Author + information
- Received December 4, 2017
- Revision received February 2, 2018
- Accepted March 28, 2018
- Published online August 27, 2018.
- Manreet K. Kanwar, MDa,∗ (, )
- Lisa C. Lohmueller, PhDb,
- Robert L. Kormos, MDc,
- Jeffrey J. Teuteberg, MDd,
- Joseph G. Rogers, MDe,
- JoAnn Lindenfeld, MDf,
- Stephen H. Bailey, MDa,
- Colleen K. McIlvennan, ANPg,
- Raymond Benza, MDa,
- Srinivas Murali, MDa and
- James Antaki, PhDb
- aCardiovascular Institute, Allegheny Health Network, Pittsburgh, Pennsylvania
- bDepartment of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania
- cHeart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
- dDepartment of Cardiovascular Medicine, Stanford University Medical Center, Stanford, California
- eDivision of Cardiology, Duke University School of Medicine, Durham, North Carolina
- fCardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- gCardiovascular Institute, University of Colorado, Aurora, Colorado
- ↵∗Address for correspondence:
Dr. Manreet Kanwar, Allegheny Health Network, 320 East North Avenue, 16th Floor, South Tower, Pittsburgh, Pennsylvania 15212.
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.
Funding for this work was provided by National Institutes of Health, Division of National Heart, Lung, and Blood Institute grants R41 HL120428 STTR Phase I Cardiac Health Risk Stratification System and R01 HL122639, CORA: A Personalized Cardiac Counselor for Optimal Therapy. Data for this study were provided by the International Registry for Mechanical Circulatory Support (INTERMACS), funded from the National Heart, Lung, and Blood Institute, National Institutes of Health, under Contract No. HHSN268201100025C. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Barry Greenberg, MD, served as Guest Editor for this paper.
- Received December 4, 2017.
- Revision received February 2, 2018.
- Accepted March 28, 2018.
- 2018 American College of Cardiology Foundation
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