Title: Prognostic Value of ALBI Score for All-Cause Mortality in Metabolic Associated Fatty
Liver Disease Patients: A Cohort Study from NHANES 2003-2018
Version: 1 Date: 31 Mar 2025
Reviewer's report:
Dear Editor,
Thank you for inviting me to review the manuscript entitled "ALBI Score and Nonalcoholic
Fatty Liver Disease Patients with All-Cause Mortality: A Cohort Study from NHANES 2003–
2018."
This manuscript evaluates the prognostic utility of the albumin-bilirubin (ALBI) score in
predicting all-cause mortality among 5,666 patients with nonalcoholic fatty liver disease
(NAFLD), using data from the NHANES 2003–2018 cohort. The authors employ weighted Cox
proportional hazards models, restricted cubic spline (RCS) analysis, and ROC curve analysis
to assess the dose-response relationship between ALBI and mortality risk. A threshold effect
analysis identifies an inflection point at ALBI = -2.69, beyond which mortality risk significantly
increases. The findings suggest that the ALBI score is a robust and reliable predictor of all-cause
mortality in NAFLD patients.
Clinical Comments
Emphasize Implications for NAFLD Screening and Management
The manuscript underscores ALBI’s prognostic utility but should explicitly address how
integrating ALBI into NAFLD management algorithms might impact clinical practice. Could
ALBI serve as a decision-making tool to guide intensity of surveillance or therapeutic
intervention?
Address Potential Impact of Comorbidities on ALBI–Mortality Association
NAFLD patients frequently harbor metabolic comorbidities (e.g., diabetes, hypertension) that
could modulate ALBI scores. The authors should consider discussing whether adjusting for these
comorbidities may refine ALBI’s predictive accuracy and strengthen its clinical utility.
pdate NAFLD Prevalence to 30.2% and Cite the Latest Literature
The manuscript currently cites older estimates of NAFLD prevalence. The authors should update
the prevalence to 30.2% as reported in a recent study, which provides a more accurate reflection
of the current global burden of NAFLD.
Suggested citation:
DOI: 10.1016/j.arcmed.2024.103043
Incorporating this updated prevalence figure strengthens the manuscript's epidemiological
foundation and contextualizes the study’s relevance in a broader public health framework.
Statistical Comments
Perform Sensitivity Analyses for Threshold Consistency
The threshold effect analysis identifies an inflection point at ALBI = -2.69, but the robustness
of this cut-off requires sensitivity analyses with alternate thresholds. Testing different cut-off
points and comparing predictive performance would strengthen confidence in this threshold.
Report Model Calibration Metrics
While the manuscript reports ROC AUC values for mortality prediction, calibration metrics such
as Brier scores or calibration plots should be included to assess model accuracy in predicting
actual event probabilities.
Address Possible Overfitting in Cox Models
Given the large number of covariates in the fully adjusted Cox models, internal validation via
bootstrapping or cross-validation should be reported to assess potential overfitting and improve
model generalizability.
Clarify Imputation Methodology for Missing Data
The manuscript mentions handling missing data but does not specify the imputation approach
(e.g., multiple imputation, complete case analysis). Detailing the method used and providing
sensitivity analyses comparing imputed vs. non-imputed models would enhance methodological
transparency.
Title: Prognostic Value of ALBI Score for All-Cause Mortality in Metabolic Associated Fatty
Liver Disease Patients: A Cohort Study from NHANES 2003-2018
Version: 1 Date: 31 Mar 2025
Reviewer's report:
Dear Editor,
Thank you for inviting me to review the manuscript entitled "ALBI Score and Nonalcoholic
Fatty Liver Disease Patients with All-Cause Mortality: A Cohort Study from NHANES 2003–
2018."
This manuscript evaluates the prognostic utility of the albumin-bilirubin (ALBI) score in
predicting all-cause mortality among 5,666 patients with nonalcoholic fatty liver disease
(NAFLD), using data from the NHANES 2003–2018 cohort. The authors employ weighted Cox
proportional hazards models, restricted cubic spline (RCS) analysis, and ROC curve analysis
to assess the dose-response relationship between ALBI and mortality risk. A threshold effect
analysis identifies an inflection point at ALBI = -2.69, beyond which mortality risk significantly
increases. The findings suggest that the ALBI score is a robust and reliable predictor of all-cause
mortality in NAFLD patients.
Clinical Comments
Emphasize Implications for NAFLD Screening and Management
The manuscript underscores ALBI’s prognostic utility but should explicitly address how
integrating ALBI into NAFLD management algorithms might impact clinical practice. Could
ALBI serve as a decision-making tool to guide intensity of surveillance or therapeutic
intervention?
