Race, Ethnicity, and Gender Differences in Patient Reported Well-Being and Cognitive Functioning Within 3 Months of Symptomatic Illness During COVID-19 Pandemic.

BACKGROUND: Differences in acute COVID-19 associated morbidity based on race, ethnicity, and gender have been well described; however, less is known about differences in subsequent longer term health-related quality of life and well-being. METHODS: This prospective cohort study included symptomatic adults tested for SARS-CoV-2 who completed baseline and 3-month follow-up surveys. Using the PROMIS-29 tool, a validated measure of health and well-being, we compared outcomes at 3 months and change in outcomes from baseline to 3 months among groups with different races, ethnicities, and/or sexes. RESULTS: Among 6044 participants, 4113 (3202 COVID +) were included. Among COVID + participants, compared to non-Hispanic White participants, Black participants had better PROMIS T-scores for cognitive function (3.6 [1.1, 6.2]) and fatigue (- 4.3 [- 6.6, - 2.0]) at 3 months and experienced more improvement in fatigue over 3 months (- 2.7 [- 4.7, - 0.8]). At 3 months, compared with males, females had worse PROMIS T-scores for cognitive function (- 4.1 [- 5.6, - 2.6]), physical function (- 2.1 [- 3.1, - 1.0]), social participation (- 2.8 [- 4.2, - 1.5]), anxiety (2.8 [1.5, 4.1]), fatigue (5.1 [3.7, 6.4]), and pain interference (2.0 [0.9, 3.2]). Females experienced less improvement in fatigue over 3 months (3.1 [2.0, 4.3]). Transgender/non-binary/other gender participants had worse 3-month scores in all domains except for sleep disturbance and pain interference. CONCLUSIONS: Three months after the initial COVID-19 infection, Black participants reported better cognitive function and fatigue, while females and other gender minoritized groups experienced lower well-being. Future studies are necessary to better understand how and why social constructs, specifically race, ethnicity, and gender, influence differences in COVID-19-related health outcomes. Trials Registration ClinicalTrials.gov Identifier: NCT04610515.

Development and multinational validation of an algorithmic strategy for high Lp(a) screening.

Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l-1) in the UK Biobank (N = 456,815), the largest cohort with protocolized Lp(a) testing. We externally validated the model in 3 large cohort studies, ARIC (N = 14,484), CARDIA (N = 4,124) and MESA (N = 4,672). The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), reduced the number needed to test to find one individual with elevated Lp(a) by up to 67.3%, based on the probability threshold, with consistent performance across external validation cohorts. ARISE could be used to optimize screening for elevated Lp(a) using commonly available clinical features, with the potential for its deployment in electronic health records to enhance the yield of Lp(a) testing in real-world settings.

Evaluating Ischemic Heart Disease in Women: Focus on Angina With Nonobstructive Coronary Arteries (ANOCA).

Ischemic heart disease (IHD) is common in women, and cardiovascular disease is a leading cause of morbidity and mortality. While obstructive coronary artery disease is the most common form of IHD, millions of women suffer from angina with nonobstructive coronary arteries (ANOCA), an umbrella term encompassing multiple nonatherosclerotic disorders of the coronary tree. The underlying pathology leading to ischemia in these syndromes may be challenging to diagnose, leaving many women without a diagnosis despite persistent symptoms that impact quality of life and adversely affect long-term cardiovascular prognosis. In the last decade, there have been significant advances in the recognition and diagnostic evaluation of ANOCA. Despite these advances, the standard approach to evaluating suspected IHD in women continues to focus predominantly on the assessment of atherosclerotic coronary artery disease, leading to missed opportunities to accurately diagnose and treat underlying coronary vasomotor disorders. The goal of this review is to describe advances in diagnostic testing that can be used to evaluate angina in women and present a pragmatic diagnostic algorithm to guide evaluation of ANOCA in symptomatic patients. The proposed approach for the assessment of ANOCA is consistent with prior expert consensus documents and guidelines but is predicated on the medical interview and pretest probability of disease to inform a personalized diagnostic strategy.

The association between prolonged SARS-CoV-2 symptoms and work outcomes.

