Prevalence of biofilms in Candida spp. bloodstream infections: A meta-analysis

María Belén Atiencia-Carrera; Fausto Sebastián Cabezas-Mera; Eduardo Tejera; António Machado

doi:10.1371/journal.pone.0263522

. 2022 Feb 3;17(2):e0263522. doi: 10.1371/journal.pone.0263522

Prevalence of biofilms in Candida spp. bloodstream infections: A meta-analysis

María Belén Atiencia-Carrera ^1,^#, Fausto Sebastián Cabezas-Mera ^1,^#, Eduardo Tejera ^2,^*, António Machado ^1,^*

Editor: Surasak Saokaew³

PMCID: PMC8812928 PMID: 35113972

Abstract

Context

Candida-related infections are nowadays a serious Public Health Problem emerging multidrug-resistant strains. Candida biofilm also leads bloodstream infections to invasive systemic infections.

Objective

The present meta-analysis aimed to analyze Candida biofilm rate, type, and antifungal resistance among hospitalized patients between 1995 and 2020.

Data sources

Web of Science, Scopus, PubMed, and Google Scholar databases were searched for English papers using the following medical subject heading terms (MESH): “invasive candidiasis”; “bloodstream infections”; “biofilm formation”; “biofilm-related infections”; “mortality”; and “prevalence”.

Study selection

The major inclusion criteria included reporting the rate of biofilm formation and the prevalence of biofilm-related to Candida species, including observational studies (more exactly, cohort, retrospective, and case-control studies). Furthermore, data regarding the mortality rate, the geographical location of the study set, and the use of anti-fungal agents in clinical isolates were also extracted from the studies.

Data extraction

Independent extraction of articles by 2 authors using predefined data fields, including study quality indicators.

Data synthesis

A total of 31 studies from publicly available databases met our inclusion criteria. The biofilm formation in the data set varied greatly from 16 to 100% in blood samples. Most of the studies belonged to Europe (17/31) and Asia (9/31). Forest plot showed a pooled rate of biofilm formation of 80.0% (CI: 67–90), with high heterogeneity (Q = 2567.45, I² = 98.83, τ² = 0.150) in random effects model (p < 0.001). The funnel plot and Egger’s linear regression test failed to find publication bias (p = 0.896). The mortality rate in Candida-related bloodstream infections was 37.9% of which 70.0% were from biofilm-associated infections. Furthermore, Candida isolates were also characterized in low, intermediate, or high biofilm formers through their level of biofilm mass (crystal violet staining or XTT assays) after a 24h growth. When comparing between countries, statistical differences were obtained (p = 0.0074), showing the lower and higher biofilm prevalence values in Italy and Spain, respectively. The prevalence of low, intermediate, and high biofilms were 36.2, 18.9, and 35.0% (p < 0.0001), respectively. C. tropicalis was the prevalent species in high biofilm formation (67.5%) showing statistically significant differences when compared to other Candida species, except for C. krusei and C. glabrata. Finally, the rates of antifungal resistance to fluconazole, voriconazole, and caspofungin related to biofilm were 70.5, 67.9 and 72.8% (p < 0.001), respectively.

Conclusions

Early detection of biofilms and a better characterization of Candida spp. bloodstream infections should be considered, which eventually will help preserve public health resources and ultimately diminish mortality among patients.

Introduction

Invasive candidiasis is a systemic mycosis caused by Candida species, being commonly described as an opportunistic infection. The population group more vulnerable for invasive candidiasis includes patients with critical illness or immunosuppression (such as hematological and solid organ malignancy, hematopoietic cell and solid organ transplantation, recent abdominal surgery, and hemodialysis), or even people with a central venous catheter, parenteral nutrition. In addition, people that received broad-spectrum antibiotics or with drug habits are also susceptible to invasive candidiasis, as well as premature newborns [1]. All these plausible scenarios lead this systemic infection to be nowadays the 4th leading nosocomial infection in the United States, demonstrating mortality of up to 40% [2]. In Europe, Bassetti and colleagues realized a multinational and multicenter study in 2019 reporting 7.07 episodes per 1000 in European intensive care units (ICUs) with a 30-day mortality of 42% [3]. While, in the Asia-Pacific region, Hsueh and colleagues reported a candidemia incidence in ICUs of 5- to 10-fold higher than in the entire hospital and a mortality rate of patients between 35% and 60% [4]. In Latin America, Nucci and colleagues realized a laboratory-based survey between November 2008 and October 2010 among 20 tertiary care hospitals in seven Latin American countries, reporting an overall incidence of 1.18 cases per 1,000 in general admissions [5]. The mortality associated with invasive candidiasis is similar or even higher in other worldwide countries [6].

To understand the dimension of this infection and its virulence, we must define the term invasive candidiasis as both forms of candidemia detected in the blood and tissues or deep organs under the mucosal surfaces (also known as deep candidiasis). Deep candidiasis can remain localized or spread causing a secondary infection [7]. Patients with a systemic infection induced by Candida spp. can be subdivided into three groups: (1) those who present with bloodstream infection (candidemia); (2) those who develop deep-seated candidiasis (most frequently intra-abdominal candidiasis); and, (3) those who develop a combination of these two groups [8].

The gold standard for the diagnosis of invasive candidiasis is the growth culture, being blood culture commonly used to diagnose candidemia while culture media is applied to diagnose deep candidiasis from tissue biopsies [9]. In this meta-analysis, we only evaluated studies using positive blood cultures to evaluate the biofilm formation and other related factors in candidiasis virulence. More exactly, the selected studies performed an in vitro biofilm assay using Candida isolates from blood samples of the patients with catheter-related candidemia (CRC) and non-CRC. In cases of patients with CRC, the standard procedure was blood cultures from obtained the catheter and peripheral veins, whereas non-CRC was indicated by the recovery of Candida spp. from only blood samples, as previously described by Guembe and colleagues [10].

Nosocomial infections are closely associated with biofilms growing attached to medical devices or host tissues [11]. Biofilms are the predominant growth state of many microorganisms, being a community of irreversible adherent cells with different phenotypic and structural properties when compared to free-floating (planktonic) cells. National Institutes of Health estimated that biofilms are responsible, in one way or another, for more than 80% of all microbial infections in the United States [12]. Candida species can produce well-structured biofilms composed of multiple types of cell and even microbial species, leading to an intrinsic resistance against a wide variety of stress factors, such as various antifungal drugs and immune defense mechanisms [13]. Although the dynamics biofilm-host is not yet fully understood, it is well-known that Candida biofilms inhibit the innate immune system of the host [14]. Therefore, our main goal was to analyze the relationship between biofilms and mortality in Candida spp. related infections, showing a severe menace to the Public Health System with serious outcomes.

Results

Study inclusion criteria and characteristics of the eligible studies

A total of 214 studies were retrieved and 70 full texts were reviewed from publicly available databases (Web of Science, Scopus, PubMed, and Google Scholar). Thirty-one studies met our inclusion criteria (Fig 1). The final data set included studies covering different global regions (most of them in Europe). All available and relevant data were extracted of each study, more exactly, biofilm rate, biofilm type, underlying disease of the patients, Candida species reported, and antifungal resistance. The data was then used to create other databases, collecting information of at least five or more papers, and consequently, each paper was cited more than once. These additional databases were chosen to realize subgroup analysis using a random-effect model and to answer relevant questions about Candida-related biofilms, such as the mortality rate related to biofilms, the geographical distribution of biofilms, the characterization of biofilm production among Candida species, and the correlation between biofilm formation and antifungal resistance (S1 and S2 Files).

As shown in Fig 1, a total data set of 31 studies was achieved for the present meta-analysis following the eligibility criteria, screening process, and quality assessment.

Overall effects of Candida biofilms

The data set reported biofilm rates of Candida-related infections among hospitalized patients between 1995 and 2020 in several countries worldwide. As shown in Table 1, the biofilm formation by Candida spp. isolates in the data set varied greatly from 16% to 100% in blood samples from hospitalized patients. Most of the data set belonged to studies realized in Europe (17/31), followed by Asia (9/31), South America (3/31), and North America (2/31).

Table 1. General information extracted from the data set selected for the present meta-analysis.

First author	Publication (year)	Region	Country	Methodology to measure biofilm	Biofilm rate, n (%)	Biofilm formation, n (%)			Correlation between biofilm and resistance	Attributable mortality, n (%)
First author	Publication (year)	Region	Country	Methodology to measure biofilm	Biofilm rate, n (%)	High	Medium	Low
Atalay	2015	Asia	Turkey	CV (450 nm)	8/50 (16)				No
Tumbarello	2007	Europe	Italy	PBS (405 nm) & XTT (490 nm)	80/294 (27.2)				No	56 (70.0)
Tortonaro	2013	Europe	Italy	XTT (490 nm)	160/451 (35.4)	116(72.5)		44(27.5)	No	11 (6.9)
Banerjee	2015	Asia	India	Branchini’s method	31/80 (38.8)				No	5 (16.1)
Tumbarello	2012	Europe	Italy	PBS (405 nm) & XTT (490 nm)	84/207 (40.6)				No	43 (51.2)
Pongracz	2016	Europe	Hungary	CV (570 nm) & XTT (490 nm)	43/93 (46.2)	12(27.9)		31(72.1)	Yes	23 (53.49)
Sida	2015	Asia	India	Branchini’s method	2/4 (50)				No
Rodrigues	2019	South America	Brazil	Christensen’s method	15/28 (53.8)				No	6 (40.0)
Gangneux	2018	Europe	France	BioFilm Ring Test	181/319 (56.7)	132(72.9)		49(27.1)	No	55 (30.4)
Shin	2002	Asia	Korea	DW (405 nm)	58/101 (57.4)				No
Pannanusorn	2012	Europe	Sweden	XTT (590 nm)	231/393 (58.7)	101(43.7)		130(56.3)	No
Tascini	2018	Europe	Italy	XTT (490 nm)	57/89 (64.0)				No	25 (43.9)
Tobudic	2011	Europe	Austria	CV (630 nm), PBS (405 nm) & XTT (620 nm)	34/47 (72.3)				No	18 (52.9)
Tulasidas	2018	Asia	India	CV (570 nm)	55/74 (74.3)				No
Pfaller	1995	North America	USA	Branchini’s method	13/17 (76.5)	3(23.1)	6(46.1)	4(30.8)	No
Pham	2019	Asia	Thailand	XTT (490 nm)	38/46 (76.4)	25(65.8)		13(34.2)	No	13 (34.2)
Guembe	2014	Europe	Spain	CV (550 nm)	45/54 (76.4)				No
Kumar	2006	Asia	India	UPW (405 nm)	30/36 (83.3)				No
Rajendran	2016	Europe	Scotland	CV (570 nm)	245/280 (87.7)	56 (22.9)	44 (17.9)	144 (58.9)	Yes
Stojanovic	2015	Europe	Serbia	CV (595 nm)	7/8 (87.5)	2 (28.6)	3 (42.8)	2 (28.6)	Yes
Turan	2018	Asia	Turkey	CV (540 nm)	145/162 (89.5)	37 (25.5)	61 (42.1)	47 (32.4)	Yes
Tulyaprawat	2020	Asia	India	XTT (490 nm)	45/48 (93.8)	26 (57.8)		19(42.2)	No
Muñoz	2018	Europe	Spain	CV (540 nm)	280/280 (100.0)	90 (32.1)	190 (67.9)		No	95 (33.9)
Soldini	2017	Europe	Italy	CV (540 nm)	190/190 (100.0)	84 (44.2)	38 (20.0)	68 (35.8)	No	89 (46.8)
Vitális	2020	Europe	Hungary	CV (550 nm)	127/127 (100.0)	28 (22.0)	69 (54.4)	30 (23.6)	No	70 (55.1)
Prigitano	2013	Europe	Italy	XTT (490 nm)	297/297 (100.0)	96 (32.3)	141(47.5)	60 (20.2)	No	65 (21.9)
Treviño-Rangel	2018	North America	México	CV (595 nm)	89/89 (100.0)				No	32 (35.9)
Marcos-Zambrano	2017	Europe	Spain	CV (540 nm)	22/22 (100.0)		13 (59.1)	9 (40.9)	Yes	3 (13.6)
Marcos-Zambrano	2014	Europe	Spain	CV (540 nm)	564/564 (100.0)	194 (34.4)	187 (33.1)	181 (32.1)	No
Thomaz	2019	South America	Brazil	CV (595 nm) & XTT (490 nm)	38/38 (100.0)	3 (7.9)		35 (92.1)	No
Herek	2019	South America	Brazil	CV (570 nm)	13/13 (100.0)	3 (23.1)	7 (53.8)	3 (23.1)	No

