Invasive candidiasis following lung transplant: An Assessment of impact utilizing a national insurance claims cohort

Kelly M Pennington; Herb Heien; Hemang Yadav; Xiaoxi Yao; Bradley White; Steve G Peters; Patricio Escalante; Che Ngufor; Raymund R Razonable; Cassie C Kennedy

doi:10.1371/journal.pone.0330162

. 2025 Aug 21;20(8):e0330162. doi: 10.1371/journal.pone.0330162

Invasive candidiasis following lung transplant: An Assessment of impact utilizing a national insurance claims cohort

Kelly M Pennington ^1,^2,^*,^‡, Herb Heien ^3,^4,^‡, Hemang Yadav ^1,², Xiaoxi Yao ^3,^4,⁵, Bradley White ¹, Steve G Peters ^1,², Patricio Escalante ¹, Che Ngufor ^1,³, Raymund R Razonable ^2,⁶, Cassie C Kennedy ^1,^2,³

Editor: Ali Amanati⁷

¹Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic Rochester, Rochester, Minnesota, United States of America

²William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic Rochester, Rochester, Minnesota, United States of America

³Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic Rochester, Minnesota, United States of America

⁴OptumLabs Eden, Eden Prairie, Minnesota, United States of America

⁵Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States of America

⁶Division of Infectious Diseases, Department of Medicine, Mayo Clinic Rochester, Rochester, Minnesota, United States of America

⁷Shiraz University of Medical Sciences, IRAN, ISLAMIC REPUBLIC OF

^✉

* E-mail: Pennington.Kelly@mayo.edu

Competing Interests: The authors have declared that no competing interests exist.

‡These authors had equal contributions to the study and manuscript development. They should be considered co-first authors

Roles

Kelly M Pennington: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Writing – original draft

Herb Heien: Conceptualization, Data curation, Formal analysis, Methodology, Writing – review & editing

Hemang Yadav: Formal analysis, Methodology, Supervision, Writing – review & editing

Xiaoxi Yao: Data curation, Formal analysis, Methodology, Supervision, Writing – review & editing

Bradley White: Investigation, Methodology, Writing – review & editing

Steve G Peters: Conceptualization, Methodology, Supervision, Validation, Writing – review & editing

Patricio Escalante: Conceptualization, Data curation, Formal analysis, Supervision, Writing – review & editing

Che Ngufor: Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Writing – review & editing, Data curation

Raymund R Razonable: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

Cassie C Kennedy: Conceptualization, Formal analysis, Methodology, Resources, Supervision, Writing – review & editing

Ali Amanati: Editor

PMCID: PMC12370120 PMID: 40839601

Abstract

Introduction

Lung transplant recipients (LTRs) are susceptible to invasive candidiasis (IC). This study aimed to assess the incidence, risk factors, and impact of IC on mortality in LTRs using a national insurance claims cohort.

Methods

We conducted a retrospective cohort study using administrative claims data from the OptumLabs® Data Warehouse. We identified LTRs from January 1, 2005, to December 31, 2023, using procedural codes. Exclusion criteria included re-transplantations and pre-transplant IC. We employed multivariable logistic regression to identify risk factors for IC and Cox Proportional Hazard models to assess the impact of IC on mortality.

Results

Among 1279 LTRs, 131 (10.2%) developed IC, primarily during the initial hospitalization for lung transplantation (index hospitalization). The median time to IC diagnosis was 32.0 days following transplant. Post-transplant extra-corporeal membrane oxygenation (ECMO) for more than 8 days was associated with IC (OR: 2.34; 95% CI 1.03 to 5.34). Mortality was higher in LTRs with IC (HR: 2.31; 95% CI: 1.45 to 3.67; p < 0.001). LTRs with IC also had longer hospital stays (median 26.0 days vs. 20.0 days; p < 0.001) and more re-operations (36.7% vs. 27.3%; p = 0.003) compared to those without IC.

Conclusion

Invasive candidiasis affects approximately 10% of lung transplant recipients, most often during the initial hospitalization. It is associated with increased mortality, prolonged hospital stays, and a greater need for surgical re-intervention. These findings highlight the importance of early identification and targeted preventive strategies to improve outcomes in this high-risk population.

Introduction

Lung transplant recipients (LTRs) are particularly vulnerable to invasive fungal infections (IFIs), which occur in up to 25% of recipients and are linked to an alarming three-fold increase in all-cause mortality [1–3]. While Aspergillus spp. have traditionally been seen as the primary cause of IFI in LTRs [4–6], more recent single-center studies suggest that invasive candidiasis (IC) may also be an important cause of IFI, particularly within the first 90 days post-lung transplant period [7,8]. Candida spp. possess virulence factors that promote adhesion to host tissues, hyphal transformation, and enzymatic degradation of epithelial barriers, allowing for tissue invasion and dissemination, particularly in immunocompromised hosts [9].

While many lung transplant centers in the United States employ antifungal prophylaxis targeting mold infections [10], variation exists in the agents used and the duration of prophylaxis. Some studies have suggested that the use of inhaled amphotericin B without systemic antifungal prophylaxis may be insufficient to prevent all forms of invasive fungal infection, including invasive candidiasis [7]. In the absence of universal prophylaxis, the incidence of candidemia at a single Canadian transplant center was reported at 3.5% with most episodes occurring within the first 30 days post-transplant [8]. Risk factors for candidemia included pre-transplant hospitalization, post-transplant extracorporeal membrane oxygenation (ECMO), and post-transplant renal replacement therapy [8]. In non-LT populations, the risk factors for IC and candidemia are clearly established and additionally include gastrointestinal perforation, length of hospital stay, and diabetes mellitus [11].

While universal antifungal prophylaxis may seem like an effective preventative measure for IC, evidence in the lung transplant population remains limited and inconclusive. Moreover, antifungal medications can have significant adverse effects, with reported rates including QTc prolongation in up to 20% of patients, hepatotoxicity in 5–15%, periostitis in approximately 10% of those receiving long-term voriconazole, and increased risk of skin cancer with prolonged triazole exposure [12,13]. Triazole antifungal medications are also expensive and interact with immunosuppressive agents, specifically calcineurin inhibitors, which have a narrow therapeutic index necessitating close, serial monitoring.

Given the unclear benefit, potential for toxicity, excessive costs, and drug interactions of antifungal prophylactic medications, it is imperative to have a clear understanding of the true impact of IC and the risk factors associated with IC in LTRs. Large claims databases, such as OptumLabs® Data Warehouse (OLDW), have been widely used to assess disease incidence and risk factors across various clinical domains [14,15]. The ability to identify a large Mult institutional cohort of LTRs, control for potential confounders, identify risk factors and diagnostics, all while following the cohort longitudinally makes this approach particularly attractive in attempting to identify the true impact of IC in LTRs.

This study aimed to describe the incidence and risk factors associated with IC in LTR and assess mortality among LTRs with IC and without IC using individuals enrolled in commercial and Medicare Advantage health plans in the US.

Methods

This study was conducted in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines. A detailed STROBE checklist has been included as a supplementary document to ensure comprehensive and transparent reporting of the research methods and findings.

Data source

We conducted a retrospective cohort study using de-identified administrative claims data from OLDW, which includes medical and pharmacy claims and enrollment records for commercial and Medicare Advantage enrollees. This database contains longitudinal health information on enrollees and patients from a diverse mixture of ages, ethnicities and geographical regions across the US [16]. OLDW provides real-world outcomes data for LTR in geographically and demographically diverse populations. In accordance with the Health Insurance Portability and Accountability Act, the use of pre-existing, de-identified claims data is exempted from Institutional Review Board review.

Study population

We extracted all single or bilateral lung transplants occurring in adults (≥ 18 years of age) from January 1, 2005 to November 30, 2023 using eligible International Classification of Diseases, Ninth Revision (ICD-9); International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); and Current Procedural Terminology (CPT) codes as previously described [17]. We required at least 90 days of continuous enrollment prior to transplant to allow for adequate baseline characterization, and at least 30 days post-transplant to ensure sufficient follow-up to assess early post-transplant outcomes. Follow-up started after the patients’ index date and continued to the end of enrollment, death, or end of the study period (December 31, 2023).

Re-transplantations were excluded by removing patients who had multiple hospital encounters with LT procedure codes. Re-transplantation cases were excluded to reduce heterogeneity, as these patients often have distinct clinical characteristics and risk profiles compared to primary transplant recipients. Patients were required to have a LT stay of at least 5 or more days, and those who had same-day admission and discharge dates were not excluded as they were considered aborted transplant procedures, also known as “dry runs.” Patients with IFI prior to their index date were also excluded to help reduce any potential confounding.

Outcomes of interest: risk factors for developing invasive candidiasis and its effect on mortality

Our study aimed to evaluate both risk factors of IC, and its effect on mortality.

In our population, IC was defined as any diagnostic code for IC (ICD-9: 112.4, 112.5, 112.8, 112.83; ICD-10: B37.1, B37.5, B37.6, B37.7) in any position following the LT procedure. These codes include candidemia (e.g., 112.5, B37.7), disseminated candidiasis (e.g., 112.5, 112.83), and organ-specific invasive infections (e.g., B37.5 for candidal peritonitis and B37.6 for candidal endocarditis).). We acknowledge that claims data may not distinguish between confirmed invasive disease and coding inaccuracies. These codes could occur during a clinic encounter, emergency department visit, or inpatient stay. Patients with IFI prior to the admission date for LT were excluded from the analysis. Mortality data were primarily obtained from the Social Security Death Master File, a national database that tracks death records. To increase completeness, particularly for deaths occurring during hospitalization, this was supplemented with information from hospital discharge status codes and insurance disenrollment records explicitly attributed to death. This approach has been validated in prior studies using the OptumLabs Data Warehouse [17,18].

