As the number of patients with heart failure continues to grow, heart transplantation (HT) remains the therapy of choice for those patients with end‐stage disease. With a conditional median survival that now exceeds 12 years, HT provides the best long‐term survival benefit for heart failure patients whose life expectancy would otherwise be 6‐24 months with medical therapy alone.1-3 Given these benefits, the quest to expand HT to more patients remains an enduring goal of the medical community. However, there continues to be a vast disparity between the number of patients who need a HT and the number of patients who actually receive one. Currently, mortality on the US waitlist is 12 deaths per 100 waitlist‐years.4 However, there is wide variation by region, with pretransplant mortality as low as 2.1 deaths per 100 waitlist‐years in some donor service areas (DSA), and as high as 23.9 deaths per 100 waitlist‐years in others.4 Successful efforts to expand the donor pool have come with more widespread use of donors considered Public Health Service increased risk, with nucleic acid amplification testing positive hepatitis C, donors who died of drug overdose, and use of donation after circulatory death, primarily in the United Kingdom and Australia. However, even with these important advances, the number of patients who are potentially eligible for HT (~250 000‐300 000 patients in the United States with stage D heart failure) is orders of magnitude greater than those who actually receive a HT (3273 HT performed in the United States in 2017).4

One of the key variables impacting expansion of the donor pool is donor heart acceptance by transplant centers. A prior analysis of the Organ Procurement and Transplantation Network (OPTN) dataset examined all potential donor hearts from 1995 to 2010.5 Khush et al identified that of 82 053 potential donor hearts, only 34% were accepted and 48% were declined. The most common reasons for donor nonacceptance were older donor age, female sex, and medical comorbidities, which was concerning given that overall donor age and presence of comorbidities increased during the study period. In fact, if one examines trends in population statistics, US adults aged 45‐64 made up 26.4% of the population (81.5 million persons) and comprised the fastest growing portion of the population between 2000 and 2010, with a growth rate of 31.5%.6 Moreover, the number of adults with hypertension, prediabetes/diabetes, and obesity is 46%, 37.6%, and 39.6%, respectively, all cardiovascular risk factors that are associated with the presence of left ventricular hypertrophy.7 Even more important is that donor heart acceptance practices contribute to variability in the probability of waitlist mortality.8 With these statistics in mind, it is critical to examine whether additional information can be used to identify donors who might otherwise be suitable despite the presence of older age and other comorbid conditions.

In this issue of the American Journal of Transplantation, Khush et al again used OPTN data to identify donor, candidate, and transplant center characteristics that influence variability in donor heart acceptance across US transplant centers.9 They examined 28 088 potential heart donors and 693 420 donor heart offers made in the United States between April 2007 and December 2015. Of these potential donors, 67% were turned down by transplant centers due to concerns about donor heart quality. There are 3 key findings that highlight the significance of this analysis: (1) characteristics that predict organ acceptance: the donor characteristics that were most predictive of organ acceptance were donor age, left ventricular ejection fraction, and height. Donor cause of death and history of hypertension were also important predictors of organ acceptance, but to a lesser degree; (2) characteristics that predict organ sequence number: Transplant center volume was the primary determinant of organ sequence number, with high‐volume transplant centers being more likely than low‐volume centers to accept organs with a higher sequence number. The median sequence number of hearts accepted for transplantation was 3 (interquartile range 1‐10), with significant variability according to transplant center size. In addition to center volume, the donor characteristics that were predictive of number of offers prior to offer acceptance were ischemic time and donor age and cause of death. When examining variation across transplant centers, the variables that explained most of the variation for organ acceptance and sequence number between centers were donor age, height, race, and cause of death; (3) characteristics that predict 30‐day and 1‐year mortality and retransplantation: most of the characteristics that predict 30‐day and 1‐year mortality were recipient characteristics (race, diabetes, hypertension, and cigarette use). Donor characteristics included ischemic time, cocaine use, and hypertension, and transplant center volume was also a predictor of 30‐day and 1‐year mortality. Of all of these factors, ischemic time was the variable most predictive of 30‐day and 1‐year mortality.

