Skip to main content
PMC home page
PLOS ONE logoLink to PLOS ONE
. 2020 Aug 21;15(8):e0238160. doi: 10.1371/journal.pone.0238160

Diagnostic and prognostic value of hematological and immunological markers in COVID-19 infection: A meta-analysis of 6320 patients

Rami M Elshazli 1, Eman A Toraih 2,3, Abdelaziz Elgaml 4,5, Mohammed El-Mowafy 4, Mohamed El-Mesery 6, Mohamed N Amin 6, Mohammad H Hussein 2, Mary T Killackey 2, Manal S Fawzy 7,8,*, Emad Kandil 9,*
Editor: Farhat Afrin10
PMCID: PMC7446892  PMID: 32822430

Abstract

Objective

Evidence-based characterization of the diagnostic and prognostic value of the hematological and immunological markers related to the epidemic of Coronavirus Disease 2019 (COVID-19) is critical to understand the clinical course of the infection and to assess in development and validation of biomarkers.

Methods

Based on systematic search in Web of Science, PubMed, Scopus, and Science Direct up to April 22, 2020, a total of 52 eligible articles with 6,320 laboratory-confirmed COVID-19 cohorts were included. Pairwise comparison between severe versus mild disease, Intensive Care Unit (ICU) versus general ward admission and expired versus survivors were performed for 36 laboratory parameters. The pooled standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated using the DerSimonian Laird method/random effects model and converted to the Odds ratio (OR). The decision tree algorithm was employed to identify the key risk factor(s) attributed to severe COVID-19 disease.

Results

Cohorts with elevated levels of white blood cells (WBCs) (OR = 1.75), neutrophil count (OR = 2.62), D-dimer (OR = 3.97), prolonged prothrombin time (PT) (OR = 1.82), fibrinogen (OR = 3.14), erythrocyte sedimentation rate (OR = 1.60), procalcitonin (OR = 4.76), IL-6 (OR = 2.10), and IL-10 (OR = 4.93) had higher odds of progression to severe phenotype. Decision tree model (sensitivity = 100%, specificity = 81%) showed the high performance of neutrophil count at a cut-off value of more than 3.74x109/L for identifying patients at high risk of severe COVID‐19. Likewise, ICU admission was associated with higher levels of WBCs (OR = 5.21), neutrophils (OR = 6.25), D-dimer (OR = 4.19), and prolonged PT (OR = 2.18). Patients with high IL-6 (OR = 13.87), CRP (OR = 7.09), D-dimer (OR = 6.36), and neutrophils (OR = 6.25) had the highest likelihood of mortality.

Conclusions

Several hematological and immunological markers, in particular neutrophilic count, could be helpful to be included within the routine panel for COVID-19 infection evaluation to ensure risk stratification and effective management.

Introduction

Coronavirus disease– 2019 (COVID-19) is a disease that was detected in December 2019 in Wuhan, China, and led to the risk of mortality of about 2% [1]. This disease is caused due to infection with a recently arising zoonotic virus known as the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [2]. Previously, infection with coronaviruses appeared in 2002 within China in the form of SARS-CoV, and it appeared later also in 2012 within Saudi Arabia that was known as Middle East Respiratory Syndrome (MERS-CoV) [3, 4]. All these coronaviruses are enveloped positive-strand RNA viruses that are isolated from bats that can be transferred from animals to humans, human to human, and animals to animals [5]. They share a similarity in the clinical symptoms in addition to specific differences that have been recently observed [5–7]. The symptoms of this disease appear with different degrees that start in the first seven days with mild symptoms such as fever, cough, shortness of breath, and fatigue [8]. Afterward, critical symptoms may develop in some patients involving dyspnea and pneumonia that require patient’s management in intensive care units to avoid the serious respiratory complications that may lead to death [9]. However, there are no specific symptoms to diagnose coronavirus infection, and accurate testing depends on the detection of the viral genome using the reverse transcription-polymerase chain reaction (RT-PCR) analysis [10].

Unfortunately, COVID-19 is not limited to its country of origin, but it has spread all over the world. Therefore, there is no wonder emerging research has been directed to provide information and clinical data of patients infected with this virus that may help to not only to the early detection in different patient categories, but it will also help in the characterization of the viral complications with other chronic diseases [1, 2, 6, 9]. However, there is no sufficient data that characterize the changes in the hematological and immunological parameters in COVID-19 patients. In the current comprehensive meta-analysis study, we aimed to analyze different hematological, inflammatory, and immunological markers in COVID-19 patients at different clinical stages in different countries that may help in the early detection of COVID-19 infection and to discriminate between severity status of the disease to decrease the death risk.

Materials and methods

Search strategy

This current meta-analysis was carried out according to the Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) statement [11] (S1 Table). Relevant literature was retrieved from Web of Science, PubMed, Scopus, and Science Direct search engines up to April 22, 2020. Our search strategy included the following terms: “Novel coronavirus 2019”, “2019 nCoV”, “COVID-19”, “Wuhan coronavirus,” “Wuhan pneumonia,” or “SARS-CoV-2”. Besides, we manually screened out the relevant potential article in the references selected. The above process was performed independently by three participants.

Study selection

No time or language restriction was applied. Inclusion criteria were as follows: (1) Types of Studies: retrospective, prospective, observational, descriptive or case control studies reporting laboratory features of COVID-19 patients; (2) Subjects: diagnosed patients with COVID-19 (3) Exposure intervention: COVID-19 patients diagnosed with Real Time-Polymerase Chain Reaction, radiological imaging, or both; with hematological testing included: complete blood picture (white blood cells, neutrophil count, lymphocyte count, monocyte count, eosinophils count, basophils, red blood cells, hemoglobin, hematocrit, and platelet count), coagulation profile (prothrombin time, international normalized ratio, activated partial thromboplastin time, thrombin time, fibrinogen, and D-dimer) or immunological parameters including inflammatory markers (ferritin, erythrocyte sedimentation rate, procalcitonin, and C-reactive protein), immunoglobulins (IgA, IgG, and IgM), complement tests (C3 and C4), interleukins (IL-4, IL-6, IL-8, IL-10, IL-2R, and TNF-α), and immune cells (B lymphocytes, T lymphocytes, CD4+ T cells, and CD8+ T cells); and (4) Outcome indicator: the mean and standard deviation or median and interquartile range for each laboratory test. The following exclusion criteria were considered: (1) Case reports, reviews, editorial materials, conference abstracts, summaries of discussions, (2) Insufficient reported data information; or (3) In vitro or in vivo studies.

Data abstraction

Four investigators separately conducted literature screening, data extraction, and literature quality evaluation, and any differences were resolved through another two reviewers. Information extracted from eligible articles in a predesigned form in excel, including the last name of the first author, date and year of publication, journal name, study design, country of the population, sample size, mean age, sex, and quality assessment.

Quality assessment

A modified version of the Newcastle-Ottawa scale (NOS) was adopted to evaluate the process in terms of queue selection, comparability of queues, and evaluation of results [12, 13]. The quality of the included studies was assessed independently by three reviewers, and disagreements were resolved by the process described above. Higher NOS scores showed a higher literature quality. NOS scores of at least six were considered high-quality literature.

Statistical analysis

All data analysis was performed using OpenMeta[Analyst] [14] and comprehensive meta-analysis software version 3.0 [15]. First, a single-arm meta‐analysis for laboratory tests was performed. The standardized mean difference (SMD) and 95%confidence intervals (CI) were used to estimate pooled results from studies. Medians and interquartile range were converted to mean and standard deviation (SD) using the following formulas: [Mean = (Q1+median+Q3)/3] and [SD = IQR/1.35], whereas, values reported in the articles as mean and 95%CI were estimated using the following formula [SD = √N * (Upper limit of CI–Lower limit of CI)/3.92]. A continuous random-effect model was applied using the DerSimonian-Laird (inverse variance) method [16, 17].

Next, in the presence of individual patient data, single-armed observed values were converted to two-armed data to act as each other’s control group based on covariate information. Only studies investigating different outcomes were considered as potential matched pairs, and two-arm meta-analysis was applied to compare between mild versus severe COVID-19 infection (based on the results of the chest radiography, clinical examination, and symptoms), ICU admission versus general ward admission, and expired versus survivors. Meta-analysis for each outcome was processed using a random-effects model since heterogeneity among studies was expected. For pairwise comparison, estimates of SMD served as quantitative measures of the strength of evidence, which were then converted to the odds ratio (OR) with 95%CI for better interpretation by clinical domains.

Decision tree to identify predictors for poor outcomes

Using laboratory features for clinical prediction, the decision tree algorithm was employed to identify the key risk factors attributed to severe COVID-19 infection, which include a count of studies ≥10. The accuracy of the model was measured by the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), which depicts the true positive rate versus the false positive rate at various discrimination thresholds. The markers that have the highest AUC were identified, and the sensitivity and specificity of the cut-off threshold level were determined. R Studio was employed using the following packages: tidyverse, magrittr, rpart, caret, and pROC.

Trial sequential analysis (TSA)

The statistical trustworthiness of this meta-analysis assessment was conducted using TSA through combining the cumulative sample sizes of all appropriate records with the threshold of statistical impact to diminish the accidental errors and enhance the intensity of expectations [18]. Two side trials with “type I error (α)” along with power set at 5% and 80% were employed. In the case of the “Z-curve” traverses the TSA monitoring boundaries, a reasonable degree of impact was accomplished, and no supplementary trials are crucial. Nevertheless, in case of the “Z-curve” failed to achieve the boundary limits, the estimated information size has not accomplished the required threshold to attract appropriate decisions and advance trials are mandatory. TSA platform (version 0.9.5.10 beta) was operated in the experiment.

