Frederick Vogel, Zachary W Sollie, Arman Kilic, Ethan Kung
{"title":"左心室辅助功能患者右心衰的预测:目前的方法和未来的方向。","authors":"Frederick Vogel, Zachary W Sollie, Arman Kilic, Ethan Kung","doi":"10.3390/jcdd12070240","DOIUrl":null,"url":null,"abstract":"<p><p>Right heart failure is a condition where the right ventricle fails to pump blood into the pulmonary artery, and, in turn, the lungs. This condition frequently presents after the implantation of a left ventricular assist device (LVAD). Ventricular assist candidates who have LVADs implanted possess various pathophysiological and cardiovascular features that contribute to the later development of RHF. With LVADs serving as bridge-to-transplantation, bridge-to-candidacy, and destination therapies, it is imperative that the pre-operative indicators of RHF are identified and assessed. Multiple predictive models and parameters have been developed to quantify the risk of post-LVAD right heart failure. Clinical, laboratory, hemodynamic, and echocardiographic parameters have all been used to develop these predictive approaches. RHF remains a major cause of morbidity and mortality after LVAD implantation. Predicting RHF helps clinicians assess treatment options, including biventricular support or avoiding high-risk surgery. In our review, we noted the varying definitions for RHF in recent models, which affected respective predictive accuracies. The pulmonary arterial pulsatile index (PAPi) and right ventricular longitudinal strain parameters were noted for their potential to enhance current models incrementally. Meanwhile, mechanistic and machine learning approaches present a more fundamental shift in the approach to making progress in this field.</p>","PeriodicalId":15197,"journal":{"name":"Journal of Cardiovascular Development and Disease","volume":"12 7","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Right Heart Failure in LVAD Candidates: Current Approaches and Future Directions.\",\"authors\":\"Frederick Vogel, Zachary W Sollie, Arman Kilic, Ethan Kung\",\"doi\":\"10.3390/jcdd12070240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Right heart failure is a condition where the right ventricle fails to pump blood into the pulmonary artery, and, in turn, the lungs. This condition frequently presents after the implantation of a left ventricular assist device (LVAD). Ventricular assist candidates who have LVADs implanted possess various pathophysiological and cardiovascular features that contribute to the later development of RHF. With LVADs serving as bridge-to-transplantation, bridge-to-candidacy, and destination therapies, it is imperative that the pre-operative indicators of RHF are identified and assessed. Multiple predictive models and parameters have been developed to quantify the risk of post-LVAD right heart failure. Clinical, laboratory, hemodynamic, and echocardiographic parameters have all been used to develop these predictive approaches. RHF remains a major cause of morbidity and mortality after LVAD implantation. Predicting RHF helps clinicians assess treatment options, including biventricular support or avoiding high-risk surgery. In our review, we noted the varying definitions for RHF in recent models, which affected respective predictive accuracies. The pulmonary arterial pulsatile index (PAPi) and right ventricular longitudinal strain parameters were noted for their potential to enhance current models incrementally. Meanwhile, mechanistic and machine learning approaches present a more fundamental shift in the approach to making progress in this field.</p>\",\"PeriodicalId\":15197,\"journal\":{\"name\":\"Journal of Cardiovascular Development and Disease\",\"volume\":\"12 7\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cardiovascular Development and Disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3390/jcdd12070240\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cardiovascular Development and Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/jcdd12070240","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Prediction of Right Heart Failure in LVAD Candidates: Current Approaches and Future Directions.
Right heart failure is a condition where the right ventricle fails to pump blood into the pulmonary artery, and, in turn, the lungs. This condition frequently presents after the implantation of a left ventricular assist device (LVAD). Ventricular assist candidates who have LVADs implanted possess various pathophysiological and cardiovascular features that contribute to the later development of RHF. With LVADs serving as bridge-to-transplantation, bridge-to-candidacy, and destination therapies, it is imperative that the pre-operative indicators of RHF are identified and assessed. Multiple predictive models and parameters have been developed to quantify the risk of post-LVAD right heart failure. Clinical, laboratory, hemodynamic, and echocardiographic parameters have all been used to develop these predictive approaches. RHF remains a major cause of morbidity and mortality after LVAD implantation. Predicting RHF helps clinicians assess treatment options, including biventricular support or avoiding high-risk surgery. In our review, we noted the varying definitions for RHF in recent models, which affected respective predictive accuracies. The pulmonary arterial pulsatile index (PAPi) and right ventricular longitudinal strain parameters were noted for their potential to enhance current models incrementally. Meanwhile, mechanistic and machine learning approaches present a more fundamental shift in the approach to making progress in this field.