Risk prediction models incorporating institutional case volume for mortality after hip fracture surgery in the elderly

Introduction While higher institutional case volume is associated with better postoperative outcomes in various types of surgery, institutional case volume has been rarely included in risk prediction models for surgical patients. This study aimed to develop and validate the predictive models incorporating institutional case volume for predicting in-hospital mortality and 1-year mortality after hip fracture surgery in the elderly. Materials and methods Data for all patients (>= 60 years) who underwent surgery for femur neck fracture, pertrochanteric fracture, or subtrochanteric fracture between January 2008 and December 2016 were extracted from the Korean National Health Insurance Service database. Patients were randomly assigned into the derivation cohort or the validation cohort in a 1:1 ratio. Risk prediction models for in-hospital mortality and 1-year mortality were developed in the derivation cohort using the logistic regression model. Covariates included age, sex, type of fracture, type of anaesthesia, transfusion, and comorbidities such as hypertension, diabetes, coronary artery disease, chronic kidney disease, cerebrovascular disease, and dementia. Two separate models, one with and the other without institutional case volume as a covariate, were constructed, evaluated, and compared using the likelihood ratio test. Based on the models, scoring systems for predicting in-hospital mortality and 1-year mortality were developed. Results Analysis of 196,842 patients showed 3.6% in-hospital mortality (7084/196,842) and 15.42% 1-year mortality (30,345/196,842). The model for predicting in-hospital mortality incorporating the institutional case volume demonstrated better discrimination (c-statistics 0.692) compared to the model without the institutional case volume (c-statistics 0.688; likelihood ratio test p value < 0.001). The performance of the model for predicting 1-year mortality was also better when incorporating institutional case volume (c-statistics 0.675 vs. 0.674; likelihood ratio test p value < 0.001). Conclusions The new institutional case volume incorporated scoring system may help to predict in-hospital mortality and 1-year mortality after hip fracture surgery in the elderly population.

Automated summary

This study aimed to develop and validate predictive models for predicting in-hospital mortality and 1-year mortality after hip fracture surgery in the elderly. The results showed that incorporating institutional case volume into the predictive models improved their discrimination.