Risk Assessment in Patients with a Left Ventricular Assist Device Across INTERMACS Profiles Using Bayesian Analysis
| Autor: | Jeffrey J. Teuteberg, James F. Antaki, Manreet Kanwar, Colleen K. McIlvennan, Srinivas Murali, Stephen H. Bailey, Lisa C. Lohmueller, Raymond L. Benza, JoAnn Lindenfeld, Joseph G. Rogers, Robert L. Kormos |
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| Rok vydání: | 2019 |
| Předmět: |
Male
medicine.medical_specialty Databases Factual Heart Ventricles International Cooperation medicine.medical_treatment Biomedical Engineering Biophysics Bioengineering Kaplan-Meier Estimate 030204 cardiovascular system & hematology Risk Assessment Article Biomaterials 03 medical and health sciences Bayes' theorem 0302 clinical medicine Internal medicine medicine Risk of mortality Humans Registries Retrospective Studies Heart Failure Receiver operating characteristic business.industry Area under the curve Bayes Theorem Retrospective cohort study Equipment Design General Medicine Middle Aged United States ROC Curve 030228 respiratory system Area Under Curve Ventricular assist device Cardiology Female Heart-Assist Devices Risk assessment business Algorithms Destination therapy |
| Zdroj: | ASAIO J |
| ISSN: | 1058-2916 |
| DOI: | 10.1097/mat.0000000000000910 |
| Popis: | Current risk stratification models to predict outcomes after a left ventricular assist device (LVAD) are limited in scope. We assessed the performance of Bayesian models to stratify post-LVAD mortality across various International Registry for Mechanically Assisted Circulatory Support (INTERMACS or IM) Profiles, device types, and implant strategies. We performed a retrospective analysis of 10,206 LVAD patients recorded in the IM registry from 2012 to 2016. Using derived Bayesian algorithms from 8,222 patients (derivation cohort), we applied the risk-prediction algorithms to the remaining 2,055 patients (validation cohort). Risk of mortality was assessed at 1, 3, and 12 months post implant according to disease severity (IM profiles), device type (axial versus centrifugal) and strategy (bridge to transplantation or destination therapy). Fifteen percentage (n = 308) were categorized as IM profile 1, 36% (n = 752) as profile 2, 33% (n = 672) as profile 3, and 15% (n = 311) as profile 4-7 in the validation cohort. The Bayesian algorithms showed good discrimination for both short-term (1 and 3 months) and long-term (1 year) mortality for patients with severe HF (Profiles 1-3), with the receiver operating characteristic area under the curve (AUC) between 0.63 and 0.74. The algorithms performed reasonably well in both axial and centrifugal devices (AUC, 0.68-0.74), as well as bridge to transplantation or destination therapy indication (AUC, 0.66-0.73). The performance of the Bayesian models at 1 year was superior to the existing risk models. Bayesian algorithms allow for risk stratification after LVAD implantation across different IM profiles, device types, and implant strategies. |
| Databáze: | OpenAIRE |
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