Serum Ferritin as a Predictor of Hospital Mortality in Critically Ill COVID-19 Patients

Round 1

Reviewer 1 Report

Positives (Strengths of the Study)

Clinical Relevance:

1. Confirms ferritin as a moderate predictorof COVID-19 mortality in ICU settings, aligning with prior studies (e.g., Zhou et al., Lino et al.).
2. Provides specific cut-offs(201.5 µg/L baseline, 878.6 µg/L peak) for risk stratification, useful in resource-limited settings.

Methodological Rigor:

1. Clear inclusion/exclusion criteria and standardized ferritin measurement (Siemens Atellica platform).

1. Robust statistical analysis (ROC curves, univariate regression).

Novel Insights:

Highlights the prognostic superiority of peak ferritin (AUC 0.754) over baseline levels (AUC 0.615), suggesting dynamic monitoring may improve predictions.

Potential for Improvement

1. Mechanistic Depth:

Does not explore why ferritin outperforms other biomarkers (e.g., IL-6) in this cohort.

Improvement: Discuss whether ferritin reflects hyperinflammation, iron dysregulation, or secondary hemophagocytosis.

1. Clinical Utility:

Fails to address how ferritin should integrate into existing prognostic scores (e.g., APACHE II, SOFA).

Improvement: Propose a combined model (e.g., ferritin + PaO₂/FiO₂) and validate its performance.

1. Generalizability:

Single-center, retrospective design limits applicability to newer variants (e.g., Omicron) or vaccinated populations.

Improvement: Compare findings to post-pandemic studies or suggest a multi-center validation.

1. Comparative Analysis:

No comparison to other biomarkers (e.g., D-dimer, LDH) in the same cohort.

Improvement: Add a multimarker ROC analysis to identify the best predictor.

1. Translation to Practice:

 Lacks discussion on ferritin-guided interventions (e.g., immunomodulators).

Improvement: Trials using ferritin to guide therapy (e.g., tocilizumab in REMAP-CAP)?

1. Lack of Multivariable Analysis

Problem: Only univariate logistic regression was performed. This ignores confounding effects (e.g., age, APACHE II, PaO₂/FiO₂).

Fix: A statistician should perform multivariable regression adjusting for ≥3 key confounders.

2. Sample Size Justification 

Problem: Power analysis claims 89% power for a small effect (d=0.38), but the actual predictive power (AUC=0.615) is weaker than assumed.

Fix: Recalculate power based on observed AUCs or clarify assumptions.

3. Scientific inaccuracies

Issue: Cardiac arrest patients were excluded, yet hyperferritinemia is hallmark of post-arrest systemic inflammation.

Fix: Reanalyze data including these patients or justify exclusion better.

Author Response

1. Lack of Multivariable Analysis

Problem: Only univariate logistic regression was performed. This ignores confounding effects (e.g., age, APACHE II, PaO₂/FiO₂).

Fix: A statistician should perform multivariable regression adjusting for ≥3 key confounders.

Reply to comment 1: Multivariable analysis was performed. Highlighted in yellow in the text.

2. Sample Size Justification 

Problem: Power analysis claims 89% power for a small effect (d=0.38), but the actual predictive power (AUC=0.615) is weaker than assumed.

Fix: Recalculate power based on observed AUCs or clarify assumptions.

Reply to comment 2: The study was a retrospective study. All patients who met the criteria between a certain time period were included in the study. Therefore, the sample size was not calculated. After the completion of the study, post-hoc power analysis was performed according to the results of comparing the baseline ferritin values between mortality conditions. Highlighted in yellow in the text.

3. Scientific inaccuracies

Issue: Cardiac arrest patients were excluded, yet hyperferritinemia is hallmark of post-arrest systemic inflammation.

Fix: Reanalyze data including these patients or justify exclusion better.

Reply to comment 3: Patients with comorbid diseases having a risk of hyperferritinemia, patients admitted to the ICU upon cardiac arrest, and patients with missing data were excluded from the study. Highlighted in yellow in the text.

Confidence intervals added to ROC in the Fig 1.

Author Response File: Author Response.pdf

Reviewer 2 Report

this paper is align with the other inthe  literature as the authors describe in to the discussion: the serum ferritin is most elevated into the non-survivor group and the authors report the specific ferritin values in relation with the two groups (reporting the specificity and sensibility of this values).

The tables and the figures are clear . The tables report the differences between the two groups in exame (survivor and non survivor) with the p value. In the tables there are also the comorbidities, and other different serum values. In the figures are report the cut-off of different values in the serum.

Author Response

This paper is align with the other inthe  literature as the authors describe in to the discussion: the serum ferritin is most elevated into the non-survivor group and the authors report the specific ferritin values in relation with the two groups (reporting the specificity and sensibility of this values).

The tables and the figures are clear . The tables report the differences between the two groups in exame (survivor and non survivor) with the p value. In the tables there are also the comorbidities, and other different serum values. In the figures are report the cut-off of different values in the serum.

Reply: Thank you very much for your positive comments.

Reviewer 3 Report

The manuscript was prepared in a comprehensible manner, the references, although not too numerous, present the current state of knowledge regarding previous studies on the possibility of using ferritin as an indicator of risk of death in acute COVID-19.

The results are developed extensively and reliably. The strength of the manuscript is to show the limitations of the present study and the difficulty of using ferritin levels as a predictor of the course of the disease.

After minor corrections by the authors, I recommend the manuscript for publication.

I have a few minor suggestions that may improve the clarity of the paper. Since the reader is not always an expert in professional terminology, I suggest clarifying the abbreviations: ROC, AUC and IC (Line 17-19) and IL-6 (Line 74)

I would also suggest an explanation or brief information on what APACHE II and PaO2/FiO2 are (Line 71)

ICU is explained in lines 55 and 60, instead of the first place it appears (line 14).

In reference to the New City Branch of Tongji Hospital (Wuhan, China) study (line 228), the duration of the study was incorrectly stated.  The referenced publication indicates that these were patients admitted to the hospital “from January 30 to March 30, 2020,” not as incorrectly quoted „from January 30, 2020 to January 30, 2020”.

Author Response

I have a few minor suggestions that may improve the clarity of the paper. Since the reader is not always an expert in professional terminology, I suggest clarifying the abbreviations: ROC, AUC and IC (Line 17-19) and IL-6 (Line 74)

I would also suggest an explanation or brief information on what APACHE II and PaO2/FiO2 are (Line 71)

ICU is explained in lines 55 and 60, instead of the first place it appears (line 14).

In reference to the New City Branch of Tongji Hospital (Wuhan, China) study (line 228), the duration of the study was incorrectly stated.  The referenced publication indicates that these were patients admitted to the hospital “from January 30 to March 30, 2020,” not as incorrectly quoted „from January 30, 2020 to January 30, 2020”.

Reply to comments: We have made the corrections you requested and highlighted them in yellow in the text.

Round 2

Reviewer 1 Report

N/A

The authors have addressed the previous concerns and made substantial improvements to the manuscript.

Author Response

Comment: The authors have addressed the previous concerns and made substantial improvements to the manuscript.

Reply: Thank you.