Semi-Markov multistate modeling approaches for multicohort event history data (preprint)

Two Cox-based multistate modeling approaches are compared for analyzing a complex multicohort event history process. The first approach incorporates cohort information as a fixed covariate, thereby providing a direct estimation of the cohort-specific effects. The second approach includes the cohort as stratum variable, thus giving an extra flexibility in estimating the transition probabilities. Additionally, both approaches may include possible interaction terms between the cohort and a given prognostic predictor. Furthermore, the Markov property conditional on observed prognostic covariates is assessed using a global score test. Whenever departures from the Markovian assumption are revealed for a given transition, the time of entry into the current state is incorporated as a fixed covariate, yielding a semi-Markov process. The two proposed methods are applied to a three-wave dataset of COVID-19-hospitalized adults in the southern Barcelona metropolitan area (Spain), and the corresponding performance is discussed. While both semi-Markovian approaches are shown to be useful, the preferred one will depend on the focus of the inference. To summarize, the cohort-covariate approach enables an insightful discussion on the the behavior of the cohort effects, whereas the stratum-cohort approach provides flexibility to estimate transition-specific underlying risks according with the different cohorts

Pragmatic, open-label, single-center, randomized, phase II clinical trial to evaluate the efficacy and safety of methylprednisolone pulses and tacrolimus in patients with severe pneumonia secondary to COVID-19: the TACROVID trial protocol (preprint)

IntroductionSome COVID-19 patients evolve to severe lung injury and systemic hyperinflammatory syndrome triggered by both the coronavirus infection and the subsequent host-immune response. Accordingly, the use of immunomodulatory agents has been suggested but still remains controversial. Our working hypothesis is that methylprednisolone pulses and tacrolimus may be an effective and safety drug combination for treating severe COVID-19 patients. Methods and analysisTACROVID is a randomized, open-label, single-center, phase II trial to evaluate the efficacy and safety of methylprednisolone pulses and tacrolimus plus standard of care (SoC) versus SoC alone, in patients at advanced stage of COVID-19 disease with lung injury and systemic hyperinflammatory response. Patients are randomly assigned (1:1) to one of two arms (42 patients in each group). The primary aim is to assess the time to clinical stability after initiating randomization. Clinical stability is defined as body temperature [≤] 37.5{degrees}C, and PaO2/FiO2 > 400 and/or SatO2/FiO2 > 300, and respiratory rate [≤]24 rpm; for 48 consecutive hours. DiscussionMethylprednisolone and tacrolimus might be beneficial to treat those COVID-19 patients progressing into severe pulmonary failure and systemic hyperinflammatory syndrome. The rationale for its use is the fast effect of methylprednisolone pulses and the ability of tacrolimus to inhibit both the CoV-2 replication and the secondary cytokine storm. Interestingly, both drugs are low-cost and can be manufactured on a large scale; thus, if effective and safe, a large number of patients could be treated in developed and developing countries. Trial registration numberNCT04341038 / EudraCT: 2020-001445-39

A Cluster-Randomized Trial of Hydroxychloroquine as Prevention of Covid-19 Transmission and Disease (preprint)

