PONE-D-26-19524-->-->Frailty prediction in heart failure patients with acute infections: the potential role of thiazide diuretics?-->-->PLOS One?>

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Additional Editor Comments (if provided):

The manuscript is generally well conducted and addresses a clinically relevant question. Most of the requested revisions concern clarification of the methodology, more cautious interpretation of the observational findings, and improvement of the presentation rather than substantial additional analyses. The manuscript would also benefit from careful language editing and a reduction of redundancy throughout the Introduction and Discussion.

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Reviewers' comments:

Reviewer’s Responses to Questions

Comments to the Author

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

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Reviewer Report

Manuscript title: Frailty prediction in heart failure patients with acute infections: the potential role of thiazide diuretics?

Journal: PLOS ONE

Manuscript ID: PONE-D-26-19524

Recommendation: Minor Revision

Overall assessment

This manuscript investigates frailty risk factors and develops a machine-learning-based prediction model among older patients hospitalized with heart failure (HF) complicated by acute infections. The authors analyzed a relatively large cohort of 1,498 patients and applied several machine learning approaches, including LASSO-based feature selection, XGBoost modeling, SHAP-based interpretation, and an online prediction tool. The study addresses a clinically relevant topic, as frailty represents an important determinant of prognosis and treatment complexity in older patients with acute cardiovascular conditions.

The manuscript is generally well structured, the methodology is described in sufficient detail, and the use of explainable artificial intelligence approaches represents a strength. The model performance appears promising, with XGBoost achieving good discrimination (AUROC 0.872) and high AUPRC values. However, several points require clarification and refinement before the manuscript can be considered for publication. In particular, the interpretation of thiazide diuretic use as a protective factor against frailty requires more cautious discussion because of the observational design and potential confounding factors.

I recommend minor revision.

________________________________________

Major comments

1. Interpretation of the association between thiazide diuretics and reduced frailty risk

The main novelty of the manuscript is the identification of thiazide diuretic use as an important predictor associated with lower frailty risk. However, the current wording sometimes implies a possible protective effect, which may overstate the findings.

Because this is a retrospective observational study, the association may be influenced by:

• confounding by indication;

• differences in disease severity;

• baseline functional status;

• renal function;

• physician treatment selection;

• concomitant HF therapies.

Patients receiving thiazide diuretics may represent a subgroup with different clinical characteristics compared with untreated patients.

The authors should:

• clearly state that the finding represents an association rather than causation;

• expand the limitations section regarding residual confounding;

• avoid suggesting that thiazide therapy may prevent or reverse frailty without prospective validation.

________________________________________

2. Definition and timing of frailty assessment

Frailty was assessed using the Clinical Frailty Scale (CFS), with frailty defined as CFS ≥5. The manuscript states that assessments were performed after resolution of acute infection and at discharge. This is important but requires additional clarification.

The authors should explain:

• whether CFS reflected pre-admission baseline frailty or acute illness-related functional decline;

• whether the acute infection itself could temporarily worsen functional status;

• whether CFS assessment at discharge may introduce reverse causality, since hospitalization-related decline may influence frailty classification.

A clearer description of the clinical context of CFS evaluation would improve interpretation.

________________________________________

3. Machine learning methodology and model validation

The methodological approach is generally appropriate; however, some aspects require clarification.

The authors should provide more details regarding:

• whether feature selection was performed strictly within the training dataset to avoid information leakage;

• whether preprocessing steps (including KNN imputation and scaling) were performed only on the training set before application to the test set;

• whether hyperparameter optimization was nested within cross-validation.

Given that the dataset originates from a single institution, external validation is necessary before clinical implementation.

The authors should emphasize that the online calculator represents a proof-of-concept tool rather than a clinically ready application.

________________________________________

4. High prevalence of frailty and model calibration

Frailty prevalence was very high (80.3%), which reflects the selected population but may influence model performance metrics. The authors appropriately considered AUROC and AUPRC; however, calibration becomes particularly important in this context.

The manuscript should provide:

• more detailed calibration results;

• discussion of potential impact of prevalence imbalance;

• clarification of whether predicted probabilities are reliable for clinical decision-making.

________________________________________

Minor comments

1. Abstract

The conclusion in the abstract should be moderated. The sentence:

“This study demonstrated that the use of thiazide diuretics was associated with reduced frailty risk”

should be revised to:

“This study identified thiazide diuretic use as an independent predictor associated with lower frailty probability.”