Independent variables of interest included sex, age, race/ethnicity, census region, single versus double lung transplant, and indication for transplant. Elixhauser comorbidity score was calculated using ICD-9 and ICD-10 diagnostic codes captured during the 90-day baseline period prior to transplant, applying the Quan et al. adaptation of the Elixhauser algorithm [19]. The Elixhauser comorbidity index was dichotomized at ≥4 to define high comorbidity burden, consistent with distributional patterns in our data and previous literature. ECMO duration was dichotomized at >8 days based on prior studies linking prolonged (>7 days) ECMO support to increased post-transplant complications [20]. Baseline factors were extracted during the respective 90-day baseline period from the index date. An algorithm to identify the indication for transplant utilized ICD-9 and ICD-10 codes aimed at the specific chronic respiratory diseases. These codes could be found in any diagnosis position on a claim during the lung transplant stay to classify the most likely indication for transplant [17].

Other factors considered were pre-transplant corticosteroid use, post-transplant re-operation within the lung transplant hospital stay, post-transplant extra-corporeal membrane oxygenation (ECMO), post-transplant renal replacement therapy, and post-transplant cytomegalovirus (CMV) disease. Pre-transplant corticosteroid use was defined as a pharmacy prescription fill for prednisone, dexamethasone, or equivalent within 30 days prior to the lung transplant procedure. Post-transplant re-operation was defined as a procedure code with description of “chest closure”, “chest exploration” or “chest washout” (CPT: 00520, 21750, 35820; ICD-9: 3451, 3452) occurring more than 24 hours after the initial transplant procedure and within the initial hospitalization for lung transplantation (index hospitalization). Post-transplant ECMO support and post-transplant renal replacement therapy were defined as ICD-9 or ICD-10 (ECMO: Z92.81; renal replacement therapy: Z99.2) diagnostic or procedure code within the index hospitalization. Post-transplant CMV disease was defined as the presence of an ICD-9 (771.1) or ICD-10 (B25.9) diagnostic code for CMV disease in any diagnostic position following transplant.

Antifungal medications prescribed within 90 days prior to a diagnostic code for IC were also considered in our analysis. Antifungal medications included caspofungin, micafungin, anidulafungin, fluconazole, itraconazole, posaconazole, voriconazole, isavuconazonium, amphotericin B deoxycholate, and liposomal amphotericin. Nebulized and intravenous amphotericin B deoxycholate and liposomal amphotericin could not be differentiated. In the absence of prior diagnostic codes for IFI, these were assumed to be nebulized.

Statistical analysis

To protect patient confidentiality and in accordance with OptumLabs® restrictions, any event frequency of 11 or fewer participants was masked. Descriptive statistics were employed to describe the overall cohort. Categorical variables were summarized as frequency (%) and were compared using chi-square test. Continuous variables were expressed as median with interquartile range (IQR), as appropriate and were compared using Student’s t test or the Wilcoxon rank-sum test, depending on distributional characteristics assessed by visual inspection and normality testing. The tables were analyzed across the IC and non-IC groups to test for differences.

Analysis of both risk factors in the development of IC and mortality from IC required us to use two different models and extraction methods. For both sets of analysis, we re-extracted patient enrollment and baseline characteristics and analyzed the data using both descriptive and multivariable methods. Each data extraction was anchored on each of the respective index dates, which created two very similar but unique sets of data for analysis. The patient counts appear approximately the same but should be thought of as two different data sets with very similar patient characteristics.

Risk factor analysis

In our first analysis, we considered all LTRs who met the inclusion criteria for our study. We then assessed a priori which risk factors were associated with IC. The analysis was complicated by the fact that IC was most likely to occur during the LT stay, thus to ensure that all considered time dependent risk factors were measured prior to IC, we defined the index date as either the inpatient admission date or the first ECMO date after the LT transplant so that the effect of ECMO (a previously described risk factor for IC) could be assessed. We subsequently excluded patients who had IC before either of these dates.

A multivariable logistic regression model was used to investigate which risk factors were associated with IC. Variables were selected a priori based on clinical relevance and literature review. Age was evaluated but excluded from the final model due to collinearity with comorbidity burden, which was prioritized given its stronger association with invasive candidiasis and greater clinical relevance.. The odds ratios (ORs) and 95% confidence intervals (CIs) were computed to estimate the strength of the associations among the baseline risk factors on the outcome of IC. Baseline risk factors were captured either on or prior to the index date.

Mortality analysis

For this analysis, we matched one exposed—defined as a lung transplant recipient (LTR) who developed IC—to two unexposed on age (±5 years), sex, index date with respect to the LT admission date, and length of hospital stay. These variables were selected to control for potential confounders known to influence both the development of IC and post-transplant mortality. Age was used as a matching variable and demonstrated good covariate balance post-matching; therefore, it was not included in the final Cox proportional hazards model. We further assessed the covariate balance between the groups by calculating the standardized differences for each of the covariates in the pre and post matched cohort. A commonly accepted cutoff of 0.10 was used as the threshold for imbalance, where values ranging from 0 to < 0.10 indicate negligible imbalance. All imbalanced variables outside of the ± 0.10 range were controlled for in the final Cox Proportional Hazard (PH) model. The proportional hazards assumption was assessed using Schoenfeld residuals, and no violations were identified.

For the PH model, the first date of IC was the index date. For patients without IC, we do not have a similar date but instead have an LT admission date as their first entrance date. To adjust for this, we first ran an exhaustive match using the LT admission date as an index date. Next, we reset all the exposed groups’ index date to the first date of IC after the LT admission date. Then a pseudo index date for the unexposed group was set to be equidistant as the exposed groups’ LT admission date to IC, where the minimal difference in the patients’ LT length of stay were given precedence in the matching criteria. This allowed us to line up the matching unexposed groups’ index dates with the exposed group’s index date and help minimize potential confounding in timing to mortality. Enrollment was re-evaluated and all baseline characteristics 90 days prior to the newly assigned index date were analyzed. We then completed a PH model along with a Kaplan Meier curve to evaluate the effect of IC on patient mortality between the groups.

All statistical tests were two-sided, and p-values were reported to quantify the strength of evidence against the null hypothesis. A p-value threshold of <0.05 was used as a general reference for interpreting the results. All analyses were conducted using SAS Enterprise Guide 7.13 (SAS Institute Inc., Cary, NC) and Stata version 16.0 (Stata Corp).

Results

We identified a total of 2,879 unique patients with LT codes from January 1, 2005, to November 30, 2023; 1,279 patients met inclusion and exclusion criteria (Fig 1). The most common indication for lung transplant was idiopathic pulmonary fibrosis (IPF) followed by non-IPF interstitial lung disease (ILD) and chronic obstructive pulmonary disease (COPD) (Table 1). Most patients underwent bilateral lung transplant. The median follow-up was 469.0 (IQR: 745.0) days. Most LTRs were prescribed antifungal medications during their LT admission. The most commonly prescribed antifungal medications were mold-active azoles (voriconazole, posaconazole, and isavuconazonium) followed by nystatin and narrower spectrum azoles (itraconazole and fluconazole).

Table 1. Non-matched cohort characteristics.

	Median (IQR) or Count (%) n = 1,279
Age	60.0 (52.0, 66.0)
Male Sex	784 (61.3%)
Region
Midwest	340 (26.6%)
West	171 (13.4%)
South and Northeast	768 (60.1%)
Race
White	924 (72.2%)
Black	124 (9.7%)
Other/ Unknown	231 (18.1%)
Reason for Transplant
COPD/ Bronchiectasis	241 (18.8%)
Cystic Fibrosis	110 (8.6%)
Idiopathic Pulmonary Fibrosis	619 (48.4%)
Other Interstitial Lung Disease	260 (20.3%)
Other	49 (3.8%)
Type of Transplant
Single	307 (24.0%)
Bilateral	972 (76.0%)
Follow-up	469.0 (224.0, 969.0)
Died during Follow-up	289 (22.6%)
Days to Death from LT	395.0 (165.0, 991.0)
Antifungal Medications During LT Stay	1,194 (93.4%)
Antifungal Medications Prescribed During LT Stay^*
Amphotericin (liposomal or deoxycholate)	50 (3.9%)
Itraconazole or Fluconazole	117 (9.2%)
Voriconazole, Posaconazole, or Isavuconazonium	699 (54.7%)
Nystatin	317 (24.8%)
Echinocandin	166 (13.0%)
Developed IC	131 (10.2%)
When IC Occurred (n = 131)
Transplant Hospitalization	69 (52.7%)
After Transplant Hospitalization	62 (47.3%)
Type of IC Based on Diagnostic Code
Locally Invasive/ Deep Seated	96 (73.3%)
Candidemia	24 (18.3%)
Other	19 (14.5%)
Time from LT to IC	32.0 (0, 192.0)

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COPD- Chronic Obstructive Pulmonary Disease, IC- Invasive Candidiasis, LT- Lung Transplant

* Some patients had more than 1 antifungal medication prescribed

Age is presented in years. Follow-up and Time from LT to IC are presented in days.

Median (IQR) where values within parentheses include the 25th and 75th percentiles.

