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38%) p<0.001. Female patients with SZ had higher metabolic score (p=0.019) and were older (p<0.001).

Metabolic syndrome is highly prevalent in OSA population (72.7%) and is much more common in female OSA patients (88%) than males OSA (68%). All OSA patients should be screened for MS so that early intervention can be done in these patients so as to prevent cardiovascular complications.

Metabolic syndrome is highly prevalent in OSA population (72.7%) and is much more common in female OSA patients (88%) than males OSA (68%). All OSA patients should be screened for MS so that early intervention can be done in these patients so as to prevent cardiovascular complications.

The relative contribution of pathophysiological mechanisms in acute coronary syndrome (ACS) towards obstructive sleep apnea (OSA) is not well-studied. We examined the correlation between severity of OSA and inflammation, myocardial necrosis, wall stress, and fibrosis.

A total of 89 patients admitted with ACS underwent a sleep study during index admission. Plasma levels of high-sensitivity C-reactive protein (hs-CRP), troponin I, N-terminal pro-brain natriuretic peptide (NT-proBNP), and suppression of tumorigenicity 2 (ST2) were prospectively analyzed. Two patients diagnosed with central sleep apnea were excluded.

The recruited patients were divided into no (AHI <5 events/hour, 9.2%), mild (5-<15, 27.6%), moderate (15-<30, 21.8%), and severe (≥30, 41.4%) OSA. Compared to the no, mild and moderate OSA groups, the severe OSA group had a higher body mass index (p=0.005). They were also more likely to present with ST-segment elevation ACS (versus non-ST-segment elevation ACS) (p=0.041), have undergone previous coronary artery bypass grafting (p=0.013), demonstrate complete coronary occlusion during baseline coronary angiography (p=0.049), and have a larger left atrial diameter measured on echocardiography (p=0.029). Likewise, the severe OSA group had higher plasma levels of hs-CRP (p=0.004), troponin I (p=0.017), and NT-proBNP (p=0.004), but not ST2 (p=0.10). After adjustment for the effects of confounding variables, OSA was independently associated with troponin I (ie, myocardial necrosis; p=0.001) and NT-proBNP (ie, myocardial wall stress; p=0.008).

Severe OSA during the acute phase of ACS was associated with extensive myocardial necrosis and high myocardial wall stress, but not with inflammation and myocardial fibrosis.

Severe OSA during the acute phase of ACS was associated with extensive myocardial necrosis and high myocardial wall stress, but not with inflammation and myocardial fibrosis.Current diagnostics of sleep apnea relies on the time-consuming manual analysis of complex sleep registrations, which is impractical for routine screening in hospitalized patients with a high probability for sleep apnea, e.g. those experiencing acute stroke or transient ischemic attacks (TIA). To overcome this shortcoming, we aimed to develop a convolutional neural network (CNN) capable of estimating the severity of sleep apnea in acute stroke and TIA patients based solely on the nocturnal oxygen saturation (SpO2) signal. selleck inhibitor The CNN was trained with SpO2 signals derived from 1379 home sleep apnea tests (HSAT) of suspected sleep apnea patients and tested with SpO2 signals of 77 acute ischemic stroke or TIA patients. The CNN's performance was tested by comparing the estimated respiratory event index (REI) and oxygen desaturation index (ODI) with manually obtained values. Median estimation errors for REI and ODI in patients with stroke or TIA were 1.45 events/hour and 0.61 events/hour, respectively. Furthermore, based on estimated REI and ODI, 77.9% and 88.3% of these patients were classified into the correct sleep apnea severity categories. The sensitivity and specificity to identify sleep apnea (REI > 5 events/hour) were 91.8% and 78.6%, respectively. Moderate-to-severe sleep apnea was detected (REI > 15 events/hour) with sensitivity of 92.3% and specificity of 96.1%. The CNN analysis of the SpO2 signal has great potential as a simple screening tool for sleep apnea. This novel automatic method accurately detects sleep apnea in acute cerebrovascular disease patients and facilitates their referral for a differential diagnostic HSAT or polysomnography evaluation.

Nocturnal hypoxemia is associated with increased cardiovascular mortality. Here, we assess whether positive airway pressure by adaptive servo-ventilation (ASV) reduces nocturnal hypoxemic burden in patients with primary central sleep apnea (primary CSA), or heart failure related central sleep apnea (CSA-HF) and treatment emergent central sleep apnea (TECSA).

Overnight oximetry data from 328 consecutive patients who underwent ASV initiation between March 2010 and May 2018 were retrospectively analyzed. Patients were stratified into three groups primary CSA (n=14), CSA-HF (n=31), TECSA (n=129). Apnea hypopnea index (AHI) and time spent below 90% SpO

(T90) was measured. Additionally, T90 due to acute episodic desaturations (T90

) and due to non-specific and non-cyclic drifts of SpO

(T90

) were assessed.

ASV reduced the AHI below 15/h in all groups. ASV treatment significantly shortened T90 in all three etiologies to a similar extent. T90

, but not T90

, was reduced by ASV across all three patient groups. AHI was identified as an independent modulator for ΔT90

upon ASV treatment (B (95% CI-1.32 (-1.73;-0.91), p<0.001), but not for ΔT90 or ΔT90

. Body mass index was one independent predictor of T90.

Across different central sleep apnea etiologies, ASV reduced AHI, but nocturnal hypoxemic burden remained high due to a non-specific component of T90 not related to episodic desaturation. Whether adjunct risk factor management such as weight-loss can further reduce T90 warrants further study.

Across different central sleep apnea etiologies, ASV reduced AHI, but nocturnal hypoxemic burden remained high due to a non-specific component of T90 not related to episodic desaturation. Whether adjunct risk factor management such as weight-loss can further reduce T90 warrants further study.Droughts are slow-moving natural hazards that gradually spread over large areas and capable of extending to continental scales, leading to severe socio-economic damage. A key challenge is developing accurate drought forecast model and understanding a models' capability to examine different drought characteristics. Traditionally, forecasting techniques have used various time-series approaches and machine learning models. However, the use of deep learning methods have not been tested extensively despite its potential to improve our understanding of drought characteristics. The present study uses a deep learning approach, specifically the Long Short-Term Memory (LSTM) to predict a commonly used drought measure, the Standard Precipitation Evaporation Index (SPEI) at two different time scales (SPEI 1, SPEI 3). The model was compared with other common machine learning method, Random Forests, Artificial Neural Networks and applied over the New South Wales (NSW) region of Australia, using hydro-meteorological variables as predictors.