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Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. 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 PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
RESUMEN
BackgroundNaming 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 US took nearly two 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." MethodsWe undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 21,072), 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. ResultsWe 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, high education, and high access to medical care. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. ConclusionsThis 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.
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As more people are vaccinated against SARS-CoV-2, many of those already infected are still suffering from Post-Acute Sequelae (PASC). Although there is no current treatment for PASC, reports from patients that the vaccine itself improves, and in some reports, worsens, PASC symptoms may lead to a deeper understanding of the causes of PASC symptoms and viable treatments. As such, we are conducting a study that measures the changes in PASC symptoms after vaccination. We are collecting baseline self-report and biospecimens for immune assays and then are following up with participants to collect the same data at 2-weeks, 6-weeks, and 12-weeks post-vaccination (first dose). Immune assays using blood specimens will include B-cell, T-cell, and myeloid cell panels; evaluation of T-cell responsiveness to SARS-CoV-2 peptides and antigen specific response; autoantibody screening (of IgG, IgM, and IgA antibodies that attack human proteins); and TCR sequencing and antigen mapping of CD8+ T-cells. Mucosal immunity will be measured using saliva specimens. The study aims to provide answers for people with PASC, especially regarding the causes of their symptoms and how the vaccine may affect them, and clues for PASC treatment.
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Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to a complex acute presentation that can affect multiple organ systems, increasing evidence points to long-term sequelae being common and impactful. The worldwide scientific community is forging ahead to characterize a wide range of outcomes associated with SARS-CoV-2 infection; however the underlying assumptions in these studies have varied so widely that the resulting data are difficult to compareFormal definitions are needed in order to design robust and consistent studies of Long COVID that consistently capture variation in long-term outcomes. Even the condition itself goes by three terms, most widely "Long COVID", but also "COVID-19 syndrome (PACS)" or, "post-acute sequelae of SARS-CoV-2 infection (PASC)". In the present study, we investigate the definitions used in the literature published to date and compare them against data available from electronic health records and patient-reported information collected via surveys. Long COVID holds the potential to produce a second public health crisis on the heels of the pandemic itself. Proactive efforts to identify the characteristics of this heterogeneous condition are imperative for a rigorous scientific effort to investigate and mitigate this threat.
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BACKGROUND: There is a paucity of service research on the effectiveness of short-term mental health clinics. AIMS: To outline the development of the Urgent Consultation Clinic (UCC), an inter-professional, short-term, mental health program in a general hospital, and to evaluate the effectiveness of the UCC from a quality improvement perspective. METHOD: Participants (n = 143) completed a battery of validated measures assessing psychological and physical symptoms, quality of life, life satisfaction, and satisfaction with services at three time-points. Inter-professional team members rated participants' overall functioning and severity of mental health problems at intake and termination. RESULTS: The median time from referral to initial UCC visit was 12 days. A significant decline in the severity of mental health symptoms was observed, with 87% of participants reporting clinically elevated symptoms at intake compared to 71% at termination. Significant improvements were observed in life satisfaction, overall functioning, and mental quality of life. Sixty-nine percent of participants rated the quality of services as good or excellent. CONCLUSIONS: The UCC model of care contributed to improved access to psychiatric evaluation and short-term treatment. This inter-professional model could be applied to other health care settings to meet the needs of patients requiring acute psychiatric services.