Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation

作者：Xiaoli Liu, Pan Hu, Wesley Yeung, Zhongheng Zhang, Vanda Ho, Chao Liu, Clark DuMontier, Patrick Thoral, Zhi Mao, Desen Cao, Roger G. Mark, Zhengbo Zhang, Mengling Feng, Deyu Li, Leo Anthony Celi · 发表于：The Lancet Digital Health · 年份：2023 · DOI：10.1016/s2589-7500(23)00128-0 · 被引用次数：58 · 研究领域：Sepsis Diagnosis and Treatment、Frailty in Older Adults、Intensive Care Unit Cognitive Disorders

BACKGROUND: Comorbidity, frailty, and decreased cognitive function lead to a higher risk of death in elderly patients (more than 65 years of age) during acute medical events. Early and accurate illness severity assessment can support appropriate decision making for clinicians caring for these patients. We aimed to develop ELDER-ICU, a machine learning model to assess the illness severity of older adults admitted to the intensive care unit (ICU) with cohort-specific calibration and evaluation for potential model bias. METHODS: In this retrospective, international multicentre study, the ELDER-ICU model was developed using data from 14 US hospitals, and validated in 171 hospitals from the USA and Netherlands. Data were extracted from the Medical Information Mart for Intensive Care database, electronic ICU Collaborative Research Database, and Amsterdam University Medical Centers Database. We used six categories of data as predictors, including demographics and comorbidities, physical frailty, laboratory tests, vital signs, treatments, and urine output. Patient data from the first day of ICU stay were used to predict in-hospital mortality. We used the eXtreme Gradient Boosting algorithm (XGBoost) to develop models and the SHapley Additive exPlanations method to explain model prediction. The trained model was calibrated before internal, external, and temporal validation. The final XGBoost model was compared against three other machine learning algorithms and five clinical scores. W...