Invasive candidiasis

Among the 1,279 LTRs included in the study, 131 (10.2%) developed IC following transplant. The majority of initial episodes of IC occurred during the transplant hospitalization. Median time from LT admission to a diagnostic code for IC was 32.0 (IQR 192.0) days. Based on the diagnostic billing codes, locally invasive candidiasis/ deep-seated candidiasis was most common followed by candidemia. Invasive candidiasis diagnostic codes in administrative claims data are limited in clinical detail and do not distinguish between specific anatomical sites (e.g., thoracic vs. bloodstream) or severity of infection, which may affect the classification of IC presentations in this study.

Lung transplant recipients who developed IC had longer hospital stays on average (26.0 days, IQR: 53.0 days) compared to those without IC (20.0 days, IQR: 25.0; p < 0.0001) (Table 2). They were also more likely to have a re-operation compared to those without IC (p = 0.003). Days on ECMO was greater in those with IC (11.0 days, IQR: 41.0) compared to those without IC (7.0 days, IQR: 13.0; p = 0.010). Although ECMO use was not different between the groups (p = 0.411).

Table 2. Characteristics of lung transplant recipients who developed invasive candidiasis compared to those who did not.

	Invasive Candidiasis Median (IQR) or Count (%) n = 131	No Invasive Candidiasis Median (IQR) or Count (%) n = 1,148	p-value
Age	60.0 (51.0, 67.0)	60.0 (52.0, 66.0)	0.500
Male Sex	85 (64.9%)	699 (60.9%)	0.371
Reason for Transplant			0.500
COPD/ Bronchiectasis	23 (17.6%)	218 (19.0%)
Cystic Fibrosis	<11 (<8.4%)	100 (8.7%)
Idiopathic Pulmonary Fibrosis	60 (45.8%)	559 (48.7%)
Other Interstitial Lung Disease	35 (26.7%)	225 (19.6%)
Other	<11 (<8.4%)	46 (4.0%)
Type of Transplant			0.581
Single	34 (26.0%)	273 (23.8%)
Bilateral	97 (74.1%)	875 (76.2%)
Follow-up	517.0 (245.0, 938.0)	468.0 (220.0, 975.0)	0.852
Prescribed Corticosteroids Prior to LT Stay	103 (78.6%)	881 (76.7%)	0.631
High Pre-Transplant Co-morbidity Burden⁺	64 (48.9%)	590 (51.4%)	0.582
Pre-Transplant Diabetes Mellitus	14 (10.7%)	217 (18.9%)	0.021
Antifungal Medications During LT Stay	117 (89.3%)	1,077 (93.8%)	0.051
Re-operation During LT Stay	52 (36.7%)	313 (27.3%)	0.003
LT Hospitalization Length of Stay	26.0 (15.0, 68.0)	20.0 (13.0, 38.0)	<0.0001
Post-Transplant ECMO	13 (9.9%)	90 (7.8%)	0.411
Days of ECMO Use	11.0 (9.0, 50.0)	8.0 (2.0, 15.0)	0.010
Renal Replacement Therapy	<11 (<8.4%)	45 (3.9%)	0.233
CMV Disease Following Transplant	40 (30.5%)	293 (25.5%)	0.221

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CMV- Cytomegalovirus, COPD- Chronic Obstructive Pulmonary Disease, ECMO- Extra-corporeal Membrane Oxygenation, LT- Lung Transplant

+ Defined Elixhauser Comorbid Conditions score of 4 or higher.

Age is presented in years. Follow-up and LT hospitalization length of stay are presented in days.

Median (IQR) where values within parentheses include the 25th and 75th percentiles.

Risk factors for invasive candidiasis: multivariable analysis

Post-transplant ECMO use of greater than 8 days was associated with approximately a 2.3-fold increase in the odds of developing invasive candidiasis. Other clinical factors, including antifungal prophylaxis in the 90 days prior to IC, CMV disease, diabetes mellitus, high comorbidity burden, bilateral lung transplant, and pre-transplant steroid use, were not meaningfully associated with increased risk. None of the primary indications for lung transplant (e.g., COPD, ILD, IPF) were associated with increased odds of developing invasive candidiasis. Full results are provided in Table 3.

Table 3. Multivariable logistic regression for risk factors for invasive candidiasis following lung transplant.

Observations = 1,279	OR	p-value	95% CI
Gender (Ref. Female)
Male Sex	1.15	0.491	0.78	1.70
Transplant Reason (Ref. COPD/ Bronchiectasis)
Cystic Fibrosis	0.70	0.450	0.28	1.74
Idiopathic pulmonary fibrosis	1.01	0.971	0.58	1.77
Other Interstitial Lung Disease	1.79	0.061	0.98	3.29
Other	0.55	0.500	0.10	3.08
Type of Transplant (Ref. Single)
Bilateral Transplant	0.90	0.643	0.59	1.39
ECMO Days (Ref. ≤ 8)
9+	2.34	0.04*	1.03	5.34
Elixhauser (Ref. ≤ 3)
4+	0.86	0.670	0.44	1.70
Baseline Risk Factors
CMV disease	1.91	0.395	0.43	8.51
Pre-Transplant Steroid Use	1.12	0.641	0.69	1.83
Antifungal Prescription	1.49	0.165	0.85	2.59
Diabetes Mellitus	1.77	0.156	0.80	3.92
Renal Failure	0.75	0.520	0.31	1.80

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CI- Confidence Interval, COPD- Chronic Obstructive Pulmonary Disease, CMV- Cytomegalovirus, OR- Odds Ratio, Ref.- Reference

Effect of invasive candidiasis on mortality

To evaluate the impact of invasive candidiasis on survival, we matched 126 lung transplant recipients with IC to 252 without IC (Fig 2). As shown in Table 4, baseline characteristics between recipients with and without IC were well balanced after matching, with standardized differences falling below the commonly accepted threshold of 0.1, indicating good covariate balance across groups. The median follow-up after the index date was 439.0 days (IQR: 770.0 days), with 97 deaths (25.7%) observed during the follow-up period. Median time to death was 347.0 days (IQR: 703.0 days). Lung transplant recipients with IC had a higher mortality rate compared to matched recipients without IC, with an adjusted hazard ratio of 2.31 (95% CI: 1.45 to 3.67), reflecting more than a twofold increase in the risk of death.

Table 4. Matched cohort of lung transplant recipients with invasive candidiasis matched to age, sex, and length of hospital stay controls (1 invasive candidiasis to 2 without invasive candidiasis).

	Invasive Candidiasis Median (IQR) or Count (%) n = 126	No Invasive Candidiasis Median (IQR) or Count (%) n = 252	Standardized Difference after match
Age	60.0 (52.0, 67.0)	60.0 (52.0, 66.0)	0.01
Male Sex	81 (64.3%)	162 (64.3%)	0.00
Reason for Transplant			0.32^*
COPD/ Bronchiectasis	>21 (>16.7%)	218 (19.0%)
Cystic Fibrosis	<11 (<8.7%)	>33 (>13.1%)
Idiopathic Pulmonary Fibrosis	61 (48.4%)	105 (41.7%)
Other Interstitial Lung Disease	33 (26.2%)	60 (26.8%)
Other	<11 (<8.7%)	<22 (8.4%)
Prescribed Corticosteroids Prior to LT Stay	108 (85.7%)	219 (86.9%)	0.03
Pre-Transplant Diabetes Mellitus	37 (29.4%)	73 (29.0%)	0.01
Antifungal Medications During LT Stay	99 (78.6%)	212 (84.1%)	0.11*
CMV Disease Following Transplant	39 (30.1%)	59 (23.4%)	0.12*

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* All imbalanced variables outside of the ± 0.10 range were controlled for in the final Cox Proportional Hazard (PH) model.

Age is presented in years.

Median (IQR) where values within parentheses include the 25th and 75th percentiles.

Per OptumLabs® data use policies, cell counts ≤11 are masked and reported using “<” or “>” symbols to protect patient privacy.

Table 5 presents the results of the Cox proportional hazards model evaluating the impact of IC on all-cause mortality. All-cause mortality was higher in those who developed IC (event rate per 100 person-years: 11.32; HR: 2.13; 95% CI 1.45 to 3.12, p < 0.001). This held true when matching IC exposed to unexposed (event rate per 100 person-years: 12.87; HR: 2.31; 95% CI 1.45 to 3.67, p < 0.001) (Fig 3).

Table 5. Cox proportional hazards model evaluating the impact of IC on all-cause mortality.

Cox Accounting for remaining unbalanced covariates after the match
	Hazard Ratio	Std. Err.	p-value	95% CI
Description
Invasive Candidiasis	2.31	0.56	< 0.0001	1.45	3.67
Region code (Cont. Midwest)
South/ Northeast	1.00	0.26	0.987	0.60	1.67
West	0.71	0.27	0.372	0.34	1.50
Race code (Cont. Black)
Unknown	2.14	1.09	0.136	0.79	5.83
White	1.56	0.76	0.363	0.60	4.07
Transplant Reasion
COPD or Bronchiectasis	0.98	0.47	0.963	0.39	2.48
IPF	1.10	0.39	0.788	0.55	2.21
Non-IPF ILD	1.26	0.47	0.531	0.61	2.62
Other	1.45	0.95	0.577	0.40	5.27
Unknown	4.64	4.73	0.133	0.63	34.27
Baseline Risk Factors
CMV Disease	2.15	0.80	0.038	1.04	4.45
Rx Antifungal Use	0.98	0.23	0.915	0.62	1.54
Baseline Elixhauser Components
Cardiac Arrhythmia	0.65	0.17	0.099	0.39	1.08
Deficiency Anemia	0.86	0.50	0.8	0.27	2.71
Chronic Pulmonary Disease	1.02	0.26	0.952	0.61	1.69
Coagulopathy	1.71	0.48	0.057	0.99	2.98
Fluid and Electrolyte Disorders	0.75	0.20	0.274	0.45	1.25
Hypertension without Complications	1.04	0.24	0.873	0.66	1.63
Hypothroidism	0.86	0.38	0.73	0.36	2.04
Liver Disease	1.71	0.51	0.073	0.95	3.08
Obesity	1.62	0.56	0.162	0.82	3.20
Other Neurological Disorders	1.97	0.61	0.031	1.07	3.63
Renal Failure	1.07	0.32	0.821	0.60	1.91
Valvular Disease	1.05	0.28	0.852	0.62	1.77

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CI- Confidence Interval, COPD- Chronic Obstructive Pulmonary Disease, CMV- Cytomegalovirus, ILD- Interstitial Lung Disease, IPF- Idiopathic Pulmonary Fibrosis

Fig 3 — The x-axis represents time from index date (in years), and the y-axis represents the probability of survival. The curves reflect unadjusted survival estimates. The effect of IC on all-cause mortality was further assessed using a Cox proportional hazards model, adjusted for residual covariate imbalance following matching (adjusted HR: 2.31; 95% CI: 1.45–3.67), as reported in the Results section.