This innovative analysis offers opportunities to educate transplant professionals in an effort to improve consistency and enhance access to organs, particularly for low‐volume centers. Organ acceptance practices should be dictated by factors that have a clear and consistent association with increased mortality. As Khush and Ball demonstrate, transplant centers demonstrated wide variability in organ acceptance practices in regard to candidate and donor characteristics that were highly predictive of posttransplant mortality. However, they also demonstrated wide variability in organ acceptance practices in regard to candidate and donor characteristics that were not predictive of posttransplant mortality, and this represents the key opportunity for change. For example, donor age was not predictive of posttransplant mortality. However, donor age is included in refusal code 830, used by centers to turn down 67% of the 28 088 potential donors included in this study. With the continued aging of the population, an educational intervention focused solely on educating centers about utilizing older donors could have a substantial impact on expanding the potential donor pool.

Although Khush has demonstrated expertise and is a thought leader in the area of donor heart utilization, another key consideration is whether the data and conclusions from this paper remain relevant under the new heart allocation system. Three priority statuses have been expanded to six, and there is no longer a local allocation priority. The first zone of allocation is 500 nautical miles from the donor hospital, which has invariably increased average ischemic time from a mean of 3‐3.4 hours.10 Similar to the findings in this analysis by Khush and Ball, Cogswell et al noted that each 30‐minute increase in ischemic time in the new allocation system has been associated with an 8% increase in the risk of posttransplant death or retransplantation.10 Khush and Ball also noted multiple recipient characteristics that are most predictive of 30‐day and 1‐year mortality and retransplantation. However, the current revised allocation system is prioritizing the illness of the recipient over all other variables. There has never been a change in heart allocation in the United States that has led to a significant increase in posttransplant mortality until now.10 If persistent, this increase in posttransplant mortality may actually make transplant cardiologists and surgeons less likely to consider liberalizing their donor acceptance criteria.

Any efforts to get transplant clinicians to vary from their well‐accepted, center‐specific practices will have to come with substantial data, as medical inertia is a key barrier to change in practice across a wide spectrum of medical conditions. Robust clinical data can, however, help to “move the needle.” For example, the use of Public Health Service increased risk donor hearts has increased substantially since it was reported that the risk of transmission of HIV, hepatitis B, or hepatitis C is less than 1%.11 Moreover, the rigorous standards that heart transplant centers are expected to perform at will have to be weighed, as how can we realistically expect centers to alter their donor acceptance practices that might lead to public reporting of a change in outcomes that would affect the very livelihood of their center? Any excitement to expand acceptance of donors that might otherwise be considered “higher risk” will have to be tempered by a new allocation system that now prioritizes transplants for waitlist candidates who are also considered “higher risk” based on their level of illness. These are key questions that deserve testing in a protocol grounded in rigorous standards of implementation science. For example, the Scientific Registry of Transplant Recipients currently publishes semiannual center‐specific data on offer acceptance ratios—centers with low acceptance ratios could be flagged to encourage higher offer acceptance. However, the use of additional variables identified in this analysis by Khush and Ball could help to prioritize variability in acceptance practices. The current model for offer acceptance utilized by the Scientific Registry of Transplant Recipients does not account for transplant center volume and does not provide the characteristics most predictive of offer acceptance. Incorporation of some of the additional variables provided in comprehensive analysis, which integrates variables most predictive of offer and organ acceptance and sequence number might give a more consistent way to identify practices that are most inconsistent from center to center, particularly if they do not affect posttransplant mortality. With the approval of UNOS and public opinion, could we design a randomized controlled trial where high‐risk waitlist candidates are offered organs that might otherwise be turned down based on donor characteristics identified in this and other analyses? Khush and Ball present compelling data that highlight key areas that could be used to improve consistency in donor acceptance practices, such that we begin to utilize even a small portion of the 67% of hearts currently being thrown away and increase the overall number of donor hearts considered acceptable for transplantation. However, these data come at a critical time when the heart transplant community is already balancing the increased risk inherently built into our recently revised allocation system. Can we afford to take a chance on “higher risk” donors and “higher risk” recipients at the same time? This is the fundamental question, and although the data presented here by Khush and Ball are quite persuasive, we suspect the heart transplant community will demand further data to fully relieve this conflict.