Assessment of heterogeneity and publication bias

After that, the heterogeneity was evaluated using Cochran’s Q statistic and quantified by using I2 statistics, which represents an estimation of the total variation across studies beyond chance. Articles were considered to have significant heterogeneity between studies when the p-value less than 0.1 or I2 greater than 50%. Subgroup analysis was performed based on the study sample size (≤50 patients compared to >50 patients) and the origin of patients (Wuhan city versus others). Also, sensitivity analyses and meta-regression with the random-effects model using restricted maximum likelihood algorithm were conducted to explore potential sources of heterogeneity.

Finally, publication bias was assessed using a funnel plot and quantified using Begg’s and Mazumdar rank correlation with continuity correction and Egger’s linear regression tests. Asymmetry of the collected studies’ distribution by visual inspection or P-value < 0.1 indicated obvious publication bias [19]. The Duval and Tweedie’s trim and fill method’s assumption were considered to reduce the bias in pooled estimates [20].

Results

Literature search

A flowchart outlining the systematic review search results is described in Fig 1A. A total of 4752 records were identified through four major electronic databases till April 22, 2020 including Web of Science (n = 557), PubMed (n = 1688), Scopus (n = 1105) and Science Direct (n = 1402). Upon reviewing the retrieved articles, a total of 1230 records were excluded for duplication, and 3522 unique records were initially identified. Following screening of titles and abstracts, several studies were excluded for being case reports (n = 44), review articles (n = 262), irrelevant publications (n = 1355), or editorial materials (n = 1809). The resulted 424 full-text publications were further assessed for eligibility, during which 372 records were removed for lacking sufficient laboratory data. Ultimately, a total of 52 eligible articles were included for the quantitative synthesis of this meta-analysis study, with 52 records represented single-arm analysis [1, 9, 21–70], 16 records represented two-arms severity analysis [24, 26, 32, 34, 37, 40, 41, 45, 46, 50, 51, 63, 64, 66, 69, 70]; meanwhile, 7 and 4 records were utilized for survival [9, 30, 53, 55, 61, 67, 68] and ICU admission [1, 31, 36, 52] analyses, respectively.

Fig 1. Literature search process.

Fig 1

Open in a new tab

(A) Workflow for screening and selecting relevant articles. (B) Map showing the location of the studies. Studies conducted in China (red), Taiwan (green), Singapore (blue), and USA (light blue) are shown with the number of studies between brackets. Data source Tableau 2020.1 Desktop Professional Edition (https://www.tableau.com/).

Characteristics of the included studies

Our review included 52 studies that were published from January 24 through April 22, 2020, including 48 articles from China [Wuhan (30), Chongqing (4), Zhejiang (4), Shanghai (2), Ningbo (1), Hong Kong (1), Shenzhen (1), Anhui (1), Macau (1), Hainan (1), Jiangsu (1), and Beijing (1)], two articles from Singapore [Singapore and Sengkang], one article from Taiwan [Taichung], and one article from USA [Washington] (Fig 1B). The main characteristics of eligible studies are shown in Table 1. A total of 6320 patients with SARS‐CoV‐2 infection were enrolled across the articles. Most records (n = 47) were retrospective case studies, while other study design included two prospective cohort studies, one observational cohort study, one descriptive case series, and one case-control study. Our team stratified 36 different laboratory parameters into seven subclasses, including complete blood picture, coagulation profile, immunological markers, immunoglobulins, complement tests, interleukins, and immune cells, as previously described in the methodology. Regarding quality score assessment, 39 studies achieved a score higher than six out of a maximum of nine (high quality), while the remaining 13 studies earned a score equal or lower than six (low quality), as shown in Table 1.

Table 1. General characteristics of the included studies.
First Author Publication* date (dd-mm) Continent Country Study design Sample size Quality score Mean age, years Female % Outcome Ref.
Zhu Z 22-April Ningbo China Retrospective case study 127 9 50.9 (15.3) 64.6% Severity [70]
Liu X 20-April Wuhan China Retrospective case study 124 8 56 (12) 57.1% Severity [40]
Chen X 18-April Wuhan China Retrospective case study 48 9 64.6 (18.1) 22.9% Severity [26]
Chen G 13-April Wuhan China Retrospective case study 21 8 57 (11.1) 19% Severity [24]
He R 12-April Wuhan China Retrospective case study 204 9 48.3 (20.7) 61.3% Severity [34]
Zhang G 09-April Wuhan China Retrospective case study 221 9 53.5 (20.4) 51.1% Severity [63]
Lei S 04-April Wuhan China Retrospective case study 34 9 53.7 (14.8) 58.8% ICU [36]
Wang L 30-March Wuhan China Retrospective case study 339 8 69 (7.4) 51% Mortality [53]
Guo T 27-March Wuhan China Retrospective case study 187 8 58.5 (14.7) 51.3% NA [33]
Zheng C 27-March Wuhan China Retrospective case study 55 7 57.2 (65.3) 43.6% Severity [66]
Chen T 26-March Wuhan China Retrospective case study 274 9 58.7 (19.2) 37.6% Mortality [9]
Tang X 26-March Wuhan China Retrospective case study 73 6 65.3 (11.1) 38.4% NA [49]
Shi S 25-March Wuhan China Retrospective case study 416 9 60 (54.8) 50.7% NA [48]
TO K 23-March Hong Kong China Observational cohort study 23 9 57.7 (27.5) 43.5% Severity [50]
Zhou Z 24-March Chongqing China Retrospective case study 62 9 47.2 (13.4) 45.2% Severity [69]
Chen Z 24-March Zhejiang China Retrospective case study 98 6 43 (17.2) 53.1% NA [27]
Wan S 21-March Chongqing China Retrospective case study 135 9 46 (14.1) 46.7% Severity [51]
Cheng Y 20-March Wuhan China Prospective cohort study 701 9 61.3 (15.5) 47.6% NA [28]
Luo S 20-March Wuhan China Retrospective case study 183 5 53.8 (NA) 44% NA [42]
Deng Y 20-March Wuhan China Retrospective case study 225 8 55.4 (11.5) 44.9% Mortality [30]
Arentz M 19-March Washington USA Retrospective case study 21 5 68.3 (36.3) 48% NA [21]
Chen J 19-March Shanghai China Retrospective case study 249 5 50.3 (20.7) 49.4% NA [25]
Cai Q 18-March Shenzhen China Retrospective case study 80 9 47.9 (18.7) 56.2% NA [22]
Gao Y 17-March Anhui China Retrospective case study 43 9 43.7 (11.8) 39.5% Severity [32]
Qian G 17-March Zhejiang China Retrospective case study 91 5 47.8 (15.2) 59.3% Severity [45]
Mo P 16-March Wuhan China Retrospective case study 155 8 54 (17.8) 44.5% NA [43]
Wang Z 16-March Wuhan China Retrospective case study 69 7 46.3 (20) 54% NA [54]
Lo I 15-March Macau China Retrospective case study 10 8 48.3 (27.4) 70% Severity [41]
Cheng Z 14-March Shanghai China Retrospective case study 11 5 50.4 (15.5) 27.3% NA [29]
Hsih W 13-March Taichung Taiwan Retrospective case study 2 5 45 (8.9) 50% NA [35]
Wu C 13-March Wuhan China Retrospective case study 201 8 51.3 (12.6) 36.3% Mortality [55]
Qin C 12-March Wuhan China Retrospective case study 452 9 57.3 (14.8) 48% Severity [46]
Zhao D 12-March Wuhan China Case-control study 19 7 43.7 (21.5) 42.1% NA [65]
Liu K 11-March Hainan China Retrospective case study 18 7 67.6 (3.3) 33.3% NA [38]
Zhou F 09-March Wuhan China Retrospective case study 191 9 56.3 (15.5) 38% Mortality [67]
Xiong Y 07-March Wuhan China Retrospective case study 42 5 49.5 (14.1) 40% NA [58]
Fan B 04-March Singapore Singapore Retrospective case study 67 9 43.7 (14.1) 44.8% ICU [31]
Young B 03-March Sengkang Singapore Descriptive case series 18 7 50.3 (31.1) 50% NA [62]
Wu J 29-February Jiangsu China Retrospective case study 80 7 46.1 (15.4) 51.2% NA [56]
Li K 29-February Chongqing China Retrospective case study 83 9 45.5 (12.3) 47% Severity [37]
Liu W 28-February Wuhan China Retrospective case study 78 9 42.7 (17.8) 50% NA [39]
Yang W 26-February Zhejiang China Retrospective case study 149 6 45.1 (13.3) 45.6% NA [60]
Wu J 25-February Chongqing China Retrospective case study 80 6 44 (11) 48% NA [57]
Shi H 24-February Wuhan China Retrospective case study 81 7 49.5 (11) 48% NA [47]
Yang X 24-February Wuhan China Retrospective case study 52 9 59.7 (13.3) 33% Mortality [61]
Zhang J 23-February Wuhan China Retrospective case study 138 9 56.3 (45.9) 49.3% Severity [64]
Zhou W 21-February Wuhan China Retrospective case study 15 8 61.7 (9.6) 33.3% Mortality [68]
Xu X 19-February Zhejiang China Retrospective case study 62 7 41.7 (14.8) 44% NA [59]
Pan F 13-February Wuhan China Retrospective case study 21 6 40 (9) 74% NA [44]
Chang D 07-February Beijing China Retrospective case study 13 6 38.7 (10.4) 23.1% NA [23]
Wang D 07-February Wuhan China Retrospective case study 138 9 55.3 (19.2) 45.7% ICU [52]
Huang C 24-January Wuhan China Prospective cohort study 41 9 49.3 (12.6) 27% ICU [1]
Open in a new tab

*All articles were published in 2020.

