Redefining Non Invasive Post Transplant Surveillance: A Bayesian Meta Analysis and Decision Curve Framework for Donor Derived Cell Free DNA in Heart Transplantation
- 1Malla Reddy Institute of Medical Sciences
Abstract
Donor-derived cell-free DNA (dd-cfDNA) is increasingly used for post transplantation non-invasive surveillance; however, its clinical interpretation remains inconsistent, with widely ranging thresholds and is typically applied as a single binary cutoff in literature. The optimal decision framework for rule-out and rule-in decisions, and whether a single threshold remains clinically meaningful, are currently uncertain. We performed a Bayesian hierarchical summary receiver operating characteristic (HSROC) meta-analysis of 14 studies (1,763 patients) evaluating dd-cfDNA against endomyocardial biopsy. To account for serial testing within individuals, we applied a cluster-corrected design effect, reducing 6,103 observations to 2,518 effective tests. Threshold-dependent sensitivity and specificity were modelled continuously. We compared a conventional single-threshold approach with a data-driven adaptive framework defining rule-out and rule-in thresholds and evaluated clinical utility by decision-curve analysis across rejection prevalences from 1% to 50%, incorporating repeat-testing strategies. The pooled area under the HSROC curve was 0.78 (95% CrI, 0.67-0.84). The Youden-optimal threshold (0.20%) yielded balanced sensitivity (0.77) and specificity (0.77) but failed to support clinical objectives of diagnosis. An adaptive framework identified a rule-out threshold of 0.16% (sensitivity 0.80) and a rule-in threshold of 0.48% (specificity 0.90), defining a indeterminate / grey zone. The residual one-in-five false-negative rate at the rule-out anchor reflects low-grade, non-cytolytic rejection, the imperfect histological reference standard and fractional suppression of the donor signal, rather than the statistical model; a result above the rule-in anchor carries a positive predictive value of approximately 38% at 10% prevalence and denotes an indication for tissue diagnosis and multimodal investigation, not for empiric treatment. Across low-to-intermediate prevalence, dd-cfDNA-guided strategies exceeded both the biopsy-all and monitor-all reference strategies; among testing strategies, repeat-if-borderline achieved the highest net benefit across the majority of the prevalence-threshold space and sustained positive net benefit over the widest operating range of any strategy, reducing false-positive biopsies without materially compromising detection. At high prevalence, where a first elevated result is usually true, biopsy-all became competitive. A single threshold is therefore clinically inadequate for post-transplant surveillance. Our tri-state, prevalence-aware framework integrating rule-out, indeterminate, and rule-in zones with selective repeat testing, more accurately reflects biomarker behavior and yields greater net benefit than any single cutoff because these anchors are pooled, population-level estimates rather than universal constants, programs should adopt this architecture and calibrate their own high-sensitivity rule-out and high-specificity rule-in thresholds to their local assay and population.
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BibTeX
@misc{john2026redefining,
title = {{Redefining Non Invasive Post Transplant Surveillance: A Bayesian Meta Analysis and Decision Curve Framework for Donor Derived Cell Free DNA in Heart Transplantation}},
author = {J. D. John and F. Henna and F. Waseem and M. A. Hassan and Z. Bacha and M. Mukhlis and B. K. Mohammed and S. Cheema and K. Shah},
howpublished = {medRxiv},
year = {2026},
month = may,
doi = {10.64898/2026.05.15.26353184},
url = {https://www.medrxiv.org/content/10.64898/2026.05.15.26353184v4},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Redefining Non Invasive Post Transplant Surveillance: A Bayesian Meta Analysis and Decision Curve Framework for Donor Derived Cell Free DNA in Heart Transplantation
AU - J. D. John
AU - F. Henna
AU - F. Waseem
AU - M. A. Hassan
AU - Z. Bacha
AU - M. Mukhlis
AU - B. K. Mohammed
AU - S. Cheema
AU - K. Shah
PY - 2026
DA - 2026/05/15
PB - medRxiv
DO - 10.64898/2026.05.15.26353184
UR - https://www.medrxiv.org/content/10.64898/2026.05.15.26353184v4
AB - Donor-derived cell-free DNA (dd-cfDNA) is increasingly used for post transplantation non-invasive surveillance; however, its clinical interpretation remains inconsistent, with widely ranging thresholds and is typically applied as a single binary cutoff in literature. The optimal decision framework for rule-out and rule-in decisions, and whether a single threshold remains clinically meaningful, are currently uncertain. We performed a Bayesian hierarchical summary receiver operating characteristic (HSROC) meta-analysis of 14 studies (1,763 patients) evaluating dd-cfDNA against endomyocardial biopsy. To account for serial testing within individuals, we applied a cluster-corrected design effect, reducing 6,103 observations to 2,518 effective tests. Threshold-dependent sensitivity and specificity were modelled continuously. We compared a conventional single-threshold approach with a data-driven adaptive framework defining rule-out and rule-in thresholds and evaluated clinical utility by decision-curve analysis across rejection prevalences from 1% to 50%, incorporating repeat-testing strategies. The pooled area under the HSROC curve was 0.78 (95% CrI, 0.67-0.84). The Youden-optimal threshold (0.20%) yielded balanced sensitivity (0.77) and specificity (0.77) but failed to support clinical objectives of diagnosis. An adaptive framework identified a rule-out threshold of 0.16% (sensitivity 0.80) and a rule-in threshold of 0.48% (specificity 0.90), defining a indeterminate / grey zone. The residual one-in-five false-negative rate at the rule-out anchor reflects low-grade, non-cytolytic rejection, the imperfect histological reference standard and fractional suppression of the donor signal, rather than the statistical model; a result above the rule-in anchor carries a positive predictive value of approximately 38% at 10% prevalence and denotes an indication for tissue diagnosis and multimodal investigation, not for empiric treatment. Across low-to-intermediate prevalence, dd-cfDNA-guided strategies exceeded both the biopsy-all and monitor-all reference strategies; among testing strategies, repeat-if-borderline achieved the highest net benefit across the majority of the prevalence-threshold space and sustained positive net benefit over the widest operating range of any strategy, reducing false-positive biopsies without materially compromising detection. At high prevalence, where a first elevated result is usually true, biopsy-all became competitive. A single threshold is therefore clinically inadequate for post-transplant surveillance. Our tri-state, prevalence-aware framework integrating rule-out, indeterminate, and rule-in zones with selective repeat testing, more accurately reflects biomarker behavior and yields greater net benefit than any single cutoff because these anchors are pooled, population-level estimates rather than universal constants, programs should adopt this architecture and calibrate their own high-sensitivity rule-out and high-specificity rule-in thresholds to their local assay and population.
ER -