Cardiovascular biomarkers as predictors of adverse outcomes in chronic Chagas cardiomyopathy

Luis E Echeverría; Lyda Z Rojas; Sergio Alejandro Gómez-Ochoa; Oscar L Rueda-Ochoa; Cristian David Sosa-Vesga; Taulant Muka; James L Januzzi; Rachel Marcus; Carlos A Morillo

doi:10.1371/journal.pone.0258622

. 2021 Oct 28;16(10):e0258622. doi: 10.1371/journal.pone.0258622

Cardiovascular biomarkers as predictors of adverse outcomes in chronic Chagas cardiomyopathy

Luis E Echeverría ^1,^2,^*, Lyda Z Rojas ³, Sergio Alejandro Gómez-Ochoa ², Oscar L Rueda-Ochoa ⁴, Cristian David Sosa-Vesga ⁵, Taulant Muka ⁶, James L Januzzi ⁷, Rachel Marcus ⁸, Carlos A Morillo ^9,¹⁰

Editor: Giuseppe Vergaro¹¹

¹Heart Failure and Cardiac Transplant Division, Fundación Cardiovascular de Colombia, Floridablanca, Colombia

²Public Health and Epidemiological Studies Group, Cardiovascular Foundation of Colombia, Floridablanca, Colombia

³Research Group and Development of Nursing Knowledge (GIDCEN-FCV), Research Center, Cardiovascular Foundation of Colombia, Floridablanca, Santander, Colombia

⁴Electrocardiography Research Group, Medicine School, Universidad Industrial de Santander, Bucaramanga, Colombia

⁵Universidad Industrial de Santander, Bucaramanga, Santander, Colombia

⁶Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland

⁷Massachusetts General Hospital, Harvard Clinical Research Institute, Boston, MA, United States of America

⁸Department of Cardiology, Washington Hospital Center, Washington, District of Columbia, United States of America

⁹Department of Cardiac Sciences, Cumming School of Medicine, Libin Cardiovascular Institute of Alberta, University of Calgary, Alberta, Canada

¹⁰Department of Medicine, Cardiology Division, McMaster University, PHRI-HHSC, Hamilton, Ontario, Canada

¹¹Fondazione Toscana Gabriele Monasterio & Scuola Superiore Sant’Anna, Pisa, Italy, ITALY

Competing Interests: Dr. Januzzi is a Trustee of the American College of Cardiology, has received grant support from Novartis Pharmaceuticals, Roche Diagnostics, Abbott, Singulex and Prevencio, consulting income from Abbott, Janssen, Novartis, Pfizer, Merck, and Roche Diagnostics, ownership in Imbria Pharmaceuticals, and participates in clinical endpoint committees/data safety monitoring boards for Abbott, AbbVie, Amgen, Boehringer- Ingelheim, Janssen, and Takeda. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The other authors have no conflict of interests to declare.

^✉

* E-mail: luisecheverria@fcv.org, luisedo10@gmail.com

Roles

Luis E Echeverría: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Validation, Writing – review & editing

Lyda Z Rojas: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Writing – review & editing

Sergio Alejandro Gómez-Ochoa: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

Oscar L Rueda-Ochoa: Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Cristian David Sosa-Vesga: Data curation, Resources, Validation, Writing – original draft, Writing – review & editing

Taulant Muka: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

James L Januzzi: Conceptualization, Investigation, Supervision, Validation, Writing – original draft, Writing – review & editing

Rachel Marcus: Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Carlos A Morillo: Conceptualization, Funding acquisition, Investigation, Supervision, Validation, Writing – review & editing

Giuseppe Vergaro: Editor

PMCID: PMC8553084 PMID: 34710112

Abstract

Background

Chronic Chagas Cardiomyopathy (CCM) is a unique form of cardiomyopathy compared to other etiologies of heart failure. In CCM, risk prediction based on biomarkers has not been well-studied. We assessed the prognostic value of a biomarker panel to predict a composite outcome (CO), including the need for heart transplantation, use of left ventricular assist devices, and mortality.

Methods

Prospective cohort study of 100 adults with different stages of CCM. Serum concentrations of amino-terminal pro-B type natriuretic peptide (NT-proBNP), galectin-3 (Gal-3), neutrophil gelatinase-associated lipocalin (NGAL), high sensitivity troponin T (hs-cTnT), soluble (sST2), and cystatin-C (Cys-c) were measured. Survival analyses were performed using Cox proportional hazard models.

Results

During a median follow-up of 52 months, the mortality rate was 20%, while the CO was observed in 25% of the patients. Four biomarkers (NT-proBNP, hs-cTnT, sST2, and Cys-C) were associated with the CO; concentrations of NT-proBNP and hs-cTnT were associated with the highest AUC (85.1 and 85.8, respectively). Combining these two biomarkers above their selected cut-off values significantly increased risk for the CO (HR 3.18; 95%CI 1.31–7.79). No events were reported in the patients in whom the two biomarkers were under the cut-off values, and when both levels were above cut-off values, the CO was observed in 60.71%.

Conclusion

The combination of NT-proBNP and hs-TnT above their selected cut-off values is associated with a 3-fold increase in the risk of the composite outcome among CCM patients. The use of cardiac biomarkers may improve prognostic evaluation of patients with CCM.

