An Electronic Medical Record-Based Model to Predict 30-Day Risk of Readmission and Death Among HIV-Infected Inpatients

Background: Readmission after hospitalization is costly, time-consuming, and remains common among HIV-infected individuals. We sought to use data from the Electronic Medical Record (EMR) to create a clinical, robust, multivariable model for predicting readmission risk in hospitalized HIV-infected patients. Methods: We extracted clinical and nonclinical data from the EMR of HIV-infected patients admitted to a large urban hospital between March 2006 and November 2008. These data were used to build automated predictive models for 30-day risk of readmission and death. Results: We identified 2476 index admissions among HIV-infected inpatients who were 73% males, 57% African American, with a mean age of 43 years. One-quarter were readmitted, and 3% died within 30 days of discharge. Those with a primary diagnosis during the index admission of HIV/AIDS accounted for the largest proportion of readmissions (41%), followed by those initially admitted for other infections (10%) or for oncologic (6%), pulmonary (5%), gastrointestinal (4%), and renal (3%) causes. Factors associated with readmission risk include: AIDS defining illness, CD4 <= 92, laboratory abnormalities, insurance status, homelessness, distance from the hospital, and prior emergency department visits and hospitalizations (c = 0.72; 95% confidence interval: 0.70 to 0.75). The multivariable predictors of death were CD4 < 132, abnormal liver function tests, creatinine >1.66, and hematocrit <30.8 (c = 0.79; 95% confidence interval: 0.74 to 0.84) for death. Conclusions: Readmission rates among HIV-infected patients were high. An automated model composed of factors accessible from the EMR in the first 48 hours of admission performed well in predicting the 30-day risk of readmission among HIV patients. Such a model could be used in real-time to identify HIV patients at highest risk so readmission prevention resources could be targeted most efficiently.