Patient Selection for Destination LVAD Therapy: Predicting Success in the Short and Long Term

Michaels, Alexander; Cowger, Jennifer

doi:10.1007/s11897-019-00434-1

Cited by 14 publications

(15 citation statements)

References 45 publications

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“…Lüneburg et al (2019) used a U-net convolutional neural network for driveline tube segmentation and showed that the deep learning techniques can efficiently recognize LVAD on driveline exit site images [OS126]. Michaels and Cowger provide a review of the HF risk assessment as a referral guide for advanced HF therapies [ 24 ].…”

Section: Resultsmentioning

confidence: 99%

Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review

et al. 2020

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Machine learning (ML) algorithms “learn” information directly from data, and their performance improves proportionally with the number of high-quality samples. The aim of our systematic review is to present the state of the art regarding the implementation of ML techniques in the management of heart failure (HF) patients. We manually searched MEDLINE and Cochrane databases as well the reference lists of the relevant review studies and included studies. Our search retrieved 122 relevant studies. These studies mainly refer to (a) the role of ML in the classification of HF patients into distinct categories which may require a different treatment strategy, (b) discrimination of HF patients from the healthy population or other diseases, (c) prediction of HF outcomes, (d) identification of HF patients from electronic records and identification of HF patients with similar characteristics who may benefit form a similar treatment strategy, (e) supporting the extraction of important data from clinical notes, and (f) prediction of outcomes in HF populations with implantable devices (left ventricular assist device, cardiac resynchronization therapy). We concluded that ML techniques may play an important role for the efficient construction of methodologies for diagnosis, management, and prediction of outcomes in HF patients. Electronic supplementary material The online version of this article (10.1007/s10741-020-10007-3) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review

et al. 2020

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“…Right ventricular failure after LVAD implant remains a major and common complication. [1][2][3] If inotropic support is insufficient or not possible due to medication-related complications such as arrhythmias, an RVAD should be considered. In patients with early post-operative RV failure after LVAD implant, percutaneous and central RVAD cannulation has been described to support the RV as a short-term bridge to recovery.…”

Section: Discussionmentioning

confidence: 99%

“…Right ventricular failure after LVAD implant remains a major and common complication 1–3 . If inotropic support is insufficient or not possible due to medication‐related complications such as arrhythmias, an RVAD should be considered.…”

Section: Discussionmentioning

confidence: 99%

“…While de novo pump thrombosis has been considerably reduced with the HeartMate3 left ventricular assist device (LVAD), stroke, mucosal bleeding, aortic regurgitation, infection, and right ventricular (RV) failure still remain problematic 1 . The overall incidence of severe RV failure requiring mechanical support in MOMENTUM3 was low, but early and late RV failure that is at least moderate in nature is associated with increased post‐operative morbidity and mortality outcomes 2–4 .…”

Section: Introductionmentioning

confidence: 99%

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Impella RP as a bridge to cardiac transplant for refractory late right ventricular failure in setting of left ventricular assist device

et al. 2020

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Right ventricular (RV) failure remains a major complication after surgical implantation of a left ventricular assist device (LVAD). While the use of a percutaneous RV assist device has been described as a short-term bridge to recovery in end-stage heart failure patients with early post-operative RV failure after index LVAD implant, management of refractory late RV failure remains challenging in these patients. We report the first successful case of extended Impella RP use as a safe and effective bridge to orthotopic heart transplant in an LVAD patient with refractory, haemodynamically significant late RV failure. The Impella RP provided support for 37 days. Haemodynamically intolerant arrhythmia precluded use of inotropic support. No adverse complications related to percutaneous Impella RP support were seen. We also review potential considerations for mechanical circulatory support strategies in this setting: central RV assist device cannulation requires sternotomy incision that can impact subsequent cardiac surgeries, while percutaneous Protek Duo insertion requires adequate vessel size and patency. With an LVAD in situ, veno-arterial extracorporeal membrane oxygenation was not considered for isolated RV support in this case. The patient is currently over 6 months post-orthotopic heart transplant.

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“…None of the existing risk prediction models for LVAD outcome, even those including large numbers of variables, include information about cognition. 23,24…”

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confidence: 99%

Predicting post-LVAD outcome: Is there a role for cognition?

et al. 2020

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Background: Cognition has been found to influence risk of stroke and death for a variety of patient groups but this association has not been examined in heart failure (HF) patients undergoing left ventricular assist device (LVAD) implant. We aimed to study the relationship between cognition, stroke, and death in a cohort of patients who received LVAD therapy. It was hypothesized that cognitive test results obtained prior to LVAD placement would predict stroke and death after surgery. Methods: We retrospectively identified 59 HF patients who had cognitive assessment prior to LVAD placement. Cognitive assessment included measures of attention, memory, language, and visualmotor speed and were averaged to produce one z-score variable per patient. Survival analyses, censored for transplant, evaluated predictors for stroke and death within a follow-up period of 900 days. Results: For patients with stroke or death during the follow up period, the average cognitive z-score predicted post-LVAD stroke (HR = 0.513, 95% CI = 0.31–0.86, p = 0.012) and death (HR = 0.166, 95% CI = 0.06–0.47, p = 0.001). Cognitive performances were worse in the patients who suffered stroke or died. No other variable predicted stroke and death within the follow up period when the cognitive variable was in the model. Conclusion: Cognitive performance was predictive of post-LVAD risk of stroke and death. Results are consistent with findings from other studies in non-LVAD samples and may reflect early signs of neurologic vulnerability. Further studies are needed to clarify the relationship between cognition and LVAD outcomes in order to optimize patient selection, management, and advanced care planning.

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Patient Selection for Destination LVAD Therapy: Predicting Success in the Short and Long Term

Cited by 14 publications

References 45 publications

Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review

Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review

Impella RP as a bridge to cardiac transplant for refractory late right ventricular failure in setting of left ventricular assist device

Predicting post-LVAD outcome: Is there a role for cognition?

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