• Caleb R Szubski, Alejandra Tellez, Alison K Klika, Meng Xu, Michael W Kattan, Jorge A Guzman, and Wael K Barsoum.
• Caleb R. Szubski is a research coordinator in the Department of Orthopaedic Surgery, Orthopaedic and Rheumatologic Institute, Alejandra Tellez is a resident physician in the Department of Dermatology, Dermatology and Plastic Surgery Institute, Alison K. Klika is a research program manager in the Department of Surgical Operations, Medical Operations, Meng Xu is a biostatistician and Michael W. Kattan is chairman of the Department of Quantitative Health Sciences, Lerner Research Institute, Jorge A. Guzman is director of the medical intensive care unit in the Department of Pulmonary and Critical Care Medicine, Respiratory Institute, and Wael K. Barsoum is vice-chairman of the Department of Orthopaedic Surgery, and chairman of the Department of Surgical Operations, Medical Operations, Cleveland Clinic, Cleveland, Ohio.
• Am. J. Crit. Care. 2014 Jul 1;23(4):e46-53.

BackgroundLong-term acute care hospitals are an option for patients in intensive care units who require prolonged care after an acute illness. Predicting use of these facilities may help hospitals improve resource management, expenditures, and quality of care delivered in intensive care.ObjectiveTo develop a predictive tool for early identification of intensive care patients with increased probability of transfer to such a hospital.MethodsData on 1967 adults admitted to intensive care at a tertiary care hospital between January 2009 and June 2009 were retrospectively reviewed. The prediction model was developed by using multiple ordinal logistic regression. The model was internally validated via the bootstrapping technique and externally validated with a control cohort of 950 intensive care patients.ResultsAmong the study group, 146 patients (7.4%) were discharged to long-term acute care hospitals and 1582 (80.4%) to home or other care facilities; 239 (12.2%) died in the intensive care unit. The final prediction algorithm showed good accuracy (bias-corrected concordance index, 0.825; 95% CI, 0.803-0.845), excellent calibration, and external validation (concordance index, 0.789; 95% CI, 0.754-0.824). Hypoalbuminemia was the greatest potential driver of increased likelihood of discharge to a long-term acute care hospital. Other important predictors were intensive care unit category, older age, extended hospital stay before admission to intensive care, severe pressure ulcers, admission source, and dependency on mechanical ventilation.ConclusionsThis new predictive tool can help estimate on the first day of admission to intensive care the likelihood of a patient's discharge to a long-term acute care hospital.©2014 American Association of Critical-Care Nurses.

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