Multidimensional Approach for Predicting 30-Day Mortality in Patients with a Hip Fracture: Development and External Validation of the Rotterdam Hip Fracture Mortality Prediction-30 Days (RHMP-30).

Abstract

The aim of this study was to develop an accurate and clinically relevant prediction model for 30-day mortality following hip fracture surgery. A previous study protocol was utilized as a guideline for data collection and as the standard for the hip fracture treatment. Two prospective, detailed hip fracture databases of 2 different hospitals (hospital A, training cohort; hospital B, testing cohort) were utilized to obtain data. On the basis of the literature, the results of a univariable analysis, and expert opinion, 26 candidate predictors of 30-day mortality were selected. Subsequently, the training of the model, including variable selection, was performed on the training cohort (hospital A) with use of adaptive least absolute shrinkage and selection operator (LASSO) logistic regression. External validation was performed on the testing cohort (hospital B). A total of 3,523 patients were analyzed, of whom 302 (8.6%) died within 30 days after surgery. After the LASSO analysis, 7 of the 26 variables were included in the prediction model: age, gender, an American Society of Anesthesiologists score of 4, dementia, albumin level, Katz Index of Independence in Activities of Daily Living total score, and residence in a nursing home. The area under the receiver operating characteristic curve of the prediction model was 0.789 in the training cohort and 0.775 in the testing cohort. The calibration curve showed good consistency between observed and predicted 30-day mortality. The Rotterdam Hip Fracture Mortality Prediction-30 Days (RHMP-30) was developed and externally validated, and showed adequate performance in predicting 30-day mortality following hip fracture surgery. The RHMP-30 will be helpful for shared decision-making with patients regarding hip fracture treatment. Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.