Risk Models Evaluated for Predicting Poor Outcome After TAVR

October 17, 2016—In Journal of the American College of Cardiology (JACC), Suzanne V. Arnold, MD, et al published a study that examined how previously developed poor outcome risk models for transcatheter aortic valve replacement (TAVR) performed in an external data set and explored the incremental contribution of geriatric domains to model performance (2016;68:1868–1877). 

As summarized in JACC, to help guide treatment choices, offer patients realistic expectations of long-term outcomes, and support decision making, a series of models have been developed to identify patients at high risk for poor outcomes after TAVR.

In the study, poor outcome after TAVR was defined as death, poor quality of life (QOL), or decline in QOL, as assessed using the Kansas City Cardiomyopathy Questionnaire. Four TAVR Poor Outcome risk models were tested: 6-month and 1-year full and clinical (reduced) models. Each model’s discrimination and calibration in the CoreValve trial data set were evaluated, and then the incremental contribution of frailty and disability markers were tested against the model’s discrimination using the incremental discrimination index.

In JACC, the investigators reported that among 2,830 TAVR patients in the CoreValve US Pivotal Extreme and High Risk trials and associated continued access registries, 31.2% experienced a poor outcome at 6 months after TAVR (death, 17.6%; very poor QOL, 11.6%; QOL decline, 2%), and 50.8% experienced a poor outcome at 1 year (death, 30.2%; poor QOL, 19.6%; QOL decline, 1%). The models demonstrated similar discrimination as in the Placement of Aortic Transcatheter Valves Trial cohorts (C-indices, 0.637–0.665) and excellent calibration. Adding frailty as a syndrome increased the C-indices by 0.000 to 0.004 (incremental discrimination index, P < .01 for all except the 1-year clinical model); disability and unintentional weight loss were the most important individual components.

Although discrimination of the TAVR Poor Outcome risk models was generally moderate, calibration was excellent among patients with different risk profiles and treated with a different TAVR device. These findings demonstrated the value of these models for individualizing outcome predictions in high-risk patients undergoing TAVR, concluded the investigators in JACC.