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.

Assessment of COVID-19 progression on day 5 from symptoms onset.

BACKGROUND: A major limitation of current predictive prognostic models in patients with COVID-19 is the heterogeneity of population in terms of disease stage and duration. This study aims at identifying a panel of clinical and laboratory parameters that at day-5 of symptoms onset could predict disease progression in hospitalized patients with COVID-19. METHODS: Prospective cohort study on hospitalized adult patients with COVID-19. Patient-level epidemiological, clinical, and laboratory data were collected at fixed time-points: day 5, 10, and 15 from symptoms onset. COVID-19 progression was defined as in-hospital death and/or transfer to ICU and/or respiratory failure (PaO2/FiO2 ratio < 200) within day-11 of symptoms onset. Multivariate regression was performed to identify predictors of COVID-19 progression. A model assessed at day-5 of symptoms onset including male sex, age > 65 years, dyspnoea, cardiovascular disease, and at least three abnormal laboratory parameters among CRP (> 80 U/L), ALT (> 40 U/L), NLR (> 4.5), LDH (> 250 U/L), and CK (> 80 U/L) was proposed. Discrimination power was assessed by computing area under the receiver operating characteristic (AUC) values. RESULTS: A total of 235 patients with COVID-19 were prospectively included in a 3-month period. The majority of patients were male (148, 63%) and the mean age was 71 (SD 15.9). One hundred and ninety patients (81%) suffered from at least one underlying illness, most frequently cardiovascular disease (47%), neurological/psychiatric disorders (35%), and diabetes (21%). Among them 88 (37%) experienced COVID-19 progression. The proposed model showed an AUC of 0.73 (95% CI 0.66-0.81) for predicting disease progression by day-11. CONCLUSION: An easy-to-use panel of laboratory/clinical parameters computed at day-5 of symptoms onset predicts, with fair discrimination ability, COVID-19 progression. Assessment of these features at day-5 of symptoms onset could facilitate clinicians' decision making. The model can also play a role as a tool to increase homogeneity of population in clinical trials on COVID-19 treatment in hospitalized patients.

How the COVID-19 Pandemic Impacted on Integrated Care Pathways for Lung Cancer: The Parallel Experience of a COVID-Spared and a COVID-Dedicated Center.

INTRODUCTION: The COVID-19 pandemic has proved to be a historic challenge for healthcare systems, particularly with regard to cancer patients. So far, very limited data have been presented on the impact on integrated care pathways (ICPs). METHODS: We reviewed the ICPs of lung cancer patients who accessed the Veneto Institute of Oncology (IOV)/University Hospital of Padua (Center 1) and the University Hospital of Verona (Center 2) before and after the COVID-19 pandemic, through sixteen indicators chosen by the members of a multidisciplinary team (MDT). RESULTS: Two window periods (March and April 2019 and 2020) were chosen for comparison. Endoscopic diagnostic procedures and major resections for early stage NSCLC patients increased at Center 1, where a priority pathway with dedicated personnel was established for cancer patients. A slight decrease was observed at Center 2 which became part of the COVID unit. Personnel shortage and different processing methods of tumor samples determined a slightly longer time for diagnostic pathway completion at both Centers. Personnel protection strategies led to a MDT reshape on a web basis and to a significant selection of cases to be discussed in both Centers. The optimization of patient access to healthcare units reduced first outpatient oncological visits, patient enrollment in clinical trials, and end-of-life cancer systemic treatments; finally, a higher proportion of hypofractionation was delivered as a radiotherapy approach for early stage and locally advanced NSCLC. CONCLUSIONS: Based on the experience of the two Centers, we identified the key steps in ICP that were impacted by the COVID-19 pandemic so as to proactively put in place a robust service provision of thoracic oncology.

Long-Term Patient-Centred Follow-up in a Prospective Cohort of Patients with COVID-19.

