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
Randomized Controlled Trial
. 2019 Mar 1;80(3):330-341.
doi: 10.1097/QAI.0000000000001925.

Clinical and Sociobehavioral Prediction Model of 30-Day Hospital Readmissions Among People With HIV and Substance Use Disorder: Beyond Electronic Health Record Data

Affiliations
Randomized Controlled Trial

Clinical and Sociobehavioral Prediction Model of 30-Day Hospital Readmissions Among People With HIV and Substance Use Disorder: Beyond Electronic Health Record Data

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

Abstract

Background: Under the Affordable Care Act, hospitals receive reduced reimbursements for excessive 30-day readmissions. However, the Centers for Medicare and Medicaid Services does not consider social and behavioral variables in expected readmission rate calculations, which may unfairly penalize systems caring for socially disadvantaged patients, including patients with HIV.

Setting: Randomized controlled trial of patient navigation with or without financial incentives in HIV-positive substance users recruited from the inpatient setting at 11 US hospitals.

Methods: External validation of an existing 30-day readmission prediction model, using variables available in the electronic health record (EHR-only model), in a new multicenter cohort of HIV-positive substance users was assessed by C-statistic and Hosmer-Lemeshow testing. A second model evaluated sociobehavioral factors in improving the prediction model (EHR-plus model) using multivariable regression and C-statistic with cross-validation.

Results: The mean age of the cohort was 44.1 years, and participants were predominantly males (67.4%), non-white (88.0%), and poor (62.8%, <$20,000/year). Overall, 17.5% individuals had a hospital readmission within 30 days of initial hospital discharge. The EHR-only model resulted in a C-statistic of 0.65 (95% confidence interval: 0.60 to 0.70). Inclusion of additional sociobehavioral variables, food insecurity and readiness for substance use treatment, in the EHR-plus model resulted in a C-statistic of 0.74 (0.71 after cross-validation, 95% confidence interval: 0.64 to 0.77).

Conclusions: Incorporation of detailed social and behavioral variables substantially improved the performance of a 30-day readmission prediction model for hospitalized HIV-positive substance users. Our findings highlight the importance of social determinants in readmission risk and the need to ask about, adjust for, and address them.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest:

AN receives research funds from Gilead Sciences FOCUS program.

Figures

Figure 1a.
Figure 1a.
Proportion of Participants Remaining Readmission-free up to 30 days, by EHR-only Model Predicted Readmission Risk Group
Figure 1b.
Figure 1b.
Thirty-Day Readmission Rate by EHR-only Model Predicted Readmission Risk Category

References

    1. Krumholz HM, Lin Z, Drye EE, et al. An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission rates among patients with acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2011;4(2):243–252. - PMC - PubMed
    1. Nijhawan AE, Clark C, Kaplan R, et al. An electronic medical record-based model to predict 30-day risk of readmission and death among HIV-infected inpatients. J Acquir Immune Defic Syndr. 2012;61(3):349–358. - PubMed
    1. Amarasingham R, Moore BJ, Tabak YP, et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care. 2010;48(11):981–988. - PubMed
    1. Singal AG, Rahimi RS, Clark C, et al. An automated model using electronic medical record data identifies patients with cirrhosis at high risk for readmission. Clin Gastroenterol Hepatol. 2013;11(10):1335–1341 e1331. - PMC - PubMed
    1. Calvillo-King L, Arnold D, Eubank KJ, et al. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med. 2013;28(2):269–282. - PMC - PubMed

Publication types