The acute patient physiologic and laboratory evaluation (APPLE) score: A severity of Illness stratification system for hospitalized dogs
2010
Hayes, G. | Mathews, K. | Doig, G. | Kruth, S. | Boston, S. | Nykamp, S. | Poljak, Z. | Dewey, C.
Background: Objective risk stratification models are used routinely in human critical care medicine. Applications include quantitative and objective delineation of illness severity for patients enrolled in clinical research, performance benchmarking, and protocol development for triage and therapeutic management. Objective: To develop an accurate, validated, and user-friendly model to stratify illness severity by mortality risk in hospitalized dogs. Animals: Eight hundred and ten consecutive intensive care unit (ICU) admissions of dogs at a veterinary teaching hospital. Methods: Prospective census cohort study. Data on 55 management, physiological, and biochemical variables were collected within 24 hours of admission. Data were randomly divided, with 598 patient records used for logistic regression model construction and 212 for model validation. Results: Patient mortality was 18.4%. Ten-variable and 5-variable models were developed to provide both a high-performance model and model maximizing accessibility, while maintaining good performance. The 10-variable model contained creatinine, WBC count, albumin, SpO2, total bilirubin, mentation score, respiratory rate, age, lactate, and presence of free fluid in a body cavity. Area under the receiver operator characteristic (AUROC) on the construction data set was 0.93, and on the validation data set was 0.91. The 5-variable model contained glucose, albumin, mentation score, platelet count, and lactate. AUROC on the construction data set was 0.87, and on the validation data set was 0.85. Conclusions and Clinical Importance: Two models are presented that enable allocation of an accurate and user-friendly illness severity index for dogs admitted to an ICU. These models operate independent of primary diagnosis, and have been independently validated.
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