Background

Chagas Disease (CD), or American trypanosomiasis, is an infectious disease caused by the protozoan parasite Trypanosoma cruzi and currently affects approximately eight million people in endemic countries in Central and South America [1]. Moreover, an estimated 300,000 infected individuals live in the United States of America, and almost 100,000 live in the European Union with this disease. Furthermore, the pooled prevalence of infection in Latin American migrants is estimated to be around 4.2% [2–5]. Underdiagnoses of CD are frequent, estimated at up to 95% in certain areas, highlighting the need for health professionals with experience in this area [6]. The acute phase of this disease often goes under-recognized, as it is rarely severe; nevertheless, 30% of infected individuals will develop a symptomatic chronic phase characterized by severe digestive or cardiac forms of the disease [7]. Chronic Chagas cardiomyopathy (CCM) is the most common form of chronic disease; characterized by an extensive arrhythmogenic and thrombogenic status, myocardial fibrosis, segmental wall motion abnormalities, and ultimately a dilated cardiomyopathy with rapidly progressive heart failure, all of which confer high morbidity and mortality [8].

Once a patient with CD develops cardiomyopathy, predicting risk for complications (such as death or need for advanced support strategies including mechanical circulatory support or cardiac transplantation) may be challenging. A relatively under-explored option for risk prediction in CD cardiomyopathy is the use of biomarkers. Population-based studies have identified various biomarkers that may predict multiple outcomes in different pathogen-related diseases [9]. However, the experience regarding biomarkers use in CD has shown that numerous challenges still remain to allow optimal use and reliably estimate the risk of CCM progression [10, 11]. Moreover, while several serum biomarkers have been identified as having a significant diagnostic and prognostic value in heart failure (HF) of other etiologies, few studies have addressed their role in CCM, which, given its unique pathogenesis, warrants direct confirmation [12–14].

Previous work has suggested that multimodal biomarker assessment can be an important tool to improve the assessment of the prognosis in this population [15, 16]. However, data are scarce regarding the use of this strategy in patients with CD. In this context, we sought to evaluate the discriminative ability of a broad range of cardiac and renal biomarkers to assess the risk of mortality and other relevant clinical outcomes in CCM patients.

Methods

Study population

This prospective cohort study was performed between July 2015 to January 2020 conducted in the Heart Failure and Heart Transplant Clinic of Fundación Cardiovascular, in Floridablanca, Colombia. The research protocol of the study was approved by the Institutional Committee on Research Ethics of the Fundación Cardiovascular de Colombia. Written consent was obtained for all patients. Adult outpatients (> 18 years old) with a positive serological diagnosis of T. cruzi infection (positive IgG antibodies) and echocardiographic (echo) or electrocardiographic (ECG) abnormalities consistent with chronic Chagas cardiomyopathy (left anterior fascicular block, right bundle branch block, atrioventricular blocks, ventricular premature beats, atrial fibrillation or flutter, bradycardia ≤50 h/min or echocardiographic finding suggesting myocardial alterations as evaluated by a cardiologist) were included. We enrolled patients across all the severity stages, including also individuals with implantable devices and refractory heart failure. The study sample was obtained from the CCM patients attending their follow-up evaluations; the first 100 individuals who fulfilled the inclusion criteria were enrolled. We excluded individuals with diabetes mellitus, coronary heart disease history, mitral stenosis, or uncontrolled hypertension. The Institutional Committee on Research Ethics approved the research protocol of the study. All patients provided written informed consent for their participation in the study.

Data collection

Information regarding socioeconomic status, lifestyle factors, and medication use was recorded. Body-mass index, left ventricular ejection fraction (LVEF) calculated by Simpson’s rule from four-chambers view, global longitudinal strain by speckle tracking (GLS), and estimated glomerular filtration rate (eGFR) were also measured. Finally, fasting serum samples were collected from each individual for the assessment of the six biomarkers. High sensitivity troponin T (hs-cTnT) was quantified with a 5th generation assay on an automated platform (ECLIA Elecsys 2010 analyzer, Roche Diagnostics, Germany). Galectin-3 (Gal-3) was assessed by using a quantitative method, specifically an ELFA (enzyme-linked fluorescent assay) technique (VIDAS, Biomerieux, Marcy l’Étoile, France). Amino-terminal pro-B type natriuretic peptide (NT-proBNP) levels were measured using the electrochemiluminescence method (Roche Diagnostics GmbH, Mannheim, Germany). The Alere Triage® NGAL test was used to assess Neutrophil Gelatinase-Associated Lipocalin (NGAL). Soluble ST2 (sST2) was measured from banked serum by Critical Diagnostics Presage™ sST2 assay kit via enzyme-linked immunosorbant assay (ELISA). Finally, Cystatin c (Cys-c) was quantified by an immunologic turbid metric assay (Tina-quant Cystatin C cobas®).

Study outcomes and follow-up

The primary outcome of this study was a composite endpoint of cardiovascular mortality, heart transplant, and left ventricular assistance device (LVAD) implantation, while the secondary outcome was cardiovascular mortality. After the baseline information collection, a telephone follow-up and review of the clinical records of each patient was performed according to a standardized protocol. Investigators contacted the patients once a month during the first six months after the initial evaluation; after that, patients were called every six months. During the telephone interview, a standardized checklist of questions aimed to identify the mentioned outcomes was used.

Statistical analysis

Categorical variables were presented as numbers and proportions, while continuous variables were reported as medians and interquartile ranges. Survival analyses were performed using the Kaplan-Meier method, life table, and Cox proportional hazard models. We considered the time to event for the CO as the number of days from enrollment to the study until the first of the components of this outcome was reached. To identify the variables that were independently predictive of mortality, univariate and multivariate analyses using Cox’s proportional regression model were performed. Due to the sample size, only age and left ventricle ejection fraction (LVEF) were included in the model, hazard ratios with its 95% confidence interval were calculated. We quantified the discriminatory ability of the biomarkers using the Harrell’s C statistic and the area under the receiver operating characteristic curve (AUC-ROC). The Youden index was used to identify the best cut-off level for each biomarker(16). A p-value <0.05 was considered statistically significant for all tests. All analyses were performed using Statistical Package STATA version 15 (Station College, Texas USA).

