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Published on: June 10, 2025
The Association Between Patient-reported Clinical Factors and 30-day Acute Care Utilization in Chronic Heart Failure
Jinying Chen1, Rajani Sadasivam1, Amanda C Blok2
1Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester.
Background:
Heart failure patients have high rates of repeat acute care use. Current efforts for risk prediction often ignore postdischarge data.
Objective:
To identify postdischarge patient-reported clinical factors associated with repeat acute care use.
Research Design:
In a prospective cohort study that followed patients with chronic heart failure for 30 days postdischarge, for 7 days after discharge (or fewer days if patients used acute care within 7 days postdischarge), patients reported health status, heart failure symptoms, medication management, knowledge of follow-up plans, and other issues using a daily interactive automatic phone call.
Subjects:
A total of 156 patients who had responded to phone surveys.
Measures:
The outcome variable was dichotomous 30-day acute care use (rehospitalization or emergency department visit). We examined the association between each patient-reported issue and the outcome, using multivariable logistic regression to adjust for confounders.
Results:
Patients were 63 years old (SD=12.4), with 51% African-American and 53% women. Within 30 days postdischarge, 30 (19%) patients used acute care. After adjustment, poor health status [odds ratio (OR)=3.53; 95% confidence interval (CI), 1.06-11.76], pain (OR=2.44; 95% CI, 1.02-5.84), and poor appetite (OR=3.05; 95% CI, 1.13-8.23) were positively associated with 30-day acute care utilization. Among 58 reports of pain in follow-up nursing notes, 39 (67%) were noncardiac, 2 (3%) were cardiac, and 17 (29%) were indeterminate.
Conclusions:
Patient-reported poor health status, pain, and poor appetite were positively associated with 30-day acute care utilization. These novel postdischarge markers require further study before incorporation into risk prediction to drive quality improvement efforts.
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