Palak Shah MD, MS , Gabriel Sayer MD , Shashank S. Sinha MD, MSc , Manreet K. Kanwar MD , Jennifer A. Cowger MD, MS , Francis D. Pagani MD, PhD , Aditi Nayak MD , Mandeep R. Mehra MD , Joseph C. Cleveland Jr. MD , Mitchell A. Psotka MD, PhD , Ramesh Singh MD , Shashank S. Desai MD , Qianhui Lu MS , Yajing Hu PhD , Allison Connolly PhD , Robert L. Kormos MD , Nir Uriel MD
{"title":"Dynamic Risk Estimation of Adverse Events in Ambulatory LVAD Patients","authors":"Palak Shah MD, MS , Gabriel Sayer MD , Shashank S. Sinha MD, MSc , Manreet K. Kanwar MD , Jennifer A. Cowger MD, MS , Francis D. Pagani MD, PhD , Aditi Nayak MD , Mandeep R. Mehra MD , Joseph C. Cleveland Jr. MD , Mitchell A. Psotka MD, PhD , Ramesh Singh MD , Shashank S. Desai MD , Qianhui Lu MS , Yajing Hu PhD , Allison Connolly PhD , Robert L. Kormos MD , Nir Uriel MD","doi":"10.1016/j.jchf.2024.05.018","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Hemocompatibility-related adverse events affect patients after left ventricular assist device (LVAD) implantation but are hard to predict.</div></div><div><h3>Objectives</h3><div>Dynamic risk modeling with a multistate model can predict risk of gastrointestinal bleeding (GIB), stroke, or death in ambulatory patients.</div></div><div><h3>Methods</h3><div>This was a secondary analysis of the MOMENTUM 3 (Multicenter Study of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3) trial. HeartMate 3 LVAD recipients who survived to hospital discharge and were followed for up to 2 years. A total of 145 variables were included in the multistate model with multivariate logistic regression. Model performance was assessed with the area under the curve in a holdout validation cohort. A risk stratification tool was created by dividing patients into categories of predicted risk using the final model variables and associated OR.</div></div><div><h3>Results</h3><div>Among 2,056 LVAD patients, the median age was 59.4 years (20.4% women, 28.6% Black). At 2 years, the incidence of GIB, stroke, and death was 25.6%, 6.0%, and 12.3%, respectively. The multistate model included 39 total variables to predict risk of GIB (16 variables), stroke (10 variables), and death (19 variables). When ambulatory patients were classified according to their risk category, the 30-day observed event rate in the highest risk group for GIB, stroke, or death was 26.9%, 1.8%, and 4.8%, respectively. The multistate model predicted GIB, stroke, and death at any 30-day period with an area under the curve of 0.70, 0.69, and 0.86, respectively.</div></div><div><h3>Conclusions</h3><div>The multistate model informs 30-day risk in ambulatory LVAD recipients and allows recalculation of risk as new patient-specific data become available. The model allows for accurate risk stratification that predicts impending adverse events and may guide clinical decision making. (MOMENTUM 3 IDE Clinical Study Protocol; <span><span>NCT02224755</span><svg><path></path></svg></span>)</div></div>","PeriodicalId":14687,"journal":{"name":"JACC. Heart failure","volume":"12 11","pages":"Pages 1898-1912"},"PeriodicalIF":10.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JACC. Heart failure","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213177924004864","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Hemocompatibility-related adverse events affect patients after left ventricular assist device (LVAD) implantation but are hard to predict.
Objectives
Dynamic risk modeling with a multistate model can predict risk of gastrointestinal bleeding (GIB), stroke, or death in ambulatory patients.
Methods
This was a secondary analysis of the MOMENTUM 3 (Multicenter Study of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3) trial. HeartMate 3 LVAD recipients who survived to hospital discharge and were followed for up to 2 years. A total of 145 variables were included in the multistate model with multivariate logistic regression. Model performance was assessed with the area under the curve in a holdout validation cohort. A risk stratification tool was created by dividing patients into categories of predicted risk using the final model variables and associated OR.
Results
Among 2,056 LVAD patients, the median age was 59.4 years (20.4% women, 28.6% Black). At 2 years, the incidence of GIB, stroke, and death was 25.6%, 6.0%, and 12.3%, respectively. The multistate model included 39 total variables to predict risk of GIB (16 variables), stroke (10 variables), and death (19 variables). When ambulatory patients were classified according to their risk category, the 30-day observed event rate in the highest risk group for GIB, stroke, or death was 26.9%, 1.8%, and 4.8%, respectively. The multistate model predicted GIB, stroke, and death at any 30-day period with an area under the curve of 0.70, 0.69, and 0.86, respectively.
Conclusions
The multistate model informs 30-day risk in ambulatory LVAD recipients and allows recalculation of risk as new patient-specific data become available. The model allows for accurate risk stratification that predicts impending adverse events and may guide clinical decision making. (MOMENTUM 3 IDE Clinical Study Protocol; NCT02224755)
期刊介绍:
JACC: Heart Failure publishes crucial findings on the pathophysiology, diagnosis, treatment, and care of heart failure patients. The goal is to enhance understanding through timely scientific communication on disease, clinical trials, outcomes, and therapeutic advances. The Journal fosters interdisciplinary connections with neuroscience, pulmonary medicine, nephrology, electrophysiology, and surgery related to heart failure. It also covers articles on pharmacogenetics, biomarkers, and metabolomics.