Prediction of Postoperative Delirium in Geriatric Hip Fracture Patients: A Clinical Prediction Model Using Machine Learning Algorithms

作者：Jacobien H. F. Oosterhoff, Aditya V. Karhade, Tarandeep Oberai, Esteban Franco‐Garcia, Job Nicolaas Doornberg, Joseph Hasbrouck Schwab · 发表于：Geriatric Orthopaedic Surgery & Rehabilitation · 年份：2021 · DOI：10.1177/21514593211062277 · 被引用次数：56 · 研究领域：Intensive Care Unit Cognitive Disorders、Hip and Femur Fractures、Anesthesia and Sedative Agents

INTRODUCTION: Postoperative delirium in geriatric hip fracture patients adversely affects clinical and functional outcomes and increases costs. A preoperative prediction tool to identify high-risk patients may facilitate optimal use of preventive interventions. The purpose of this study was to develop a clinical prediction model using machine learning algorithms for preoperative prediction of postoperative delirium in geriatric hip fracture patients. MATERIALS & METHODS: Geriatric patients undergoing operative hip fracture fixation were queried in the American College of Surgeons National Surgical Quality Improvement Program database (ACS NSQIP) from 2016 through 2019. A total of 28 207 patients were included, of which 8030 (28.5%) developed a postoperative delirium. First, the dataset was randomly split 80:20 into a training and testing subset. Then, a random forest (RF) algorithm was used to identify the variables predictive for a postoperative delirium. The machine learning-model was developed on the training set and the performance was assessed in the testing set. Performance was assessed by discrimination (c-statistic), calibration (slope and intercept), overall performance (Brier-score), and decision curve analysis. RESULTS: The included variables identified using RF algorithms were (1) age, (2) ASA class, (3) functional status, (4) preoperative dementia, (5) preoperative delirium, and (6) preoperative need for mobility-aid. The clinical prediction model reached good di...