Obstructive sleep apnea-hypopnea syndrome (OSA) is being identified increasingly as an important health issue. It is typified by repeated episodes of upper airway collapse during sleep leading to occasional hypoxaemia, sleep fragmentation and poor sleep quality. OSA is also being considered as an independent risk factor for hypertension, diabetes and cardiovascular diseases, leading to increased multi-morbidity and mortality. Cluster analysis, a powerful statistical set of techniques, may help in investigating and classifying homogeneous groups of patients with similar OSA characteristics. This study aims to investigate the (possible) different groups of patients in an OSA population, and to analyse the relationships among the main clinical variables in each group to better understand the impact of OSA on patients. Starting from a well-characterized OSA population of 198 subjects afferent to our sleep centre, we identified three different communities of OSA patients. The first has a very severe disease [apnea-hypopnea index (AHI) = 65.91 ± 22.47] and sleep disorder has a strong impact on daily life: a low level of diurnal partial pressure of oxygen (PaO ) (77.39 ± 11.64 mmHg) and a high prevalence of hypertension (64%); the second, with less severe disease (AHI = 28.88 ± 17.13), in which sleep disorders seem to be less important for diurnal PaO and have a minimum impact on comorbidity; and the last with very severe OSA (AHI = 57.26 ± 15.09) but with a low risk of nocturnal hypoxaemia (T90 = 11.58 ± 8.54) and less sleepy (Epworth Sleepiness Scale 10.00 ± 4.77).

The presence and the association of comorbidities seem to be higher in patients suffering from OSA, OS and OHS. Subjects suffering of OHS present a high prevalence of main diseases despite their younger age compared with others patients with SDB. Sleepiness may have a role, at least in a subset of these patients, into the development of comorbidities.

Introduction: Obstructive sleep apnea syndrome has become an important public health concern. Polysomnography is traditionally considered an established and effective diagnostic tool providing information on the severity of obstructive sleep apnea syndrome and the degree of sleep fragmentation. However, the numerous steps in the polysomnography test to diagnose obstructive sleep apnea syndrome are costly and time consuming. This study aimed to test the efficacy and clinical applicability of different machine learning methods based on demographic information and questionnaire data to predict obstructive sleep apnea syndrome severity. Materials and methods: We collected data about demographic characteristics, spirometry values, gas exchange (PaO2, PaCO2) and symptoms (Epworth Sleepiness Scale, snoring, etc.) of 313 patients with previous diagnosis of obstructive sleep apnea syndrome. After principal component analysis, we selected 19 variables which were used for further preprocessing and to eventually train seven types of classification models and five types of regression models to evaluate the prediction ability of obstructive sleep apnea syndrome severity, represented either by class or by apnea–hypopnea index. All models are trained with an increasing number of features and the results are validated through stratified 10-fold cross validation. Results: Comparative results show the superiority of support vector machine and random forest models for classification, while support vector machine and linear regression are better suited to predict apnea–hypopnea index. Also, a limited number of features are enough to achieve the maximum predictive accuracy. The best average classification accuracy on test sets is 44.7 percent, with the same average sensitivity (recall). In only 5.7 percent of cases, a severe obstructive sleep apnea syndrome (class 4) is misclassified as mild (class 2). Regression results show a minimum achieved root mean squared error of 22.17. Conclusion: The problem of predicting apnea–hypopnea index or severity classes for obstructive sleep apnea syndrome is very difficult when using only data collected prior to polysomnography test. The results achieved with the available data suggest the use of machine learning methods as tools for providing patients with a priority level for polysomnography test, but they still cannot be used for automated diagnosis.

The role of colony stimulating factors (CSFs) in cystic fibrosis (CF) circulating neutrophils has not been thoroughly evaluated, considering that the neutrophil burden of lung inflammation in these subjects is very high. The aim of this study was to assess granulocyte-CSF (G-CSF) and granulocyte-macrophage-CSF (GM-CSF) levels in CF patients in various clinical conditions and how these cytokines impact on activation and priming of neutrophils. G-CSF and GM-CSF levels were measured in sputum and serum samples of stable CF patients (n = 21) and in CF patients with acute exacerbation before and after a course of antibiotic therapy (n = 19). CSFs were tested on non CF neutrophils to investigate their effects on reactive oxygen species (ROS) production, degranulation (CD66b, elastase, lactoferrin, MMP-9), and chemotaxis. At very low concentrations found in CF patients (0.005–0.1 ng/ml), both cytokines inhibited ROS production, while higher concentrations (1–5 ng/ml) exerted a stimulatory effect. While either CSF induced elastase and MMP-9 secretion, lactoferrin levels were increased only by G-CSF. Chemotaxis was inhibited by GM-CSF, but was increased by G-CSF. However, when present together at low concentrations, CSFs increased basal and fMLP-stimulated ROS production and chemotaxis. These results suggest the CSF levels that circulating neutrophils face before extravasating into the lungs of CF patients may enhance their function contributing to the airway damage.

The treatment of cystic fibrosis (CF) patients homozygous for the F508del mutation with Orkambi®, a combination of a corrector (lumacaftor) and a potentiator (ivacaftor) of the mutated CFTR protein, resulted in some amelioration of the respiratory function. However, a great variability in the clinical response was also observed. The aim of this study was to evaluate the response to Orkambi® in a small cohort of F508del/F508del patients (n = 14) in terms of clinical and laboratory parameters, including ex vivo CFTR activity in mononuclear cells (MNCs), during a 12-month treatment. Patients responded with an increase in percent predicted forced expiratory volume in 1 s (FEV1%) and body mass index (BMI) as well as with a decrease in white blood cell (WBC) total counts and serum C-reactive protein (CRP) levels, although not significantly. Sweat chloride and CFTR-dependent chloride efflux were found to decrease and increase, respectively, as compared with pre-therapy values. CFTR and BMI showed a statistically significant correlation during Orkambi® treatment. Clustering analysis showed that CFTR, BMI, sweat chloride, FEV1%, and WBC were strongly associated. These data support the notion that CFTR-dependent chloride efflux in MNCs should be investigated as a sensitive outcome measure of Orkambi® treatment in CF patients.