Differential associations between abnormal cardiac left ventricular geometry types and cerebral white matter disease.

OBJECTIVES: Reduced cardiac outflow due to left ventricular hypertrophy has been suggested as a potential risk factor for development of cerebral white matter disease. Our study aimed to examine the correlation between left ventricular geometry and white matter disease volume to establish a clearer understanding of their relationship, as it is currently not well-established. METHODS: Consecutive patients from 2016 to 2021 who were ≥18 years and underwent echocardiography, cardiac MRI, and brain MRI within one year were included. Four categories of left ventricular geometry were defined based on left ventricular mass index and relative wall thickness on echocardiography. White matter disease volume was quantified using an automated algorithm applied to axial T2 FLAIR images and compared across left ventricular geometry categories. RESULTS: We identified 112 patients of which 34.8 % had normal left ventricular geometry, 20.5 % had eccentric hypertrophy, 21.4 % had concentric remodeling, and 23.2 % had concentric hypertrophy. White matter disease volume was highest in patients with concentric hypertrophy and concentric remodeling, compared to eccentric hypertrophy and normal morphology with a trend-P value of 0.028. Patients with higher relative wall thickness had higher white matter disease volume (10.73 ± 10.29 cc vs 5.89 ± 6.46 cc, P = 0.003), compared to those with normal relative wall thickness. CONCLUSION: Our results showed that abnormal left ventricular geometry is associated with higher white matter disease burden, particularly among those with abnormal relative wall thickness. Future studies are needed to explore causative relationships and potential therapeutic options that may mediate the adverse left ventricular remodeling and its effect in slowing white matter disease progression.

Incidence and Outcomes of New-Onset Right Bundle Branch Block Following Transcatheter Aortic Valve Replacement.

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.

Health Disparities, Clinical Trials, and the Digital Divide.

In the past few years, there have been rapid advances in technology and the use of digital tools in health care and clinical research. Although these innovations have immense potential to improve health care delivery and outcomes, there are genuine concerns related to inadvertent widening of the digital gap consequentially exacerbating health disparities. As such, it is important that we critically evaluate the impact of expansive digital transformation in medicine and clinical research on health equity. For digital solutions to truly improve the landscape of health care and clinical trial participation for all persons in an equitable way, targeted interventions to address historic injustices, structural racism, and social and digital determinants of health are essential. The urgent need to focus on interventions to promote health equity was made abundantly clear with the coronavirus disease 2019 pandemic, which magnified long-standing social and racial health disparities. Novel digital technologies present a unique opportunity to embed equity ideals into the ecosystem of health care and clinical research. In this review, we examine racial and ethnic diversity in clinical trials, historic instances of unethical research practices in biomedical research and its impact on clinical trial participation, and the digital divide in health care and clinical research, and we propose suggestions to achieve digital health equity in clinical trials. We also highlight key digital health opportunities in cardiovascular medicine and dermatology as exemplars, and we offer future directions for development and adoption of patient-centric interventions aimed at narrowing the digital divide and mitigating health inequities.

Managing hypertension in African Americans with heart failure: A guide for the primary care clinician.

Hypertension is the predominant risk factor for cardiovascular disease related morbidity and mortality among Black adults in the United States. It contributes significantly to the development of heart failure and increases the risk of death following heart failure diagnosis. It is also a leading predisposing factor for hypertensive disorders of pregnancy and peripartum cardiomyopathy in Black women. As such, all stakeholders including health care providers, particularly primary care clinicians (including physicians and advanced practice providers), patients, and communities must be aware of the consequences of uncontrolled hypertension among Black adults. Appropriate treatment strategies should be identified and implemented to ensure timely and effective blood pressure management among Black individuals, particularly those with, and at risk for heart failure.

Using Technology to Deliver Cardiovascular Care in African Countries.

PURPOSE OF REVIEW: This review aims to explore the applications of digital technology in cardiovascular care across African countries. It highlights the opportunities and challenges associated with leveraging technology to enhance patient self-monitoring, remote patient-clinician interactions, telemedicine, clinician and patient education, and research facilitation. The purpose is to highlight how technology can transform cardiovascular care in Africa. RECENT FINDINGS: Recent findings indicate that the increasing penetration of mobile phones and internet connectivity in Africa offers a unique opportunity to improve cardiovascular care. Smartphone-based applications and text messaging services have been employed to promote self-monitoring and lifestyle management, although challenges related to smartphone ownership and digital literacy persist. Remote monitoring of patients by clinicians using home-based devices and wearables shows promise but requires greater accessibility and validation studies in African populations. Telemedicine diagnosis and management of cardiovascular conditions demonstrates significant potential but faces adoption challenges. Investing in targeted clinician and patient education on novel digital technology and devices as well as promoting technology-assisted research for participant recruitment and data collection can facilitate cardiovascular care advancements in Africa. Technology has the potential to revolutionize cardiovascular care in Africa by improving access, efficiency, and patient outcomes. However, barriers related to limited resources, supportive infrastructure, digital literacy, and access to devices must be addressed. Strategic actions, including investment in digital infrastructure, training programs, community collaboration, and policy advocacy, are crucial to ensuring equitable integration of digital health solutions.

