No abstract

No abstract

Background Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for “Post COVID-19 condition, unspecified.” Methods We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. Results We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. Conclusions This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.

Article retraction in research is rising, yet retracted articles continue to be cited at a disturbing rate. This paper presents an analysis of recent retraction patterns, with a unique emphasis on the role author self-cites play, to assist the scientific community in creating counter-strategies. This was accomplished by examining the following: (1) A categorization of retracted articles more complete than previously published work. (2) The relationship between citation counts and after-retraction self-cites from the authors of the work, and the distribution of self-cites across our retraction categories. (3) The distribution of retractions written by both the author and the editor across our retraction categories. (4) The trends for seven of our nine defined retraction categories over a 6-year period. (5) The average journal impact factor by category, and the relationship between impact factor, author self-cites, and overall citations. Our findings indicate new reasons for retractions have emerged in recent years, and more editors are penning retractions. The rates of increase for retraction varies by category, and there is statistically significant difference of average impact factor between many categories. 18 % of authors self-cite retracted work post retraction with only 10 % of those authors also citing the retraction notice. Further, there is a positive correlation between self-cites and after retraction citations.

Accurate stratification of patients with Post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies and could enable more focussed investigation of the molecular pathogenetic mechanisms of this disease. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling long COVID phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Using unsupervised machine learning (k-means clustering), we found six distinct clusters of long COVID patients, each with distinct profiles of phenotypic abnormalities with enrichments in pulmonary, cardiovascular, neuropsychiatric, and constitutional symptoms such as fatigue and fever. There was a highly significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. We show that the clusters we identified in one hospital system were generalizable across different hospital systems. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on long COVID.