Manreet K Kanwar1, Lisa C Lohmueller2, Robert L Kormos3, Jeffrey J Teuteberg4, Joseph G Rogers5, JoAnn Lindenfeld6, Stephen H Bailey7, Colleen K McIlvennan8, Raymond Benza7, Srinivas Murali7, James Antaki2. 1. Cardiovascular Institute, Allegheny Health Network, Pittsburgh, Pennsylvania. Electronic address: Manreet.kanwar@ahn.org. 2. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania. 3. Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania. 4. Department of Cardiovascular Medicine, Stanford University Medical Center, Stanford, California. 5. Division of Cardiology, Duke University School of Medicine, Durham, North Carolina. 6. Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee. 7. Cardiovascular Institute, Allegheny Health Network, Pittsburgh, Pennsylvania. 8. Cardiovascular Institute, University of Colorado, Aurora, Colorado.
Abstract
OBJECTIVES: This study investigates the use of a Bayesian statistical models to predict survival at various time points in patients undergoing left ventricular assist device (LVAD) implantation. BACKGROUND: LVADs are being increasingly used in patients with end-stage heart failure. Appropriate patient selection continues to be key in optimizing post-LVAD outcomes. METHODS: Data used for this study were derived from 10,277 adult patients from the INTERMACS (Inter-Agency Registry for Mechanically Assisted Circulatory Support) who had a primary LVAD implanted between January 2012 and December 2015. Risk for mortality was calculated retrospectively for various time points (1, 3, and 12 months) after LVAD implantation, using multiple pre-implantation variables. For each of these endpoints, a separate tree-augmented naïve Bayes model was constructed using the most predictive variables. RESULTS: A set of 29, 26, and 31 pre-LVAD variables were found to be predictive at 1, 3, and 12 months, respectively. Predictors of 1-month mortality included low Inter-Agency Registry for Mechanically Assisted Circulatory Support profile, number of acute events in the 48 h before surgery, temporary mechanical circulatory support, and renal and hepatic dysfunction. Variables predicting 12-month mortality included advanced age, frailty, device strategy, and chronic renal disease. The accuracy of all Bayesian models was between 76% and 87%, with an area under the receiver operative characteristics curve of between 0.70 and 0.71. CONCLUSIONS: A Bayesian prognostic model for predicting survival based on the comprehensive INTERMACS registry provided highly accurate predictions of mortality based on pre-operative variables. These models may facilitate clinical decision-making while screening candidates for LVAD therapy.
OBJECTIVES: This study investigates the use of a Bayesian statistical models to predict survival at various time points in patients undergoing left ventricular assist device (LVAD) implantation. BACKGROUND:LVADs are being increasingly used in patients with end-stage heart failure. Appropriate patient selection continues to be key in optimizing post-LVAD outcomes. METHODS: Data used for this study were derived from 10,277 adult patients from the INTERMACS (Inter-Agency Registry for Mechanically Assisted Circulatory Support) who had a primary LVAD implanted between January 2012 and December 2015. Risk for mortality was calculated retrospectively for various time points (1, 3, and 12 months) after LVAD implantation, using multiple pre-implantation variables. For each of these endpoints, a separate tree-augmented naïve Bayes model was constructed using the most predictive variables. RESULTS: A set of 29, 26, and 31 pre-LVAD variables were found to be predictive at 1, 3, and 12 months, respectively. Predictors of 1-month mortality included low Inter-Agency Registry for Mechanically Assisted Circulatory Support profile, number of acute events in the 48 h before surgery, temporary mechanical circulatory support, and renal and hepatic dysfunction. Variables predicting 12-month mortality included advanced age, frailty, device strategy, and chronic renal disease. The accuracy of all Bayesian models was between 76% and 87%, with an area under the receiver operative characteristics curve of between 0.70 and 0.71. CONCLUSIONS: A Bayesian prognostic model for predicting survival based on the comprehensive INTERMACS registry provided highly accurate predictions of mortality based on pre-operative variables. These models may facilitate clinical decision-making while screening candidates for LVAD therapy.
