Explore the vast range of titles available.

Systematic screening for liver fibrosis using FIB-4 score is recommended in primary care for patients with chronic liver disease risk factors. This study assesses the prevalence and characteristics of patients at risk for advanced fibrosis in a weight loss program. This multicenter retrospective cohort study includes obese and overweight subjects participating in a weight loss program across 110 French centers. 34 510 participants with baseline FIB-4 available were included, predominantly women (78.3%), median age of 54 years, 70% obese. Baseline FIB-4 values were <1.3, 1.3–2.67 or >2.67 in 80.9%, 18.1% and 1% of the participants, respectively. When moving from the lower risk category (<1.3) to the highest (>2.67), the rates of metabolic comorbidities such as diabetes rose (from 3.2% to 13.3%). After 5 (3–7) months, all anthropometric parameters improved. A follow-up FIB-4 value was available in 20.7% participants. Among high-risk, 43% changed classes, 4.6% moving to the lower risk-category.

Abstract Study Objectives In-laboratory polysomnography is the current gold standard for objective sleep measurements in clinical trials, but this does not capture night-to-night variability in sleep parameters. This study analyzed variability in sleep parameters recorded over multiple nights of sleep in an ecological setting using a portable sleep monitor and then estimated the minimum sample sizes required to reliably account for inter- and intra-individual variability in sleep parameters. Methods Participants were males who self-reported the absence of sleep disorders, and used a sleep monitoring device (Dreem Headband, Dreem, France) over multiple nights of sleep. Night-to-night variability of sleep parameters was determined over five consecutive weeknights using coefficients of variation (CV), and the minimal number of individuals and nights needed to reliably determine each sleep parameter was assessed. Results Night-to-night variability for the whole group (n = 94; 470 nights) was high (CV 0.44–0.58) for N2, N3, sleep onset and persistent sleep latencies, and wake after sleep onset (WASO), medium (CV 0.22–0.28) for N1 and N3 percentage, awakenings and REM latency, and low (CV 0.04–0.19) for sleep efficiency, N2 and REM percentages, total sleep time (TST) and micro-arousal index. Minimum sample sizes for reliable assessment of TST and WASO were 2 nights with 10 participants and 4 nights with 50 participants, respectively. Conclusions Night-to-night variability of sleep parameters is underestimated and under-recognized. These data on variability in commonly used sleep parameters will facilitate better estimation of sample sizes and number of nights required in clinical trials based on the outcomes of interest. Graphical Abstract Graphical Abstract

The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea. An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors. Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies.

More advanced knowledge is needed on how COPD alters the clinical presentation of obstructive sleep apnea (OSA) and how the association of both diseases, known as 'overlap syndrome' (OVS), impacts on cardiovascular health. To investigate differences between patients with OVS and those with moderate-to-severe OSA alone. A cross-sectional study conducted in the French National Sleep Apnea Registry between January 1997 and January 2017. Univariable and multivariable logistic regression models were used to compare OVS versus OSA alone on symptoms and cardiovascular health. 46,786 patients had moderate-to-severe OSA. Valid spirometry was available for 16,466 patients: 14,368 (87%) had moderate-to-severe OSA alone and 2098 (13%) had OVS. A lower proportion of OVS patients complained of snoring, morning headaches and excessive daytime sleepiness compared to OSA alone (median Epworth Sleepiness Scale score: 9 [interquartile range (IQR) 6-13] versus 10 (IQR 6-13), respectively; P <0.02). Similarly, a lower proportion of OVS patients (35.6% versus 39.4%, respectively; P <0.01) experienced sleepiness while driving. In contrast, 63.5% of the OVS population experienced nocturia compared to 58.0% of the OSA population (P<0.01). Apnea hypopnea index (36 [25; 52] vs 33.1 [23.3; 50]), oxygen desaturation index (28 [15; 48] vs 25.2 [14; 45]) and mean nocturnal SaO2 (92 [90; 93.8] vs 93 [91.3; 94]) were significantly more altered in the OVS group. Associated COPD had no effect on the prevalence of hypertension and stroke. After controlling for main confounders, COPD severity was associated in a dose-response relationship with a higher prevalence of coronary heart disease, heart failure and peripheral arteriopathy. In adults with moderate-to-severe OSA, OVS was minimally symptomatic, but exhibited higher odds for prevalent coronary heart disease, heart failure and peripheral arteriopathy.

Background The comorbidity burden has a negative impact on lung-cancer survival. Several comorbidity scores have been described and are currently used. The current challenge is to select the comorbidity score that best reflects their impact on survival. Here, we compared seven usable comorbidity scores (Charlson Comorbidity Index, Age adjusted Charlson Comorbidity Index, Charlson Comorbidity Index adapted to lung cancer, National Cancer Institute combined index, National Cancer Institute combined index adapted to lung cancer, Elixhauser score, and Elixhauser adapted to lung cancer) with coded administrative data according to the tenth revision of the International Statistical Classification of Diseases and Related Health Problems to select the best prognostic index for predicting four-month survival. Materials and methods This cohort included every patient with a diagnosis of lung cancer hospitalized for the first time in the thoracic oncology unit of our institution between 2011 and 2015. The seven scores were calculated and used in a Cox regression method to model their association with four-month survival. Then, parameters to compare the relative goodness-of-fit among different models (Akaike Information Criteria, Bayesian Information Criteria), and discrimination parameters (the C-statistic and Harrell’s c-statistic) were calculated. A sensitivity analysis of these parameters was finally performed using a bootstrap method based on 1,000 samples. Results In total, 633 patients were included. Male sex, histological type, metastatic status, CCI, CCI-lung, Elixhauser score, and Elixhauser-lung were associated with poorer four-month survival. The Elixhauser score had the lowest AIC and BIC and the highest c-statistic and Harrell’s c-statistic. These results were confirmed in the sensitivity analysis, in which these discrimination parameters for the Elixhauser score were significantly different from the other scores. Conclusions Based on this cohort, the Elixhauser score is the best prognostic comorbidity score for predicting four-month survival for hospitalized lung cancer patients.

