Baylor Medical Center at Garland

Dyslipidemias are a significant risk factor for stroke. Still, there is limited data on mortality trends where both dyslipidemia and stroke are either contributing or underlying causes of death among adults aged ≥ 25 years. We aimed to evaluate the demographic, regional, and temporal trends in dyslipidemia and stroke-related mortality among adults from 2010 to 2020. The CDC WONDER mortality data were utilized to identify deaths with both dyslipidemia and stroke as either underlying or contributing causes of death in adults aged ≥ 25 years. Age-adjusted mortality rates (AAMRs) per 100,000 and annual percent change (APC) were calculated and further categorized by year, sex, race/ethnicity, region, and urban-rural status. Joinpoint regression was used to determine changes in trends over time. Between 2010 and 2020, a total of 106,813 dyslipidemia and stroke-related deaths occurred among adults aged ≥ 25 years, with the most in medical facilities (35.7%), at home (27.7%), or in nursing/long-term care facilities (26.9%). The AAMR increased from 3.47 in 2010 to 5.47 in 2020, stable through 2018 (APC 1.6 [95% CI, −0.4 to 2.7]) then rising sharply (APC 15.1 [95% CI, 7.5 to 19.5]). Men had higher mortality than women (AAMR: 4.44 vs. 3.66). The Non-Hispanic (NH) Black or African American population had the highest overall AAMR (4.97), followed by the NH White population (3.99), the NH American Indian or Alaska Native population (3.87), the NH Asian or Pacific Islander population (3.59), and the Hispanic or Latino population (3.25) Vermont (10.15) and Georgia (1.99) had the highest and lowest state-level rates, respectively. Regionally, the West (4.61) exceeded the Northeast (3.43). Nonmetropolitan areas (4.81) had consistently higher mortality than metropolitan areas (3.85). Dyslipidemia and stroke-related mortality increased significantly after a period of stability. The highest AAMRs were observed in men, NH Black individuals, and people living in the Western US and nonmetropolitan areas. Effective policies are required to reduce these mortality rates and improve cardiovascular health.

Social determinants of health, including neighborhood deprivation measured by the Area Deprivation Index, are key drivers of disparities in health outcomes, including survival after cardiac surgery. We evaluated whether the association between neighborhood deprivation and survival differs across racial and ethnic groups undergoing coronary artery bypass grafting. We conducted a retrospective study of 739 335 Medicare beneficiaries who underwent isolated coronary artery bypass grafting between 2008 and 2019. A flexible parametric survival model with time-dependent effects was implemented to estimate standardized (over demographics, clinical, and procedural characteristics) survival probabilities. Primary end points were differences in 30-day and 5-year standardized survival probabilities between beneficiaries from the lowest and highest quintiles of neighborhood deprivation (LDNs and HDNs), stratified by race and ethnicity. Unadjusted median survival was substantially higher among beneficiaries in LDNs compared with HDNs (10.65 [95% CI, 10.55-10.76] versus 9.20 [95% CI, 9.14-9.27] years). In our risk-adjusted model, the magnitude of this difference varied significantly by race and ethnicity. At 30 days, standardized survival differences between LDNs and HDNs were 1.49% [95% CI, 0.45-2.53] among Asian American or Native Hawaiian/Pacific Islander, 1.06% [95% CI, 0.94-1.19] among White, 0.94% [95% CI,0.583-1.31] among Black, and 0.56% [95% CI, 0.25-0.91] among Hispanic beneficiaries. At 5 years, Asian American or Native Hawaiian/Pacific Islander and White beneficiaries showed the largest standardized survival differences between LDNs and HDNs (8.07% [95% CI, 5.37-10.77] and 5.01% [95% CI, 4.62 -5.39], respectively), whereas Black and Hispanic beneficiaries had smaller differences (2.00% [95% CI, 0.71-3.28] and 1.15% [95% CI, 0.05-2.26], respectively). Although LDN (compared with HDN) residence was associated with improved survival after coronary artery bypass grafting, these survival differences were not equally distributed across race and ethnicity. Policies aiming to reduce socioeconomic disadvantage may yield uneven outcomes unless tailored to the specific challenges faced by different racial and ethnic populations.

Artificial intelligence (AI) is in the era of rapid evolution. Like other healthcare fields, AI has significantly impacted sleep medicine. We aim to explain the evolving role of AI in sleep medicine and provide clinicians with key information related to its benefits and limitations. AI technologies, like machine learning and deep learning, improve the detection of sleep disorders, such as obstructive sleep apnea, insomnia, and narcolepsy, through advanced data analysis from tools like polysomnography and consumer sleep devices. AI also enables targeted therapies by endotyping sleep disorders, optimizing patient care, and reducing unnecessary treatments. On the other hand, there are many challenges that need to be addressed before using AI in clinical settings. Ethical issues regarding patient privacy, biases, and transparency regarding data use are some of the key challenges. AI could transform sleep medicine by enhancing diagnostic accuracy and personalizing treatment plans. Effective collaboration between clinicians and AI experts is necessary to use AI optimally in clinical settings.