ABSTRACT
Over the past year, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing SARS-Covid-19 pandemic evolution. With the advent of data-sharing, computational approaches have gained a key role in evaluating future scenarios and offering predictions in a constantly evolving social environment. To design optimal social, hospitalization and economical strategies that guarantee control over the pandemic progression, we developed a data-driven modelling framework with the aim to provide reliable near future predictions under constantly evolving social and pandemic events. The framework is flexible enough to be used at a single hospital, regional or national level. We used a variety of data such as social, serological, testing and clinical data to compute the infection dynamics and the hospital workload for France. We developed inference methods to calibrate model parameters from observed hospitalization statistics over adjustable time periods. We applied our model to study the age stratified pandemic evolution inside and outside hospitals until February 2021, and the competition between vaccinations and the novel delta variant. We obtained several predictions about hidden pandemic properties such as fractions of infected, infection hospitality and infection fatality ratios. We show that reproduction numbers and herd immunity levels are not universal but strongly depend on the underlying social dynamics. We find that with normal social interactions the present vaccination status and rate is not sufficient to prevent a new pandemic wave driven by the delta variant.
Subject(s)
COVID-19 , Cross InfectionABSTRACT
ObjectivesIn severe COVID-19 pneumonia, the appropriate timing and dosing of corticosteroids(CS) is not known. Patient subgroups for which CS could be more beneficial also need appraisal. The aim of this study was to assess the effect of early CS in COVID-19 pneumonia patients admitted to the ICU on the occurrence of 60-day mortality, ICU-acquired-bloodstream infections(ICU-BSI), and hospital-acquired pneumonia and ventilator-associated pneumonia(HAP-VAP).MethodsWe included patients with COVID-19 pneumonia admitted to 11 ICUs belonging to the French OutcomeReaTM network from January to May 2020. We used survival models with ponderation with inverse probability of treatment weighting (IPTW). Inflammation was defined as Ferritin >1000 µg/l or D-Dimers >1000 µg/l or C-Reactive Protein >100 mg/dL.ResultsThe study population comprised 302 patients having a median age of 61.6(53-70) years of whom 78.8% were male and 58.6% had at least one comorbidity. The median SAPS II was 33(25-44). Invasive mechanical ventilation was required in 34.8% of the patients. Sixty-six (21.8%) patients were in the Early-CS-subgroup. Most of them (n=55, 83.3%) received high doses of steroids. Overall, 60-day mortality was 29.4%. The risks of 60-day mortality (IPTWHR =0.88;95% CI 0.55 to 1.39, p=0.58), ICU-BSI and HAP-VAP were similar in the two groups. Importantly, early CS treatment was associated with a lower mortality rate in patients aged 60 years or more (IPTWHR, 0.51;95% CI, 0.29 – 0.91; p=0.02). But, CS was associated with an increased risk of death for the patients younger than 60 years without inflammation on admission (IPTWHR =8.17;95% CI, 1.76, 37.85; p=0.01).ConclusionFor patients with COVID-19 pneumonia, early CS treatment was not associated with patient survival. Interestingly, inflammation and age can significantly influence the effect of CS.
Subject(s)
Pneumonia , Critical Illness , COVID-19 , InflammationABSTRACT
Among 197 COVID-19 patients hospitalized in ICU, 88 (44.7%) experienced at least one bacterial infection, with pneumonia (39.1%) and bloodstream infections (15,7%) being the most frequent. Unusual findings include frequent suspicion of bacterial translocations originating from the digestive tract as well as bacterial persistence in the lungs despite adequate therapy.