1 Tennessee Valley Healthcare System (TVHS) Veterans Administration Medical Center, Veteran's Health Administration, Nashville, Tennessee.
2 Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, California.
3 Division of Hospital Medicine, Department of Medicine, University of California, San Diego, California.
4 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin.
5 Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.
6 Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
7 Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee.
8 Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Vanderbilt University Medical Center, Nashville, Tennessee.
9 VA San Diego Healthcare System, San Diego, California.
10 Division of Gastroenterology, Department of Medicine, University of California, San Diego, California.
11 Division of Epidemiology, University of Utah, Salt Lake City, Utah.
12 Veterans Affairs, Salt Lake City Health Care System, Salt Lake City, Utah.
13 Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
Hepatorenal syndrome (HRS) is a life-threatening complication of cirrhosis and early detection of evolving HRS may provide opportunities for early intervention. We developed a HRS risk model to assist early recognition of inpatient HRS.
We analysed a retrospective cohort of patients hospitalised from among 122 medical centres in the US Department of Veterans Affairs between 1 January 2005 and 31 December 2013. We included cirrhotic patients who 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.
The cohort included 19 368 patients comprising 32 047 inpatient admissions. The event rate for hospitalised HRS was 2810/31 047 (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.
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.