Title: Preoperative quantitative quadriceps muscle ultrasound to predict POD for
gastrointestinal surgery in older patients
Version: 0 Date: 08 Feb 2025
Reviewer's report:
Manuscript Review for BMC Gastroenterology
General Comments:
The manuscript presents a novel investigation into preoperative quadriceps ultrasound
parameters as predictors of postoperative delirium (POD) in older gastrointestinal surgery
patients. While the study design is robust and addresses an important clinical question, several
methodological and interpretive issues require attention to strengthen validity and clinical
applicability.
Major Issues:
Overfitting of the Prediction Model
The reported AUC of 0.966 (95% CI: 0.950–0.982) is exceptionally high for a clinical prediction
model, raising concerns about overfitting. Although the sample size meets the “10 events per
variable” guideline, the single-center design and lack of external validation likely contribute to
optimistic performance estimates.
Recommendation:
Perform external validation in a multicenter cohort.
Clarify whether variable selection (e.g., stepwise regression) was adjusted for multiple testing.
Discuss potential reasons for the high AUC (e.g., collinearity between predictors, homogeneity
of the cohort).
Ultrasound Measurement Reliability
A single trained anesthesiologist performed all ultrasound measurements. Inter- and intra-rater
reliability data are absent, which undermines reproducibility.
Recommendation:
Report intraclass correlation coefficients (ICC) for Q-MT and Q-EI measurements.
Include a supplementary figure demonstrating ultrasound image analysis methodology.
Missing Data Handling
The manuscript states that variables with <10% missing data were imputed but does not specify
which variables were affected or the imputation method’s impact on results.
Recommendation:
Provide a supplementary table detailing missing data rates and imputation sensitivity analysis.
Exclusion of High-Risk Populations
Patients with sensory impairments (deafness, blindness) were excluded, potentially biasing the
sample and underestimating POD incidence.
Recommendation:
Acknowledge this limitation explicitly and discuss how it might affect generalizability.
Cutoff Values for MMSE and Ultrasound Parameters
The MMSE cutoff of 27.5 (vs. the traditional #24) and ultrasound parameter thresholds (e.g., Q-
MT = 20.305 mm) appear data-driven, risking overfitting.
Recommendation:
Validate cutoffs in an independent cohort.
Compare MMSE thresholds with established literature on preoperative cognitive impairment
and POD.
Minor Issues:
Inconsistent Reporting of Predictors
Table 2 lists CRP and VAS as significant in univariate analysis but excludes them from the final
model. The rationale for exclusion (e.g., collinearity) is unclear.
Recommendation:
Clarify variable selection criteria (e.g., backward elimination p-value thresholds).
Emergency Surgery as a Confounder
Emergency surgery was significant in univariate analysis (OR = 1.54, p = 0.038) but not retained
in the multivariate model. This contrasts with literature identifying emergency surgery as a
strong POD predictor.
Recommendation:
Discuss potential interactions between emergency surgery and other variables (e.g., frailty, CCI).
Lack of Neuroinflammatory Biomarkers
The study emphasizes the gut-brain axis but omits biomarkers of systemic inflammation (e.g.,
IL-6) that may mediate POD.
Recommendation:
Address this gap in the Discussion and propose future research directions.
Outdated References
Some references (e.g., Maldonado 2013) predate recent advances in delirium pathophysiology
(e.g., neuroinflammation, cholinergic dysfunction).
Recommendation:
Update citations to reflect current mechanistic understanding (e.g., add references to 2022–2024
studies).
Clinical Implementation Feasibility
The nomogram’s reliance on ultrasound measurements may limit scalability in resource-
constrained settings.
Recommendation:
Discuss barriers to implementation and propose simplified alternatives (e.g., frailty screening
combined with MMSE).
Additional Recommendations:
Figures/Tables:
Include a figure illustrating ultrasound measurement landmarks.
Provide ROC curves for the nomogram alongside individual predictors in the main text
(currently in supplemental?).
Statistical Methods:
Report variance inflation factors (VIFs) to assess multicollinearity.
Clarify whether continuous variables (e.g., age) were modeled linearly or with splines.
Ethical Considerations:
Explicitly state whether excluded patients (e.g., those with communication impairments) were
offered alternative participation pathways.
Conclusion:
The study introduces a promising approach to POD prediction using preoperative muscle
ultrasound. However, the exceptionally high AUC and methodological limitations necessitate
cautious interpretation. Addressing these concerns through revisions and external validation
would enhance the manuscript’s impact.