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The prevalence of biofilm formation was calculated with 95% CI through random-model and significance level ≤0.05 (p-value). The sample size and prevalence were used to calculate the combined biofilm produced. Attribute mortality was calculated by the number of deaths among patients with biofilm in blood samples. The information summarized in the table did not show information on the patients’ underlying diseases and resistance. The methodologies used to measure biofilm in the studies were based in the optical density (nm, i.e., wavelength in the assay) of the biomass from growth culture, more exactly: XTT—using micro plate reader with yellow tetrazolium salt; CV—using micro plate reader with crystal violet staining; UPW—using micro plate reader with ultra-pure water; DW—using microplate reader with distilled water; Branchini’s method—evaluating the adherent growth of the biofilm’s slime production; BioFilm Ring Test—using micro plate reader with a BioFilm Index (BFI) software; and, Christensen’s method—evaluating the adherent growth of the biofilm in Falcon tube with safranin or trypan blue staining.

Although the methodologies to quantify biofilm biomass varied between studies, these methodologies are based on the optical density (OD) obtained by the combination of a certain colorimetric compound or a simple dissolution in a buffer or water with the growth of the isolated Candida sp. and then it’s compared with reference Candida strains in the same growth conditions. The main methodologies in our study set were crystal violet (CV) assays using microplate reader (51.6%; 16/31), assays with tetrazolium dye (2,3-bis-(2-methoxy-4-nitro-5-sulphenyl)-(2H)-tetrazolium-5-carboxanilide, XTT) using micro plate reader (35.5%; 11/31), and Branchini’s method (9.7%; 3/31). The Branchini’s method, also called slime production method, is based on the production of a viscid slime layer by the growth of the Candida isolate in a tube containing Sabouraud broth [15].

Regardless of the applied methodology in the studies, all these authors were able to evaluate biofilm formation among Candida isolates. However, only 18 of 31 studies were able to categorize the biofilm formation, and so just 5 studies were able to evaluate a positive correlation between biofilm presence and increment of antifungal resistance in the treatment. Finally, the incidence of mortality among patients varied considerably among studies, reporting the values of attributable mortality between 6.9 and 70%. All the information extracted is available in the supplementary section.

Analysis of the forest plot was then realized with data set, showing a pooled rate of biofilm formation of 80.0% (CI: 67–90), as shown in Fig 2. The heterogeneity indices obtained using random effects model (p < 0.001) were Q = 2567.45 (p < 0.001), I² = 98.83, and τ ² = 0.150. The pooled rate of biofilm formation obtained needs to be carefully analyzed given the high value of heterogeneity. This will be addressed in our discussion.

A funnel plot was realized to evaluate the existence of publication bias in the final data set (Fig 3). Furthermore, Egger’s linear regression test was also used to reveal any publication bias and possible asymmetric data distribution in the funnel plot.

No publication bias was identified by the Egger’s linear regression test (p = 0.896). However, as we will discuss in the next section the qualitative analysis of the funnel clearly suggests some biases from the departure of the geometry from the expected triangular form. The funnel plot of this study illustrates the effect size (biofilm prevalence) on the x-axis and the standard error (SE) on the y-axis. In case of no publication bias in the data set, the studies are distributed evenly around the pooled effect size. The smaller studies should appear near the bottom due to their higher variance when compared to the larger studies, which should be placed at the top of the plot. The diagonal lines show the expected 95% confidence intervals around the summary estimate. In the absence of heterogeneity, the studies of the data set should lie within the funnel defined by these diagonal lines. However, heterogeneity and some asymmetries among the studies of the data set were illustrated by the funnel plot. In our case, we found studies with low errors (similar sizes) but with drastic differences in the biofilm prevalence. This type of pattern probably indicates the presence of confounding variables (sub-groups undelaying structures) which are not included in the global analysis.

Although an obvious biofilm prevalence was found in the data set, the selected studies poorly described the underlying conditions of the patient with biofilm production. The analysis of these conditions among the patients was merely descriptive, as shown in Table 2.

Table 2. The reported clinical background of the patients with Candida-related bloodstream infections in the study set.

Study set	Total	Biofilm	Mortality	Mortality-related biofilm	Adult clinical conditions																								Pediatric clinical conditions
Study set	Total	Biofilm	Mortality	Mortality-related biofilm	CA	IT	MV	CD	Neu	ND	CO	PD	GI	QMT	DI	AL	CRF	UC	CVC	RI	NGT	TPN	GAD	HIV	ANF	ANT	SC	ICU	PCVC	PVC	PB	LWB
Stojanovic et al., 2015	8	7	0	0	4	4	NR	NR	NR	NR	NR	NR	NR	NR	5	2	3	NR	6	NR	4	5	NR	NR	NR	6	4	6	NR	NR	NR	NR
Banerjee et al., 2015	80	31	16	5	11	NR	9	5	6	6	7	11	19	NR	17	16	13	27	58	28	NR	NR	NR	1	NR	42	9	NR	0	19	14	13
Guembe et al., 2014	54	45	0	0	16	NR	NR	6	NR	NR	NR	NR	6	NR	NR	NR	NR	NR	23	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	10
Pongracz et al., 2016	93	43	43	23	25	19	NR	NR	NR	NR	NR	NR	NR	NR	20	NR	NR	NR	NR	NR	NR	22	NR	11	NR	NR	51	NR	NR	NR	NR	NR
Vitalis et al., 2020	127	127	70	70	28	13	87	NR	NR	NR	NR	NR	NR	NR	41	NR	NR	NR	NR	NR	NR	68	NR	13	162	91	8	100	NR	NR	NR	NR
Kumar et al., 2006	36	30	0	0	35	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	1	NR	NR	NR	NR	NR	NR	NR	NR
Tumbarello et al., 2012	207	84	82	43	42	16	NR	NR	NR	NR	29	17	9	NR	NR	NR	21	NR	56	NR	27	58	NR	1	NR	75	38	NR	NR	NR	NR	NR
Tumbarello et al., 2007	294	80	154	56	88	82	NR	NR	10	NR	NR	NR	16	NR	NR	NR	NR	136	30	NR	NR	72	NR	NR	NR	NR	100	57	NR	NR	NR	NR
Marcos-Zambrano et al., 2017	22	22	0	0	21	13	NR	NR	4	NR	NR	NR	1	NR	76	NR	4	NR	19	NR	NR	13	NR	1	7	NR	4	2	NR	NR	NR	NR
Tortonaro et al., 2013	451	160	13	11	136	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	219	158	NR	NR	17	NR
Muñoz et al., 2018	280	280	0	95	151	22	50	91	18	70	78	59	NR	53	69	NR	61	NR	201	NR	NR	152	NR	6	62	253	136	28	NR	NR	NR	NR
Soldini et al., 2017	190	190	89	89	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	152	NR	NR	132	NR	NR	NR	177	NR	28	NR	NR	NR	NR
Tascini et al., 2018	89	57	42	25	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	47	80	NR	25	62	NR	NR	75	NR	35	NR	NR	NR	NR	NR
Treviño-Rangel et al., 2018	89	89	32	32	NR	NR	24	NR	NR	NR	13	NR	NR	7	NR	NR	NR	37	50	1	NR	30	NR	13	30	53	38	NR	NR	NR	4	NR
Shin et al., 2002	101	58	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	41	NR	NR	35	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Atalay et al., 2015	50	8	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	18	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Gangneux et al., 2018	319	181	105	55	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Herek et al., 2019	13	13	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Marcos-Zambrano et al., 2014	564	564	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Pannanusorn et al., 2012	393	231	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Pfaller et al., 1995	17	13	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Pham et al., 2019	46	38	23	13	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Prigitano et al., 2013	297	297	130	65	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Rajendran et al., 2016	280	245	0	0	NR	NR	NR	NR	NR	NR	NR	NR	121	30	153	128	NR	NR	NR	118	NR	123	133	NR	119	NR	40	128	NR	NR	NR	NR
Rodrigues et al., 2019	28	15	13	6	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Sida et al., 2015	4	2	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Thomaz et al., 2019	38	38	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Tobudic et al., 2011	47	34	25	18	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Tulasidas et al., 2018	74	55	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Tulyaprawat et al., 2020	48	45	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR
Turan et al., 2018	162	145	0	0	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR	NR

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CA: malignancy; IT: Immunosuppressive Therapy; MV: Mechanical Ventilation; CD: Cardiovascular Disease; Neu: Neutropenia; ND: Neurological Disorders, CO: Corticoids; PD: Pulmonary Disorders; GI: Gastro Intestinal and Hepatically Disease; QMT: Chemotherapy; DI: Diabetes; AL: Alcoholism; CRF: Chronic Renal Failure; UC; Urinary Catheter; CVC: Central Venous Catheter; RI: Renal Insufficiency; NGT: Nasogastric Tube, TPN: Total Parenteral Nutrition; GAD: Genetic Autoimmune Disorders; HIV: Human Immunodeficiency Virus; ANF: Prior Antifungal Therapy; ANT: Prior Antibacterial Therapy; SC: Surgical conditions; ICU: Intensive Care Unit; PCVC: Pediatric Central Venus Catheter; PVC: Peripheric Venus Catheter; PB: Preterm Bird; LBW: Low Weight Bird; NR: Not Reported in the study.

The lack of a detail description of the clinical background and host factors in the patients among the studies represents a main drawback of the present meta-analysis precluding the evaluation of clinical or patient factors and the ability of Candida isolates to establish biofilm. Nonetheless, the ability to establish biofilm is a virulence factor by itself and should be evaluate as risk factor in the treatment of patients with Candida-related blood infections. As summarized in Table 2, only 16 of 31 studies reported some sort of clinical background of the patients with Candida-related bloodstream infections. From this subset of studies, patients evidenced mainly the following clinical conditions: hematological or solid cancer (68.8%, 11/16), surgery interventions (62.5%, 10/16); patients with central venous catheter (56.3%, 9/16); adults under total parenteral nutrition (50.0%, 8/16); patients with human immunodeficiency virus (HIV; 50.0%, and 8/16); patients with diabetes (43.8%; 7/16); patients in the intensive care unit (ICU; 37.5%, and 6/16); patients with immunosuppressive therapy (37.5%, 6/16) and, the remaining clinical backgrounds were only described in 25% or less of the studies in this subset, such as neutropenia (4/16), cardiovascular diseases (3/16), pulmonary diseases (3/16), urinary catheter (3/16), chemotherapy (2/16), and renal insufficiency (2/16). The heterogeneity of the clinical background of the patients and the gap of the host epidemiological factors in these studies excluded further analysis between Candida-related biofilm isolates and clinical history.

Mortality among patients with Candida biofilm

Further subgroup analysis using a random-effect model was realized to differentiate the Candida-related mortality rates between bloodstream infections with planktonic cells and biofilm formation. From the initial data set, only 15 studies evaluated the mortality among patients with Candida-related bloodstream infections. As shown in Table 3, the pooled mortality rate due to Candida-related bloodstream infections was 37.9% (95% CI: 26.2–50.2) of which the mortality associated with biofilm-forming infections was 70.0% (95% CI: 52.8–84.8).

