Explore the vast range of titles available.

BackgroundCPAP reduces blood pressure (BP) in patients with symptomatic obstructive sleep apnoea (OSA). Whether the same benefit is present in patients with minimally symptomatic OSA is unclear, thus a meta-analysis of existing trial data is required.MethodsThe electronic databases Medline, Embase and trial registries were searched. Trials were eligible if they included patients with minimally symptomatic OSA, had randomised them to receive CPAP or either sham-CPAP or no CPAP, and recorded BP at baseline and follow-up. Individual participant data were obtained. Primary outcomes were absolute change in systolic and diastolic BP.FindingsFive eligible trials were found (1219 patients) from which data from four studies (1206 patients) were obtained. Mean (SD) baseline systolic and diastolic BP across all four studies was 131.2 (15.8) mm Hg and 80.9 (10.4) mm Hg, respectively. There was a slight increase in systolic BP of 1.1 mm Hg (95% CI −0.2 to 2.3, p=0.086) and a slight reduction in diastolic BP of 0.8 mm Hg (95% CI −1.6 to 0.1, p=0.083), although the results were not statistically significant. There was some evidence of an increase in systolic BP in patients using CPAP <4 h/night (1.5 mm Hg, 95% CI −0.0 to 3.1, p=0.052) and reduction in diastolic BP in patients using CPAP >4 h/night (−1.4 mm Hg, 95% CI −2.5 to −0.4, p=0.008). CPAP treatment reduced both subjective sleepiness (p<0.001) and OSA severity (p<0.001).InterpretationAlthough CPAP treatment reduces OSA severity and sleepiness, it seems not to have a beneficial effect on BP in patients with minimally symptomatic OSA, except in patients who used CPAP for >4 h/night.

Population-based studies showing the negative impact of type 2 diabetes (T2D) on lung function are overviewed. Among the well-recognized pathophysiological mechanisms, the metabolic pathways related to insulin resistance (IR), low-grade chronic inflammation, leptin resistance, microvascular damage, and autonomic neuropathy are emphasized. Histopathological changes are exposed, and findings reported from experimental models are clearly differentiated from those described in humans. The accelerated decline in pulmonary function that appears in patients with cystic fibrosis (CF) with related abnormalities of glucose tolerance and diabetes is considered as an example to further investigate the relationship between T2D and the lung. Furthermore, a possible causal link between antihyperglycemic therapies and pulmonary function is examined. T2D similarly affects breathing during sleep, becoming an independent risk factor for higher rates of sleep apnea, leading to nocturnal hypoxemia and daytime sleepiness. Therefore, the impact of T2D on sleep breathing and its influence on sleep architecture is analyzed. Finally, the effect of improving some pathophysiological mechanisms, primarily IR and inflammation, as well as the optimization of blood glucose control on sleep breathing is evaluated. In summary, the lung should be considered by those providing care for people with diabetes and raise the central issue of whether the normalization of glucose levels can improve pulmonary function and ameliorate sleep-disordered breathing. Therefore, patients with T2D should be considered a vulnerable group for pulmonary dysfunction. However, further research aimed at elucidating how to screen for the lung impairment in the population with diabetes in a cost-effective manner is needed.Current evidence supporting the link among type 2 diabetes, pulmonary dysfunction, and sleep disorders is reviewed. We conclude the lung is a new target for the deleterious effects of diabetes.

Question We evaluated whether the time between first respiratory support and intubation of patients receiving invasive mechanical ventilation (IMV) due to COVID-19 was associated with mortality or pulmonary sequelae. Materials and methods Prospective cohort of critical COVID-19 patients on IMV. Patients were classified as early intubation if they were intubated within the first 48 h from the first respiratory support or delayed intubation if they were intubated later. Surviving patients were evaluated after hospital discharge. Results We included 205 patients (140 with early IMV and 65 with delayed IMV). The median [p 25 ;p 75 ] age was 63 [56.0; 70.0] years, and 74.1% were male. The survival analysis showed a significant increase in the risk of mortality in the delayed group with an adjusted hazard ratio (HR) of 2.45 (95% CI 1.29–4.65). The continuous predictor time to IMV showed a nonlinear association with the risk of in-hospital mortality. A multivariate mortality model showed that delay of IMV was a factor associated with mortality (HR of 2.40; 95% CI 1.42–4.1). During follow-up, patients in the delayed group showed a worse DLCO (mean difference of − 10.77 (95% CI − 18.40 to − 3.15), with a greater number of affected lobes (+ 1.51 [95% CI 0.89–2.13]) and a greater TSS (+ 4.35 [95% CI 2.41–6.27]) in the chest CT scan. Conclusions Among critically ill patients with COVID-19 who required IMV, the delay in intubation from the first respiratory support was associated with an increase in hospital mortality and worse pulmonary sequelae during follow-up.