BackgroundCurrent strategies for preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are limited to non-pharmacological interventions. Hydroxychloroquine (HCQ) has been proposed as a postexposure therapy to prevent Coronavirus disease 2019 (Covid-19) but definitive evidence is lacking. MethodsWe conducted an open-label, cluster-randomized trial including asymptomatic contacts exposed to a PCR-positive Covid-19 case in Catalonia, Spain. Clusters were randomized to receive no specific therapy (control arm) or HCQ 800mg once, followed by 400mg daily for 6 days (intervention arm). The primary outcome was PCR-confirmed symptomatic Covid-19 within 14 days. The secondary outcome was SARS-CoV-2 infection, either symptomatically compatible or a PCR-positive result regardless of symptoms. Adverse events (AEs) were assessed up to 28 days. ResultsThe analysis included 2,314 healthy contacts of 672 Covid-19 index cases identified between Mar 17 and Apr 28, 2020. A total of 1,198 were randomly allocated to usual care and 1,116 to HCQ therapy. There was no significant difference in the primary outcome of PCR-confirmed, symptomatic Covid-19 disease (6.2% usual care vs. 5.7% HCQ; risk ratio 0.89 [95% confidence interval 0.54-1.46]), nor evidence of beneficial effects on prevention of SARS-CoV-2 transmission (17.8% usual care vs. 18.7% HCQ). The incidence of AEs was higher in the intervention arm than in the control arm (5.9% usual care vs 51.6% HCQ), but no treatment-related serious AEs were reported. ConclusionsPostexposure therapy with HCQ did not prevent SARS-CoV-2 disease and infection in healthy individuals exposed to a PCR-positive case. Our findings do not support HCQ as postexposure prophylaxis for Covid-19. ClinicalTrials.gov registration numberNCT04304053

The natural history of symptomatic COVID-19 in Catalonia, Spain: a multi-state model including 109,367 outpatient diagnoses, 18,019 hospitalisations, and 5,585 COVID-19 deaths among 5,627,520 people (preprint)

Background The natural history of Coronavirus Disease 2019 (COVID-19) has yet to be fully described, with most previous reports focusing on hospitalised patients. Using linked patient-level data, we set out to describe the associations between age, gender, and comorbidities and the risk of outpatient COVID-19 diagnosis, hospitalisation, and/or related mortality. Methods A population-based cohort study including all individuals registered in Information System for Research in Primary Care (SIDIAP). SIDIAP includes primary care records covering > 80% of the population of Catalonia, Spain, and was linked to region-wide testing, hospital and mortality records. Outpatient diagnoses of COVID-19, hospitalisations with COVID-19, and deaths with COVID-19 were identified between 1st March and 6th May 2020. A multi-state model was used, with cause-specific Cox survival models estimated for each transition. Findings A total of 5,664,652 individuals were included. Of these, 109,367 had an outpatient diagnosis of COVID-19, 18,019 were hospitalised with COVID-19, and 5,585 died after either being diagnosed or hospitalised with COVID-19. Half of those who died were not admitted to hospital prior to their death. Risk of a diagnosis with COVID-19 peaked first in middle-age and then again for oldest ages, risk for hospitalisation after diagnosis peaked around 70 years old, with all other risks highest at oldest ages. Male gender was associated with an increased risk for all outcomes other than outpatient diagnosis. The comorbidities studied (autoimmune condition, chronic kidney disease, chronic obstructive pulmonary disease, dementia, heart disease, hyperlipidemia, hypertension, malignant neoplasm, obesity, and type 2 diabetes) were all associated with worse outcomes. Interpretation There is a continued need to protect those at high risk of poor outcomes, particularly the elderly, from COVID-19 and provide appropriate care for those who develop symptomatic disease. While risks of hospitalisation and death are lower for younger populations, there is a need to limit their role in community transmission. These findings should inform public health strategies, including future vaccination campaigns.

COVID19-Global: A shiny application to perform a global comparative data visualization for the SARS-CoV-2 epidemic (preprint)

Data visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. The COVID19-Global online web application systematically produces daily updated data visualization and analysis of the SARS-CoV-2 epidemic on a global scale. It collects automatically daily data on COVID-19 diagnosed cases and mortality worldwide from January 1st, 2020 onwards. We have implemented comparative data visualization between countries for the most common indicators in epidemiological surveillance to follow an epidemic: attack rate, population fatality rate, case fatality rate, and basic reproduction number. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

COVID19-Tracker: A shiny app to produce comprehensive data visualization for SARS-CoV-2 epidemic in Spain (preprint)

Data visualization is an essential tool for exploring and communicating findings in medical research, and especially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of the SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases, intensive care unit admissions, and mortality, from February 24th, 2020 onwards. Two applications have already been developed; 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate, and; 3) To assess the effect of the lockdown measures on the trends of incident data. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.