Leveraging Digital Health to Improve the Cardiovascular Health of Women.

Purpose of Review: In this review, we present a comprehensive discussion on the population-level implications of digital health interventions (DHIs) to improve cardiovascular health (CVH) through sex- and gender-specific prevention strategies among women. Recent Findings: Over the past 30 years, there have been significant advancements in the diagnosis and treatment of cardiovascular diseases, a leading cause of morbidity and mortality among men and women worldwide. However, women are often underdiagnosed, undertreated, and underrepresented in cardiovascular clinical trials, which all contribute to disparities within this population. One approach to address this is through DHIs, particularly among racial and ethnic minoritized groups. Implementation of telemedicine has shown promise in increasing adherence to healthcare visits, improving BP monitoring, weight control, physical activity, and the adoption of healthy behaviors. Furthermore, the use of mobile health applications facilitated by smart devices, wearables, and other eHealth (defined as electronically delivered health services) modalities has also promoted CVH among women in general, as well as during pregnancy and the postpartum period. Overall, utilizing a digital health approach for healthcare delivery, decentralized clinical trials, and incorporation into daily lifestyle activities has the potential to improve CVH among women by mitigating geographical, structural, and financial barriers to care. Summary: Leveraging digital technologies and strategies introduces novel methods to address sex- and gender-specific health and healthcare disparities and improve the quality of care provided to women. However, it is imperative to be mindful of the digital divide in specific populations, which may hinder accessibility to these novel technologies and inadvertently widen preexisting inequities.

Impact of Pulmonary Hypertension Hemodynamic Phenotype on Incident Atrial Fibrillation.

INTRODUCTION: Atrial fibrillation/flutter (AF) is common among patients with pulmonary hypertension (PH) and is associated with poor clinical outcomes. AF has been shown to occur more commonly among patients with postcapillary PH, although AF also occurs among patients with precapillary PH. The goal of this study was to evaluate the independent impact of PH hemodynamic phenotype on incident AF among patients with PH. METHODS: We retrospectively identified 262 consecutive patients, without a prior diagnosis of atrial arrhythmias, seen at the PH clinic at Mayo Clinic, Florida, between 1997 and 2017, who had right heart catheterization and echocardiography performed, with follow-up for outcomes through 2021. Kaplan-Meier analysis and Cox-proportional hazards regression modeling were used to evaluate the independent effect of PH hemodynamic phenotype on incident AF. RESULTS: Our study population was classified into two broad PH hemodynamic groups: precapillary (64.9%) and postcapillary (35.1%). The median age was 59.5 years (Q1: 48.4, Q3: 68.4), and 72% were female. In crude models, postcapillary PH was significantly associated with incident AF (HR 2.17, 95% CI: 1.26-3.74, p = 0.005). This association was lost following multivariable adjustment, whereas left atrial volume index remained independently associated with incident AF (aHR 1.30, 95% CI: 1.09-1.54, p = 0.003). CONCLUSION: We found PH hemodynamic phenotype was not significantly associated with incident AF in our patient sample; however, echocardiographic evidence of left atrial remodeling appeared to have a greater impact on AF development. Larger studies are needed to validate these findings and identify potential modifiable risk factors for AF in this population.

Utility of an Artificial Intelligence Enabled Electrocardiogram for Risk Assessment in Liver Transplant Candidates.

BACKGROUND: Post-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown. AIMS: The aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant. METHODS: A retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation. RESULTS: The performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction. CONCLUSIONS: A positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice.

Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model.

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.

Screening for peripartum cardiomyopathies using artificial intelligence in Nigeria (SPEC-AI Nigeria): Clinical trial rationale and design.