NA: not applicable.

Pooled estimates of laboratory parameters: Single-arm meta-analysis

The final pooled estimates of single-arm meta-analysis included 52 eligible articles. The pooled mean of laboratory parameters and 95%CI among SARS-CoV-2 infected patients, including hematological, immunological, and inflammatory variables, is illustrated in Table 2. Our results depicted a wide variability between studies for each laboratory marker. Apart from immunoglobulins, IL-2R, and IL-8, significant heterogeneity was observed. Subgroup analysis by sample size and city of origin and sensitivity analysis failed to reveal the source of variation for each parameter. Additionally, meta-regression also rendered insignificant results.

Table 2. Pooled estimates of single-arm meta-analysis for laboratory parameters in COVID-19 patients.

Laboratory testing Number studies Sample size Estimate 95% CI P-value Q P-value I2 T2
CBC
 White blood cells 47 5967 5.82 5.24, 6.40 <0.001 7136.1 <0.001 99.35 3.83
 Neutrophil count 31 3814 3.70 3.48, 3.92 <0.001 525.8 <0.001 93.9 0.31
 Lymphocyte count 45 6017 0.99 0.91, 1.08 <0.001 7645.2 <0.001 99.3 0.07
 Monocyte count 18 2586 0.42 0.39, 0.44 <0.001 263.7 <0.001 93.5 0.003
 Eosinophils count 4 546 0.02 0.01, 0.024 <0.001 10.6 0.014 71.6 0.0
 Red blood cells 2 507 4.42 3.81, 4.67 <0.001 50.8 <0.001 98.03 0.095
 Hemoglobin 26 3114 129.1 125.0, 133.3 <0.001 1504.3 <0.001 98.3 103.4
 Platelet count 34 4347 178.4 171.9, 184.9 <0.001 390.2 <0.001 91.5 273.5
Coagulation profile
 Prothrombin time 22 3287 12.38 11.8, 12.9 <0.001 3415.7 <0.001 99.3 1.905
 APTT 19 3023 31.8 30.2, 33.4 <0.001 1312.1 <0.001 98.6 11.96
 Thrombin time 2 754 21.9 8.29, 35.57 0.002 1908.1 <0.001 99.94 96.86
 D-dimer 27 3857 1.25 0.67, 1.82 <0.001 40947.5 <0.001 99.9 2.22
 Fibrinogen 2 781 2.45 0.61, 4.29 0.009 46.19 <0.001 97.83 1.729
Inflammatory markers
 Ferritin 8 528 889.5 773.2, 1005.7 <0.001 16.61 0.020 57.8 14138.9
 ESR 13 1013 37.85 29.07, 46.6 <0.001 692.4 <0.001 98.26 239.7
 Procalcitonin 25 3010 0.10 0.07, 0.12 <0.001 3913.6 <0.001 99.3 0.003
 C-reactive protein 36 4409 28.11 24.7, 31.4 <0.001 3432.1 <0.001 98.9 79.35
Immunoglobulins
 IgA 2 101 2.21 2.15, 2.27 <0.001 0.089 0.76 0.0 0.0
 IgG 2 101 11.54 11.2, 11.8 <0.001 1.88 0.17 46.9 0.023
 IgM 2 101 1.00 0.96, 1.04 <0.001 1.11 0.29 10.32 0.0
Complement test
 C3 2 101 0.95 0.80, 1.10 <0.001 28.02 <0.001 96.43 0.011
 C4 2 101 0.24 0.21, 0.27 <0.001 28.08 <0.001 96.44 0.0
Interleukins
 IL-2R 2 101 762.3 732.4, 792.2 <0.001 0.33 0.56 0.0 0.0
 IL-4 2 276 2.98 1.09, 4.87 0.002 958.765 <0.001 99.9 1.85
 IL-6 12 926 11.56 9.82, 13.3 <0.001 144.7 <0.001 92.4 6.19
 IL-8 2 101 18.4 17.08, 19.84 <0.001 1.54 0.21 35.3 0.39
 IL-10 3 292 6.33 4.39, 8.27 <0.001 133.1 <0.001 98.4 2.89
 TNF-α 3 292 6.72 1.33, 12.12 0.015 2933.6 <0.001 99.9 22.7
Immune cells
 CD4+ T cells 6 296 361.1 254.0, 468.2 <0.001 88.7 <0.001 94.3 15973.1
 CD8+ T cells 5 285 219.6 157.1, 282.0 <0.001 46.17 <0.001 91.3 4437.2
 T lymphocytes 2 167 704.3 254.5, 1154.0 0.002 27.6 <0.001 96.3 101500
Open in a new tab

Test of association: standardized mean difference, Random model. 95% CI: 95% confidence interval, Q statistic: a measure of weighted squared deviations that denotes the ratio of the observed variation to the within-study error, I2: the ratio of true heterogeneity to total observed variation, T2: Tau squared, and it is referred to the extent of variation among the effects observed in different studies. Laboratory markers (INR and B lymphocytes) were reported in only one study thus were not shown. CBC: Complete blood picture, APTT: Activated partial thromboplastin time, ESR: Erythrocyte sedimentation rate. Ig: immunoglobulin, IL-2R: Interleukin-2 receptor, TNF- α: tumor necrosis factor-alpha.

Pooled estimates of laboratory parameters according to disease severity: Pairwise meta-analysis

Two-arms meta-analyses were then conducted for three pairwise comparisons; (1) Severe versus mild COVID, (2) ICU admitted patients versus the general ward, and (3) Expired versus survivors (Table 3).

Table 3. Pooled estimates of two-arms meta-analysis for laboratory parameters in COVID-19 patients.

Laboratory test No of studies Sample size Effect size Heterogeneity
SMD (95%CI) OR (95% CI) P-value I2 P-value
(A) Severity Mild Severe
White blood cells 14 1007 634 0.31 (0.11, 0.52) 1.75 (1.21, 2.54) 0.002 62.9 <0.001
Neutrophil count 14 959 599 0.53 (0.3, 0.76) 2.62 (1.72, 3.97) <0.001 67.61 <0.001
Lymphocyte count 16 680 1128 -0.66 (-0.9, -0.41) 0.30 (0.19, 0.47) <0.001 77.36 <0.001
Monocyte count 5 390 500 -0.08 (-0.23, 0.05) 0.86 (0.67, 1.12) 0.23 0.0 0.49
Hemoglobin 4 70 200 -0.22 (-0.51, 0.06) 0.67 (0.40, 1.12) 0.12 0.0 0.91
Platelet count 7 219 588 -0.32 (-0.47, -0.16) 0.56 (0.42, 0.74) <0.001 0.0 0.76
Prothrombin time 6 215 521 0.33 (0.004, 0.67) 1.82 (1.00, 3.33) 0.047 72.0 0.003
APTT 5 146 386 -0.23 (-0.79, 0.33) 0.66 (0.24, 1.82) 0.42 85.5 <0.001
D-dimer 9 301 719 0.76 (0.53, 0.99) 3.97 (2.62, 6.02) <0.001 55.65 0.021
Ferritin 2 297 176 1.003 (-0.08, 2.09) 6.17 (0.87, 43.9) 0.07 79.21 0.028
Fibrinogen 3 45 144 0.63 (0.27, 0.99) 3.14 (1.64, 6.00) <0.001 0.0 0.81
ESR 2 302 277 0.26 (0.08, 0.44) 1.60 (1.16, 2.22) 0.004 0.0 0.43
Procalcitonin 10 565 716 0.86 (0.5, 1.22) 4.76 (2.48, 9.14) <0.001 86.1 <0.001
C-reactive protein 13 605 928 1.02 (0.65, 1.4) 6.36 (3.22, 12.5) <0.001 88.2 <0.001
IgA 2 355 301 0.13 (-0.03, 0.29) 1.27 (0.95, 1.69) 0.11 3.398 0.30
IgG 2 355 301 0.21 (-0.301, 0.72) 1.46 (0.58, 3.69) 0.41 88.3 0.003
IgM 2 355 301 -2.37 (-6.64, 1.89) 0.01 (0.00, 30.6) 0.27 99.56 <0.001
Complement 3 2 355 301 0.18 (-0.1, 0.47) 1.39 (0.83, 2.32) 0.20 64.70 0.09
Complement 4 2 355 301 0.13 (-0.16, 0.43) 1.27 (0.74, 2.16) 0.38 66.83 0.08
IL-4 2 355 301 1.01 (-0.85, 2.87) 6.25 (0.2, 181.1) 0.28 97.17 <0.001
IL-6 7 85 246 0.41 (0.014, 0.81) 2.10 (1.02, 4.32) 0.043 84.38 <0.001
IL-10 3 371 412 0.88 (0.43, 1.33) 4.93 (2.18, 11.1) <0.001 82.81 0.003
TNF-α 3 371 412 0.6 (-0.17, 1.37) 2.97 (0.74, 11.9) 0.12 94.28 <0.001
CD4+ T cells 2 80 145 -1.87 (-2.39, -1.36) 0.03 (0.01, 0.09) <0.001 29.8 0.23
CD8+ T cells 2 80 145 -1.8 (-2.12, -1.48) 0.04 (0.02, 0.07) <0.001 0.0 0.71
(B) Admission Floor ICU
White blood cells 3 64 149 0.85 (0.54, 1.15) 4.67 (2.70, 8.10) <0.001 0.0 0.56
Neutrophil count 4 73 207 1.86 (0.59, 3.14) 29.1 (2.9, 291.8) 0.004 93.14 <0.001
Lymphocyte count 4 73 207 -0.81 (-1.36, -0.27) 0.23 (0.09, 0.62) 0.003 68.59 0.023
Monocyte count 3 60 179 -0.308 (-1.15, 0.53) 0.57 (0.13, 2.59) 0.47 83.77 0.002
Hemoglobin 2 22 86 -1.1 (-1.97, -0.24) 0.14 (0.03, 0.64) 0.012 66.31 0.08
Platelet count 4 73 207 -0.06 (-0.33, 0.2) 0.90 (0.56, 1.45) 0.64 0.0 0.54
Prothrombin time 3 64 149 0.43 (0.09, 0.76) 2.18 (1.19, 3.99) 0.012 14.28 0.31
APTT 3 64 149 -0.22 (-0.51, 0.07) 0.67 (0.40, 1.13) 0.14 0.0 0.78
D-dimer 3 64 149 0.79 (0.35, 1.24) 4.19 (1.88, 9.35) <0.001 44.94 0.16
(C) Mortality Alive Died
White blood cells 6 736 392 0.91 (0.61, 1.22) 5.21 (3.00, 9.05) <0.001 78.05 <0.001
Neutrophil count 3 475 222 1.01 (0.4, 1.63) 6.25 (2.05, 19.0) 0.001 90.9 <0.001
Lymphocyte count 7 756 424 -0.85 (-1.28, -0.41) 0.21 (0.10, 0.47) <0.001 89.33 <0.001
Monocyte count 4 483 229 -0.18 (-0.47, 0.1) 0.72 (0.43, 1.21) 0.21 57.48 0.070
Hemoglobin 5 600 271 0 (-0.15, 0.15) 1.00 (0.76, 1.31) 0.99 4.988 0.378
Platelet count 6 640 315 -0.46 (-0.71, -0.21) 0.43 (0.28, 0.68) <0.001 59.52 0.030
Prothrombin time 6 640 315 0.64 (0.25, 1.03) 3.19 (1.58, 6.47) 0.001 83.0 <0.001
APTT 4 483 229 -0.096 (-0.51, 0.31) 0.83 (0.40, 1.75) 0.646 78.23 0.003
D-dimer 5 620 283 1.02 (0.85, 1.18) 6.36 (4.72, 8.58) <0.001 10.63 0.34
Ferritin 3 338 211 0.94 (0.26, 1.62) 5.50 (1.6, 18.83) 0.006 91.63 <0.001
ESR 2 201 157 0.33 (0.08, 0.58) 1.82 (1.16, 2.86) 0.008 20.03 0.263
Procalcitonin 3 580 239 0.96 (0.43, 1.49) 5.70 (2.18, 14.9) <0.001 81.48 0.005
C-reactive protein 4 591 331 1.08 (0.65, 1.52) 7.09 (3.23, 15.5) <0.001 87.31 <0.001
IL-6 4 612 276 1.45 (1.11, 1.78) 13.87 (7.6, 25.4) <0.001 75.44 0.007
CD4+ T cells 2 314 109 -0.67 (-1.01, -0.33) 0.30 (0.16, 0.55) <0.001 44.57 0.17
CD8+ T cells 2 314 109 -0.832 (-1.1, -0.59) 0.22 (0.15, 0.34) <0.001 0.0 0.423
Open in a new tab

