A comparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care unit

Objective: To compare statistical and connectionist models for the prediction of chronicity which is influenced by patient disease and external factors. Design: Retrospective development of predictive criteria and subsequent prospective testing of the same predictive criteria, using multiple logistic regression and three architecturally distinct neural networks; revision of predictive criteria. Setting: Surgical intensive care unit (ICU) equipped with a clinical information system in a ±1000-bed university hospital. Patients: Four hundred ninety-one patients with ICU length of stay 3 days who survived at least an additional 4 days. Interventions: None. Measurements and Main Results: Chronicity was defined as a length of stay >7 days. Neural networks predicted chronicity more reliably than the statistical model regardless of the former's architecture. However, the neural networks' ability to predict this chronicity degraded over time. Conclusions: Connectionist models may contribute to the prediction of clinical trajectory, including outcome and resource utilization, in surgical ICUs.