Early Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning

TL;DR

This study develops an interpretable machine learning model to predict 30-day mortality in ICU patients with hypertensive kidney disease, achieving high accuracy and providing uncertainty estimates to aid clinical decision-making.

Contribution

The paper introduces a novel interpretable machine learning framework with uncertainty quantification for early mortality prediction in HKD ICU patients, utilizing the DREAM algorithm and SHAP analysis.

Findings

CatBoost model achieved AUROC of 0.88.

Model identified key predictors like altered consciousness and vasopressor use.

Uncertainty estimates enable personalized risk assessment.

Abstract

Background: Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality, but tailored risk prediction tools are lacking. Early identification of high-risk individuals is crucial for clinical decision-making. Methods: We developed a machine learning framework to predict 30-day in-hospital mortality among ICU patients with HKD using early clinical data from the MIMIC-IV v2.2 database. A cohort of 1,366 adults was curated with strict criteria, excluding malignancy cases. Eighteen clinical features-including vital signs, labs, comorbidities, and therapies-were selected via random forest importance and mutual information filtering. Several models were trained and compared with stratified five-fold cross-validation; CatBoost demonstrated the best performance. Results: CatBoost achieved an AUROC of 0.88 on the independent test set, with sensitivity of 0.811 and specificity of 0.798. SHAP values and Accumulated Local Effects (ALE) plots showed the model relied on meaningful predictors such as altered consciousness, vasopressor use, and coagulation status. Additionally, the DREAM algorithm was integrated to estimate patient-specific posterior risk distributions, allowing clinicians to assess both predicted mortality and its uncertainty. Conclusions: We present an interpretable machine learning pipeline for early, real-time risk assessment in ICU patients with HKD. By combining high predictive performance with uncertainty quantification, our model supports individualized triage and transparent clinical decisions. This approach shows promise for clinical deployment and merits external validation in broader critical care populations.