Adedinsewo, Demilade A

Background Studies suggest that hypoperfusion from heart failure (HF) may contribute to cognitive decline. However, the relationship between the HF subtypes and cognition remains poorly described in real‐world populations. Methods We conducted a cross‐sectional study of patients aged ≥18 years seen at the Mayo Clinic in 2022 who underwent echocardiogram and cognitive assessment within 1 year. HF was classified per the 2022 American Heart Association as no HF, HF with preserved ejection fraction (EF), HF with mildly reduced EF, and HF with reduced EF. Cognitive impairment was categorized using the Montreal Cognitive Assessment score as normal (≥26), mild (18–25), and moderate to severe (<18). Linear regression assessed EF–Montreal Cognitive Assessment correlation and logistic regression the association between HF subtypes and cognitive dysfunction, adjusting for age, sex, race, and vascular risk factors. Results A total of 1592 patients met inclusion criteria (mean age, 75.7±13.8 years; 48.5% women). Lower EF was associated with worse cognitive performance (postadjustment β=0.033, P =0.013). Patients with HF with reduced EF and patients with HF with preserved EF had significantly lower Montreal Cognitive Assessment scores than those without HF (17.6±6.2 and 18.9±5.8 versus 19.9±6.0, P <0.001). The prevalence of moderate to severe cognitive impairment was highest in HF with reduced EF (43.3%), followed by HF with preserved EF (34.4%), no heart failure (28.6%), and HF with mildly reduced EF (23.4%) (adjusted trend P =0.032). Conclusions HF, independent of ejection fraction alone, is associated with cognitive impairment. Further studies should confirm these findings and evaluate the impact of interventions targeting EF and HF subtypes on cognitive outcomes.

BACKGROUND: The incidence and prognosis of right bundle branch block (RBBB) following transcatheter aortic valve replacement (TAVR) are unknown. Hence, we sought to characterize the incidence of post-TAVR RBBB and determine associated risks of permanent pacemaker (PPM) implantation and mortality. METHODS: All patients 18 years and above without preexisting RBBB or PPM who underwent TAVR at US Mayo Clinic sites and Mayo Clinic Health Systems from June 2010 to May 2021 were evaluated. Post-TAVR RBBB was defined as new-onset RBBB in the postimplantation period. The risks of PPM implantation (within 90 days) and mortality following TAVR were compared for patients with and without post-TAVR RBBB using Kaplan-Meier analysis and Cox proportional hazards modeling. The risks of PPM implantation (within 90 days) and mortality following TAVR were compared for patients with and without post-TAVR RBBB using Kaplan-Meier analysis and Cox proportional hazards modeling. RESULTS: Of 1992 patients, 15 (0.75%) experienced new RBBB post-TAVR. There was a higher degree of valve oversizing among patients with new RBBB post-TAVR versus those without (17.9% versus 10.0%; P =0.034). Ten patients (66.7%) with post-TAVR RBBB experienced high-grade atrioventricular block and underwent PPM implantation (median 1 day; Q1, 0.2 and Q3, 4), compared with 268/1977 (13.6%) without RBBB. Following propensity score adjustment for covariates (age, sex, balloon-expandable valve, annulus diameter, and valve oversizing), post-TAVR RBBB was significantly associated with PPM implantation (hazard ratio, 8.36 [95% CI, 4.19–16.7]; P <0.001). No statistically significant increase in mortality was seen with post-TAVR RBBB (hazard ratio, 0.83 [95% CI, 0.33–2.11]; P =0.69), adjusting for age and sex. CONCLUSIONS: Although infrequent, post-TAVR RBBB was associated with elevated PPM implantation risk. The mechanisms for its development and its clinical prognosis require further study.