Continuous Random-Effects model, SMD: Standardized mean difference, OR 95% CI: Odds ratio 95% confidence interval, I2: the ratio of true heterogeneity to total observed variation. APTT: Activated partial thromboplastin time, ESR: Erythrocyte sedimentation rate. Ig: immunoglobulin, IL: Interleukin, TNF-α: tumor necrosis factor-alpha.

Laboratory parameters of 16 eligible records were utilized to compare between severe and non-severe patients. Severe cohorts were more likely to have high blood levels of white blood cells (OR = 1.75, 95%CI = 1.21–2.54, p = 0.002), neutrophil count (OR = 2.62, 95%CI = 1.72–3.97, p <0.001), prothrombin time (OR = 1.82, 95%CI = 1.00–3.33, p = 0.047), D-dimer (OR = 3.97, 95%CI = 2.62–6.02, p <0.001), fibrinogen (OR = 3.14, 95%CI = 1.64–6.00, p <0.001), erythrocyte sedimentation rate (OR = 1.60, 95%CI = 1.16–2.22, p <0.001), procalcitonin (OR = 4.76, 95%CI = 2.48–9.14, p <0.001), IL-6 (OR = 2.10, 95%CI = 1.02–4.32, p = 0.043), and IL-10 (OR = 4.93, 95%CI = 2.18–11.1, p <0.001). In contrast, patients with normal lymphocyte count (OR = 0.30, 95%CI = 0.19–0.47, p <0.001), platelet count (OR = 0.56, 95%CI = 0.42–0.74, p <0.001), CD4+ T cells (OR = 0.04, 95%CI = 0.02–0.07, p <0.001), and CD8+ T cells (OR = 0.03, 95%CI = 0.01–0.09, p <0.001) were less likely to develop severe form of COVID-19 disease (Table 3A).

Significant heterogeneity was observed in eight of these parameters, namely WBC (I2 = 62.9%, p <0.001), neutrophil count (I2 = 67.6%, p <0.001), lymphocyte count (I2 = 77.4%, p <0.001), prothrombin time (I2 = 72%, p = 0.003), D-dimers (I2 = 55.6%, p = 0.021), procalcitonin (I2 = 86.1%, p <0.001), IL-6 (I2 = 84.4%, p <0.001), and IL-10 (I2 = 82.8%, p = 0.003).

Pooled estimates of laboratory parameters according to ICU admission: Pairwise meta-analysis

A total of 4 eligible articles were recognized to include laboratory features of ICU and floor patients. Our data revealed having elevated levels of WBCs (OR = 5.21, 95%CI = 3.0–9.05, p <0.001), neutrophils (OR = 6.25, 95%CI = 2.05–19.0, p = 0.001), D-dimer (OR = 4.19, 95%CI = 1.88–9.35, p <0.001), and prolonged prothrombin time (OR = 2.18, 95%CI = 1.19–3.99, p = 0.012) were associated with increased odds of ICU admission, while normal lymphocyte count (OR = 0.23, 95%CI = 0.09–0.62, p = 0.003) and hemoglobin (OR = 0.14, 95%CI = 0.03–0.64, p = 0.012) conferred lower risk of ICU admission (Table 3B).

Remarkable heterogeneity was obvious in studies of neutrophil count (I2 = 93.1%, p <0.001), lymphocyte count (I2 = 68.5%, p = 0.023), and hemoglobin (I2 = 66.3%, p = 0.08). These parameters were enclosed in two to four studies; therefore, further tracing for the source of heterogeneity was not applicable.

Pooled estimates of laboratory parameters according to mortality: Pairwise meta-analysis

Of the included articles, 7 studies contained separate results for laboratory testing in survival versus expired patients. As depicted in Table 3C, our data revealed increased odds of having elevated levels of WBC (OR = 5.21, 95%CI = 3.0–9.05, p <0.001), neutrophils (OR = 6.25, 95%CI = 2.05–19.0, p = 0.001), prothrombin time (OR = 3.19, 95%CI = 1.58–6.47, p = 0.001), D-dimer (OR = 6.36, 95%CI = 4.72–8.58, p <0.001), ferritin (OR = 5.50, 95%CI = 1.6–18.8, p = 0.006), ESR (OR = 1.82, 95%CI = 1.16–2.86, p = 0.008), procalcitonin (OR = 5.70, 95%CI = 2.18–14.9, p <0.001), CRP (OR = 7.09, 95%CI = 3.23–15.5, p <0.001), and IL-6 (OR = 13.87, 95%CI = 7.6–25.4, p <0.001) in expired cases. However, patients with normal lymphocyte count (0.21 (0.10, 0.47, p <0.001), platelet count (0.43 (0.28, 0.68, p <0.001), CD4+ T cells (OR = 0.30 (0.16, 0.55, p <0.001), and CD8+ T cells (OR = 0.22 (0.15, 0.34, p <0.001) had higher chance of survival (Table 3C).

Considerable heterogeneity was also noted in some of these parameters, namely WBC (I2 = 78.0%, p <0.001), neutrophilic count (I2 = 90.9%, p <0.001), lymphocyte count (I2 = 89.3%, p <0.001), platelet count (I2 = 59.5%, p = 0.030), ferritin (I2 = 91.6%, p <0.001), procalcitonin (I2 = 81.5%, p = 0.005), CRP (I2 = 87.3%, p <0.001), and IL-6 (I2 = 75.4%, p = 0.007). Given the small number of enrolled studies with discriminated data on patients who survived or died, we failed to identify the source of heterogeneity.

Subgroup and sensitivity analysis

For the studies which included a comparison between mild and severe patients, subgroup and sensitivity analyses were performed for five laboratory markers (WBC, neutrophil count, lymphocyte count, procalcitonin, and CRP). First, to identify how each study affects the overall estimate of the rest of the studies, we performed leave-one-out sensitivity analyses. Results did not contribute to give explanations to heterogeneity. In contrast, subgroup analysis revealed homogeneity with certain categorizations. For WBCs lab results, heterogeneity was resolved on stratification by the origin of study population [Wuhan population: I2 = 73.4%, p = 0.002, other cities: I2 = 0%, p = 0.53] and month of publication [April: I2 = 74.5%, p = 0.001, February/March: I2 = 47.5%, p = 0.06]. Regarding neutrophilic count, the variance in the results resolved in articles with large sample size >50 patients (I2 = 46.2%, p = 0.06). Moreover, the degree of dissimilarities of procalcitonin results found in different studies was ameliorated in April publications (I2 = 41.5%, p = 0.16) and in those with low sample size (I2 = 0%, p = 0.80). Similarly, homogeneity was generated in CRP results in articles with low sample size (I2 = 0%, p = 0.58) (Table 4).

