Neural network prediction of 30-day mortality following primary total hip arthroplasty

• DOI: 10.1016/j.jor.2021.11.013
• Publication Date: 2021-11-25T01:44:20Z
• Authors: Safa C. Fassihi; Abhay Mathur; Matthew J. Best; Aaron Z. Chen; Alex Gu; Theodore Quan; Kevin Y. Wang; Chapman Wei; Joshua C. Campbell; Savyasachi C. Tha...
• Keywords: 03 medical and health sciences; 0302 clinical medicine; 3. Good health

The purpose is to utilize an artificial neural network (ANN) model to determine the most important variables in predicting mortality following total hip arthroplasty (THA).Patients that underwent primary THA were included from a national database. Demographic, preoperative, and intraoperative variables were analyzed based on their contribution to 30-day mortality with the use of an ANN model.The five most important factors in predicting mortality following THA were preoperative international normalized ratio, age, body mass index, operative time, and preoperative hematocrit.ANN modeling represents a novel approach to determining perioperative factors that predict mortality following THA.