Title: Application of continuous renal replacement therapy (CRRT) in patients with severe acute
pancreatitis: an analytical study
Version: 2 Date: 17 Jun 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
1. Retrospective Design and Potential Selection Bias
The retrospective nature limits causal inference and is prone to selection bias. There is no
mention of how missing data were handled statistically.
Exclusion of patients with incomplete records or early death may skew results by eliminating
high-risk individuals, potentially underestimating CRRT's effects on mortality.
2. Inconsistent Definition of “Improved Prognosis”
The primary outcome classification (voluntary discharge vs. death) oversimplifies complex
clinical trajectories. "Voluntary discharge" does not necessarily equate to recovery or clinical
improvement and may introduce misclassification.
3. CRRT Timing Analysis Underdeveloped
While the study highlights that initiation within 36 hours is optimal, the method for determining
this cut-off is unclear (e.g., ROC curve, time-dependent Cox model). Time as a continuous
variable is not adequately explored.
CRRT initiation timing was identified as protective but excluded from the predictive model due
to potential bi-directionality. This weakens the study's core premise of optimizing timing.
4. Nomogram Limitations in Real-World Utility
The predictive model excludes one of the most clinically relevant factors (CRRT timing), which
may reduce its real-world applicability, especially in borderline cases.
No external validation was conducted, and calibration/decision curve analyses were not reported.
5. Small and Single-Center Sample Size
A sample of 282 over nearly a decade may reflect low case accrual or selective inclusion,
reducing statistical power and limiting generalizability. There's no report of how many total
SAP cases were screened.
The lack of multicenter or prospective validation restricts the external applicability of findings.
6. Potential Confounding from Concurrent Interventions
The analysis does not adjust for other treatments (e.g., antibiotics, nutritional support, vasoactive
agents) that may independently influence prognosis or interact with CRRT effects.
7. CRRT Protocol Not Fully Described
The description of CRRT lacks detail on filter types, blood flow rates, anticoagulation strategies,
and session duration—all known factors that can impact cytokine clearance and patient
outcomes.
It is unclear whether CRRT modalities were standardized or varied by physician discretion.
8. Lack of Long-Term Outcomes
The study only assesses in-hospital outcomes. No follow-up data (e.g., 30-day or 90-day
mortality, renal recovery) are provided, missing insights into sustained CRRT benefits or harms.
9. Ambiguous Subgroup Definitions
The criteria used to define “patients without strong CRRT indications” are vague. It is unclear
how objective and reproducible this classification is, which affects the model's reliability in
clinical decision-making.
10. Overreliance on Univariate Filtering for Regression
Logistic regression was built only on variables with p < 0.10 in univariate analysis. This
approach risks omitting confounders and may lead to model instability.
11. Ethical Oversight and Patient Consent
Although ethical approval was obtained and data anonymization measures were thorough, there
is no mention of data audit or verification to ensure data integrity across the 9-year span.
Title: Application of continuous renal replacement therapy (CRRT) in patients with severe acute
pancreatitis: an analytical study
Version: 2 Date: 17 Jun 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
1. Retrospective Design and Potential Selection Bias
The retrospective nature limits causal inference and is prone to selection bias. There is no
mention of how missing data were handled statistically.
Exclusion of patients with incomplete records or early death may skew results by eliminating
high-risk individuals, potentially underestimating CRRT's effects on mortality.
2. Inconsistent Definition of “Improved Prognosis”
The primary outcome classification (voluntary discharge vs. death) oversimplifies complex
clinical trajectories. "Voluntary discharge" does not necessarily equate to recovery or clinical
improvement and may introduce misclassification.
3. CRRT Timing Analysis Underdeveloped
While the study highlights that initiation within 36 hours is optimal, the method for determining
this cut-off is unclear (e.g., ROC curve, time-dependent Cox model). Time as a continuous
variable is not adequately explored.
CRRT initiation timing was identified as protective but excluded from the predictive model due
to potential bi-directionality. This weakens the study's core premise of optimizing timing.
4. Nomogram Limitations in Real-World Utility
The predictive model excludes one of the most clinically relevant factors (CRRT timing), which
may reduce its real-world applicability, especially in borderline cases.
No external validation was conducted, and calibration/decision curve analyses were not reported.
5. Small and Single-Center Sample Size
A sample of 282 over nearly a decade may reflect low case accrual or selective inclusion,
reducing statistical power and limiting generalizability. There's no report of how many total
SAP cases were screened.