Table 4. Tracing the source of heterogeneity of laboratory markers in studies comparing mild and severe COVID-19 patients.

Lab test Feature Categories Count of studies Pooled estimates Heterogeneity Meta-regression
SMD LL UL P-value I2 P-value Coefficient LL UL P-value
White blood cells Overall 14 0.317 0.113 0.52 0.002 62.90% 0.001
Origin of patients Others 8 0.113 -0.083 0.308 0.26 0% 0.53 Reference
Wuhan 6 0.490 0.198 0.783 0.00 73.40% 0.002 0.31 0.03 0.58 0.029
Sample size ≤50 5 0.164 -0.553 0.881 0.65 71.30% 0.007 Reference
>50 9 0.387 0.208 0.566 <0.001 52.60% 0.031 0.30 -0.10 0.72 0.14
Publication month Feb/Mar 8 0.251 0.039 0.464 0.021 47.50% 0.06 Reference
April 6 0.445 0.005 0.884 0.047 74.50% 0.001 0.11 -0.16 0.38 0.43
Neutrophils Overall 14 0.534 0.306 0.762 <0.001 67.62% <0.001
Origin of patients Others 8 0.439 0.139 0.740 0.004 50.88% 0.047 Reference
Wuhan 6 0.632 0.280 0.985 <0.001 78.29% <0.001 0.045 -0.21 0.30 0.20
Sample size ≤50 5 0.286 -0.503 1.076 0.47 75.94% 0.002 Reference
>50 9 0.65 0.472 0.828 <0.001 46.2% 0.06 0.606 0.20 1.01 0.003
Publication month Feb/Mar 8 0.428 0.181 0.675 <0.001 54.4% 0.032 Reference
April 6 0.709 0.273 1.44 0.001 73.19% 0.002 0.312 0.06 0.55 0.014
Lymphocytes Overall 16 -0.663 -0.909 -0.417 <0.001 77.36% <0.001
Origin of patients Others 9 -0.626 -0.962 -0.291 <0.001 66.51% 0.002 Reference
Wuhan 7 -0.710 1.097 -0.323 <0.001 85.72% <0.001 0.092 -0.31 0.49 0.64
Sample size ≤50 5 -0.506 -1.169 0.156 0.13 66.1% 0.019 Reference
>50 11 -0.714 -0.983 -0.444 <0.001 80.98% <0.001 -0.342 -0.85 0.169 0.18
Publication month Feb/Mar 9 -0.452 -0.712 -0.192 <0.001 66.65% 0.002 Reference
April 7 -0.979 -1.354 -0.604 <0.001 70.53% 0.002 -0.572 -0.97 -0.17 0.006
Procalcitonin Overall 10 0.868 0.508 1.228 <0.001 88.16% <0.001
Origin of patients Others 5 1.038 0.370 1.706 <0.001 86.16% <0.001 Reference
Wuhan 5 0.686 0.331 1.041 <0.001 75.38% 0.003 -0.318 -0.97 0.33 0.34
Sample size ≤50 3 0.768 0.334 1.203 <0.001 0% 0.80 Reference
>50 7 0.903 0.459 1.348 <0.001 88.62% <0.001 0.054 -0.72 0.83 0.89
Publication month Feb/Mar 6 0.956 0.404 1.509 <0.001 91.51% <0.001 Reference
April 4 0.757 0.409 1.105 <0.001 41.54% 0.16 -0.096 -0.80 0.61 0.78
C-reactive protein Overall 13 1.027 0.65 1.40 <0.001 88.2% <0.001
Origin of patients Others 8 1.24 0.65 1.83 <0.001 87.8% <0.001 Reference
Wuhan 5 0.389 0.30 1.07 <0.001 80.7% <0.001 -0.58 -1.27 0.10 0.09
Sample size ≤50 3 0.831 0.341 1.322 <0.001 0% 0.58 Reference
>50 10 1.08 0.651 1.512 <0.001 82.3% <0.001 0.37 -0.55 1.29 0.42
Publication month Feb/Mar 8 1.014 0.502 1.525 <0.001 88.23% <0.001 Reference
April 5 1.07 0.548 1.600 <0.001 75.1% 0.003 0.13 -0.59 0.86 0.71
Open in a new tab

SMD: Standardized mean difference, LL: lower limit, UL: upper limit, I2: the ratio of true heterogeneity to total observed variation. Significant values indicate significance at P < 0.05.

Meta-regression analysis

Considering the number of the included studies with severity, ICU admission, and mortality data was rather small, we performed meta-regression analyses for only five parameters (mentioned above) in studies comparing mild and severe disease (Table 4).

For WBCs, higher difference between mild and severe cohorts was noted in Wuhan studies than other population (coefficient = 0.31, 95%CI = 0.03, 0.58, p = 0.029). Moreover, articles with larger sample size exhibited a wider variation of neutrophilic count between severe and non-severe cases (coefficient = 0.60, 95%CI = 0.20, 1.01, p = 0.003). For the same marker, later studies published in April also showed higher difference compared to those published in February and March (coefficient = 0.31, 95%CI = 0.06, 0.55, p = 0.014). In contrast, more reduction of lymphocytes was observed in April articles than earlier ones (coefficient = -0.57, 95%CI = -0.97, -0.17, p = 0.006).

Publication bias

Publication bias was performed to the same five parameters with study count ≥10 (S1 Fig). Visual inspection of the funnel plots suggested symmetrical distribution for all laboratory parameters tested. The Egger test (p > 0.1) confirmed that there was no substantial evidence of publication bias; Egger’s regression p values were 0.44, 0.50, 0.68, 0.56, and 0.22 for WBC, neutrophil count, lymphocyte count, procalcitonin, and CRP, respectively.

Decision tree and Receiver Operating Characteristic (ROC) curve

To identify predictors for severity, decision tree analysis was applied using multiple laboratory results. High performance of classification was found with the usage of a single parameter; neutrophilic count identified severe patients with 100% sensitivity and 81% specificity at a cut-off value of >3.74 identified by the specified decision tree model. Further analysis of the area under the curve of input data is shown in Table 5.

Table 5. Receiver operating characteristics results for severity of COVID-19.

Lab test AUC Threshold Sensitivity Specificity P-value
WBC 0.801 ± 0.09 5.47 85.7 85.7 0.007
Neutrophil 0.831 ± 0.09 3.74 78.5 100 0.003
Lymphocyte 0.867 ± 0.06 0.98 81.2 87.5 <0.001
Platelets 0.836 ± 0.11 177.6 71.4 71.4 0.035
PT 0.583 ± 0.17 12.9 50.0 83.3 0.63
Procalcitonin 0.845 ± 0.09 0.06 80.0 90.0 0.007
D-dimer 0.876 ± 0.08 0.48 88.9 77.8 0.007
CRP 0.875 ± 0.08 38.2 84.6 92.3 0.001
IL-6 0.632 ± 1.6 22.9 71.4 71.4 0.40
Open in a new tab

AUC: area under the curve, WBC: white blood cells, PT: prothrombin time, CRP: C-reactive protein, IL-6: interleukin 6. Bold values indicate significance at P < 0.05.

Trial sequential analysis

As elaborated by the decision tree algorithm for the role of neutrophilic count on decision-making to discriminate between COVID-19 patients with a mild and severe presentation, TSA was employed on that particular laboratory parameter to test for the presence of sufficient studies from which results were drawn. The sample size of studies containing neutrophilic count information and classifying cohorts into mild and severe COVID-19 infection accounted for a total of 1,558 subjects. TSA illustrated crossing of the monitoring boundary by the cumulative Z-curve before reaching the required sample size, suggesting that the cumulative proof was acceptable, and no additional future studies are needed to authenticate the significances (Fig 2).

Fig 2. Trial sequential analysis.

Fig 2

Open in a new tab

Trial sequential analysis (TSA) for the neutrophil count. The acquired sample size of the neutrophil count was 1558 subjects and the cumulative Z-curve crossed the monitoring boundary before reaching the required sample size (n = 540), suggesting that the cumulative proof was reliable, and no additional trials are required to achieve the significance.

Discussion

During the last few months, the prevalence of COVID-19 infection was increased daily among different countries overall in the world. Thus, the need to assess the disease severity and mortality are required to limit the pervasiveness of this pandemic [71]. A diverse of abnormal laboratory parameters including hematological, inflammatory as well as immunological markers thought to be raised throughout COVID-19 outbreak [2, 72]. In this comprehensive meta-analysis, our team attempted to interpret the distinct questions raised about the various spectrum of laboratory parameters associated with the severity and mortality of COVID-19. At the beginning of this workflow, our team investigated different hematological, inflammatory, and immunological variables of 6320 patients diagnosed with COVID-19. Our findings using random-effect models revealed increased levels of WBCs and neutrophil counts that were significantly associated with higher odds ratio among severe, ICU admission and Expired patients with COVID-19. On the contrary, the levels of lymphocyte and platelet counts were lowered among severe and expired patients with COVID-19. Also, we observed depletion in quantities of CD4+ T cells and CD8+ T cells among severe and mortality patients.

