Novel risk tool predicts post-TAVR readmissions

SAN DIEGO — A team of researchers created a first-ever risk tool to predict 30-day readmission rates in patients undergoing transcatheter aortic valve replacement.

The tool proved successful in identifying those at risk for readmission, which occurs in up to 15% to 20% of patients within 30 days of TAVR.

Using the Nationwide Readmissions Database, Sahil Khera, MD, and colleagues identified patients who underwent TAVR from January 2013 to September 2015. The researchers employed complex survey methods, hierarchal regression and other strategies to create a prediction rule to determine probability for readmission within 30 days.

A total of 39,305 patients underwent TAVR, 16.2% of whom were readmitted within 30 days.

The risk tool focused on the following nine variables:

acute kidney injury;

anemia;

atrial fibrillation;

chronic kidney disease;

chronic liver disease;

chronic lung disease;

end-stage renal disease on dialysis;

length of stay longer than 5 days; and

discharge disposition;

The researchers calculated a risk score using the aforementioned variables, and assigned each variable a certain number of points based on predicted risk for readmission. A total score of greater than 212 was associated with a 30% 30-day readmission risk, while a score of greater than 182 was associated with a 25% 30-day readmission risk, according to findings presented here.

“We aimed to devise an easy to use risk-prediction tool to predict readmissions for patients undergoing TAVR,” Khera said during a press conference. He cautioned that use of this tool would not result in refusing TAVR to a patient at high risk for readmission based on his or her predicted risk score. Rather, the goal is to “identify or get a signal from these patients that they are at high risk,” he said.

“The creation of this tool and the results of the study are very reassuring because it allows for a better understanding how patients should be managed peri-TAVR. By recognizing patients at higher risk of readmission, we can guide post-discharge care coordination and improve transitions of care to decrease readmission, improve quality of life, reduce health care costs and ultimately impact mortality rates,” Khera said here.

The researchers plan to continue to evaluate the risk tool and analyze its incorporation into electronic medical records in hospital systems across the country in the near future. – by Katie Kalvaitis

Reference:

Khera S, et al. Late Breaking Clinical Science II. Presented at: Society for Cardiovascular Angiography and Interventions Scientific Sessions; April 25-28, 2018; San Diego.

Disclosure: Khera reports no relevant financial disclosures.