Multi-modal AI: Integrating electrocardiography with molecular biomarkers for noninvasive detection of cardiac allograft rejection

@article{kchen2026multimodal,
  title = {{Multi-modal AI: Integrating electrocardiography with molecular biomarkers for noninvasive detection of cardiac allograft rejection}},
  author = {K. Chen, MD, MS and V. Koesmahargyo and R. Ronan and C.R. Barbhaiya and S.A. Bernstein and A. Kushnir and L. Garber and F. Yang and A. Aizer and A. Reyentovich and L.A. Chinitz and R.I. Goldberg and L. Jankelson},
  journal = {JHLT Open},
  year = {2026},
  month = sep,
  doi = {10.1016/j.jhlto.2026.100698},
  url = {https://doi.org/10.1016/j.jhlto.2026.100698},
  note = {CC BY-NC-ND},
}

TY  - JOUR
TI  - Multi-modal AI: Integrating electrocardiography with molecular biomarkers for noninvasive detection of cardiac allograft rejection
AU  - K. Chen, MD, MS
AU  - V. Koesmahargyo
AU  - R. Ronan
AU  - C.R. Barbhaiya
AU  - S.A. Bernstein
AU  - A. Kushnir
AU  - L. Garber
AU  - F. Yang
AU  - A. Aizer
AU  - A. Reyentovich
AU  - L.A. Chinitz
AU  - R.I. Goldberg
AU  - L. Jankelson
PY  - 2026
DA  - 2026/09/25
JO  - JHLT Open
DO  - 10.1016/j.jhlto.2026.100698
UR  - https://doi.org/10.1016/j.jhlto.2026.100698
AB  - Noninvasive rejection biomarkers (AlloMap, AlloSure) demonstrate high negative predictive value but positive predictive values of only 10–30%, driving avoidable endomyocardial biopsies. We developed a multi-modal AI model integrating electrocardiography with molecular biomarkers in 389 heart transplant recipients (5338 ECG–biopsy pairs), performing the first head-to-head comparison of AI-ECG, AlloMap, AlloSure, and their integration against biopsy-proven rejection. Individual modalities showed modest discrimination (AI-ECG AUC 0.65; AlloSure 0.67; AlloMap 0.56), while combining biomarkers alone did not improve performance (AUC 0.65). In the multi-modal test subset of ECGs (n=38, 2 rejection events), the integrated model achieved AUC 0.81 with 100% specificity (0/36 false positives) and 97% negative predictive value, eliminating all 19 false-positive combined-biomarker referrals. These hypothesis-generating results support prospective multi-center validation to determine whether requiring concordance across electrical and biomarker signals reduces unnecessary biopsies while preserving high negative predictive value. Background: Noninvasive rejection biomarkers (AlloMap gene-expression profiling (GEP); AlloSure donor-derived cell-free DNA (dd-cfDNA)) deliver high negative predictive value but positive predictive values of only 10–30%, driving avoidable endomyocardial biopsies. Each biomarker interrogates a single biological compartment. Whether integrating AI-enabled electrocardiography (AI-ECG) with these molecular assays improves specificity has not been tested. Methods: We retrospectively assembled 5338 unique ECG–endomyocardial-biopsy pairs from 389 adult heart transplant recipients at a single center (2018–2024). A ResNet-18 convolutional neural network was trained on 8-lead ECG waveforms with clinical covariates. In the 340-patient subset with same encounter AlloMap and AlloSure testing (405 ECG–biomarker–biopsy triplets), an integrated model fused the ECG embedding with biomarker values via late concatenation. We performed the first head-to-head, same-cohort comparison of AI-ECG, AlloMap, AlloSure, and their integration against biopsy-proven rejection (ISHLT ≥2R ACR or pAMR ≥2). Discrimination was evaluated with bootstrap 95% confidence intervals. Results: Individual modalities showed modest discrimination (AI-ECG AUC 0.65, 95% CI 0.58–0.72; AlloSure 0.67, 0.56–0.78; AlloMap 0.56, 0.46–0.67); combining biomarkers alone did not improve performance (AUC 0.65). In the multi-modal test subset (n=38 ECG-biopsy pairs, 2 rejection events), the integrated model achieved AUC 0.81 (0.50–1.00), 100% specificity (36/36; Clopper–Pearson 90–100%), and 97% NPV. At the same NPV threshold, all 19 false-positive combined-biomarker referrals in this subset would have been avoided, though this estimate reflects a single operating point in a small sample. AUC gains over single modalities did not reach statistical significance (DeLong p=0.31–0.34) given the limited event count. Conclusions: A multi-modal AI model that requires concordance across electrical, immune-transcriptional, and donor-DNA signals showed numerically higher specificity for cardiac allograft rejection than any single modality, although the corresponding AUC differences did not reach statistical significance. These hypothesis-generating findings suggest that cross-compartment concordance may help discriminate true rejection from biomarker noise, but the small multi-modal test set precludes definitive conclusions; a pre-specified prospective multi-center trial powered for ≥50 rejection events is the necessary next step to test this hypothesis.
SN  - 2950-1334
ER  -