Results

Population characteristics

One hundred individuals were included (55% males with a median age of 62 years [IQR 53–70]. 25% of the patients had a normal LVEF, but only 10% of the included patients had a normal global longitudinal strain (GLS) value. As expected, most of the biomarker measurements were elevated. Median NT-proBNP was 704 (IQR 170–2846) pg/mL, hs-cTnT: 11.7 (IQR 5.6–22.6) ng/L, sST2: 24.7 (IQR 20.1–31.9) ng/mL, Gal-3 had a median value of 14.2 (IQR 11.5–18.3) ng/mL, while median Cys-C and NGAL values were 1.1 (IQR 0.9–1.4) mg/L and 96.5 (IQR 69.0–145.5) ng/mL, respectively. Table 1 shows a summary of the included population characteristics by the CO.

Table 1. Baseline characteristics the evaluated chronic Chagas cardiomyopathy patients (n = 100).

Variable	Composite Outcome		p-value
Variable	No (%)	Yes (%)	p-value
Sex
Female	34 (45.33)	11 (44)	0.908
Male	41 (54.67)	14 (56)
Age (years)	61 (53–67)	66 (56–74)	0.112
Area of Residence
Urban	56 (74.67)	20 (80)	0.589
Rural	19 (25.33)	5 (20)
BMI (kg/m ² )	26.4 (23.4–28.6)	22.8 (19.6–28.6)	<0.001
NYHA			0.004
I	27 (36)	5 (20)
II	36 (48)	7 (28)
III	11 (14.67)	12 (48)
IV	1 (1.33)	1 (4)
LVEF (%)	49 (37–60)	25 (18–30)	<0.001
GLS (%)	-14.5 (-19.5; -9.3)	-7.2 (-11.1; -2.9)	<0.001
Pharmacotherapy
ACEI or ARB	51 (68)	20 (80)	0.252
Beta-blockers	53 (70.67)	24 (96)	0.009
Aldosterone Antagonists	38 (50.67)	20 (80)	0.010
Diuretics	28 (37.33)	22 (88)	<0.001
Amiodarone	21 (28.57)	6 (25)	0.771
Digitalis	8 (10.67)	9 (36)	0.003
Ivabradine	1 (1.33)	2 (8)	0.091
Antiplatelet agents	26 (34.67)	3 (12)	0.031
Anticoagulants	23 (30.67)	14 (56)	0.023
Biomarkers
NT-proBNP (pg/mL)	352 (89–1423)	5583 (1566–8703)	<0.001
hs-cTnT (ng/mL)	9.2 (4.04–15.97)	30 (17.00–69.32)	<0.001
NGAL (ng/mL)	83 (64–112)	147 (108–254)	<0.001
Cys-C (mg/L)	1.05 (0.87–1.26)	1.54 (1.22–1.89)	<0.001
Creatinine (mg/dL)	1.08 (0.92–1.24)	1.2 (1.03–1.54)	0.046
Galectin-3 (ng/mL)	13.7 (10.8–16.2)	19.6 (14.6–24.1)	<0.001
(s)ST2 (ng/mL)	22.9 (18.7–28.6)	37.4 (26.7–59.9)	<0.001
GFR (ml/min/1.73m²)	61 (55–61)	56 (43–61)	0.037

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This table contains % for categorical variables and median (first and third quartile) for continuous variables. Abbreviations: BMI: Body Mass Index; NYHA: New York Heart Association; LVEF: Left Ventricle Ejection Fraction; GLS: Global Longitudinal Strain; ACEI: Angiotensin-Converting Enzyme Inhibitor; ARB: Angiotensin Receptor Blocker; NTproBNP: N-terminal brain natriuretic propeptide; Gal-3: Galectin-3 (Gal-3); NGAL: Neutrophil gelatinase-associated lipocalin (NGAL); sST2: Soluble ST2; Cys-c: Cystatin-C; Hs-cTnT: High sensitivity cardiac troponin T.

Incidence and rate of outcomes

After a mean of 52 months (Q1 = 42; Q3 = 54) of follow-up, there were 25 events (20 deaths, four transplants, and one LVAD implantation). The mortality in this cohort was 20% (95% CI 13.2% - 29.2%), with a mortality rate of 0.15 per 1000 person-years (95% CI 0.09–0.23). The overall incidence of this composite outcome was 25% (95% CI 16.8–34.6%), with a rate of 0.18 per 1000 person-years (95% CI 0.12–0.28). We must highlight that we did not have any loss to follow-up during this period.

Biomarkers as predictors of adverse outcomes in CCM

All biomarkers were significantly associated with the CO (Fig 1); nevertheless, in age and LVEF adjusted models, log-transformed continuous concentrations of each biomarker were associated with the CO, except for NGAL and Galectin-3 (Table 2). Considering each biomarker dichotomously, only two evaluated biomarkers (ST2 and hs-cTnT) were significantly associated with the composite outcome (Table 2).

Table 2. Prognostic value of the log-transformed biomarker levels in a continuous manner and using cut-off points for the composite outcome in patients with CCM (n = 100).