While the early effects of the COVID-19 pandemic on the United States labor market are well-established, less is known about the long-term impact of SARS-CoV-2 infection and Long COVID on employment. To address this gap, we analyzed self-reported data from a prospective, national cohort study to estimate the effects of SARS-CoV-2 symptoms at three months post-infection on missed workdays and return to work. The analysis included 2,939 adults in the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study who tested positive for their initial SARS-CoV-2 infection at the time of enrollment, were employed before the pandemic, and completed a baseline and three-month electronic survey. At three months post-infection, 40.8% of participants reported at least one SARS-CoV-2 symptom and 9.6% of participants reported five or more SARS-CoV-2 symptoms. When asked about missed work due to their SARS-CoV-2 infection at three months, 7.2% of participants reported missing ≥10 workdays and 13.9% of participants reported not returning to work since their infection. At three months, participants with ≥5 symptoms had a higher adjusted odds ratio of missing ≥10 workdays (2.96, 95% CI 1.81-4.83) and not returning to work (2.44, 95% CI 1.58-3.76) compared to those with no symptoms. Prolonged SARS-CoV-2 symptoms were common, affecting 4-in-10 participants at three-months post-infection, and were associated with increased odds of work loss, most pronounced among adults with ≥5 symptoms at three months. Despite the end of the federal Public Health Emergency for COVID-19 and efforts to "return to normal", policymakers must consider the clinical and economic implications of the COVID-19 pandemic on people's employment status and work absenteeism, particularly as data characterizing the numerous health and well-being impacts of Long COVID continue to emerge. Improved understanding of risk factors for lost work time may guide efforts to support people in returning to work.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome After SARS-CoV-2 Infection.

Importance: Chronic symptoms reported following an infection with SARS-CoV-2, such as cognitive problems, overlap with symptoms included in the definition of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Objective: To evaluate the prevalence of ME/CFS-like illness subsequent to acute SARS-CoV-2 infection, changes in ME/CFS symptoms through 12 months of follow-up, and the association of ME/CFS symptoms with SARS-CoV-2 test results at the acute infection-like index illness. Design, Setting, and Participants: This prospective, multisite, longitudinal cohort study (Innovative Support for Patients with SARS-CoV-2 Infections Registry [INSPIRE]) enrolled participants from December 11, 2020, to August 29, 2022. Participants were adults aged 18 to 64 years with acute symptoms suggestive of SARS-CoV-2 infection who received a US Food and Drug Administration-approved SARS-CoV-2 test at the time of illness and did not die or withdraw from the study by 3 months. Follow-up surveys were collected through February 28, 2023. Exposure: COVID-19 status (positive vs negative) at enrollment. Main Outcome and Measures: The main outcome was the weighted proportion of participants with ME/CFS-like illness based on the 2015 Institute of Medicine clinical case definition using self-reported symptoms. Results: A total of 4378 participants were included in the study. Most were female (3226 [68.1%]). Mean (SD) age was 37.8 (11.8) years. The survey completion rates ranged from 38.7% (3613 of 4738 participants) to 76.3% (1835 of 4738) and decreased over time. The weighted proportion of participants identified with ME/CFS-like illness did not change significantly at 3 through 12 months of follow-up and was similar in the COVID-19-positive (range, 2.8%-3.7%) and COVID-19-negative (range, 3.1%-4.5%) groups. Adjusted analyses revealed no significant difference in the odds of ME/CFS-like illness at any time point between COVID-19-positive and COVID-19-negative individuals (marginal odds ratio range, 0.84 [95% CI, 0.42-1.67] to 1.18 [95% CI, 0.55-2.51]). Conclusions and Relevance: In this prospective cohort study, there was no evidence that the proportion of participants with ME/CFS-like illness differed between those infected with SARS-CoV-2 vs those without SARS-CoV-2 infection up to 12 months after infection. A 3% to 4% prevalence of ME/CFS-like illness after an acute infection-like index illness would impose a high societal burden given the millions of persons infected with SARS-CoV-2.

Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

Comparing Methods for Identifying Post-Market Patient Preferences at the Point of Decision-Making: Insights from Patients with Chronic Pain Considering a Spinal Cord Stimulator Device.

