Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables |
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| Authors: | Rocco J. LaFaro Suryanarayana Pothula Keshar Paul Kubal Mario Emil Inchiosa Venu M. Pothula Stanley C. Yuan David A. Maerz Lucresia Montes Stephen M. Oleszkiewicz Albert Yusupov Richard Perline Mario Anthony Inchiosa Jr. |
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| Affiliation: | 1. Department of Surgery, New York Medical College, Valhalla, New York, United States of America.; 2. Department of Anesthesiology, New York Medical College, Valhalla, New York, United States of America.; 3. Department of Pharmacology, New York Medical College, Valhalla, New York, United States of America.; 4. Revolution Analytics, Inc., Mountain View, California, United States of America.; 5. The SAS Institute, Cary, North Carolina, United States of America.; Jiangnan University, CHINA, |
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| Abstract: | BackgroundAdvanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics.MethodsThirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual.ResultsFactors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001).ConclusionsANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities. |
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