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

Jacobien H F Oosterhoff; Aditya V Karhade; Tarandeep Oberai; Esteban Franco-Garcia; Job N Doornberg; Joseph H Schwab

doi:10.1177/21514593211062277

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

Geriatr Orthop Surg Rehabil. 2021 Dec 13:12:21514593211062277. doi: 10.1177/21514593211062277. eCollection 2021.

Authors

Jacobien H F Oosterhoff^{1

2

3}, Aditya V Karhade¹, Tarandeep Oberai³, Esteban Franco-Garcia⁴, Job N Doornberg^{3

5}, Joseph H Schwab¹

Affiliations

¹ Department of Orthopaedic Surgery, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
² Department of Orthopaedic Surgery, University of Amsterdam, Amsterdam Movement Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands.
³ Department of Orthopaedics & Trauma Surgery, Flinders Medical Centre and Flinders University, Adelaide SA Australia.
⁴ Division of Palliative Care & Geriatric Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁵ Department of Orthopaedic Surgery, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.

Abstract

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 discrimination (c-statistic = .79), almost perfect calibration (intercept = -.01, slope = 1.02), and excellent overall model performance (Brier score = .15). The clinical prediction model was deployed as an open-access web-application: https://sorg-apps.shinyapps.io/hipfxdelirium/.

Discussion & conclusions: We developed a clinical prediction model that shows promise in estimating the risk of postoperative delirium in geriatric hip fracture patients. The clinical prediction model can play a beneficial role in decision-making for preventative measures for patients at risk of developing a delirium. If found to be externally valid, clinicians might use the available web-based application to help incorporate the model into clinical practice to aid decision-making and optimize preoperative prevention efforts.

Keywords: clinical prediction model; delirium; geriatric trauma; hip fracture; machine learning; personalized medicine.