Background Although there has been a decrease in the incidence of ST‐segment–elevation myocardial infarction (STEMI) in the United States, this trend might be stagnant or increasing in young women. We assessed the trends, characteristics, and outcomes of STEMI in women aged 18 to 55 years. Methods and Results We identified 177 602 women aged 18 to 55 with the primary diagnosis of STEMI from the National Inpatient Sample during years 2008 to 2019. We performed trend analyses to assess hospitalization rates, cardiovascular disease (CVD) risk factor profile, and in‐hospital outcomes stratified by three age subgroups (18–34, 35–44, and 45–55 years). We found STEMI hospitalization rates were decreased in the overall study cohort from 52 per 100 000 hospitalizations in 2008 to 36 per 100 000 in 2019. This was driven by decreased proportion of hospitalizations in women aged 45 to 55 years (74.2% to–71.7%; P <0.001). Proportion of STEMI hospitalizationincreased in women aged 18–34 (4.7%–5.5%; P <0.001) and 35–44 years (21.2%–22.7%; P <0.001). The prevalence of traditional and non‐traditional female‐specific or female‐predominant CVD risk factors increased in all age subgroups. The adjusted odds of in‐hospital mortality in the overall study cohort and age subgroups were unchanged throughout the study period. Additionally, we observed an increase in the adjusted odds of cardiogenic shock, acute stroke, and acute kidney injury in the overall cohort over the study period. Conclusions STEMI hospitalizations are increasing among women aged <45 years, and in‐hospital mortality has not changed over the past 12 years in women aged <55 years. Future studies on the optimization of risk assessment and management of STEMI in young women are urgently needed.

AbstractBackgroundElevated transmitral gradient post transcatheter mitral valve edge‐to‐edge repair (TEER) has been associated with worse outcomes. Whether an elevated baseline transmitral diastolic mean gradient (MG) ≥5 mmHg is associated with hemodynamic outcomes after TEER is unknown.MethodsA total of 164 consecutive patients undergoing TEER at Mayo Clinic between June 2014 and May 2018 were analyzed in this retrospective study. Baseline demographics, as well as clinical, echocardiographic, and procedural data were obtained. Data on direct left atrial pressure (LAP) before and after TEER were recorded. Logistic regression models were constructed to evaluate the association between preprocedure transmitral diastolic mean gradient (pre‐MG) and (1) improvement in LAP following TEER, (2) postprocedure transmitral diastolic mean gradient (post‐MG). A decrease in LAP post TEER was considered an improvement in hemodynamic response. Pre‐MG was categorized as: ≥5 and <5 mmHg.ResultsMedian age of the cohort was 81.5 years (Q1: 76.3, Q3: 87) and 34% were female. At baseline, median transmitral diastolic MG was 4 mmHg (Q1: 3, Q3: 5) and median LAP was 19 mmHg (Q1:16, Q3: 23.5). In a multivariable model, adjusted for age and sex, patients with pre‐MG ≥ 5 mmHg were less likely to see an improvement in LAP post TEER (adjusted odds ratio [aOR]: 0.22, 95% confidence interval [CI]: 0.09, 0.55; p = 0.001) and more likely to have elevated post‐MG (aOR; 7.08, 95% CI: 2.93, 17.13; p < 0.001).ConclusionHigher pre‐MG (≥5 mmHg) was associated with a lower reduction in LAP and higher residual transmitral gradient following TEER suggesting other potential contributors to increased LAP besides mitral regurgitation as a cause of elevated baseline MG.

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

Background: Transcatheter aortic valve replacement (TAVR) is now an approved alternative to surgical aortic valve replacement for the treatment of severe aortic stenosis. As the clinical adoption of TAVR expands, it remains important to identify predictors of mortality after TAVR. We aimed to evaluate the impact of sex differences in aortic valve calcium score (AVCS) on long-term mortality following TAVR in a large patient sample. Methods: We included consecutive patients who successfully underwent TAVR for treatment of severe native aortic valve stenosis from June 2010 to May 2021 across all US Mayo Clinic sites with follow-up through July 2021. AVCS values were obtained from preoperative computed tomography of the chest. Additional clinical data were abstracted from medical records. Kaplan-Meier curves and Cox-proportional hazard regression models were employed to evaluate the effect of AVCS on long-term mortality. Results: A total of 2543 patients were evaluated in the final analysis. Forty-one percent were women, median age was 82 years (Q1: 76, Q3: 86), 18.4% received a permanent pacemaker following TAVR, and 88.5% received a balloon expandable valve. We demonstrate an increase in mortality risk with higher AVCS after multivariable adjustment ( P <0.001). When stratified by sex, every 500-unit increase in AVCS was associated with a 7% increase in mortality risk among women (adjusted hazard ratio, 1.07 [95% CI, 1.02–1.12]) but not in men. Conclusions: We demonstrate a notable sex difference in the association between AVCS and long-term mortality in a large TAVR patient sample. This study highlights the potential value of AVCS in preprocedural risk stratification, specifically among women undergoing TAVR. Additional studies are needed to validate this finding.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman’s life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes—termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence–enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.