This member-only article appears in the November issue of Patient Safety Monitor Journal.
A new risk model provides a simple and inexpensive way to determine whether acute myocardial infarction (AMI) patients are at high risk for hospital readmission.
The risk model, which is detailed in a recent study published in the Journal of the American Heart Association (JAHA), features seven variables that can be scored in as little as five minutes during a patient’s first day of hospital admission. With a simple calculation at the bedside or in an electronic health record, physicians can determine whether a heart attack patient is at high risk for readmission and can then order interventions to help the patient avoid a return to the hospital after discharge.
Research published by the Healthcare Cost and Utilization Project shows that about one in six AMI patients are readmitted to a hospital within 30 days of discharge, with annual healthcare costs estimated at $1 billion. Targeting AMI patients who are at high risk of readmission also helps hospitals avoid financial penalties under the federal Hospital Readmissions Reduction Program and promotes cost-effective interventions, the JAHA researchers wrote.
“Although federal readmission penalties have incentivized readmissions reduction intervention strategies (known as transitional care interventions), these interventions are resource intensive, are most effective when implemented well before discharge, and have been only modestly successful when applied indiscriminately to all inpatients,” the researchers wrote. “The acute myocardial infarction READMITS score (renal function, elevated brain natriuretic peptide, age, diabetes mellitus, nonmale sex, intervention with timely percutaneous coronary intervention, and low systolic blood pressure) is the best at identifying patients at high risk for 30‐day hospital readmission; is easy to implement in clinical settings; and provides actionable data in real time.”
The AMI READMITS risk model is superior to other models, they wrote. “The few currently available AMI readmission risk prediction models have poor-to-modest predictive ability and are not readily actionable in real time.”
The JAHA research, which examined health outcomes for 826 AMI patients at six hospitals in north Texas, has several key findings:
The AMI READMITS score accurately predicts which heart attack patients are at high risk or low risk of readmission. In the JAHA research, about one third of AMI patients that were deemed at high risk through the AMI READMITS score had a 30-day readmission. Only 2% of patients considered at low risk experienced a readmission.
- The AMI READMITS score can accurately predict readmission risk during the first 24 hours of a hospital inpatient admission, which gives clinicians the ability to make timely interventions.
- Clinical severity metrics such as shock, heart strain or failure, and renal dysfunction, as well as timely percutaneous coronary intervention, were strongly associated with readmission risk.
Why this model matters
Assessing the readmission risk of AMI patients during the first day of hospital admission is crucial, says Oahn Nguyen, MD, MAS, the lead author of the JAHA research and an assistant professor at UT Southwestern Medical Center in Dallas. “[The model] gives you more time to intervene and try to prevent someone from having to come back to the hospital. It gives you more time to optimize someone’s path to recovery,” she said.
She said development of the AMI READMITS risk model is the first step toward significantly reducing readmissions for AMI patients. “Studies of interventions to reduce readmissions for other conditions suggest that the earlier you can intervene, the better. One caveat is those interventions have yet to be assessed in acute myocardial infarction.”
The current primary strategy to prevent readmissions for heart attack patients is transitional care intervention, and the AMI READMITS score helps physicians target patients for this intervention, she said.