Purpose: To compare three methods for identifying patient preferences (MIPPs) at the point of decision-making: analysis of video-recorded patient-clinician encounters, post-encounter interviews, and post-encounter surveys. Patients and Methods: For the decision of whether to use a spinal cord stimulator device (SCS), a video coding scheme, interview guide, and patient survey were iteratively developed with 30 SCS decision-making encounters in a tertiary academic medical center pain clinic. Burke's grammar of motives was used to classify the attributed source or justification for a potential preference for each preference block. To compare the MIPPs, 13 patients' encounters with their clinician were video recorded and subsequently analyzed by 4 coders using the final video coding scheme. Six of these patients were interviewed, and 7 surveyed, immediately following their encounters. Results: For videos, an average of 66 (range 33-106) sets of utterances potentially indicating a patient preference (a preference block), surveys 33 (range 32-34), and interviews 25 (range 18-30) were identified. Thirty-eight unique themes (75 subthemes), each a preference topic, were identified from videos, surveys 19 themes (12 subthemes), and interviews 39 themes (54 subthemes). The proportion of preference blocks that were judged as expressing a preference that was clearly important to the patient or affected their decision was highest for interviews (72.8%), surveys (68.0%), and videos (27.0%). Videos mostly attributed preferences to the patient's situation (scene) (65%); interviews, the act of receiving or living with SCS (43%); surveys, the purpose of SCS (40%). Conclusion: MIPPs vary in the type of preferences identified and the clarity of expressed preferences in their data sets. The choice of which MIPP to use depends on projects' goals and resources, recognizing that the choice of MIPP may affect which preferences are found.

Development and Iterative Design of Peer-based Discussions about Heart Health for Women.

Objective: To support women in their health journeys around cardiovascular risk reduction, providing: access to information; space to discuss complex and personal issues that may factor into decision making; and skills to support conversations with healthcare providers. Methods: We used a participatory design approach to develop peer sessions led by a non-clinical facilitator and a cardiologist, for women to learn, think, and discuss information and decision-making around cardiovascular risk. These sessions took place in-person and over video. A host invited her friends, family or network to the events. Sessions were designed to bring information to women at all stages of cardiovascular risk, including women with no prior experiences with heart health and women with established cardiovascular disease. The sessions provided information about cardiovascular risk and facilitated discussion around risk factors and treatment. Sessions were recorded, transcribed and analyzed using a grounded theory approach to identify emerging themes. Results: We conducted 12 groups, with an average of 6 women per group. Women were of all ages and spectrums of cardiovascular risk. The three major themes that emerged were: Starting Place (attitudes, beliefs, and behaviors toward cardiovascular care are informed by a blend of one's family history, personal interactions with their health and the health system, and approach to managing uncertainty); Lack of Sense-Making (seemingly simple recommendations to support heart health were much more complicated when related to their own risk and experience), and Self-Judgement and Judgement by Others (without support or validation, women may feel judged by others for not doing enough). Conclusion: Peer sessions can illuminate the complicated issues women face when approaching decisions regarding cardiovascular health. These sessions may offer an alternative to the healthcare setting, for women to wrestle with complex issues that relate to their risk, and better prepare for visits with their healthcare providers.

Secondary Prevention in Patients With Stroke Versus Myocardial Infarction: Analysis of 2 National Cohorts.

BACKGROUND: The implementation of preventive therapies among patients with stroke remains inadequately explored, especially when compared with patients with myocardial infarction (MI), despite sharing similar vascular risk profiles. We tested the hypothesis that participants with a history of stroke have a worse cardiovascular prevention profile in comparison to participants with MI. METHODS AND RESULTS: In cross-sectional analyses within the UK Biobank and All of Us Research Program, involving 14 760 (9193 strokes, 5567 MIs) and 7315 (2948 strokes, 4367 MIs) participants, respectively, we evaluated cardiovascular prevention profiles assessing low-density lipoprotein (<100 mg/dL), blood pressure (systolic, <140 mm Hg; and diastolic, <90 mm Hg), statin and antiplatelet use, and a cardiovascular prevention score that required meeting at least 3 of these criteria. The results revealed that, within the UK Biobank, patients with stroke had significantly lower odds of meeting all the preventive criteria compared with patients with MI: low-density lipoprotein control (odds ratio [OR], 0.73 [95% CI, 0.68-0.78]; P<0.001), blood pressure control (OR, 0.63 [95% CI, 0.59-0.68]; P<0.001), statin use (OR, 0.45 [95% CI, 0.42-0.48]; P<0.001), antiplatelet therapy use (OR, 0.30 [95% CI, 0.27-0.32]; P<0.001), and cardiovascular prevention score (OR, 0.42 [95% CI, 0.39-0.45]; P<0.001). Similar patterns were observed in the All of Us Research Program, with significant differences across all comparisons (P<0.05), and further analysis suggested that the odds of having a good cardiovascular prevention score were influenced by race and ethnicity as well as neighborhood deprivation levels (interaction P<0.05 in both cases). CONCLUSIONS: In 2 independent national cohorts, patients with stroke showed poorer cardiovascular prevention profiles and lower adherence to guideline-directed therapies compared with patients with MI. These findings underscore the need to explore the reasons behind the underuse of secondary prevention in vulnerable stroke survivors.

