Association Between Cytokines and Liver Histology in Children with Nonalcoholic Fatty Liver Disease.

BackgroundReliable non-invasive markers to characterize inflammation, hepatocellular ballooning, and fibrosis in nonalcoholic fatty liver disease (NAFLD) are lacking. We investigated the relationship between plasma cytokine levels and features of NAFLD histology to gain insight into cellular pathways driving NASH and to identify potential non-invasive discriminators of NAFLD severity and pattern.MethodsCytokines were measured from plasma obtained at enrollment in pediatric participants in NASH Clinical Research Network studies with liver biopsy-proven NAFLD. Cytokines were chosen a priori as possible discriminators of NASH and its components. Minimization of Akaike Information Criterion (AIC) was used to determine cytokines retained in multivariable models.ResultsOf 235 subjects, 31% had "Definite NASH" on liver histology, 43% had "Borderline NASH", and 25% had NAFLD but not NASH. Total plasminogen activator inhibitor 1 (PAI1) and activated PAI1 levels were higher in pediatric participants with Definite NASH and with lobular inflammation. Interleukin-8 (IL-8) was higher in those with stage 3-4 fibrosis and lobular inflammation. sIL-2rα was higher in children with stage 3-4 fibrosis and portal inflammation. In multivariable analysis, PAI1 variables were discriminators of Borderline/Definite NASH, definite NASH, lobular inflammation and ballooning. IL-8 increased with steatosis and fibrosis severity; sIL-2rα increased with fibrosis severity and portal inflammation. IL-7 decreased with portal inflammation and fibrosis severity.ConclusionsPlasma cytokines associated with histology varied considerably among NASH features, suggesting promising avenues for investigation. Future, more targeted analysis is needed to identify the role of these markers in NAFLD and to evaluate their potential as non-invasive discriminators of disease severity

Obstructive sleep apnea syndrome (OSAS), metabolic syndrome and mental health in small enterprise workers. Feasibility of an action for health.

Objective: To determine the frequency of obstructive sleep apnea syndrome (OSAS), metabolic syndrome and common mental disorders in the working population of 11 small enterprises and the feasibility of a program of action for health. Method: The clinical risk of OSAS, the prevalence of metabolic syndrome, and the level of psychological disorders were assessed during routine medical examination at the workplace in 2012. The response to medical advice was assessed in 2013 Results: 12.3% of the workers were suspected of being affected by OSAS. One or more components of metabolic syndrome were present in 24.5% of cases. OSAS in \u201chealthy\u201d workers was significantly associated with the presence of one or more components of metabolic syndrome (OR=3.83; 95%CI 1.45-10.13) and with a psychological disorders score in the highest quartile (OR= 4.67; 95%CI= 1.72-12.64). Workers with suspected OSAS were reluctant to follow advice about undergoing further tests under the NHS. However, in some cases, confirmation of the OSAS diagnosis and subsequent treatment led to an improvement in metabolic condition. Conclusion: Although participation in treatment was limited, anecdotal cases support the idea that prevention of obstructive sleep apnea in the workplace might be useful for workers\u2019 health

Unexplained Practice Variation in Primary Care Providers' Concern for Pediatric Obstructive Sleep Apnea

Objective To examine primary care provider (PCP) screening practice for obstructive sleep apnea (OSA) and predictive factors for screening habits. A secondary objective was to describe the polysomnography (PSG) completion proportion and outcome. We hypothesized that both provider and child health factors would predict PCP suspicion of OSA. Methods A computer decision support system that automated screening for snoring was implemented in five urban primary care clinics in Indianapolis, Indiana. We studied 1086 snoring children between 1 and 11 years seen by 26 PCPs. We used logistic regression to examine the association between PCP suspicion of OSA and child demographics, child health characteristics, provider characteristics, and clinic site. Results PCPs suspected OSA in 20% of snoring children. Factors predicting PCP concern for OSA included clinic site (p < .01; OR=0.13), Spanish language (p < .01; OR=0.53), provider training (p=.01; OR=10.19), number of training years (p=.01; OR=4.26) and child age (p<.01), with the youngest children least likely to elicit PCP concern for OSA (OR=0.20). No patient health factors (e.g., obesity) were significantly predictive. Proportions of OSA suspicion were variable between clinic sites (range 6% to 28%) and between specific providers (range 0% to 63%). Of children referred for PSG (n=100), 61% completed the study. Of these, 67% had OSA. Conclusions Results suggest unexplained small area practice variation in PCP concern for OSA amongst snoring children. It is likely that many children at-risk for OSA remain unidentified. An important next step is to evaluate interventions to support PCPs in evidence-based OSA identification