Table 3. Pooled mortality rates in bloodstream infections due to Candida spp.

	k	Mortality rate (95% CI) (%)	Random model
	k	Mortality rate (95% CI) (%)	Q	I²	τ	p
All Candida spp. bloodstream infections	15	37.9 (26.2–50.2)	493.82	97.2	0.237	< 0.0001
Biofilm-forming	15	70.0 (52.8–84.8)	345.47	95.9	0.331	< 0.0001

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k, Number of studies; Q, I² and τ, Heterogeneity indexes; p, Random effect model significance level. Mortality rates were estimated within 30 days after diagnosis and confirmation of Candida spp. bloodstream infection. The studies considered (k = 15) were those in which a sample corresponded to an individual and reported deaths related to biofilm-formers strains.

In both scenarios, the mortality rate was statistically incremented among hospitalized patients (p < 0.0001). However, biofilm-related infections evidenced almost the double value of mortality rate in patients, when compared to all Candida-related bloodstream infections.

Geographical distribution of biofilm-forming Candida spp. isolates

The prevalence rate of biofilm-related infections significantly varied among studies of different countries and regions. Therefore, a subgroup analysis was realized between the biofilm formation rates and the geographical region to evaluate possible statistically significant differences (Table 4). Subgroup analysis evaluated the biofilm prevalence between regions and countries with a minimum of published studies, at least two and three studies per region and country, respectively. However, Egger’s test was not applied due to the low number of studies in this analysis.

Table 4. Subgroup analysis for different geographical regions and countries.

Subgroups	k	Prevalence (95% CI) (%)	Random model
Subgroups	k	Prevalence (95% CI) (%)	Q	I²	τ	p*
Region			Q	I²	τ	p*
Europe	17	81.0 (63.3–94.0)	2267.21	99.3	0.407	0.4049
Asia	9	67.9 (48.1–85.0)	171.49	95.3	0.283
South America	3	91.6 (50.7–100.0)	31.83	93.7	0.387
North America	2	94.0 (55.1–100.0)	12.94	92.3	0.319
Country (≥3 studies)
Italy	6	69.1 (32.0–95.8)	1095.33	99.5	0.471	0.0074
India	5	72.3 (46.2–92.7)	55.54	92.8	0.267
Spain	4	98.9 (93.5–100.0)	33.85	91.1	0.126
Brazil	3	91.6 (50.7–100.0)	31.83	93.7	0.387

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k, Number of studies; Q, I² and τ, Heterogeneity indexes; p*, Significance level in subgroup analysis.

Although the biofilm prevalence varied among regions, there were no statistically significant differences (p = 0.4049). Europe reported a greater number of studies and showed an intermediate biofilm prevalence among Candida spp. infections. Meanwhile, when comparing prevalence rates between countries, a statistically significant value was obtained (p = 0.0074). In the pairwise comparison analyses, Spain was significantly superior to Brazil (p < 0.0001), Italy (p = 0.0263), and India (p = 0.0030).

Biofilm-forming capability in Candida spp. isolates

Candida spp. isolates vary in their ability to form biofilms, being usually categorized as low (LBF), intermediate (IBF), and high biofilm formers (HBF) according to biomass production (S1–S3 Figs). Briefly, biofilm forming capacity was assessed using the crystal violet or XTT assays, measuring the biofilm mass. Candida isolates were cultured in 96-well plates at 37°C for 24 h and the biomass of each isolate was measured. Then, isolates were grouped based on their level of biomass, more exactly: low biofilm formers (LBF) showed a biomass production below the 1^st quartile (Q₁; Abs_isolate < 0.432), intermediate biofilm formers (IBF) evidenced a biomass production in the 2^nd quartile (Q₂; 0.432 < Abs_isolate < 1.07), and high biofilm formers (HBF) demonstrated a biomass production higher the 1^st quartile 3^rd quartile (Q₃; Abs_isolate > 1.07), as previously described by Monfredini et al. [16] and Vitális et al. [17]. Eighteen studies reported this biofilm classification and so a subgroup analysis was realized (Table 5).

Table 5. Overall effects in subgroups based on biofilm-forming capability.

Biofilm-forming capability	k	Prevalence (95% CI) (%)	Egger’s test	Random model
Biofilm-forming capability	k	Prevalence (95% CI) (%)	p	Q	I²	τ	p*
High (HBF)	18	35.0 (26.6–43.9)	0.768	313.94	94.58	0.177	< 0.0001
Intermediate (IBF)	18	18.9 (7.8–33.1)	0.457	1074.52	98.42	0.334	< 0.0001
Low (LBF)	18	36.2 (24.7–48.5)	0.370	623.25	97.27	0.253	< 0.0001

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k, Number of studies; Q, I² and τ, Heterogeneity indexes; p*, Random effect model significance level in subgroup analysis. The selected studies (k = 18) categorized the strains according to their biofilm-forming capability using only methods based on biomass quantification through spectrophotometric measures.

Statistically significant differences were found among Candida isolates according to their biofilm-forming capability (p < 0.0001), evidencing a low number of Candida isolates related to intermediate biofilms. No publication bias was detected in both subgroups according to Egger’s linear regression test.

Evaluation of biofilm formation between different Candida species

Although Candida spp. isolates vary in their ability to form biofilms, little is known about this biofilm-forming ability among Candida species. Each category of biofilm was further evaluated among Candida species to evaluate the most virulent Candida species (S1 Table). When analyzing HBF (Table 6), C. tropicalis was the most prevalent HBF overpassing C. albicans and C. parapsilosis by a factor of 2. More precisely, the HBF prevalence of C. tropicalis was the highest showing statistically significant differences with the other Candida species, except for C. krusei (p = 0.5477) and C. glabrata (p = 0.0896).

Table 6. Subgroup analysis between different Candida species.

Species	k	BF strains (n)	Prevalence of HBF % (95% CI)	Random model
Species	k	BF strains (n)	Prevalence of HBF % (95% CI)	Q	I²	τ	p*
C. albicans	22	1461	30.3 (20.5–41.0)	225.66	95.6	0.173	0.0454^a
*non-albicans Candida* species**	26	1868	43.6 (34.5–52.9)	306.69	87.6	0.230	0.0454^a
C. albicans	22	1461	30.3 (20.5–41.0)	225.66	95.6	0.173
C. glabrata	17	387	37.6 (0.1–71.0)	95.0	95.8	0.325	< 0.0001^b
C. tropicalis	17	331	67.5 (58.3–76.3)	11.71	31.7	0.069
C. parapsilosis	20	744	29.6 (20.3–39.9)	69.9	84.3	0.154
C. krusei	10	68	52.8 (0.1–94.9)	30.12	83.4	0.409
^ Other species**	20	338	40.7 (26.5–55.6)	22.49	60.0	0.139

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k, Number of studies; Q, I² and τ, Heterogeneity indexes; p*, Random effect model significance level in subgroup analysis.

^a Comparison between C. albicans and non-albicans Candida species.

^b Comparison between all Candida species.

** Other species includes C. dublinensis (n = 12), C. quilliermondi (n = 25), C. lusitaniae (n = 10), C. haemulonii (n = 4), C. lypolitica (n = 1), C. pelliculosa (n = 1) and unreported species (n = 285).

In order to comprehend how these two major factors: countries and Candida species could actually explain the high heterogeneity showed in our data, we carried out a meta-regression analysis. The inclusion of both variables as interacting variables in a multiplicative model (R² = 59.13%, p < 0.0001) explained more than an additive model (R² = 43.48%, p < 0.0001), regarding the prevalence of biofilm formation.

Evaluation of antifungal resistance pattern among Candida isolates

Multiple antifungal resistance among candidiasis has become a serious public health issue, leading to clinical complications and expensive costs. A subgroup analysis based on antifungal resistance was also realized among our study set. Due to the different methodologies used to test susceptibility, the number of studies not enough to analyze statistically antifungal resistance rates between Candida species. As shown in Table 7, the rates of antifungal resistance to fluconazole, voriconazole, and caspofungin related to biofilm-forming strains were 70.5, 67.9, and 72.8%, respectively.

Table 7. Summary of subgroup analysis for antifungal resistance in Candida spp. isolates.

Studies	k	Antifungal resistance rate % (95% CI)
Studies	k	Fluconazole	Voriconazole	Caspofungin
Mixed/Planktonic cells	3	15.1 (0.7–41.2)	1.6 (0.1–4.4)	3.1 (0.0–20.76)
Biofilm-forming strains	2	70.5 (54.6–84.5)	67.9 (51.8–82.3)	72.8 (55.1–87.8)
Cochran’s Q*		11.68	85.15	22.88
p-value**		0.0006	< 0.0001	< 0.0001
Not reported/ Other methods	26	-	-	-

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k, Number of studies; Q*, Test of heterogeneity between groups; p**, Random effect model significance level in subgroup analysis. Subgroup analysis based on antifungal resistance contains k = 5 studies. Egger’s test may lack the statistical power to detect bias when the number of studies is small (i.e., k < 10).

When comparing to planktonic cells, all Candida-related biofilm isolates showed a statistical increment of resistance against the three antifungals evaluated in the study (p < 0.001).

Discussion

The present study evaluated a possible relationship between Candida-related biofilm formation, bloodstream infections, and mortality among hospitalized patients. Invasive mycoses are responsible every year for more than two million infections worldwide and for, at least, as many deaths as tuberculosis or malaria. Candidiasis, aspergillosis, cryptococcosis, and pneumocystosis cause more than 90% of reported deaths associated with invasive mycoses [18]. Among them, the most frequent mycosis is invasive candidiasis causing high morbidity in critically ill patients [19].

Overall effects of Candida biofilms in infections and mortality

As previously referred, around 70.0% of candidemia reports were caused by biofilm-forming strains. However, its biofilm formation was less than in isolates from urogenital infections [20–23] and even respiratory tract infections [22, 23]. Still, the rate of candidemia-associated biofilm infections was higher than oral-related biofilm infections [24] and more than invasive infections [25]. These findings are in agreement with the Institute of Health in the United States, which estimates that biofilms are responsible, in one way or another, for over 80% of all microbial infections [12]. Yet, the reports of Candida-associated biofilm infections varied greatly between published studies possibly due to the lack of differentiation between Candida species, the experience of the researchers, the number of Candida isolates in the study set, and the diversity of biofilm detection and quantification methodologies and its subsequent classification within the study set, such as crystal violet assay, biomass measure, XTT reduction assay, and microtiter plate method [8, 12].

Another issue concerns the lack of differentiation between planktonic and biofilm-related Candida infections in the diagnosis of the clinical laboratories at public health system [19, 26]. The traditional clinical microbiology laboratories have focused on testing planktonically isolated microorganisms and reporting the susceptibility to various antimicrobials under planktonic growth conditions [27]. While the authors from the studies of this meta-analysis applied a further analysis by evaluating the ability of biofilm production in Candida isolates through an in vitro biofilm assay. In Candida biofilms, traditional techniques require device removal followed by culture or microscopy of a catheter segment, while catheter-sparing diagnostic tests include paired quantitative blood cultures. However, as previously indicated by Høiby et al. (2015) and Bouza et al. (2013), the number of positive peripheral blood cultures also seems to be a promising diagnostic tool to diagnose catheter-related candidemia without directly removing the catheter [27, 28]. Therefore, an implementation of a new gold standard methodology is vital to a better characterization of microbial-associated infections avoiding unproductive treatments among hospitalized patients. The mortality rate caused by biofilm formation in Candida-related infections was almost double when compared to planktonic infections. Other studies already stated the burden of invasive candidiasis and its severe outcomes [1, 29], indicating biofilm formation and antifungal resistance as main risk factors among patients. Moreover, we report a pooled attributable mortality of 37.9% to Candida-related bloodstream infection with planktonic cells, which is in agreement with previous reports [1, 18, 30, 31]. These studies reported a mortality range between 25 and 40%, showing a higher mortality incidence among ICU or burn patients, and immunocompromised patients [32]. While the mortality associated with biofilm-forming strains was 70.0% in Candida-related bloodstream infections. However, this correlation has been debated by several authors [10, 16, 33, 34], reporting different mortality rates (25–70%).

