JACC. Advances · 2026
Predictors of Bleeding After Percutaneous Left Ventricular Assist Device Support: Findings From the J-PVAD Registry.
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Sano Madoka, Toyota Toshiaki, Kohjitani Hirohiko, Azumi Yuta, Hayashi Hideyuki, Ito Shinya, Murai Ryosuke, Ooka Junichi, Sasaki Yasuhiro, Taniguchi Tomohiko, Kobori Atsushi, Ehara Natsuhiko, Kinoshita Makoto, Furukawa Yutaka, J-PVAD Investigators
Аннотация
BACKGROUND: Bleeding is a clinically important complication during percutaneous left ventricular assist devices (pLVAD) support, yet its determinants remain unclear.
OBJECTIVES: The 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).
METHODS: A 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.
RESULTS: The 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.
CONCLUSIONS: Bleeding 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.
OBJECTIVES: The 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).
METHODS: A 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.
RESULTS: The 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.
CONCLUSIONS: Bleeding 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.