Authors: Clyde W Yancy; Mariell Jessup; Biykem Bozkurt; Javed Butler; Donald E Casey; Monica M Colvin; Mark H Drazner; Gerasimos S Filippatos; Gregg C Fonarow; Michael M Givertz; Steven M Hollenberg; JoAnn Lindenfeld; Frederick A Masoudi; Patrick E McBride; Pamela N Peterson; Lynne Warner Stevenson; Cheryl Westlake Journal: J Card Fail Date: 2017-04-28 Impact factor: 5.712
Authors: Jennifer Ann Cowger; Lindsay Castle; Keith David Aaronson; Mark S Slaughter; Sina Moainie; Mary Walsh; Christopher Salerno Journal: ASAIO J Date: 2016 May-Jun Impact factor: 2.872
Authors: Mark S Slaughter; Joseph G Rogers; Carmelo A Milano; Stuart D Russell; John V Conte; David Feldman; Benjamin Sun; Antone J Tatooles; Reynolds M Delgado; James W Long; Thomas C Wozniak; Waqas Ghumman; David J Farrar; O Howard Frazier Journal: N Engl J Med Date: 2009-11-17 Impact factor: 91.245
Authors: Jennifer Cowger; Kartik Sundareswaran; Joseph G Rogers; Soon J Park; Francis D Pagani; Geetha Bhat; Brian Jaski; David J Farrar; Mark S Slaughter Journal: J Am Coll Cardiol Date: 2012-12-19 Impact factor: 24.094
Authors: Natasha A Loghmanpour; Robert L Kormos; Manreet K Kanwar; Jeffrey J Teuteberg; Srinivas Murali; James F Antaki Journal: JACC Heart Fail Date: 2016-06-08 Impact factor: 12.035
Authors: Anton Sabashnikov; Prashant N Mohite; Bartlomiej Zych; Diana García; Aron-Frederik Popov; Alexander Weymann; Nikhil P Patil; Rachel Hards; Massimo Capoccia; Thorsten Wahlers; Fabio De Robertis; Toufan Bahrami; Mohamed Amrani; Nicholas R Banner; André R Simon Journal: ASAIO J Date: 2014 Mar-Apr Impact factor: 2.872
Authors: Manreet K Kanwar; Mardi Gomberg-Maitland; Marius Hoeper; Christine Pausch; David Pittrow; Geoff Strange; James J Anderson; Carol Zhao; Jacqueline V Scott; Marek J Druzdzel; Jidapa Kraisangka; Lisa Lohmueller; James Antaki; Raymond L Benza Journal: Eur Respir J Date: 2020-08-27 Impact factor: 16.671
Authors: Imad M Hariri; Todd Dardas; Manreet Kanwar; Rebecca Cogswell; Igor Gosev; Ezequiel Molina; Susan L Myers; James K Kirklin; Palak Shah; Francis D Pagani; Jennifer A Cowger Journal: J Heart Lung Transplant Date: 2021-07-24 Impact factor: 10.247
Authors: Sebastian Roth; René M'Pembele; Alexandra Stroda; Josephine Voit; Giovanna Lurati Buse; Stephan U Sixt; Ralf Westenfeld; Amin Polzin; Philipp Rellecke; Igor Tudorache; Markus W Hollmann; Udo Boeken; Payam Akhyari; Artur Lichtenberg; Ragnar Huhn; Hug Aubin Journal: ESC Heart Fail Date: 2022-05-05
Authors: J Hunter Mehaffey; Ryan Cantor; Susan Myers; Nicholas R Teman; John A Kern; Gorav Ailawadi; Francis Pagani; James Kirklin; Kenan Yount; Leora Yarboro Journal: JTCVS Open Date: 2022-01-22