It is also important to mention that the ability to quicky proliferate and to establish biofilm is not exclusively dependent of the type of Candida species and even strains in a blood-related infection, but it is also influenced by their interaction with host homeostasis and variations (mucosal pH shifts or nutritional changes), previous use of antibiotics, and immune system alterations (such as secondary effect of stress or immunosuppressant therapy) [35].

The I² observed in the forest plot indicate a high heterogenic data. The I² is a measurement of the heterogeneity that is not caused by variations in the sample size considered in each study. Therefore, this high value and also the geometry of the funnel plot indicates the possibility of major sources of variation across the studies. Some of the sources of variations can clearly be related with the differences previously described (i.e., methodology, Candida species, etc.) and consequently the pooled effect around the 80% need to be considered with caution. Several factors can be modulating this pooled effect leading to higher and/or lower values. In this context, the present meta-analysis was unable to study any correlations between clinical or epidemiological factors and mortality in patients with biofilm-related blood infections. These heterogeneity and gaps on the selected studies constitute the main drawback of our study. However, it is also well-known that the ability to establish biofilms among Candida species is an important virulence factor contributing to a more severe infection in patients [36] and it is worth to be studied. The observed heterogeneity was the leading cause to consider the effect of several variables like geographical distribution and Candida species. However, the missing information in the consulted scientific literature can be an important source of unexplained variation.

Geographical distribution of Candida biofilm-related infections

World incidence of invasive candidiasis is difficult to estimate because the criteria used for diagnosing and categorizing invasive candidiasis are quite different [6, 8, 9]. Also, most studies restricted many factors in their group set, such as the range age of patients and their health status. The present meta-analysis recollected data from diverse study sets demonstrating the Candida-related biofilm infections as a main nosocomial infection, but only 16 of 31 studies partially reported the clinical background of the patients (Table 2), such as patients suffering from immunodeficiency, receiving organ transplantations, under major surgery, or treated with cancer chemotherapy and different primary hospitalizations, and no epidemiological factors were available. Only a study realized in a tertiary care hospital of southern India reported the clinical backgrounds in adult and pediatric patients [37], evidencing central venous catheter and low weight at birth as the most prevalent risk factors in these population sets, respectively.

Generally, the number of patients in surveillance studies is very low and there are many gaps in our knowledge on the true epidemiology of invasive candidiasis in many regions of the world [19]. As expected, around 55% of our data set belonged to European studies (17/31), where the rate of biofilm-related infections varied greatly among countries showing Spain with statistical differences in the incidence of Candida-related biofilm infections in hospitalized patients in comparison with other countries. However, Cesta and colleagues recently reported Italy as the one region with a higher number of deaths caused by antibiotic-resistant bacteria and biofilm-related infections [38]. Due to European Centre for Disease Prevention and Control (ECDC) reported a spread of multi-drug resistant strains (MDR) in Italy, in particular of the bacterial species of Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii [38], it is plausible that the Candida-related biofilm incidence among hospitalized patients in Italy had been underrated. Likewise, only two and three studies in our data set belong to North and South America, respectively. All three studies of South America were indeed from Brazil, demonstrating one of the highest Candida-related biofilm incidences among hospitalized patients (91.6%). However, no further information was available in the remaining Latin-American countries with the criteria selection of the present meta-analysis.

We can notice in the meta-analysis that the values of I², Q and other indicators also suggest a high heterogeneity within each group. It is an indicator that other factors can be involved. For example, if we consider only the articles from Italy, we can notice that the sample size in 5 of 6 studies do not considerably differ but the effect size is quite different (this will impact directly in the funnel plot geometry as presented in Fig 3). In three studies, we found a low prevalence of biofilm formation [33, 39, 40] while in other two articles we found a high prevalence of biofilm formation [41, 42]. This distribution suggests that factors quite beyond the geography are possible causes of heterogeneity within groups.

Association between different Candida species in biofilm and infections

The number of Candida species with clinical importance in humans is relatively small, more exactly, Candida albicans, Candida glabrata, Candida tropicalis, Candida parapsilosis, and Candida dubliniensis [43]. C. albicans is the most reported Candida species worldwide in different ethnic populations [34, 44–47], being responsible for the majority of oral and systemic candidiasis cases. However, there has been an increase in the number of reports about non-albicans Candida infection in the last years and even surpassing C. albicans in terms of incidence and attributable mortality [25, 31, 34, 42, 48–51]. This new scenario could be attributed to the implementation of better molecular techniques in the identification of Candida species [21, 29, 52].

Our results demonstrated C. tropicalis as the most prevalent HBF evidencing statistical dominance among Candida species. Although C. tropicalis is described as a species with normal to high biofilm-forming capacity [36], it is commonly related to infections in prosthetic joints, endodontic issues, ulcerative colitis [53–55]. C. tropicalis biofilm is characterized by chains of cells with thin, but large, amounts of extracellular matrix material with low sums of carbohydrate and protein [36, 40]. Furthermore, Silva and colleagues showed that matrix material extracted from biofilms of C. tropicalis and C. albicans contained carbohydrates, proteins, hexosamine, phosphorus and uronic acid [55]. However, hexosamine was the major component quantified in C. tropicalis biofilm (27%). C. tropicalis biofilms are described as a dense network of yeast cells with evident different filamentous morphologies [36].

After C. tropicalis, the present meta-analysis showed C. krusei and C. glabrata as the second and third most prevalent HBF among Candida species, more exactly, 52.8 and 37.6%, respectively. C. krusei is characterized by a thick multilayered biofilm of pseudohyphal forms embedded within the polymer matrix [56], being categorized with a high ability to establish biofilm [36]. Several mucosal infections and pneumonia are caused by C. krusei [23, 56]. Although C. glabrata is known to develop less biofilm, it is characterized to produce high content of both protein and carbohydrate [40, 57]. C. glabrata is commonly associated with infections among patients with total parenteral nutrition, periodontal disease, ventilator-associated and non-healing surgical wounds [58]. C. glabrata biofilms are structured on multilayers of blastospores with high cohesion among them [55]. The elucidation of these biofilm-forming abilities and properties among Candida species could provide a promising step toward the improvement of treatments.

Until this point, we have showed that Candida species and geographical distribution can be related with our data heterogeneity. The actual combination of both variables in a multiple meta-regression model as interacting variables explained more than the 50% of the global variability. The lack of clinical information and many other discussed variables are probably related, at least partially, with the remained variability. Unfortunately, as previously explained, this information is not accessible for most of the studies and constitute by itself a recommendation in further studies.

Antifungal resistance among Candida-related biofilm infections

Candida spp. infections had successfully become more difficult to treat in the last decade due to the growth of immunogenic diseases, the disproportionate use of immunosuppressive drugs, malnutrition, endocrine disorders, the widespread use of indwelling medical devices, broad-spectrum antibiotics, aging, and an increase of the number of patients among the population [36, 59]. Thus, the morbidity and mortality associated with candidiasis are still very high, even using the actual antifungal drugs [59]. The main antifungal drugs applied to Candida infections are azoles, polyenes, and echinocandins [60]. Briefly, azoles (such as fluconazole and voriconazole) block ergosterol synthesis by targeting the enzyme lanosterol 14α-demethylase and leading to an accumulation of toxic sterol pathway intermediates. While echinocandins (such as caspofungin) aim for the synthesis of 1,3-β-glucan (a cell wall component), being the ideal antifungal drug of choice in severe cases of candidemia [61, 62]. As previously referred, the rates of antifungal resistance to fluconazole, caspofungin, and voriconazole in biofilm cells surpassed planktonic cells by a factor of 4.7, 23.5, and 42.4, respectively. Despite the number of studies comparing resistance between planktonic and biofilm cells among Candida species is still scarce, these results are in agreement with the literature postulations [36, 63]. Numerous reasons are attributed to this enormous resistance against antifungal drugs in Candida-related biofilms, such as high cell density, growth rate reduction, nutrient limitation, matrix extracellular production, presence of persister (dormant and non-dividing) cells, phenotypic shift, and high sterols content on membrane cell [36, 59, 63]. So, the treatment for Candida-biofilm infections requires a comprehensive knowledge of the complex mechanisms underlying the interaction between a biofilm and its host.

Although no efficient treatment for Candida biofilms has been found yet, several promising strategies are being explored. New therapeutic targets, such as the genes involved in biofilm development and the quorum-sensing systems, are considered an alternative treatment to the currently antifungal drugs.

Conclusions

In summary, several studies on the prevalence of Candida biofilms in bloodstream infections have been published across the world, allowing some conclusions on its mortality, species, and virulence in different geographic regions. However, a lot of information is missing, such as the lack of a thorough clinical background from the patients and the diversity of the primary infections from the patients. Further studies are needed to close gaps in our understanding of the incidence of Candida biofilms and to monitor trends in antifungal resistance and species shifts.

To the authors’ best knowledge, this meta-analysis is one of the few that explored the association of biofilm production among different Candida species in bloodstream infections [64–67], using data published worldwide and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Although the present meta-analysis was performed methodically, there are some limitations of this study: (1) heterogeneity exists in some subgroup and overall analyses; (2) relationship between mortality and each Candida-related biofilm species could not be assessed; and, (3) a detailed analysis of antifungal resistance in Candida biofilms was not possible. These limitations are due to a lack of sufficient published data. Therefore, early detection of biofilms and a better characterization of Candida spp. bloodstream infections should be considered, which eventually will help preserve public health resources and ultimately diminish mortality among patients.

Materials and methods

Data selection, search strategy, and study guidelines

This study was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) strategies (S1 File) [68]. Web of Science, Scopus, PubMed, and Google Scholar databases were searched for English papers using the following medical subject heading terms (MESH): “invasive candidiasis”; “bloodstream infections”; “biofilm formation”; “biofilm-related infections”; “mortality”; and, “prevalence”.

In each electronic database, a combination of MESH terms was used to conduct the search applying the following strategy (in the MEDLINE for example): ‘‘(Candida) AND (biofilm [Title/Abstract]) AND (mortality).” All studies published until 30th July of 2020 were retrieved. The articles reporting the prevalence of bloodstream infections biofilm-related, the mortality rates, and the species identification of Candida isolates were included. The references of these articles were also checked for finding additional records. The data selection was limited to human clinical isolates and studies in English. All references were compiled into a database Zotero Library and then managed using Excel.

Screening process

Duplicates were initially identified and eliminated in Zotero after entering all the recognized studies into an Excel self-created database (S2 File). All articles were assessed by two reviewers (MBA-C and FSC-M) by screening titles, abstracts, topics, and finally full texts. At each level, the reviewers independently screened the articles and finally merged their conclusions. An additional examination of the selected articles was realized by a third author (AM) focused on the homogeneity of the eligibility criteria of previous reviewers in the initial data set. Discrepancies were resolved by discussion before finalizing the records for the evaluation of eligibility criteria. In case of disagreements, the third assessor (AM) was assigned to make a final decision.

Eligibility criteria

All studies without information about biofilm formation or clinical Candida isolates were consequently excluded. The method to quantify biofilm biomass was not a criterion to include or exclude any paper in this meta-analysis. Concerning antifungal resistance rate, only studies that used the standard susceptibility tests according to the Clinical and Laboratory Standards Institute (CLSI) or European Committee on Antimicrobial Susceptibility Testing EUCAST were selected for the present study.

