ArticleJACC. Advances2026
Predictors of Bleeding After Percutaneous Left Ventricular Assist Device Support: Findings From the J-PVAD Registry.
Article in JACC. Advances, 2026. 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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15 authors.
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Abstract
backgroundBleeding is a clinically important complication during percutaneous left ventricular assist devices (pLVAD) support, yet its determinants remain unclear.
objectivesThe objective of the study was to identify predictors of bleeding after pLVAD implantation using a nationwide Japanese registry, J-PVAD (Japanese registry for Percutaneous Ventricular Assist Device).
methodsA total of 5,608 patients with complete follow-up were analyzed and stratified into acute coronary syndrome (ACS) and non-ACS strata. The primary outcome measure was any bleeding within 30 days. Multivariable Cox proportional hazards model were used to identify predictors within each stratum, with competing-risk analyses using Fine-Gray models performed for sensitivity. Machine learning (ML) with Shapley Additive exPlanations analysis was conducted as a complementary approach.
resultsThe cohort included 2,667 ACS and 2,941 non-ACS patients. The 30-day incidence of bleeding was similar between the ACS and Non-ACS strata (25.7% vs 26.2%; P = 0.951), with most events occurring within several days after implantation. Additional mechanical circulatory support (MCS) was the strongest factor associated with bleeding in both strata (ACS: adjusted HR: 1.60; non-ACS: adjusted HR 1.75; both P < 0.001). Competing-risk analyses accounting for early mortality yielded consistent results (ACS: subdistribution HR: 1.52; non-ACS: subdistribution HR: 1.74). ML analyses consistently identified additional MCS use as the dominant contributor to bleeding. Shapley Additive exPlanations-based clustering suggested 2 high-risk phenotypes characterized by procedural complexity including additional MCS use and inflammatory-hemodynamic instability.
conclusionsBleeding after pLVAD support is common and occurs early, with comparable incidence across ACS and non-ACS strata. Additional MCS use is the dominant determinant of bleeding risk. ML-based phenotyping provides complementary insights into heterogeneous bleeding mechanisms.
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