This better reflects the observational design.

________________________________________

2. Introduction

The introduction is comprehensive but could be shortened. Some statements regarding machine learning as a “virtual clinician” are somewhat exaggerated and should be toned down to maintain scientific neutrality.

________________________________________

3. Methods – inclusion criteria

The exclusion of NYHA class I patients should be justified more clearly, as this criterion may introduce selection bias.

________________________________________

4. Missing data handling

The authors used KNN imputation for missing values. Please clarify:

• whether missingness was assumed to be missing completely at random (MCAR), missing at random (MAR), or not assessed;

• whether sensitivity analyses using alternative imputation methods were considered.

________________________________________

5. Feature selection

The manuscript combines univariate analysis followed by LASSO selection. This approach may exclude clinically relevant variables that are not statistically significant individually but contribute to multivariable prediction.

The authors should briefly justify this strategy.

________________________________________

6. SHAP interpretation

The SHAP analysis is a valuable component of the study. However, the authors should clarify whether SHAP values represent:

• global feature importance;

• directionality of association;

• individual patient-level interpretation.

________________________________________

7. Figures and supplementary material

The figures should be checked to ensure that:

• axes and legends are sufficiently explained;

• abbreviations are defined;

• readers without machine learning expertise can interpret the results.

8. Discussion

The discussion should include a clearer comparison with previous frailty prediction models in HF and geriatric populations.

The authors should also discuss whether the identified predictors (albumin, renal function, lymphocyte percentage, functional capacity, age) reflect known biological pathways of frailty rather than disease-specific predictors.

I strongly suggest to see and discuss the following references:

Multidimensional frailty: Testa G et al., Multidimensional frailty evaluation in elderly outpatients with chronic heart failure: A prospective study. Eur J Prev Cardiol. 2019 Jul;26(10):1115-1117.

Low arterial pressure and catabolic syndrome: Curcio F et al. . The reverse metabolic syndrome in the elderly: Is it a "catabolic" syndrome? Aging Clin Exp Res. 2018 Jun;30(6):547-554.

9. Data availability statement

The current data availability statement appears inconsistent with PLOS ONE requirements. Since clinical data were analyzed, the authors should clarify whether anonymized data can be made available through an appropriate repository or upon reasonable request, according to ethical restrictions.

Suggested final decision to Editor

Minor Revision

The manuscript presents an interesting and potentially valuable machine-learning approach for frailty prediction in older patients with heart failure and acute infections. The study has methodological strengths, particularly the use of explainable AI techniques. The main issue requiring revision is the interpretation of the association between thiazide diuretics and frailty, which should be framed more cautiously given the retrospective observational design. Additional clarification regarding methodology, validation, and limitations would improve the manuscript substantially. Some important references should be added to revised form of the manuscript

Reviewer #2: The manuscript has a strong methodological foundation, a large sample size, and is hbased on a clinically relevant question. The main opportunities for improvement are in presentation rather than scientific content. Specifically, I would recommend:

reducing redundancy by approximately 20–30%, improving transitions between paragraphs, shortening overly long explanatory sections, consolidating repeated descriptions of previous studies' limitations, and making the rationale progress more directly from the clinical problem to the need for an interpretable prediction model. I estimate these revisions could reduce the manuscript by 1,000–1,500 words while making it more concise and easier to follow, without sacrificing scientific rigor.

ie Some background paragraphs explain concepts already familiar to most readers.

For example, detailed descriptions of machine learning visualization tumor imaging examples are not essential.

The paragraph discussing tumor imaging can probably be removed entirely because it does not directly support the rationale of this study. Instead, simply state that SHAP improves model interpretability.

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Reviewer #1: No

Reviewer #2: No

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Frailty prediction in heart failure patients with acute infections: the potential role of thiazide diuretics?

PONE-D-26-19524R1

Dear Dr. Shen,

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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Kind regards,

Francesco Curcio, M.D., Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

According to the reviewers, the manuscript has been substantially improved and now merits publication.

Reviewers' comments:

Reviewer’s Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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

Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: 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

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Reviewer #1: All questions arised have been answered. No further comments. The manuscript merits to be published in Plos ONE.

Reviewer #2: (No Response)

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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: No

Reviewer #2: No

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