Discussion

Invasive candidiasis occurred in approximately 10.2% of LTRs in our national cohort, with a median time to diagnosis of 32 days. This finding is consistent with a prior single-center study by Baker et al., which reported an IC prevalence of 11.4% and a similar median time to diagnosis of 31 days [7]. In contrast, Marinelli et al. described a lower prevalence of 3.5%; however, their study only evaluated episodes of candidemia and did not include other forms of invasive Candida infections, such as surgical site or pleural space infections [8]. This difference highlights the importance of case definition in determining incidence estimates. Our study used administrative billing codes, which allow for large-scale cohort identification but may include both candidemia and deep-seated non-bloodstream infections. The broader definition used in our claims-based approach may explain the higher observed prevalence compared to studies that only included microbiologically confirmed bloodstream infections. Furthermore, differences in antifungal prophylaxis practices—such as the use of inhaled amphotericin B versus systemic triazoles—may have contributed to variability in observed rates across studies. These findings align with prior large-scale surveillance data from the Transplant-Associated Infection Surveillance Network (TRANSNET), which identified Candida species as the most common cause of invasive fungal infections across solid organ transplant recipients [3]. Our findings build on this by focusing specifically on lung transplant recipients and demonstrating that IC remains a common and clinically important complication, even in the context of widespread antifungal exposure.

We found that post-transplant ECMO support of greater than 8 days is a risk factor for IC in LTRs. Post-transplant ECMO support and renal replacement therapy were risk factors reported by Marinelli et. al.[8] and coincides with risk factors reported in other critically ill populations [11,21]. Re-operation has not previously been reported as a risk factor for IC in LTRs. However, it is a known risk factor for IFI in other solid organ transplant recipients, and an open chest has been reported as a potential risk factor for IC in heart transplant recipients [22,23]. In our data, due to both the small sample size and the need to show a clear chronology directionality with potential risk factors, we were not able to fully explore re-operation within the index hospitalization or post-transplant renal replacement therapy as risk factors for IC. While re-operation was higher in the IC group, we cannot be certain if that was a risk factor for or consequence of IC. We saw no difference in the incidence of post-transplant renal replacement therapy between the groups. We decided to focus on ECMO support as a potential risk factor because of its established association with invasive fungal infections in transplant and critically ill populations, and because the timing of ECMO initiation provided a reliable anchor to establish chronological directionality between exposure and development of IC. This maintained a 90-day washout period of IC and a proper baseline period to collect risk factors of contracting IC post-index date. While the other measures are important, the direction of causality would be less reliable in the model if they were present.

Our dataset does not include information on ECMO cannulation sites. While femoral access may increase the risk of invasive candidiasis due to higher potential for contamination and vascular complications, further studies are needed to confirm this association.

Absence of antifungal prophylaxis during the index hospitalization was not a risk factor for IC in our cohort, but around 90% of patients were prescribed some type of antifungal medication making it difficult to discern if absence of an antifungal medication would increase the risk of IC. Given that over 90% of patients received antifungal prophylaxis, our ability to detect a protective effect was likely limited by the lack of variability in exposure, and the absence of observed benefit should be interpreted with caution.

Traditional risk factors for IC in non-immunosuppressed patients such as diabetes mellitus and co-morbidity burden were not associated with increased risk of IC in our cohort. Likewise, CMV disease has been implicated as a risk factor for IFI [23], but this was not a risk factor for IC in our cohort. These differences are possibly due to the small sample size or possibly because other studies have grouped IC and invasive mold infections as a composite outcome. Pathophysiologically, CMV as a risk factor for invasive mold infections makes sense as it causes direct tissue damage within the lungs weakening alveolar barrier defenses [24].

We found that lung transplant recipients with invasive candidiasis had a more than twofold increased risk of death compared to matched recipients without IC, consistent with the adjusted hazard ratio of 2.31 reported in our Cox proportional hazards model. Marinelli et. al. found in their single center cohort study that LTRs with candidemia had an increased 30-day post-transplant mortality [8]. A 3-fold increase in mortality in the first year following transplant has also been described in thoracic transplant recipients at Stanford University between 1980 and 2004 [25]. While we have had significant advancements in antifungal medications since the Stanford University cohort, mortality rates in LTRs with IC remains staggeringly high. Future efforts should be focused on preventative measures for the development of IC in LTRs. This needs to go beyond pharmacologic prophylaxis, as most patients in our cohort were on some type of antifungal medication and the incidence of IC was around 10%.

While our findings align with prior single-center studies, confirmation in other cohorts—particularly those with different antifungal prophylaxis practices or transplant protocols—will be important to validate these results and assess generalizability. Our findings do have practical implications for clinical management and resource allocation in LT care. Identifying prolonged ECMO support as a risk factor for IC highlights a high-risk subgroup where heightened surveillance or targeted antifungal strategies may be warranted. Additionally, the observed association between IC and increased mortality underscores the need for early recognition and timely intervention. The data also inform antifungal stewardship efforts by emphasizing that universal prophylaxis may not fully prevent IC and should be balanced against drug-related toxicities. As such, these results support a risk-based approach to IC prevention and may help guide clinical decision-making in the early post-transplant period.

Limitations

The primary constraint inherent to any observational study is the potential for uncontrolled confounding variables. In our investigation, due to the absence of pertinent data within the de-identified cohort, we were unable to consider additional risk factors such as intensified immunosuppressive regimens, pre-transplant colonization status, or parenteral nutrition. Nevertheless, we diligently managed known confounding factors by including variables such as sex, age, race/ethnicity, geographical region, year of transplant, primary diagnosis, type of transplant (single versus double or heart/lung), and comorbidities in our analysis.

We acknowledge the potential for selection bias introduced by excluding patients with a history of invasive candidiasis prior to transplant while retaining those with prior antifungal exposure, which may reflect underlying differences in baseline health status. Additionally, a slightly higher exclusion rate among patients who later developed IC (13% vs. 8%) could suggest unmeasured pre-transplant differences that we were unable to fully control for.

One notable advantage afforded by the OLDW is the real-world, enriched racial, ethnic, and geographic diversity of the cohort it encompasses, setting it apart from single observational studies. In comparison to frequently used datasets like the Scientific Registry of Transplant Recipients (SRTR), although our lung transplant cohort is relatively smaller, it furnishes additional data not readily available in SRTR. However, it’s imperative to acknowledge a drawback associated with this dataset; it is confined to patients enrolled in commercial and Medicare Advantage health plans. This limitation may potentially curtail the generalizability of our study, as it does not encompass uninsured patients or those enrolled in other government health plans.

Furthermore, the inherent nature of the de-identified dataset introduces certain limitations. For instance, our reliance on the Social Security Death Master File for mortality data comes with constraints, notably the limitations of this database since 2011, which may not capture up to one-third of deaths [26]. To mitigate this limitation, we supplemented the Social Security Death Master File with information derived from discharge status and insurance discontinuation due to death (insurance knowledge of death), particularly since the majority of early LT deaths occur within the hospital setting. While we acknowledge the possibility of missing a small proportion of patients who may have passed away outside the hospital environment, we anticipate that this phenomenon would be comparable between the groups we are comparing. Lastly, it is important to recognize that we employed billing codes to identify IC. These codes are contingent on accurate diagnosis and coding by the treating healthcare provider, introducing a potential source of variability and error in our analysis.

We dichotomized ECMO duration and comorbidity burden for ease of interpretation and consistency with prior studies, but acknowledge this approach may have limited our ability to detect dose-response relationships. Our analytic approach to evaluating post-IC mortality involved assigning matched unexposed LTRs a pseudo-index date aligned with the timing of IC diagnosis in the exposed group. While this allowed for comparison of outcomes from similar post-transplant timepoints, it may introduce bias if the assumptions underlying index date alignment are not fully met. Modeling IC as a time-dependent covariate in a Cox model represents an alternative approach and may help validate our findings in future studies.

Conclusion

In our multi-institutional cohort, IC has a prevalence of around 10% in LTRs with most episodes initially occurring during the index hospitalization. Post-transplant ECMO support for greater than 8 days increases the risk of IC, and patients with IC have more re-operations and longer hospital length of stay. LTRs who developed IC had a two-fold increased risk of death.