The lack of multicenter or prospective validation restricts the external applicability of findings.
6. Potential Confounding from Concurrent Interventions
The analysis does not adjust for other treatments (e.g., antibiotics, nutritional support, vasoactive
agents) that may independently influence prognosis or interact with CRRT effects.
7. CRRT Protocol Not Fully Described
The description of CRRT lacks detail on filter types, blood flow rates, anticoagulation strategies,
and session duration—all known factors that can impact cytokine clearance and patient
outcomes.
It is unclear whether CRRT modalities were standardized or varied by physician discretion.
8. Lack of Long-Term Outcomes
The study only assesses in-hospital outcomes. No follow-up data (e.g., 30-day or 90-day
mortality, renal recovery) are provided, missing insights into sustained CRRT benefits or harms.
9. Ambiguous Subgroup Definitions
The criteria used to define “patients without strong CRRT indications” are vague. It is unclear
how objective and reproducible this classification is, which affects the model's reliability in
clinical decision-making.
10. Overreliance on Univariate Filtering for Regression
Logistic regression was built only on variables with p < 0.10 in univariate analysis. This
approach risks omitting confounders and may lead to model instability.
11. Ethical Oversight and Patient Consent
Although ethical approval was obtained and data anonymization measures were thorough, there
is no mention of data audit or verification to ensure data integrity across the 9-year span.
Please review this paper.
user
Title: Application of continuous renal replacement therapy (CRRT) in patients with severe acute
pancreatitis: an analytical study
Version: 2 Date: 17 Jun 2025
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
1. Retrospective Design and Potential Selection Bias
The retrospective nature limits causal inference and is prone to selection bias. There is no
mention of how missing data were handled statistically.
Exclusion of patients with incomplete records or early death may skew results by eliminating
high-risk individuals, potentially underestimating CRRT's effects on mortality.
2. Inconsistent Definition of “Improved Prognosis”
The primary outcome classification (voluntary discharge vs. death) oversimplifies complex
clinical trajectories. "Voluntary discharge" does not necessarily equate to recovery or clinical
improvement and may introduce misclassification.
3. CRRT Timing Analysis Underdeveloped
While the study highlights that initiation within 36 hours is optimal, the method for determining
this cut-off is unclear (e.g., ROC curve, time-dependent Cox model). Time as a continuous
variable is not adequately explored.
CRRT initiation timing was identified as protective but excluded from the predictive model due
to potential bi-directionality. This weakens the study's core premise of optimizing timing.
4. Nomogram Limitations in Real-World Utility
The predictive model excludes one of the most clinically relevant factors (CRRT timing), which
may reduce its real-world applicability, especially in borderline cases.
No external validation was conducted, and calibration/decision curve analyses were not reported.
5. Small and Single-Center Sample Size
A sample of 282 over nearly a decade may reflect low case accrual or selective inclusion,
reducing statistical power and limiting generalizability. There's no report of how many total
SAP cases were screened.
The lack of multicenter or prospective validation restricts the external applicability of findings.
6. Potential Confounding from Concurrent Interventions
The analysis does not adjust for other treatments (e.g., antibiotics, nutritional support, vasoactive
agents) that may independently influence prognosis or interact with CRRT effects.
7. CRRT Protocol Not Fully Described
The description of CRRT lacks detail on filter types, blood flow rates, anticoagulation strategies,
and session duration—all known factors that can impact cytokine clearance and patient
outcomes.
It is unclear whether CRRT modalities were standardized or varied by physician discretion.
8. Lack of Long-Term Outcomes
The study only assesses in-hospital outcomes. No follow-up data (e.g., 30-day or 90-day
mortality, renal recovery) are provided, missing insights into sustained CRRT benefits or harms.
9. Ambiguous Subgroup Definitions
The criteria used to define “patients without strong CRRT indications” are vague. It is unclear
how objective and reproducible this classification is, which affects the model's reliability in
clinical decision-making.
10. Overreliance on Univariate Filtering for Regression
Logistic regression was built only on variables with p < 0.10 in univariate analysis. This
approach risks omitting confounders and may lead to model instability.
11. Ethical Oversight and Patient Consent
Although ethical approval was obtained and data anonymization measures were thorough, there
is no mention of data audit or verification to ensure data integrity across the 9-year span.
assistant