Open-Circuit Mouthpiece Ventilation: Indications, Evidence and Practicalities

Open-circuit mouthpiece ventilation (MPV) is a method of noninvasive ventilation, which can be used to provide full-time support, induce lung volume recruitment, increase cough efficacy, defer tracheostomy and possibly improve survival and quality of life in advanced-stage neuromuscular patients. MPV might also be applicable to other chronic respiratory diseases as well as in acute exacerbations of chronic obstructive pulmonary disease and can also be employed for the extubation of unweanable neuromuscular patients. A candidate for MPV should be able to rotate his neck adequately, grab the mouthpiece with his lips and maintain sufficient control of the upper airway muscles. MPV is usually provided in the volume assisted-controlled mode with a tidal volume between 0.7 and 1.5 L, zero PEEP and backup rate set to the lower allowed value, allowing the patient to define his own ventilatory pattern. The “low pressure” and “apnea” alarm should be switched off, if possible, or special setting adjustments should be used to prevent their activation. Comprehensive patient training and dedicated nursing time are important for the application of MPV. MPV is considered a safe method for the majority of the patients, but accidental mouthpiece loss is an important concern

Functional Comorbidity Index and health-related quality of life in patients with obstructive sleep apnea

Introduction: The role of comorbidities in determining health-related quality of life (HRQL) in obstructive sleep apnea (OSA) pa-tients has not been thoroughly investigated. Commonly used comorbidity tools, such as Charlson Comorbidity Index (CCI), have been designed with mortality as the outcome variable. A new tool, the Functional Comorbidity Index (FCI), has been especially developed to assess the effect of comorbidities on the “physical functioning” subscale of the Medical Outcomes Short Form-36 Health Survey (SF-36). 1) To determine the role of FCI in the prediction of the effect of comorbidities on HRQL in OSA. 2) To determine whether FCI and CCI are equally robust in predicting the effect of comorbidities on HRQL in OSA. Material and methods: Two hundred and fifty-five OSA patients were enrolled. Patients completed the SF-36 and the Medical Outcomes Study Sleep Scale (MOS-SS) forms, while their comorbidity status was assessed by FCI and CCI. The SF-36 physical (PCS-36) and mental component summary (MCS-36) scores were also calculated. Results: PCS-36 was predicted by FCI (p < 0.001), male gender (p = 0.001), BMI (p = 0.002) and the “awakening with “breathlessness/headache” MOS-SS subscale (p = 0.011) (R2 = 0.348). Among these predictors, FCI exerted the most important quantitative effect. MCS-36 was predicted only by the “sleep disturbance” (p = 0.005) and the “awakening with breathlessness/headache” MOS-SS subscales (p < 0.001) (R2  = 0.221). Conclusions: In patients with OSA, FCI is an independent predictor of the physical aspect of their HRQL. FCI is more robust than CCI in assessing the effect of comorbidities on HRQL in OSA

Solitary splenic metastasis of squamous lung cancer: a case report

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Functional Comorbidity Index and Health-Related Quality of Life in Patients with Obstructive Sleep Apnea

Introduction: The role of comorbidities in determining health-related quality of life (HRQL) in obstructive sleep apnea (OSA) pa-tients has not been thoroughly investigated. Commonly used comorbidity tools, such as Charlson Comorbidity Index (CCI), have been designed with mortality as the outcome variable. A new tool, the Functional Comorbidity Index (FCI), has been especially developed to assess the effect of comorbidities on the “physical functioning” subscale of the Medical Outcomes Short Form-36 Health Survey (SF-36). (1) To determine the role of FCI in the prediction of the effect of comorbidities on HRQL in OSA. (2) To determine whether FCI and CCI are equally robust in predicting the effect of comorbidities on HRQL in OSA. Material and Methods: Two hundred and fifty-five OSA patients were enrolled. Patients completed the SF-36 and the Medical Outcomes Study Sleep Scale (MOS-SS) forms, while their comorbidity status was assessed by FCI and CCI. The SF-36 physical (PCS-36) and mental component summary (MCS-36) scores were also calculated. Results: PCS-36 was predicted by FCI (p < 0.001), male gender (p = 0.001), BMI (p = 0.002) and the “awakening with “breathlessness/headache” MOS-SS subscale (p = 0.011) (R2 = 0.348). Among these predictors, FCI exerted the most important quantitative effect. MCS-36 was predicted only by the “sleep disturbance” (p = 0.005) and the “awakening with breathlessness/headache” MOS-SS subscales (p < 0.001) (R2 = 0.221). Conclusions: In patients with OSA, FCI is an independent predictor of the physical aspect of their HRQL. FCI is more robust than CCI in assessing the effect of comorbidities on HRQL in OSA