随着心力衰竭患者数量的持续增长，心脏移植 (HT) 仍然是这些终末期疾病患者的首选治疗方法。HT 的条件中位生存期现在超过 12 年，为心力衰竭患者提供了最佳的长期生存益处，否则单独进行药物治疗的预期寿命为 6-24 个月。1-3鉴于这些好处，将 HT 扩展到更多患者的探索仍然是医学界的持久目标。然而，需要 HT 的患者数量与实际接受 HT 的患者数量之间仍然存在巨大差异。目前，美国候补名单上的死亡率为每 100 个候补名单年有 12 人死亡。4个然而，各地区之间存在很大差异，移植前死亡率在一些捐助者服务地区 (DSA) 低至每 100 等待名单年 2.1 人死亡，而在其他地区则高达每 100 等待名单年 23.9 人死亡。4个扩大捐献者池的成功努力伴随着被认为公共卫生服务增加风险的捐献者的更广泛使用，核酸扩增检测丙型肝炎呈阳性，死于药物过量的捐献者，以及在循环死亡后使用捐献者，主要是在美国王国和澳大利亚。然而，即使取得了这些重要进展，可能符合 HT 条件的患者数量（美国约有 250,000-300,000 名 D 期心力衰竭患者）仍比实际接受 HT 的患者数量多几个数量级 (3273 HT于2017年在美国演出）。4个

影响供体库扩大的关键变量之一是移植中心对供体心脏的接受。器官采购和移植网络 (OPTN) 数据集的先前分析检查了 1995 年至 2010 年的所有潜在捐献者心脏。5Khush 等人发现，在 82 053 颗潜在的捐赠心脏中，只有 34% 被接受，48% 被拒绝。捐献者不接受的最常见原因是捐献者年龄较大、女性性别和医学合并症，考虑到在研究期间总体捐献者年龄和合并症的存在增加，这是令人担忧的。事实上，如果检查人口统计趋势，45-64 岁的美国成年人占人口的 26.4%（8150 万人），并且是 2000 年至 2010 年人口增长最快的部分，增长率为 31.5% . 6此外，患有高血压、前驱糖尿病/糖尿病和肥胖症的成人人数分别为 46%、37.6% 和 39.6%，这些都是与左心室肥大相关的心血管危险因素。7更重要的是，捐献者心脏接受做法会导致候补死亡率概率的变化。8考虑到这些统计数据，关键是要检查是否可以使用额外的信息来识别尽管存在年龄较大和其他合并症但可能适合的捐赠者。

在本期《美国移植杂志》中，Khush 等人再次使用 OPTN 数据来确定供体、候选者和移植中心的特征，这些特征会影响美国移植中心对供体心脏接受度的差异。9他们检查了 2007 年 4 月至 2015 年 12 月期间在美国提供的 28 088 名潜在心脏捐献者和 693 420 名捐献者心脏报价。在这些潜在捐献者中，67% 由于担心捐献者心脏质量而被移植中心拒绝。有 3 个关键发现突出了该分析的重要性：(1) 预测器官接受的特征：最能预测器官接受的供体特征是供体年龄、左心室射血分数和身高。供体死亡原因和高血压病史也是器官接受的重要预测因素，但程度较轻；(2)预测器官序号的特征：移植中心体积是器官序号的主要决定因素，高容量移植中心比低容量中心更有可能接受序列号更高的器官。接受移植的心脏的中位序列数为 3（四分位数范围 1-10），根据移植中心的大小有显着差异。除了中心体积外，在接受提议之前预测提议数量的供体特征是缺血时间、供体年龄和死亡原因。在检查移植中心之间的差异时，解释中心之间器官接受和序列号差异的大部分变量是供体年龄、身高、种族和死亡原因；(3) 预测 30 天和 1 年死亡率和再移植的特征：大多数预测 30 天和 1 年死亡率的特征是接受者特征（种族、糖尿病、高血压和吸烟）。供体特征包括缺血时间、可卡因使用和高血压，移植中心体积也是 30 天和 1 年死亡率的预测因子。在所有这些因素中，缺血时间是最能预测 30 天和 1 年死亡率的变量。

