Definition of the Post-COVID syndrome using a symptom-based Post-COVID score in a prospective, multi-center, cross-sectoral cohort of the German National Pandemic Cohort Network (NAPKON)

<jats:title>Abstract</jats:title><jats:sec> <jats:title>Purpose</jats:title> <jats:p>The objective examination of the Post-COVID syndrome (PCS) remains difficult due to heterogeneous definitions and clinical phenotypes. The aim of the study was to verify the functionality and correlates of a recently developed PCS score.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>The PCS score was applied to the prospective, multi-center cross-sectoral cohort (in- and outpatients with SARS-CoV-2 infection) of the 'National Pandemic Cohort Network (NAPKON, Germany)'. Symptom assessment and patient-reported outcome measure questionnaires were analyzed at 3 and 12 months (3/12MFU) after diagnosis. Scores indicative of PCS severity were compared and correlated to demographic and clinical characteristics as well as quality of life (QoL, EQ-5D-5L).</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>Six hundred three patients (mean 54.0 years, 60.6% male, 82.0% hospitalized) were included. Among those, 35.7% (215) had no and 64.3% (388) had mild, moderate, or severe PCS. PCS severity groups differed considering sex and pre-existing respiratory diseases. 3MFU PCS worsened with clinical severity of acute infection (<jats:italic>p</jats:italic> = .011), and number of comorbidities (<jats:italic>p</jats:italic> = .004). PCS severity was associated with poor QoL at the 3MFU and 12MFU (<jats:italic>p</jats:italic> < .001).</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>The PCS score correlated with patients’ QoL and demonstrated to be instructive for clinical characterization and stratification across health care settings. Further studies should critically address the high prevalence, clinical relevance, and the role of comorbidities.</jats:p> </jats:sec><jats:sec> <jats:title>Trail registration number</jats:title> <jats:p>The cohort is registered at <jats:ext-link xmlns:xlink='http://www.w3.org/1999/xlink' ext-link-type='uri' xlink:href='http://www.clinicaltrials.gov'>www.clinicaltrials.gov</jats:ext-link> under NCT04768998.</jats:p> </jats:sec&gt

Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients

AbstractAnonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks. However, the degree of protection must be balanced against the impact on statistical properties. We studied an extreme case of this trade-off: the statistical validity of an open medical dataset based on the German National Pandemic Cohort Network (NAPKON), which was prepared for publication using a strong anonymization procedure. Descriptive statistics and results of regression analyses were compared before and after anonymization of multiple variants of the original dataset. Despite significant differences in value distributions, the statistical bias was found to be small in all cases. In the regression analyses, the median absolute deviations of the estimated adjusted odds ratios for different sample sizes ranged from 0.01 [minimum = 0, maximum = 0.58] to 0.52 [minimum = 0.25, maximum = 0.91]. Disproportionate impact on the statistical properties of data is a common argument against the use of anonymization. Our analysis demonstrates that anonymization can actually preserve validity of statistical results in relatively low-dimensional data.</jats:p