Title: Preoperative quantitative quadriceps muscle ultrasound to predict POD for
gastrointestinal surgery in older patients
Version: 0 Date: 08 Feb 2025
Reviewer's report:
Manuscript Review for BMC Gastroenterology
General Comments:
The manuscript presents a novel investigation into preoperative quadriceps ultrasound
parameters as predictors of postoperative delirium (POD) in older gastrointestinal surgery
patients. While the study design is robust and addresses an important clinical question, several
methodological and interpretive issues require attention to strengthen validity and clinical
applicability.
Major Issues:
Overfitting of the Prediction Model
The reported AUC of 0.966 (95% CI: 0.950–0.982) is exceptionally high for a clinical prediction
model, raising concerns about overfitting. Although the sample size meets the “10 events per
variable” guideline, the single-center design and lack of external validation likely contribute to
optimistic performance estimates.
Recommendation:
Perform external validation in a multicenter cohort.
Clarify whether variable selection (e.g., stepwise regression) was adjusted for multiple testing.
Discuss potential reasons for the high AUC (e.g., collinearity between predictors, homogeneity
of the cohort).
Ultrasound Measurement Reliability
A single trained anesthesiologist performed all ultrasound measurements. Inter- and intra-rater
reliability data are absent, which undermines reproducibility.
Recommendation:
Report intraclass correlation coefficients (ICC) for Q-MT and Q-EI measurements.
Include a supplementary figure demonstrating ultrasound image analysis methodology.
Missing Data Handling
The manuscript states that variables with <10% missing data were imputed but does not specify
which variables were affected or the imputation method’s impact on results.
Recommendation:
Provide a supplementary table detailing missing data rates and imputation sensitivity analysis.
Exclusion of High-Risk Populations
Patients with sensory impairments (deafness, blindness) were excluded, potentially biasing the
sample and underestimating POD incidence.
Recommendation:
Acknowledge this limitation explicitly and discuss how it might affect generalizability.
Cutoff Values for MMSE and Ultrasound Parameters
The MMSE cutoff of 27.5 (vs. the traditional #24) and ultrasound parameter thresholds (e.g., Q-
MT = 20.305 mm) appear data-driven, risking overfitting.
Recommendation:
Validate cutoffs in an independent cohort.
Compare MMSE thresholds with established literature on preoperative cognitive impairment
and POD.
Minor Issues:
Inconsistent Reporting of Predictors
Table 2 lists CRP and VAS as significant in univariate analysis but excludes them from the final
model. The rationale for exclusion (e.g., collinearity) is unclear.
Recommendation:
Clarify variable selection criteria (e.g., backward elimination p-value thresholds).
Emergency Surgery as a Confounder
Emergency surgery was significant in univariate analysis (OR = 1.54, p = 0.038) but not retained
in the multivariate model. This contrasts with literature identifying emergency surgery as a
strong POD predictor.
Recommendation:
Discuss potential interactions between emergency surgery and other variables (e.g., frailty, CCI).
Lack of Neuroinflammatory Biomarkers
The study emphasizes the gut-brain axis but omits biomarkers of systemic inflammation (e.g.,
IL-6) that may mediate POD.
Recommendation:
Address this gap in the Discussion and propose future research directions.
Outdated References
Some references (e.g., Maldonado 2013) predate recent advances in delirium pathophysiology
(e.g., neuroinflammation, cholinergic dysfunction).
Recommendation:
Update citations to reflect current mechanistic understanding (e.g., add references to 2022–2024
studies).
Clinical Implementation Feasibility
The nomogram’s reliance on ultrasound measurements may limit scalability in resource-
constrained settings.
Recommendation:
Discuss barriers to implementation and propose simplified alternatives (e.g., frailty screening
combined with MMSE).
Additional Recommendations:
Figures/Tables:
Include a figure illustrating ultrasound measurement landmarks.
Provide ROC curves for the nomogram alongside individual predictors in the main text
(currently in supplemental?).
Statistical Methods:
Report variance inflation factors (VIFs) to assess multicollinearity.
Clarify whether continuous variables (e.g., age) were modeled linearly or with splines.
Ethical Considerations:
Explicitly state whether excluded patients (e.g., those with communication impairments) were
offered alternative participation pathways.
Conclusion:
The study introduces a promising approach to POD prediction using preoperative muscle
ultrasound. However, the exceptionally high AUC and methodological limitations necessitate
cautious interpretation. Addressing these concerns through revisions and external validation
would enhance the manuscript’s impact.
