SAN FRANCISCO — A simple model based on five variables could help clinicians predict which patients are at greater risk for 30-day readmission after heart failure, according to a researcher at the American Academy of Physician Assistant 2015 meeting.
“This prediction model is a user-friendly tool that can be easily integrated into one of the routine nursing assessments,” reported Margaret Lucas, BA, PA-S, of Campbell University College of Pharmacy & Health Science in Buies Creek, North Carolina.
She and colleagues conducted a retrospective cohort study of 275 adults admitted to the Harnett Health System Hospital between Dec. 2012 and Dec. 2013 with a heart failure diagnosis to determine 30-day readmission risk factors.
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The researchers identified five variables that were statistically significant predictors of 30-day all-cause readmission (p<0.05 for all).
They then calculated a risk score for each variable using Chi-square analyses and determined the odds of readmission above and below each point category:
- Prior hospitalization during adulthood: yes, 2 points; no, -2 points
- Arterial pH <7.350: yes, 4.5 points; no -4.5 points
- Creatinine levels > 1.3mg/dL: yes, 2.5 points; no, -2.5 points
- Hemoglobin levels <11.3 g/dL for women and <13.2 g/dL for men: yes, 2 points; no, -2 points
- History of congestive heart failure: yes, 2 points; no, -2 points
Patients with a score in the 4.5 to 13.5 range were considered high risk, those in the -4.5 to 3.5 were considered moderate risk, and those in the -13.5 to -5.5 range were considered low risk, the researchers found.
Other predictive factors not included in the risk assessment model included use of an assistive ambulatory device, white race, blood urea nitrogen >20mg/dL, prior hospitalization during adulthood, and Medicaid or Medicare as the primary form of payment.
“The clinical utility of a model that identifies patients who are more likely to be rehospitalized ties directly to both discharge planning and improved transition of care,” Lucas said. “The earlier during hospitalization that at-risk patients are identified, the sooner intervention of any kind can be implemented to prevent readmission.”
Future studies should look at differentiating between those readmissions that are potentially preventable and the readmissions for which proactive intervention could be beneficial, she said.
