摘要
BACKGROUNDLong COVID, defined as presence of COVID-19 related symptoms 28 days or more after the onset of acute SARS-CoV-2 infection, is an emerging challenge to healthcare systems. The objective of this study was to phenotype recovery trajectories of non-hospitalized COVID-19 individuals. METHODSWe performed an international, multi-center, exploratory online survey study on demographics, comorbidities, COVID-19 symptoms and recovery status of non-hospitalized SARS-CoV-2 infected adults (Austria: n=1157), and Italy: n= 893). RESULTSWorking age subjects (Austria median: 43 yrs (IQR: 31 - 53), Italy: 45 yrs (IQR: 35 - 55)) and females (65.1% and 68.3%) predominated the study cohorts. Course of acute COVID-19 was characterized by a high symptom burden (median 13 (IQR: 9 - 18) and 13 (7 - 18) out of 44 features queried), a 47.6 - 49.3% rate of symptom persistence beyond 28 days and 20.9 - 31.9% relapse rate. By cluster analysis, two acute symptom phenotypes could be discerned: the non-specific infection phenotype and the multi-organ phenotype (MOP), the latter encompassing multiple neurological, cardiopulmonary, gastrointestinal and dermatological features. Clustering of long COVID subjects yielded three distinct subgroups, with a subset of 48.7 - 55 % long COVID individuals particularly affected by post-acute MOP symptoms. The number and presence of specific acute MOP symptoms and pre-existing multi-morbidity was linked to elevated risk of long COVID. CONCLUSIONThe consistent findings of two independent cohorts further delineate patterns of acute and post-acute COVID-19 and emphasize the importance of symptom phenotyping of home-isolated COVID-19 patients to predict protracted convalescence and to allocate medical resources. Key PointsO_ST_ABSQuestionC_ST_ABSWhich acute symptom patterns of acute COVID-19 are associated with prolonged symptom persistence, symptom relapse or physical performance impairment? FindingsIn this multicenter international comparative survey study on non-hospitalized SARS- CoV-2 infected adults (Austria: n = 1157, Italy: n = 893) we identified distinct and reproducible phenotypes of acute and persistent features. Acute multi-organ symptoms including neurological and cardiopulmonary manifestations are linked to elevated risk of long COVID. MeaningThese findings suggest to employ symptom phenotyping of home-isolated COVID-19 patients to predict protracted convalescence and to allocate medical resources.
主题 s
COVID-19 , Severe Acute Respiratory Syndrome , Gastrointestinal Diseases摘要
Background COVID-19 is associated with long-term pulmonary symptoms and may result in chronic pulmonary impairment. The optimal procedures to prevent, identify, monitor, and treat these pulmonary sequelae are elusive. Research question To characterize the kinetics of pulmonary recovery, risk factors and constellations of clinical features linked to persisting radiological lung findings after COVID-19. Study design and methods A longitudinal, prospective, multicenter, observational cohort study including COVID-19 patients (n = 108). Longitudinal pulmonary imaging and functional readouts, symptom prevalence, clinical and laboratory parameters were collected during acute COVID-19 and at 60-, 100- and 180-days follow-up visits. Recovery kinetics and risk factors were investigated by logistic regression. Classification of clinical features and study participants was accomplished by k-means clustering, the k-nearest neighbors (kNN), and naive Bayes algorithms. Results At the six-month follow-up, 51.9% of participants reported persistent symptoms with physical performance impairment (27.8%) and dyspnea (24.1%) being the most frequent. Structural lung abnormalities were still present in 45.4% of the collective, ranging from 12% in the outpatients to 78% in the subjects treated at the ICU during acute infection. The strongest risk factors of persisting lung findings were elevated interleukin-6 (IL6) and C-reactive protein (CRP) during recovery and hospitalization during acute COVID-19. Clustering analysis revealed association of the lung lesions with increased anti-S1/S2 antibody, IL6, CRP, and D-dimer levels at the early follow-up suggesting non-resolving inflammation as a mechanism of the perturbed recovery. Finally, we demonstrate the robustness of risk class assignment and prediction of individual risk of delayed lung recovery employing clustering and machine learning algorithms. Interpretation Severity of acute infection, and systemic inflammation is strongly linked to persistent post-COVID-19 lung abnormality. Automated screening of multi-parameter health record data may assist the identification of patients at risk of delayed pulmonary recovery and optimize COVID-19 follow-up management. Clinical Trial Registration ClinicalTrials.gov: NCT04416100