6 research outputs found
Incidence and Predictors of Permanent Pacemaker Implantation after Valve Surgery – A Single Center Experience
Depressive symptoms in patients with obstructive sleep apnea: biological mechanistic pathways.
This study examined the association between depressive symptoms, as well as depressive symptom dimensions, and three candidate biological pathways linking them to Obstructive sleep apnea (OSA): (1) inflammation; (2) circulating leptin; and (3) intermittent hypoxemia. Participants included 181 obese adults with moderate-to-severe OSA enrolled in the Cardiovascular Consequences of Sleep Apnea (COSA) trial. Depressive symptoms were measured using the Beck Depression Inventory-II (BDI-II). We assessed inflammation using C-reactive protein levels (CRP), circulating leptin by radioimmunoassay using a double antibody/PEG assay, and intermittent hypoxemia by the percentage of sleep time each patient had below 90% oxyhemoglobin saturation. We found no significant associations between BDI-II total or cognitive scores and CRP, leptin, or percentage of sleep time below 90% oxyhemoglobin saturation after controlling for relevant confounding factors. Somatic symptoms, however, were positively associated with percentage of sleep time below 90% saturation (β = 0.202, P = 0.032), but not with CRP or circulating leptin in adjusted models. Another significant predictor of depressive symptoms included sleep efficiency (
Effect of Continuous Positive Airway Pressure, Weight Loss, or Continuous Positive Airway Pressure Plus Weight Loss on Central Hemodynamics and Arterial Stiffness.
Obesity and obstructive sleep apnea tend to coexist. Little is known about the effects of obstructive sleep apnea, obesity, or their treatment on central aortic pressures and large artery stiffness. We randomized 139 adults with obesity (body mass index \u3e30 kg/m(2)) and moderate-to-severe obstructive sleep apnea to (1) continuous positive airway pressure (CPAP) therapy (n=45), (2) weight loss (WL) therapy (n=48), or (3) combined CPAP and WL (n=46) for 24 weeks. We assessed the effect of these interventions on central pressures and carotid-femoral pulse wave velocity (a measure of large artery stiffness), measured with arterial tonometry. Central systolic pressure was reduced significantly only in the combination arm (-7.4 mm Hg; 95% confidence interval, -12.5 to -2.4 mm Hg; P=0.004), without significant reductions detected in either the WL-only (-2.3 mm Hg; 95% confidence interval, -7.5 to 3.0; P=0.39) or the CPAP-only (-3.1 mm Hg; 95% confidence interval, -8.3 to 2.0; P=0.23) arms. However, none of these interventions significantly changed central pulse pressure, pulse pressure amplification, or the central augmentation index. The change in mean arterial pressure (P=0.008) and heart rate (P=0.027) induced by the interventions was significant predictors of the change in carotid-femoral pulse wave velocity. However, after adjustment for mean arterial pressure and heart rate, no significant changes in carotid-femoral pulse wave velocity were observed in any group. In obese subjects with obstructive sleep apnea, combination therapy with WL and CPAP is effective in reducing central systolic pressure. However, this effect is largely mediated by changes in mean, rather than central pulse pressure. WL and CPAP, alone or in combination, did not reduce large artery stiffness in this population.
CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00371293
Timing and Outcomes After Coronary Angiography Following Out-of-Hospital Cardiac Arrest Without Signs of ST-Segment Elevation Myocardial Infarction.
BACKGROUND: There is broad consensus that resuscitated out-of-hospital cardiac arrest (OHCA) patients with ST-segment elevation myocardial infarction (STEMI) should receive immediate coronary angiography (CAG); however, factors that guide patient selection and optimal timing of CAG for post-arrest patients without evidence of STEMI remain incompletely described.
OBJECTIVE: We sought to describe the timing of post-arrest CAG in actual practice, patient characteristics associated with decision to perform immediate vs. delayed CAG, and patient outcomes after CAG.
METHODS: We conducted a retrospective cohort study at seven U.S. academic hospitals. Resuscitated adult patients with OHCA were included if they presented between January 1, 2015 and December 31, 2019 and received CAG during hospitalization. Emergency medical services run sheets and hospital records were analyzed. Patients without evidence of STEMI were grouped and compared based on time from arrival to CAG performance into early (≤ 6 h) and delayed (\u3e 6 h).
RESULTS: Two hundred twenty-one patients were included. Median time to CAG was 18.6 h (interquartile range [IQR] 1.5-94.6 h). Early catheterization was performed on 94 patients (42.5%) and delayed catheterization was performed on 127 patients (57.5%). Patients in the early group were older (61 years [IQR 55-70 years] vs. 57 years [IQR 47-65] years) and more likely to be male (79.8% vs. 59.8%). Those in the early group were more likely to have clinically significant lesions (58.5% vs. 39.4%) and receive revascularization (41.5% vs. 19.7%). Patients were more likely to die in the early group (47.9% vs. 33.1%). Among survivors, there was no significant difference in neurologic recovery at discharge.
CONCLUSIONS: OHCA patients without evidence of STEMI who received early CAG were older and more likely to be male. This group was more likely to have intervenable lesions and receive revascularization
Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021
Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings: In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation: Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Funding: Bill & Melinda Gates Foundation