Development and Validation of a Risk Prediction Model for In-Hospital Mortality After Transcatheter Aortic Valve Replacement

IMPORTANCE: Patient selection for transcatheter aortic valve replacement (TAVR) should include assessment of the risks of TAVR compared with surgical aortic valve replacement (SAVR). Existing SAVR risk models accurately predict the risks for the population undergoing SAVR, but comparable models to predict risk for patients undergoing TAVR are currently not available and should be derived from a population that underwent TAVR. OBJECTIVE: To use a national population of patients undergoing TAVR to develop a statistical model that will predict in-hospital mortality after TAVR. DESIGN, SETTING, AND PARTICIPANTS: Patient data were obtained from the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy (STS/ACC TVT) Registry. The model was developed from 13 718 consecutive US patients undergoing TAVR in centers participating in the STS/ACC TVT Registry from November 1, 2011, to February 28, 2014. Validation was conducted using 6868 records of consecutive patients undergoing TAVR from March 1 to October 8, 2014. Covariates were selected through a process of expert opinion and statistical analysis. The association between in-hospital mortality and baseline covariates was estimated using logistic regression. The final set of predictors was selected via stepwise variable selection. Data were collected and analyzed from November 1, 2011, to February 28, 2014. MAIN OUTCOMES AND MEASURES: In-hospital TAVR mortality. RESULTS: The development sample included 13 718 patient records from 265 participant sites (of 13 672 with data available, 6680 men 48.9%; 6992 women 51.1%; mean SD age, 82.1 8.3 years). The final validation cohort included 6868 patients from 314 participating centers (3554 men 51.7%; 3314 women 48.3%; mean SD age, 81.6 8.8 years). In-hospital mortality occurred in 730 patients (5.3%). The C statistic for discrimination was 0.67 (95% CI, 0.65-0.69) in the development group and 0.66 (95% CI, 0.62-0.69) in the validation group. The final model covariates (reported as odds ratios; 95% CIs) were age (1.13; 1.06-1.20), glomerular filtration rate per 5-U increments (0.93; 0.91-0.95), hemodialysis (3.25; 2.42-4.37), New York Heart Association functional class IV (1.25; 1.03-1.52), severe chronic lung disease (1.67; 1.35-2.05), nonfemoral access site (1.96; 1.65- 2.33), and procedural acuity categories 2 (1.57; 1.20-2.05), 3 (2.70; 2.05-3.55), and 4 (3.34; 1.59-7.02). Calibration analysis demonstrated no significant difference between the model (predicted vs observed) calibration line (-0.18 and 0.97 for intercept and slope, respectively) compared with the ideal calibration line. CONCLUSIONS AND RELEVANCE: Data from the STS/ACC TVT Registry have been used to develop a predictive model of in-hospital mortality for patients undergoing TAVR. Validation based on a population of patient records not used in model development demonstrates discrimination and calibration indices that are more favorable than other models used in populations with TAVR. This model should be a valuable adjunct for patient counseling, local quality improvement, and national monitoring for appropriateness of selection of patients for TAVR.