Search | Search by Center | Search by Source | Keywords in Title
Chen J, Sadasivam R, Blok AC, Ritchie CS, Nagawa C, Orvek E, Patel K, Houston TK. The Association Between Patient-reported Clinical Factors and 30-day Acute Care Utilization in Chronic Heart Failure. Medical care. 2020 Apr 1; 58(4):336-343.
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.