Reviews, editorials, congress or meeting abstracts, literature in languages other than English, case reports, and letters to editors were excluded from the final data set. Finally, articles without full text available, duplicate reports on different databases, and studies with unclear or missing data were also omitted.

Data extraction and quality assessment

Methodological quality assessment of the studies was performed using a checklist for necessary items as outlined in the Critical Appraisal Skills Programmed checklists [69]. For each article, a series of critical questions were asked. If the pertinent data were presented, the question was scored ‘‘yes.” If there was any doubt or no information in the study, that question was marked as ‘‘no”. A data extraction form was designed to extract the relevant characteristics of each study (S1 and S2 Files). The extracted information included the first authors’ names, time of the study, year of publication, location, sample size, biofilm formation rate, Candida species and its categorization (as C. albicans and non-albicans Candida species), the correlation between biofilm formation and antifungal resistance, and the type of biofilm. The type of biofilm was categorized as low biofilm formers (LBF), intermediate biofilm formers (IBF), and high biofilm formers (HBF). The initial two authors (MBA-C and FSC-M) extracted all data, further confirmation and final evaluation were realized by the lead authors (AM and ET).

Data analysis and statistical methods

Meta-analysis was performed using several R packages ("meta" [70], "metafor" [71], "poibin" [72], and "stringr" [73]) of R version 3.4.3 [74] and RStudio version 1.3.1073 [75] (S3 File). The rate of biofilm formation was computed, and values were reported with confidence intervals (CI) of 95%. The heterogeneity was assessed by the Cochrane Q and I² tests. The I² metric indicates the amount of heterogeneity that is not related with sampling size variation. Moreover, it is also independent of the number of studies included in the meta-analysis (in contrast to the Cochrane Q metric). Considering the heterogeneity indices, the random-effects model was then used for meta-analysis of the selected studies, and the Freeman-Tukey transformation was also applied to calculate the pooled frequencies. To estimate the between-study variance in a random-effects model we use tau-squared, and its square root is the estimated standard deviation of underlying effects across studies. Subgroup analyses were conducted based on the type of biofilm, biofilm-related species, geographical regions, and antifungal resistance rates. Outliers’ analysis was done with the Baujat diagram, while quantitative Egger weighted regression test and Funnel plot were used to evaluate the eventual existence of publication bias. In statistical analysis, p-values <0.05 were considered as significant statistical results. We used the multiple meta-regression analysis with the "metareg" function from "meta" to explore the contribution to model heterogeneity of several variables. In this approach, the maximum-likelihood method was used.

Supporting information

S1 Fig. Forest plot of the meta-analysis of the prevalence of high biofilm producers in Candida spp. isolates.

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S2 Fig. Forest plot of the meta-analysis of the prevalence of intermediate biofilm producers in Candida spp. isolates.

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S3 Fig. Forest plot of the meta-analysis of the prevalence of low biofilm producers in Candida spp. isolates.

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S1 Table. Subgroup analysis between different Candida species and biofilm-forming capability.

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S1 File. The PRISMA statement for reporting meta-analysis of the present study.

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S2 File. The databases of the present study for metanalyses process.

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S3 File. The R-code used in the present meta-analysis.

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Click here for additional data file.^{(19.7KB, txt)}

Acknowledgments

A special recognition deserves all colleagues of the Microbiology Institute of USFQ and COCIBA, as well as the Research Office of Universidad San Francisco de Quito.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was supported by COCIBA Research Grant 2018-2019 through project ID: 12260 entitled “Adhesión inicial y resistencia antimicrobiana de Candida sp. aisladas de la microbiota humana”, under regulations of the Ministry of Health of Ecuador (Contrato Marco de Acceso a los Recursos Genéticos No. MAE-DNB-CM-2016-0046).

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PLoS One. doi: 10.1371/journal.pone.0263522.r001

Decision Letter 0

Surasak Saokaew

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8 Nov 2021

PONE-D-21-23498Prevalence of Biofilms in Candida spp. bloodstream infections: A Meta-analysisPLOS ONE

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Reviewer #1: Although this paper has merit in concept the authors appear to have tried to undertake a review that requires clinical knowledge without the background to do so. The ability of an organism to produce biofilm under laboratory conditions does not necessarily represent the condition of the organism under its clinically infectious state, especially when the review is aimed at only blood cultures, rather than taking into account infections linked to indwelling surfaces such as heart valves and prosthetics.

The authors attempt to compare levels of biofilm production between papers but do not comment on methodology utilised to assess biofilm production to support the reader in understanding whether they are reviewing comparable methods. Discussion of clinical presentations include items such as neonates and TPN without linking the fact that pre-term infants are likely to be on TPN and therefore this is correlation not causation. Is some of the data linked to considerable variation in mortality linked to this lack of clinical interpretation or patient factors or items such as drug availability. Were lines left in situ as a continuous source for instance - without information on whether the clinical analysis is comparable it is difficult to say, as noted with the laboratory comparisons, if the data is truly comparable.

Therefore although the paper is of interest and could be modified for publication with further information I do not feel it is publishable in its current form.

Reviewer #2: The authors present a meta-analysis of Candida infections, and compare prevalence among geographic locations, Candida species, phenotypes (planktonic vs. biofilm; “biofilm forming capability”), and they also consider Candida resistance to a few anti-fungal agents.

There are some major issues, the most serious is that there are many confusing issues with the statistical diagnostics and reporting of statistical results. Secondly, the authors could provide a lot more details about how biofilm associated infections were diagnosed.

1. Statements re: standard statistical tests for publication bias could be better worded. It’s great that the authors include the funnel plot so that readers can decide for themselves whether the plot suggests publication bias. Asymmetry IS evident in the plot, it is just that Egger’s test fails to find that the asymmetry is drastic enough to suggest publication bias. In light of this, the statement in lines 24-25 about the funnel plot and Egger's test showing no publication bias is incorrect (p=0.896), replace with a statement that says that Egger's test failed to show publication bias, or failed to find publication bias. Similarly, also change the incorrect wording on line 113, “the symmetry of the funnel plot confirmed the hypothesis of absence of bias,” it did not.

2. Even more worrisome is that the funnel plot does not show a funnel at all, what does this mean?!?! Are the authors conclusions still valid?

3. For the PLOS ONE audience, please explain why the funnel plot should look like a funnel, and explain the axes.

4. Line 100, what does the p-value < 0.001 associate with? Not the t-stat of 0.387! The Q stat is the test statistic for the test of heterogeneity across studies. What is the t-stat for? Both these stats have associated p-values, but only one is reported here.

5. Tables 2 and 3, What are k, P, p*, Q, I and $\\tau$ in the table? Is k the number of studies? Is P the p-value for Egger's test? For the general PLOS audience not familiar with meta-analyses, please provide brief summaries of these.

6. Lines 149-150, “Although the biofilm prevalence varied among regions, no statistically significant value was obtained in this subgroup analysis” is uninformative. Consider replacing with “Although the biofilm prevalence varied among regions, there were no statistically significant differences (p = 0.4049).”

7. Lines 151-152, the authors are keen to point out getting a small p-value, “Meanwhile, when comparing prevalence rates between countries, a statistically significant value was obtained (p = 0.0074)”. The authors should make clear WHY there was small p-value. That p-value says there is some statistically significant difference between at least 2 countries, but the authors do not make clear which countries are statistically significantly different. In lines 153 and 154 the authors mention that Italy has the lowest rate and that Spain has the highest. Is Spain statistically significantly the highest than all other countries? Or maybe Spain is statistically significantly higher than just Italy? The authors make a nebulous reference to this comparison again in lines 236-237 which seems to suggest that Spain is statistically significantly higher than just Italy. Please clarify.

8. Table 4, why do the authors use notation P* instead of p* as in Table 3? What does P* mean?

9. Lines 173-175, the authors state “68.8% of isolates from C. tropicalis were high biofilm formers, showing statistically significant differences among Candida species according to its ability to form high biofilms (p < 0.0001).” So was C tropicalis statistically significantly highest compared to any other HBF species in Table 5? or was it just statistically significantly higher than a subset? Make clear what the subset is!

10. I do not understand what the authors are trying to say in lines 192-194, please re-write.

11. Lines 56-57, I am surprised that a sole blood culture can show biofilm infection? I would expect that for a biofilm related infection to be determined, then biofilm would have to be identified in an associated catheter or medical device or in tissue. This is similar to how a catheter-related-blood-stream-infection is diagnosed. Please describe how biofilm infection can be assessed solely from a blood sample.

12. Lines 213-214, the authors indicate a “lack of differentiation between planktonic and biofilm-related Candida infections in the diagnosis of the clinical laboratories at public health system” What is the current methodology used by these labs? Why were the authors able to differentiate between planktonic and biofilm infections?

Minor comments:

Abstract:

13. Lines 20 and 71, Please make some mention which databases were used for the literature review up front in the abstract and results, for example say “from publicly available data bases”. Right now, we do not learn which data bases were used until the methods section that comes at the end of the paper.

14. Line 23, Not sure the Q statistic ought to be reported in the abstract. If authors do want to leave it in, for the general PLOSONE audience, state what Q means and what it is used for.

15. Line 24 I^2 = 98.83% is huge! What does it mean?

16. Lines 26-27, two statements are made re: mortality (planktonic and biofilm), then the p-value < 0.0001 is stated. What test and which parameter does the p-value go with?

17. Line 29, what is meant by a low, medium or high biofilm? If you want to mention this in the abstract, then be clear here what is meant. The authors finally mention what is meant in the caption to Table 4 and line 163. Please explain how this determination “biofilm forming capacity” was made? Was it based on a categorization of the blood work outcome?

Intro:

18. Line 44, here the authors report on prevalence of nosocomial infections in the US. Since the meta-analysis focuses on Europe and Asia, please report Candida infection rate in Europe and Asia.

Results:

19. Line 75, Why and How were these 5 papers chosen? Figure 1 does not address these 5 or how they were chosen. This question is addressed later, but a brief explanation should be included here.

20. Table 2, I think by “All Candida spp. bloodstream infections” the authors mean planktonic associated infections, please clarify.

21. The sentence in lines 163-164 adds no more information than what is already in Table 4, I suggest removing this sentence.

Discussion:

22. Line 203, the statement re: “4/5” is not supported by Table 6. Table 6 instead suggests 70%. please clarify.

23. Line 307, the authors state “This meta-analysis is one of the few …” Where do the authors cite the other meta-analyses? Please refer to them here.

Methods:

24. Line 369, RStudio is a way to interact with R! It is OK to cite RStudio, but also cite the R software itself in the list of citations: R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

25. Line 370, please properly cite the R packages. To find the citations, within R, use citation("meta"), etc.

26. Line 372, Please state what the random effect was. I am assuming the random effect was for study, but say so.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Elaine Cloutman-Green

Reviewer #2: No

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PLoS One. 2022 Feb 3;17(2):e0263522. doi: 10.1371/journal.pone.0263522.r002

Author response to Decision Letter 0

29 Nov 2021

Revised Manuscript

Answer to the Reviewer’s comments

Based on the following comments and suggestions, we have made new modifications to the previous original manuscript. These additional changes are also shown in the newly revised manuscript with track changes.

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: No

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Author’s answer – We are very grateful to both Reviewers for their constructive comments and thoughtful suggestions that really allowed us to improve the initial draft manuscript.

Reviewer 1 Report

Reviewer #1 (Doctor Elaine Cloutman-Green):

Although this paper has merit in concept the authors appear to have tried to undertake a review that requires clinical knowledge without the background to do so. The ability of an organism to produce biofilm under laboratory conditions does not necessarily represent the condition of the organism under its clinically infectious state, especially when the review is aimed at only blood cultures, rather than taking into account infections linked to indwelling surfaces such as heart valves and prosthetics.