Abbreviations

COPD: Chronic obstructive pulmonary disease
C.I.: Confidence interval
PH: Cox proportional hazard model
CPT: Current procedural terminology
CMV: Cytomegalovirus
ECMO: Extra-corporeal membrane oxygenation
IPF: Idiopathic pulmonary fibrosis
ICD-9: International classification of diseases, ninth revision
ICD-10: International statistical classification of diseases and related health problems, tenth revision
I.C.: invasive candidiasis
IFI: Invasive fungal infection
ILD: Interstitial lung disease
L.T.: Lung transplant
LTR: Lung transplant recipient
O.R.: Odds ratio
OLDW: OptumLabs® data warehouse
US: United States.

Data Availability

Data Sharing Statement: This study was conducted using de-identified administrative claims from OptumLabs Data Warehouse. These data are third party data owned by OptumLabs and contain sensitive patient information; therefore, the data is only available upon request. Interested researchers engaged in HIPAA compliant research may contact connected@optum.com for data access requests. The data use requires researchers to pay for rights to use and access the data. These data are subject to restrictions on sharing as a condition of access.

Funding Statement

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

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PLoS One. doi: 10.1371/journal.pone.0330162.r001

Decision Letter 0

Ali Amanati

25 Mar 2025

PONE-D-25-06214Invasive Candidiasis Following Lung Transplant: An Assessment of Impact Utilizing a National Insurance Claims CohortPLOS ONE

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Reviewer #1: - Please don’t use abbreviates in the abstract (ECMO).

- Introduction lines 6 and 7 are wrong. Please see this article: Mayer FL, Wilson D, Hube B. Candida albicans pathogenicity mechanisms. Virulence. 2013 Feb 15;4(2):119-28. doi: 10.4161/viru.22913. Epub 2013 Jan 9. PMID: 23302789; PMCID: PMC3654610.

- Lines 10-11 according to reference 7 is not true.

- Please define invasive candidiasis “In our population, IC was defined as any diagnostic code for IC (ICD-9: 112.4, 112.5, 112.8, 112.4, 112.83, 112.5;5 ICD-10: B37.1, B37.5, B37.6, B37.7) in any position following the LT procedure”

Reviewer #2: The paper presented is a very interesting retrospective analysis of the risk factors leading to Candidiasis infection after lung transplant. The following issues regarding the statistical considerations should be addressed prior to publication:

1) There appears to be selection bias in that patients with a prior history of IC were excluded, but patients with a prior exposure to anti-fungals were not. Furthermore, based on Figure 1 it appears 13% of IC cases but only 8% of non-IC cases were excluded. This suggests possible selection bias.

2) It is unclear what the exact selection methods were for variable choices in the multivariable regression models. Based on the language Page 5 Lines 11-28, many factors were considered. However Table 3 reports the OR for only a select set of variables. For example why was age not included in the risk analysis? Similar issue for PH regression.

3) For the PH model of mortality, it is unclear why the analysis was complicated so with the challenge of the index date for cases and controls. Why not use the index date as LT for both cases and controls, but use date of IC as a time-dependent covariate? The choice to manipulate the index date for controls opens up potential bias in the results. The overall statistical question is asking does an IC infection post LT impact overall mortality. Give that the median time from LT admission to IC was 32d (IQR 0 to 192) suggests possible confounding of analysis and interpretation of the results.

4) Page 6, Lines 49-50: As over 90% of subjects had antifungal prophylactic therapy it is not surprising there is no observed benefit.

5) Page 6, Lines 47-54: this paragraph is a reiteration of the data in Table 3.

6) Why are the results of the PH model not shown as a table? The HR in Figure 3 should be that from the PH model, and the legend should clarify this.

7) Given this is a retrospective look at over 18 years of data, it would be useful to report on the annual rate of infections (maybe over 3 year windows?) and test if the overall rate of infection decreased over the years.

Minor issues:

1) Abstract indicates data was collected January 1, 2005, to July 31, 2023 while Page 4 Lines 49-50 indicate data study period was January 1, 2005 to December 31, 2023. Suggest abstract should be revised.

2) Revise Page 5 Line 43 to read more clearly: “Analysis of both risk factors in the development of IC and mortality from IC required us to use two different models and extraction methods.”

3) Table 1 and Table 2 should be combined with the first column reporting cohort total, second column IC, third column non-IC.

4) Table 3 can exclude the Z-score value. Also “Control” is an improper term, use “Reference” instead.

Reviewer #3: (abstract) Write the aim of the study at past tense.

(abstract) Define ECMO abbreviation.

(general comment) Do not start a sentence with an abbreviation.

(abstract)Define "index hospitalization".

(introduction) Please be specific when you say "at high risk".

(introduction) Define early in "early post-lung transplant period"

(introduction) Please provide the frequencies for "antifungal medications can have significant adverse effects".

(introduction) Write the aim of the study at past tense.

(methods) Who can access OLDW? It is open access?

(methods) "Re-transplantations were excluded" why?

(methods) Detail the method use to collect mortality data.

(methods) Detail the Elixhauser comorbidity score.

(methods) "compared using a t test" even those with deviations from the theoretical normal distribution?

(methods) Describe the methods used to decide which variables entered in multivariable model.

(methods) "we matched one case" the case is defined as patient with IF? Please clarify.

(methods) Tell the readers the level of significance used in the statistical analysis.

(results) Define axes and units of measurements on Kaplan-Meier graph.

(results) Table 1 - 4: do not include the units of measurements in the body of the table. Generally, when round brackets are used the lower and upper values are not included in the range; please clarify.

(results) Report p-values with at least 3 decimals

(discussion) "Our study utilized administrative claims data to evaluate both risk factors and the impact of IC in a diverse cohort of LTRs." This is duplicated information and should be deleted.

(discussion) Are your results expected to be confirm on other cohorts?

(discussion) Discuss the practical utility of the reported results.

Reviewer #4: This article does not mention diagnostic methods. How is pulmonary candidiasis diagnosed? The medications taken by the patient were not mentioned in this article. How much did their use contribute to the disease's development? What is the diagnostic code for IC?

Reviewer #5: Congratulations to this mostly very well written manuscript. It has been one of very few manuscripts that I reviewed during the last few years that didn't have any major statistical flaws. The only reason that I selected major instead of minor revision is the number of questions that I have and that should be clarified before this manuscript is published. I hope they are helpful:

1. The authors write in the Statistical Analysis section that in the descriptive tables, they show mean + sd or median and IQR as appropriate. However, in Tables 1 and 2 there only is the median/IQR shown. Is the reason for this that all variables seem to be nonnormally distributed? Or just for was of presentation (to avoid confusions due to switching between mean and median)? In any case, only the median/IQR should be mentioned in the methods.

2. Follow-up question from the above: As far as I understood, all tests performed in Table 2 are t-tests? But if the (or some of the) data are nonnormally distributed, a nonparametric test should be used. Please change that.

3. Please delete the stars denoting statistical significance from the tables. Nowadays, there is a strong trend towards quantifying the evidence against the null hypothesis instead of making a binary cutoff to distinguish "significant" from "non-significant". I don't mind you discussing significance in the text (although I clearly prefer writing something like "There was no evidence for a difference"), but do not use stars in the tables.

4. How were the covariates for the multivariable logistic regression model chosen? I don't fully understand the line "We then assessed a priori which risk factors were associated." How did you do that? Is that a fancy way of writing "We asked experts which risk factors to include"? Please clarify.

By the way, I very much like that you obviously didn't choose the covariates based on their p-value or something wrong like that. But some information how you actually chose them would be very helpful.

5. I recommend to use the word "Reference category" instead of "Control" in Table 3. Control can be misunderstood as being a control group, especially as you also have matching in your manuscript.

6. Both the ECMO days and the Elixhauser variable were dichotomized, but for example in Table 2 the Elixhauser is included as count variable. I don't like dichotomizing very much because you usually loose information. Have you tried to include them in a continuous way? If so, what was the result? Have you looked at the descriptives if it might be reasonable to include them in a non-linear way? And if that is not an option (with a good reason): How were the thresholds chosen? If there is a clinical reasoning behind that (if for example the threshold is used in clinical practice) this should be stated and a reference for this should be given. If the threshold was selected by the authors, please also write how and why you did that.

7. Two hazard ratios are mentioned in the text. First, 2.13, then 2.31 "when matching IC cases to controls", and the latter one is also printed below the Kaplan-Meier curve. I don't understand which model the first value comes from. Is that a simple, unmatched model with just one influential variable? I also find it irritating that the value 2.31 comes from the adjusted model that the authors mention, but that it is printed below the KM curve suggests to me that it comes from an unadjusted model. Please clarify. Think about not showing the HR below the curve, as the HR comes from an adjusted model.

8. Please print the results of the final Cox model in an additional table. I think it important to also see what hazard ratios the other influential variables show.

9. Did you check the proportional hazards assumption? Please mention that and the results of this.

10. I don't understand the > and < signs in Table 4. Please explain.

Reviewer #6: Overall

I read with great interest the article entitled “Invasive Candidiasis Following Lung Transplant: An Assessment of Impact Utilizing a National Insurance Claims Cohort”, which falls within the aim of this journal. This study aimed to assess the incidence, risk factors, and impact of invasive candidiasis on mortality in lung transplant recipients using administrative claims data from individuals enrolled in commercial and Medicare Advantage health plans in the US.

In my honest opinion, the topic and results are interesting for audiences and the paper is almost well structured enough to attract the readers’ attention. However, the authors should consider and clarify some points and improve the paper, as suggested below [*: major points, #: minor points].

Abstract section

# Please note that the tone of your study’s objective in the Introduction part should align with the verb tense used in the Methods and Results parts. Therefore, instead of “This study aims to…”, consider using ‘This study aimed to….’.

