A Tool to Estimate Risk of 30-day Mortality and Complications After Hip Fracture Surgery: Accurate Enough for Some but Not All Purposes? A Study From the ACS-NSQIP Database

Surgical repair of hip fracture carries substantial short-term risks of mortality and complications. The risk-reward calculus for most patients with hip fractures favors surgical repair. However, some patients have low prefracture functioning, frailty, and/or very high risk of postoperative mortality, making the choice between surgical and nonsurgical management more difficult. The importance of high-quality informed consent and shared decision-making for frail patients with hip fracture has recently been demonstrated. A tool to accurately estimate patient-specific risks of surgery could improve these processes. With this study, we sought (1) to develop, validate, and estimate the overall accuracy (C-index) of risk prediction models for 30-day mortality and complications after hip fracture surgery; (2) to evaluate the accuracy (sensitivity, specificity, and false discovery rates) of risk prediction thresholds for identifying very high-risk patients; and (3) to implement the models in an accessible web calculator. In this comparative study, preoperative demographics, comorbidities, and preoperatively known operative variables were extracted for all 82,168 patients aged 18 years and older undergoing surgery for hip fracture in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) between 2011 and 2017. Eighty-two percent (66,994 of 82,168 ) of patients were at least 70 years old, 21% (17,007 of 82,168 ) were at least 90 years old, 70% (57,260 of 82,168 ) were female, and 79% (65,301 of 82,168 ) were White. A total of 5% (4260 of 82,168) of patients died within 30 days of surgery, and 8% (6786 of 82,168) experienced a major complication. The ACS-NSQIP database was chosen for its clinically abstracted and reliable data from more than 600 hospitals on important surgical outcomes, as well as rich characterization of preoperative demographic and clinical predictors for demographically diverse patients. Using all the preoperative variables in the ACS-NSQIP dataset, least absolute shrinkage and selection operator (LASSO) logistic regression, a type of machine learning that selects variables to optimize accuracy and parsimony, was used to develop and validate models to predict two primary outcomes: 30-day postoperative mortality and any 30-day major complications. Major complications were defined by the occurrence of ACS-NSQIP complications including: on a ventilator longer than 48 hours, intraoperative or postoperative unplanned