Photo Credit: Wand Prapan
AI-ECG may have clinical implications and provide additional insight into general risk stratification among patients with Takotsubo cardiomyopathy.
Artificial intelligence-augmented ECG (AI-ECG) may help identify underlying patterns associated with worse outcomes in patients with Takotsubo cardiomyopathy (TC), which is beneficial for stratifying high-risk patients with TC, according to recent study findings.
“In [TC], there is a lack of evidence and predictive tools for recurrences and worse outcomes despite an increase in the incidence of TC,” said Amir Lerman, MD, and colleagues in their study, which was posted online in the Journal of the American Heart Association.
“Recent studies have indicated high rates of future major adverse cardiovascular events [MACE] in patients with [TC], but there is no well‐established tool for risk stratification,” they stressed.
Rates of MACE in Patients With TC
According to Dr. Lerman and colleagues, TC, also known as stress‐induced cardiomyopathy or apical ballooning syndrome, was once thought to be a benign syndrome. Although it is a reversible cardiomyopathy, this patient population has higher rates of subsequent MACE events.
Due to an incomplete understanding of TC’s underlying pathophysiological mechanisms, the study authors stress there is still a lack of evidence and predictive tools for recurrences and worse outcomes—despite increased recognition and improved technologies for TC evaluation.
“Considering the recent reports indicating an increase in the incidence of TC, risk stratification and prediction tools in patients with TC are of clinical importance,” Dr. Lerman and colleagues said.
Utilizing AI in Cardiology Care
The study authors highlighted the recent application of AI algorithms to 12‐lead ECG. They explained that this made it possible to detect multiple, complex, nonlinear changes in the ECG and create a powerful, noninvasive biomarker for cardiovascular disease. This is because the ECG test is rapid, simple, reproducible, and low-cost.
With these benefits in mind, they hypothesized that AI‐ECG parameters might reflect ECG correlates of underlying nontransient myocardial dysfunction. Further, the authors said it may detect subtle underlying patterns associated with worse outcomes in patients with TC.
To better understand the prognostic value of the developed and validated AI‐ECG algorithms in patients with TC, Dr. Lerman and colleagues observed 305 patients who met the study’s TC criteria.
According to the study findings, 81 patients enrolled in the study (26.6%) experienced MACE during the follow‐up of 4.8 (2.3–9.1) years, including 17 cardiovascular deaths (5.6%), 21 recurrences of TC (6.9%), 11 nonfatal MIs (3.6%), 28 heart failure admissions (9.2%), and 4 strokes (1.3%). Of note, TC recurrence was not fatal in any study participant.
Dr Lerman and colleagues said, “patients with TC who experienced subsequent MACE were more likely to have a history of hypertension, AF, chronic heart failure (CHF), renal insufficiency, and higher GEIST prognosis score than those without MACE (P=0.026, 0.008, 0.002, <0.001, respectively).”
AI-ECG showed a higher frequency in the history of hypertension, AF, CHF, and renal insufficiency, as well as higher GEIST prognosis scores among patients with TC who experienced subsequent MACE.
The Future of AI-ECG in Clinical Practice
“The number of high‐risk AI‐ECG findings could provide significant predictive efficacy of MACE, independent of conventional risk factors,” said Dr. Lerman and colleagues.
Based on the findings of this recent study, the authors highlighted that AI-ECG can be used as both a digital biomarker to identify underlying nontransient myocardial dysfunction and a detection tool for subtle underlying patterns associated with worse outcomes in patients with TC.
“Given the long‐term mortality of patients with TC comparable to that of acute coronary syndrome despite its reversible nature in most cases, risk stratification using AI‐ECG algorithms potentially provides identification of high‐risk patients with TC who require early intensive therapeutic strategies, leading to the prevention of subsequent major adverse cardiac and cerebrovascular events,” Dr. Lerman and colleagues stressed.
Due to the need for accurate risk stratification and therapeutic strategies, the authors concluded that AI-ECG may have clinical implications and provide additional insight into general risk stratification for this patient population.
“The risk stratification based on [AI-ECG] findings might provide additional insight into the management of patients with TC,” they said.
