Explainable machine learning–based prediction of postoperative pulmonary infection after aneurysmal subarachnoid hemorrhage clipping and exploratory analysis of enteral nutrition timing: a dual-cohort retrospective study
- 1Department of Nursing, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital , ,
- 2Department of Neurosurgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital , ,
Abstract
Background:: Postoperative pulmonary infection (PPI) is a common complication after microsurgical clipping for aneurysmal subarachnoid hemorrhage (aSAH) and is associated with impaired recovery and greater use of hospital resources.
Objective: To develop and temporally validate an explainable machine-learning model for PPI risk stratification and to explore the association between enteral nutrition (EN) initiation timing and subsequent PPI risk.
Methods: This dual-cohort retrospective study was reported in accordance with Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis plus Artificial Intelligence (TRIPOD+AI) principles. Candidate predictors were selected using a combined least absolute shrinkage and selection operator (LASSO) regression and random-forest importance strategy. Nine machine-learning algorithms were developed and compared in terms of discrimination, calibration, and clinical utility using decision-curve analysis.
Results: In the validation cohort, the Naive Bayes model showed the best overall performance [area under the receiver operating characteristic curve (AUC), 0.811; 95% confidence interval (CI), 0.755–0.855]. SHAP analysis identified the major contributors to the primary 24-h prediction model. Separately, a secondary exploratory analysis of continuous EN initiation timing identified a nonlinear pattern, with a bootstrap median model-derived inflection point of 24.0 h (95% bootstrap interval, 23.0–27.0 h).
Conclusions: This explainable model may support early PPI risk stratification after aSAH clipping. The observed EN timing signal should be interpreted as exploratory and warrants prospective validation before clinical implementation.
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BibTeX
@article{li2026explainable,
title = {{Explainable machine learning–based prediction of postoperative pulmonary infection after aneurysmal subarachnoid hemorrhage clipping and exploratory analysis of enteral nutrition timing: a dual-cohort retrospective study}},
author = {Lin Li and Xiang Li and Shufang Shi},
journal = {Frontiers in neurology},
year = {2026},
month = oct,
doi = {10.3389/fneur.2026.1873566},
url = {https://europepmc.org/article/PMC/PMC13686907},
note = {CC BY},
}
RIS
TY - JOUR
TI - Explainable machine learning–based prediction of postoperative pulmonary infection after aneurysmal subarachnoid hemorrhage clipping and exploratory analysis of enteral nutrition timing: a dual-cohort retrospective study
AU - Lin Li
AU - Xiang Li
AU - Shufang Shi
PY - 2026
DA - 2026/10/08
JO - Frontiers in neurology
DO - 10.3389/fneur.2026.1873566
UR - https://europepmc.org/article/PMC/PMC13686907
AB - Background:: Postoperative pulmonary infection (PPI) is a common complication after microsurgical clipping for aneurysmal subarachnoid hemorrhage (aSAH) and is associated with impaired recovery and greater use of hospital resources. Objective: To develop and temporally validate an explainable machine-learning model for PPI risk stratification and to explore the association between enteral nutrition (EN) initiation timing and subsequent PPI risk. Methods: This dual-cohort retrospective study was reported in accordance with Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis plus Artificial Intelligence (TRIPOD+AI) principles. Candidate predictors were selected using a combined least absolute shrinkage and selection operator (LASSO) regression and random-forest importance strategy. Nine machine-learning algorithms were developed and compared in terms of discrimination, calibration, and clinical utility using decision-curve analysis. Results: In the validation cohort, the Naive Bayes model showed the best overall performance [area under the receiver operating characteristic curve (AUC), 0.811; 95% confidence interval (CI), 0.755–0.855]. SHAP analysis identified the major contributors to the primary 24-h prediction model. Separately, a secondary exploratory analysis of continuous EN initiation timing identified a nonlinear pattern, with a bootstrap median model-derived inflection point of 24.0 h (95% bootstrap interval, 23.0–27.0 h). Conclusions: This explainable model may support early PPI risk stratification after aSAH clipping. The observed EN timing signal should be interpreted as exploratory and warrants prospective validation before clinical implementation.
SN - 1664-2295
ER -