Using Neural Networks to Identify Patients Unlikely to Achieve a Reduction in Bodily Pain After Total Hip Replacement Surgery

Abstract

OBJECTIVES: Fourteen patient-provided variables were chosen as potential predictors for improvement after total hip replacement surgery. These variables included patient demographic information, as well as preoperative physical function. METHODS: A neural network was trained to predict the relative success of total hip replacement surgery using this presurgical patient survey information. The outcome measure was improvement in the Medical Outcomes Study 36 Short Form Health Survey pain score between the preoperative assessment and the 1-year postoperative assessment. For the study sample, 221 patients were selected who had complete information for the composite outcome variable. A backpropagation feedforward neural network was trained to predict the output variable using the jackknife method. RESULTS: Performance of the neural network was assessed by calculating the area under the receiver operating characteristic curve for the network's ability to predict whether the pain score was improved after total hip replacement surgery. The observed area under the receiver operating characteristic curve was 0.79. For comparison, a linear regression model built using the same data had a receiver operating characteristic area of 0.74 (P = 0.23). CONCLUSIONS: This research therefore showed the ability of neural networks to predict the success of total hip replacement more accurately. Our results further indicate that it may be possible to predict which patients are at greatest risk of a poor outcome.

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