BACKGROUND: Artificial intelligence (AI), and more specifically deep learning, models have demonstrated the potential to augment physician diagnostic capabilities and improve cardiovascular health if incorporated into routine clinical practice. However, many of these tools are yet to be evaluated prospectively in the setting of a rigorous clinical trial-a critical step prior to implementing broadly in routine clinical practice. OBJECTIVES: To describe the rationale and design of a proposed clinical trial aimed at evaluating an AI-enabled electrocardiogram (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria. DESIGN: The protocol will enroll 1,000 pregnant and postpartum women who reside in Nigeria in a prospective randomized clinical trial. Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. Women aged 18 and older, seen for routine obstetric care at 6 sites (2 Northern and 4 Southern) in Nigeria will be included. Participants will be randomized to the study intervention or control arm in a 1:1 fashion. This study aims to enroll participants representative of the general obstetric population at each site. The primary outcome is a new diagnosis of cardiomyopathy, defined as left ventricular ejection fraction (LVEF) < 50% during pregnancy or within 12 months postpartum. Secondary outcomes will include the detection of impaired left ventricular function (at different LVEF cut-offs), and exploratory outcomes will include the effectiveness of AI-ECG tools for cardiomyopathy detection, new diagnosis of cardiovascular disease, and the development of composite adverse maternal cardiovascular outcomes. SUMMARY: This clinical trial focuses on the emerging field of cardio-obstetrics and will serve as foundational data for the use of AI-ECG tools in an obstetric population in Nigeria. This study will gather essential data regarding the utility of the AI-ECG for cardiomyopathy detection in a predominantly Black population of women and pave the way for clinical implementation of these models in routine practice. TRIAL REGISTRATION: Clinicaltrials.gov: NCT05438576.

Trends and Outcomes of ST-Segment-Elevation Myocardial Infarction Among Young Women in the United States.

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.

The impact of baseline transmitral diastolic mean gradient on left atrial pressure reduction in patients undergoing transcatheter mitral valve edge-to-edge repair.

BACKGROUND: Elevated 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. METHODS: A 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. RESULTS: Median 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). CONCLUSION: Higher 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.

Community-based participatory research application of an artificial intelligence-enhanced electrocardiogram for cardiovascular disease screening: A FAITH! Trial ancillary study.

Objective: With the emergence of artificial intelligence (AI)-based health interventions, systemic racism remains a concern as these advancements are frequently developed without race-specific data analysis or validation. To evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of participants enrolled in a community-based clinical trial as a proof-of-concept ancillary study for community-based screening. Methods: Enrollees completed cardiovascular testing including standard 12-lead ECG and a limited echocardiogram (TTE). All ECGs were analyzed using previously published institution-based AI algorithms. AI-ECG predictions were generated for age, sex, and decreased left ventricular ejection fraction (LVEF). Diagnostic accuracy of the AI-ECG for decreased LVEF and sex was quantified using area under the receiver operating characteristic curve (AUC). Correlation between actual age and AI-ECG predicted age was assessed using Pearson correlation coefficients. Results: Fifty-four participants completed both an ECG and TTE (mean age 55 years [range 31-87 years]; 66.7% female). All participants were in sinus rhythm, and the median LVEF of the cohort was 60-65%. The AI-ECG for decreased LVEF demonstrated excellent performance with an AUC of 0.892 (95% confidence interval [CI] 0.708-1); sensitivity=50% (95% CI 9.5-90.5%; n=1/2) and specificity=96% (95% CI 86.8-98.9%; n=49/51). The AI-ECG for participant sex demonstrated similar performance with AUC of 0.944 (95% CI 0.891-0.998); sensitivity=100% (95% CI 82.4-100.0%; n=18/18) and specificity=77.8% (95% CI 61.9-88.3%; n=28/36). The AI-ECG predicted mean age was 55 years (range 26.9-72.6 years) with a strong correlation to actual age (R=0.769; p<0.001). Conclusion: Our analyses of previously developed AI-ECG algorithms for prediction of age, sex, and decreased LVEF demonstrated reliable performance in this community-based, African-American cohort. This novel, community-centric delivery of AI could provide valuable screening resources and appropriate referrals for early detection of highly-morbid CVD for under-resourced patient populations.

Real-world performance, long-term efficacy, and absence of bias in the artificial intelligence enhanced electrocardiogram to detect left ventricular systolic dysfunction.

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.

Sex Differences in the Impact of Aortic Valve Calcium Score on Mortality After Transcatheter Aortic Valve Replacement.

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.

Racial and Ethnic Disparities in the Trends and Outcomes of Cardiogenic Shock Complicating Peripartum Cardiomyopathy.

