Impact of CPAP Therapy Adherence on Global Cognition in Patients with Moderate to Severe Obstructive Sleep Apnea: A One-Year Follow-Up.

Background: Obstructive sleep apnea increases (OSA) cognitive impairment risk. The objective of this study was to determine the impact of continuous positive airway pressure (CPAP) adherence on global cognition using the Montreal Cognitive Assessment questionnaire (MoCA). Materials and Methods: Thirty-four new patients diagnosed with moderate or severe OSA (apnea-hypopnea index AHI ≥ 15 events/h) from the CPAP group were compared with thirty-one moderate to severe OSA patients from the no-CPAP group. In addition, all patients completed the MoCA test, a patient health questionnaire (PHQ-9) for depressive symptoms, and a generalized anxiety questionnaire (GAD-7) for anxiety symptoms, at baseline, after 6 months, and after 1 year. Results: At baseline, there were no significant differences between the two groups regarding total MoCA scores, 20.9 ± 3.5 CPAP group to 19.7 ± 2.9 no-CPAP group (p = 0.159), PHQ-9 (p = 0.651), and GAD-7 (p = 0.691). After one year, improvement was observed for a total MoCA score of 22.7 ± 3.5 (p < 0.001) for the CPAP group, and significant variance of score between groups was more accentuated for delayed recall and attention (p < 0.001) sub-topic. Moreover, PHQ-9, GAD-7 scores, and the Epworth Sleepiness Scale (ESS) decreased significantly (p < 0.001) after CPAP therapy. The MoCA score was significantly correlated with years of education (r = 0.74, p < 0.001) and had a negative correlation with body mass index (BMI) (r = -0.34), ESS (r = -0.30) and PHQ-9 (r = -0.34). Conclusions: One year of CPAP adherence improved global cognition associated with OSA.

Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks.

Background and Objectives: Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. Materials and Methods: 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. Results: The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters' variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan. Conclusions: A CN based computer aided diagnosis could provide the much-required data needed to successfully manage DILDs.