Development of prognostic models to estimate the probability of lung injury one year after COVID-19-related hospitalization-a prospective study.

Background: Long-term effects of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection still under study. The objectives of this study were to identify persistent pulmonary lesions 1 year after coronavirus disease 2019 (COVID-19) hospitalization and assess whether it is possible to estimate the probability that a patient develops these complications in the future. Methods: A prospective study of ≥18 years old patients hospitalized for SARS-COV-2 infection who develop persistent respiratory symptoms, lung function abnormalities or have radiological findings 6-8 weeks after hospital discharge. Logistic regression models were used to identify prognostic factors associated with a higher risk of developing respiratory problems. Models performance was assessed in terms of calibration and discrimination. Results: A total of 233 patients [median age 66 years [interquartile range (IQR): 56, 74]; 138 (59.2%) male] were categorized into two groups based on whether they stayed in the critical care unit (79 cases) or not (154). At the end of follow-up, 179 patients (76.8%) developed persistent respiratory symptoms, and 22 patients (9.4%) showed radiological fibrotic lesions with pulmonary function abnormalities (post-COVID-19 fibrotic pulmonary lesions). Our prognostic models created to predict persistent respiratory symptoms [post-COVID-19 functional status at initial visit (the higher the score, the higher the risk), and history of bronchial asthma] and post-COVID-19 fibrotic pulmonary lesions [female; FVC% (the higher the FVC%, the lower the probability); and critical care unit stay] one year after infection showed good (AUC 0.857; 95% CI: 0.799-0.915) and excellent performance (AUC 0.901; 95% CI: 0.837-0.964), respectively. Conclusions: Constructed models show good performance in identifying patients at risk of developing lung injury one year after COVID-19-related hospitalization.

A cross-country study on the impact of governmental responses to the COVID-19 pandemic on perinatal mental health.

This study aimed to analyse the role of governmental responses to the coronavirus disease 2019 (COVID-19) outbreak, measured by the Containment and Health Index (CHI), on symptoms of anxiety and depression during pregnancy and postpartum, while considering the countries' Inequality-adjusted Human Development Index (IHDI) and individual factors such as age, gravidity, and exposure to COVID-19. A cross-sectional study using baseline data from the Riseup-PPD-COVID-19 observational prospective international study (ClinicalTrials.gov: NCT04595123) was carried out between June and October 2020 in 12 countries (Albania, Brazil, Bulgaria, Chile, Cyprus, Greece, Israel, Malta, Portugal, Spain, Turkey, and the United Kingdom). Participants were 7645 pregnant women or mothers in the postpartum period-with an infant aged up to 6 months-who completed the Edinburgh Postnatal Depression Scale (EPDS) or the Generalised Anxiety Disorder Assessment (GAD-7) during pregnancy or the postpartum period. The overall prevalence of clinically significant depression symptoms (EPDS ≥ 13) was 30%, ranging from 20,5% in Cyprus to 44,3% in Brazil. The prevalence of clinically significant anxiety symptoms (GAD-7 ≥ 10) was 23,6% (ranging from 14,2% in Israel and Turkey to 39,5% in Brazil). Higher symptoms of anxiety or depression were observed in multigravida exposed to COVID-19 or living in countries with a higher number of deaths due to COVID-19. Furthermore, multigravida from countries with lower IHDI or CHI had higher symptoms of anxiety and depression. Perinatal mental health is context-dependent, with women from more disadvantaged countries at higher risk for poor mental health. Implementing more restrictive measures seems to be a protective factor for mental health, at least in the initial phase of the COVID-19.

Longitudinal relationship of liver injury with inflammation biomarkers in COVID-19 hospitalized patients using a joint modeling approach.

The mechanisms underlying liver disease in patients with COVID-19 are not entirely known. The aim is to investigate, by means of novel statistical techniques, the changes over time in the relationship between inflammation markers and liver damage markers in relation to survival in COVID-19. The study included 221 consecutive patients admitted to the hospital during the first COVID-19 wave in Spain. Generalized additive mixed models were used to investigate the influence of time and inflammation markers on liver damage markers in relation to survival. Joint modeling regression was used to evaluate the temporal correlations between inflammation markers (serum C-reactive protein [CRP], interleukin-6, plasma D-dimer, and blood lymphocyte count) and liver damage markers, after adjusting for age, sex, and therapy. The patients who died showed a significant elevation in serum aspartate transaminase (AST) and alkaline phosphatase levels over time. Conversely, a decrease in serum AST levels was observed in the survivors, who showed a negative correlation between inflammation markers and liver damage markers (CRP with serum AST, alanine transaminase [ALT], and gamma-glutamyl transferase [GGT]; and D-dimer with AST and ALT) after a week of hospitalization. Conversely, most correlations were positive in the patients who died, except lymphocyte count, which was negatively correlated with AST, GGT, and alkaline phosphatase. These correlations were attenuated with age. The patients who died during COVID-19 infection displayed a significant elevation of liver damage markers, which is correlated with inflammation markers over time. These results are consistent with the role of systemic inflammation in liver damage during COVID-19.

