Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012 Nov 1;61(3):349-58.
doi: 10.1097/QAI.0b013e31826ebc83.

An electronic medical record-based model to predict 30-day risk of readmission and death among HIV-infected inpatients

Ank E Nijhawan  1 , Christopher Clark, Richard Kaplan, Billy Moore, Ethan A Halm, Ruben Amarasingham
Affiliations

An electronic medical record-based model to predict 30-day risk of readmission and death among HIV-infected inpatients

Ank E Nijhawan et al. J Acquir Immune Defic Syndr. .

Abstract

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.

PubMed Disclaimer

Publication types