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.

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.

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.

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.

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.

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.

Detecting cardiomyopathies in pregnancy and the postpartum period with an electrocardiogram-based deep learning model.

AIMS: Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period. METHODS AND RESULTS: We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, <45%, and <50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF < 45%), and 0.87 (LVEF < 50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively. CONCLUSIONS: An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.

Acute Rhabdomyolysis Following Synthetic Cannabinoid Ingestion.

CONTEXT: Novel psychoactive substances, including synthetic cannabinoids, are becoming increasingly popular, with more patients being seen in the emergency room following acute ingestion. These substances have been associated with a wide range of adverse effects. However, identification of complications, clinical toxicity, and management remain challenging. CASE REPORT: We present the case of a young African-American male who developed severe agitation and bizarre behavior following acute K2 ingestion. Laboratory studies revealed markedly elevated serum creatine phosphokinase (CPK) with normal renal function. The patient was managed with aggressive intravenous (IV) fluid hydration and treatment of underlying psychiatric illness. CONCLUSION: We recommend the routine evaluation of renal function and CPK levels with early initiation of IV hydration among patients who present to the emergency department following acute ingestion of synthetic cannabinoids to identify potential complications early as well as institute early supportive therapy.

Comparison of Outcomes between Individuals with Pure and Mixed Lupus Nephritis: A Retrospective Study.

INTRODUCTION: Lupus nephritis (LN) is a serious organ manifestation of systemic lupus erythematosus. Histologic overlap is relatively common in the six pathologic classes (I to VI) of LN. For example, mixed proliferative LN (MPLN) often includes features of classes III & V or classes IV & V combined. We performed a comparative evaluation of renal outcomes in patients with MPLN to patients with pure proliferative LN (PPLN) against pre-specified renal outcomes, and we also identified predictor of clinical outcomes among those with PPLN and MPLN. HYPOTHESIS: Individuals with MPLN will have worse short-term renal outcomes compared to those with PPLN. METHODS: We retrospectively reviewed 278 adult LN patients (≥18 years old) identified from an Emory University Hospital registry of native renal biopsies performed between January 2000 and December 2011. The final analytic sample consisted of individuals with a diagnosis of PPLN (n = 60) and MPLN (n = 96). We analyzed differences in clinical and laboratory characteristics at baseline. We also assessed associations between LN category and renal outcomes (complete remission and time to ESRD) with logistic and Cox proportional hazards models within two years of baseline. RESULTS: The study population was predominantly female (83.97%) and African American (71.8%) with a mean age of 33.4 years at baseline. Over a median follow up of 1.02 years, we did not find any statistically significant associations between MPLN and the development of ESRD or remission when compared to patients with PPLN (adjusted HR = 0.30, 95% CI = 0.07, 1.26). CONCLUSION: There was no association between mixed or pure histopathologic features of LN at presentation and rate of complete or partial remission but higher baseline eGFR was associated with a lower probability of complete remission among patients with lupus nephritis.

Racial and Ethnic Disparities in Graft and Recipient Survival in Elderly Kidney Transplant Recipients.

OBJECTIVES: To investigate racial and ethnic differences in graft and recipient survival in elderly kidney transplant recipients. DESIGN: Retrospective cohort. SETTING: First-time, kidney-only transplant recipients aged 60 and older of age at transplantation transplanted between July 1996 and October 2010 (N = 44,013). PARTICIPANTS: United Network for Organ Sharing (UNOS) database. MEASUREMENTS: Time to graft failure and death obtained from the UNOS database and linkage to the Social Security Death Index. Neighborhood poverty from 2000 U.S. Census geographic data. RESULTS: Of the 44,013 recipients in the sample, 20% were black, 63% non-Hispanic white, 11% Hispanic, 5% Asian, and the rest "other racial groups." In adjusted Cox models, blacks were more likely than whites to experience graft failure (hazard ratio (HR) = 1.23, 95% confidence interval (CI) = 1.15-1.32), whereas Hispanics (HR = 0.77, 95% CI = 0.70-0.85) and Asians (HR = 0.70, 95% CI = 0.61-0.81) were less likely to experience graft failure. Blacks (HR = 0.84, 95% CI = 0.80-0.88), Hispanics (HR = 0.68, 95% CI = 0.64-0.72), and Asians (HR = 0.62, 95% CI = 0.57-0.68) were less likely than whites to die after renal transplantation. CONCLUSION: Elderly blacks are at greater risk of graft failure than white transplant recipients but survive longer after transplantation. Asians have the highest recipient and graft survival, followed by Hispanics. Further studies are needed to assess additional factors affecting graft and recipient survival in elderly adults and to investigate outcomes such as quality of life.

Maternal anxiety and breastfeeding: findings from the MAVAN (Maternal Adversity, Vulnerability and Neurodevelopment) Study.

BACKGROUND: Maternal anxiety and depression may impair maternal intention, motivation, and self-efficacy in multiple domains associated with child health including breastfeeding. OBJECTIVE: We tested the hypothesis that mothers who experience substantial anxiety during pregnancy or the postpartum period are at increased risk for reduced initiation, exclusivity, and continuation of breastfeeding. METHODS: We obtained data on 255 Canadian pregnant women from the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) study recruited between June 2004 and February 2009. We utilized data collected from 18 to 23 weeks gestation through 12 months postpartum. Multivariate logistic regression was used to assess whether scores on the Hamilton Anxiety Scale (HAM-A) and State-Trait Anxiety Inventory (STAI) were associated with initiation, exclusivity, and continuation of breastfeeding. RESULTS: Prenatal anxiety was not associated with breastfeeding outcomes. In adjusted models, a single point increase in HAM-A scores at 3 months postpartum was associated with an 11% reduction in the odds of exclusive breastfeeding at 6 months (adjusted odds ratio [aOR] = 0.89; 95% CI, 0.80-0.99). A single point increase in STAI State and STAI Trait scores at 3 months postpartum was associated with a 4% (aOR = 0.96; 95% CI, 0.92-0.99) and 7% (aOR = 0.93; 95% CI, 0.86-1.00) reduction, respectively, in the odds of any breastfeeding at 12 months. CONCLUSION: Our findings suggest a relationship between maternal anxiety and reduced exclusivity and continuation of breastfeeding. Maternal anxiety should be actively monitored and managed appropriately in the postpartum period to support optimal breastfeeding practices.