Please review this paper.
user
Title: Preoperative quantitative quadriceps muscle ultrasound to predict POD for
gastrointestinal surgery in older patients
Version: 0 Date: 08 Feb 2025
Reviewer's report:
Manuscript Review for BMC Gastroenterology
General Comments:
The manuscript presents a novel investigation into preoperative quadriceps ultrasound
parameters as predictors of postoperative delirium (POD) in older gastrointestinal surgery
patients. While the study design is robust and addresses an important clinical question, several
methodological and interpretive issues require attention to strengthen validity and clinical
applicability.
Major Issues:
Overfitting of the Prediction Model
The reported AUC of 0.966 (95% CI: 0.950–0.982) is exceptionally high for a clinical prediction
model, raising concerns about overfitting. Although the sample size meets the “10 events per
variable” guideline, the single-center design and lack of external validation likely contribute to
optimistic performance estimates.
Recommendation:
Perform external validation in a multicenter cohort.
Clarify whether variable selection (e.g., stepwise regression) was adjusted for multiple testing.
Discuss potential reasons for the high AUC (e.g., collinearity between predictors, homogeneity
of the cohort).
Ultrasound Measurement Reliability
A single trained anesthesiologist performed all ultrasound measurements. Inter- and intra-rater
reliability data are absent, which undermines reproducibility.
Recommendation:
Report intraclass correlation coefficients (ICC) for Q-MT and Q-EI measurements.
Include a supplementary figure demonstrating ultrasound image analysis methodology.
Missing Data Handling
The manuscript states that variables with <10% missing data were imputed but does not specify
which variables were affected or the imputation method’s impact on results.
Recommendation:
Provide a supplementary table detailing missing data rates and imputation sensitivity analysis.
Exclusion of High-Risk Populations
Patients with sensory impairments (deafness, blindness) were excluded, potentially biasing the
sample and underestimating POD incidence.
Recommendation:
Acknowledge this limitation explicitly and discuss how it might affect generalizability.
Cutoff Values for MMSE and Ultrasound Parameters
The MMSE cutoff of 27.5 (vs. the traditional #24) and ultrasound parameter thresholds (e.g., Q-
MT = 20.305 mm) appear data-driven, risking overfitting.
Recommendation:
Validate cutoffs in an independent cohort.
Compare MMSE thresholds with established literature on preoperative cognitive impairment
and POD.
Minor Issues:
Inconsistent Reporting of Predictors
Table 2 lists CRP and VAS as significant in univariate analysis but excludes them from the final
model. The rationale for exclusion (e.g., collinearity) is unclear.
Recommendation:
Clarify variable selection criteria (e.g., backward elimination p-value thresholds).
Emergency Surgery as a Confounder
Emergency surgery was significant in univariate analysis (OR = 1.54, p = 0.038) but not retained
in the multivariate model. This contrasts with literature identifying emergency surgery as a
strong POD predictor.
Recommendation:
Discuss potential interactions between emergency surgery and other variables (e.g., frailty, CCI).
Lack of Neuroinflammatory Biomarkers
The study emphasizes the gut-brain axis but omits biomarkers of systemic inflammation (e.g.,
IL-6) that may mediate POD.
Recommendation:
Address this gap in the Discussion and propose future research directions.
Outdated References
Some references (e.g., Maldonado 2013) predate recent advances in delirium pathophysiology
(e.g., neuroinflammation, cholinergic dysfunction).
Recommendation:
Update citations to reflect current mechanistic understanding (e.g., add references to 2022–2024
studies).
Clinical Implementation Feasibility
The nomogram’s reliance on ultrasound measurements may limit scalability in resource-
constrained settings.
Recommendation:
Discuss barriers to implementation and propose simplified alternatives (e.g., frailty screening
combined with MMSE).
Additional Recommendations:
Figures/Tables:
Include a figure illustrating ultrasound measurement landmarks.
Provide ROC curves for the nomogram alongside individual predictors in the main text
(currently in supplemental?).
Statistical Methods:
Report variance inflation factors (VIFs) to assess multicollinearity.
Clarify whether continuous variables (e.g., age) were modeled linearly or with splines.
Ethical Considerations:
Explicitly state whether excluded patients (e.g., those with communication impairments) were
offered alternative participation pathways.
Conclusion:
The study introduces a promising approach to POD prediction using preoperative muscle
ultrasound. However, the exceptionally high AUC and methodological limitations necessitate
cautious interpretation. Addressing these concerns through revisions and external validation
would enhance the manuscript’s impact.
assistant