Report From a Forum on US Heart Allocation Policy

Kobashigawa, Jon A.; Johnson, Maryl R.; Rogers, Joseph G.; Vega, J. David; Colvin-Adams, Monica; Edwards, L. B.; Meyer, Dan M.; Luu, Minh B.; Reinsmoen, Nancy L.; Dipchand, Anne I.; Feldman, David S.; Kormos, Robert L.; Mancini, Donna; Webber, Steven A.

doi:10.1111/ajt.13033

Cited by 36 publications

(24 citation statements)

References 12 publications

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“…4 HCM was once considered a rare disease with a high mortality rate but now is known to be one of the most common congenital heart diseases with an adult annual mortality rate of about 0.5% and a “burned out“ phase occurring in 3% of the HCM population. The risk of sudden death is greatest in patients < 30 years of age and is reduced with usage of implantable cardioverter defibrillators.…”

Section: Discussionmentioning

confidence: 99%

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Does Survival on the Heart Transplant Waiting List Depend on the Underlying Heart Disease?

Hsich

Rogers

McNamara

et al. 2016

JACC: Heart Failure

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Objective The aim was to identify differences in survival based on type of heart disease while awaiting orthotopic heart transplantation (OHT). Background Restrictive cardiomyopathy (RCM), congenital heart disease (CHD), and hypertrophic cardiomyopathy (HCM) patients may be at a disadvantage while awaiting OHT since they often are poor candidates for mechanical circulatory support and/or inotropes. Methods We included all adults in the Scientific Registry of Transplant Recipients database awaiting OHT from 2004–2014 and evaluated outcomes based on type of heart disease. The primary endpoint was time to all-cause mortality censored at last patient follow-up and time of transplantation. Multivariable Cox proportional hazards models were performed to evaluate survival by type of cardiomyopathy. Results There were 14447 DCM, 823 RCM, 11799 ischemic cardiomyopathy (ICM), 602 HCM, 964 CHD, 584 valvular disease, and 1528 “other” (including 1216 for re-transplantation). During median follow-up of 3.7 months, 4943 died (1253 F, 3690 M). After adjusting for possible confounding variables including age, renal function, inotropes, mechanical ventilation and mechanical circulatory support, the adjusted hazard ratio (aHR) by diagnoses relative to DCM were RCM aHR 1.70 (1.43–2.02), ICM aHR 1.10 (1.03–1.18), HCM aHR 1.23 (0.98–1.54), valvular disease aHR 1.30 (1.07–1.57), CHD aHR 1.37 (1.17–1.61) and “Other” aHR 1.51 (1.34–1.69). Sex was a significant modifier of mortality for ICM, RCM and “other” (P<0.05 for interaction). Conclusion In the United States, patients with RCM, CHD and prior heart transplantation had a higher risk of death awaiting OHT than patients with a DCM, ICM, HCM and valvular heart disease.

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Section: Discussionmentioning

confidence: 99%

“…In addition, there is concern that hypertrophic cardiomyopathy (HCM) patients may have a poor prognosis and may not qualify for high priority transplantation based on our current allocation system. 4 In one national study analyzing survival among patients removed from the heart transplant waiting list, HCM and RCM were among the highest predictors of death. 5 …”

mentioning

confidence: 99%

Does Survival on the Heart Transplant Waiting List Depend on the Underlying Heart Disease?

Hsich

Rogers

McNamara

et al. 2016

JACC: Heart Failure

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“…Data from several sources are used to calibrate and validate the model from 2006, the last year in which changes to the heart allocation policy were made, 15 to the end of 2014. Patient records for 30,394 adults who are reported in the UNOS database were used.…”

Section: Methodsmentioning

confidence: 99%

Efficient and Fair Heart Allocation Policies for Transplantation

Hasankhani

Khademi

2017

MDM Policy & Practice

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Background: The optimal allocation of limited donated hearts to patients on the waiting list is one of the top priorities in heart transplantation management. We developed a simulation model of the US waiting list for heart transplantation to investigate the potential impacts of allocation policies on several outcomes such as pre- and posttransplant mortality. Methods: We used data from the United Network for Organ Sharing (UNOS) and the Scientific Registry of Transplant Recipient (SRTR) to simulate the heart allocation system. The model is validated by comparing the outcomes of the simulation with historical data. We also adapted fairness schemes studied in welfare economics to provide a framework to assess the fairness of allocation policies for transplantation. We considered three allocation policies, each a modification to the current UNOS allocation policy, and analyzed their performance via simulation. The first policy broadens the geographical allocation zones, the second modifies the health status order for receiving hearts, and the third prioritizes patients according to their waiting time. Results: Our results showed that the allocation policy similar to the current UNOS practice except that it aggregates the three immediate geographical allocation zones, improves the health outcomes, and is “closer” to an optimal fair policy compared to all other policies considered in this study. Specifically, this policy could have saved 319 total deaths (out of 3738 deaths) during the 2006 to 2014 time horizon, in average. This policy slightly differs from the current UNOS allocation policy and allows for easy implementation. Conclusion: We developed a model to compare the outcomes of heart allocation policies. Combining the three immediate geographical zones in the current allocation algorithm could potentially reduce mortality rate and is closer to an optimal fair policy.

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“…Currently, heart allocation policy is undergoing additional review as the current geographic system used in heart allocation is recognized as a major limitation to ensuring the sickest patients are transplanted [26]. The new policy will have additional tiers to better capture the clinical acuity of a waitlisted patient, and incorporates broader geographic sharing of donor hearts [27, 28]. …”

Section: Broader Geographic Sharingmentioning

confidence: 99%

Contemporary Issues in Lung Transplant Allocation Practices

Tsuang

2017

Curr Transpl Rep

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Purpose of Review To discuss the current state of donor lung allocation in the United States, and future opportunities to increase the efficiency of donor lung allocation. Recent Findings The current donor lung allocation system prioritizes clinical acuity by use of the Lung Allocation Score (LAS) which has reduced waitlist mortality since its implementation in 2005. Access to donor lungs can be further improved through policy changes using broader geographic sharing, and developing new technology such as ex vivo lung perfusion to recover marginal donor lungs. Summary The number of lung transplants in the U.S. continues to increase annually. However, the demand for donor lungs continues to be outpaced by an ever growing waitlist. Efficient allocation can be achieved through improved allocation policies and new technology.

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Report From a Forum on US Heart Allocation Policy

Cited by 36 publications

References 12 publications

Does Survival on the Heart Transplant Waiting List Depend on the Underlying Heart Disease?

Does Survival on the Heart Transplant Waiting List Depend on the Underlying Heart Disease?

Efficient and Fair Heart Allocation Policies for Transplantation

Contemporary Issues in Lung Transplant Allocation Practices

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