ArticleEuropean journal of medical research2025
Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
backgroundDelirium frequently complicates elderly chronic kidney disease (CKD) patients due to multifactorial vulnerability. Early detection in geriatric intensive care unit (ICU) settings is challenged by traditional assessments' communication deficits. Machine learning refines predictions through multidimensional pattern recognition. We develop an interpretable online tool for timely high-risk delirium identification, guiding interventions to enhance outcomes.
methodsElderly chronic kidney disease patients were selected using International Classification of Diseases (ICD) codes, with delirium defined per Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Baseline traits, vitals, lab parameters, comorbidities, and clinical scores were collected. Dual feature selection employed Least Absolute Shrinkage and Selection Operator (Lasso) regression and Boruta. Eight machine learning models underwent assessment via Decision Curve Analysis (DCA) and calibrated curves. The superior model was interpreted using SHapley Additive exPlanations (SHAP) values and deployed as a web-based risk calculator.
resultsTen key predictors were identified through dual feature selection. The Gradient Boosting Machine (GBM) model demonstrated good calibration and sustained high predictive accuracy in both internal and external validation. SHAP analysis revealed Glasgow Coma Scale (GCS) score, Sequential Organ Failure Assessment (SOFA) score, and sedative usage as clinically significant predictors. The finalized model was deployed as a clinically applicable web-based risk calculator.
conclusionThis study's prediction model accurately assesses delirium risk in elderly CKD patients, showing robust performance and clinical utility. Its web-based dynamic calculator supplies personalized risk evaluation in ICU settings, supporting early identification and proactive interventions to enhance clinical management.
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