# Please ensure that the final sentence of the Results part clarifies whether the finding is statistically significant. Therefore, include the relevant statistical measures.

* Writing the Conclusion part is crucial, but it is currently insufficient. Additionally, the first sentence is very similar to the first line of the previous part. So please revise it. You may use the following suggestion or a similar one: ‘Invasive candidiasis affects approximately 10% of lung transplant recipients and is linked to higher mortality, prolonged hospitalization, and increased surgical interventions. These findings underscore the importance of early identification and targeted preventive strategies to improve post-transplant outcomes’.

Introduction section

* Please note that the phrase “Since Candida spp. do not have the ability to digest and invade tissues” is not entirely accurate based on various studies, including this one: https://doi.org/10.1016/j.rsma.2023.103258. So kindly revise it.

# Please note that the phrase “its effectiveness has not been established in the lung transplant population” is stated too definitively. It would be preferable to rephrase it as ‘evidence exists but is inconclusive’.

# Please note that here “One tool that has been utilized to assess clinical incidence and risk factor questions in other clinical domains are large claims databases, such as OptumLabs® Data Warehouse”, “One tool” does not align with “are”. It is recommended to restructure the sentence. A suggested revision is: ‘Large claims databases, such as OptumLabs® Data Warehouse (OLDW), have been widely used to assess disease incidence and risk factors across various clinical domains’.

Methods section

* Regarding your sentence: “We required subjects to have at least 90 days of continuous health plan coverage prior to their lung transplant date and at least 30 days post”. Please clarify the rationale behind selecting the 90-day pre-transplant and 30-day post-transplant timeframes.

# In the part Outcomes of Interest, please note that ICD code 112.5 appears to be duplicated.

* Since your study follows a cohort design, it is preferable not to use the terms “Case” and “Control”, which are more relevant to case-control studies. Please revise these terms throughout the text and figures, replacing them with ‘diseased’ and ‘non-diseased’, respectively. Additionally, when referring to mortality outcomes, please use ‘exposed’ and ‘unexposed’ instead.

# In the Mortality Analysis part, it would be preferable to briefly explain the rationale behind matching for the selected variables. For instance for confounding variables.

Results section

# Please ensure consistency in punctuation. In this instance, (619; 48.4%) uses a semicolon, whereas a comma would be more appropriate for consistency.

* Please note that (Q1, Q3) is not equivalent to the interquartile range (IQR), as IQR is calculated as Q3 minus Q1. It is recommended to report IQR as a single value in the text while maintaining the (Q1, Q3) format in tables for clarity.

# Regarding the sentence “The most commonly prescribed antifungal were mold-active azoles”, please note that “antifungal” should be written as ‘antifungals’ or, preferably, ‘Antifungal Medications’ to maintain consistency with the terminology used in the tables.

# In the first paragraph of this section, please remove all instances of (n, x%) as this information is already provided in the table.

# Please revise the sentence “Of these, 131 (10.2%) LTRs developed IC following LT”. to explicitly refer to ‘lung transplant recipients’ instead of using “Of these”.

* Preferably, ensure that the variable titles used in the tables are consistent with those in the main text. For example, term like “locally invasive candidiasis/deep-seated candidiasis” in the text are somewhat ambiguous, whereas the table clearly refers to “Pulmonary Candidiasis”. Please make the necessary revisions and include any additional explanations in parentheses.

# Please clarify what you mean by “IC diagnostic codes are limited”.

* In the Invasive Candidiasis part, please remove all instances of (n, x%) since this information is already provided in the tables. Additionally, mentioning other details is unnecessary; instead, simply state that all had a P-value < 0.05 (also please clarify that if you have considered this threshold in the Statistical Analysis part). Please apply this recommendation to other parts as well.

# For the sentence: “Post-transplant ECMO (OR: 2.34; 95% CI 1.03 to 5.34, p = 0.043) use of greater than 8 days was the only risk factor significantly associated with post-transplant IC on multivariable modeling”. Please remove the information inside the parentheses as previously suggested. Additionally, after “associated with”, add ‘was associated with around 2.3-fold increase in’ to clarify the magnitude of the association.

# Please clarify this phrase “No single indication for transplant increased the odds of IC”.

# Please note that the first two sentences in the Effect of Invasive Candidiasis on Mortality part are not appropriate for this part.

* Please ensure that Table 4 is adequately interpreted, as its interpretation is even more important than that of Table 3.

* Please provide appropriate titles for both figures, as they currently lack titles.

# Regarding Table 3, please note that it is unnecessary to include SE and Z values. It is recommended to remove these values from the table.

* Ensure that consistent variable names are used across all tables and carefully review them for uniformity. Additionally, clarify the rationale for including certain variables in Table 4 that are absent from Table 3 (e.g., COPD/Bronchiectasis).

Discussion section

* Please note that this section currently requires further elaboration from literature. It is recommended to include a more detailed comparison of your study with previous research and, if possible, please discuss the reasons for any discrepancies, which could be attributed to for example, different methodologies.

* Regarding the sentence: “We decided to focus on ECMO support as a potential risk factor by setting the index date ...”, please clarify the rationale for selecting ECMO support.

# Please note that the sentence “We cannot glean ECMO configuration placement position from our dataset. It is likely that femoral lines portend a higher risk of IC than ...” presents a claim without sufficient scientific support. It is recommended to revise and integrate the following phrase: ‘Our dataset does not include information on ECMO cannulation sites. While femoral access may increase the risk of IC, further studies are needed to confirm this association’.

* Regarding your sentence: “We found that patients with IC have more than twice the probability of mortality compared to their matched partners”. I did not find the corresponding results in the previous section.

Good Luck,

Reviewer #7: A very comprehensive study. Data are available, so it is a nice addition to the potential resources made available to others researching similar topics. It is a "complex read" but that is very much essential here. I noted a misspell [Invasive Candidiasis. . . . 1 paragraph, last line, "diagnositic"(sic)] -- (Can't believe I saw it however, due to the 'density' of this writing). I like the detail oriented approach even for the following: implementation of Elixhauser over Charlton Comorbidity Scoring--that makes sense with, provides and example for, a study focused on Labs data.

**********

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Reviewer #1: Yes: Parisa Badiee

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

Reviewer #5: No

Reviewer #6: No

Reviewer #7: Yes: Brian L Altonen

**********

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PLoS One. 2025 Aug 21;20(8):e0330162. doi: 10.1371/journal.pone.0330162.r002

Author response to Decision Letter 1

10 May 2025

Dear Editorial Team:

We thank the editorial team and all reviewers for their time and detailed evaluation of our manuscript, “Invasive Candidiasis Following Lung Transplant: An Assessment of Impact Utilizing a National Insurance Claims Cohort.” We are especially grateful for the thoughtful critiques, which have significantly strengthened the clarity, methodological transparency, and overall quality of the manuscript. In the pages that follow, we provide detailed responses to each reviewer comment. All revisions made to the manuscript are noted with corresponding page and line numbers. We believe these changes enhance the rigor and impact of our work and sincerely appreciate the opportunity to revise and resubmit.

Reviewer #1:

We thank Reviewer #1 for their thoughtful review and valuable feedback. We are pleased that you found the topic of invasive candidiasis following lung transplantation to be of interest. Your comments helped us clarify key points and improve the overall quality of the manuscript. Our response to individual comments are outlined below:

- Please don’t use abbreviates in the abstract (ECMO).

Thank you. This has been written in long form with abbreviation parenthetically given the commonality of the term ECMO in the healthcare profession.

Thank you for this important correction and for pointing us to the relevant literature. We agree that our original statement was inaccurate. We have revised the sentence to better reflect the known pathogenic mechanisms of Candida species, including their ability to adhere to and invade host tissues. The revised text on page 4, lines5-8 now reads:

“Candida spp. possess a range of virulence factors that facilitate epithelial adhesion, invasion, and tissue damage, contributing to pathogenesis in the setting of impaired host immunity or barrier disruption.”

- Lines 10-11 according to reference 7 is not true.

We thank the reviewer for this important clarification. Upon re-reviewing Reference 7, we agree that it does not support the broad statement regarding breakthrough IC with nebulized AmBisome. We have revised the sentence to remove the overstatement and better reflect the cited literature. The revised text on page 4, lines 9-11 now reads:

“While many lung transplant centers in the United States employ antifungal prophylaxis targeting mold infections(10), variation exists in the agents used and the duration of prophylaxis. Some studies have suggested that the use of inhaled amphotericin B without systemic antifungal prophylaxis may be insufficient to prevent all forms of invasive fungal infection, including invasive candidiasis (7).”

We appreciate the reviewer’s request for clarification. In our study, invasive candidiasis (IC) was defined using a set of International Classification of Diseases (ICD-9 and ICD-10) diagnostic codes intended to capture clinically significant invasive infections. We have added the following to Page 5, Lnes 6-9: “These codes include candidemia (e.g., 112.5, B37.7), disseminated candidiasis (e.g., 112.5, 112.83), and organ-specific invasive infections (e.g., B37.5 for candidal peritonitis and B37.6 for candidal endocarditis). We acknowledge that claims data may not distinguish between confirmed invasive disease and coding inaccuracies.”

Reviewer #2:

The paper presented is a very interesting retrospective analysis of the risk factors leading to Candidiasis infection after lung transplant. The following issues regarding the statistical considerations should be addressed prior to publication:

We thank Reviewer #2 for their thoughtful and constructive feedback. We are pleased that you found the study of interest. We appreciate your detailed review of the statistical methodology and have addressed each of your points below to enhance the clarity and rigor of our analysis.