Persistent Sleep Quality Deterioration among Post-COVID-19 Patients: Results from a 6-Month Follow-Up Study

Background: To date, evidence about sleep disturbances among post-COVID-19 patients is limited. This study aimed to evaluate sleep quality after hospitalization due to SARS-CoV-2 infection. Methods: In-person follow-up was conducted in patients with prior hospitalization due to COVID-19 1(Τ1), 3(Τ2), and 6 (Τ3) months after hospital discharge. Patients were asked to complete questionnaires concerning sleep quality: the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), the Athens Insomnia Scale (AIS), the Fatigue Severity Scale (FSS), and the Stop-BANG (S-B) questionnaire. Results: In total, 133 patients were enrolled (mean age: 56.0 ± 11.48 years, 59.4% males). The most frequently reported comorbidity was arterial hypertension (29.8% of patients), while 37.4% of patients had no comorbidities. The majority of participants exhibited poor sleep quality (global PSQI ≥ 5) at T1 (84.3%), T2 (75.7%), and T3 (77.4%). Insomnia was observed in 56.5%, 53.5%, and 39.2% of participants, respectively (AIS ≥ 6). An FSS score ≥ 4 was observed in 51.2%, 33.7%, and 29.1% of participants at T1, T2, T3, respectively. Elapsed time was found to be negatively and independently associated with the global PSQI, PSQI C5-Sleep disturbance, PSQI C7-Daytime dysfunctions, FSS, and AIS after adjustment for possible confounders. No significant difference was found between groups with good and poor sleep quality (based on the global PSQI) with respect to gender (p = 0.110), age (p = 0.528), BMI (p = 0.816), smoking status (p = 0.489), hypertension (p = 0.427), severity of disease (p = 0.224), the Charlson Comorbidity Index (p = 0.827), or the length of hospital stay (p = 0.162). Participants with excessive daytime sleepiness (EDS) and patients with severe fatigue (FSS ≥ 4) were significantly younger. Females presented a higher rate of insomnia symptoms (55.7% vs. 44.3%, p < 0.001). Conclusions: Several sleep disturbances were observed after hospital discharge for COVID-19 pneumonia at certain time points; However, the improvement over time was remarkable in most domains of the assessed questionnaires

Epidemiological Characteristics and Outcomes from 187 Patients with COVID-19 Admitted to 6 Reference Centers in Greece: An Observational Study during the First Wave of the COVID-19 Pandemic

Introduction: Epidemiological data from patients with COVID-19 has been recently published in several countries. Nationwide data of hospitalized patients with COVID-19 in Greece remain scarce. Material and methods:This was an observational, retrospective study from 6 reference centers between February 26 and May 15, 2020. Results: The patients were mostly males (65.7%) and never smokers (57.2%) of median age 60 (95% CI: 57.6–64) years. The majority of the subjects (98%) were treated with the standard-of-care therapeutic regimen at that time, including hydroxychlo-roquine and azithromycin. Median time of hospitalization was 10 days (95% CI: 10–12). Twenty-five (13.3%) individuals were intubated and 8 died (4.2%). The patients with high neutrophil-to-lymphocyte ratio (NLR) (> 3.58) exhibited more severe disease as indicated by significantly increased World Health Organization (WHO) R&D ordinal scale (4; 95% CI: 4–4 vs. 3; 95% CI: 3–4, p = 0.0001) and MaxFiO2% (50; 95% CI: 38.2–50 vs 29.5; 95% CI: 21–31, p < 0.0001). The patients with increased lactate dehydrogenase (LDH) levels (> 270 IU/ml) also exhibited more advanced disease compared to the low LDH group (< 270 IU/ml) as indicated by both WHO R&D ordinal scale (4; 95% CI: 4–4 vs. 4; 95% CI: 3–4, p = 0.0001) and MaxFiO2% (50; 95% CI: 35–60 vs. 28; 95% CI: 21–31, p < 0.0001). Conclusion: We present the first epidemiological report from a low-incidence and mortality COVID-19 country. NLR and LDH may represent reliable disease prognosticators leading to timely treatment decisions