| Literature DB >> 33380509 |
Joan B Soriano1,2, Grant Waterer3, José L Peñalvo4, Jordi Rello2,5,6.
Abstract
Entities:
Year: 2021 PMID: 33380509 PMCID: PMC7778876 DOI: 10.1183/13993003.04423-2020
Source DB: PubMed Journal: Eur Respir J ISSN: 0903-1936 Impact factor: 16.671
FIGURE 1Sinuhe, The Egyptian, by Mika Waltari (1945).
Characteristics of each prospective cohort of clinical cases, analysis and main findings on post-COVID-19 respiratory outcomes
| 103 patients across 6 medical centres in Norway | 3 months (∼90 days) after admission | Descriptive analysis of patients based on ICU admission. Univariate logistic model for severity indices and respiratory outcomes. Multivariate logistic model for respiratory outcomes related to ICU stay. | Approximately 50% patients presented persistent dyspnoea on exertion, and 25% reduced | |
| 145 patients across 4 medical centres in Austria | 60 and 100 days after admission | Overall and subgroup descriptive analyses for time-related differences. Secondary analyses using adjusted generalised linear models to account for time-series. | Major improvement of symptoms over time, however, 41% patients presented symptoms after 100 days: most frequently dyspnoea (36%) and impaired lung function (21%). Small proportion of patients with cardiac impairment or pulmonary hypertension. Frequent finding in CT scans of lung pathologies (63%) without fibrosis. | |
| 113 patients across 9 medical centres in Switzerland | 4 months (∼120 days) after discharge | Descriptive analysis of patient's outcomes stratified into mild and severe cases. Adjusted logistic models for radiological features related to disease severity. |
COVID-19: coronavirus disease 2019; ICU: intensive care unit; DLCO: diffusion capacity of the lung for carbon monoxide; CT: computed tomography.
Recommendations for future clinical observational studies on post COVID-19 condition
| 1) Reports should follow all/most STROBE recommendations for observational research, and attach their checklist [24] |
| 2) Minimum follow-up of 6 months |
| 3) Early, active identification of subjects at risk of severe sequelae |
| 4) Use reference groups (hospital controls |
| 5) Tests, questionnaires and tools to assess patient outcomes should be pre-specified as per protocol |
| 6) A minimum dataset to merge variables/values/patients in a standard dictionary should be implemented |
| 7) Characterise risk factors known for persistence of symptoms: high blood pressure, overweight/obesity, smoking, mental health conditions, other comorbidities and their treatment, |
| 8) Recording of real-time data with apps, remote sensors and e-health |
| 9) Assess mental status and post-traumatic stress disorder |
| 10) Assess quality of life in patients (and their carers) objectively |
| 11) Identify early potential pharmacological ( |
| 12) Report at least three sets of serial measurements over time, to fully assess recovery |
| 13) Use objective techniques, like cardiopulmonary exercise testing, to assess exercise impairment |
| 14) Assess effects of targeted rehabilitation |
| 15) Differentiate from systemic exertion intolerance disease, formerly known as chronic fatigue syndrome (SEID/CFS) |
| 16) Identify laboratory tests or biomarkers for characterisation of post COVID-19 condition |
| 17) Assess correlation between symptoms and abnormal peak oxygen consumption |
COVID-19: coronavirus disease 2019; STROBE: STrengthening the Reporting of OBservational studies in Epidemiology.