Persistent symptoms and risk factors predicting prolonged time to symptom-free after SARS­CoV­2 infection: an analysis of the baseline examination of the German COVIDOM/NAPKON-POP cohort.

PURPOSE: We aimed to assess symptoms in patients after SARS-CoV-2 infection and to identify factors predicting prolonged time to symptom-free. METHODS: COVIDOM/NAPKON-POP is a population-based prospective cohort of adults whose first on-site visits were scheduled ≥ 6 months after a positive SARS-CoV-2 PCR test. Retrospective data including self-reported symptoms and time to symptom-free were collected during the survey before a site visit. In the survival analyses, being symptom-free served as the event and time to be symptom-free as the time variable. Data were visualized with Kaplan-Meier curves, differences were tested with log-rank tests. A stratified Cox proportional hazard model was used to estimate adjusted hazard ratios (aHRs) of predictors, with aHR < 1 indicating a longer time to symptom-free. RESULTS: Of 1175 symptomatic participants included in the present analysis, 636 (54.1%) reported persistent symptoms after 280 days (SD 68) post infection. 25% of participants were free from symptoms after 18 days [quartiles: 14, 21]. Factors associated with prolonged time to symptom-free were age 49-59 years compared to < 49 years (aHR 0.70, 95% CI 0.56-0.87), female sex (aHR 0.78, 95% CI 0.65-0.93), lower educational level (aHR 0.77, 95% CI 0.64-0.93), living with a partner (aHR 0.81, 95% CI 0.66-0.99), low resilience (aHR 0.65, 95% CI 0.47-0.90), steroid treatment (aHR 0.22, 95% CI 0.05-0.90) and no medication (aHR 0.74, 95% CI 0.62-0.89) during acute infection. CONCLUSION: In the studied population, COVID-19 symptoms had resolved in one-quarter of participants within 18 days, and in 34.5% within 28 days. Over half of the participants reported COVID-19-related symptoms 9 months after infection. Symptom persistence was predominantly determined by participant's characteristics that are difficult to modify.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

BACKGROUND: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. OBJECTIVES: The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. METHODS: All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. RESULTS: Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. CONCLUSION: An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Fatigue and cognitive impairment after COVID-19: A prospective multicentre study.

Background: Reliable estimates of frequency, severity and associated factors of both fatigue and cognitive impairment after COVID-19 are needed. Also, it is not clear whether the two are distinct sequelae of COVID-19 or part of the same syndrome." Methods: In this prospective multicentre study, frequency of post-COVID fatigue and cognitive impairment were assessed in n = 969 patients (535 [55%] female) ≥6 months after SARS-CoV-2 infection with the FACIT-Fatigue scale (cut-off ≤30) and Montreal Cognitive Assessment (≤25 mild, ≤17 moderate impairment) between November 15, 2020 and September 29, 2021 at University Medical Center Schleswig-Holstein, Campus Kiel and University Hospital Würzburg in Germany. 969 matched non-COVID controls were drawn from a pre-pandemic, randomised, Germany-wide population survey which also included the FACIT-Fatigue scale. Associated sociodemographic, comorbid, clinical, psychosocial factors and laboratory markers were identified with univariate and multivariable linear regression models. Findings: On average 9 months after infection, 19% of patients had clinically relevant fatigue, compared to 8% of matched non-COVID controls (p < 0.001). Factors associated with fatigue were female gender, younger age, history of depression and the number of acute COVID symptoms. Among acute COVID symptoms, altered consciousness, dizziness and myalgia were most strongly associated with long-term fatigue. Moreover, 26% of patients had mild and 1% had moderate cognitive impairment. Factors associated with cognitive impairment were older age, male gender, shorter education and a history of neuropsychiatric disease. There was no significant correlation between fatigue and cognitive impairment and only 5% of patients suffered from both conditions. Interpretation: Fatigue and cognitive impairment are two common, but distinct sequelae of COVID-19 with potentially separate pathophysiological pathways. Funding: German Federal Ministry of Education and Research (BMBF).

Impact of coronavirus disease (COVID-19) pandemic on physical activity of patients with cardiac implantable electronic devices-A remote monitoring study.

OBJECTIVES: The study aims to investigate the impact of COVID-19 pandemic on physical activity and frequency of implantable cardioverter-defibrillator (ICD) therapies of patients with cardiac implantable electronic devices. METHODS AND RESULTS: Physical activity, heart rate and ICD-therapies were assessed via routine remote monitoring over two years. We focussed on a 338-day period during COVID-19 pandemic that was divided in 6 time-intervals defined by public health interventions and compared to the previous regular year. Paired nonparametric longitudinal analysis was performed to detect differences between time-intervals. To model effects of age, sex and time we applied a nonparametric ANOVA-type-statistic. 147 patients with cardiac implantable electronic devices were analysed. Longitudinal analysis of physical activity in 2019 and 2020 showed a specific weekly and seasonal pattern. Physical activity was reduced during the pandemic (mean daily physical activity 2019: 12.4% vs. 2020: 11.5%; p<0.0001) with the strongest reductions (fold changes 0.885/0.889, p<0.0001/p<0.0001) during the two lockdown-periods. In older patients (>70 years), physical activity was decreased in every time-interval of the year 2020. In time-intervals of eased restrictions, physical activity of younger patients (≤70 years) was not different compared to 2019. No variation in mean heart rate, arrhythmia-burden and count of ICD-therapies was found. CONCLUSION: Physical activity shows fluctuations dependent on days of the week and time of the year. During the pandemic, physical activity was reduced in patients with cardiac implantable electronic devices with the strongest reductions during lockdown-periods. Younger patients resumed former levels of physical activity in times of eased restrictions while older patients remained less active. Thus, activation of the elderly population is important to prevent long-term health impairments due to the pandemic.

