Machine Learning Model for Sepsis Prediction in Prolonged and Chronic Critical Illness: Development and Validation Using Retrospective Real-World ICU Data

TL;DR

This study develops a machine learning model to predict sepsis in patients with prolonged or chronic critical illness, finding it works well in its specific group but fails in broader ICU populations.

Contribution

The novel contribution is developing and validating a sepsis prediction model specifically for prolonged/chronic critical illness patients using real-world ICU data.

Findings

A PCI/CCI-focused XGBoost model achieved an AUROC of 0.82 in its cohort but only 0.47 in external populations.

A universal model trained on mixed data showed reduced discrimination in PCI/CCI patients.

Respiratory rate, heart rate, body temperature, and age were key features for prediction.

Abstract

Background: No machine learning (ML) models for sepsis prediction have been specifically developed for patients with prolonged or chronic critical illness (PCI/CCI). Objective: This study aimed to develop and validate an ML-based sepsis prediction model for this cohort. Methods: We analyzed ICU admissions from the Russian Intensive Care Dataset (RICD, 575 patients with PCI/CCI) and two public ICU datasets from the PhysioNet (>40,000 patients with acute critical illness). Models were trained within a right-aligned prediction framework using a case–crossover–control sampling approach and a 6 h prediction window. Two strategies were evaluated: (1) a PCI/CCI-focused model trained on RICD with external testing on PhysioNet data and (2) a universal model trained on combined RICD and PhysioNet cohorts. Models were developed with tree-based algorithms (XGBoost, LightGBM, Random Forest, AdaBoost), with internal and external validation. Primary outcome was model discrimination (AUROC). Subgroup analyses were performed for sepsis phenotypes. Results: The PCI/CCI-focused XGBoost model achieved an AUROC of 0.82 in the RICD cohort but failed to generalize to external ICU populations (AUROC 0.47). A universal model trained on mixed data demonstrated reduced discrimination in PCI/CCI patients (AUROC mean difference 0.02, p = 0.0012). Respiratory rate, heart rate, body temperature, and age were among the most important features. Predictive performance was higher in hypoinflammatory sepsis phenotype (AUROC 0.84 vs. 0.81 for hyperinflammatory, p < 0.001). Despite worthless positive predictive value (up to 21%) for PCI/CCI-focused model, negative predictive value exceeded 97%. Conclusions: A right-aligned ML model tailored to PCI/CCI demonstrated strong internal performance for sepsis exclusion but limited cross-population generalizability, underscoring the need for population-specific prediction models and prospective validation before clinical application.

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