Suzanne V Arnold1, Sean M O'Brien2, Sreekanth Vemulapalli2, David J Cohen3, Amanda Stebbins2, J Matthew Brennan2, David M Shahian4, Fred L Grover5, David R Holmes6, Vinod H Thourani7, Eric D Peterson2, Fred H Edwards8. 1. Saint Luke's Mid America Heart Institute and the University of Missouri-Kansas City, Kansas City, Missouri. Electronic address: suz.v.arnold@gmail.com. 2. Duke University, Durham, North Carolina. 3. Saint Luke's Mid America Heart Institute and the University of Missouri-Kansas City, Kansas City, Missouri. 4. Lahey Hospital and Medical Center and Harvard Clinical Research Institute, Boston, Massachusetts. 5. University of Colorado School of Medicine, Aurora, Colorado. 6. Mayo Clinic, Rochester, Minnesota. 7. Medstar Washington Hospital Center/Georgetown University, Washington, District of Columbia. 8. University of Florida College of Medicine-Jacksonville, Jacksonville, Florida.
Abstract
OBJECTIVES: The aim of this study was to develop and validate a risk adjustment model for 30-day mortality after transcatheter aortic valve replacement (TAVR) that accounted for both standard clinical factors and pre-procedural health status and frailty. BACKGROUND: Assessment of risk for TAVR is important both for patient selection and provider comparisons. Prior efforts for risk adjustment have focused on in-hospital mortality, which is easily obtainable but can be biased because of early discharge of ill patients. METHODS: Using data from patients who underwent TAVR as part of the Society of Thoracic Surgeons/American College of Cardiology TVT (Transcatheter Valve Therapy) Registry (June 2013 to May 2016), a hierarchical logistic regression model to estimate risk for 30-day mortality after TAVR based only on pre-procedural factors and access site was developed and internally validated. The model included factors from the original TVT Registry in-hospital mortality model but added the Kansas City Cardiomyopathy Questionnaire (health status) and gait speed (5-m walk test). RESULTS: Among 21,661 TAVR patients at 188 sites, 1,025 (4.7%) died within 30 days. Independent predictors of 30-day death included older age, low body weight, worse renal function, peripheral artery disease, home oxygen, prior myocardial infarction, left main coronary artery disease, tricuspid regurgitation, nonfemoral access, worse baseline health status, and inability to walk. The predicted 30-day mortality risk ranged from 1.1% (lowest decile of risk) to 13.8% (highest decile of risk). The model was able to stratify risk on the basis of patient factors with good discrimination (C = 0.71 [derivation], C = 0.70 [split-sample validation]) and excellent calibration, both overall and in key patient subgroups. CONCLUSIONS: A clinical risk model was developed for 30-day death after TAVR that included clinical data as well as health status and frailty. This model will facilitate tracking outcomes over time as TAVR expands to lower risk patients and to less experienced sites and will allow an objective comparison of short-term mortality rates across centers.
OBJECTIVES: The aim of this study was to develop and validate a risk adjustment model for 30-day mortality after transcatheter aortic valve replacement (TAVR) that accounted for both standard clinical factors and pre-procedural health status and frailty. BACKGROUND: Assessment of risk for TAVR is important both for patient selection and provider comparisons. Prior efforts for risk adjustment have focused on in-hospital mortality, which is easily obtainable but can be biased because of early discharge of ill patients. METHODS: Using data from patients who underwent TAVR as part of the Society of Thoracic Surgeons/American College of Cardiology TVT (Transcatheter Valve Therapy) Registry (June 2013 to May 2016), a hierarchical logistic regression model to estimate risk for 30-day mortality after TAVR based only on pre-procedural factors and access site was developed and internally validated. The model included factors from the original TVT Registry in-hospital mortality model but added the Kansas City Cardiomyopathy Questionnaire (health status) and gait speed (5-m walk test). RESULTS: Among 21,661 TAVR patients at 188 sites, 1,025 (4.7%) died within 30 days. Independent predictors of 30-day death included older age, low body weight, worse renal function, peripheral artery disease, home oxygen, prior myocardial infarction, left main coronary artery disease, tricuspid regurgitation, nonfemoral access, worse baseline health status, and inability to walk. The predicted 30-day mortality risk ranged from 1.1% (lowest decile of risk) to 13.8% (highest decile of risk). The model was able to stratify risk on the basis of patient factors with good discrimination (C = 0.71 [derivation], C = 0.70 [split-sample validation]) and excellent calibration, both overall and in key patient subgroups. CONCLUSIONS: A clinical risk model was developed for 30-day death after TAVR that included clinical data as well as health status and frailty. This model will facilitate tracking outcomes over time as TAVR expands to lower risk patients and to less experienced sites and will allow an objective comparison of short-term mortality rates across centers.
