Novel risk predictor identifies likelihood of readmission after AMI

A novel risk score incorporating patient demographics and clinical characteristics known ahead of hospital discharge can accurately identify CVD patients at low and high risk for 90-day readmission after an initial acute MI (AMI), according to research published in the October edition of Circulation: Cardiovascular Quality and Outcomes.

As healthcare transitions from a volume-based reimbursement model to a value-based one, physicians will need new tools to assess performance metrics, first author Vinay Kini, MD, MSHP, and colleagues wrote in the journal. While 30-day readmissions after AMI have long been a standard performance measurement for hospitals, the shift to value-based care will place payers’ focus on longer-term outcomes, like 90-day readmissions.

“Episode-based payment will require clinicians and hospitals to develop novel strategies to improve quality of care not only during hospitalization but also in the period after discharge,” Kini, an assistant professor of cardiology at the University of Colorado School of Medicine, and coauthors wrote.

They said nearly a fifth of patients hospitalized for AMI see readmission, but physicians remain unsure of how to identify patients at the greatest risk for rehospitalization. Meanwhile, 22 and 12 percent of yearly costs associated with 90-day AMI outcomes can be attributed to medical interventions and PCIs, respectively.

“To succeed in these new episode payment models (EPMs), clinicians and hospitals need tools that can be used to assess the risk of 90-day readmission during a patient’s index hospitalization for AMI, so that the intensity of follow-up, such as home health services, can be targeted to patient risk,” the authors wrote.

In their study, Kini et al. considered Medicare beneficiaries with a primary diagnosis of acute MI between 2008 and 2014. The team performed hierarchical proportional hazards regression, accounting for death as a competing risk, to assess predictors of all-cause readmission in a total of 86,849 patients.

According to the study, Kini and colleagues accounted for patient-level characteristics like age, sex, race, admission stats, cardiac status at first medical contact, heart rate, blood pressure, CVD history and past cardiac procedures and tests in their prediction model. They also considered risk factors like hypertension, tobacco use, diabetes, cancer and atrial fibrillation.

Of the 23,912 readmissions identified in the group, 55 percent occurred within 30 days of the initial AMI event and 81 percent took place within 60 days of the event. The most common reasons for readmission were congestive heart failure (16 percent), coronary atherosclerosis (11 percent), AMI (10 percent) and dysrhythmias (5 percent). Sepsis, pneumonia, chest pain and hemorrhage were also high on the list.

The risk prediction model was able to distinguish patients as either low risk (13.1 percent chance of 90-day readmission) or high risk (42.9 percent odds of readmission).

Cian P. McCarthy, MB, BCh, and Ambarish Pandey, MD, MSCS, said in a related editorial Kini et al.’s risk score is strong in its novelty and clinical relevance—and it couldn’t get much more convenient. But the study was limited in that it didn’t consider hospital-level characteristics and didn’t adjust results for guideline-recommended medical therapies and cardiac rehab.

“Developing predictive models such as the one created by Kini et al. are a welcome first step in response to recent changes in value-based healthcare policies,” McCarthy and Pandey wrote. “Future studies are needed to validate their score in external cohorts, expand its horizon to make it more inclusive of other relevant predictors of readmission and to test it prospectively to determine if it may indeed reduce readmission rates but also posthospitalization mortality.

“Patients out of hospital sight may not necessarily be out of harm.”