Predictors of Health-Related Quality of Life Among Women Participating in an Appointment-Based Cardiac Rehabilitation Program.

BACKGROUND: An alternative patient-centered appointment-based cardiac rehabilitation (CR) program has led to significant improvements in health outcomes for patients with cardiovascular disease. However, less is known about the effects of this approach on health-related quality of life (HRQoL), particularly for women. OBJECTIVE: We examined the effects of a patient-centered appointment-based CR program on HRQoL by sex and examined predictors of HRQoL improvements specifically for women. METHODS: Data were used from an urban single-center CR program at Yale New Haven Health (2012-2017). We collected information on patient demographics, socioeconomic status, and clinical characteristics. The Outcome Short-Form General Health Survey (SF-36) was used to measure HRQoL. We evaluated sex differences in SF-36 scores using t tests and used a multivariate linear regression model to examine predictors of improvements in HRQoL (total SF-36 score) for women. RESULTS: A total of 1530 patients with cardiovascular disease (23.7% women, 4.8% Black; mean age, 64 ± 10.8 years) were enrolled in the CR program. Women were more likely to be older, Black, and separated, divorced, or widowed. Although women had lower total SF-36 scores on CR entry, there was no statistically significant difference in CR adherence or total SF-36 score improvements between sexes. Women who were employed and those with chronic obstructive pulmonary disease were more likely to have improvements in total SF-36 scores. CONCLUSION: Both men and women participating in an appointment-based CR program achieved significant improvements in HRQoL. This approach could be a viable alternative to conventional CR to optimize secondary outcomes for patients.

Digital Health Interventions for Hypertension Management in US Populations Experiencing Health Disparities: A Systematic Review and Meta-Analysis.

Importance: Hypertension remains a leading factor associated with cardiovascular disease, and demographic and socioeconomic disparities in blood pressure (BP) control persist. While advances in digital health technologies have increased individuals' access to care for hypertension, few studies have analyzed the use of digital health interventions in vulnerable populations. Objective: To assess the association between digital health interventions and changes in BP and to characterize tailored strategies for populations experiencing health disparities. Data Sources: In this systematic review and meta-analysis, a systematic search identified studies evaluating digital health interventions for BP management in the Cochrane Library, Ovid Embase, Google Scholar, Ovid MEDLINE, PubMed, Scopus, and Web of Science databases from inception until October 30, 2023. Study Selection: Included studies were randomized clinical trials or cohort studies that investigated digital health interventions for managing hypertension in adults; presented change in systolic BP (SBP) or baseline and follow-up SBP levels; and emphasized social determinants of health and/or health disparities, including a focus on marginalized populations that have historically been underserved or digital health interventions that were culturally or linguistically tailored to a population with health disparities. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Data Extraction and Synthesis: Two reviewers extracted and verified data. Mean differences in BP between treatment and control groups were analyzed using a random-effects model. Main Outcomes and Measures: Primary outcomes included mean differences (95% CIs) in SBP and diastolic BP (DBP) from baseline to 6 and 12 months of follow-up between digital health intervention and control groups. Shorter- and longer-term follow-up durations were also assessed, and sensitivity analyses accounted for baseline BP levels. Results: A total of 28 studies (representing 8257 participants) were included (overall mean participant age, 57.4 years [range, 46-71 years]; 4962 [60.1%], female). Most studies examined multicomponent digital health interventions incorporating remote BP monitoring (18 [64.3%]), community health workers or skilled nurses (13 [46.4%]), and/or cultural tailoring (21 [75.0%]). Sociodemographic characteristics were similar between intervention and control groups. Between the intervention and control groups, there were statistically significant mean differences in SBP at 6 months (-4.24 mm Hg; 95% CI, -7.33 to -1.14 mm Hg; P = .01) and SBP changes at 12 months (-4.30 mm Hg; 95% CI, -8.38 to -0.23 mm Hg; P = .04). Few studies (4 [14.3%]) reported BP changes and hypertension control beyond 1 year. Conclusions and Relevance: In this systematic review and meta-analysis of digital health interventions for hypertension management in populations experiencing health disparities, BP reductions were greater in the intervention groups compared with the standard care groups. The findings suggest that tailored initiatives that leverage digital health may have the potential to advance equity in hypertension outcomes.