Author’s answer – We want to thank Doctor Elaine Cloutman-Green (Reviewer 1) for her constructive comments and thoughtful suggestions that really allowed us to improve the initial draft manuscript. Based on her comments and suggestions, we clarified confusing data and statements that initially we were not able to see in the original manuscript. We added a new table 2 in the revised manuscript describing the available information about the clinical background of the patients, which the authors evaluated the ability of Candida isolates to produce biofilm (Please check the new Table 2 on page 13 and the paragraph describing the available clinical background from the patients on page 15 lines 207-229 of the revised manuscript with track changes). It is well-known that the ability of a microorganism to produce biofilm in vitro does not necessarily represent the condition found in the infectious state of the patients; however, it is a virulence factor that may contribute to a more severe infection, and it is usually not detected in a standard evaluation of the isolated microorganism in the clinical procedures, which it was the main goal of the present study. Our results clearly correlated the ability to produce biofilm from Candida isolates with a higher mortality rate among patients with invasive candidiasis from different clinical backgrounds. Still, we recognized that the main drawbacks of the present study were the lack of a thorough clinical background from the patients and the diversity of the primary infections from the patients, such as infections linked to indwelling surfaces. Therefore, these limitations were added to the Results and Conclusions sections to better clarify the Readers (Please check these limitations on page 15 lines 207-209 at Results section and on page 28 lines 490-492 at Conclusions section of the revised manuscript with track changes).

Author’s answer – As well-appointed by Reviewer 1 (Doctor Elaine Cloutman-Green), although we stated in the Eligibility criteria section (on page 30 lines 536-537 of the revised manuscript with track changes) that all methods to quantify biofilm biomass was not a criterion to include or exclude any paper in this meta-analysis, we did not report the methodologies applied in these studies neither the biofilm classification criteria. Therefore, we added a new column in Table 1 with its respective legend and a new paragraph in the Overall effects of Candida biofilms section describing the methodologies applied in these studies and their biofilm classification criteria (Please check the new Table 1 on pages 7-8 and the new paragraph on pages 8-9 lines 142-150 of the revised manuscript with track changes). Although the methodologies to quantify biofilm biomass varied between studies, these methodologies are based on the optical density (OD) obtained by the combination of a certain colorimetric compound with the growth of the isolated Candida sp. and then it’s compared with reference strains in the same growth conditions. So, it is possible to compare results of biofilm production among Candida species between studies. The same procedure is also realized in biofilm classification criteria, but unfortunately, only 18 of the 31 studies realized this further evaluation, as reported in the Biofilm-forming capability in Candida spp. isolates section (Please check the description on page 18 lines 268-276 of the revised manuscript with track changes). Still, as described in the Discussion section, this diversity of methodologies could partially explain the inconsistency among the reports of Candida-associated biofilm infections in the published studies. Likewise, other numerous factors could also contribute to this heterogeneity of reports, such as the lack of differentiation between Candida species, the experience of the researchers, and the number of Candida isolates in the study set. These other factors were added in the Discussion section to avoid misunderstanding of the Readers (Please check the amendments on page 21 lines 339-344 of the revised manuscript with track changes).

Concerning clinical presentations of the patients, we recognized that the original manuscript contained items and statements (such as neonates, TPN, and drug availability) lacking clinical interpretation or patient factors in the Results and Discussion sections. Several rectifications of these items and statements were realized in the revised manuscript trying to elucidate the gaps of the study and to avoid misunderstanding of the Readers (Please check these amendments on Results and Discussion sections on page 15 lines 207-222 and pages 22-24 lines 367-398, respectively, of the revised manuscript with track changes). However, as previously mentioned, these limitations were added to the Conclusions section to avoid misunderstanding of the Readers (Please check these limitations on page 28 lines 490-492 of the revised manuscript with track changes).

Therefore although the paper is of interest and could be modified for publication with further information I do not feel it is publishable in its current form.

Author’s answer – We are very grateful to Reviewer 1 for her constructive comments and thoughtful suggestions that really allowed us to improve the original manuscript. Several modifications were made to clarify the clinical background of the patients (the new Table 2, and the amendments in Results and Discussion sections) and the classification of biofilm production by Candida isolates in the selected studies (the new Table 1, and the amendments in Results section), also recognizing the limitations of the present meta-analysis. We hope that Reviewer 1 finds this version suitable for publication in PLOS ONE journal.

Reviewer 2 Report

Reviewer #2:

The authors present a meta-analysis of Candida infections, and compare prevalence among geographic locations, Candida species, phenotypes (planktonic vs. biofilm; “biofilm forming capability”), and they also consider Candida resistance to a few anti-fungal agents.

Author’s answer – We want to thank Reviewer 2 for the constructive comments and thoughtful suggestions that really allowed us to improve the original manuscript. As recommended by the Reviewer, several rectifications were made in the statistical diagnostics and results to clarify confusing issues and to avoid misunderstanding of the Readers. These amendments are detailed in the following answers to Reviewer 2 and illustrated in the revised manuscript with track changes.

Author’s answer – As well-appointed by Reviewer 2, the standard tests for publication bias could be more accurately described and the presence of asymmetry is undeniable. To avoid misunderstanding by the Readers, we modified the original sentences on page 3 lines 43-44 and line 113 as suggested by Reviewer 2 (Please check these amendments on page 2 lines 30-31 and on page 11 lines 178-181 of the revised manuscript with track changes).

2. Even more worrisome is that the funnel plot does not show a funnel at all, what does this mean?!?! Are the authors conclusions still valid?

Author’s answer – As well-questioned by Reviewer 2, the funnel plot illustrated heterogeneity in the results among the studies of the data set. This heterogeneity does not invalidate the conclusions of the present manuscript. However, the high value of heterogeneity obtained in the pooled rate of biofilm formation in the data set, through the forest and funnel plots, needs to be carefully analyzed.

As explained in the Discussion section of the revised manuscript with track changes, our Q and I2 values suggest high heterogeneity within each group, indicating that this heterogeneity is probably multifactorial, supported for a multiple meta-regression model results that explained more than 50% of the global variability as interacting variables. For example, if we consider only the articles from Italy, the sample size in 5 of 6 articles does not considerably differ but the effect size is quite different impacting, therefore, the funnel plot distribution. Furthermore, the asymmetry is probably not due to publication bias as the underlying cause, but to other information biases. Conclusions are still valid but should be interpreted with caution due to several factors that can be modulating them.

The triangular geometry of the funnel plot is basically a relationship between the effect-size and the error is associated with sampling distribution across studies. In our case, we found studies with low errors (similar sizes) but with drastic differences in the biofilm prevalence. This type of pattern probably indicates the presence of confounding variables (sub-groups undelaying structures), which are not included in the global analysis. Due to this high heterogeneity and considering the important observation of Reviewer 2, we include three segments in the Discussion section addressing this problem (Please check these explanations on pages 22-23 lines 372-385, on pages 24-25 lines 415-422, and on page 26 lines 455-460 of the revised manuscript with track changes) and we also included another segment describing a further meta-regression analysis using a combination of interacting variables in two models (Please check these results on pages 19-20 lines 304-308 of the revised manuscript with track changes).

References

Page, MJ, Sterne, JAC, Higgins, JPT, Egger, M. Investigating and dealing with publication bias and other reporting biases in meta-analyses of health research: A review. Res Syn Meth. 2021; 12: 248– 259. https://doi.org/10.1002/jrsm.1468

Sterne J A C, Sutton A J, Ioannidis J P A, Terrin N, Jones D R, Lau J et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials BMJ 2011; 343: d4002.

https://doi.org/10.1136/bmj.d4002

3. For the PLOS ONE audience, please explain why the funnel plot should look like a funnel and explain the axes.

Author’s answer – As well-recommended by Reviewer 2, the brief and simple explanation about the funnel plot, their axes, and expected results were written for the Readers of PLOS ONE. More exactly, we add a summary of the funnel plot on page 12 lines 185-192 of the revised manuscript with track changes with the following text:

“The funnel plot of this study illustrates the effect size (biofilm prevalence) on the x-axis and the standard error (SE) on the y-axis. In case of no publication bias in the data set, the studies are distributed evenly around the pooled effect size. The smaller studies should appear near the bottom due to their higher variance when compared to the larger studies, which should be placed at the top of the plot. The diagonal lines show the expected 95% confidence intervals around the summary estimate. In the absence of heterogeneity, the studies of the data set should lie within the funnel defined by these diagonal lines. However, heterogeneity and some asymmetries among the studies of the data set were illustrated by the funnel plot.”

Finally, an explanation of each axis was also added below Fig. 3 (Please check this description on page 11 lines 176-177 of the revised manuscript with track changes) with the following short text:

“Studies are represented by a point. The X-axis represents the effect size (biofilm prevalence), and the Y-axis shows the standard error.”

Reference

Sterne JA, Egger M. Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. J Clin Epidemiol. 2001 Oct;54(10):1046-55. doi: 10.1016/s0895-4356(01)00377-8. PMID: 11576817.

Author’s answer – As questioned by Reviewer 2, the p-value < 0.001 referred to the prevalence of biofilm rate in the data set, showing a statistical prevalence. This explanation was clarified in the original sentence, and we also replaced the t-stat value with the �2 value (�2 = 0.150). The initial p-value mentioned is associated with the model, which justifies the random-effect analysis. The t-test was not used, but rather tau-squared which estimates the variance between the effect sizes of the studies in the model. Unfortunately, there is still no consensus on which cut-off points to use to interpret this test. Finally, we also included the p-value of Cochran's Q (Please check these amendments on page 9 lines 157-161 of the revised manuscript with track changes).

Author’s answer – As well-proposed by Reviewer 2, a summary of each statistic symbol was included in the section “Data analysis and statistical methods” and the meaning of each statistic symbol was added in the legends below the tables for the general PLOS audience not familiar with meta-analyses (Please check these legends now on tables 3 and 4 at pages 16-17 and the brief summary on page 31 lines 561-569 of the revised manuscript with track changes). Lastly, the k is the number of studies, the p-value (in lower case) mentioned corresponds to Egger's test, and the p*-value corresponds to the random-effect model significance level. It is important to mention that the notation of P* was wrongly put in the tables due to automated correction of p* (in lower case) and we apologize for this mistake.

Author’s answer – As advised by Reviewer 2, the sentence from lines 149-150 was replaced by “Although the biofilm prevalence varied among regions, there were no statistically significant differences (p = 0.4049).” (Please check this adjustment on page 17 lines 257-258 of the revised manuscript with track changes).

Author’s answer – As well-appointed by Reviewer 2, pairwise comparisons were realized between Spain and other countries demonstrating that Spain was statistically superior to Brazil (p <0 .0001), Italy (p = 0.0263), and India (p = 0.0030). We clarified these results on page 17 lines 261-264 in the Results section and also on page 24 lines 401-404 in the Discussion section. The p-value corresponds to the test for pairwise compared subgroup differences.

8. Table 4, why do the authors use notation P* instead of p* as in Table 3? What does P* mean?

Author’s answer – As previously answered to Reviewer 2, the notation of P* was wrongly put in the tables due to the automated correction of p* (in lower case). We rectified this error, and we apologize for this mistake (Please check this rectification now on Table 5 on page 18 of the revised manuscript with track changes).

Author’s answer – As well-observed by Reviewer 2, C. tropicalis was statistically higher in this subgroup analysis between all Candida species. To better clarify the Readers, we recalculated and performed a pairwise comparison between each of the Candida species clarifying the original sentence (Please check this rectification now on page 19 lines 294-296 of the revised manuscript with track changes), more exactly:

“More precisely, the HBF prevalence of C. tropicalis was the highest showing statistically significant differences with the other Candida species, except for C. krusei (p = 0.5477) and C. glabrata (p = 0.0896)”.