SARS-CoV-2 in Conjunctiva and Tears and Ocular Symptoms of Patients with COVID-19.

This study investigates the presence of SARS-CoV-2 in conjunctival secretions and tears and evaluates ocular symptoms in a group of patients with COVID-19. We included 56 hospitalized patients with COVID-19 in this cross-sectional cohort study. Conjunctival secretions and tears were collected using flocked swabs and Schirmer strips for SARS-CoV-2 reverse-transcriptase polymerase chain reaction (RT-PCR). Assessment of ocular surface manifestations included an OSDI (Ocular Surface Disease Index) questionnaire. Patients had been admitted to hospital for an average of 2.4 days (range 0-7) and had shown general symptoms for an average of 7.1 days (range 1-20) prior to ocular testing. Four (7.1%) of 56 conjunctival swabs and four (4%) of 112 Schirmer strips were positive for SARS-CoV-2. The mean E-gene cycle threshold values (Ct values) were 31.2 (SD 5.0) in conjunctival swabs and 32.9 (SD 2.7) in left eye Schirmer strips. Overall, 17 (30%) patients presented ocular symptoms. No association was found between positive ocular samples and ocular symptoms. This study shows that SARS-CoV-2 can be detected on the conjunctiva and tears of patients with COVID-19. Contact with the ocular surface may transmit the virus and preventive measures should be taken in this direction.

Impact of the Covid-19 pandemic on perinatal mental health (Riseup-PPD-COVID-19): protocol for an international prospective cohort study.

BACKGROUND: Corona Virus Disease 19 (COVID-19) is a new pandemic, declared a public health emergency by the World Health Organization, which could have negative consequences for pregnant and postpartum women. The scarce evidence published to date suggests that perinatal mental health has deteriorated since the COVID-19 outbreak. However, the few studies published so far have some limitations, such as a cross-sectional design and the omission of important factors for the understanding of perinatal mental health, including governmental restriction measures and healthcare practices implemented at the maternity hospitals. Within the Riseup-PPD COST Action, a study is underway to assess the impact of COVID-19 in perinatal mental health. The primary objectives are to (1) evaluate changes in perinatal mental health outcomes; and (2) determine the risk and protective factors for perinatal mental health during the COVID-19 pandemic. Additionally, we will compare the results between the countries participating in the study. METHODS: This is an international prospective cohort study, with a baseline and three follow-up assessments over a six-month period. It is being carried out in 11 European countries (Albania, Bulgaria, Cyprus, France, Greece, Israel, Malta, Portugal, Spain, Turkey, and the United Kingdom), Argentina, Brazil and Chile. The sample consists of adult pregnant and postpartum women (with infants up to 6 months of age). The assessment includes measures on COVID-19 epidemiology and public health measures (Oxford COVID-19 Government Response Tracker dataset), Coronavirus Perinatal Experiences (COPE questionnaires), psychological distress (BSI-18), depression (EPDS), anxiety (GAD-7) and post-traumatic stress symptoms (PTSD checklist for DSM-V). DISCUSSION: This study will provide important information for understanding the impact of the COVID-19 pandemic on perinatal mental health and well-being, including the identification of potential risk and protective factors by implementing predictive models using machine learning techniques. The findings will help policymakers develop suitable guidelines and prevention strategies for perinatal mental health and contribute to designing tailored mental health interventions. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04595123 .

Development and validation of a clinical score to estimate progression to severe or critical state in COVID-19 pneumonia hospitalized patients.

The prognosis of a patient with COVID-19 pneumonia is uncertain. Our objective was to establish a predictive model of disease progression to facilitate early decision-making. A retrospective study was performed of patients admitted with COVID-19 pneumonia, classified as severe (admission to the intensive care unit, mechanic invasive ventilation, or death) or non-severe. A predictive model based on clinical, laboratory, and radiological parameters was built. The probability of progression to severe disease was estimated by logistic regression analysis. Calibration and discrimination (receiver operating characteristics curves and AUC) were assessed to determine model performance. During the study period 1152 patients presented with SARS-CoV-2 infection, of whom 229 (19.9%) were admitted for pneumonia. During hospitalization, 51 (22.3%) progressed to severe disease, of whom 26 required ICU care (11.4); 17 (7.4%) underwent invasive mechanical ventilation, and 32 (14%) died of any cause. Five predictors determined within 24 h of admission were identified: Diabetes, Age, Lymphocyte count, SaO2, and pH (DALSH score). The prediction model showed a good clinical performance, including discrimination (AUC 0.87 CI 0.81, 0.92) and calibration (Brier score = 0.11). In total, 0%, 12%, and 50% of patients with severity risk scores ≤ 5%, 6-25%, and > 25% exhibited disease progression, respectively. A risk score based on five factors predicts disease progression and facilitates early decision-making according to prognosis.