Expanded adaptation of an artificial intelligence model for predicting chemotherapy-induced cardiotoxicity using baseline electrocardiograms
Authors: Yagi R , Goto S , MacRae C A , Deo R C 
Published: October 2022

Introduction

While effective as chemotherapeutics, anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD), which adversely affects the prognosis of patients with malignancies1–5. Despite guideline recommendations6–9, repeated echocardiograms are rarely performed10 with delayed diagnosis of CTRCD leading to unrecoverable cardiac dysfunction11. Recently, artificial intelligence (AI) was shown to be capable of detecting reduced left ventricular ejection fraction (LVEF) solely from electrocardiogram (ECG)12. Furthermore, this model was predictive of a future decrease in LVEF. Therefore, we hypothesized that an AI model detecting reduced LVEF (AI-EF model) could predict CTRCD from ECGs.

Purpose

To assess whether the AI-EF model could detect patients at a high risk of CTRCD by analyzing ECGs taken immediately prior to the initiation of cardiotoxic chemotherapy.

Methods

Among patients who received chemotherapy with a regimen including anthracyclines in two institutions between June 1st, 2015 and October 1st, 2020, those who underwent both an ECG and echocardiogram ≤90 days prior to initial treatment were selected. The ECGs were analyzed by the AI-EF model and patients were divided into two groups according to the scores from the model. CTRCD was defined as LVEF &lt;53% and ≥10% decrease in LVEF from the baseline at any time after the start of chemotherapy13. The cumulative incidence of CTRCD was compared for the two groups using Kaplan-Meier curves, log-rank test, a univariate Cox proportional hazard model, and a multivariable Cox proportional hazard model adjusting for known risk factors for CTRCD. Finally, a prediction model for CTRCD using readily available clinical variables with the AI-EF score was compared with the model using the same variables without the AI-EF score.

Results

1,158 patients were included in this study. 99 of them developed CTRCD during follow-up. The AI-EF model displayed excellent risk stratification of developing CTRCD: while 7.1% in the low AI-EF score group developed CTRCD, 12.9% of the patients in the high AI-EF score group developed CTRCD (hazard ratio (HR), 2.14; 95% confidence interval (CI), 1.43–3.19; log-rank p&lt;0.001; Figure 1). This finding was robust across subgroups such as cancer types, the initial dose of anthracycline and baseline LVEF, and consistent after adjusting for multiple risk factors (adjusted HR, 2.10; 95% CI, 1.37–3.22; p&lt;0.001; Figure 2). Furthermore, the addition of the AI-EF score significantly improved the accuracy of predicting CTRCD compared to clinical features alone (time-dependent area under the received operating curve (AUROC) for 2 years, 77.1; 95% CI, 71.8–82.3 for the model with AI-EF score and AUROC 73.9; 95% CI, 69.0–80.1 for the model without AI-EF score; p=0.02).

Conclusion

The AI-EF model, by utilizing baseline ECG, could stratify patients according to the risk of CTRCD and robustly augmented CTRCD prediction.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): American Heart AssociationVerily