The evolution of patient-specific precision biomarkers to guide personalized heart-transplant care

Deng, Mario C.

doi:10.1080/23808993.2021.1840273

Cited by 8 publications

(6 citation statements)

References 103 publications

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“…The CareDx Allomap test and newer developments, such as donor-derived cell-free DNA have been studied more extensively with a variety of heart transplant patients and yet are far from the worldwide standard of care. 4,5,8 Some of this is due to the cost and availability of these testing modalities that are not universal outside of the United States. However, there is tremendous clinical inertia as well, which maintains the usual status quo with EMB and hemodynamic assessment as fundamental aspects of surveillance especially in the first year following heart transplantation.…”

Section: Replacing the Endomyocardial Biopsy: Easier Said Than Donementioning

confidence: 99%

“…10 One of the last major heart transplant trials, the Invasive Monitoring Attenuation Through Gene Expression trial was published more than a decade ago, and yet EMB remains a hallmark of the first post transplant year for many patients today. 4,8 To make progress in the future will require several key steps:…”

Section: Replacing the Endomyocardial Biopsy: Easier Said Than Donementioning

confidence: 99%

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Replacing the Endomyocardial Biopsy: Easier Said Than Done

Baran¹

2022

Transplantation

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Section: Replacing the Endomyocardial Biopsy: Easier Said Than Donementioning

confidence: 99%

Section: Replacing the Endomyocardial Biopsy: Easier Said Than Donementioning

confidence: 99%

Replacing the Endomyocardial Biopsy: Easier Said Than Done

Baran¹

2022

Transplantation

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“…Although the use of these assays has become more routine, their role in reducing EMB frequency has not yet been well established, and validation studies have been ongoing [17,18]. One of the more significant recent developments comes from Agbor-Enoh et al who performed one such validation with a multicenter prospective cohort study of 165 patients [19 ▪ ].…”

Section: Biomarker Testing For Acute Rejectionmentioning

confidence: 99%

Noninvasive methods to reduce cardiac complications postheart transplant

Zhou

Wolfson

Vaidya

2021

Current Opinion in Organ Transplantation

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Purpose of reviewLong-term success of heart transplantation is limited by allograft rejection and cardiac allograft vasculopathy (CAV). Classic management has relied on frequent invasive testing to screen for early features of rejection and CAV to allow for early treatment. In this review, we discuss new developments in the screening and prevention of allograft rejection and CAV. Recent findingsNewer noninvasive screening techniques show excellent sensitivity and specificity for the detection of clinically significant rejection. New biomarkers and treatment targets continue to be identified and await further studies regarding their utility in preventing allograft vasculopathy. SummaryNoninvasive imaging and biomarker testing continue to show promise as alternatives to invasive testing for allograft rejection. Continued validation of their effectiveness may lead to new surveillance protocols with reduced frequency of invasive testing. Furthermore, these noninvasive methods will allow for more personalized strategies to reduce the complications of long-term immunosuppression whereas continuing the decline in the overall rate of allograft rejection.

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“…These methods include the use of gene expression profiling 6 (GEP) and donor-derived cell-free DNA (dd-cfDNA), 7 , 8 with both methods requiring a blood draw. 9 However, studies utilizing these methods have shown limited discrimination 6–8 to detection of acute cellular rejection (ACR).…”

Section: Introductionmentioning

confidence: 99%

Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model

Adedinsewo

Hardway

Morales-Lara

et al. 2023

European Heart Journal - Digital Health

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Background and aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and Results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7,590 unique ECG-biopsy pairs, belonging to 1,427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) and 95% (19/20; 95% CI: 75%-100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61-0.96) and 100% (2/2; 95% CI: 16%-100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate to severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

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The evolution of patient-specific precision biomarkers to guide personalized heart-transplant care

Cited by 8 publications

References 103 publications

Replacing the Endomyocardial Biopsy: Easier Said Than Done

Replacing the Endomyocardial Biopsy: Easier Said Than Done

Noninvasive methods to reduce cardiac complications postheart transplant

Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model

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