Background: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory. Objective: The aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance. Methods: This is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea–Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient’s CPAP compliance from the very first days of usage; (2) machine learning–based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient’s compliance, satisfaction, and health care costs. Results: Randomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13% (8/60) were women. Patients in the intervention arm had a mean (95% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients’ satisfaction was excellent in both arms, and up to 88% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean €90.2 (SD 53.14) (US $105.76 [SD 62.31]); intervention: mean €96.2 (SD 62.13) (US $112.70 [SD 72.85]); P=.70; €1=US $1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis. Conclusions: A machine learning–based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients’ empowerment in the management of chronic diseases. Trial Registration: ClinicalTrials.gov NCT03116958; https://clinicaltrials.gov/ct2/show/NCT03116958

Advanced glycation end-products (AGEs) activate specific receptors (RAGE) promoting inflammation and oxidative stress. The lungs, with high RAGE expression, may be particularly susceptible to AGE-related injury. This study assessed whether baseline skin AGE levels, measured by skin autofluorescence (SAF), predict pulmonary function decline in middle-aged adults with cardiovascular risk factors. This ancillary analysis of the ILERVAS cohort included adults aged 45–70 years with cardiovascular risk factors but without diabetes or chronic kidney disease. Baseline data included demographics, lifestyle, and fasting blood tests. SAF was measured using AGE Reader™, and spirometry performed at baseline and after a median follow-up of 4 years. Associations between baseline SAF and annual declines in FEV 1 , FVC, and FEV 1 /FVC were analysed using adjusted models and generalized additive models, stratified by smoking status. Among 658 participants (median age 56 years, 48% female), median baseline SAF was 1.90 AU [1.60; 2.20]. Baseline lung function was preserved, with median FEV 1 , FVC and FEV 1 /FVC of 2795 mL [2270; 3,341], 3,525 mL [2870; 4300], and 78.6% [74.4; 82.8]. Annual declines were −  81.9 mL [− 120.6; − 43.3] for FEV 1 , − 99.6 mL [− 159.3; − 37.9] for FVC, and − 0.04% [− 0.85; 0.70] for FEV 1 /FVC. No significant associations were found between SAF and spirometry changes. Results were consistent across smoking subgroups. Baseline skin AGE levels did not predict pulmonary function decline over four years in middle-aged adults with cardiovascular risk factors. While SAF reflects cumulative AGE exposure, it has limited prognostic value for lung function in this population.

Osteoarthritis is a disabling condition that is often associated with other comorbidities. Total hip or knee arthroplasty is an effective surgical treatment for osteoarthritis when indicated, but comorbidities can impair their results by increasing complications and social and economic costs. Integrated care (IC) models supported by eHealth can increase efficiency through defragmentation of care and promote patient-centeredness. This study aims to assess the effectiveness and cost-effectiveness of implementing a mobile health (mHealth)-enabled IC model for complex chronic patients undergoing primary total hip or knee arthroplasty. As part of the Horizon 2020 Personalized Connected Care for Complex Chronic Patients (CONNECARE) project, a prospective, pragmatic, two-arm, parallel implementation trial was conducted in the rural region of Lleida, Catalonia, Spain. For 3 months, complex chronic patients undergoing total hip or knee arthroplasty and their caregivers received the combined benefits of the CONNECARE organizational IC model and the eHealth platform supporting it, consisting of a patient self-management app, a set of integrated sensors, and a web-based platform connecting professionals from different settings, or usual care (UC). We assessed changes in health status (12-item short-form survey [SF-12]), unplanned visits and admissions during a 6-month follow-up, and the incremental cost-effectiveness ratio. A total of 29 patients were recruited for the mHealth-enabled IC arm, and 30 patients were recruited for the UC arm. Both groups were statistically comparable for baseline characteristics, such as age; sex; type of arthroplasty; and Charlson index, American Society of Anesthesiologists classification, Barthel index, Hospital Anxiety and Depression scale, Western Ontario and McMaster Universities Osteoarthritis Index, and Pfeiffer mental status questionnaire scores. Patients in both groups had significant increases in the SF-12 physical domain and total SF-12 score, but differences in differences between the groups were not statistically significant. IC patients had 50% fewer unplanned visits (P=.006). Only 1 hospital admission was recorded during the follow-up (UC arm). The IC program generated savings in different cost scenarios, and the incremental cost-effectiveness ratio demonstrated cost-effectiveness. Chronic patients undergoing hip or knee arthroplasty can benefit from the implementation of patient-centered mHealth-enabled IC models aimed at empowering patients and facilitating transitions from specialized hospital care to primary care. Such models can reduce unplanned contacts with the health system and reduce overall health costs, proving to be cost-effective. Overall, our findings support the notion of system-wide cross-organizational care pathways supported by mHealth as a successful way to implement IC for patients undergoing elective surgery.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers. Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers. On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the A 42, least squares (LS) for -tau and Huber for -tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting A 42, while the ALL subset most accurately predicted -tau and -tau due to the lowest test errors. Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.