Nevertheless, in patients with the COVID-19 outbreak, the WBC count can vary [73]. Other reports indicated that leukopenia, leukocytosis, and lymphopenia have been reported, although lymphopenia appears most common [74, 75]. Another study supported that lymphopenia is an effective and reliable indicator of the severity and hospitalization in COVID-19 patients [76]. The additional report suggested that COVID-19 illness might be implicated with CD4+ and CD8+ T cells depletion through acting on lymphocytes, especially T lymphocytes [34]. A recent meta-analysis study discovered that the severity among COVID-19 patients might correlate with higher levels of WBCs count and lower levels of lymphocyte, CD4+ T cells, and CD8+ T cells counts [72]. In this respect, we could speculate that the depletion in the number of lymphocytes count is directly proportional with the severity of COVID-19 infection and the high survival rate of the disease is associated with the ability to renovate lymphocyte cells, particularly T lymphocytes which are crucial for destroying the infected viral particles [77]. During disease severity, remarkable thrombocytopenia was observed and confirmed by Lippi and his colleagues that revealed a reduction of platelet count among severe and died patients with COVID-19 supporting that thrombocytopenia could consider as an exacerbating indicator during the progression of the disease [78]. Therefore, our findings could support Shi et al. conclusion that high WBC count with lymphopenia could be considered as a differential diagnostic criterion for COVID-19 [79].

Considering coagulation profile, our team observed a prolonged in most coagulation markers among severe, ICU and expired patients, especially prothrombin time, fibrinogen, D-dimer, but with normal proportions of activated partial thromboplastin time (APTT) that could focus the light on the pathogenesis of COVID-19 infection through interfering with extrinsic coagulation pathway. A recently published report concluded similar findings in the form of observation of higher levels prothrombin time, D-dimer along fibrin degradation products among non-survival compared with survival patients [80].

Numerous studies illustrated the pathogenesis action of COVID-19 with the induction of cytokine storm throughout the progressive phase of the infection [72, 81, 82]. The generation of cytokine storm within COVID-19 patients required increased levels of IFN-γ and IL-1β that could stimulate the cellular response of T helper type 1 (Th1) which has a crucial function in the acceleration of specific immunity against COVID-19 outbreak [81]. Due to the elevated levels of IL-2R and IL-6 accompanied by the advancement of COVID-19, several cytokines secreted by T helper type 2 (Th2) cells that could neutralize the inflammatory responses including IL-4 and IL-10 [72, 81]. Our findings revealed a significantly associated with elevated levels of anti-inflammatory cytokines involving IL-6 and IL-10 among severe and expired patients with COVID-19. A recent study indicated a similar assumption with these findings and identified elevated levels of IL-6 and IL-10 among non-survived compared with survived patients [9]. Another confirmation of this conclusion is confirmed by a newly published meta-analysis report that indicated an exaggerated elevation of IL-6 and IL-10 throughout the severe level of COVID-19 infection [72].

Concerning the inflammatory markers associated with the COVID-19 pandemic, this comprehensive meta-analysis study observed higher concentrations of C-reactive protein (CRP) and procalcitonin besides elevated erythrocyte sedimentation rate (ESR) levels among severe and expired patients with COVID-19. Recently, Henry et al. established a meta-analysis survey and corroborated this finding with a higher significance of CRP and procalcitonin levels [72]. Other recent reports identified higher levels of CRP among severe patients with COVID-19 infection [76]. An additional meta-analysis survey established based on four recent articles indicated prolonged levels of procalcitonin among severe patients with COVID-19 [83]. In this respect, we might speculate the potential role of procalcitonin as a prognostic biomarker during the severe status of COVID-19. Finally, our team revealed increased levels of serum ferritin among non-survived patients compared with survived patients, and this significant outcome was observed in another meta-analysis study among severe and non-survival patients with COVID-19 infection [72].

This comprehensive meta-analysis confronted several limitations that raised throughout the processing of the outcomes. First, the insufficient laboratory data concerning the interest of design causing the increasing bias among different covariates. Second, the variation in the characteristics among different articles concerning the severity and survival of COVID-19. Third, the small sample sizes of some studies besides most of the concerned articles were established within China, especially Wuhan. Finally, there was an observed publication bias and heterogeneity in this comprehensive meta-analysis.

Conclusion

In conclusion, several laboratory parameters could associate with the severity and mortality of COVID-19 infection and should be screened and measured continuously during the progression of this pandemic. These parameters included WBCs count, lymphocytes, platelet count, prothrombin time, D-dimer, and fibrinogen. Also, various interleukins could serve as anti-inflammatory markers such as IL-6, and IL-10 and should be evaluated. The estimation of other inflammatory biomarkers like CRP and procalcitonin could be helpful in the monitor the severity of the disease.

Supporting information

S1 Table. PRISMA checklist.

(DOC)

Click here for additional data file. (66KB, doc)
S2 Table. Reported timing of data collection and criteria of severity in eligible studies.

(DOCX)

Click here for additional data file. (97KB, docx)
S1 Fig. Publication bias.

Funnel plot of standard error by the standardized difference in means for (A) White blood cells, (B) Neutrophil count, (C) Lymphocyte count, (D) Procalcitonin, and (E) C-reactive protein. The standard error provides a measure of the precision of the effect size as an estimate of the population parameter. It starts with zero at the top. Studies with smaller sample sizes produce less precise estimated effects with a broader base. The pooled estimated effects would be expected to scatter symmetrically around the total overall estimate of the meta-analysis (represented by the vertical line). Each circle represents a study (black circle). In the case of asymmetry, Duval and Tweedie’s trim and fill method predict the missing studies (red circle). Begg’s and Egger’s tests were performed. P values <0.1 were set to have a significant bias.

(TIF)

Click here for additional data file. (736.3KB, tif)

Acknowledgments

We thank all authors who provided published information for our meta-analysis.