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep

Service Selection using Predictive Models and Monte-Carlo Tree Search

This article proposes a method for automated service selection to improve treatment efficacy and reduce re-hospitalization costs. A predictive model is developed using the National Home and Hospice Care Survey (NHHCS) dataset to quantify the effect of care services on the risk of re-hospitalization. By taking the patient's characteristics and other selected services into account, the model is able to indicate the overall effectiveness of a combination of services for a specific NHHCS patient. The developed model is incorporated in Monte-Carlo Tree Search (MCTS) to determine optimal combinations of services that minimize the risk of emergency re-hospitalization. MCTS serves as a risk minimization algorithm in this case, using the predictive model for guidance during the search. Using this method on the NHHCS dataset, a significant reduction in risk of re-hospitalization is observed compared to the original selections made by clinicians. An 11.89 percentage points risk reduction is achieved on average. Higher reductions of roughly 40 percentage points on average are observed for NHHCS patients in the highest risk categories. These results seem to indicate that there is enormous potential for improving service selection in the near future

Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction A Machine Learning Study

BACKGROUND: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained during the management of patients with myocardial infarction (MI). Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks. RESEARCH QUESTION: Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who experienced an in-hospital cardiovascular event after acute MI? STUDY DESIGN AND METHOD: This is a prospective observational cohort study of 124 patients with acute MI of whom the STOP-BANG score classified 34 as low (27.4%), 30 as intermediate (24.2%), and 60 as high (48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction) to identify those patients who experienced an in-hospital cardiovascular event (ie, death, ventricular arrhythmias, atrial fibrillation, recurrent angina, reinfarction, stroke, worsening heart failure, or cardiogenic shock) after definitive MI treatment. Receiver operating characteristic curves were used to compare ML performance against STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction, independently. RESULTS: There were an increasing proportion of cardiovascular events across the low, intermediate, and high OSA risk groups (P = .005). ML selected 7 accessible variables (ie, Killip class, leukocytes, GRACE score, c reactive protein, oxygen saturation, STOP-BANG score, and N-terminal prohormone of B-type natriuretic peptide); their integration outperformed all comparators (area under the curve, 0.83 [95% CI, 0.74-0.90]; P <.01). INTERPRETATION: The integration of the STOP-BANG score into clinical evaluation (considering Killip class, GRACE score, and simple laboratory values) of subjects who were admitted for an acute MI because of ML can significantly optimize the identification of patients who will experience an in-hospital cardiovascular event

Association between preoperative obstructive sleep apnea and preoperative positive airway pressure with postoperative intensive care unit delirium

Importance: Obstructive sleep apnea has been associated with postoperative delirium, which predisposes patients to major adverse outcomes. Positive airway pressure may be an effective intervention to reduce delirium in this population. Objectives: To determine if preoperative obstructive sleep apnea is associated with postoperative incident delirium in the intensive care unit and if preoperative positive airway pressure adherence modifies the association. Design, Setting, and Participants: A retrospective single-center cohort study was conducted at a US tertiary hospital from November 1, 2012, to August 31, 2016, among 7792 patients admitted to an intensive care unit who underwent routine Confusion Assessment Method for the intensive care unit after major surgery. Patients were adults who had undergone a complete preoperative anesthesia assessment, received general anesthesia, underwent at least 1 delirium assessment, were not delirious preoperatively, and had a preoperative intensive care unit stay of less than 6 days. Statistical analysis was conducted from August 20, 2019, to January 11, 2020. Exposures: Self-reported obstructive sleep apnea, billing diagnosis of obstructive sleep apnea, or STOP-BANG (Snoring, Tiredness, Observed Apnea, Blood Pressure, Body Mass Index, Age, Neck Circumference and Gender) questionnaire score greater than 4, as well as self-reported use of preoperative positive airway pressure. Main Outcomes and Measures: Delirium within 7 days of surgery. Results: A total of 7792 patients (4562 men; mean [SD] age, 59.2 [15.3] years) met inclusion criteria. Diagnosed or likely obstructive sleep apnea occurred in 2044 patients (26%), and delirium occurred in 3637 patients (47%). The proportion of patients with incident delirium was lower among those with obstructive sleep apnea than those without (897 of 2044 [44%] vs 2740 of 5748 [48%]; unadjusted risk difference, -0.04; 99% credible interval [CrI], -0.07 to -0.00). Positive airway pressure adherence had minimal association with delirium (risk difference, -0.00; 99% CrI, -0.09 to 0.09). Doubly robust confounder adjustment eliminated the association between obstructive sleep apnea and delirium (risk difference, -0.01; 99% CrI, -0.04 to 0.03) and did not change that of preoperative positive airway pressure adherence (risk difference, -0.00, 99% CrI, -0.07 to 0.07). The results were consistent across multiple sensitivity analyses. Conclusions and Relevance: After risk adjustment, this study found no association between obstructive sleep apnea and postoperative delirium in the context of usual care in the intensive care unit, with 99% CrIs excluding clinically meaningful associations. With limited precision, no association was found between positive airway pressure adherence and delirium. Selection bias and measurement error limit the validity and generalizability of these observational associations; however, they suggest that interventions targeting sleep apnea and positive airway pressure are unlikely to have a meaningful association with postoperative intensive care unit delirium

