Kakuta, Tsunekazu

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: Mechanisms underlying bioprosthetic valve deterioration (BVD) are multifactorial and incompletely elucidated. Reparative circulating progenitor cells (CPCs), and conversely calcification-associated osteocalcin expressing CPCs (CPC-OCN), have been linked to native aortic valve deterioration. However, their role in BVD remains elusive. This study sought to evaluate the contribution of different subpopulations of CPCs in BVD. Methods: This prospective study enrolled 121 patients who had peripheral blood mononuclear cells (PBMC) isolated prior to bioprosthetic aortic valve replacement (surgical 104, transcatheter 17) and had an echocardiographic follow-up ≥2 years after the procedure. Using flow cytometry, fresh PBMC were analyzed for the surface markers CD34, CD133, and osteocalcin (OCN). BVD was evaluated by hemodynamic valve deterioration (HVD) using echocardiography, which was defined as an elevated mean transprosthetic gradient ≥30mmHg and/or at least moderate intraprosthetic regurgitation. Multivariable models were constructed to evaluate the association of each subpopulation of CPCs and other clinical characteristics with HVD. Results: Sixteen patients (13.2%) developed HVD during follow-up for a median of 5.9 years. Patients with HVD showed significantly lower levels of reparative CD34 + CD133 + cells and higher levels of OCN + cells than those without HVD (CD34 + CD133 + cells: 125 [80, 210] vs 270 [130, 420], P=0.002; OCN + cells: 3060 [523, 5528] vs 670 [180, 1930], P=0.005 respectively). Decreased level of CD34 + CD133 + cells was a significant predictor of HVD. Conclusions: Circulating levels of CD34 + CD133 + cells and OCN + cells were significantly associated with the subsequent occurrence of HVD in patients undergoing bioprosthetic aortic valve replacement. CPCs might play a vital role in the mechanism, risk stratification and a potential therapeutic target for patients with bioprosthetic valve deterioration.

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.

Background Mechanisms underlying bioprosthetic valve deterioration are multifactorial and incompletely elucidated. Reparative circulating progenitor cells, and conversely calcification‐associated osteocalcin expressing circulating progenitor cells, have been linked to native aortic valve deterioration. However, their role in bioprosthetic valve deterioration remains elusive. This study sought to evaluate the contribution of different subpopulations of circulating progenitor cells in bioprosthetic valve deterioration. Methods and Results This single‐center prospective study enrolled 121 patients who had peripheral blood mononuclear cells isolated before bioprosthetic aortic valve replacement and had an echocardiographic follow‐up ≥2 years after the procedure. Using flow cytometry, fresh peripheral blood mononuclear cells were analyzed for the surface markers CD34, CD133, and osteocalcin. Bioprosthetic valve deterioration was evaluated by hemodynamic valve deterioration (HVD) using echocardiography, which was defined as an elevated mean transprosthetic gradient ≥30 mm Hg or at least moderate intraprosthetic regurgitation. Sixteen patients (13.2%) developed HVD during follow‐up for a median of 5.9 years. Patients with HVD showed significantly lower levels of reparative CD34 + CD133 + cells and higher levels of osteocalcin‐positive cells than those without HVD (CD34 + CD133 + cells: 125 [80, 210] versus 270 [130, 420], P =0.002; osteocalcin‐positive cells: 3060 [523, 5528] versus 670 [180, 1930], P =0.005 respectively). Decreased level of CD34 + CD133 + cells was a significant predictor of HVD (hazard ratio, 0.995 [95% CI, 0.990%–0.999%]). Conclusions Circulating levels of CD34 + CD133 + cells and osteocalcin‐positive cells were significantly associated with the subsequent occurrence of HVD in patients undergoing bioprosthetic aortic valve replacement. Circulating progenitor cells might play a vital role in the mechanism, risk stratification, and a potential therapeutic target for patients with bioprosthetic valve deterioration.