Redefining Non Invasive Post Transplant Surveillance: A Bayesian Meta Analysis and Decision Curve Framework for Donor Derived Cell Free DNA in Heart Transplantation

Donor derived cell free DNA (ddcfDNA) 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 ddcfDNA 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 (Youden index) with a data driven adaptive framework defining rule out and rule in thresholds. Clinical utility was evaluated using decision curve analysis across a range of rejection prevalences (10% to 30%), incorporating repeat testing strategies. The pooled area under the HSROC curve was 0.78 (95% CrI, 0.67 to 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. Across all prevalence scenarios, ddcfDNA guided strategies provided positive net benefit compared with biopsy all and biopsy none approaches. A repeat if borderline strategy consistently achieved the highest net benefit, particularly in low and intermediate risk settings, by reducing false positive biopsies without materially compromising detection. A single threshold interpretation is not clinically adequate for post heart 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 improves clinical decision making. These findings support a shift away from binary thresholds toward dynamic, context dependent use of ddcfDNA in transplant surveillance.