Biomarker	HR^*	Adjusted Model		AUC
Biomarker	HR^*	95% CI	p-value	AUC
NT-proBNP	2.02	1.43–2.85	0.000	85.06
hs-cTnT	2.65	1.71–4.14	0.000	85.82
sST2	3.98	1.82–8.66	0.000	84.31
Galectin-3	2.41	0.91–6.32	0.075	83.55
Cys-C	8.17	1.51–44.29	0.015	83.46
NGAL	2.05	0.84–4.97	0.112	82.38
sST2 (>35 vs. ≤35)	3.62	1.49–8.78	0.004	84.68
hs-cTnT (>15 vs. ≤15)	3.24	1.26–8.33	0.014	83.51
Galectin-3 (>16.7 vs. ≤16.7)	1.65	0.68–4.04	0.265	82.70
NT-proBNP (>1000 vs. ≤1000)	3.57	0.93–13.68	0.063	82.66
Cys-C (>1.1 vs. ≤1.1)	1.48	0.52–4.18	0.451	81.29
NGAL (>96.5 vs. ≤96.5)	1.47	0.51–4.211	0.464	81.39
sST2 >35 + NT-proBNP >1000^¥	11.84	1.97–70.85	0.007	86.10
sST2 >35 + hs-cTnT >15^¥	4.01	1.66–9.66	0.002	85.20
NT-proBNP >1000 + hs-cTnT >15^¥	3.18	1.31–7.79	0.011	84.07
sST2 >35 + NT-proBNP >1000 + hs-cTnT >15^¥	11.25	1.94–65.14	0.007	85.75

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*HR adjusted by age and left ventricular ejection fraction.

^¥The HR is derived from comparing the group of patients with the levels of the biomarkers over the cut-off values vs. those with the evaluated biomarkers below the selected cut-offs.

To analyze the potential additive value of combining sST2 and NT-proBNP measurements for predicting the composite outcome, the sample was divided into four groups based on these biomarkers cut-off points. There were only two (3.92%) deaths, HT or LVAD implantations in the patients with levels of both sST2 below 35 ng/mL and NT-proBNP under 1000 pg/ml; in contrast, the composite outcome was present in 81.25% (n = 13) of those with both these two biomarkers over the selected cut-off values, conferring a significantly higher risk when compared to those patients without sST2 or NT-proBNP elevations (HR 11.84; 95% CI 1.97–70.85) (Fig 2). In addition, the AUC for this combination was significantly higher than the ones for dichotomic sST2 (p = 0.001) and NT-proBNP (p = 0.038). Furthermore, considering that sST2 is not readily available in most clinical contexts, we analyzed the value of combined NT-proBNP and hs-TnT analysis for predicting the composite outcome (Fig 3). The results were similar to the ST2 and NT-proBNP combination, with no cases of the CO in the group of patients with NT-proBNP <1000 pg/ml and Hs-TnT <15 ng/ml, while in the group of individuals with NT-proBNP >1000 pg/ml and Hs-TnT >15 ng/ml almost 61% (n = 17) had one of the events of the CO (Fig 3). Furthermore, this group’s CO risk compared to those without hs-TnT or NT-proBNP alterations over the cut-offs was significantly higher (HR 3.18; 95%CI 1.31–7.79). The AUC value for the combined NT-proBNP and Hs-cTnT was also significantly higher than the one of the dichotomic NT-proBNP (p = 0.037) but was not different from the one of the dichotomic Hs-cTnT (p = 0.064). Similar results were observed when performing a sensitivity analysis focusing on mortality as the outcome (S1 Table). Finally, the combined analysis using the three biomarkers over their selected cut-off values revealed an 11-fold increased risk of the CO (HR 11.25; 95% CI 1.94–65.14) (Fig 4).

Discussion

This prospective cohort study represents the first study to extensively analyze the prognostic value of a series of cardiorenal biomarkers in CCM. Five serum biomarkers (NT-proBNP, hs-cTnT, sST2, Gal-3, and Cys-C) were independently associated with mortality and the CO. Additionally, NT-proBNP and hs-TnT showed the highest prognostic value in predicting the CO in this population. Of note, Hs-cTnT had a similar predictive performance to sST2; however, hs-cTnT has the advantage of being widely available in the clinical setting. A multimarker strategy has been used in other scenarios of HF patients, showing to improve the prognostic performance of every single biomarker assessment. We analyzed the value of the biomarkers combination in CCM patients, finding that the combination of NT-proBNP and sST2 or hs-cTnT significantly increased the prognostic accuracy in predicting our CO.

The need for reliable serum biomarkers for predicting outcomes such as mortality and therapeutic response in CCM has been assessed in multiple studies [9, 10, 17, 18]. Natriuretic peptides are the most studied biomarker in HF associated with CCM [12]. However, few studies have addressed the prognostic value of this and several other biomarkers in CCM. Moreira et al. evaluated natriuretic peptides as mortality predictors at three years of follow-up in CCM patients, finding that higher atrial natriuretic peptide (ANP) and BNP concentrations were significantly associated with a higher risk of death/heart transplant [17]. Sherbuk et al. confirmed these results in a study that analyzed the prognostic value of BNP, NT-proBNP, creatine kinase-myocardial band (CK-MB), troponin I, matrix metalloproteinase (MMP-2), MMP-9, and tissue inhibitor of metalloproteinases (TIMP) 1 and 2 in a group of 50 T. cruzi-infected Stage D Bolivian patients. In this study, higher baseline levels of BNP, NT-proBNP, CK-MB, and MMP-2 were significantly associated with increased mortality at 14 months [14]. Interestingly, in our study dichotomic Hs-cTnT had a similar prognostic value when compared to NT-proBNP and Hs-cTnT combination, a finding that may improve the assessment of CCM patients in low-resource settings, in which Hs-cTnT may be cheaper and more easily performed.

To date, no study evaluating the prognostic value of hs-cTnT, Gal-3, NGAL, sST2, and Cys-c in CCM has been reported, and our group recently reported the only study that has assessed the role of these biomarkers in CD in a cross-sectional fashion [19]. However, these serum biomarkers have already been studied in HF of other etiologies, showing promising results [20–22]. In the present study, we found that higher concentrations of these biomarkers were associated with higher risk features, and in the case of all but NGAL and Galectin-3, they were all associated with a higher risk for future cardiovascular events even after adjusting for age and LVEF. Considered dichotomously in the manner a clinician might analyze biomarker results, the biomarkers studied predicted a 3 to 11-fold increase in the risk of having any composite outcome events. Given that CCM most often occurs in resource-constrained health care environments, these results might help clinicians in decision making, including consideration for transplantation evaluation.