10. I do not understand what the authors are trying to say in lines 192-194, please re-write.

Author’s answer – We apologized for this confusing sentence. As well-suggested by Reviewer 2, we re-wrote the original sentence to avoid misunderstanding by the Readers (Please check the new sentence on page 20 lines 321-325 of the revised manuscript with track changes), more exactly:

“When comparing to planktonic cells, Candida-related biofilm isolates showed a statistical increment of resistance against the three antifungals evaluated in the study (p < 0.001).”

Author’s answer – As wished by Reviewer 2, a more detailed explanation was added about how biofilm infection was assessed from a blood sample (Please check this description on pagse 4-5 lines 88-93 of the revised manuscript with track changes). Briefly, the selected studies performed an in vitro biofilm assay using Candida isolates from blood samples of patients. It is important to mention that the study set of the present meta-analysis included patients with catheter-related candidemia (CRC) and non-CRC. In cases of patients with CRC, the standard procedure was blood cultures from obtained the catheter and peripheral veins, whereas non-CRC was indicated by the recovery of Candida spp. from only blood samples, as previously described by Guembe et al. (2014). Finally, a better portrayal of the methodologies applied in the selected studies was added to Table 1 and in the Results section to better clarify for the Readers (Please check the new Table 1 on pages 7-8 and the new paragraph on pages 8-9 lines 142-150 of the revised manuscript with track changes).

Reference

Guembe M, Guinea J, Marcos-Zambrano L, Fernández-Cruz A, Peláez T, Muñoz P, Bouza E. Is biofilm production a predictor of catheter-related candidemia? Med Mycol. 2014 May;52(4):407-10. doi: 10.1093/mmy/myt031. Epub 2014 Apr 28. PMID: 24782103.

Author’s answer – As well-questioned by Reviewer 2 and previously discussed by Høiby and colleagues (2015), the traditional clinical microbiology laboratories have focused on culturing and testing planktonically (=non-aggregated) growing microorganisms and have reported the susceptibility to various antibiotics and antiseptics under planktonic growth conditions.

Meanwhile, the authors of selected studies of this manuscript applied a further analysis by evaluating the ability of biofilm production in Candida isolates through an in vitro biofilm assay. Furthermore, in Candida biofilms, traditional techniques require device removal followed by culture or microscopy of a catheter segment, while catheter-sparing diagnostic tests include paired quantitative blood cultures. However, as previously indicated by Høiby et al. (2015) and Bouza et al. (2013), the number of positive peripheral blood cultures also seems to be a promising diagnostic tool to diagnose catheter-related candidemia without directly removing the catheter.

Finally, all these clarifications were added in the revised manuscript (Please check this description on pages 21-22 lines 346-355 of the revised manuscript with track changes).

References

Høiby N, Bjarnsholt T, Moser C, Bassi GL, Coenye T, Donelli G, Hall-Stoodley L, Holá V, Imbert C, Kirketerp-Møller K, Lebeaux D, Oliver A, Ullmann AJ, Williams C; ESCMID Study Group for Biofilms and Consulting External Expert Werner Zimmerli. ESCMID guideline for the diagnosis and treatment of biofilm infections 2014. Clin Microbiol Infect. 2015 May;21 Suppl 1:S1-25. doi: 10.1016/j.cmi.2014.10.024. Epub 2015 Jan 14. PMID: 25596784.

Bouza E, Alcalá L, Muñoz P, Martín-Rabadán P, Guembe M, Rodríguez-Créixems M; GEIDI and the COMIC study groups. Can microbiologists help to assess catheter involvement in candidaemic patients before removal? Clin Microbiol Infect. 2013 Feb;19(2):E129-35. doi: 10.1111/1469-0691.12096. Epub 2012 Dec 10. PMID: 23231412.

Minor comments:

Abstract:

Author’s answer– As well-recommended by Reviewer 2, we mentioned the databases used in the study, more exactly:

Original line 20 (now on page 2 lines 37-38 of the revised manuscript with track changes): “A total of 31 studies from publicly available databases met our inclusion criteria.”

Original line 71 (now on page 5 lines 108-109 of the revised manuscript with track changes): “A total of 214 studies were retrieved and 70 full texts were reviewed from publicly available databases (Web of Science, Scopus, PubMed, and Google Scholar).”

Finally, it is important to mention that the Abstract section was rewritten following the PLOS ONE guidelines for a meta-analysis study (Please check the new Abstract section on pages 2-3 of the revised manuscript with track changes).

14. Line 23, Not sure the Q statistic ought to be reported in the abstract. If authors do want to leave it in, for the general PLOSONE audience, state what Q means and what it is used for.

Author’s answer– We decided to maintain the Q statistic because it is standard procedure in most meta-analysis studies that we read before. However, as suggested by Reviewer 2, we added the indication that Q is related to the heterogeneity of the results together with I2 and t2, indicating the high heterogeneity obtained through the random-effects model.

Original line 23 (now on page 2 lines 39-41 of the revised manuscript with track changes): “Forest plot showed a pooled rate of biofilm formation of 80.0 % (CI: 67–90), with high heterogeneity (Q = 2567.45, I2 = 98.83, t2 = 0.150) in random effects model (p<0.001).”

15. Line 24 I^2 = 98.83% is huge! What does it mean?

Author’s answer– As already mentioned in the responses to comments number 2 and 3 of Reviewer 2, the high heterogeneity is probably due to multifactorial causes, which is supported by the results of our meta-regression model. As clarified on pages 19-20 lines 304-308 of the revised manuscript with track changes, this meta-regression model explains more than 50% of the overall variability as interacting variables, specifically geographical distribution, and Candida species. In addition, it is also reported that the lack of information about the clinical background among patients in the selected studies (data set) is the main drawback and may be an important source of unexplained variation in the present meta-analysis. These results are extensively explained in several paragraphs in the Discussion section, more exactly, on pages 22-23 lines 372-385, on pages 24-25 lines 415-422, and on page 26 lines 455-460 of the revised manuscript with track changes.

16. Lines 26-27, two statements are made re: mortality (planktonic and biofilm), then the p-value < 0.0001 is stated. What test and which parameter does the p-value go with?

Author’s answer– As well-appointed by Reviewer 2, the p-value < 0.0001 was stated in the Abstract section and leading to misunderstanding by the Readers. The p-value < 0.0001 was obtained through the random effect model significance level, which is already clarified in Table 2 (now Table 3 in the revised manuscript) and associated with the mortality rate in biofilm-associated infections. However, we re-wrote the original sentence to avoid misinterpretation by the Readers (Please check this description on page 3 lines 44-45 of the revised manuscript with track changes), more exactly:

“The mortality rate in Candida-related bloodstream infections was 37.9% of which 70.0% were from biofilm-associated infections.”

Author’s answer– As suggested by Reviewer 2, we clarified that the classification of Candida isolates in low, intermediate, or high biofilm formers was based on the level of biofilm mass (crystal violet staining or XTT assays) in the Abstract section (Please check this description on page 3 lines 46-48 of the revised manuscript with track changes). Also, we explained how the classification of Candida isolates was made in the studies of the data set in the Results section (Please check this explanation on page 18 lines 268-275 of the revised manuscript with track changes), more exactly:

“Briefly, biofilm forming capacity was assessed using the crystal violet or XTT assays, measuring the biofilm mass. Candida isolates were cultured in 96-well plates at 37°C for 24 h and the biomass of each isolate was measured. Then, isolates were grouped based on their level of biomass, more exactly: low biofilm formers (LBF) showed a biomass production below the 1st quartile (Q1; Absisolate < 0.432), intermediate biofilm formers (IBF) evidenced a biomass production in the 2nd quartile (Q2; 0.432 < Absisolate < 1.07), and high biofilm formers (HBF) demonstrated a biomass production higher the 1st quartile 3rd quartile (Q3; Absisolate > 1.07), as previously described by Monfredini et al. (2018) and Vitális et al. (2020).”

References

Monfredini PM, Souza ACR, Cavalheiro RP, Siqueira RA, Colombo AL. Clinical impact of Candida spp. biofilm production in a cohort of patients with candidemia. Med Mycol. 2018 Oct 1;56(7):803-808. doi: 10.1093/mmy/myx133. PMID: 29228246.

Vitális E, Nagy F, Tóth Z, Forgács L, Bozó A, Kardos G, Majoros L, Kovács R. Candida biofilm production is associated with higher mortality in patients with candidaemia. Mycoses. 2020 Apr;63(4):352-360. doi: 10.1111/myc.13049. Epub 2020 Jan 23. PMID: 31943428.

Intro:

18. Line 44, here the authors report on prevalence of nosocomial infections in the US. Since the meta-analysis focuses on Europe and Asia, please report Candida infection rate in Europe and Asia.

Author’s answer– As suggested by Reviewer 2, we added the report on candidemia incidence in Europe and Asia, as well as one study realized in Latin America evolving seven countries (Please check this information on page 4 lines 70-76 of the revised manuscript with track changes), more exactly:

“In Europe, Bassetti and colleagues realized a multinational and multicenter study in 2019 reporting 7.07 episodes per 1000 in European intensive care units (ICUs) with a 30-day mortality of 42% [3]. While, in the Asia-Pacific region, Hsueh and colleagues reported a candidemia incidence in ICUs of 5- to 10-fold higher than in the entire hospital and a mortality rate of patients between 35% and 60% [4]. In Latin America, Nucci and colleagues realized a laboratory-Based Survey between November 2008 and October 2010 among 20 tertiary care hospitals in seven Latin American countries, reporting an overall incidence of 1.18 cases per 1,000 in general admissions [5].”

References

Bassetti, M., Giacobbe, D.R., Vena, A. et al. Incidence and outcome of invasive candidiasis in intensive care units (ICUs) in Europe: results of the EUCANDICU project. Crit Care 23, 219 (2019). https://doi.org/10.1186/s13054-019-2497-3.

Hsueh PR, Graybill JR, Playford EG, Watcharananan SP, Oh MD, Ja'alam K, Huang S, Nangia V, Kurup A, Padiglione AA. Consensus statement on the management of invasive candidiasis in Intensive Care Units in the Asia-Pacific Region. Int J Antimicrob Agents. 2009 Sep;34(3):205-9. doi: 10.1016/j.ijantimicag.2009.03.014. Epub 2009 May 5. PMID: 19409759.

Nucci M, Queiroz-Telles F, Alvarado-Matute T, Tiraboschi IN, Cortes J, Zurita J, et al. Epidemiology of Candidemia in Latin America: A Laboratory-Based Survey. PLoS One. 2013;8: e59373. doi:10.1371/journal.pone.0059373

Results:

19. Line 75, Why and How were these 5 papers chosen? Figure 1 does not address these 5 or how they were chosen. This question is addressed later, but a brief explanation should be included here.

Author’s answer– As well-recommended by Reviewer 2, we added a brief explanation about the selection of at least 5 or more papers to realize subgroup analysis using a random-effect model and to answer other relevant questions about Candida-related biofilms (such as the mortality rate related to biofilms, the geographical distribution of biofilms, the characterization of biofilm production among Candida species, and the correlation between biofilm formation and antifungal resistance). Please check this explanation on page 6 lines 115-119 of the revised manuscript with track changes.

20. Table 2, I think by “All Candida spp. bloodstream infections” the authors mean planktonic associated infections, please clarify.

Author’s answer– As suggested by Reviewer 2, we clarified the information about “All Candida spp. bloodstream infections” in the text before the original Table 2 (now Table 3 in the revised manuscript), more exactly:

Original lines 129-131 (now on page 16 lines 234-237 of the revised manuscript with track changes)

“As shown in Table 3, the pooled mortality rate due to Candida-related bloodstream infections was 37.9% (95% CI: 26.2-50.2) of which the mortality associated with biofilm-forming infections was 70.0% (95% CI: 52.8–84.8).”