Severity, predictors and clinical correlates of Post-COVID syndrome (PCS) in Germany: A prospective, multi-centre, population-based cohort study.

Background: Post-COVID syndrome (PCS) is an important sequela of COVID-19, characterised by symptom persistence for >3 months, post-acute symptom development, and worsening of pre-existing comorbidities. The causes and public health impact of PCS are still unclear, not least for the lack of efficient means to assess the presence and severity of PCS. Methods: COVIDOM is a population-based cohort study of polymerase chain reaction (PCR) confirmed cases of SARS-CoV-2 infection, recruited through public health authorities in three German regions (Kiel, Berlin, Würzburg) between November 15, 2020 and September 29, 2021. Main inclusion criteria were (i) a PCR confirmed SARS-CoV-2 infection and (ii) a period of at least 6 months between the infection and the visit to the COVIDOM study site. Other inclusion criteria were written informed consent and age ≥18 years. Key exclusion criterion was an acute reinfection with SARS-CoV-2. Study site visits included standardised interviews, in-depth examination, and biomaterial procurement. In sub-cohort Kiel-I, a PCS (severity) score was developed based upon 12 long-term symptom complexes. Two validation sub-cohorts (Würzburg/Berlin, Kiel-II) were used for PCS score replication and identification of clinically meaningful predictors. This study is registered at clinicaltrials.gov (NCT04679584) and at the German Registry for Clinical Studies (DRKS, DRKS00023742). Findings: In Kiel-I (n = 667, 57% women), 90% of participants had received outpatient treatment for acute COVID-19. Neurological ailments (61·5%), fatigue (57·1%), and sleep disturbance (57·0%) were the most frequent persisting symptoms at 6-12 months after infection. Across sub-cohorts (Würzburg/Berlin, n = 316, 52% women; Kiel-II, n = 459, 56% women), higher PCS scores were associated with lower health-related quality of life (EQ-5D-5L-VAS/-index: r = -0·54/ -0·56, all p < 0·0001). Severe, moderate, and mild/no PCS according to the individual participant's PCS score occurred in 18·8%, 48·2%, and 32·9%, respectively, of the Kiel-I sub-cohort. In both validation sub-cohorts, statistically significant predictors of the PCS score included the intensity of acute phase symptoms and the level of personal resilience. Interpretation: PCS severity can be quantified by an easy-to-use symptom-based score reflecting acute phase disease burden and general psychological predisposition. The PCS score thus holds promise to facilitate the clinical diagnosis of PCS, scientific studies of its natural course, and the development of therapeutic interventions. Funding: The COVIDOM study is funded by the Network University Medicine (NUM) as part of the National Pandemic Cohort Network (NAPKON).

[Seroprevalence of COVID-19 and Psychosocial Effects in the General Population: Results of the STAAB-COVID-One Program]. / Seroprävalenz von COVID-19 und psychosoziale Auswirkungen in der Allgemeinbevölkerung: Ergebnisse des STAAB-COVID-One Programms.

BACKGROUND: Research of SARS-CoV-2 has so far largely focused on symptomatic cases. The STAAB-COVID study therefore examined the seroprevalence of COVID-19 in the general population and the psychosocial effects of the pandemic. METHODS: From June-October 2020, a sub-study was conducted within the "Characteristics and Course of Heart Failure Stages A-B and Determinants of Progression (STAAB)" cohort study. 4,860 study participants identified from a representative age-stratified sample of Würzburg residents were asked to provide a blood sample and to fill in a questionnaire. All participants also received an offer to take part in a point prevalence assessment (nasal swab taken from the participant at the beginning of November 2020). RESULTS: A total of 3,034 subjects took part in the STAAB-COVID program (response rate 62%). Antibodies against SARS-CoV-2 were detected in 33 participants (1.1%; 95% confidence interval 0.7-1.5%). Higher values on the GAD-7 anxiety scale were associated with lower rates of SARS-CoV-2 antibodies (Odds Ratio=0.78 for each+1 point in GAD-7; 95% confidence interval 0.65-0.95). Within this rather anxious group of subjects, however, the rate of cancellation of medical appointments was also increased (Odds Ratio=1.13 for each+1 point in GAD-7; 95% confidence interval 1.10-1.16). An acute infection was detected in six of a total of 2,451 participants in the point prevalence assessment (0.24%; 95% confidence interval 0.09-0.53%). CONCLUSION: Between the first and second COVID-19 waves in Germany, we found a low level of SARS-CoV-2 contamination in the city of Würzburg. A more anxious personality was associated with a lower seroprevalence. Conducting the study was largely facilitated by the existing cohort study.

Corona Health-A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic.

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.