Despite the benefit for most patients, a small number of patients still show poor outcomes after transcatheter aortic valve implantation (TAVR). This work was able to recalibrate a previously developed risk model to predict post TAVR quality of life and provide us with a potential tool to properly estimate the chances of good recovery, or to predict futility and altogether give up the procedure.
The prior prediction model was developed using data from randomized clinical studies of high-risk TAVR patients. However, this model did not work well with lower risk or unselected ‘real life’ patients. The aim of this study was to optimize this risk model to better predict poor outcomes after TAVR.
At one year, of 13351 patients undergoing TAVR in 252 centers in the US from 2011 to 2015, 38.9% had poor outcomes; 20.7% because of death, and 18.2% because of poor or worsening quality of life.
This high rate has fortunately been receding: it has gone from 42% in 2012 to 37.8% in 2015 (p=0.076).
The original risk model based on randomized studies did not adjust well to this population of unselected patients.
The study recalibrated model coefficients and retested its capacity to predict poor outcomes, which improved its prediction capacity (both globally and in subgroups) reaching a C index of 0.65, and excellent calibration.
A great number of patients still show poor results after TAVR. This recalibrated prediction model at one year is far more accurate and might help us better identify TAVR candidates.
Original title: Predicting Quality of Life at 1 Year After Transcatheter Aortic Valve Replacement in a Real-World Population.
Reference: Suzanne V. Arnold et al. Circ Cardiovasc Qual Outcomes. 2018;11:e004693.
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