Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications.

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.

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools.

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.

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.

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.

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.

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.

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.

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.

A Case-Control Study of Peripartum Cardiomyopathy Using the Rochester Epidemiology Project.

BACKGROUND: The incidence of peripartum cardiomyopathy (PPCM) is known through referral center databases that may be affected by referral, misclassification, and other biases. We sought to determine the community-based incidence and natural history of PPCM using the Rochester Epidemiology Project. METHODS AND RESULTS: Incident cases of PPCM occurring between January 1, 1970, and December 31, 2014, were identified in Olmsted County, Minnesota. A total of 15 PPCM cases were confirmed yielding an incidence of 20.3 cases per 100,000 live births in Olmsted County, Minnesota. Clinical information, disease characteristics, and outcomes were extracted from medical records in a 27-county region of the Rochester Epidemiology Project including Olmsted County and matched in a 1:2 ratio with pregnant women without PPCM. A total of 48 women were identified with PPCM in the expanded 27-county region. There was 1 death and no transplants over a median of 7.3 years of follow-up. Six of the 23 women with subsequent pregnancies developed recurrent PPCM, all of whom recovered. Migraine and anxiety were identified as novel possible risk factors for PPCM. CONCLUSIONS: The population-based incidence of PPCM was 20.3 cases per 100,000 live births in Olmsted County, Minnesota. Cardiovascular outcomes were generally excellent in this community cohort.

30-day patient reported outcomes can be predicted by change in left atrial pressure and not change in transmitral gradient following MitraClip.

BACKGROUND: Change in left atrial pressure (LAP) has been shown to be associated with symptom improvement post-MitraClip; however, the association between acute procedural changes in transmitral diastolic mean gradient (MG) compared to LAP and symptom improvement is not well established. METHODS: 164 consecutive patients undergoing MitraClip at Mayo Clinic between June 2014 and May 2018 were included. Preclip and postclip MG and LAP were recorded. Baseline demographics, clinical, and echocardiographic outcomes, including 30-day New York Heart Association (NYHA) functional status were obtained from patient charts. RESULTS: Median age was 81.5 years (IQR: 76.3, 87), 34% were female and 94.5% had NYHA class III and IV functional status at baseline. At baseline, median MG was 4 mmHg (IQR: 3, 5) and LAP was 19 mmHg (IQR: 16, 23.5). Following MitraClip deployment, the median MG was 4 mmHg (IQR: 3, 6) and the median LAP was 17 mmHg (IQR: 14, 21), 69.5% of patients had less than moderate MR. There was no statistically significant association between change in MG and NYHA functional class at 30 days (OR = 0.95, 95% CI: 0.76-1.20). However, a reduction in LAP following MitraClip deployment was significantly associated with improvement in NYHA functional status at 30 days following adjustments for age and sex (aOR 3.36, 95% CI: 1.34-8.65). There was no significant correlation between change in mean LAP and change in MG (p = .98). CONCLUSION: Unlike change in left atrial pressure, change in MG post-MitraClip was not associated with patient reported outcomes at 30 days and did not correlate with change in left atrial pressure. Long-term follow up is needed to evaluate the impact of LA pressure on symptoms.

Clinical Outcomes following Left Atrial Appendage Occlusion: A Single-Center Experience.

INTRODUCTION: Percutaneous left atrial appendage closure is an established alternative to anticoagulation therapy for stroke prophylaxis among patients with nonvalvular atrial fibrillation. There are currently no guidelines on the choice of antithrombotic therapy following placement of the Watchman® device, the optimal time to discontinue anticoagulation or the duration of follow-up imaging after device deployment. Our main objective was to evaluate clinical outcomes among these patients. METHODS: We conducted a retrospective review of patients who received a Watchman® device at Mayo Clinic sites between January 2010 and December 2018. We constructed Cox-proportional hazard models to evaluate the effect of specific variables on clinical outcomes. RESULTS: 231 patients were identified (33% female), median age was 77 years, CHA2DS2-VASc score was 5 and HASBLED score was 4. We found no difference in clinically significant bleeding based on initial antithrombotic choice. However, patients with prior gastrointestinal bleeding were more likely to have a bleeding event in the first 6 weeks following Watchman® implantation (HR 9.40, 95% CI 2.15-41.09). Device sizes of 24-27 mm were significantly associated with a decreased risk of thromboembolic events (HR 0.15, 95% CI 0.04-0.55) compared to 21-mm devices. Peridevice leak (PDL) sizes appeared to either remain the same or increase on follow-up imaging. DISCUSSION/CONCLUSIONS: This observational study showed no statistically significant difference in bleeding risk related to initial antithrombotic choice. Smaller device sizes were associated with thromboembolic events, and longitudinal PDL assessment using transesophageal echocardiography showed these frequently do not decrease in size. Larger studies are needed.