Study limitations

Our study has some significant limitations, including the limited sample size and the lack of inclusion of patients in the disease’s indeterminate form (positive for T. cruzi, with normal ECG and echo), which could have provided insight into the role of cardiovascular biomarkers in asymptomatic individuals. In this cohort, we report a mortality rate of 0.15 per 1000 person-years at two years, which is significantly lower compared to previously reported event rates in similar studies [15]. The reasons for this difference are not apparent but may be related to differences in the compliance with optimal pharmacological treatment schemes across populations, implantable cardioverter defibrillator insertion rates, and follow-up by a specialized heart failure clinic [15, 23, 24]. Furthermore, we could not assess the role of implantable cardioverter-defibrillator use in the results, as ICD shocks may have been a relevant part of the CO considering the arrhythmogenic nature of CCM. Finally, the lack of repeated measures of the assessed biomarkers precluded assessing potential variations of the patients’ clinical status during the follow-up period, for example, after changes in medical therapy or lifestyle interventions.

Conclusion

Chronic Chagas cardiomyopathy manifests with high short-term morbidity, mortality, and significant expenditure for the healthcare system. In our prospective cohort study, we found that four serum biomarkers (NT-proBNP, hs-cTnT, sST2, and Cys-C) were significantly associated with a higher risk of the CO among patients with CCM. The combination of NT-proBNP and sST2 or hs-cTnT provided the highest discrimination for major cardiovascular outcomes primarily driven by death. Larger multi-center studies should validate these results to identify certain biomarkers optimal for improving the prediction of adverse outcomes in CCM and potentially developing a risk score that may improve targeted therapies in this high-risk population.

Supporting information

S1 Graphical abstract. Central illustration or graphical abstract.

A multimarker approach combining NT-proBNP and sST2 or hs-cTnT predicted mortality and adverse cardiovascular outcomes accurately after a median follow-up of 52 months in Chronic Chagas Cardiomyopathy. The presence of two of these biomarkers over their cut-off values reflect a higher risk of mortality, indicating the need for a closer follow-up and consideration of advanced therapies. On the other hand, patients with two of these biomarkers under their cut-off values are at lower risk of adverse outcomes, potentially allowing usual follow-up and the maintenance of the guided medical therapy (GMT).

(TIF)

Click here for additional data file.^{(782.4KB, tif)}

S1 Table. Prognostic value of the log-transformed biomarker levels in a continuous manner and using cut-off points for the mortality outcome in patients with CCM (n = 100).

(DOCX)

Click here for additional data file.^{(13.9KB, docx)}

Acknowledgments

The authors thank patients with Chagas Disease for their participation in this study.

Abbreviations and acronyms

AUC-ROC: Area under the receiver operating characteristic curve
CO: Composite Outcome
CCM: Chronic Chagas Cardiomyopathy
CD: Chagas Disease
Cys-c: Cystatin-C
ECG: Electrocardiogram
Echo: Echocardiogram
Gal-3: Galectin-3
Hs-cTnT: High sensitivity cardiac troponin T
NGAL: Neutrophil gelatinase-associated lipocalin
NT-proBNP: amino-terminal pro-B type natriuretic peptide
sST2: Soluble ST2

Data Availability

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

Funding Statement

LEE and LZR were supported by the Colombian government through Departamento Administrativo de Ciencia, Tecnología e Innovación-COLCIENCIAS (project code: 501453730398, CT 380–2011); URL: https://minciencias.gov.co/. LEE was supported by the Universität St. Gallen through the Seed Money Grants from the Leading House for Latin America of this institution (project code: 39-703).The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0258622.r001

Decision Letter 0

Giuseppe Vergaro

26 Jan 2021

PONE-D-20-36713

Cardiovascular Biomarkers as Predictors of Adverse Outcomes in Chronic Chagas Cardiomyopathy

PLOS ONE

Dear Dr. Echeverría,

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.

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Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Partly

Reviewer #2: Yes

**********

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

Reviewer #1: No

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

**********

5. Review Comments to the Author

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

Reviewer #1: The article entitled “Cardiovascular Biomarkers as Predictors of Adverse Outcomes in Chronic Chagas Cardiomyopathy” intends to establish some biomarkers related to outcomes in chronic Chagas cardiomyopathy. Some aspects need to be addressed by the authors to clarify their results and conclusions.

1. Since Chagas Disease was abbreviated as CD, it should be included in the abbreviations list.

2. The study population selection was described as consecutive CCM patients filling the inclusion criteria, but no mention was made to those patients with pacemakers or ICD. Were those exclusion criteria?

3. It is important to define what was the clinical status of the patients during screening and inclusion. Were they only stable outpatients only or included hospitalized ones?

4. Since no other samples were obtained during follow-up and their values may be affected by medication adjustments such as increasing diuretics or beta blockers it is important to know how those adjustments may have impacted the results. A comment is needed.

5. Why hospitalizations were not part of the composite outcome? Maybe this could be a censoring point since IV medications may be used and new NYHA class achieved. Please comment on that.

6. It seems that no patient had an ICD implanted during follow-up. Since it is a high risk sample it may have occurred and could change survival. This could be treated as a censored endopoint. Please explain.

7. Finally, did you have any lost of follow-up? No censoring was noted in the tables. Please include a comment.

8. In the Methods section, the authors do not report the collection of GLS presented in the results section and also do not report the method used for left ventricular ejection fraction (unidimensional, Simpson, Teicholz, etc). The description is relevant.

9. In the follow-up procedures, you only mentioned a telephone interview for data collection. How did you confirmed a cardiovascular cause of mortality? I suggest including the structured questionnaire as supplemental material.

