Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study

作者：Xiaoli Liu, Clark DuMontier, Pan Hu, Chao Liu, Wesley Yeung, Zhi Mao, Vanda Ho, Patrick Thoral, Po‐Chih Kuo, Jie Hu, Deyu Li, Desen Cao, Roger G. Mark, FeiHu Zhou, Zhengbo Zhang, Leo Anthony Celi · 发表于：The Journals of Gerontology Series A · 年份：2022 · DOI：10.1093/gerona/glac107 · 被引用次数：14 · 研究领域：Sepsis Diagnosis and Treatment、Chronic Disease Management Strategies、Heart Failure Treatment and Management

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU. METHODS: The study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions. RESULTS: Thirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively; discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively; and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Gl...