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The CovILD study is a prospective, multicenter, observational cohort study to systematically follow up patients after coronavirus disease-2019 (COVID-19). We extensively evaluated 145 COVID-19 patients at 3 follow-up visits scheduled for 60, 100, and 180 days after initial confirmed diagnosis based on typical symptoms and a positive reverse transcription-polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). We employed comprehensive pulmonary function and laboratory tests, including serum concentrations of IgG against the viral spike (S) glycoprotein, and compared the results to clinical data and chest computed tomography (CT). We found that at the 60 day follow-up, 131 of 145 (90.3%) participants displayed S-specific serum IgG levels above the cut-off threshold. Notably, the highly elevated IgG levels against S glycoprotein positively correlated with biomarkers of immune activation and negatively correlated with pulmonary function and the extent of pulmonary CT abnormalities. Based on the association between serum S glycoprotein-specific IgG and clinical outcome, we generated an S-specific IgG-based recovery score that, when applied in the early convalescent phase, accurately predicted delayed pulmonary recovery after COVID-19. Therefore, we propose that S-specific IgG levels serve as a useful immunological surrogate marker for identifying at-risk individuals with persistent pulmonary injury who may require intensive follow-up care after COVID-19. [ABSTRACT FROM AUTHOR]

Background: The optimal procedures to prevent, identify, monitor, and treat long-term pulmonary sequelae of COVID-19 are elusive. Here, we characterized the kinetics of respiratory and symptom recovery following COVID-19. Methods: We conducted a longitudinal, multicenter observational study in ambulatory and hospitalized COVID-19 patients recruited in early 2020 (n = 145). Pulmonary computed tomography (CT) and lung function (LF) readouts, symptom prevalence, and 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 participants was accomplished by unsupervised and semi-supervised multiparameter clustering and machine learning. Results: At the 6-month follow-up, 49% of participants reported persistent symptoms. The frequency of structural lung CT abnormalities ranged from 18% in the mild outpatient cases to 76% in the intensive care unit (ICU) convalescents. Prevalence of impaired LF ranged from 14% in the mild outpatient cases to 50% in the ICU survivors. Incomplete radiological lung recovery was associated with increased anti-S1/S2 antibody titer, IL-6, and CRP levels at the early follow-up. We demonstrated that the risk of perturbed pulmonary recovery could be robustly estimated at early follow-up by clustering and machine learning classifiers employing solely non-CT and non-LF parameters. Conclusions: The severity of acute COVID-19 and protracted systemic inflammation is strongly linked to persistent structural and functional lung abnormality. Automated screening of multiparameter health record data may assist in the prediction of incomplete pulmonary recovery and optimize COVID-19 follow-up management. [ABSTRACT FROM AUTHOR]

This study evaluates the predictive value of circulating inflammatory markers, especially neopterin, in patients with coronavirus disease 2019 (COVID-19). Within this retrospective analysis of 115 hospitalized COVID-19 patients, elevated neopterin levels upon admission were significantly associated with disease severity, risk for intensive care unit admission, need for mechanical ventilation, and death. Therefore, neopterin is a reliable predictive marker in patients with COVID-19 and may help to improve the clinical management of patients. [ABSTRACT FROM AUTHOR]

Subjective illness perception (IP) can differ from physician's clinical assessment results. Herein, we explored patient's IP during coronavirus disease 2019 (COVID-19) recovery. Participants of the prospective observation CovILD study (ClinicalTrials.gov : NCT04416100) with persistent somatic symptoms or cardiopulmonary findings one year after COVID-19 were analyzed (n = 74). Explanatory variables included demographic and comorbidity, COVID-19 course and one-year follow-up data of persistent somatic symptoms, physical performance, lung function testing, chest computed tomography and trans-thoracic echocardiography. Factors affecting IP (Brief Illness Perception Questionnaire) one year after COVID-19 were identified by regularized modeling and unsupervised clustering. In modeling, 33% of overall IP variance (R2) was attributed to fatigue intensity, reduced physical performance and persistent somatic symptom count. Overall IP was largely independent of lung and heart findings revealed by imaging and function testing. In clustering, persistent somatic symptom count (Kruskal-Wallis test: η2 = 0.31, p <.001), fatigue (η2 = 0.34, p <.001), diminished physical performance (χ 2 test, Cramer V effect size statistic: V = 0.51, p <.001), dyspnea (V = 0.37, p =.006), hair loss (V = 0.57, p <.001) and sleep problems (V = 0.36, p =.008) were strongly associated with the concern, emotional representation, complaints, disease timeline and consequences IP dimensions. Persistent somatic symptoms rather than abnormalities in cardiopulmonary testing influence IP one year after COVID-19. Modifying IP represents a promising innovative approach to treatment of post-COVID-19 condition. Besides COVID-19 severity, individual IP should guide rehabilitation and psychological therapy decisions. • Long-term sequelae affect many COVID-19 patients. • This prospective cohort study assesses illness perceptions (IP) after 1 year. • Persistent somatic symptoms have a strong influence on individual IP. • IP are largely independent of abnormalities in cardiopulmonary testing. • IP should be addressed during rehabilitation and in multimodal therapies. [ABSTRACT FROM AUTHOR]

Background: Severe coronavirus disease 2019 (COVID-19) is frequently associated with hyperinflammation and hyperferritinemia. The latter is related to increased mortality in COVID-19. Still, it is not clear if iron dysmetabolism is mechanistically linked to COVID-19 pathobiology.Methods: We herein present data from the ongoing prospective, multicentre, observational CovILD cohort study (ClinicalTrials.gov number, NCT04416100), which systematically follows up patients after COVID-19. 109 participants were evaluated 60 days after onset of first COVID-19 symptoms including clinical examination, chest computed tomography and laboratory testing.Results: We investigated subjects with mild to critical COVID-19, of which the majority received hospital treatment. 60 days after disease onset, 30% of subjects still presented with iron deficiency and 9% had anemia, mostly categorized as anemia of inflammation. Anemic patients had increased levels of inflammation markers such as interleukin-6 and C-reactive protein and survived a more severe course of COVID-19. Hyperferritinemia was still present in 38% of all individuals and was more frequent in subjects with preceding severe or critical COVID-19. Analysis of the mRNA expression of peripheral blood mononuclear cells demonstrated a correlation of increased ferritin and cytokine mRNA expression in these patients. Finally, persisting hyperferritinemia was significantly associated with severe lung pathologies in computed tomography scans and a decreased performance status as compared to patients without hyperferritinemia.Discussion: Alterations of iron homeostasis can persist for at least two months after the onset of COVID-19 and are closely associated with non-resolving lung pathologies and impaired physical performance. Determination of serum iron parameters may thus be a easy to access measure to monitor the resolution of COVID-19.Trial Registration: ClinicalTrials.gov number: NCT04416100. [ABSTRACT FROM AUTHOR]