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BackgroundProspective and longitudinal data on pulmonary injury over one year after acute coronavirus disease 2019 (COVID-19) are sparse. Research questionWith this study, we aim to investigate pulmonary outcome following SARS-CoV-2 infection including pulmonary function, computed chest tomography, respiratory symptoms and quality of life over 12 months. Study design and Methods180 patients after acute COVID-19 were enrolled into a single-centre, prospective observational study and examined 6 weeks, 3, 6 and 12 months after onset of COVID-19 symptoms. Chest CT-scans, pulmonary function and symptoms assessed by St. Georges Respiratory Questionnaire were used to evaluate objective and subjective respiratory limitations. Patients were stratified according to acute COVID-19 disease severity. ResultsOf 180 patients enrolled, 42/180 were not hospitalized during acute SARS-CoV-2 infection, 29/180 were hospitalized without need for oxygen, 43/180 with need for low-flow and 24/180 with high-flow oxygen, 26/180 required invasive mechanical ventilation and 16/180 were treated with ECMO. After acute COVID-19, pulmonary restriction and reduced carbon monoxide diffusion capacity was associated with disease severity after the acute phase and improved over 12 months except for those requiring ECMO treatment. Patients with milder disease showed a predominant reduction of ventilated area instead of simple restriction. The CT score of lung involvement in the acute phase increased significantly with COVID-19 severity and was associated with restriction and reduction in diffusion capacity in follow-up. Respiratory symptoms improved for patients in higher severity groups during follow-up, but not for patients with mild initially disease. InterpretationSeverity of respiratory failure during COVID-19 correlates with the degree of pulmonary function impairment and respiratory quality of life in the year after acute infection. Patients with mild vs. severe disease show different patterns of lung involvement and symptom resolution. Clinical Trial RegistrationThe study is registered at the German registry for clinical studies (DRKS00021688)
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Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care. Trial registrationGerman Clinical Trials Register DRKS00021688
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PurposeSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide causing a global health emergency. Pa-COVID-19 aims to provide comprehensive data on clinical course, pathophysiology, immunology and outcome of COVID-19, in order to identify prognostic biomarkers, clinical scores, and therapeutic targets for improved clinical management and preventive interventions. MethodsPa-COVID-19 is a prospective observational cohort study of patients with confirmed SARS-CoV-2 infection treated at Charite - Universitatsmedizin Berlin. We collect data on epidemiology, demography, medical history, symptoms, clinical course, pathogen testing and treatment. Systematic, serial blood sampling will allow deep molecular and immunological phenotyping, transcriptomic profiling, and comprehensive biobanking. Longitudinal data and sample collection during hospitalization will be supplemented by long-term follow-up. ResultsOutcome measures include the WHO clinical ordinal scale on day 15 and clinical, functional and health-related quality of life assessments at discharge and during follow-up. We developed a scalable dataset to (i) suit national standards of care (ii) facilitate comprehensive data collection in medical care facilities with varying resources and (iii) allow for rapid implementation of interventional trials based on the standardized study design and data collection. We propose this scalable protocol as blueprint for harmonized data collection and deep phenotyping in COVID-19 in Germany. ConclusionWe established a basic platform for harmonized, scalable data collection, pathophysiological analysis, and deep phenotyping of COVID-19, which enables rapid generation of evidence for improved medical care and identification of candidate therapeutic and preventive strategies. The electronic database accredited for interventional trials allows fast trial implementation for candidate therapeutic agents.