Korleski Jack B, Koch Regina M, Ho Thanh P, Robinson Steven I, Okuno Scott H, Herrmann Joerg, Siontis Brittany L
Department of Internal Medicine, Mayo Clinic, Rochester MN.
Department of Oncology, Mayo Clinic, Rochester MN.
Mayo Clin Proc Digit Health. 2025 Jul 4;3(3):100247. doi: 10.1016/j.mcpdig.2025.100247. eCollection 2025 Sep.
The objective of this study was to understand the utility of artificial intelligence-enabled electrocardiogram (AI-ECG) to assess the tolerability of anthracycline chemotherapy.
From December 18, 2006 to October 15, 2020, we identified adults with sarcoma who were treated with anthracycline chemotherapy at our institution who had an ECG within 1 year prior to treatment initiation. Utilizing previously defined AI-ECG nomograms, we obtained age and ejection fraction (EF) predictions. Changes in AI-ECG age were correlated with chemotherapy tolerance (the rates of dose reductions, treatment delays, and early discontinuation). We measured the sensitivity and specificity of the ECG to predict an EF of less than 50% or 35% prior to treatment and compared how changes in the AI-ECG EF prediction related to changes in echocardiography-based EF.
Forty patients met the eligibility criteria. Sixty-eight percent of the patients were men. The median age was 56.5 years (18-76 years). We did not find differences in chemotherapy tolerance between patients who had an elevated or decreased ECG age. There was a trend `toward higher rates of dose reductions in patients with high ECG aging (odds ratio, 5.13; =.32). The AI-ECG low EF prediction had a sensitivity of 100% and a specificity of 94% to isolate patients with an EF of less than 50% prior to treatment. Two patients' EF decreased more than 10% after treatment, and both cases showed significant increases in the low EF prediction.
Overall, AI-based predictions on ECG tracings could be a way to monitor for decreases in EF during treatment with anthracycline chemotherapy. We recommend further studies to evaluate AI-ECG aging as a marker for chemotherapy tolerance.
本研究的目的是了解人工智能心电图（AI-ECG）在评估蒽环类化疗耐受性方面的效用。
从2006年12月18日至2020年10月15日，我们确定了在本机构接受蒽环类化疗的成年肉瘤患者，这些患者在开始治疗前1年内进行了心电图检查。利用先前定义的AI-ECG列线图，我们获得了年龄和射血分数（EF）预测值。AI-ECG年龄的变化与化疗耐受性（剂量减少率、治疗延迟率和早期停药率）相关。我们测量了心电图预测治疗前EF小于50%或35%的敏感性和特异性，并比较了AI-ECG EF预测值的变化与基于超声心动图的EF变化之间的关系。
40例患者符合纳入标准。68%的患者为男性。中位年龄为56.5岁（18 - 76岁）。我们未发现心电图年龄升高或降低的患者在化疗耐受性方面存在差异。心电图年龄增长较高的患者有剂量减少率更高的趋势（比值比，5.13；P =.32）。AI-ECG低EF预测在识别治疗前EF小于50%的患者时，敏感性为100%，特异性为94%。两名患者治疗后EF下降超过10%，且两例低EF预测值均显著增加。
总体而言，基于人工智能对心电图描记的预测可能是监测蒽环类化疗期间EF降低的一种方法。我们建议进一步研究以评估AI-ECG年龄增长作为化疗耐受性标志物的情况。