To determine the effect of continuous positive airway pressure (CPAP), the gold standard treatment for obstructive sleep apnea syndrome (OSAS), on gait control in severe OSAS patients. We conducted a randomized, double-blind, parallel-group, sham-controlled monocentric study in Grenoble Alpes University Hospital, France. Gait parameters were recorded under single and dual-task conditions using a visuo-verbal cognitive task (Stroop test), before and after the 8-week intervention period. Stride-time variability, a marker of gait control, was the primary study endpoint. Changes in the determinants of gait control were the main secondary outcomes. ClinicalTrials.gov Identifier: (NCT02345694). 24 patients [median (Q1; Q3)]: age: 59.5 (46.3; 66.8) years, 87.5% male, body mass index: 28.2 (24.7; 29.8) kg. m −2 , apnea–hypopnea index: 51.6 (35.0; 61.4) events/h were randomized to be treated by effective CPAP (n = 12) or by sham-CPAP (n = 12). A complete case analysis was performed, using a mixed linear regression model. CPAP elicited no significant improvement in stride-time variability compared to sham-CPAP. No difference was found regarding the determinants of gait control. This study is the first RCT to investigate the effects of CPAP on gait control. Eight weeks of CPAP treatment did not improve gait control in severe non-obese OSAS patients. These results substantiate the complex OSAS-neurocognitive function relationship.

Background Although polypharmacy has been described among cancer patients, very few studies have focused on those with lung cancer. We aimed to assess whether polypharmacy and comorbidity have an impact on systemic parenteral treatment administration and survival among lung-cancer patients. Methods In this retrospective monocenter cohort study, we included patients hospitalized in thoracic oncology for the first time between 2011 and 2015. The Elixhauser score was used to assess comorbidity and polypharmacy was estimated with a threshold of at least five prescribed medications. The Fine and Gray competitive risk model was used to estimate the impact of polypharmacy and comorbidity on systemic parenteral treatment administration within the first two months of hospitalization. The effect of comorbidity and polypharmacy on overall survival was evaluated by Cox proportional hazards analysis. Results In total, 633 patients were included (71% men), with a median age of 66 years. The median Elixhauser score was 6 and median overall survival was four months. Among the patients, 24.3% were considered to be receiving polypharmacy, with a median number of medications of 3, and 49.9% received systemic parenteral treatment within two months after hospitalization. Severe comorbidity (Elixhauser score > 11), but not polypharmacy, was independently associated with a lower rate of systemic parenteral treatment prescription (SdHR = 0.4 [0.3;0.6], p < 0.01) and polypharmacy, but not a high comorbidity score, was independently associated with poorer four-month survival (HR = 1.4 [1.1;1.9], p < 0.01) Conclusions This first study to evaluate the consequences of comorbidity and polypharmacy on the care of lung-cancer patients shows that a high comorbidity burden can delay systemic parenteral treatment administration, whereas polypharmacy has a negative impact on four-month survival.

Abstract Study Objectives The COVID-19 pandemic has had dramatic effects on society and people’s daily habits. In this observational study, we recorded objective data on sleep macro- and microarchitecture repeatedly over several nights before and during the COVID-19 government-imposed lockdown. The main objective was to evaluate changes in patterns of sleep duration and architecture during home confinement using the pre-confinement period as a control. Methods Participants were regular users of a sleep-monitoring headband that records, stores, and automatically analyzes physiological data in real time, equivalent to polysomnography. We measured sleep onset duration, total sleep time, duration of sleep stages (N2, N3, and rapid eye movement [REM]), and sleep continuity. Via the user’s smartphone application, participants filled in questionnaires on how lockdown changed working hours, eating behavior, and daily life at home. They also filled in the Insomnia Severity Index, reduced Morningness–Eveningness Questionnaire, and Hospital Anxiety and Depression Scale questionnaires, allowing us to create selected subgroups. Results The 599 participants were mainly men (71%) of median age 47 (interquartile range: 36–59). Compared to before lockdown, during lockdown individuals slept more overall (mean +3·83 min; SD: ±1.3), had less deep sleep (N3), more light sleep (N2), and longer REM sleep (mean +3·74 min; SD: ±0.8). They exhibited less weekend-specific changes, suggesting less sleep restriction during the week. Changes were most pronounced in individuals reporting eveningness preferences, suggesting relative sleep deprivation in this population and exacerbated sensitivity to societal changes. Conclusion This unique dataset should help us understand the effects of lockdown on sleep architecture and on our health.