10. Table 1:

a. Please include in the legend that data is presented as median and interquartile range.

b. No patient received Amiodarone? This is strange in a high risk sample of CCM since arrhythmia is a frequent finding. Can you provide an explanation?

c. The group with events seems to include those with acute decompensated heart failure (NT-proBNP median value was 5583pg/mL) at inclusion. It seems that at baseline you had patients acutely ill. Can you comment on that?

d. Patients with this condition may have kidney markers Cystain C and NGAL increased due to renal involvement in an acute heart failure patient even before serum creatinine increases.

11. You must present data of univariate and multivariate analysis since it is important to known if performing those expensive tests provides an advantage beyond LVEF or GLS, traditional prognostic markers.

12. You stated a mortality of 20% in your sample and included a confidence interval that I do not understand. The same applies to your composite outcome (25%). Is this correct?

13. A potential collinearity may exist between the biomarkers studied and LVEF. Have you evaluated this?

14. The number of patients in the Kaplan-Meier curves for each interval are needed in the figures.

Reviewer #2: In this manuscript Echeverria et al explored the roles of cardiovascular biomarker as predictors of adverse outcomes in patients with Chagas Cardiomyopathy.

The study is clear and well conducted, though I have some observations:

- A clearer definition of the population included should be performed, specifying whether those patients are chronic outpatients or acute on chronic inpatients.

- Spline models should be considered to determine the optimal cut-offs for the biomarkers identified. The higher cutoffs used (i.e. 15 pg/mL for Troponin T and 1000 pg/mL for NT-proBNP) could be responsible for the non-significant HR in the dichotomized analysis.

- The number of events should also be clarified

- The adjusted model for LVEF and age is not sufficient. Other models including sex and/or GLS and especially optimal medical therapy should also be included in the model. GLS might in fact be more informative than LVEF on the prognosis. Similarly, OMT could represent a bias when interpreting survival analysis in different patients with different baseline medications.

- The three-biomarker association as predictor of outcome has only been assessed with the Cox analysis. Therefore, a Kaplan-Meier analysis of this model should also be performed.

- The Authors suggest in the Discussion section that the dichotomic troponin value has a similar prognostic value than the combined model with NT-proBNP and troponin T. However, since the two biomarkers explore two different pathophysiological backgrounds and might carry different information, their prognostic value might be affected by the underlying stage of the disease. Please, clarify.

- The quality of the figures should be increased and figure 1 with the algorithm should either made easier to read or included as a supplemental figure.

**********

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Reviewer #1: No

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PLoS One. 2021 Oct 28;16(10):e0258622. doi: 10.1371/journal.pone.0258622.r002

Author response to Decision Letter 0

29 Mar 2021

Response to Reviewers

1. Since Chagas Disease was abbreviated as CD, it should be included in the abbreviations list.

Response: We agree with the reviewer, this abbreviation was now added to the list.

Response: We appreciate this clarification, we indeed included patients with CCM and implantable devices. We have now stated this in the methods section, specifically in the “Study population” paragraph.

3. It is important to define what was the clinical status of the patients during screening and inclusion. Were they only stable outpatients only or included hospitalized ones?

Response: We thank the reviewer for this question. We have now clarified in the methods section that only outpatients were included.

Response: We agree with the reviewer regarding this critical aspect of our study. This is a clear limitation for all studies with single measurement of biomarkers. However, all the patients included are being treated in a single center by the same group of cardiologists of the heart failure service, so we expect that changes in medical therapy may be homogeneous. We have included a small paragraph stating this in the limitations section.

5. Why hospitalizations were not part of the composite outcome? Maybe this could be a censoring point since IV medications may be used and new NYHA class achieved. Please comment on that.

Response: We understand the reviewer concerns, as hospitalizations are frequently used as part of composite outcomes in heart failure studies. However, our aim was to assess the prognostic value of the biomarkers for predicting mortality and outcomes that are tightly related to it (Heart transplant and LVAD implantation are usually last resource measures in patients without response to conventional therapy whose survival is otherwise extremely low in the short term). Considering the relatively high incidence of our composite outcome, we believe we could focus our analysis in the predictors of these outcomes, as they have a higher impact than hospitalizations.

Response: We thank the reviewer for this question. We did not record ICD implantation as an outcome of the study, as we focused on the specific outcomes of mortality, HT and LVAD implantation.

7. Finally, did you have any lost of follow-up? No censoring was noted in the tables. Please include a comment.

Response: We agree with the reviewer that this is a relevant aspect. As the sample size was not very large and all the patients were being treated in our center, we were able of performing a complete follow-up without losses. We have added a comment on this in the results section.

Response: We thank the reviewer for this relevant suggestion. We have now added this information in the paragraph of “Data collection” of the Methods section.

Response: We understand the reviewer´s concern. We want to clarify that we verified the cause of mortality by asking the relatives of the patient to send us the clinical records of the hospitalization in which the patient died. As most of the patients are treated in our clinic, we had direct access to their records in the institution to confirm the cause of mortality.

10. Table 1:

a. Please include in the legend that data is presented as median and interquartile range.

Response: It is now included in the legend as follows: “This table contains % for categorical variables and median (first and third quartile) for continuous variables.”

b. No patient received Amiodarone? This is strange in a high risk sample of CCM since arrhythmia is a frequent finding. Can you provide an explanation?

Response: We agree with the reviewer this information is relevant for reporting. Therefore we have now added the data on amiodarone in the table.