这种创新分析提供了教育移植专业人员的机会，以努力提高一致性并增加对器官的访问，特别是对于低容量中心。器官接受实践应由与死亡率增加有明确和一致关联的因素决定。正如 Khush 和 Ball 所证明的那样，移植中心在器官接受实践方面表现出很大的差异性，这些差异与高度预测移植后死亡率的候选者和供体特征有关。然而，他们还表明，在器官接受实践中，在不能预测移植后死亡率的候选者和供体特征方面存在很大差异，这代表了变革的关键机会。例如，供体年龄不能预测移植后死亡率。然而，捐赠者年龄包含在拒绝代码 830 中，中心使用该代码拒绝本研究中包括的 28 088 名潜在捐赠者中的 67%。随着人口的持续老龄化，仅专注于教育中心利用老年捐助者的教育干预可能会对扩大潜在捐助者库产生重大影响。

尽管 Khush 展示了专业知识并且是捐献心脏利用领域的思想领袖，但另一个关键考虑因素是本文的数据和结论在新的心脏分配系统下是否仍然相关。三个优先级状态已扩展到六个，不再有本地分配优先级。第一个分配区域距离供体医院 500 海里，平均缺血时间总是从平均 3-3.4 小时增加。10与 Khush 和 Ball 的分析结果类似，Cogswell 等人指出，新分配系统中的缺血时间每增加 30 分钟，移植后死亡或再移植的风险就会增加 8%。10Khush 和 Ball 还注意到多个受体特征最能预测 30 天和 1 年死亡率和再移植。但是，当前修订后的分配系统将接受者的疾病优先于所有其他变量。迄今为止，美国的心脏分配从未发生过导致移植后死亡率显着增加的变化。10如果持续存在，这种移植后死亡率的增加实际上可能会使移植心脏病专家和外科医生不太可能考虑放宽他们的供体接受标准。

任何让移植临床医生改变他们广为接受的、针对特定中心的做法的努力都必须有大量数据，因为医疗惯性是在广泛的医疗条件下改变实践的主要障碍。然而，可靠的临床数据可以帮助“改变现状”。例如，自从据报道 HIV、乙型肝炎或丙型肝炎的传播风险低于 1% 以来，使用公共卫生服务增加风险的捐献心脏大幅增加。11此外，必须权衡心脏移植中心预期执行的严格标准，因为我们如何现实地期望中心改变他们的捐赠者接受做法，这可能导致公开报告结果的变化，从而影响生计他们的中心？任何扩大接受可能被认为是“高风险”捐赠者的兴奋都必须通过一个新的分配系统来缓和，该系统现在优先考虑根据他们的疾病水平也被认为是“高风险”的候补候选人进行移植。这些关键问题值得在基于严格的实施科学标准的协议中进行测试。例如，移植接受者科学登记处目前每半年发布一次关于接受率的中心特定数据——可以标记接受率低的中心以鼓励更高的接受率。但是，使用 Khush 和 Ball 在该分析中确定的其他变量可能有助于确定验收实践中可变性的优先级。移植接受者科学登记处使用的当前报价接受模型没有考虑移植中心的体积，也没有提供最能预测报价接受的特征。结合综合分析中提供的一些额外变量，它整合了最能预测提供和器官接受的变量以及序列号，可能会提供一种更一致的方法来识别中心与中心之间最不一致的做法，特别是如果它们不影响移植后死亡率。在 UNOS 和公众舆论的批准下，我们是否可以设计一项随机对照试验，向高风险候补名单候选人提供器官，否则这些器官可能会根据本次分析和其他分析中确定的捐助者特征而被拒绝？Khush 和 Ball 提供了令人信服的数据，这些数据突出了可用于提高捐赠者接受实践一致性的关键领域，这样我们就可以开始利用目前被丢弃的 67% 的心脏中的一小部分，并增加捐赠者心脏的总数认为可以接受移植。然而，这些数据是在心脏移植界已经在平衡我们最近修订的分配系统中内在增加的风险的关键时刻发布的。我们能否同时对“高风险”捐赠者和“高风险”接受者冒险？这是一个基本问题，虽然 Khush 和 Ball 在这里提供的数据很有说服力，但我们怀疑心脏移植界将需要更多数据来完全缓解这种冲突。