Evidence map›Paper›PMID 40578266›Full record

ArticleJACC. Heart failure2025

Patterns of Left Ventricular Regional Wall Motion Abnormalities After Brain Death and Their Clinical Significance.

Alex J Chang, Brian J Wayda, Shiqi Zhang, Yingjie Weng, Kiran K Khush, Jonathan G Zaroff

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Article in JACC. Heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Alex J ChangDepartment of Medicine, Kaiser Permanente San Francisco Medical Center, San Francisco, California, USA.
Brian J WaydaDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA.
Shiqi ZhangQuantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Yingjie WengQuantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Kiran K KhushDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA.
Jonathan G ZaroffDepartment of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, California, USA; Division of Research, Kaiser Permanente Northern California, Oakland, California, USA. Electronic address: jonathan.g.zaroff@kp.org.

Funding

Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Evidence Based Evaluation and Acceptance of Donor Hearts for TransplantationR01HL125303 · NHLBI · STANFORD UNIVERSITY · PI KHUSH, KIRAN KAUR · 2015 to 2019
$3.0M
NCATS NIH HHS UL1 TR001085NHLBI NIH HHS R01 HL125303
6 · The paper itself

Abstract

backgroundLeft ventricular dysfunction is common in potential heart donors after brain death, but specific regional wall motion abnormality (RWMA) patterns in this population have not been well described.

objectivesThis study aims to define and characterize RWMA patterns in potential heart donors after brain death by using a machine learning algorithm.

methodsThe Donor Heart Study enrolled 4,333 potential heart donors after brain death. All had a transthoracic echocardiogram (TTE) including RWMA assessment, with each segment analyzed for WMS (Wall Motion Score). Those with any RWMA (ie, any WMS >1) were classified using k-means clustering, and each cluster's associations with donor clinical characteristics, heart use, and recipient survival were assessed.

resultsThe final analytical cohort included 913 initial TTEs. We identified 4 unique RWMA phenotypes: focal basal septal (FBS) (n = 500), basal and midventricular (n = 311), apical (n = 66), and global hypokinesis (n = 36). These phenotypes exhibited similar donor characteristics but differed in troponin, N-terminal pro-B-type natriuretic peptide levels (both lowest in FBS), and left ventricular ejection fraction (LVEF) (highest in FBS). On subsequent TTEs (performed in 314 donors with any RWMA), all phenotypes demonstrated significant improvement in LVEF. The FBS phenotype had the highest donor heart acceptance for transplantation (68%). Of the hearts accepted for transplantation, there were no significant differences by RWMA phenotype in recipient survival.

conclusionsLeft ventricular dysfunction after brain death exhibits distinct RWMA phenotypes, which differ in terms of selected biomarkers and LVEF, but not in recipient survival of the hearts accepted for transplantation.

Indexed as

Brain DeathHeart TransplantationHeart VentriclesTissue DonorsVentricular Dysfunction, LeftAdultClinical RelevanceEchocardiographyFemaleHumansMachine LearningMaleMiddle AgedStroke VolumeVentricular Function, Leftdonor heartleft ventricular dysfunctionmachine learningoutcomesregional wall motion abnormality

Identifiers

PMID40578266
PMCPMC12341468

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.