Timing of antiretroviral therapy initiation in a nationally representative sample of HIV-infected adults receiving medical care in the United States.

Early antiretroviral therapy (ART) initiation reduces the risk of disease progression and HIV transmission, but data on time from HIV care entry to ART initiation are lacking. Using data from the Medical Monitoring Project (MMP), a population-based probability sample of HIV-infected adults receiving medical care in the United States, we assessed time from care entry to ART initiation among persons diagnosed May 2004-April 2009 and used multivariable Cox proportional-hazards models to identify factors associated with time to ART initiation. Among 1094 MMP participants, 83.9% reported initiating ART, with median time to ART initiation of 10 months. In multivariable models, blacks compared to whites [hazard ratio (HR) 0.82; 95% confidence interval (CI) 0.70-0.98], persons without continuous health insurance (HR 0.82; CI 0.70-0.97), heterosexual women and men who have sex with men compared to heterosexual men (HR 0.66; CI 0.51-0.85 and HR 0.71; CI 0.60-0.84, respectively), and persons without AIDS at care entry (HR 0.37; CI 0.31-0.43) had significantly longer times to ART initiation. Overall, time to ART initiation was suboptimal by current standards and significant disparities were noted among certain subgroups. Efforts to encourage prompt ART initiation should address delays among those without health insurance and among certain sociodemographic subgroups.

Impact of maternal characteristics on the effect of maternal influenza vaccination on fetal outcomes.

BACKGROUND: Maternal infections during pregnancy have been associated with adverse fetal and infant health outcomes, and vaccination against influenza is the most effective tool to prevent morbidity and mortality due to seasonal and pandemic influenza. We evaluated the association between receipt of the inactivated seasonal influenza vaccine on preterm and small for gestational age (SGA) births, with the aim to assess racial and socioeconomic variations in vaccine effect. METHODS: We conducted a retrospective analysis of state-wide surveillance data from Georgia for the most recent four years available at the beginning of the study, a total of 8393 live births in Georgia from January 1, 2005 through December 31, 2008. We constructed multivariable logistic regression models and calculated odds ratios (OR) estimates with corresponding 95% confidence intervals (CI) to evaluate the effect of maternal influenza vaccination on SGA (birth weight <10th percentile for gestational age) and preterm (gestational age at birth <37 weeks) births while controlling for potential confounders. RESULTS: Among all women, we found significant strong associations between maternal influenza vaccination and reduced odds of a preterm birth during the widespread influenza activity period [OR=0.39, 95% CI: 0.18, 0.83]. In this period, vaccination was protective against SGA births among women at higher risk for influenza related morbidity - women enrolled in the Women, Infant and Child (WIC) program [OR=0.20, 95% CI: 0.04, 0.98] and Black women [OR=0.15 95% CI: 0.02, 0.94]; maternal influenza vaccination was associated with reduced odds of a preterm birth among white women [OR=0.34, 95% CI: 0.12, 0.91] and women of higher socio-economic status [OR=0.30, 95% CI: 0.12, 0.74]. CONCLUSION: Influenza vaccination during pregnancy was significantly associated with reduced odds of small for gestational age and preterm births during the widespread influenza activity period. Vaccination effects varied by socio-demographic characteristics.

Near-elimination of folate-deficiency anemia by mandatory folic acid fortification in older US adults: Reasons for Geographic and Racial Differences in Stroke study 2003-2007.

BACKGROUND: The United States implemented mandatory folic acid fortification of enriched cereal grains in 1998. Although several studies have documented the resulting decrease in anemia and folate deficiency, to our knowledge, no one has determined the prevalence of folate-deficiency anemia after fortification. OBJECTIVE: We determined the prevalence of folate deficiency and folate-deficiency anemia within a sample of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. DESIGN: The REGARDS cohort is a prospective cohort of 30,239 black and white participants living in the contiguous United States. We measured serum folate concentrations in a random sample of 1546 REGARDS participants aged ≥50 y with baseline hemoglobin and red blood cell mean corpuscular volume measurements. Folate deficiency was defined as a serum folate concentration <6.6 nmol/L (<3.0 ng/mL), and anemia was defined as a hemoglobin concentration <13 g/dL in men and <12 g/dL in nonpregnant women (WHO criteria). Folate-deficiency anemia was defined as the presence of both folate deficiency and anemia. RESULTS: The mean hemoglobin concentration was 13.6 g/dL, and 15.9% of subjects had anemia. The median serum folate concentration was 34.2 nmol/L (15.1 ng/mL), and only 2 of 1546 participants 0.1%) were folate deficient. Both subjects were African American women with markedly elevated C-reactive protein concentrations, macrocytosis, and normal serum cobalamin concentrations; only one subject was anemic. Overall, the prevalence of folate-deficiency anemia was <0.1% (1 of 1546 subjects). CONCLUSION: Our data suggest that, after mandatory folic acid fortification, the prevalence of folate-deficiency anemia is nearly nonexistent in a community-dwelling population in the United States.