Determinants of persistence of symptoms and impact on physical and mental wellbeing in Long COVID: A prospective cohort study.

BACKGROUND: Residual symptoms can be detected for several months after COVID-19. To better understand the predictors and impact of symptom persistence we analyzed a prospective cohort of COVID-19 patients. METHODS: Patients were followed for 9 months after COVID-19 onset. Duration and predictors of persistence of symptoms, physical health and psychological distress were assessed. RESULTS: 465 patients (54% males, 51% hospitalized) were included; 37% presented with at least 4 symptoms and 42% complained of symptom lasting more than 28 days. At month 9, 20% of patients were still symptomatic, showing mainly fatigue (11%) and breathlessness (8%). Hospitalization and ICU stay vs. non-hospitalized status increased the median duration of fatigue of 8 weeks. Age > 50 years (OR 2.50), ICU stay (OR 2.35), and presentation with 4 or more symptoms (OR 2.04) were independent predictors of persistence of symptoms at month 9. A total of 18% of patients did not return to optimal pre-COVID physical health, while 19% showed psychological distress at month 9. Hospital admission (OR 2.28) and persistence of symptoms at day 28 (OR 2.21) and month 9 (OR 5.16) were independent predictors of suboptimal physical health, while female gender (OR 5.27) and persistence of symptoms at day 28 (OR 2.42) and month 9 (OR 2.48) were risk factors for psychological distress. CONCLUSIONS: Patients with advanced age, ICU stay and multiple symptoms at onset were more likely to suffer from long-term symptoms, which had a negative impact on both physical and mental wellbeing. This study contributes to identify the target populations and Long COVID consequences for planning long-term recovery interventions.

CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias.

PURPOSE: To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. MATERIAL AND METHODS: CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C-), respectively. C- patients, however, presented with interstitial lung involvement. A subgroup of C-, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. RESULTS: The first model classified C + and C- pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C- (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). CONCLUSION: Whole lung ML models based on radiomics can classify C + and C- interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.

Fever and dyspnoea in a tracheostomised patient.

Pneumonia of unknown origin in tracheostomised patient https://bit.ly/3hZHBA0.

Does the frequency of switching inhalers represent a predictive factor of exacerbation in asthma?

OBJECTIVE: Management of asthma includes monitoring of inhaler technique and level of adherence to treatment. Both factors could be influenced by high frequency of switching inhaler devices. We explored whether switching inhalers is an independent predictive factor of exacerbations. METHODS: Data were collected from 2015 to 2017 from the outpatient clinic of asthma at the University of Palermo, Italy. This observational study consisted of two phases: Phase 1 included subjects of at least three visits in the previous year who reported the frequency of inhalers switched; Phase 2 included subjects of at least two visits during the second year, and the rate of switches and exacerbations was recorded. We included adult (24-84 years old) mild/moderate asthmatics under regular inhaled treatment; uncontrolled asthma was defined as poor symptom control, exacerbations (≥2/year) requiring oral corticosteroids (OCS), or serious exacerbations (≥1/year) requiring hospitalization. RESULTS: A total of 109 records were retrieved for the analysis. A significant correlation between the rate of switches in Phase 1 and exacerbations in Phase 2 was found (p = 0.001). Age and the rates of exacerbations in Phase 1 were also independently associated with a higher number of exacerbations in Phase 2 (p < 0.0001). The multivariate regression model showed that the numbers of switches, as well as exacerbations in Phase 1, were independently correlated to the number of exacerbations in Phase 2 (p = 0.003). CONCLUSIONS: The frequency of switching inhalers independently affects the risk of exacerbations in asthma. These results imply that changing inhaler requires careful management in clinical practice.