title: Development and Validation of a Risk Prediction Model for In-Hospital Mortality After Transcatheter Aortic Valve Replacement.
abstract: 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.
  
  
  
  To use a national population of patients undergoing TAVR to develop a statistical model that will predict in-hospital mortality after TAVR.
  
  
  
  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.
  
  
  
  In-hospital TAVR mortality.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Department of Surgery, University of Florida College of Medicine-Jacksonville.
authors.forename: Fred H
authors.initials: FH
authors.lastname: Edwards
authors.affiliation: Department of Medicine, St Luke's Mid America Heart Institute, Kansas City, Missouri.
authors.forename: David J
authors.initials: DJ
authors.lastname: Cohen
authors.affiliation: Duke Clinical Research Institute, Durham, North Carolina.
authors.forename: Sean M
authors.initials: SM
authors.lastname: O'Brien
authors.affiliation: Duke Clinical Research Institute, Durham, North Carolina.
authors.forename: Eric D
authors.initials: ED
authors.lastname: Peterson
authors.affiliation: Department of Cardiovascular Disease, Baylor Scott and White Health Care System, Plano, Texas.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Mack
authors.affiliation: Department of Surgery, Center for Quality and Safety, Massachusetts General Hospital, Boston.
authors.forename: David M
authors.initials: DM
authors.lastname: Shahian
authors.affiliation: Department of Surgery, University of Colorado School of Medicine, Aurora.
authors.forename: Frederick L
authors.initials: FL
authors.lastname: Grover
authors.affiliation: Department of Cardiovascular Medicine, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates.
authors.forename: E Murat
authors.initials: EM
authors.lastname: Tuzcu
authors.affiliation: Division of Cardiothoracic Surgery, Emory University School of Medicine, Atlanta, Georgia.
authors.forename: Vinod H
authors.initials: VH
authors.lastname: Thourani
authors.affiliation: Department of Medicine, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora.
authors.forename: John
authors.initials: J
authors.lastname: Carroll
authors.affiliation: Duke Clinical Research Institute, Durham, North Carolina.
authors.forename: J Matthew
authors.initials: JM
authors.lastname: Brennan
authors.affiliation: Department of Medicine and the Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco.
authors.forename: Ralph G
authors.initials: RG
authors.lastname: Brindis
authors.affiliation: Department of Medicine, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora.
authors.forename: John
authors.initials: J
authors.lastname: Rumsfeld
authors.affiliation: Department of Medicine, Mayo Clinic, Rochester, Minnesota.
authors.forename: David R
authors.initials: DR
authors.lastname: Holmes
mesh_terms: D000369:Aged, 80 and over; D006328:Cardiac Catheterization; D005260:Female; D005544:Forecasting; D006801:Humans; D007297:Inpatients; D008297:Male; D015233:Models, Statistical; D012306:Risk; D065467:Transcatheter Aortic Valve Replacement / Q000401:mortality*