Bhanushali, Aditi K

Background: Studies in the past have demonstrated an association between sarcoidosis and pulmonary embolism (PE), but little is known about its impact, especially in young adults. We aim to determine the burden and impact of PE in young adults hospitalized with sarcoidosis. Methods: This study investigates the prevalence and consequences of PE in young adults aged 18–44 years hospitalized with sarcoidosis using data from the National Inpatient Sample (2016–2020) with relevant International Classification of Diseases (ICD)-10 codes. This study aims to address the primary outcomes of the burden and trends of PE and its associated impact on in-hospital mortality. Additionally, it explores secondary outcomes such as healthcare resource utilization and discharge patterns. Results: Among 50,385 young adults (median age 37 years) hospitalized with sarcoidosis, 1120 had PE, showing a linear increase from 2% in 2016 to 2.9% ( P < 0.001) in 2020. The sarcoidosis–PE+ group, predominantly in young blacks, exhibited a higher prevalence of comorbidities such as obesity, smoking, drug use, peripheral vascular disease, hypothyroidism and prior history of venous thrombus embolism. In contrast, the sarcoidosis–PE− group had a higher prevalence of diabetes (with/without chronic complication), hyperlipidemia and chronic pulmonary disease. Multivariable regression analysis adjusted for all potential sociodemographic and comorbid variables showed higher odds of all-cause mortality (odds ratio [OR]: 2.92, 95% confidence interval [CI]: 1.18–7.24, P < 0.02) in the sarcoidosis–PE+ group as compared to the sarcoidosis–PE− group. The sarcoidosis–PE+ group also had higher hospital costs and length of stay. Conclusion: The higher odds of in-hospital mortality with an increased length of stay and hospital cost observed in the sarcoidosis–PE+ group highlight the often underreported complications of sarcoidosis, particularly in a younger demographic. The relevance of this study stems from its potential to uncover trends that could lead to improved diagnostic and therapeutic strategies.

Background: The association of prediabetes (pDM) with stroke risk in non-diabetic geriatric patients with AF is unclear. The findings from this analysis contribute to a better understanding of the interrelation between pDM, AF, and stroke, highlighting the need for targeted interventions and risk management strategies in this vulnerable population. Methods: Utilizing the 2019 National Inpatient Sample and employing ICD-10 CM coding, we identified all non-diabetic geriatric admissions with AF. Patients were categorized into two groups: pDM+ and pDM-. The primary outcomes of interest were stroke incidence, odds and potential sex disparities in odds as a risk factor for stroke in geriatric patients with AF. Results: Of the total 218130 non-diabetic geriatric AF admissions, 3525(1.6%) had pDM. The pDM+ cohort was relatively younger (median 75 vs 77 years), and often consisted of females (57.9%; p<0.359), white (80.1%), and from the higher-income quartile (28.5%); (p<0.001). The preDM+ group had significantly higher rates of complicated HTN (50.2 vs 43.6%), hyperlipidemia (66.4 vs 53.7%), obesity (30.2 vs 13.6%), PVD (15.3 vs 9.9%), stroke (1.4 vs 0.8%). After adjusting for baseline characteristics/comorbidities, the pDM+ cohort with AF admissions showed a higher rate and significantly higher odds of stroke vs the pDM- cohort (1.4% vs. 0.8%, OR 2.00, 95%CI 1.06-3.77; p=0.033). On subgroup analyses, males had a stronger association (OR 5.48, 95%CI 1.43-21.02; p=0.013) vs. females. Conclusion: Our research revealed that geriatric patients hospitalized with AF who had concomitant pDM were twice likely to develop stroke, with male preponderance. Further research is warranted to develop tailored interventions to mitigate the risk of stroke in AF patients with pDM.

Introduction: Lipoprotein (a) [Lp(a)] has been recognized as an independent predictor of atherosclerotic cardiovascular disease (ASCVD). However, there is limited data on the association between Lp(a) levels and risk of recurrent myocardial infarction (MI)/coronary revascularization in patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI). Methods: PubMed/Medline, EMBASE, and Scopus were searched for studies evaluating the association of Lp(a) with recurrent ischemic events from the year 2000-2023. Patients with coronary artery disease or ACS with baseline Lp(a) who underwent PCI or coronary stenting and had outcomes as recurrent MI/ coronary revascularization were included. Random effects models and I 2 statistics were used for pooled hazard ratios (HR), odds ratio (OR), and heterogeneity assessment. A leave-one-out sensitivity analysis was also performed. Results: The initial search resulted in 651 articles, of which nine studies with 27964 patients (mean age 58-66 years, 74.79% male) were included. Of the nine studies, six were retrospective and three prospective. Mean±SD/Median (IQ) follow-up duration was four years [range 2-7 years]. There was an increased risk of recurrent MI/coronary revascularization associated with Lp(a) ≥30 mg/dL (OR, 1.44, 95% CI: 1.09-1.89, I 2 = 79.24%, p=0.009) (Fig 1a) and Lp(a) ≥10-30 mg/dL (OR, 1.19, 95% CI: 1.009-1.416, I 2 = 0%, p=0.039) (Fig 1b), but failed to reach significance with Lp(a) as a continuous variable (OR, 1.004, 95% CI: 0.998-1.009, I 2 = 85.02%, p=0.221) (Fig 1c). Leave-one-out sensitivity analysis showed an equivalent result for various Lp(a) cutoffs (Fig 1a, 1b, and 1c). Conclusions: Our meta-analysis suggests an association of raised Lp(a) with increased risk of recurrent MI/coronary revascularization in ACS patients who underwent PCI.

Background: Cardiovascular disease is the leading cause of death worldwide, with a notable impact on postmenopausal women. However, contemporary large-scale population-based data for the burden and disparities of Major Adverse Cardiovascular and Cerebrovascular Events (MACCE) in this population remains limited. Methods: The National Inpatient Sample (2020, ICD10 codes, age>55 years) was queried to identify postmenopausal women's hospitalizations. Socio-demographics, racial subgroups, comorbidities, and MACCE rates were retrieved with a more granular comparative analysis in postmenopausal women. A p<0.05 was considered statistically significant. Results: Of the total 8435195 postmenopausal women hospitalizations (median age 75 years), 1021375 (12.1%) were identified with MACCE. MACCE cohort, in comparison to non-MACCE cohort, had significantly higher prevalence of hypertension (77.1% vs 71.3), diabetes (39.8% vs 34.0%), hyperlipidemia (53.7% vs 48.4%), PVD (9.8% vs 7.1%), prior MI (8.3 vs 5.7%), prior TIA/stroke (10.5 vs 9%) and CKD (29.4 vs 23.4%). Significant clinical predictors for all-cause mortality were found to be race Asian/PI vs white ( 1.31 [1.09-1.18]), median household income national quartile (1.16 [1.13-1.20]), hypertension (1.18[1.16-1.20]) diabetes (1.20 [1.19-1.22]), hyperlipidemia (1.12[1.11-1.14]), prior MI (1.37[1.34-1.40]) and prior TIA stroke (1.0 5 [1.03-1.07] (all p<0.05). Conclusion: Among hospitalizations, Asian/Pacific Islanders, patients from the lowermost income quartile with co-existent hypertension, diabetes, hyperlipidemia, CKD, and cardiovascular comorbidities independently revealed the highest MACCE rates. Special emphasis needs to be laid on socio-demographics, comorbidities, and racial differences for CVD disease prevention, and risk factor modification in postmenopausal women.

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.