Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity Journal Article
| Authors: | Shaffer, B. C.; Brown, S.; Chinapen, S.; Mangold, K. E.; Lahoud, O.; Lopez-Jimenez, F.; Schaffer, W.; Liu, J.; Giralt, S.; Devlin, S.; Shah, G.; Scordo, M.; Papadopoulos, E.; Landau, H.; Usmani, S.; Perales, M. A.; Friedman, P. A.; Gersh, B. J.; Attia, I. Z.; Noseworthy, P. A.; Kosmidou, I. |
| Article Title: | Artificial intelligence enabled interpretation of ECG images to predict hematopoietic cell transplantation toxicity |
| Abstract: | Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P < .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment. © 2024 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved. |
| Keywords: | adult; controlled study; aged; middle aged; major clinical study; myeloproliferative disorder; busulfan; mortality; drug dose reduction; methotrexate; follow up; multiple myeloma; cohort analysis; cyclophosphamide; melphalan; amyloidosis; hematopoietic stem cell transplantation; retrospective study; acute lymphoblastic leukemia; risk assessment; acute leukemia; myelodysplastic syndrome; whole body radiation; artificial intelligence; prophylaxis; diabetes mellitus; comorbidity; graft versus host reaction; allogeneic hematopoietic stem cell transplantation; heart arrhythmia; echocardiography; toxicity; immunosuppressive treatment; calcineurin inhibitor; tacrolimus; electrocardiography; atrial fibrillation; cyclosporine; autologous hematopoietic stem cell transplantation; vascular disease; plasma cell dyscrasia; human; male; female; article; deep learning; mortality risk; ai interpretation of pretransplant ecg images is a novel diagnostic tool to predict transplant arrhythmia risk; ai-ecg prediction of af also informed the risk of mortality after allo transplantation |
| Journal Title: | Blood Advances |
| Volume: | 8 |
| Issue: | 21 |
| ISSN: | 2473-9529 |
| Publisher: | American Society of Hematology |
| Date Published: | 2024-11-12 |
| Start Page: | 5603 |
| End Page: | 5611 |
| Language: | English |
| DOI: | 10.1182/bloodadvances.2024013636 |
| PUBMED: | 39158065 |
| PROVIDER: | scopus |
| PMCID: | PMC11550362 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: Brian C. Shaffer -- Source: Scopus |
