Neural network prediction of 30-day mortality following primary total hip arthroplasty.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 34898926.
- Also identified by DOI 10.1016/j.jor.2021.11.013 and PMC identifier 8636995.
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Abstract
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.
Anatomy
- hip