This Simple Tool Predicts Readmission Risk for Heart Attack Patients

To gauge readmission risk for patients with acute myocardial infarction, clinicians can easily assess seven variables during the first day of a hospital admission.

A new risk model provides a simple and inexpensive way to determine whether acute myocardial infarction patients are at high risk for hospital readmission.

Research published by the Healthcare Cost and Utilization Project shows that about 1 in 6 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 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 order interventions that can help patients avoid a return to the hospital after discharge.

"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 researchers wrote.

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."

Key findings
 

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 told HealthLeaders.

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.

"Transitional care intervention is a bundle of care to promote a safe transition from hospital to home. One way I like to think of it is deploying a medical SWAT team in the hospital to make sure that everything you can do for a patient is being done to ensure the transition from the hospital to the community is as smooth as possible," Nguyen said.

A "SWAT team" approach to care is often costly, so the capability of the AMI READMITS score to target patients who are at high risk of readmission improves the cost-effectiveness of care.

There are several primary elements to transitional care intervention:

• Medication counseling to make sure AMI patients know how to take them
   
• Making sure patients get their medications when they leave the hospital
   
• Connecting patients with the most appropriate outpatient care such as setting up clinic appointments
   
• Conducting phone calls to the homes of patients to check on their health status after discharge

Major strengths of the AMI READMITS score include the risk model's simplicity and low cost, she said.

"Our goal in creating this model was creating something that was simple and pragmatic; so, it's parsimonious because there are only seven variables that go into it. The seven variables are also information that is commonly and routinely collected during most hospitalizations."

The AMI READMITS risk model does not require sophisticated support systems, Nguyen said.

"In an age when there is a lot of hype about machine learning and big data, we were able to distill the big data of an electronic health record down to small, simple, parsimonious data that is easily applied at the bedside by clinicians," she said.

Expense is minimal for the AMI READMITS risk model.

"It's low cost because a clinician could look at our [research], then see how many of the seven factors a patient has in the hospital. You can literally spend less than five minutes summing up the points in the model scale, add them up, and determine whether a patient is at high risk or not. It does not take a fancy new IT infrastructure to implement," Nguyen said.