To describe the overall sleep health of the Catalan population using data from the 2015 Catalan Health Survey and to compare the performance of two sleep health indicators: sleep duration and a 5-dimension sleep scale (SATED). Multistage probability sampling representative of the non-institutionalized population aged 15 or more years, stratified by age, gender and municipality size, was used, excluding nightshift-workers. A total of 4385 surveys were included in the analyses. Associations between sleep health and the number of reported chronic diseases were assessed using non-parametric smoothed splines. Differences in the predictive ability of age-adjusted logistic regression models of self-rated health status were assessed. Multinomial logistic regression models were used to assess SATED determinants. Overall mean (SD) sleep duration was 7.18 (1.16) hours; and SATED score 7.91 (2.17) (range 0-10), lower (worse) scores were associated with increasing age and female sex. Alertness and efficiency were the most frequently impaired dimensions across age groups. SATED performed better than sleep duration when assessing self-rated health status (area under the curve = 0.856 vs. 0.798; p-value <0.001), and had a linear relationship with the number of reported chronic diseases, while the sleep duration relationship was u-shaped. Sleep health in Catalonia is associated with age and gender. SATED has some advantaged compared to sleep duration assessment, as it relates linearly to health indicators, has a stronger association with self-rated health status, and provides a more comprehensive assessment of sleep health. Therefore, the inclusion of multi-dimensional sleep health assessment tools in national surveys should be considered.

We hypothesized that obstructive sleep apnea (OSA) can predispose individuals to lower airway infections and community-acquired pneumonia (CAP) due to upper airway microaspiration. This study evaluated the association between OSA and CAP. We performed a case-control study that included 82 patients with CAP and 41 patients with other infections (control group). The controls were matched according to age, sex and body mass index (BMI). A respiratory polygraph (RP) was performed upon admission for patients in both groups. The severity of pneumonia was assessed according to the Pneumonia Severity Index (PSI). The associations between CAP and the Epworth Sleepiness Scale (ESS), OSA, OSA severity and other sleep-related variables were evaluated using logistic regression models. The associations between OSA, OSA severity with CAP severity were evaluated with linear regression models and non-parametric tests. No significant differences were found between CAP and control patients regarding anthropometric variables, toxic habits and risk factors for CAP. Patients with OSA, defined as individuals with an Apnea-Hypopnea Index (AHI) ≥10, showed an increased risk of CAP (OR = 2·86, 95%CI 1·29-6·44, p = 0·01). Patients with severe OSA (AHI≥30) also had a higher risk of CAP (OR = 3·18, 95%CI 1·11-11·56, p = 0·047). In addition, OSA severity, defined according to the AHI quartile, was also significantly associated with CAP (p = 0·007). Furthermore, OSA was significantly associated with CAP severity (p = 0·0002), and OSA severity was also associated with CAP severity (p = 0·0006). OSA and OSA severity are associated with CAP when compared to patients admitted to the hospital for non-respiratory infections. In addition, OSA and OSA severity are associated with CAP severity. These results support the potential role of OSA in the pathogenesis of CAP and could have clinical implications. This link between OSA and infection risk should be explored to investigate the relationships among gastroesophageal reflux, silent aspiration, laryngeal sensory dysfunction and CAP. ClinicalTrials.gov NCT01071421.

Background Patients with heart failure with reduced ejection fraction (HFrEF) and central sleep apnea (CSA) are at a very high risk of fatal outcomes. Objective To test whether the circulating miRNome provides additional information for risk stratification on top of clinical predictors in patients with HFrEF and CSA. Methods The study included patients with HFrEF and CSA from the SERVE-HF trial. A three-step protocol was applied: microRNA (miRNA) screening (n = 20), technical validation (n = 60), and biological validation (n = 587). The primary outcome was either death from any cause, lifesaving cardiovascular intervention, or unplanned hospitalization for worsening of heart failure, whatever occurred first. MiRNA quantification was performed in plasma samples using miRNA sequencing and RT-qPCR. Results Circulating miR-133a-3p levels were inversely associated with the primary study outcome. Nonetheless, miR-133a-3p did not improve a previously established clinical prognostic model in terms of discrimination or reclassification. A customized regression tree model constructed using the Classification and Regression Tree (CART) algorithm identified eight patient subphenotypes with specific risk patterns based on clinical and molecular characteristics. MiR-133a-3p entered the regression tree defining the group at the lowest risk; patients with log(NT-proBNP) ≤ 6 pg/mL (miR-133a-3p levels above 1.5 arbitrary units). The overall predictive capacity of suffering the event was highly stable over the follow-up (from 0.735 to 0.767). Conclusions The combination of clinical information, circulating miRNAs, and decision tree learning allows the identification of specific risk subphenotypes in patients with HFrEF and CSA.