title: Low Accuracy of the HeartMate Risk Score for Predicting Mortality Using the INTERMACS Registry Data.
abstract: Selection is a key determinant of clinical outcomes after left ventricular assist device (LVAD) placement in patients with end-stage heart failure. The HeartMate II risk score (HMRS) has been proposed to facilitate risk stratification and patient selection for continuous flow pumps. This study retrospectively assessed the performance of HMRS in predicting 90 day and 1 year mortality in patients within the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS). A total of 11,523 INTERMACS patients who received a continuous flow LVAD between 2010 and 2015 were retrospectively categorized per their calculated HMRS to predict their 90 day and 1 year risk of mortality. The performance of the score was evaluated by the area under curve (AUC) of the receiver operator characteristic. We also performed multiple regression analysis using variables from the HMRS calculation on the INTERMACS data. The HMRS model showed moderate discrimination for both 90 day and 1 year mortality prediction with AUCs of 61% and 59%, respectively. The predictions had similar accuracy irrespective of whether the pump was axial or centrifugal flow. Multivariable analysis using independent variables used in the original HMRS analysis revealed different set of variables to be predictive of 90 day mortality than those used to calculate HMRS. HMRS predicts both 90 day and 1 year mortality with poor discrimination when applied to a large cohort of LVAD patients. Newer risk prediction models are therefore needed to optimize the therapeutic application of LVAD therapy. Patient selection for appropriate use of LVADs is critical. Currently available risk stratification tools (HMRS) continue to be limited in their ability to accurately predict mortality after LVAD. This study highlights these limitations when applied to a large, comprehensive, multicenter database. HMRS predicts mortality with only modest discrimination when applied to a large cohort of LVAD patients. Better risk stratification tools are needed to optimize outcomes.
authors.affiliation: From the *Cardiovascular Institute, Allegheny Health Network, Pittsburgh, Pennsylvania; †Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania; ‡Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; and §Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina.
authors.forename: Manreet K
authors.initials: MK
authors.lastname: Kanwar
authors.forename: Lisa C
authors.initials: LC
authors.lastname: Lohmueller
authors.forename: Robert L
authors.initials: RL
authors.lastname: Kormos
authors.forename: Natasha A
authors.initials: NA
authors.lastname: Loghmanpour
authors.forename: Raymond L
authors.initials: RL
authors.lastname: Benza
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Mentz
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Bailey
authors.forename: Srinivas
authors.initials: S
authors.lastname: Murali
authors.forename: James F
authors.initials: JF
authors.lastname: Antaki
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D006333:Heart Failure / Q000401:mortality / Q000628:therapy; D006353:Heart-Assist Devices / Q000009:adverse effects*; D006801:Humans; D008297:Male; D008875:Middle Aged; D018579:Patient Selection; D012042:Registries; D012189:Retrospective Studies; D012306:Risk