We appreciate the reviewer’s observation regarding potential selection bias. Patients with a prior diagnosis of invasive candidiasis (IC) were excluded to ensure that all identified IC events occurred de novo after lung transplantation, allowing for more accurate assessment of post-transplant risk factors and outcomes. In contrast, prior antifungal exposure was not used as an exclusion criterion, as antifungals may be prescribed for a variety of prophylactic or therapeutic indications unrelated to IC and do not necessarily indicate prior infection.

With respect to the exclusion discrepancy noted in Figure 1, we thank the reviewer for highlighting this detail. The slightly higher exclusion rate among patients who later developed IC (13% vs. 8%) likely reflects the fact that patients with IC may have had more complex clinical courses or prolonged pre-transplant hospitalizations, during which time IC was diagnosed and coded. While this introduces a possibility of selection bias, we have added a statement acknowledging this as a limitation on page 8, line 8-11: “We acknowledge the potential for selection bias introduced by excluding patients with a history of invasive candidiasis prior to transplant while retaining those with prior antifungal exposure, which may reflect underlying differences in baseline health status. Additionally, a slightly higher exclusion rate among patients who later developed IC (13% vs. 8%) could suggest unmeasured pre-transplant differences that we were unable to fully control for.”

Thank you for this insightful comment. We apologize for the lack of clarity regarding our variable selection process for the multivariable models. In the logistic regression model assessing risk factors for IC, we included a priori selected clinical variables based on prior literature and clinical plausibility. These included transplant-related factors (e.g., ECMO use, re-operation), demographic characteristics (e.g., sex, region), and comorbidities. Although age was considered, it was ultimately excluded from the final model due to lack of significant univariable association and collinearity with other factors such as comorbidity burden. We have now clarified this in the Methods section on page 6, lines 3-5: “Variables were selected a priori based on clinical relevance and literature review. Age was evaluated but excluded from the final model due to collinearity with comorbidity burden and lack of a significant univariable association with IC.”

For the Cox proportional hazards model, we used matched cohorts (on age ±5 years, sex, index date relative to transplant, and hospital length of stay), and the final model included only variables that remained imbalanced after matching. Since age was a matching variable and showed good covariate balance (standardized difference <0.1), it was not included in the final PH model. We have added text to clarify this approach in the Methods on page 6, lines 11-12: “Age was used as a matching variable (±5 years) and demonstrated good covariate balance post-matching; therefore, it was not included in the final Cox proportional hazards model.”

We thank the reviewer for this important comment and agree that modeling IC as a time-dependent covariate is a valid alternative approach. Our goal, however, was to assess mortality following an episode of IC, and to compare outcomes in those with and without IC who were at a similar timepoint post-transplant and had similar post-operative trajectories (as reflected by hospital length of stay and index date proximity). To do this, we aligned the timing of follow-up between cases and controls by anchoring the index date for each control to mirror the case's time from transplant to IC diagnosis, thus mitigating immortal time bias and facilitating interpretation of post-IC outcomes.

We recognize that this approach may introduce its own limitations, particularly with regard to assumptions around matching and event timing. We have acknowledged this as a limitation in the Discussion page 8, lines 34-38: “Our analytic approach to evaluating post-IC mortality involved assigning matched controls a pseudo-index date aligned with the timing of IC diagnosis in cases. While this allowed for comparison of outcomes from similar post-transplant timepoints, it may introduce bias if the assumptions underlying index date alignment are not fully met. Modeling IC as a time-dependent covariate in a Cox model represents an alternative approach and may help validate our findings in future studies.”

4) Page 6, Lines 49-50: As over 90% of subjects had antifungal prophylactic therapy it is not surprising there is no observed benefit.

We agree with the reviewer that the high prevalence of antifungal prophylaxis in our cohort likely limited our ability to detect a protective association. This high background rate reduces variability in exposure, potentially obscuring any true effect of prophylaxis on invasive candidiasis (IC) risk. We have added language to the Discussion on page 7, lines 44-48: “Given that over 90% of patients received antifungal prophylaxis, our ability to detect a protective effect was likely limited by the lack of variability in exposure, and the absence of observed benefit should be interpreted with caution.”

5) Page 6, Lines 47-54: this paragraph is a reiteration of the data in Table 3.

We appreciate the reviewer’s perspective. While the content in this paragraph does overlap with Table 3, we believe that summarizing key findings from the multivariable analysis in the text improves accessibility and supports interpretation for readers who may not focus on tables alone. These results help contextualize the lack of association for several anticipated risk factors, such as CMV disease and diabetes mellitus, and emphasize that only prolonged ECMO support was statistically significant. We have reviewed the paragraph to ensure it remains concise and non-redundant and believe its inclusion strengthens the narrative presentation of our findings. Edited text is on page 6, lines 54-60: “Post-transplant ECMO (OR: 2.34; 95% CI 1.03 to 5.34, p =0.043) use of greater than 8 days was the only risk factor significantly associated with post- transplant IC on multivariable modeling. Other clinical factors, including the presence of antifungal prophylaxis during the 90 days prior to IC (OR: 1.49; 95% CI 0.85 to 2.59, p=0.165), CMV disease (OR: 1.91; 95% CI 0.43 to 8.51, p = 0.395), diabetes mellitus (OR: 1.77; 95% CI 0.80 to 3.92, p=0.156), high co-morbidity burden (OR: 0.86; 95% CI 0.44 to 1.70, p=0.67), bilateral lung transplant (OR: 0.90; 95% CI 0.59 to 1.39, p=0.643), and pre-transplant steroid use (OR: 1.12; 95% CI 0.69, 1.83, p=0.641) were not significant risk factors for IC. No single indication for transplant increased the odds of IC.

6) Why are the results of the PH model not shown as a table? The HR in Figure 3 should be that from the PH model, and the legend should clarify this.

We appreciate the reviewer’s suggestion to enhance clarity. We have added a new table (now Table 5) summarizing the results of the Cox proportional hazards model, including the hazard ratio, 95% confidence interval, and p-value. This allows for clearer interpretation and complements the Kaplan-Meier curve presented in Figure 3. We have also updated the Figure 3 legend to specify that the hazard ratio shown is derived from the Cox model.

Figure 3 Legend now states, “Kaplan-Meier survival curve comparing lung transplant recipients with and without invasive candidiasis (IC). The hazard ratio (HR: 2.31; 95% CI: 1.45–3.67) is derived from the Cox proportional hazards model adjusted for variables with residual imbalance following matching.”

Results section on page 7, lines 7-10 now states, “Table 5 presents the results of the Cox proportional hazards model evaluating the impact of IC on all-cause mortality. All-cause mortality was significantly higher in those who developed IC (event rate per 100 person-years: 11.32; HR: 2.13; 95% CI 1.45 to 3.12, p<0.001). This held true when matching IC cases to controls (event rate per 100 person-years: 12.87; HR: 2.31; 95% CI 1.45 to 3.67, p<0.001) (Figure 3).

Minor issues:

Thank you for identifying this inconsistency. We have revised the abstract to reflect the correct study period of January 1, 2005, to December 31, 2023, in alignment with the Methods section.

2) Revise Page 5 Line 43 to read more clearly: “Analysis of both risk factors in the development of IC and mortality from IC required us to use two different models and extraction methods.”

Thank you. We have updated this statementon on page 5, line 47-48.

3) Table 1 and Table 2 should be combined with the first column reporting cohort total, second column IC, third column non-IC.

We appreciate the reviewer’s suggestion. We considered combining Tables 1 and 2 but felt that presenting baseline characteristics (Table 1) and post-transplant outcomes (Table 2) separately improves readability and keeps the focus clear for each table. Combining them would result in a substantially larger and denser table, potentially making it more difficult for readers to interpret key findings. We have retained the current format to preserve clarity and emphasize the distinction between baseline variables and outcome data.

4) Table 3 can exclude the Z-score value. Also “Control” is an improper term, use “Reference” instead.

Thank you for this helpful suggestion. We have removed the Z-score column from Table 3 and replaced the term “Control” with “Reference” to more appropriately indicate the referent category in the regression model.

Reviewer #3:

We thank Reviewer #3 for their thoughtful review and constructive feedback. We appreciate your time and expertise in evaluating our manuscript and have carefully addressed each of your comments to improve the clarity, accuracy, and overall quality of the work.

(abstract) Write the aim of the study at past tense.

This has been updated on page 3, lines 3-4: “This study aimed to assess the incidence, risk factors, and impact of IC on mortality in LTRs using a national insurance claims cohort.”

(abstract) Define ECMO abbreviation.

This has been done at the suggestion of Reviewer 1.

(general comment) Do not start a sentence with an abbreviation.

Thank you for this ed

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Submitted filename: Letter to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0330162.r003

Decision Letter 1

Ali Amanati

14 Jun 2025

PONE-D-25-06214R1Invasive Candidiasis Following Lung Transplant: An Assessment of Impact Utilizing a National Insurance Claims CohortPLOS ONE

Dear Dr. Pennington,

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 Jul 29 2025 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:

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We look forward to receiving your revised manuscript.

Kind regards,

Ali Amanati

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear authors, ‎

‎The invited reviewers posted new comments. So, the manuscripts ‎‎require a ‎round of revision.‎ Please provide a point-by-point response to the ‎‎reviewer's ‎comments and highlight all the ‎amends on your manuscript with ‎‎yellow color.‎ ‎

Yours,

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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 #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #4: All comments have been addressed

Reviewer #5: (No Response)

Reviewer #6: (No Response)

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: I Don't Know

Reviewer #5: Yes

Reviewer #6: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: No

Reviewer #5: No

Reviewer #6: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #5: Yes

Reviewer #6: Yes

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6. Review Comments to the Author

Reviewer #1: The manuscript reviewed by 6 reviewer and revised as my previous comments. It is suitable for publication.