Psychological Health and Ischemic Heart Disease in Women: A Review of Current Evidence and Clinical Considerations across the Healthspan.

PURPOSE OF REVIEW: Psychological health encompasses a constellation of negative and positive factors-i.e., psychosocial stress, depression, anxiety, trauma, loneliness and social isolation, anger and hostility, optimism, and a sense of purpose. This narrative review presents current evidence at the intersection of psychological health, risk of ischemic heart disease (IHD), and IHD-related outcomes, with an emphasis on associations in women. RECENT FINDINGS: For women, relations between psychological health and IHD reflect important sex and gender differences in biological and psychosocial factors. Although efforts devoted to understanding psychological health and IHD risk have varied by psychological factor-scientific evidence is strongest for psychosocial stress and depression, while anxiety, trauma, and positive psychological factors warrant more investigation-less optimal psychological health is consistently associated with an earlier and greater risk of IHD morbidity and mortality in women. Still, many past prospective studies of psychological factors and IHD risk had a limited representation of women, did not include analyses by sex, or failed to account for other influential, sex-specific factors. Thus, there are multiple pathways for further, rigorous investigation into psychological health-IHD associations, mechanisms, and empirically supported psychological interventions to mitigate IHD risk among women. Given the robust evidence linking psychological health with women's risk for IHD, implementing routine, brief, psychological screening is recommended. Significant life events, developmental milestones specific to women, and IHD diagnoses or events could cue further psychological assessment and referral, efforts which will mutually strengthen the evidence for integrated psychological and IHD care and delivery of such care to this vulnerable group.

Bundled Payments for Care Improvement and Quality of Care and Outcomes in Heart Failure.

Importance: The Centers for Medicare & Medicaid Services (CMS) Bundled Payments for Care Improvement (BPCI) program was launched in 2013 with a goal to improve care quality while lowering costs to Medicare. Objective: To compare changes in the quality and outcomes of care for patients hospitalized with heart failure according to hospital participation in the BPCI program. Design, Setting, and Participants: This cross-sectional study used a difference-in-difference approach to evaluate the BPCI program in 18 BPCI hospitals vs 211 same-state non-BPCI hospitals for various process-of-care measures and outcomes using American Heart Association Get With The Guidelines-Heart Failure registry and CMS Medicare claims data from November 1, 2008, to August 31, 2018. Data were analyzed from May 2022 to May 2023. Exposures: Hospital participation in CMS BPCI Model 2 Heart Failure, which paid hospitals in a fee-for-service process and then shared savings or required reimbursement depending on how the total cost of an episode of care compared with a target price. Main Outcomes and Measures: Primary end points included 7 quality-of-care measures. Secondary end points included 9 outcome measures, including in-hospital mortality and hospital-level risk-adjusted 30-day and 90-day all-cause readmission rate and mortality rate. Results: During the study period, 8721 patients were hospitalized in the 23 BPCI hospitals and 94 530 patients were hospitalized in the 224 same-state non-BPCI hospitals. Less than a third of patients (30 723 patients, 29.8%) were 75 years or younger; 54 629 (52.9%) were female, and 48 622 (47.1%) were male. Hospital participation in BPCI Model 2 was not associated with significant differential changes in the odds of various process-of-care measures, except for a decreased odds of evidence-based ß-blocker at discharge (adjusted odds ratio [aOR], 0.63; 95% CI, 0.41-0.98; P = .04). Participation in the BPCI was not associated with a significant differential change in the odds of receiving angiotensin-converting enzyme inhibitors/angiotensin receptor blockers or angiotensin receptor-neprilysin inhibitors at discharge, receiving an aldosterone antagonist at discharge, having a cardiac resynchronization therapy (CRT)-defibrillator or CRT pacemaker placed or prescribed at discharge, having implantable cardioverter-defibrillator (ICD) counseling or an ICD placed or prescribed at discharge, heart failure education being provided among eligible patients, or having a follow-up visit within 7 days or less. Participation in the BPCI was associated with a significant decrease in odds of in-hospital mortality (aOR, 0.67; 95% CI, 0.51-0.86; P = .002). Participation was not associated with a significant differential change in hospital-level risk-adjusted 30-day or 90-day all-cause readmission rate and 30-day or 90-day all-cause mortality rate. Conclusion and Relevance: In this study, hospital participation in the BPCI Model 2 Heart Failure program was not associated with improvement in process-of-care quality measures or 30-day or 90-day risk-adjusted all-cause mortality and readmission rates.