Arousal frequency is associated with increased fatigue in obstructive sleep apnea

Fatigue is an important and often underemphasized symptom in patients with obstructive sleep apnea (OSA). Sleep fragmentation, i.e., arousals and disruptions in sleep architecture, is common in patients with OSA and may potentially contribute to their fatigue. We hypothesized that arousal frequency and changes in sleep architecture contribute to the fatigue experienced by patients with OSA. Seventy-three patients with diagnosed but untreated OSA (AHI ≥ 15) were enrolled in this study. A baseline polysomnogram was obtained, and fatigue was measured with the Multidimensional Fatigue Symptom Inventory-short form (MFSI-sf). We evaluated the association between fatigue and arousals and various polysomongraphic variables, including sleep stages and sleep efficiency. Significant correlations between MFSI-sf subscale scores and various arousal indices were noted. Emotional fatigue scores were associated with total arousal index (r = 0.416, p = .021), respiratory movement arousal index (r = 0.346, p = .025), and spontaneous movement arousal index (r = 0.378, p = .025). Physical fatigue scores were associated with total arousal index (r = 0.360, p = .033) and respiratory movement arousal index (r = 0.304, p = .040). Percent of stage 1 sleep and REM sleep were also associated with physical and emotional fatigue scores. Hierarchal linear regression analysis demonstrated that emotional fatigue scores were independently associated with spontaneous movement arousals after controlling for age, body mass index, depression, and sleep apnea severity. These findings suggest that arousals may contribute to the fatigue seen in patients with OSA

Obstructive Sleep Apnea With or Without Excessive Daytime Sleepiness: Clinical and Experimental Data-Driven Phenotyping

Introduction: Obstructive sleep apnea (OSA) is a serious and prevalent medical condition with major consequences for health and safety. Excessive daytime sleepiness (EDS) is a common\u2014but not universal\u2014accompanying symptom. The purpose of this literature analysis is to understand whether the presence/absence of EDS is associated with different physiopathologic, prognostic, and therapeutic outcomes in OSA patients. Methods: Articles in English published in PubMed, Medline, and EMBASE between January 2000 and June 2017, focusing on no-EDS OSA patients, were critically reviewed. Results: A relevant percentage of OSA patients do not complain of EDS. EDS is a significant and independent predictor of incident cardiovascular disease (CVD) and is associated with all-cause mortality and an increased risk of metabolic syndrome and diabetes. Male gender, younger age, high body mass index, are predictors of EDS. The positive effects of nasal continuous positive airway pressure (CPAP) therapy on blood pressure, insulin resistance, fatal and non-fatal CVD, and endothelial dysfunction risk factors have been demonstrated in EDS-OSA patients, but results are inconsistent in no-EDS patients. The most sustainable cause of EDS is nocturnal hypoxemia and alterations of sleep architecture, including sleep fragmentation. These changes are less evident in no-EDS patients that seem less susceptible to the cortical effects of apneas. Conclusions: There is no consensus if we should consider OSA as a single disease with different phenotypes with or without EDS, or if there are different diseases with different genetic/epigenetic determinants, pathogenic mechanisms, prognosis, and treatment.The small number of studies focused on this issue indicates the need for further research in this area. Clinicians must carefully assess the presence or absence of EDS and decide accordingly the treatment. This approach could improve combination therapy targeted to a patient\u2019s specific pathology to enhance both efficacy and long-term adherence to OSA treatment and significantly reduce the social, economic, and health negative impact of OSA