Inclusion of Functional Status Measures in the Risk Adjustment of 30-Day Mortality After Transcatheter Aortic Valve Replacement: A Report From the Society of Thoracic Surgeons/American College of Cardiology TVT Registry.

MedStar author(s):
Citation: Jacc: Cardiovascular Interventions. 11(6):581-589, 2018 Mar 26PMID: 29566805Institution: MedStar Heart & Vascular InstituteForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Decision Support Techniques | *Health Status | *Health Status Indicators | *Heart Valve Diseases/su [Surgery] | *Transcatheter Aortic Valve Replacement/mo [Mortality] | Aged | Aged, 80 and over | Cardiomyopathies/di [Diagnosis] | Cardiomyopathies/mo [Mortality] | Clinical Decision-Making | Female | Frailty/di [Diagnosis] | Frailty/mo [Mortality] | Heart Valve Diseases/di [Diagnosis] | Heart Valve Diseases/mo [Mortality] | Hospital Mortality | Humans | Male | Physical Fitness | Predictive Value of Tests | Registries | Reproducibility of Results | Risk Assessment | Risk Factors | Societies, Medical | Surveys and Questionnaires | Time Factors | Transcatheter Aortic Valve Replacement/ae [Adverse Effects] | Treatment Outcome | United States/ep [Epidemiology] | Walk Test | Walking SpeedYear: 2018Local holdings: Available online through MWHC library: 2008 - presentISSN:
  • 1936-8798
Name of journal: JACC. Cardiovascular interventionsAbstract: 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.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.Copyright (c) 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.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).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.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.All authors: Arnold SV, Brennan JM, Cohen DJ, Edwards FH, Grover FL, Holmes DR, O'Brien SM, Peterson ED, Shahian DM, Stebbins A, STS/ACC TVT Registry, Thourani VH, Vemulapalli SFiscal year: FY2018Digital Object Identifier: Date added to catalog: 2018-04-20
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Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article 29566805 Available 29566805

Available online through MWHC library: 2008 - present

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.

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.

Copyright (c) 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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).

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.

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.

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