Association of marital/partner status with hospital readmission among young adults with acute myocardial infarction.

INTRODUCTION: Despite evidence supporting the benefits of marriage on cardiovascular health, the impact of marital/partner status on the long-term readmission of young acute myocardial infarction (AMI) survivors is less clear. We examined the association between marital/partner status and 1-year all-cause readmission and explored sex differences among young AMI survivors. METHODS: Data were from the VIRGO study (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients), which enrolled young adults aged 18-55 years with AMI (2008-2012). The primary end point was all-cause readmission within 1 year of hospital discharge, obtained from medical records and patient interviews and adjudicated by a physician panel. We performed Cox proportional hazards models with sequential adjustment for demographic, socioeconomic, clinical, and psychosocial factors. Sex-marital/partner status interaction was also tested. RESULTS: Of the 2,979 adults with AMI (2002 women [67.2%]; mean age 48 [interquartile range, 44-52] years), unpartnered individuals were more likely to experience all-cause readmissions compared with married/partnered individuals within the first year after hospital discharge (34.6% versus 27.2%, hazard ratio [HR] = 1.31; 95% confidence interval [CI], 1.15-1.49). The association attenuated but remained significant after adjustment for demographic and socioeconomic factors (adjusted HR, 1.16; 95% CI, 1.01-1.34), and it was not significant after further adjusting for clinical factors and psychosocial factors (adjusted HR, 1.10; 95%CI, 0.94-1.28). A sex-marital/partner status interaction was not significant (p = 0.69). Sensitivity analysis using data with multiple imputation and restricting outcomes to cardiac readmission yielded comparable results. CONCLUSIONS: In a cohort of young adults aged 18-55 years, unpartnered status was associated with 1.3-fold increased risk of all-cause readmission within 1 year of AMI discharge. Further adjustment for demographic, socioeconomic, clinical, and psychosocial factors attenuated the association, suggesting that these factors may explain disparities in readmission between married/partnered versus unpartnered young adults. Whereas young women experienced more readmission compared to similar-aged men, the association between marital/partner status and 1-year readmission did not vary by sex.

National Trends in Racial and Ethnic Disparities in Use of Recommended Therapies in Adults with Atherosclerotic Cardiovascular Disease, 1999-2020.

Importance: Despite efforts to improve the quality of care for patients with atherosclerotic cardiovascular disease (ASCVD), it is unclear whether the US has made progress in reducing racial and ethnic differences in utilization of guideline-recommended therapies for secondary prevention. Objective: To evaluate 21-year trends in racial and ethnic differences in utilization of guideline-recommended pharmacological medications and lifestyle modifications among US adults with ASCVD. Design, Setting, and Participants: This cross-sectional study includes data from the National Health and Nutrition Examination Survey between 1999 and 2020. Eligible participants were adults aged 18 years or older with a history of ASCVD. Data were analyzed between March 2022 and May 2023. Exposure: Self-reported race and ethnicity. Main Outcome and Measures: Rates and racial and ethnic differences in the use of guideline-recommended pharmacological medications and lifestyle modifications. Results: The study included 5218 adults with a history of ASCVD (mean [SD] age, 65.5 [13.2] years, 2148 women [weighted average, 44.2%]), among whom 1170 (11.6%) were Black, 930 (7.7%) were Hispanic or Latino, and 3118 (80.7%) were White in the weighted sample. Between 1999 and 2020, there was a significant increase in total cholesterol control and statin use in all racial and ethnic subgroups, and in angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) utilization in non-Hispanic White individuals and Hispanic and Latino individuals (Hispanic and Latino individuals: 17.12 percentage points; 95% CI, 0.37-37.88 percentage points; P = .046; non-Hispanic White individuals: 12.14 percentage points; 95% CI, 6.08-18.20 percentage points; P < .001), as well as smoking cessation within the Hispanic and Latino population (-27.13 percentage points; 95% CI, -43.14 to -11.12 percentage points; P = .002). During the same period, the difference in smoking cessation between Hispanic and Latino individuals and White individuals was reduced (-24.85 percentage points; 95% CI, -38.19 to -11.51 percentage points; P < .001), but racial and ethnic differences for other metrics did not change significantly. Notably, substantial gaps persisted between current care and optimal care throughout the 2 decades of data analyzed. In the period of 2017 to 2020, optimal regimens were observed in 47.4% (95% CI, 39.3%-55.4%), 48.7% (95% CI, 36.7%-60.6%), and 53.0% (95% CI, 45.6%-60.4%) of Black, Hispanic and Latino, and White individuals, respectively. Conclusions and Relevance: In this cross-sectional study of US adults with ASCVD, significant disparities persisted between current care and optimal care, surpassing any differences observed among demographic groups. These findings highlight the critical need for sustained efforts to bridge these gaps and achieve better outcomes for all patients, regardless of their racial and ethnic backgrounds.

