Back to the future: the novel art of digital auscultation applied in a prospective observational study of critically ill Covid-19 patients.

BACKGROUND: The Covid-19 pandemic has caused immense pressure on Intensive Care Units (ICU). In patients with severe ARDS due to Covid-19, respiratory mechanics are important for determining the severity of lung damage. Lung auscultation could not be used during the pandemic despite its merit. The main objective of this study was to investigate associations between lung auscultatory sound features and lung mechanical properties, length of stay (LOS) and survival, in adults with severe Covid-19 ARDS. METHODS: Consecutive patients admitted to a large ICU between 2020 and 2021 (n = 173) were included. Digital stethoscopes obtained auscultatory sounds and stored them in an on-line database for replay and further processing using advanced AI techniques. Correlation and regression analysis explored relationships between digital auscultation findings and lung mechanics or the ICU outcome. The resulting annotated lung sounds database is also publicly available as supplementary material. RESULTS: The presence of squawks was associated with the ICU LOS, outcome and 90-day mortality. Other features (age, SOFA score & oxygenation index upon admission, minimum crackle entropy) had significant impact on outcome. Additional features affecting the 90-d survival were age and mean crackle entropy. Multivariate logistic regression showed that survival was affected by age, baseline SOFA, baseline oxygenation index and minimum crackle entropy. CONCLUSIONS: Respiratory mechanics were associated with various adventitious sounds, whereas the lung sound analytics and the presence of certain adventitious sounds correlated with the ICU outcome and the 90-d survival. Spectral features of crackles sounds can serve as prognostic factors for survival, highlighting the importance of digital auscultation.

Self-perceived quality of sleep among COPD patients in Greece: the SLEPICO study.

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide accompanied by a substantial social and economic burden for the patient and the society. Poor sleep quality among COPD patients is frequently unnoticed and unaddressed by physicians and patients themselves, although it is a major source of further deterioration of these patients' quality of life. The aim of the present study was to record the quality of sleep in COPD patients among the Greek population and correlate these findings with various features of these patients, using the COPD and Asthma Sleep Impact Scale (CASIS). This was a cross-sectional observational study. Forty different variables (demographics, vital sign measurements, COPD-related medical history parameters, comorbidities, CASIS questionnaire results, COPD assessment test, COPD severity based on spirometry measurements, COPD stage based on the ABCD assessment approach, inhaled COPD treatment report) were collected from 3454 nation-wide COPD patients (Greece). The study sample consisted of COPD patients, mainly male (73%) with a median age of 69 years and a median BMI of 27.2. More than half of COPD patients (60.6%) suffered from moderate disease severity and 23.8% from severe disease, while less than half (42.1%) suffered from at least one exacerbation of the disease over the last year prior study enrollment. About 14% reported frequent to very frequent issues affecting their sleep quality, between a fourth and a third of them reported occasional night sleep disturbances, and at least half of them reported no or very infrequent problems in their night sleep. Our study indicates that the COPD assessment test (CAT) and the spirometry-based disease severity can predict the poorness in the quality of sleep (F2,3451 = 1397.5, p < 0.001, adj. R2 = 0.45) as assessed by CASIS score, and that the latter also correlates with age (ρ = 0.122, p < 0.001) and disease duration (ρ = 0.104, p < 0.001). On the contrary, there appears to be no correlation between sleep quality and number of exacerbations. Finally, untreated patients with COPD suffer from poorer quality of sleep compared to treated subjects, independently of the use of inhaled corticosteroids (F2,3451 = 21.65, p < 0.001). The results of the SLEPICO study show that increased age, prolonged disease duration, and especially CAT score ≥ 10, and severe COPD stage, might act as important indicators for deterioration in the quality of sleep, with potential consequences in the daily routine of those patients, thus urging potentially for further pharmacological interventions or modifications.

A simplified quantitative acid-base approach for patients with acute respiratory diseases.

The Stewart-Figge acid-base model has been criticized for being mathematically complex. We aimed to develop simpler formalisms, which can be used at the bedside. The following simplifications were used: (1) [Ca2+] and [Mg2+] are replaced by their mid-reference concentrations (2) pH is set to 7.4. In the new model [SIDa] is replaced by its adjusted form, [SIDa, adj] = [Na+] + [K+] - [Cl-] + 6.5 and [SIG] is replaced by "bicarbonate gap", [BICgap] = [SIDa, adj] - (0.28⋅[Albumin]) - (1.82⋅[Phosphatei])- [HCO3̄]. The diagnostic performance of the model was tested in 210 patients with acute respiratory diseases and 17 healthy volunteers. [BICgap] was also compared to albumin-corrected anion gap ([AGc]). The concordant correlation coefficient between [SIDa, adj] and [SIDa] and between [BICgap] and [SIG] was 0.98 in both comparisons. The mean bias (limits of agreement) of [SIDa, adj] - [SIDa] and of [BICgap] - [SIG] were 0.53 meq/l (- 0.46 to 1.53) and 0.50 meq/l (- 0.70 to 1.70), respectively. A [SIDa, adj] < 50.4 meq/l had an accuracy of 0.995 (p < 0.001) for the diagnosis of strong ion (SI) acidosis, while a [SIDa, adj] > 52.5 meq/l had an accuracy of 0.997 (p < 0.001) for the diagnosis of SI alkalosis. A [BICgap] > 11.6 meq/l predicted unmeasured ion (UI) acidosis with an accuracy of 0.997 (p < 0.001), while an [AGc] > 19.88 meq/l predicted UI acidosis with an accuracy of 0.994 (p < 0.001). The "[BICgap] model" is a reliable tool for the assessment of acid-base disorders in patients with acute respiratory diseases. [BICgap] is not inferior to [AGc] in the diagnosis of UI acidosis.