Authors: Harlan M Krumholz; Ralph G Brindis; John E Brush; David J Cohen; Andrew J Epstein; Karen Furie; George Howard; Eric D Peterson; Saif S Rathore; Sidney C Smith; John A Spertus; Yun Wang; Sharon-Lise T Normand Journal: Circulation Date: 2005-12-19 Impact factor: 29.690
Authors: John D Carroll; Sreekanth Vemulapalli; Dadi Dai; Roland Matsouaka; Eugene Blackstone; Fred Edwards; Frederick A Masoudi; Michael Mack; Eric D Peterson; David Holmes; John S Rumsfeld; E Murat Tuzcu; Frederick Grover Journal: J Am Coll Cardiol Date: 2017-07-04 Impact factor: 24.094
Authors: Matthew R Reynolds; Elizabeth A Magnuson; Kaijun Wang; Vinod H Thourani; Mathew Williams; Alan Zajarias; Charanjit S Rihal; David L Brown; Craig R Smith; Martin B Leon; David J Cohen Journal: J Am Coll Cardiol Date: 2012-07-18 Impact factor: 24.094
Authors: Joakim Alfredsson; Amanda Stebbins; J Matthew Brennan; Roland Matsouaka; Jonathan Afilalo; Eric D Peterson; Sreekanth Vemulapalli; John S Rumsfeld; David Shahian; Michael J Mack; Karen P Alexander Journal: Circulation Date: 2016-02-26 Impact factor: 29.690
Authors: David M Overman; Jeffrey P Jacobs; Richard L Prager; Cameron D Wright; David R Clarke; Sara K Pasquali; Sean M O'Brien; Rachel S Dokholyan; Paul Meehan; Donna E McDonald; Marshall L Jacobs; Constantine Mavroudis; David M Shahian Journal: World J Pediatr Congenit Heart Surg Date: 2013-01
Authors: Suzanne J Baron; Suzanne V Arnold; Kaijun Wang; Elizabeth A Magnuson; Khaja Chinnakondepali; Raj Makkar; Howard C Herrmann; Susheel Kodali; Vinod H Thourani; Samir Kapadia; Lars Svensson; David L Brown; Michael J Mack; Craig R Smith; Martin B Leon; David J Cohen Journal: JAMA Cardiol Date: 2017-08-01 Impact factor: 14.676
Authors: Michael J Reardon; Nicolas M Van Mieghem; Jeffrey J Popma; Neal S Kleiman; Lars Søndergaard; Mubashir Mumtaz; David H Adams; G Michael Deeb; Brijeshwar Maini; Hemal Gada; Stanley Chetcuti; Thomas Gleason; John Heiser; Rüdiger Lange; William Merhi; Jae K Oh; Peter S Olsen; Nicolo Piazza; Mathew Williams; Stephan Windecker; Steven J Yakubov; Eberhard Grube; Raj Makkar; Joon S Lee; John Conte; Eric Vang; Hang Nguyen; Yanping Chang; Andrew S Mugglin; Patrick W J C Serruys; Arie P Kappetein Journal: N Engl J Med Date: 2017-03-17 Impact factor: 91.245
Authors: Manesh R Patel; Steven P Marso; David Dai; Kevin J Anstrom; Kendrick A Shunk; Jeptha P Curtus; J Matthew Brennan; Art Sedrakyan; John C Messenger; Pamela S Douglas Journal: JACC Cardiovasc Interv Date: 2012-10 Impact factor: 11.195
