Artificial Intelligence Enabled Interpretation of ECG Images to Predict Hematopoietic Cell Transplantation Toxicity.
Brian C ShafferSamantha BrownStephanie ChinapenKathryn MangoldOscar B LahoudFrancisco Lopez-JimenezWendy L SchafferJennifer E LiuSergio A GiraltSean M DevlinGunjan L ShahMichael ScordoEsperanza B PapadopoulosHeather J LandauSaad Z UsmaniMiguel-Ángel PeralesM P H Paul A FriedmanBernard GershItzhak Zachi AttiaPeter NoseworthyIoanna KosmidouPublished in: Blood advances (2024)
Artificial intelligence enabled interpretation of electrocardiogram waveform images (AI-ECG) can identify patterns predictive of future adverse cardiac events. We hypothesized such an approach, which is well described in general medical and surgical patients, would provide prognostic information with respect to the risk of cardiac complications and overall mortality in patients undergoing hematopoietic cell transplantation (HCT) for blood malignancy. We retrospectively subjected ECGs obtained pre-HCT to an externally trained, deep learning model designed to predict risk of atrial fibrillation (AF). Included were 1,377 patients (849 autologous HCT and 528 allogeneic HCT recipients). Median follow-up was 2.9 years. The three-year cumulative incidence of AF was 9% (95% CI: 7-12%) in autologous HCT patients and 13% (10-16%) in allogeneic HCT patients. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with development of clinical AF (Hazard Ratio (HR) 7.37, 3.53-15.4, p <0.001), inferior overall survival (HR: 2.4; 1.3-4.5, p = 0.004), and greater risk of non-relapse mortality (HR 3.36, 1.39-8.13, p = 0.007), without increased risk of relapse. Significant associations with mortality were only noted in allo HCT recipients, where the risk of non-relapse mortality was greater. Compared to calcineurin inhibitor-based graft versus host disease prophylaxis, the use of post-transplantation cyclophosphamide resulted in greater 90-day incidence of AF (13% versus 5%, p = 0.01), corresponding to temporal changes in AI-ECG AF prediction post HCT. In summary, AI-ECG can inform risk of post-transplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment after HCT.
Keyphrases
- artificial intelligence
- deep learning
- atrial fibrillation
- end stage renal disease
- ejection fraction
- newly diagnosed
- risk assessment
- risk factors
- cell cycle arrest
- bone marrow
- healthcare
- big data
- heart rate
- heart rate variability
- type diabetes
- cardiovascular events
- left ventricular
- cell proliferation
- blood pressure
- convolutional neural network
- low dose
- body composition
- oxidative stress
- mesenchymal stem cells
- signaling pathway
- venous thromboembolism
- cell therapy
- left atrial
- high intensity
- high dose
- climate change
- optical coherence tomography
- adverse drug