Response: We understand the reviewer’s concern. It is relevant to highlight that despite having elevated NT-proBNP values, patients can present with a progressive deterioration of their clinical status, not requiring hospitalization at the assessment time. For example, some of our patients come to the clinic for their outpatient assessment with recent results with NT-proBNP values of even 10000 pg/mL, and they exhibit only moderate efforts dyspnea. Therefore, although we know these are the patients with the higher risk of hospitalizations in the short term, they cannot be considered acutely ill at the moment of enrollment in the study even if they showed these high values of NT-proBNP.

d. Patients with this condition may have kidney markers Cystain C and NGAL increased due to renal involvement in an acute heart failure patient even before serum creatinine increases.

Response: We agree with the reviewer’s comment. As Cystatin-C represents a more sensitive marker of filtration than creatinine, we expected this result. Moreover, as NGAL is a marker of tubular injury, we also expected to observe elevated values in cardiorenal syndrome patients, although it could not be amended as the only explanation for this observation.

Response: We thank the reviewer for this suggestion. We want to clarify that univariate and multivariate analyses are presented in Tables 1 and 2, respectively.

12. You stated a mortality of 20% in your sample and included a confidence interval that I do not understand. The same applies to your composite outcome (25%). Is this correct?

Response: We thank the reviewer for this important clarification. We observed that the confidence interval for this proportion was repeated for the two outcomes. We have now changed the intervals adequately.

13. A potential collinearity may exist between the biomarkers studied and LVEF. Have you evaluated this?

Response: We agree that this is a relevant aspect to assess. We evaluated collinearity in all the models we generated, and no evidence of it was observed between the assessed variables.

14. The number of patients in the Kaplan-Meier curves for each interval are needed in the figures.

Response: We have now included this information in the figures.

Reviewer #2: In this manuscript Echeverria et al explored the roles of cardiovascular biomarker as predictors of adverse outcomes in patients with Chagas Cardiomyopathy.

The study is clear and well conducted, though I have some observations:

1. A clearer definition of the population included should be performed, specifying whether those patients are chronic outpatients or acute on chronic inpatients.

Response: We thank the reviewer for this question. We have now clarified in the methods section that only chronic outpatients were included.

2. Spline models should be considered to determine the optimal cut-offs for the biomarkers identified. The higher cutoffs used (i.e. 15 pg/mL for Troponin T and 1000 pg/mL for NT-proBNP) could be responsible for the non-significant HR in the dichotomized analysis.

Response: Thank you very much for the suggestions to use spline models. We have used Youden index to select the cut-off points, now this was specified in statistical analysis.

3. The number of events should also be clarified

Response: Thanks for the comment now the events number was clarified in “incidence and rate of outcomes”.

4. The adjusted model for LVEF and age is not sufficient. Other models including sex and/or GLS and especially optimal medical therapy should also be included in the model. GLS might in fact be more informative than LVEF on the prognosis. Similarly, OMT could represent a bias when interpreting survival analysis in different patients with different baseline medications.

Response: We thank the reviewer for this important comment, but given the sample size and number of events, the recommendations in the literature to include variables in a multivariate model is 10 events per variable, then the maximum that we could include in the model are two variables, otherwise we could be overestimating the model. In relation to treatment, all the patients included are being treated in a single center by the same group of cardiologists of the heart failure service, so we expect that changes in medical therapy may be homogeneous. We have included a small paragraph stating this in the limitations section.

5. The three-biomarker association as predictor of outcome has only been assessed with the Cox analysis. Therefore, a Kaplan-Meier analysis of this model should also be performed.

Response: We thank the reviewer for this suggestion. A new Kaplan Meier figure was included.

6. The Authors suggest in the Discussion section that the dichotomic troponin value has a similar prognostic value than the combined model with NT-proBNP and troponin T. However, since the two biomarkers explore two different pathophysiological backgrounds and might carry different information, their prognostic value might be affected by the underlying stage of the disease. Please, clarify.

Response: We thank the reviewer for this suggestion. Both NT-proBNP and Hs-cTnT are recommended for assessing prognosis in heart failure patients. In this context, troponin T is related to Chagas cardiomyopathy specific inflammation, which is expected to remain somehow stable across the chronic disease course. On the other hand, NT-proBNP is released as a response to myocardial tissue stretching; thus, it may be more useful for predicting outcomes in patients with dilated cardiomyopathy (Stages C and D). Therefore, we could hypothesize that NT-proBNP could have a better performance in patients with more severe forms of disease. Nevertheless, our sample size was not sufficient to adequately validate this hypothesis. We expect to prove it in future studies.

7. The quality of the figures should be increased and figure 1 with the algorithm should either made easier to read or included as a supplemental figure.

Response: We have now improved the quality of the graphics (increased the dpi) and clarified the graphical abstract legend.

PLoS One. doi: 10.1371/journal.pone.0258622.r003

Decision Letter 1

Giuseppe Vergaro

12 May 2021

PONE-D-20-36713R1

Cardiovascular Biomarkers as Predictors of Adverse Outcomes in Chronic Chagas Cardiomyopathy

PLOS ONE

Dear Dr. Echeverría,

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, as some points still deserve clarification after the first review. 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 June 11th. 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,

Giuseppe Vergaro, M.D.

Academic Editor

PLOS ONE

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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: (No Response)

Reviewer #2: All comments have been addressed

**********

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

Reviewer #1: Partly

Reviewer #2: Yes

**********

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

Reviewer #1: No

Reviewer #2: Yes

**********

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

Reviewer #1: No

Reviewer #2: No

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: We thank the authors for addressing all questions raised by this reviewer. Some points stiil need further clarification.

1. Now you stated the refractory heart failure patients were included. Most of them in the group with events. This may explain the high number of events during the first year. This may clearly impact your results and need to be addressed.

2. TAble 1 needs to have absolute numbers included with percentagens in brackets to be consistent with the notation presented : N(%) in the first line.