Authors: Philip Green; David J Cohen; Philippe Généreux; Tom McAndrew; Suzanne V Arnold; Maria Alu; Nirat Beohar; Charanjit S Rihal; Michael J Mack; Samir Kapadia; Danny Dvir; Mathew S Maurer; Mathew R Williams; Susheel Kodali; Martin B Leon; Ajay J Kirtane Journal: Am J Cardiol Date: 2013-05-29 Impact factor: 2.778
Authors: Martin B Leon; Craig R Smith; Michael J Mack; Raj R Makkar; Lars G Svensson; Susheel K Kodali; Vinod H Thourani; E Murat Tuzcu; D Craig Miller; Howard C Herrmann; Darshan Doshi; David J Cohen; Augusto D Pichard; Samir Kapadia; Todd Dewey; Vasilis Babaliaros; Wilson Y Szeto; Mathew R Williams; Dean Kereiakes; Alan Zajarias; Kevin L Greason; Brian K Whisenant; Robert W Hodson; Jeffrey W Moses; Alfredo Trento; David L Brown; William F Fearon; Philippe Pibarot; Rebecca T Hahn; Wael A Jaber; William N Anderson; Maria C Alu; John G Webb Journal: N Engl J Med Date: 2016-04-02 Impact factor: 91.245
Authors: Taku Inohara; Pratik Manandhar; Andrzej S Kosinski; Roland A Matsouaka; Shun Kohsaka; Robert J Mentz; Vinod H Thourani; John D Carroll; Ajay J Kirtane; Joseph E Bavaria; David J Cohen; Todd L Kiefer; Jeffrey G Gaca; Samir R Kapadia; Eric D Peterson; Sreekanth Vemulapalli Journal: JAMA Date: 2018-12-04 Impact factor: 56.272
Authors: Parth P Patel; Abdallah El Sabbagh; Patrick W Johnson; Rayan Suliman; Najiyah Salwa; Andrea Carolina Morales-Lara; Peter Pollak; Mohamad Yamani; Pragnesh Parikh; Sushilkumar K Sonavane; Carolyn Landolfo; Mohamad Adnan Alkhouli; Mackram F Eleid; Mayra Guerrero; F David Fortuin; John Sweeney; Peter A Noseworthy; Rickey E Carter; Demilade Adedinsewo Journal: Circ Cardiovasc Imaging Date: 2022-08-03 Impact factor: 8.589
Authors: Jackson M King; Morgan T Black; Ruyun Jin; Gary L Grunkemeier; Branden R Reynolds; Brydan D Curtis; Robert W Hodson; Erika A Strehl; Sameer A Gafoor; Matthew D Forrester; Emily J Cox; Michael E Ring Journal: J Interv Cardiol Date: 2022-06-25 Impact factor: 1.776
Authors: Gabby Elbaz-Greener; Feng Qiu; John G Webb; Kayley A Henning; Dennis T Ko; Andrew Czarnecki; Idan Roifman; Peter C Austin; Harindra C Wijeysundera Journal: J Am Heart Assoc Date: 2019-06-05 Impact factor: 5.501
Authors: Sandra B Lauck; Maggie Yu; Lillian Ding; Sean Hardiman; Daniel Wong; Janarthanan Sathananthan; Jian Ye; Albert Chan; Steven Hodge; Simon Robinson; David A Wood; John G Webb Journal: CJC Open Date: 2021-04-24