Validity of an artificial neural network in predicting discharge destination from a postacute geriatric rehabilitation unit

Objective: To develop an artificial neural network (ANN) designed to predic t discharge destination from postacute geriatric rehabilitation units. Design: Nonconcurrent prospective study. Setting: Postacute geriatric rehabilitation units: a 20-bed unit in a nonpr oprietary skilled nursing facility and a 40-bed unit in a suburban private facility. Patients: Consecutive sample of 661 patients admitted between January 1995 and February 1999, including a derivation group of 452 patients and a valid ation group of 209 patients. Interventions: A feed-forward, back-propagation neural network to predict d ischarge destination. Main Outcome Measure: Discharge destination from postacute geriatric rehabi litation. Results: An ANN was trained on clinical pattern set derived from 452 patien ts and validated prospectively on 209 consecutive patients admitted to post acute geriatric rehabilitation units. The neural network achieved a sensiti vity of 85.7% (95% confidence interval [CI], 83.7-89.4) and specificity of 94.1% (95% CI, 84.4-99.1) in identifying discharge destination with a corre sponding area under the curve of 95.7% (95% CI, 92.1-98.3). Conclusion: An ANN can predict discharge to the community postacute rehabil itation with a high degree of accuracy. It could have particular value to p redict return to the community for older adults with multiple comorbidities after an acute hospitalization.