Department Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta (S.A., C.Y., V.S.V.).
Department of Pathology and Laboratory Medicine at the Hospital of the University of Pennsylvania (P.L.), Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Circ Heart Fail. 2024 Feb;17(2):e010950. doi: 10.1161/CIRCHEARTFAILURE.123.010950. Epub 2024 Feb 13.
Cardiac allograft rejection is the leading cause of early graft failure and is a major focus of postheart transplant patient care. While histological grading of endomyocardial biopsy samples remains the diagnostic standard for acute rejection, this standard has limited diagnostic accuracy. Discordance between biopsy rejection grade and patient clinical trajectory frequently leads to both overtreatment of indolent processes and delayed treatment of aggressive ones, spurring the need to investigate the adequacy of the current histological criteria for assessing clinically important rejection outcomes.
N=2900 endomyocardial biopsy images were assigned a rejection grade label (high versus low grade) and a clinical trajectory label (evident versus silent rejection). Using an image analysis approach, n=370 quantitative morphology features describing the lymphocytes and stroma were extracted from each slide. Two models were constructed to compare the subset of features associated with rejection grades versus those associated with clinical trajectories. A proof-of-principle machine learning pipeline-the cardiac allograft rejection evaluator-was then developed to test the feasibility of identifying the clinical severity of a rejection event.
The histopathologic findings associated with conventional rejection grades differ substantially from those associated with clinically evident allograft injury. Quantitative assessment of a small set of well-defined morphological features can be leveraged to more accurately reflect the severity of rejection compared with that achieved by the International Society of Heart and Lung Transplantation grades.
Conventional endomyocardial samples contain morphological information that enables accurate identification of clinically evident rejection events, and this information is incompletely captured by the current, guideline-endorsed, rejection grading criteria.
心脏同种异体移植排斥是早期移植物衰竭的主要原因,也是心脏移植后患者护理的重点。虽然心内膜心肌活检样本的组织学分级仍然是急性排斥反应的诊断标准,但该标准的诊断准确性有限。活检排斥分级与患者临床轨迹之间的不一致经常导致对惰性过程的过度治疗和对侵袭性过程的延迟治疗,这促使人们需要研究当前评估临床上重要排斥反应结果的组织学标准是否充分。
共对 2900 份心内膜心肌活检图像进行了排斥分级标签(高级别与低级别)和临床轨迹标签(明显排斥与隐匿性排斥)的分配。使用图像分析方法,从每张幻灯片中提取了描述淋巴细胞和基质的 370 个定量形态特征。构建了两个模型来比较与排斥分级相关的特征子集与与临床轨迹相关的特征子集。然后开发了一个心脏同种异体移植排斥评估器的概念验证机器学习管道,以测试识别排斥事件临床严重程度的可行性。
与传统排斥分级相关的组织病理学发现与与临床上明显的同种异体损伤相关的发现有很大不同。与国际心肺移植协会分级相比,通过定量评估一小部分定义明确的形态特征,可以更准确地反映排斥的严重程度。
传统的心内膜心肌样本包含可准确识别临床上明显排斥反应事件的形态学信息,而当前指南推荐的排斥分级标准并未完全捕捉到这些信息。