Address Potential Impact of Comorbidities on ALBI–Mortality Association
NAFLD patients frequently harbor metabolic comorbidities (e.g., diabetes, hypertension) that
could modulate ALBI scores. The authors should consider discussing whether adjusting for these
comorbidities may refine ALBI’s predictive accuracy and strengthen its clinical utility.
pdate NAFLD Prevalence to 30.2% and Cite the Latest Literature
The manuscript currently cites older estimates of NAFLD prevalence. The authors should update
the prevalence to 30.2% as reported in a recent study, which provides a more accurate reflection
of the current global burden of NAFLD.
Suggested citation:
DOI: 10.1016/j.arcmed.2024.103043
Incorporating this updated prevalence figure strengthens the manuscript's epidemiological
foundation and contextualizes the study’s relevance in a broader public health framework.
Statistical Comments
Perform Sensitivity Analyses for Threshold Consistency
The threshold effect analysis identifies an inflection point at ALBI = -2.69, but the robustness
of this cut-off requires sensitivity analyses with alternate thresholds. Testing different cut-off
points and comparing predictive performance would strengthen confidence in this threshold.
Report Model Calibration Metrics
While the manuscript reports ROC AUC values for mortality prediction, calibration metrics such
as Brier scores or calibration plots should be included to assess model accuracy in predicting
actual event probabilities.
Address Possible Overfitting in Cox Models
Given the large number of covariates in the fully adjusted Cox models, internal validation via
bootstrapping or cross-validation should be reported to assess potential overfitting and improve
model generalizability.
Clarify Imputation Methodology for Missing Data
The manuscript mentions handling missing data but does not specify the imputation approach
(e.g., multiple imputation, complete case analysis). Detailing the method used and providing
sensitivity analyses comparing imputed vs. non-imputed models would enhance methodological
transparency.
Please review this paper.
user
Title: Prognostic Value of ALBI Score for All-Cause Mortality in Metabolic Associated Fatty
Liver Disease Patients: A Cohort Study from NHANES 2003-2018
Version: 1 Date: 31 Mar 2025
Reviewer's report:
Dear Editor,
Thank you for inviting me to review the manuscript entitled "ALBI Score and Nonalcoholic
Fatty Liver Disease Patients with All-Cause Mortality: A Cohort Study from NHANES 2003–
2018."
This manuscript evaluates the prognostic utility of the albumin-bilirubin (ALBI) score in
predicting all-cause mortality among 5,666 patients with nonalcoholic fatty liver disease
(NAFLD), using data from the NHANES 2003–2018 cohort. The authors employ weighted Cox
proportional hazards models, restricted cubic spline (RCS) analysis, and ROC curve analysis
to assess the dose-response relationship between ALBI and mortality risk. A threshold effect
analysis identifies an inflection point at ALBI = -2.69, beyond which mortality risk significantly
increases. The findings suggest that the ALBI score is a robust and reliable predictor of all-cause
mortality in NAFLD patients.
Clinical Comments
Emphasize Implications for NAFLD Screening and Management
The manuscript underscores ALBI’s prognostic utility but should explicitly address how
integrating ALBI into NAFLD management algorithms might impact clinical practice. Could
ALBI serve as a decision-making tool to guide intensity of surveillance or therapeutic
intervention?
Address Potential Impact of Comorbidities on ALBI–Mortality Association
NAFLD patients frequently harbor metabolic comorbidities (e.g., diabetes, hypertension) that
could modulate ALBI scores. The authors should consider discussing whether adjusting for these
comorbidities may refine ALBI’s predictive accuracy and strengthen its clinical utility.
pdate NAFLD Prevalence to 30.2% and Cite the Latest Literature
The manuscript currently cites older estimates of NAFLD prevalence. The authors should update
the prevalence to 30.2% as reported in a recent study, which provides a more accurate reflection
of the current global burden of NAFLD.
Suggested citation:
DOI: 10.1016/j.arcmed.2024.103043
Incorporating this updated prevalence figure strengthens the manuscript's epidemiological
foundation and contextualizes the study’s relevance in a broader public health framework.
Statistical Comments
Perform Sensitivity Analyses for Threshold Consistency
The threshold effect analysis identifies an inflection point at ALBI = -2.69, but the robustness
of this cut-off requires sensitivity analyses with alternate thresholds. Testing different cut-off
points and comparing predictive performance would strengthen confidence in this threshold.
Report Model Calibration Metrics
While the manuscript reports ROC AUC values for mortality prediction, calibration metrics such
as Brier scores or calibration plots should be included to assess model accuracy in predicting
actual event probabilities.
Address Possible Overfitting in Cox Models
Given the large number of covariates in the fully adjusted Cox models, internal validation via
bootstrapping or cross-validation should be reported to assess potential overfitting and improve
model generalizability.
Clarify Imputation Methodology for Missing Data
The manuscript mentions handling missing data but does not specify the imputation approach
(e.g., multiple imputation, complete case analysis). Detailing the method used and providing
sensitivity analyses comparing imputed vs. non-imputed models would enhance methodological
transparency.
assistant