Development and Multinational Validation of a Novel Algorithmic Strategy for High Lp(a) Screening.

Importance: Elevated lipoprotein(a) [Lp(a)] is associated with atherosclerotic cardiovascular disease (ASCVD) and major adverse cardiovascular events (MACE). However, fewer than 0.5% of patients undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Objective: We developed and validated a machine learning model to enable targeted screening for elevated Lp(a). Design: Cross-sectional. Setting: 4 multinational population-based cohorts. Participants: We included 456,815 participants from the UK Biobank (UKB), the largest cohort with protocolized Lp(a) testing for model development. The model's external validity was assessed in Atherosclerosis Risk in Communities (ARIC) (N=14,484), Coronary Artery Risk Development in Young Adults (CARDIA) (N=4,124), and Multi-Ethnic Study of Atherosclerosis (MESA) (N=4,672) cohorts. Exposures: Demographics, medications, diagnoses, procedures, vitals, and laboratory measurements from UKB and linked electronic health records (EHR) were candidate input features to predict high Lp(a). We used the pooled cohort equations (PCE), an ASCVD risk marker, as a comparator to identify elevated Lp(a). Main Outcomes and Measures: The main outcome was elevated Lp(a) (≥150 nmol/L), and the number-needed-to-test (NNT) to find one case with elevated Lp(a). We explored the association of the model's prediction probabilities with all-cause and cardiovascular mortality, and MACE. Results: The Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE) used low-density lipoprotein cholesterol, statin use, triglycerides, high-density lipoprotein cholesterol, history of ASCVD, and anti-hypertensive medication use as input features. ARISE outperformed cardiovascular risk stratification through PCE for predicting elevated Lp(a) with a significantly lower NNT (4.0 versus 8.0 [with or without PCE], P<0.001). ARISE performed comparably across external validation cohorts and subgroups, reducing the NNT by up to 67.3%, depending on the probability threshold. Over a median follow-up of 4.2 years, a high ARISE probability was also associated with a greater hazard of all-cause death and MACE (age/sex-adjusted hazard ratio [aHR], 1.35, and 1.38, respectively, P<0.001), with a greater increase in cardiovascular mortality (aHR, 2.17, P<0.001). Conclusions and Relevance: ARISE optimizes screening for elevated Lp(a) using commonly available clinical features. ARISE can be deployed in EHR and other settings to encourage greater Lp(a) testing and to improve identifying cases eligible for novel targeted therapeutics in trials. KEY POINTS: Question: How can we optimize the identification of individuals with elevated lipoprotein(a) [Lp(a)] who may be eligible for novel targeted therapeutics?Findings: Using 4 multinational population-based cohorts, we developed and validated a machine learning model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), to enable targeted screening for elevated Lp(a). In contrast to the pooled cohort equations that do not identify those with elevated Lp(a), ARISE reduces the "number-needed-to-test" to find one case with elevated Lp(a) by up to 67.3%.Meaning: ARISE can be deployed in electronic health records and other settings to enable greater yield of Lp(a) testing, thereby improving the identification of individuals with elevated Lp(a).