21. The sentence in lines 163-164 adds no more information than what is already in Table 4, I suggest removing this sentence.

Author’s answer– As advised by Reviewer 2, we removed the sentence on lines 163-164 of the original manuscript (Please check this deletion on page 18 lines 281-283 of the revised manuscript with track changes).

Discussion:

22. Line 203, the statement re: “4/5” is not supported by Table 6. Table 6 instead suggests 70%. please clarify.

Author’s answer– As well-observed by Reviewer 2, we apologized for this mistake. It is around 70.0% and not 80.0% as stated in the sentence of the Discussion section. We rectified the sentence in the original line 203 to avoid misunderstanding by the Readers (Please check the new sentence on page 21 lines 334-335 of the revised manuscript with track changes).

23. Line 307, the authors state “This meta-analysis is one of the few …” Where do the authors cite the other meta-analyses? Please refer to them here.

Author’s answer– As suggested by Reviewer 2, we cited the other meta-analyses in the sentence of the Conclusions section (Please check these references in the sentence on page 28 lines 494-497 of the revised manuscript with track changes), more exactly:

“To the authors’ best knowledge, this meta-analysis is one of the few that explored the association of biofilm production among different Candida species in bloodstream infections [64–67], using data published worldwide and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline.”

References

64. Pammi M, Holland L, Butler G, Gacser A, Bliss JM. Candida parapsilosis is a significant neonatal pathogen: a systematic review and meta-analysis. Pediatr Infect Dis J. 2013 May;32(5):e206-16. doi: 10.1097/INF.0b013e3182863a1c. PMID: 23340551; PMCID: PMC3681839.

65. Buehler SS, Madison B, Snyder SR, Derzon JH, Cornish NE, Saubolle MA, Weissfeld AS, Weinstein MP, Liebow EB, Wolk DM. Effectiveness of Practices To Increase Timeliness of Providing Targeted Therapy for Inpatients with Bloodstream Infections: a Laboratory Medicine Best Practices Systematic Review and Meta-analysis. Clin Microbiol Rev. 2016 Jan;29(1):59-103. doi: 10.1128/CMR.00053-14. PMID: 26598385; PMCID: PMC4771213.

66. Kobayashi T, Marra AR, Schweizer ML, Ten Eyck P, Wu C, Alzunitan M, Salinas JL, Siegel M, Farmakiotis D, Auwaerter PG, Healy HS, Diekema DJ. Impact of Infectious Disease Consultation in Patients With Candidemia: A Retrospective Study, Systematic Literature Review, and Meta-analysis. Open Forum Infect Dis. 2020 Aug 3;7(9):ofaa270. doi: 10.1093/ofid/ofaa270. PMID: 32904995; PMCID: PMC7462368.

67. Pinto H, Simões M, Borges A. Prevalence and Impact of Biofilms on Bloodstream and Urinary Tract Infections: A Systematic Review and Meta-Analysis. Antibiotics (Basel). 2021 Jul 8;10(7):825. doi: 10.3390/antibiotics10070825. PMID: 34356749; PMCID: PMC8300799.

Methods:

Author’s answer– As well-appointed by Reviewer 2, RStudio is only a way to interact with R. So we rectified the sentence also citing R software (Please check the amendment and this reference in the sentence on page 31 lines 559-561 of the revised manuscript with track changes), more exactly:

“Meta-analysis was performed using several R packages ("meta", "metafor", "poibin", and "stringr") of R version 3.4.3 [70] and RStudio version 1.3.1073 [71] (S3 File) .”

References

70. R Core Team. R: A language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing; 2021. Available: https://www.r-project.org/.

71. RStudio Team. RStudio: Integrated Development for R. Boston (USA): RStudio, Inc.; 2021. Available: http://www.rstudio.com/

25. Line 370, please properly cite the R packages. To find the citations, within R, use citation("meta"), etc.

Author’s answer– As previously answered to Reviewer 2, the original sentence was rectified properly citing R software and the R packages (Please check the amendment and this reference in the sentence on page 31 lines 559-561 of the revised manuscript with track changes), more exactly:

“Meta-analysis was performed using several R packages ("meta", "metafor", "poibin", and "stringr") of R version 3.4.3 [70] and RStudio version 1.3.1073 [71] (S3 File).”

26. Line 372, Please state what the random effect was. I am assuming the random effect was for study, but say so.

Author’s answer– As advised by Reviewer 2, we rectified the sentence by stating that the random-effects model was used for the study (Please check the amendment on page 31 lines 566-568 of the revised manuscript with track changes).

Author’s answer – We appreciated all suggestions and efforts made by Reviewer 2 that really allowed us to improve the initial draft manuscript. We hope that Reviewer 2 finds this version suitable for publication in PLOS ONE journal.

Attachment

Submitted filename: Response to Reviewers-Final.docx

Click here for additional data file.^{(82.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0263522.r003

Decision Letter 1

Surasak Saokaew

10 Jan 2022

PONE-D-21-23498R1Prevalence of biofilms in Candida spp. bloodstream infections: a meta-analysisPLOS ONE

Dear Dr. Antonio Machado,

Please submit your revised manuscript by January 18, 2022. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Surasak Saokaew, PharmD, PhD, BPHCP, FACP, FCPA

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: The authors did a very good job addressing the issues from the first review. The only remaining suggestion I have is to properly cite the R packages in the methods section, and add the following references to the bibliography:

For "meta"

Balduzzi S, Rücker G, Schwarzer G (2019). “How to perform a meta-analysis with R: a practical tutorial.” Evidence-Based Mental Health, 153–160.

For "metafor"

Viechtbauer W (2010). “Conducting meta-analyses in R with the metafor package.” Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03.

For "poibin"

Hong, Y. (2013). On computing the distribution function for the Poisson binomial distribution. Computational Statistics & Data Analysis, Vol. 59, pp. 41-51.

For "stringr"

Wickham H (2021). stringr: Simple, Consistent Wrappers for Common String Operations. http://stringr.tidyverse.org, https://github.com/tidyverse/stringr.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #2: No

PLoS One. 2022 Feb 3;17(2):e0263522. doi: 10.1371/journal.pone.0263522.r004

Author response to Decision Letter 1

10 Jan 2022

Reviewer 2 Report

Author’s answer –We thank Reviewer 2 for their insight and supportive comments. These amendments are detailed in the following answer to Reviewer 2 and illustrated in the revised manuscript with track changes. We hope that Reviewer 2 finds this version suitable for publication in PLOS ONE journal.

Reviewer #2:

6. Review Comments to the Author

For "meta"

Balduzzi S, Rücker G, Schwarzer G (2019). “How to perform a meta-analysis with R: a practical tutorial.” Evidence-Based Mental Health, 153–160.

For "metafor"

Viechtbauer W (2010). “Conducting meta-analyses in R with the metafor package.” Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03.

For "poibin"

Hong, Y. (2013). On computing the distribution function for the Poisson binomial distribution. Computational Statistics & Data Analysis, Vol. 59, pp. 41-51.

For "stringr"

Wickham H (2021). stringr: Simple, Consistent Wrappers for Common String Operations. http://stringr.tidyverse.org, https://github.com/tidyverse/stringr.

Author’s answer– As well-appointed by Reviewer 2, we properly cited the R packages in the methods section and added the references to the bibliography (Please check these amendments on page 30 lines 518-519 and page 41 lines 745-753 of the revised manuscript with track changes, respectively). Also, we want to thank Reviewer 2 to give us the proper citations of the R packages that allowed us to quickly rectify our mistake.

Attachment

Submitted filename: Response to Reviewers-Final.docx

Click here for additional data file.^{(30.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0263522.r005

Decision Letter 2

Surasak Saokaew

21 Jan 2022

Prevalence of biofilms in Candida spp. bloodstream infections: a meta-analysis

PONE-D-21-23498R2

Dear Dr. Antonio Machado,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Surasak Saokaew, PharmD, PhD, BPHCP, FACP, FCPA

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0263522.r006

Acceptance letter

Surasak Saokaew

25 Jan 2022

PONE-D-21-23498R2

Prevalence of biofilms in Candida spp. bloodstream infections: a meta-analysis

Dear Dr. Machado:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Surasak Saokaew

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Forest plot of the meta-analysis of the prevalence of high biofilm producers in Candida spp. isolates.

(TIF)

Click here for additional data file.^{(442.5KB, tif)}

S2 Fig. Forest plot of the meta-analysis of the prevalence of intermediate biofilm producers in Candida spp. isolates.

(TIF)

Click here for additional data file.^{(717.2KB, tif)}

S3 Fig. Forest plot of the meta-analysis of the prevalence of low biofilm producers in Candida spp. isolates.

(TIF)

Click here for additional data file.^{(435.8KB, tif)}

S1 Table. Subgroup analysis between different Candida species and biofilm-forming capability.

(DOCX)

Click here for additional data file.^{(20.1KB, docx)}

S1 File. The PRISMA statement for reporting meta-analysis of the present study.

(DOCX)

Click here for additional data file.^{(39.5KB, docx)}

S2 File. The databases of the present study for metanalyses process.

(XLSX)

Click here for additional data file.^{(648.6KB, xlsx)}

S3 File. The R-code used in the present meta-analysis.

(TXT)

Click here for additional data file.^{(19.7KB, txt)}

Attachment

Submitted filename: Response to Reviewers-Final.docx

Click here for additional data file.^{(82.9KB, docx)}

Attachment

Submitted filename: Response to Reviewers-Final.docx

Click here for additional data file.^{(30.7KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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Prevalence of biofilms in Candida spp. bloodstream infections: A meta-analysis

María Belén Atiencia-Carrera

Fausto Sebastián Cabezas-Mera

Eduardo Tejera

António Machado

Roles

Abstract

Context

Objective

Data sources

Study selection

Data extraction

Data synthesis

Conclusions

Introduction

Results

Study inclusion criteria and characteristics of the eligible studies

Fig 1. Prisma flow chart of included and excluded studies of the selection process.

Overall effects of Candida biofilms

Table 1. General information extracted from the data set selected for the present meta-analysis.

Fig 2. Forest plot of the meta-analysis of the prevalence of biofilm formation in Candida spp. isolated from blood clinical samples.

Fig 3. Funnel plot of the meta-analysis on the biofilm formation rate in Candida spp. isolated from blood clinical samples.

Table 2. The reported clinical background of the patients with Candida-related bloodstream infections in the study set.

Mortality among patients with Candida biofilm

Table 3. Pooled mortality rates in bloodstream infections due to Candida spp.

Geographical distribution of biofilm-forming Candida spp. isolates

Table 4. Subgroup analysis for different geographical regions and countries.

Biofilm-forming capability in Candida spp. isolates

Table 5. Overall effects in subgroups based on biofilm-forming capability.

Evaluation of biofilm formation between different Candida species

Table 6. Subgroup analysis between different Candida species.

Evaluation of antifungal resistance pattern among Candida isolates

Table 7. Summary of subgroup analysis for antifungal resistance in Candida spp. isolates.

Discussion

Overall effects of Candida biofilms in infections and mortality

Geographical distribution of Candida biofilm-related infections

Association between different Candida species in biofilm and infections

Antifungal resistance among Candida-related biofilm infections

Conclusions

Materials and methods

Data selection, search strategy, and study guidelines

Screening process

Eligibility criteria

Data extraction and quality assessment

Data analysis and statistical methods

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Surasak Saokaew

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Surasak Saokaew

Roles

Author response to Decision Letter 1

Decision Letter 2

Surasak Saokaew

Roles

Acceptance letter

Surasak Saokaew

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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