Prognostic Value of the Charlson Comorbidity Index for Mortality and Machine Learning–Based Prediction in Critically Ill Patients with Paralytic Ileus: Retrospective Cohort Study

TL;DR

This study shows that the Charlson Comorbidity Index helps predict mortality in ICU patients with paralytic ileus and improves machine learning models for hospital mortality prediction.

Contribution

The study identifies the optimal CCI threshold for mortality prediction and demonstrates its integration into high-performing machine learning models for critically ill patients with paralytic ileus.

Findings

CCI score of 4.5 is the optimal threshold for predicting mortality in ICU patients with paralytic ileus.

Higher CCI scores are strongly associated with increased hospital, 28-day, and 90-day mortality.

A machine learning model using CCI and other clinical features achieved high accuracy in predicting hospital mortality.

Abstract

The burden of paralytic ileus (PI) in the intensive care unit remains high, and the Charlson Comorbidity Index (CCI) is strongly associated with the prognosis of several acute and chronic diseases. However, evidence specifically evaluating the prognostic value of CCI in intensive care unit patients with PI remains limited.

This study aimed to investigate the association between CCI and clinical prognosis in critically ill patients with PI.

In this study, data were extracted from the Medical Information Mart for Intensive Care IV (version 2.2), a large, publicly available critical care database, and used to determine the optimal cut-off value of CCI for predicting mortality in patients with PI using the receiver operating characteristic curves, and the association between CCI and mortality was evaluated using Cox regression and restricted cubic spline analysis. A machine learning (ML) prediction model was then constructed to predict hospital mortality by combining CCI and other clinical characteristics.

The study included 863 patients with PI (age: median 65.4, IQR 54.6‐75.5 y; male: 575/863, 66.6%). The receiver operating characteristic curve identified an optimal cut-off value of 4.5 for CCI. The multivariate Cox regression analysis showed that compared to the lowest CCI quartile, patients with elevated CCI levels were more likely to have elevated hospital (Q4: hazard ratio [HR] 2.447, 95% CI 1.210‐4.951), 28-day (Q4: HR 3.891, 95% CI 1.956‐7.740), and 90-day (Q4: HR 3.994, 95% CI 2.224‐7.173) all-cause mortality were significantly associated with elevated CCI levels; however, the association with ICU mortality (Q4: HR 1.892, 95% CI 0.653‐5.480) was weak. Among the 11 ML models, the light gradient boosting machine model performed best, with internal validation results showing an area under the curve of 0.811, a geometric mean of 0.670, and an F1-score of 0.895.

The CCI is an important predictor of hospital, 28-day, and 90-day all-cause mortality in critically ill patients with PI, and the optimal threshold is 4.5. ML models, including the CCI, show high accuracy in predicting hospital mortality, and the CCI occupies an important position in the model. This suggests that the CCI helps to identify high-risk patients, supports clinical decision-making, and improves prognosis.

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