A Clinical Risk Prediction Model to Identify Patients with Hepatorenal Syndrome at Hospital Admission.
Int J Clin Pract. 2019 Jul 26;:e13393
Authors: Koola JD, Chen G, Malin BA, Fabbri D, Siew ED, Ho SB, Patterson OV, Matheny ME
BACKGROUND: Hepatorenal syndrome (HRS) is a life-threatening complication of cirrhosis, and early detection of evolving HRS may provide opportunities for early intervention. We developed an HRS risk model to assist early recognition of inpatient HRS.
METHODS: We analyzed a retrospective cohort of patients hospitalized from among 122 medical centers in the U.S. Department of Veterans Affairs between January 1, 2005 and December 31, 2013. We included cirrhotic patients and had Kidney Disease Improving Global Outcomes criteria based acute kidney injury on admission. We developed a logistic regression risk prediction model to detect HRS on admission using 10 variables. We calculated 95% confidence intervals on the model building dataset and, subsequently, calculated performance on a 1000 sample holdout test set. We report model performance with area under the curve (AUC) for discrimination and several calibration measures.
RESULTS: The cohort included 19,368 patients comprising 32,047 inpatient admissions. The event rate for hospitalized HRS was 2,810/31,047 (9.1%) and 79/1000 (7.9%) in the model building and validation datasets, respectively. The variable selection procedure designed a parsimonious model involving ten predictor variables. Final model performance in the validation dataset had an AUC of 0.87, Brier score of 0.05, slope of 1.10, and intercept of 0.04.
CONCLUSIONS: We developed a probabilistic risk model to diagnose HRS within 24 hours of hospital admission using routine clinical variables in the largest ever published HRS cohort. The performance was excellent, and this model may help identify high-risk patients for HRS and promote early intervention. This article is protected by copyright. All rights reserved.
PMID: 31347754 [PubMed - as supplied by publisher]