NYMC Faculty Publications

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

Author Type(s)

Faculty

DOI

10.1371/journal.pone.0145395

Journal Title

PLoS One

First Page

0145395

Last Page

0145395

Document Type

Article

Publication Date

1-1-2015

Department

Pharmacology

Keywords

Cardiac Surgical Procedures, Female, Humans, Intensive Care Units, Length of Stay, Linear Models, Male, Neural Networks, Computer, Odds Ratio, Risk Assessment

Disciplines

Medicine and Health Sciences

Abstract

BACKGROUND: Advanced 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.

METHODS: Thirty 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.

RESULTS: Factors 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).

CONCLUSIONS: ANN 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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