Development and Validation of Multivariable Prediction Models for In-Hospital Death, 30-Day Death, and Change in Residence After Hip Fracture Surgery and the “Stratify-Hip” Algorithm

Abstract

Abstract Background To develop and validate the stratify-hip algorithm (multivariable prediction models to predict those at low, medium, and high risk across in-hospital death, 30-day death, and residence change after hip fracture). Methods Multivariable Fine-Gray and logistic regression of audit data linked to hospital records for older adults surgically treated for hip fracture in England/Wales 2011–14 (development n = 170 411) and 2015–16 (external validation, n = 90 102). Outcomes included time to in-hospital death, death at 30 days, and time to residence change. Predictors included age, sex, pre-fracture mobility, dementia, and pre-fracture residence (not for residence change). Model assumptions, performance, and sensitivity to missingness were assessed. Models were incorporated into the stratify-hip algorithm assigning patients to overall low (low risk across outcomes), medium (low death risk, medium/high risk of residence change), or high (high risk of in-hospital death, high/medium risk of 30-day death) risk. Results For complete-case analysis, 6 780 of 141 158 patients (4.8%) died in-hospital, 8 693 of 149 258 patients (5.8%) died by 30 days, and 4 461 of 119 420 patients (3.7%) had residence change. Models demonstrated acceptable calibration (observed:expected ratio 0.90, 0.99, and 0.94), and discrimination (area under curve 73.1, 71.1, and 71.5; Brier score 5.7, 5.3, and 5.6) for in-hospital death, 30-day death, and residence change, respectively. Overall, 31%, 28%, and 41% of patients were assigned to overall low, medium, and high risk. External validation and missing data analyses elicited similar findings. The algorithm is available at https://stratifyhip.co.uk. Conclusions The current study developed and validated the stratify-hip algorithm as a new tool to risk stratify patients after hip fracture.