INTRODUCTION: To better define COVID-19 long-term impact we prospectively analysed patient-centred outcomes, including general health and symptom duration. METHODS: Barthel index (BI), St. George's Respiratory Questionnaire adapted to patients with COVID-19 (aSGRQ) and WHO Clinical Progression Scale (CPS) were measured at enrolment and at 6 weeks from the onset of symptoms. Persistence of most frequently reported symptoms was assessed at 6 weeks and, among symptomatic patients, at 12 weeks from the onset of symptoms. Predictors of impaired general health over time were identified using an ordinal multilevel multivariate model. RESULTS: A total of 448 patients (55% men, median age 56 years) were enrolled. WHO-CPS showed mild, moderate and severe disease in 48%, 42% and 10% of patients at admission and mild disease in all patients at follow-up, respectively. BI and aSGRQ were normal in 96% and 93% patients before COVID-19 but only in 47% and 16% at COVID-19 diagnosis and in 87% and 65% at 6-week follow-up. Male gender was identified by all three assessments as a predictor of impaired general health (BI, OR 2.14, p < 0.0001; aSGRQ, OR 0.53, p = 0.003; WHO-CPS, OR 1.56, p = 0.01). Other predictors included age, ICU admission and comorbidities (e.g. cardiovascular disease and cancer) for BI, hospital admission for aSGRQ, age and presence of comorbidities for WHO-CPS. At 6- and 12-week follow-up, 39% and 20% of patients, respectively, were still reporting symptoms. Fatigue and breathlessness were the most frequently reported symptoms. CONCLUSIONS: Long-term follow-up facilitates the monitoring of health impairment and symptom persistence and can contribute to plan tailored 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.

Prevalence of SARS-CoV-2, Verona, Italy, April-May 2020.

We used random sampling to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 infection in Verona, Italy. Of 1,515 participants, 2.6% tested positive by serologic assay and 0.7% by reverse transcription PCR. We used latent class analysis to estimate a 3.0% probability of infection and 2.0% death rate.

Asthma in a large COVID-19 cohort: Prevalence, features, and determinants of COVID-19 disease severity.

BACKGROUND: Asthma prevalence among COVID-19 patients seems to be surprisingly low. However the clinical profile of COVID-19 asthmatic patients and potential determinants of higher susceptibility/worse outcome have been scarcely investigated. We aimed to describe the prevalence and features of asthmatic patients hospitalized for COVID-19 and to explore the association between their clinical asthma profile and COVID-19 severity. METHODS: Medical records of patients admitted to COVID-Units of six Italian cities major hospitals were reviewed. Demographic and clinical data were analyzed and compared according to the COVID-19 outcome (death/need for ventilation vs discharge at home without requiring invasive procedures). RESULTS: Within the COVID-Units population (n = 2000) asthma prevalence was 2.1%. Among the asthmatics the mean age was 61.1 years and 60% were females. Around half of patients were atopic, blood eosinophilia was normal in most of patients. An asthma exacerbation in the 6 months before the Covid-Unit admittance was reported by 18% of patients. 24% suffered from GINA step 4-5 asthma, and 5% were under biologic treatment. 31% of patients were not on regular treatment and a negligible use of oral steroid was recorded. Within the worse outcome group, a prevalence of males was detected (64 vs 29%, p = 0.026); they suffered from more severe asthma (43 vs 14%, p = 0.040) and were more frequently current or former smokers (62 vs 25%, p = 0.038). CONCLUSIONS: Our report, the first including a large COVID-19 hospitalized Italian population, confirms the low prevalence of asthma. On the other side patients with GINA 4/5 asthma, and those not adequately treated, should be considered at higher risk.

Prevalence of asymptomatic SARS-CoV-2-positive individuals in the general population of northern Italy and evaluation of a diagnostic serological ELISA test: a cross-sectional study protocol.

INTRODUCTION: As of 30 April 2020, the novel betacoronavirus SARS-CoV-2 had infected more than 3 172 000 individuals, killing over 224 000 people and spreading to more than 200 countries. Italy was the most affected country in Europe and the third most affected in the world in terms of the number of cases. Therefore, the aims of this study are: (1) to estimate the prevalence of asymptomatic SARS-CoV-2-positive individuals among the general population of Verona; (2) to assess the accuracy (sensitivity, specificity and predictive values) of an ELISA serological test for the screening of SARS-CoV-2. METHODS AND ANALYSIS: The study will be carried out on a random sample of subjects aged at least 10 years from the general population of Verona. Participants will undergo the measurement of vital parameters (oxygen saturation measured by oximeter, respiratory rate and body temperature detected by laser thermometer), the administration of a COVID-19-related symptoms questionnaire, the collection of a blood sample and a nasopharyngeal swab. Our evaluation will include the statistical technique of Latent Class Analysis, which will be the basis for the estimation of prevalence. ETHICS AND DISSEMINATION: The study protocol has been approved by the Ethics Committee of Verona and Rovigo provinces on 15 April 2020 (internal protocol number 2641CESC). The study results will be submitted for publication in international, peer-reviewed journals and the complete dataset will be deposited in a public repository. Most relevant data will be made available to policy-makers as well as disseminated to stakeholders and to the community.