Delayed entry into HIV medical care in a nationally representative sample of HIV-infected adults receiving medical care in the USA.

Before widespread antiretroviral therapy (ART), an estimated 17% of people delayed HIV care. We report national estimates of the prevalence and factors associated with delayed care entry in the contemporary ART era. We used Medical Monitoring Project data collected from June 2009 through May 2011 for 1425 persons diagnosed with HIV from May 2004 to April 2009 who initiated care within 12 months. We defined delayed care as entry >three months from diagnosis. Adjusted prevalence ratios (aPRs) were calculated to identify risk factors associated with delayed care. In this nationally representative sample of HIV-infected adults receiving medical care, 7.0% (95% confidence interval [CI]: 5.3-8.8) delayed care after diagnosis. Black race was associated with a lower likelihood of delay than white race (aPR 0.38). Men who have sex with women versus women who have sex with men (aPR 1.86) and persons required to take an HIV test versus recommended by a provider (aPR 2.52) were more likely to delay. Among those who delayed 48% reported a personal factor as the primary reason. Among persons initially diagnosed with HIV (non-AIDS), those who delayed care were twice as likely (aPR 2.08) to develop AIDS as of May 2011. Compared to the pre-ART era, there was a nearly 60% reduction in delayed care entry. Although relatively few HIV patients delayed care entry, certain groups may have an increased risk. Focus on linkage to care among persons who are required to take an HIV test may further reduce delayed care entry.

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.

Artificial intelligence-based screening for cardiomyopathy in an obstetric population: A pilot study.

Background: Cardiomyopathy is a leading cause of pregnancy-related mortality and the number one cause of death in the late postpartum period. Delay in diagnosis is associated with severe adverse outcomes. Objective: To evaluate the performance of an artificial intelligence-enhanced electrocardiogram (AI-ECG) and AI-enabled digital stethoscope to detect left ventricular systolic dysfunction in an obstetric population. Methods: We conducted a single-arm prospective study of pregnant and postpartum women enrolled at 3 sites between October 28, 2021, and October 27, 2022. Study participants completed a standard 12-lead ECG, digital stethoscope ECG and phonocardiogram recordings, and a transthoracic echocardiogram within 24 hours. Diagnostic performance was evaluated using the area under the curve (AUC). Results: One hundred women were included in the final analysis. The median age was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, and 21% as Hispanic. Five percent and 6% had left ventricular ejection fraction (LVEF) <45% and <50%, respectively. The AI-ECG model had near-perfect classification performance (AUC: 1.0, 100% sensitivity; 99%-100% specificity) for detection of cardiomyopathy at both LVEF categories. The AI-enabled digital stethoscope had an AUC of 0.98 (95% CI: 0.95, 1.00) and 0.97 (95% CI: 0.93, 1.00), for detection of LVEF <45% and <50%, respectively, with 100% sensitivity and 90% specificity. Conclusion: We demonstrate an AI-ECG and AI-enabled digital stethoscope were effective for detecting cardiac dysfunction in an obstetric population. Larger studies, including an evaluation of the impact of screening on clinical outcomes, are essential next steps.

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05-4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85-3.62; P = 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576.

Valproate prescriptions for nonepilepsy disorders in reproductive-age women.

BACKGROUND: Scientific evidence has consistently shown taking valproate during pregnancy increases risks of congenital malformations and cognitive impairment. As such, elimination of its use would be an important step in birth defects prevention. There are guidelines discouraging its use among women with epilepsy, but none exists for women without epilepsy, nor is the prevalence of valproate for nonepilepsy indications known. METHODS: Using de-identified data from the National Hospital and Ambulatory Medical Care Surveys (1996-2007), we examined individual prescriptions for reproductive-age adolescent girls and adult women ages 15 to 44 years in the United States, and estimated the number of antiepileptic drug and valproate prescriptions in the aggregate. We classified our study population using International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes, as women with epilepsy and women without epilepsy. The prevalence of antiepileptic drug and valproate prescriptions among women without epilepsy was estimated as prescriptions per 1000 patient visits for every 3-year time interval and the overall study period. RESULTS: We found 83% of valproate prescriptions were issued to women without epilepsy and 74% of these were for psychiatric diagnoses. The prevalence of antiepileptic drug prescriptions among women without epilepsy tripled during the study period (10.3 [1996-1998] vs. 34.9 [2005-2007] per 1000 patient visits), whereas valproate prescriptions remained relatively stable (3.1 [1996-1998] vs. 3.7 [2005-2007] per 1000 patient visits). CONCLUSION: Most women of reproductive age who receive a valproate prescription do not have epilepsy. Valproate prescriptions did not decline, despite increasing knowledge of its teratogenicity. Reducing valproate use among women of reproductive age, especially among those who use the drug for psychiatric indications, would prevent birth defects and cognitive deficits.

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.