Predictors of 30-Day Re-admission in Cardiac Patients at Heart Hospital, Qatar.

Background: Cardiovascular disease patients are more likely to be readmitted within 30 days of being discharged alive. This causes an enormous burden on health-care systems in terms of poor care of patients and misutilization of resources.

Aims And Objective: This study aims to find out the risk factors associated with 30-day readmission in cardiac patients at Heart Hospital, Qatar.

Methods: A total of 10,550 cardiac patients who were discharged alive within 30 days at the heart hospital in Doha, Qatar, from January 2015 and December 2019 were analyzed. The bootstrap method, an internal validation statistical technique, was applied to present representative estimates for the population.

Results: Out of the 10,550 cardiac patients, there were 8418 (79.8%) index admissions and 2132 (20.2%) re-admitted at least once within 30 days after the index admission. The re-admissions group was older than the index admission group (65.6 ± 13.2 vs. 56.0 ± 13.5, = 0.001). Multinomial regression analysis showed that females were 30% more likely to be re-admitted than males (adjusted odds ratio [aOR] 1.30, 95% confidence interval [CI]: 1.11-1.50, = 0.001). Diabetes (aOR 1.36, 95% CI: 1.20-1.53, = 0.001), chronic renal failure (aOR 1.93, 95% CI: 1.66-2.24, = 0.001), previous MI (aOR 3.22, 95% CI: 2.85-3.64, = 0.001), atrial fibrillation (aOR 2.17, 95% C.I. : 1.10-2.67, = 0.01), cardiomyopathy (aOR 1.72, 95% CI 1.47-2.02, = 0.001), and chronic heart failure (aOR 1.56, 95% C.I.: 1.33-1.82, = 0.001) were also independent predictors for re-admission in the regression model. C-statistics showed these variables could predict 82% accurately hospital readmissions within 30 days after being discharged alive.

Conclusion: The model was more than 80% accurate in predicting 30-day readmission after being discharged alive. The presence of five or more risk factors was found to be crucial for readmissions within 30 days. The study may help design interventions that may result in better outcomes with fewer resources in the population.