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Neural network prediction of 30-day mortality following primary total hip arthroplasty
Safa C Fassihi1, Abhay Mathur1, Matthew J Best2
1Department of Orthopedic Surgery, George Washington Hospital, 2300 M St NW, Washington, DC, 20037, USA.
Purpose:
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).
Methods:
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.
Results:
The five most important factors in predicting mortality following THA were preoperative international normalized ratio, age, body mass index, operative time, and preoperative hematocrit.
Conclusion:
ANN modeling represents a novel approach to determining perioperative factors that predict mortality following THA.
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