3. An important point that needs clarification is the option for including AGE in the multivariate analysis. It was not significantly disticnt between the two groups, but you selected it. GLS, NYHA class and beta-blockers are highly significant in univariate analysis, but they were not selected. Can you provide an explanation?

4. Use of ICD may have a significant impact on the results. You simply stated that ICD use was not recorded. Since Cardiac transplantations and use of LVAd were described, you certainly had patients with previously implanted ICDs. Sudden cardiac death is the main reason for death in CD, so it is important to clearly identify the causes of death in your sample. An ICD shock is certainly an outcome that will be missed and may affect your results.

5. Your figures still need quality improvement.

6. The results of ROC curves described in methods need to be presented since they are the first step in the selection of those cut-off point used.

Reviewer #2: The Authors have partly addressed my concerns, though the revised figures are not presented in the revised manuscript. Nonetheless, I deem the article improved and suited for publication

**********

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Reviewer #2: No

PLoS One. 2021 Oct 28;16(10):e0258622. doi: 10.1371/journal.pone.0258622.r004

Author response to Decision Letter 1

20 Jul 2021

Reviewer 1:

Response: We understand the reviewer's concerns regarding the high number of events during the first year. Beyond the inclusion of Stage D patients, we must highlight the more severe course of Chronic Chagas Cardiomyopathy compared to other heart failure etiologies. Therefore, a 25% composite outcome incidence in the evaluated period does not necessarily represent an unusual observation in this context. Furthermore, the biomarkers also identified a relevant group of patients with a very low incidence of adverse outcomes. We have now highlighted this relevant aspect in the limitations section.

2. TAble 1 needs to have absolute numbers included with percentagens in brackets to be consistent with the notation presented: N(%) in the first line.

Response: We agree with the reviewer. This has been corrected in the table.

Response: We thank the reviewer for this relevant recommendation. We understand his concern, so we evaluated the additive value of including the NYHA class and beta-blockers by comparing the ROC curves of the models. We observed that adding these two variables did not increase the model's ROC area for any of the biomarkers evaluated. Then, we considered that the most parsimonious model, including only age and LVEF, could be optimal due to the small sample size.

Response: We agree with the reviewer on this aspect. However, we were unable to register information regarding ICD shocks. We have now included this in the limitations section.

5. Your figures still need quality improvement.

Response: We agree with the reviewer. This issue has been addressed in the current version. Please download the images directly from the PDF document or the submission system to allow a full-quality view.

6. The results of ROC curves described in methods need to be presented since they are the first step in the selection of those cut-off point used.

Response: We thank the reviewer for his relevant comment. The results of the AUC-ROC for each continuous biomarker assessed can be found in the last column of Table 2.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0258622.r005

Decision Letter 2

Giuseppe Vergaro

4 Oct 2021

Cardiovascular Biomarkers as Predictors of Adverse Outcomes in Chronic Chagas Cardiomyopathy

PONE-D-20-36713R2

Dear Dr. Echeverría,

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

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

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Kind regards,

Giuseppe Vergaro, M.D.

Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0258622.r006

Acceptance letter

Giuseppe Vergaro

14 Oct 2021

PONE-D-20-36713R2

Cardiovascular Biomarkers as Predictors of Adverse Outcomes in Chronic Chagas Cardiomyopathy

Dear Dr. Echeverría:

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

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

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

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

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Giuseppe Vergaro

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Graphical abstract. Central illustration or graphical abstract.

(TIF)

Click here for additional data file.^{(782.4KB, tif)}

S1 Table. Prognostic value of the log-transformed biomarker levels in a continuous manner and using cut-off points for the mortality outcome in patients with CCM (n = 100).

(DOCX)

Click here for additional data file.^{(13.9KB, docx)}

Attachment

Submitted filename: Response to reviewers PONE 09062021.docx

Click here for additional data file.^{(16.5KB, docx)}

Data Availability Statement

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

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PERMALINK

Cardiovascular biomarkers as predictors of adverse outcomes in chronic Chagas cardiomyopathy

Luis E Echeverría

Lyda Z Rojas

Sergio Alejandro Gómez-Ochoa

Oscar L Rueda-Ochoa

Cristian David Sosa-Vesga

Taulant Muka

James L Januzzi

Rachel Marcus

Carlos A Morillo

Roles

Abstract

Background

Methods

Results

Conclusion

Background

Methods

Study population

Data collection

Study outcomes and follow-up

Statistical analysis

Results

Population characteristics

Table 1. Baseline characteristics the evaluated chronic Chagas cardiomyopathy patients (n = 100).

Incidence and rate of outcomes

Biomarkers as predictors of adverse outcomes in CCM

Fig 1. Kaplan Meier survival analyses for the composite outcome stratified by biomarker results above and below selected cut-off values.

Table 2. Prognostic value of the log-transformed biomarker levels in a continuous manner and using cut-off points for the composite outcome in patients with CCM (n = 100).

Fig 2. Bar graph and Kaplan Meier survival analyses for the composite outcome stratified by combined NT-proBNP and ST2 according to selected cut-off values.

Fig 3. Bar graph and Kaplan Meier survival analyses for the composite outcome stratified by combined NT-proBNP and Hs-cTnT according to selected cut-off values.

Fig 4. Kaplan Meier survival analysis for the composite outcome stratified by combined NT-proBNP, Hs-cTnT and sST2 according to selected cut-off values.

Discussion

Study limitations

Conclusion

Supporting information

Acknowledgments

Abbreviations and acronyms

Data Availability

Funding Statement

References

Decision Letter 0

Giuseppe Vergaro

Roles

Author response to Decision Letter 0

Decision Letter 1

Giuseppe Vergaro

Roles

Author response to Decision Letter 1

Decision Letter 2

Giuseppe Vergaro

Roles

Acceptance letter

Giuseppe Vergaro

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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