Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

• Autor(a) principal: Shi,L.
• Data de Publicação: 2013
• Outros Autores: Wang,X.C., Wang,Y.S.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Medical and Biological Research
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2013001100993
• topic: Artificial neural network; Intertrochanteric fracture; Outcome prediction; One-year mortality

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.