Data Availability

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

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Huang C., et al. , Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 2020. 395(10223): p. 497–506. 10.1016/S0140-6736(20)30183-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Rodriguez-Morales A.J., et al. , Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis, 2020: p. 101623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Al-Tawfiq J.A. and Gautret P., Asymptomatic Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection: Extent and implications for infection control: A systematic review. Travel Med Infect Dis, 2019. 27: p. 27–32. 10.1016/j.tmaid.2018.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Ksiazek T.G., et al. , A novel coronavirus associated with severe acute respiratory syndrome. N Engl J Med, 2003. 348(20): p. 1953–66. 10.1056/NEJMoa030781 [DOI] [PubMed] [Google Scholar]
  • 5.Rodriguez-Morales A.J., et al. , History is repeating itself: Probable zoonotic spillover as the cause of the 2019 novel Coronavirus Epidemic. Infez Med, 2020. 28(1): p. 3–5. [PubMed] [Google Scholar]
  • 6.Chen N., et al. , Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet, 2020. 395(10223): p. 507–513. 10.1016/S0140-6736(20)30211-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Yin Y. and Wunderink R.G., MERS, SARS and other coronaviruses as causes of pneumonia. Respirology, 2018. 23(2): p. 130–137. 10.1111/resp.13196 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Lechien J.R., et al. , Clinical and Epidemiological Characteristics of 1,420 European Patients with mild-to-moderate Coronavirus Disease 2019. J Intern Med, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Chen T., et al. , Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ, 2020. 368: p. m1091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Udugama B., et al. , Diagnosing COVID-19: The Disease and Tools for Detection. ACS Nano, 2020. 14(4): p. 3822–3835. 10.1021/acsnano.0c02624 [DOI] [PubMed] [Google Scholar]
  • 11.McInnes M.D.F., et al. , Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA, 2018. 319(4): p. 388–396. 10.1001/jama.2017.19163 [DOI] [PubMed] [Google Scholar]
  • 12.Kapadia M.Z., et al. , Weight Loss Instead of Weight Gain within the Guidelines in Obese Women during Pregnancy: A Systematic Review and Meta-Analyses of Maternal and Infant Outcomes. PLOS ONE, 2015. 10(7): p. e0132650 10.1371/journal.pone.0132650 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Stang A., Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. European Journal of Epidemiology, 2010. 25(9): p. 603–605. 10.1007/s10654-010-9491-z [DOI] [PubMed] [Google Scholar]
  • 14.Wallace B.C., et al. , Closing the Gap between Methodologists and End-Users: R as a Computational Back-End. 2012, 2012. 49(5): p. 15. [Google Scholar]
  • 15.Pierce C.A., Software Review: Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2006). Comprehensive Meta-Analysis (Version 2.2.027) [Computer software]. Englewood, NJ: Biostat. Organizational Research Methods, 2008. 11(1): p. 188–191. [Google Scholar]
  • 16.DerSimonian R. and Laird N., Meta-analysis in clinical trials. Control Clin Trials, 1986. 7(3): p. 177–88. 10.1016/0197-2456(86)90046-2 [DOI] [PubMed] [Google Scholar]
  • 17.Andreano A., Rebora P., and Valsecchi M.G., Measures of single arm outcome in meta-analyses of rare events in the presence of competing risks. Biometrical Journal, 2015. 57(4): p. 649–660. 10.1002/bimj.201400119 [DOI] [PubMed] [Google Scholar]
  • 18.Elshazli R.M., et al. , Genetic polymorphisms of TP53 (rs1042522) and MDM2 (rs2279744) and colorectal cancer risk: An updated meta-analysis based on 59 case-control studies. Gene, 2020. 734. [DOI] [PubMed] [Google Scholar]
  • 19.Lin L. and Chu H., Quantifying publication bias in meta-analysis. Biometrics, 2018. 74(3): p. 785–794. 10.1111/biom.12817 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Duval S. and Tweedie R., A Nonparametric “Trim and Fill” Method of Accounting for Publication Bias in Meta-Analysis. Journal of the American Statistical Association, 2000. 95(449): p. 89–98. [Google Scholar]
  • 21.Arentz M., et al. , Characteristics and Outcomes of 21 Critically Ill Patients With COVID-19 in Washington State. JAMA, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Cai Q., et al. , Experimental Treatment with Favipiravir for COVID-19: An Open-Label Control Study. Engineering, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Chang, et al. , Epidemiologic and Clinical Characteristics of Novel Coronavirus Infections Involving 13 Patients Outside Wuhan, China. JAMA, 2020. 323(11): p. 1092–1093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Chen G., et al. , Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Chen J., et al. , Clinical progression of patients with COVID-19 in Shanghai, China. J Infect, 2020. 80(5): p. e1–e6. 10.1016/j.jinf.2020.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Chen X., et al. , Detectable serum SARS-CoV-2 viral load (RNAaemia) is closely correlated with drastically elevated interleukin 6 (IL-6) level in critically ill COVID-19 patients. Clin Infect Dis, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Chen Z., et al. , High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages. Eur J Radiol, 2020. 126: p. 108972. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Cheng Y., et al. , Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Cheng Z., et al. , Clinical Features and Chest CT Manifestations of Coronavirus Disease 2019 (COVID-19) in a Single-Center Study in Shanghai, China. AJR Am J Roentgenol, 2020: p. 1–6. [DOI] [PubMed] [Google Scholar]
  • 30.Deng Y., et al. , Clinical characteristics of fatal and recovered cases of coronavirus disease 2019 (COVID-19) in Wuhan, China: a retrospective study. Chin Med J (Engl), 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Fan B.E., et al. , Hematologic parameters in patients with COVID-19 infection. Am J Hematol, 2020. [DOI] [PubMed] [Google Scholar]
  • 32.Gao Y., et al. , Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19. J Med Virol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Guo T., et al. , Cardiovascular Implications of Fatal Outcomes of Patients With Coronavirus Disease 2019 (COVID-19). JAMA Cardiol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.He R., et al. , The clinical course and its correlated immune status in COVID-19 pneumonia. Journal of Clinical Virology, 2020: p. 104361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Hsih W.-H., et al. , Featuring COVID-19 cases via screening symptomatic patients with epidemiologic link during flu season in a medical center of central Taiwan. Journal of Microbiology, Immunology and Infection, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lei S., et al. , Clinical characteristics and outcomes of patients undergoing surgeries during the incubation period of COVID-19 infection. EClinicalMedicine, 2020: p. 100331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Li K., et al. , The Clinical and Chest CT Features Associated with Severe and Critical COVID-19 Pneumonia. Invest Radiol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Liu K., et al. , Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. J Infect, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Liu W., et al. , Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. Chin Med J (Engl), 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Liu X., et al. , Potential therapeutic effects of dipyridamole in the severely ill patients with COVID-19. Acta Pharm Sin B, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Lo I.L., et al. , Evaluation of SARS-CoV-2 RNA shedding in clinical specimens and clinical characteristics of 10 patients with COVID-19 in Macau. Int J Biol Sci, 2020. 16(10): p. 1698–1707. 10.7150/ijbs.45357 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Luo S., Zhang X., and Xu H., Don’pt Overlook Digestive Symptoms in Patients With 2019 Novel Coronavirus Disease (COVID-19). Clin Gastroenterol Hepatol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Mo P., et al. , Clinical characteristics of refractory COVID-19 pneumonia in Wuhan, China. Clin Infect Dis, 2020. [Google Scholar]
  • 44.Pan F., et al. , Time Course of Lung Changes On Chest CT During Recovery From 2019 Novel Coronavirus (COVID-19) Pneumonia. Radiology, 2020: p. 200370. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Qian G.Q., et al. , Epidemiologic and Clinical Characteristics of 91 Hospitalized Patients with COVID-19 in Zhejiang, China: A retrospective, multi-centre case series. QJM, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Qin C., et al. , Dysregulation of immune response in patients with COVID-19 in Wuhan, China. Clin Infect Dis, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Shi H., et al. , Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Shi S., et al. , Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Tang X., et al. , Comparison of Hospitalized Patients With ARDS Caused by COVID-19 and H1N1. Chest, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.To K.K., et al. , Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. Lancet Infect Dis, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Wan S., et al. , Clinical features and treatment of COVID-19 patients in northeast Chongqing. J Med Virol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Wang D., et al. , Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA, 2020. 323(11): p. 1061–1069. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Wang L., et al. , Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up. J Infect, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Wang Z., et al. , Clinical Features of 69 Cases with Coronavirus Disease 2019 in Wuhan, China. Clin Infect Dis, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Wu C., et al. , Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Wu J., et al. , Clinical Characteristics of Imported Cases of COVID-19 in Jiangsu Province: A Multicenter Descriptive Study. Clin Infect Dis, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Wu J., et al. , Chest CT Findings in Patients With Coronavirus Disease 2019 and Its Relationship With Clinical Features. Invest Radiol, 2020. 55(5): p. 257–261. 10.1097/RLI.0000000000000670 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Xiong Y., et al. , Clinical and High-Resolution CT Features of the COVID-19 Infection: Comparison of the Initial and Follow-up Changes. Invest Radiol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Xu X.W., et al. , Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ, 2020. 368: p. m606 10.1136/bmj.m606 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Yang W., et al. , Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19):A multi-center study in Wenzhou city, Zhejiang, China. J Infect, 2020. 80(4): p. 388–393. 10.1016/j.jinf.2020.02.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Yang X., et al. , Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Young B.E., et al. , Epidemiologic Features and Clinical Course of Patients Infected With SARS-CoV-2 in Singapore. JAMA, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Zhang G., et al. , Clinical features and short-term outcomes of 221 patients with COVID-19 in Wuhan, China. J Clin Virol, 2020. 127: p. 104364 10.1016/j.jcv.2020.104364 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Zhang J.J., et al. , Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy, 2020. [DOI] [PubMed] [Google Scholar]
  • 65.Zhao D., et al. , A comparative study on the clinical features of COVID-19 pneumonia to other pneumonias. Clin Infect Dis, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Zheng C., et al. , Risk-adapted Treatment Strategy For COVID-19 Patients. Int J Infect Dis, 2020. 94: p. 74–77. 10.1016/j.ijid.2020.03.047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Zhou F., et al. , Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet, 2020. 395(10229): p. 1054–1062. 10.1016/S0140-6736(20)30566-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Zhou W., et al. , Potential benefits of precise corticosteroids therapy for severe 2019-nCoV pneumonia. Signal Transduction and Targeted Therapy, 2020. 5(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Zhou Z., et al. , Coronavirus disease 2019: initial chest CT findings. Eur Radiol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Zhu Z., et al. , Clinical value of immune-inflammatory parameters to assess the severity of coronavirus disease 2019. International Journal of Infectious Diseases, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Emami A., et al. , Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19: a Systematic Review and Meta-Analysis. Arch Acad Emerg Med, 2020. 8(1): p. e35 [PMC free article] [PubMed] [Google Scholar]
  • 72.Henry B.M., et al. , Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med, 2020. [DOI] [PubMed] [Google Scholar]
  • 73.Di Gennaro F., et al. , Coronavirus Diseases (COVID-19) Current Status and Future Perspectives: A Narrative Review. Int J Environ Res Public Health, 2020. 17(8). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Lippi G., Simundic A.M., and Plebani M., Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19). Clin Chem Lab Med, 2020. [DOI] [PubMed] [Google Scholar]
  • 75.Lagier J.C., et al. , Testing the repatriated for SARS-Cov2: Should laboratory-based quarantine replace traditional quarantine? Travel Med Infect Dis, 2020: p. 101624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Tan C., et al. , C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early. J Med Virol, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Henry B.M., COVID-19, ECMO, and lymphopenia: a word of caution. Lancet Respir Med, 2020. 8(4): p. e24 10.1016/S2213-2600(20)30119-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Lippi G., Plebani M., and Henry B.M., Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A meta-analysis. Clinica Chimica Acta, 2020. 506: p. 145–148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Shi Y., et al. , COVID-19 infection: the perspectives on immune responses. Cell Death Differ, 2020. 27(5): p. 1451–1454. 10.1038/s41418-020-0530-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Tang N., et al. , Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. Journal of Thrombosis and Haemostasis, 2020. 18(4): p. 844–847. 10.1111/jth.14768 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Ye Q., Wang B., and Mao J., The pathogenesis and treatment of the ‘Cytokine Storm’ in COVID-19. J Infect, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Zhao M., Cytokine storm and immunomodulatory therapy in COVID-19: Role of chloroquine and anti-IL-6 monoclonal antibodies. Int J Antimicrob Agents, 2020: p. 105982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Lippi G. and Plebani M., Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. Clin Chim Acta, 2020. 505: p. 190–191. 10.1016/j.cca.2020.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Farhat Afrin

12 Jun 2020

PONE-D-20-13889

Diagnostic and prognostic value of hematological and immunological markers in COVID-19 infection: A meta-analysis of 6320 patients

PLOS ONE

Dear Dr. Fawzy,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The manuscript highlights the L. infantum loads and inflammation in the genital tract of naturally infected dogs in an endemic area of Brazil by qPCR and IHC. Besides vertical transmission, it also suggests venereal transmission from both the sexes as also suggested by other studies. The positive aspect is the higher number of animals used in the present study; otherwise, there are no new findings compared to similar studies conducted in the past. 

The manuscript cannot be accepted as it presently stands. There are serious concerns that need to be addressed as elaborated in Reviewer’s comments. 