Importance: Cardiogenic shock (CS) is a recognized complication of peripartum cardiomyopathy (PPCM) associated with poor prognosis. Although racial and ethnic disparities have been described in the occurrence and outcomes of PPCM, it is unclear if these disparities persist among patients with PPCM and CS. Objectives: To evaluate the temporal trends in CS incidence among hospitalized patients with PPCM stratified by race and ethnicity and to investigate the racial and ethnic differences in hospital mortality, mechanical circulatory support (MCS) use, and heart transplantation (HT). Design, Setting, and Participants: This multicenter retrospective cohort study included hospitalized patients with PPCM complicated by CS in the US from 2005 to 2019 identified from the National Inpatient Sample (NIS). Data analysis was conducted in November 2021. Exposure: PPCM complicated by CS. Main Outcomes and Measures: The main outcome was incidence of CS in PPCM stratified by race and ethnicity. The secondary outcome was racial and ethnic differences in hospital mortality, MCS use, and HT. Results: Of 55 804 hospitalized patients with PPCM, 1945 patients had CS, including 947 Black patients, 236 Hispanic patients, and 702 White patients, translating to an incidence rate of 35 CS events per 1000 patients with PPCM. The mean (SD) age was 31 (9) years. Black and Hispanic patients had higher CS incidence rates (39 events per 1000 patients with PPCM) compared with White patients (33 events per 1000 patients with PPCM). CS incidence rates significantly increased across all races and ethnicities over the study period. Overall, the odds of developing CS were higher in Black patients (aOR, 1.17 [95% CI, 1.15-1.57]; P < .001) and Hispanic patients (aOR, 1.37 [95% CI, 1.17-1.59]; P < 001) compared with White patients during the study period. Compared with White patients, the odds of in-hospital mortality were higher in Black (adjusted odds ratio [aOR], 1.67 [95% CI, 1.21-2.32]; P = .002) and Hispanic (aOR, 2.20 [95% CI, 1.45-3.33]; P < .001) patients. Hispanic patients were more likely to receive any type of MCS device (aOR, 2.23 [95% CI, 1.60-3.09]; P < .001), intraaortic balloon pump (aOR, 1.65 [95% CI, 1.11-2.44]; P < .001), and ventricular assisted device (aOR, 4.45 [95% CI, 2.45-8.08]; P < .001), compared with White patients. Black patients were more likely to receive VAD (aOR, 2.69 [95% CI, 1.63-4.42]; P < .001) compared with White patients. Black and Hispanic patients were significantly less likely to receive HT compared with White patients (Black patients: aOR, 0.51 [95% CI, 0.33-0.78]; P = .02; Hispanic patients: aOR, 0.15 [95% CI, 0.06-0.42]; P < .001). Conclusions and Relevance: These findings highlight significant racial disparities in mortality and HT among hospitalized patients with PPCM complicated by CS in the US. More research to identify factors of racial and ethnic disparities is needed to guide interventions to improve outcomes of patients with PPCM.

Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications.

Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.

Evaluating atrial fibrillation artificial intelligence for the ED: statistical and clinical implications.

OBJECTIVE: An artificial intelligence (AI) algorithm has been developed to detect the electrocardiographic signature of atrial fibrillation (AF) present on an electrocardiogram (ECG) obtained during normal sinus rhythm. We evaluated the ability of this algorithm to predict incident AF in an emergency department (ED) cohort of patients presenting with palpitations without concurrent AF. METHODS: This retrospective study included patients 18 years and older who presented with palpitations to one of 15 ED sites and had a 12­lead ECG performed. Patients with prior AF or newly diagnosed AF during the ED visit were excluded. Of the remaining patients, those with a follow up ECG or Holter monitor in the subsequent year were included. We evaluated the performance of the AI-ECG output to predict incident AF within one year of the index ECG by estimating an area under the receiver operating characteristics curve (AUC). Sensitivity, specificity, and positive and negative predictive values were determined at the optimum threshold (maximizing sensitivity and specificity), and thresholds by output decile for the sample. RESULTS: A total of 1403 patients were included. Forty-three (3.1%) patients were diagnosed with new AF during the following year. The AI-ECG algorithm predicted AF with an AUC of 0.74 (95% CI 0.68-0.80), and an optimum threshold with sensitivity 79.1% (95% Confidence Interval (CI) 66.9%-91.2%), and specificity 66.1% (95% CI 63.6%-68.6%). CONCLUSIONS: We found this AI-ECG AF algorithm to maintain statistical significance in predicting incident AF, with clinical utility for screening purposes limited in this ED population with a low incidence of AF.