Machine learning-based mortality prediction in hip fracture patients using biomarkers.

Abstract

The purpose of this retrospective study was to assess whether mortality following a hip fracture can be predicted by a machine learning model trained on basic blood and lab test data as well as basic demographic data. Additionally, the purpose was to identify the key variables most associated with 1-, 5-, and 10-year mortality and investigate their clinical significance. Input data included 3751 hip fracture patient records sourced from the Medical Information Mart for Intensive Care IV database, which provided records from in-hospital database systems at the Beth Israel Deaconess Medical Center. The 1-year mortality rate for all patients studied was 21% and for those aged 80+ was 29%. We assessed 10 different machine learning classification models, finding LightGBM to have the strongest 1-year mortality prediction performance, with accuracy of 81%, AUC of 0.79, sensitivity of 0.34, and specificity of 0.98 on the test set. The strongest-weighted features of the 1-year model included age, glucose, red blood cell distribution width, mean corpuscular hemoglobin concentration, white blood cells, urea nitrogen, prothrombin time, platelet count, calcium levels, and partial thromboplastin time. Most of these were also in the top 10 features of the LightGBM 5- and 10-year prediction models trained. Testing for these high-ranking biomarkers in new hip fracture patients can aid clinicians in assessing the likelihood of poor outcomes for hip fracture patients, and additional research can use these biomarkers to develop a mortality risk score.