Tryon, David

Background Recent studies have indicated high rates of future major adverse cardiovascular events in patients with Takotsubo cardiomyopathy (TC), but there is no well‐established tool for risk stratification. This study sought to evaluate the prognostic value of several artificial intelligence‐augmented ECG (AI‐ECG) algorithms in patients with TC. Methods and Results This study examined consecutive patients in the prospective and observational Mayo Clinic Takotsubo syndrome registry. Several previously validated AI‐ECG algorithms were used for the estimation of ECG‐ age, probability of low ejection fraction, and probability of atrial fibrillation. Multivariable models were constructed to evaluate the association of AI‐ECG and other clinical characteristics with major adverse cardiac events, defined as cardiovascular death, recurrence of TC, nonfatal myocardial infarction, hospitalization for congestive heart failure, and stroke. In the final analysis, 305 patients with TC were studied over a median follow‐up of 4.8 years. Patients with future major adverse cardiac events were more likely to be older, have a history of hypertension, congestive heart failure, worse renal function, as well as high‐risk AI‐ECG findings compared with those without. Multivariable Cox proportional hazards analysis indicated that the presence of 2 or 3 high‐risk findings detected by AI‐ECG remained a significant predictor of major adverse cardiac events in patients with TC after adjustment by conventional risk factors (hazard ratio, 4.419 [95% CI, 1.833–10.66], P =0.001). Conclusions The combined use of AI‐ECG algorithms derived from a single 12‐lead ECG might detect subtle underlying patterns associated with worse outcomes in patients with TC. This approach might be beneficial for stratifying high‐risk patients with TC.

Introduction: Recent studies have indicated high rates of future major adverse cardiovascular events in patients with Takotsubo cardiomyopathy (TTC). This study sought to evaluate the prognostic value of our institutional artificial intelligence-augmented electrocardiography (AI-ECG) algorithms in patients with TTC. Methods: This study enrolled consecutive patients in the prospective and observational Mayo Clinic Takotsubo syndrome registry. TTC was diagnosed according to the Mayo Clinic diagnostic. Our clinic’s previously developed and validated AI-ECG algorithms were used for detection of ECG-Age, probability of low ejection fraction, and probability of atrial fibrillation.Multivariable models were constructed to evaluate the association of AI-ECG and other clinical characteristics with major adverse cardiac events (MACE), defined as cardiac death, recurrence of TTC, nonfatal myocardial infarction, hospitalization for congestive heart failure, and stroke. Results: In the final analysis, 305 patients were studied over a median follow-up of 4.8 years. Patients with MACE were more likely to be older, have history of hypertension and chronic heart failure (CHF), worse renal function as well as having high-risk AI-ECG findings compared to those without. In the multivariate Cox proportional hazard analysis, the presence of 3 or 4 high-risk findings detected by AI-ECG, remained a significant predictor of MACE in the patients with TTC after adjusted by conventional risk factors (hazard ratio (HR) 2.17, 95% CI [1.33, 3.53], P=0.002; HR 2.44, 95% CI [1.38, 4.29], P=0.002, respectively). Conclusions: The combined use of developed AI-ECG algorithms derived from a single 12-lead ECG might identify ECG correlates of underlying non-transient myocardial dysfunction and detect subtle underlying patterns associated with worse outcomes in patients with TTC. This approach might be beneficial for stratifying high-risk patients with TTC.