What is the impact of post-COVID-19 syndrome on health-related quality of life and associated factors: a cross-sectional analysis.

BACKGROUND: After the acute phase, symptoms or sequelae related to post-COVID-19 syndrome may persist for months. In a population of patients, previously hospitalized and not, followed up to 12 months after the acute infection, we aim to assess whether and to what extent post-COVID-19 syndrome may have an impact on health-related quality of life (HRQoL) and to investigate influencing factors. METHODS: We present the cross-sectional analysis of a prospective study, including patients referred to the post-COVID-19 service. Questionnaires and scales administered at 3, 6, 12 months were: Short-Form 36-item questionnaire (SF-36); Visual Analogue Scale of the EQ5D (EQ-VAS); in a subgroup, Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI-II) and Pittsburgh Sleep Quality Index (PSQI). Linear regression models were fitted to identify factors associated with HRQoL. RESULTS: We considered the first assessment of each participant (n = 572). The mean scores in SF-36 and in EQ-VAS were significantly lower than the Italian normative values and remained stable over time, except the mental components score (MCS) of the SF-36 and EQ-VAS which resulted in lower ratings at the last observations. Female gender, presence of comorbidities, and corticosteroids treatment during acute COVID-19, were associated with lower scores in SF-36 and EQ-VAS; patients previously hospitalized (54%) reported higher MCS. Alterations in BAI, BDI-II, and PSQI (n = 265)were associated with lower ratings in SF-36 and EQ-VAS. CONCLUSIONS: This study provides evidence of a significantly bad perception of health status among persons with post-COVID-19 syndrome, associated with female gender and, indirectly, with disease severity. In case of anxious-depressive symptoms and sleep disorders, a worse HRQoL was also reported. A systematic monitoring of these aspects is recommended to properly manage the post-COVID-19 period.

No Efficacy of the Combination of Lopinavir/Ritonavir Plus Hydroxychloroquine Versus Standard of Care in Patients Hospitalized With COVID-19: A Non-Randomized Comparison.

Objectives: No specific treatment has been approved for COVID-19. Lopinavir/ritonavir (LPV/r) and hydroxychloroquine (HCQ) have been used with poor results, and a trial showed advantages of combined antiviral therapy vs. single antivirals. The aim of the study was to assess the effectiveness of the combination of antivirals (LPV/r and HCQ) or their single use in COVID-19 hospitalized patients vs. standard of care (SoC). Methods: Patients ≥18 years with SARS-CoV-2 infection, defined as positive RT-PCR from nasal/oropharyngeal (NP/OP) swab or positive serology, admitted at L. Spallanzani Institute (Italy) were included. Primary endpoint: time to invasive ventilation/death. Secondary endpoint: time to two consecutive negative SARS-CoV-2 PCRs in NP/OP swabs. In order to control for measured confounders, a marginal Cox regression model with inverse probability weights was used. Results: A total of 590 patients were included in the analysis: 36.3% female, 64 years (IQR 51-76), and 91% with pneumonia. Cumulative probability of invasive ventilation/death at 14 days was 21.2% (95% CI 17.6, 24.7), without difference between SOC, LPV/r, hydroxychloroquine, HCQ + LPV/r, and SoC. The risk of invasive ventilation/death in the groups appeared to vary by baseline ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2). Overall cumulative probability of confirmed negative nasopharyngeal swabs at 14 days was 44.4% (95% CI 38.9, 49.9), without difference between groups. Conclusion: In this retrospective analysis, we found no difference in the rate of invasive ventilation/death or viral shedding by different strategies, as in randomized trials performed to date. Moreover, even the combination HCQ + LPV/r did not show advantages vs. SoC.

Risk and predictive factors of prolonged viral RNA shedding in upper respiratory specimens in a large cohort of COVID-19 patients admitted to an Italian reference hospital.

BACKGROUND: Limited data are available about the predictors and outcomes associated with prolonged SARS-CoV-2 RNA shedding (VS). METHODS: A retrospective study including COVID-19 patients admitted to an Italian hospital between March 1 and July 1, 2020. Predictors of viral clearance (VC) and prolonged VS from the upper respiratory tract were assessed by Poisson regression and logistic regression analyses. The causal relation between VS and clinical outcomes was evaluated through an inverse probability weighted Cox model. RESULTS: The study included 536 subjects. The median duration of VS from symptoms onset was 18 days. The estimated 30-day probability of VC was 70.2%. Patients with comorbidities, lymphopenia at hospital admission, or moderate/severe respiratory disease had a lower chance of VC. The development of moderate/severe respiratory failure, delayed hospital admission after symptoms onset, baseline comorbidities, or D-dimer >1000ng/mL at admission independently predicted prolonged VS. The achievement of VC doubled the chance of clinical recovery and reduced the probability of death/mechanical ventilation. CONCLUSIONS: Respiratory disease severity, comorbidities, delayed hospital admission and inflammatory markers negatively predicted VC, which resulted to be associated with better clinical outcomes. These findings highlight the importance of prompt hospitalization of symptomatic patients, especially where signs of severity or comorbidities are present.

Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.

BACKGROUND AND AIMS: There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. METHODS AND RESULTS: Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age ≥85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs ≥ 90 mL/min/1.73 m2; HR = 2.3; 1.5-3.6 for C-reactive protein levels ≥10 vs ≤ 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses. CONCLUSIONS: Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.