Reviewer #2: Thank you for addressing my comments. Although I would prefer to see the mortality modeled with IC as a time-dependent covariate, the choice of a psudo index date is not unheard of in statistical modeling. The manuscript is much improved with better clarity.

Reviewer #4: The authors present a well-conducted and clinically relevant retrospective cohort study that addresses a critical and underexplored aspect of post-lung transplantation care. The use of a large national insurance claims database provides robust real-world evidence on the incidence and impact of invasive candidiasis (IC) in lung transplant recipients. The methodology is sound. The manuscript is clearly written, the results are compelling, and the conclusions are well-supported by the data. I recommend acceptance of this article.

Reviewer #5: Thank you for answering all my questions and changing the manuscript accordingly. There is one last thing that should be formulated differently:

The authors explain (both in "my" answer and in one for reviewer 2) that they chose the variables for the regression model based on clinical knowledge and literature, but they state that age was excluded "due to collinearity with other factors and lack of a significant univariable association". But exactly significance or non-significance in a univariable model should not be used to decide a bout including a variable, because the p-value can change considerably as soon as additional variables are taken into account.

Therefore, the authors should delete this part of the sentence. If there indeed is strong (multi-)collinearity, this is reason enough to not include age. If, however, the collinearity is not very strong, I suggest to include age in the model as an additional covariate. In any case, it has to be explored and explained properly.

Reviewer #6: I sincerely thank the authors for their thoughtful and thorough revisions to the manuscript. Almost all of my previous comments have been addressed. Before resubmitting, I recommend reviewing the revised version without Track Changes to ensure there are no minor issues, such as the presence of two periods at the end of the conclusion sentence in the abstract. Also, please note that the code “112.4” appears twice in the following sentence: “In our population, ... for IC (ICD-9: 112.4, 112.5, 112.8, 112.4, 112.83; ICD-10: 16 B37.1, B37.5, B37.6, B37.7) in ... procedure”.

Additionally, I appreciate your attention to comment “Since your study follows a cohort design, it is preferable not to use the terms “Case” and “Control”, which are more relevant to case-control studies. Please revise these terms throughout the text and figures, replacing them with ‘diseased’ and ‘non-diseased’, respectively. Additionally, when referring to mortality outcomes, please use ‘exposed’ and ‘unexposed’ instead”. However, a few instances of these terms still appear in the text and should preferably be corrected.

Good Luck,

**********

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 #1: Yes: Parisa Badiee

Reviewer #2: No

Reviewer #4: No

Reviewer #5: No

Reviewer #6: No

**********

Attachment

Submitted filename: Re-reviewed by Seyedeh Elaheh Bagheri.pdf

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PLoS One. 2025 Aug 21;20(8):e0330162. doi: 10.1371/journal.pone.0330162.r004

Author response to Decision Letter 2

18 Jul 2025

Dear Editorial Team:

Reviewer #1:

The manuscript reviewed by 6 reviewer and revised as my previous comments. It is suitable for publication.

We sincerely thank the reviewer for their positive feedback and continued support of our manuscript.

Reviewer #2:

Thank you for addressing my comments. Although I would prefer to see the mortality modeled with IC as a time-dependent covariate, the choice of a psudo index date is not unheard of in statistical modeling. The manuscript is much improved with better clarity.

We appreciate the reviewer’s thoughtful feedback and are grateful for the recognition of the improvements made to the manuscript. We acknowledge that modeling IC as a time-dependent covariate is a valid and elegant alternative. In this analysis, we selected a pseudo index date approach to align with our dataset structure and facilitate matching on temporal proximity and hospital length of stay. We agree that future work could benefit from incorporating time-dependent modeling to validate and extend our findings.

Reviewer #4:

The authors present a well-conducted and clinically relevant retrospective cohort study that addresses a critical and underexplored aspect of post-lung transplantation care. The use of a large national insurance claims database provides robust real-world evidence on the incidence and impact of invasive candidiasis (IC) in lung transplant recipients. The methodology is sound. The manuscript is clearly written, the results are compelling, and the conclusions are well-supported by the data. I recommend acceptance of this article.

We thank the reviewer for their positive feedback and thoughtful assessment of our study. We greatly appreciate the recommendation for acceptance.

Reviewer #5:

Thank you for answering all my questions and changing the manuscript accordingly. There is one last thing that should be formulated differently:

We thank the reviewer for this important and insightful comment. We agree that variable selection should not rely on statistical significance in univariable models, and we appreciate the opportunity to clarify our rationale.

In our analysis, age demonstrated moderate collinearity with comorbidity burden (Elixhauser score), and including both in the multivariable model introduced instability in the model estimates. Given that comorbidity burden was more strongly associated with our outcome of interest and is clinically relevant in this context, we prioritized its inclusion over age. We have revised the manuscript to reflect this clarification and have removed the reference to univariable significance. The updated text now reads:

“Age was evaluated but excluded from the final model due to collinearity with comorbidity burden, which was prioritized given its stronger association with IC and greater clinical relevance.”

Reviewer #6:

I sincerely thank the authors for their thoughtful and thorough revisions to the manuscript. Almost all of my previous comments have been addressed. Before resubmitting, I recommend reviewing the revised version without Track Changes to ensure there are no minor issues, such as the presence of two periods at the end of the conclusion sentence in the abstract.

We thank the reviewer for their careful review and kind comments. We have removed the extraneous period in the abstract and carefully reviewed the revised manuscript for any remaining formatting or typographical errors before resubmission.

Also, please note that the code “112.4” appears twice in the following sentence: “In our population, ... for IC (ICD-9: 112.4, 112.5, 112.8, 112.4, 112.83; ICD-10: 16 B37.1, B37.5, B37.6, B37.7) in ... procedure”.

This has been corrected in the revised version, and the duplicate 112.4 code has been removed.

Thank you for this important clarification and for previously highlighting the need to align terminology with cohort study design conventions. We carefully reviewed the full manuscript and have now revised all remaining instances of the terms “case” and “control.” Specifically, we have replaced “case”/“control” with “diseased”/“non-diseased” in sections referring to IC status, and with “exposed”/“unexposed” when referring to matched groups in the mortality analysis. Figure titles and flowchart labels have been updated accordingly. We appreciate the reviewer’s close reading and attention to methodological precision.

Once again, we would like to express our gratitude to the reviewers for their valuable feedback, which has been instrumental in refining our manuscript. Thank you for your consideration and for the opportunity to resubmit our work.

Sincrerely,

Kelly M. Pennington, MD

Attachment

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PLoS One. doi: 10.1371/journal.pone.0330162.r005

Decision Letter 2

Ali Amanati

29 Jul 2025

Invasive Candidiasis Following Lung Transplant: An Assessment of Impact Utilizing a National Insurance Claims Cohort

PONE-D-25-06214R2

Dear Dr. Kelly Pennington,

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.

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Ali Amanati

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PLOS ONE

Additional Editor Comments (optional):

The authors have effectively utilized all available resources and data to enhance the manuscript, making it ‎more scientifically robust than before. Therefore, based on my opinion and the esteemed ‎reviewers' ‎‎comments, it could be published in its current form.‎

Yours‎,‎

PLoS One. doi: 10.1371/journal.pone.0330162.r006

Acceptance letter

Ali Amanati

PONE-D-25-06214R2

PLOS ONE

Dear Dr. Pennington,

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Associated Data

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

Supplementary Materials

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Submitted filename: Letter to Reviewers.docx

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Data Availability Statement

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PERMALINK

Invasive candidiasis following lung transplant: An Assessment of impact utilizing a national insurance claims cohort

Kelly M Pennington

Herb Heien

Hemang Yadav

Xiaoxi Yao

Bradley White

Steve G Peters

Patricio Escalante

Che Ngufor

Raymund R Razonable

Cassie C Kennedy

Roles

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Methods

Data source

Study population

Outcomes of interest: risk factors for developing invasive candidiasis and its effect on mortality

Statistical analysis

Risk factor analysis

Mortality analysis

Results

Fig 1. Flow chart for the development of the final non-matched cohort.

Table 1. Non-matched cohort characteristics.

Invasive candidiasis

Table 2. Characteristics of lung transplant recipients who developed invasive candidiasis compared to those who did not.

Risk factors for invasive candidiasis: multivariable analysis

Table 3. Multivariable logistic regression for risk factors for invasive candidiasis following lung transplant.

Effect of invasive candidiasis on mortality

Fig 2. Flow chart for development of the matched cohort of lung transplant recipients with invasive candidiasis matched to age, sex, and length of hospital stay controls (1 with invasive candidiasis to 2 without invasive candidiasis).

Table 4. Matched cohort of lung transplant recipients with invasive candidiasis matched to age, sex, and length of hospital stay controls (1 invasive candidiasis to 2 without invasive candidiasis).

Table 5. Cox proportional hazards model evaluating the impact of IC on all-cause mortality.

Fig 3. Kaplan-Meier survival curve comparing lung transplant recipients with and without invasive candidiasis (IC).

Discussion

Limitations

Conclusion

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Ali Amanati

Roles

Author response to Decision Letter 1

Decision Letter 1

Ali Amanati

Roles

Author response to Decision Letter 2

Decision Letter 2

Ali Amanati

Roles

Acceptance letter

Ali Amanati

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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