Further, the authors should incorporate the following suggestions:

  1. Every method should be supported by a reference.

  2. Few references need to be updated. 

  3. The authors have mentioned the results of other studies at several instances while discussing their findings, which is confusing. The exact values from other studies need not be mentioned every time.

  4. The dogs were naturally infected and of different ages. The authors should discuss why they found similar parasite loads in the different genital organs (testis, epididymis, vulva and vagina) and significantly low in prostrate and uterus, unlike other studies. They have mentioned the anatomic proximity of the organs. 

  5. The title should be modified to bring out the crux of the study; it is too detailed at the moment.

Please submit your revised manuscript by Jul 27 2020 11:59PM. 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'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Farhat Afrin, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments:

The manuscript highlights the L. infantum loads and inflammation in the genital tract of naturally infected dogs in an endemic area of Brazil by qPCR and IHC. Besides vertical transmission, it also suggests venereal transmission from both the sexes as also suggested by other studies. The positive aspect is the higher number of animals used in the present study; otherwise, there are no new findings compared to similar studies conducted in the past.

The manuscript cannot be accepted as it presently stands. There are serious concerns that need to be addressed as elaborated in Reviewer’s comments.

Further, the authors should incorporate the following suggestions:

1) Every method should be supported by a reference.

2) Few references need to be updated.

3) The authors have mentioned the results of other studies at several instances while discussing their findings, which is confusing. The exact values from other studies need not be mentioned every time.

4) The dogs were naturally infected and of different ages. The authors should discuss why they found similar parasite loads in the different genital organs (testis, epididymis, vulva and vagina) and significantly low in prostrate and uterus, unlike other studies. They have mentioned the anatomic proximity of the organs.

5) The title should be modified to bring out the crux of the study; it is too detailed at the moment.

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that Figure 1B in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

2.1.    You may seek permission from the original copyright holder of Figure 1B to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

2.2.    If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

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

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

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

Reviewer #1: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The review is written well. But it needs several refinements.

1. The abstract (results): The values of parameters must be given with indicative conclusions. The reader will read the abstract before deciding to read the full article or not. Unfortunately the abstract is not well written.

2. Too many tables and same data is presented in the figures 2 and 3. I strongly feel that that Figure 2 and 3 are not represented correctly. Each panel of these figures deserve a separate independent figure. The journal also advises how to format a figure/graphs. The authors should use that format. The current form of these two important figures (having so many panels within) are not readable and have been made irrelevant.

3. The conclusions are also not crisp and clear. and need rephrasing with clear message. The words like various cytokines, markers, means nothing. We all know that all the cytokines and inflammatory markers are high in COVID-19, but this review can only be relevant if the author give clear message, eg. If D-Dimer is more significant or IL-6 value. (This is just an illustration)

**********

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.

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

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Prof. Sarman Singh

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Aug 21;15(8):e0238160. doi: 10.1371/journal.pone.0238160.r002

Author response to Decision Letter 0

14 Jun 2020

Journal Requirements

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response

The authors ensure that our manuscript meets PLOS ONE's style requirements, including those for file naming.

2. We note that Figure 1B in your submission contain [map/satellite] images which may be copyrighted.

Response:

The map in Fig 1 B is curated by the authors using Tableau 2020.1 Desktop Professional Edition (https://www.tableau.com/). These data were provisded in the figure legend to clarify this issue and cite the vendor in the text.

Reviewer #1

The review is written well. But it needs several refinements.

Author Response:

Dear Prof. Sarman Singh

We appreciate the time put in reviewing this manuscript. Thank you for the constructive comments. The authors followed it.

1. The abstract (results): The values of parameters must be given with indicative conclusions. The reader will read the abstract before deciding to read the full article or not. Unfortunately, the abstract is not well written.

Author Response:

Thank you for the remark. The abstract has been revised to be attractive for future readers.

2. Too many tables and same data is presented in the figures 2 and 3. I strongly feel that that Figure 2 and 3 are not represented correctly. Each panel of these figures deserve a separate independent figure. The journal also advises how to format a figure/graphs. The authors should use that format. The current form of these two important figures (having so many panels within) are not readable and have been made irrelevant.

Author Response:

We followed reviewers’ suggestions. We removed Figure 2 as the representative data included in the tables and let figure 3 as supplementary material (Figure S1) based on the referee's valued suggestion. The authors only represented the sequential trial analysis in a separate main figure (Figure 2).

3. The conclusions are also not crisp and clear. and need rephrasing with clear message. The words like various cytokines, markers, means nothing. We all know that all the cytokines and inflammatory markers are high in COVID-19, but this review can only be relevant if the author give clear message, e.g. If D-Dimer is more significant or IL-6 value. (This is just an illustration)

Author Response:

Thank you for the remark. The conclusion has been revised and highlighted according to the valued suggestion.

Decision Letter 1

Farhat Afrin

15 Jul 2020

PONE-D-20-13889R1

Diagnostic and prognostic value of hematological and immunological markers in COVID-19 infection: A meta-analysis of 6320 patients

PLOS ONE

Dear Dr. Fawzy,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 29 2020 11:59PM. 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'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Farhat Afrin, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

The authors need to address the issues raised by the reviewer.

[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: All comments have been addressed

Reviewer #3: (No Response)

**********

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: None. Comments Addressed now.

Authors revised the suggestions and comments and proceed to improve and correct properly.

Reviewer #3: The overall performance is good, still, many issues should be addressed

1. Line 154:  Next, in the presence of individual patient data, single-armed observed values were converted to two-armed data to act as each other’s control group based on covariate information.

Please provide Reference for articles presented with individual data.

2. Line 162: For severity pairwise comparison, estimates of SMD served as quantitative measures of the strength of evidence against the null hypothesis of no difference in the population between mild and severe COVID-19 manifestations.

Line 165: SMD of <0.2, 0.2-0.8, and >0.8 indicated mild, moderate, and severe strength.

Line 166:  For ICU admission, survival analysis, overall effect size estimates in SMD were then converted to the odds ratio (OR) with 95%CI for better interpretation by clinical domains.

What about moderate group defined by SMD 0.2-0.8?

3. Line: 168: Decision tree to identify predictors for poor outcomes

In the manuscript, only severity was analyzed.

4. Line169:  Using laboratory features for clinical prediction, the decision tree algorithm was employed to identify the key risk factors attributed to severe COVID-19 infection.

For Risk factor? Not for cutoff value? No matter whatever it is, please provide the decision tree results as supplemental material.

5. Line 216: Ultimately, a total of 52 eligible articles were included for the quantitative synthesis of this meta-analysis study, with 52 records represented single-arm analysis, 16 records represented two-arms

Sixteen included articles have both single- and two-arms design?

And please specify the arms here, what is it?

6. Line 236:  descriptive case series, and one case-control study.

Line 212 excluded for being case records (n = 44)

What is difference between case series and case records here?

7.Table 1

1)Journal name and Publication date are not necessary to be included.

But, patients gender, age, outcomes should be provided.

8. table 2

What are the outcome measures here to predict by biological parameters?

9.Table 5

Specify the outcome “severity of COVID19”in the title.

10.Figure 2.

RSA sample size or RIS should be notified.

11.Subgroup analyses

why not conduct subgroup analyses by quality? diagnosis criteria for severity? or methods of biological parameters measurements?

12.Time point of collection of lab parameters and clinic symptoms?

**********

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.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Aug 21;15(8):e0238160. doi: 10.1371/journal.pone.0238160.r004

Author response to Decision Letter 1

25 Jul 2020

The rebuttal letter that responds to each point raised by the academic editor and the reviewer has been uploaded as a separate file labeled 'Response sheet', by the end of the manuscript as it includes some illustrations which are hard to be included here. Thanks

Attachment

Submitted filename: 2-Response sheet.docx

Click here for additional data file. (150KB, docx)

Decision Letter 2

Farhat Afrin

12 Aug 2020

Diagnostic and prognostic value of hematological and immunological markers in COVID-19 infection: A meta-analysis of 6320 patients

PONE-D-20-13889R2

Dear Dr. Fawzy,

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,

Farhat Afrin, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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 #3: All comments have been addressed

**********

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: (No Response)

**********

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

Reviewer #3: (No Response)

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: (No Response)

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: (No Response)

**********

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.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #3: No

Acceptance letter

Farhat Afrin

14 Aug 2020

PONE-D-20-13889R2

Diagnostic and prognostic value of hematological and immunological markers in COVID-19 infection: A meta-analysis of 6320 patients

Dear Dr. Fawzy:

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. Farhat Afrin

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 Table. PRISMA checklist.

    (DOC)

    Click here for additional data file. (66KB, doc)
    S2 Table. Reported timing of data collection and criteria of severity in eligible studies.

    (DOCX)

    Click here for additional data file. (97KB, docx)
    S1 Fig. Publication bias.

    Funnel plot of standard error by the standardized difference in means for (A) White blood cells, (B) Neutrophil count, (C) Lymphocyte count, (D) Procalcitonin, and (E) C-reactive protein. The standard error provides a measure of the precision of the effect size as an estimate of the population parameter. It starts with zero at the top. Studies with smaller sample sizes produce less precise estimated effects with a broader base. The pooled estimated effects would be expected to scatter symmetrically around the total overall estimate of the meta-analysis (represented by the vertical line). Each circle represents a study (black circle). In the case of asymmetry, Duval and Tweedie’s trim and fill method predict the missing studies (red circle). Begg’s and Egger’s tests were performed. P values <0.1 were set to have a significant bias.

    (TIF)

    Click here for additional data file. (736.3KB, tif)
    Attachment

    Submitted filename: 2-Response sheet.docx

    Click here for additional data file. (150KB, docx)

    Data Availability Statement

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

    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES