Heart waitlist survival in adults with an intra-aortic balloon pump relative to other Status 2, Status 1, and inotrope Status 3 patients

Hanff, Thomas C.; Browne, Adeline; Dickey, Jacqueline; Gaines, Holly; Harhay, Michael O.; Goodwin, Matt; Selzman, Craig H.; Fang, James C.; Drakos, Stavros G.; Stehlik, Josef

doi:10.1016/j.healun.2022.10.010

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Contrary to expectations, the type of tMCS device (IABP vs. Impella) was less significant than patient-related factors in predicting Status 2 failure. However, it shows that using Impella can increase the risk of failure to bridge to transplantation, which is consistent with the current stream of evidence that IABP-supported patients fare better than most other status 2 listed patients ( 14 , 15 ). Impella provides robust circulatory support for the left ventricle, with maximal blood flow over 5 L/min.…”

Section: Discussionsupporting

confidence: 86%

An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation

Mardini,

Bai,

Bledsoe

et al. 2024

Front. Cardiovasc. Med.

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BackgroundThe use of Intra-aortic Balloon Pump (IABP) and Impella devices as a bridge to heart transplantation (HTx) has increased significantly in recent times. This study aimed to create and validate an explainable machine learning (ML) model that can predict the failure of status two listings and identify the clinical features that significantly impact this outcome.MethodsWe used the UNOS registry database to identify HTx candidates listed as UNOS Status 2 between 2018 and 2022 and supported with either Impella (5.0 or 5.5) or IABP. We used the eXtreme Gradient Boosting (XGBoost) algorithm to build and validate ML models. We developed two models: (1) a comprehensive model that included all patients in our cohort and (2) separate models designed for each of the 11 UNOS regions.ResultsWe analyzed data from 4,178 patients listed as Status 2. Out of them, 12% had primary outcomes indicating Status 2 failure. Our ML models were based on 19 variables from the UNOS data. The comprehensive model had an area under the curve (AUC) of 0.71 (±0.03), with a range between 0.44 (±0.08) and 0.74 (±0.01) across different regions. The models' specificity ranged from 0.75 to 0.96. The top five most important predictors were the number of inotropes, creatinine, sodium, BMI, and blood group.ConclusionUsing ML is clinically valuable for highlighting patients at risk, enabling healthcare providers to offer intensified monitoring, optimization, and care escalation selectively.

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Section: Discussionsupporting

confidence: 86%

An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation

Mardini,

Bai,

Bledsoe

et al. 2024

Front. Cardiovasc. Med.

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“…Most recently, Hanff and colleagues [ 59 ] observed under the new allocation system, there is significant heterogeneity among patients listed as Status 2 and some subgroups may have waitlist mortality risk more closely approximating Status 1 or Status 3. Moreover, there is little evidence to guide the escalation of therapy in these patients (e.g., continued inotropic support vs. insertion of IABP vs. initiation of other temporary MCS).…”

Section: New Strategies For Bridging To Heart Transplantmentioning

confidence: 99%

Bridging Over Troubled Waters—How the United States 2018 Heart Allocation System Altered Transplant Bridging Strategies

James,

Smith

2024

Rev. Cardiovasc. Med.

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As we approach the five-year anniversary of the 2018 heart allocation system in the United States, it is imperative to consider the changing landscape of mechanical circulatory support and the strategies used to bridge patients into heart transplants. This manuscript reviews the history of the heart allocation system, as well as the conditions that led to its multiple revisions. We discuss initial outcomes following the implementation of the new allocation system, including the impact on waitlist mortality and post-transplant outcomes. We also give special consideration to changes in bridging strategies using venoarterial extracorporeal membrane oxygenation (VA ECMO), intra-aortic balloon pumps, and durable left ventricular assist devices (LVADs).

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“…The emphasis on granular listing criteria and improved risk stratification has led to a surge in the utilization of temporary MCS, notably IABP, as a bridge to transplantation ( Figure 2 ). However, disparities in mortality risk among listed patients underscore the need for continued refinement of allocation algorithms to ensure equitable access to cardiac transplantation based on medical urgency [ 6 ] .…”

mentioning

confidence: 99%

A Milestone in Cardiac Care: The Intra-Aortic Balloon Pump in Cardiac Surgery and Transplantation

Perazzo,

Steffen,

Gaiotto

et al. 2024

Braz J Cardiovasc Surg

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Heart waitlist survival in adults with an intra-aortic balloon pump relative to other Status 2, Status 1, and inotrope Status 3 patients

Cited by 7 publications

References 11 publications

An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation

An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation

Bridging Over Troubled Waters—How the United States 2018 Heart Allocation System Altered Transplant Bridging Strategies

A Milestone in Cardiac Care: The Intra-Aortic Balloon Pump in Cardiac Surgery and Transplantation

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