Spectrum of SARS-CoV-2-Related Clinical Syndromes in Children: A Year in the Life

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a wide pediatric clinical spectrum. Initial reports suggested that children had milder symptoms compared with adults; then diagnosis of multisystem inflammatory syndrome in children (MIS-C) emerged. We performed a retrospective cohort study of hospitalized patients at a children’s hospital over 1 year. Our objectives were to study the demographic and clinical profile of pediatric SARS-CoV-2-associated diagnoses. Based on the clinical syndrome, patients were classified into coronavirus disease 2019 (COVID-19; non-MIS-C) and MIS-C cohorts. Among those who tested positive, 67% were symptomatic. MIS-C was diagnosed in 24 patients. Both diagnoses were more frequent in Caucasians. Both cohorts had different symptom profiles. Inflammatory markers were several-fold higher in MIS-C patients. These patients had critical care needs and longer hospital stays. More COVID-19 patients had respiratory complications, while MIS-C cohort saw cardiovascular involvement. Health care awareness of both syndromes is important for early recognition, diagnosis, and prompt treatment.

Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study

Multidimensional integrative data analysis of digital phenotyping is crucial for elucidating the pathologies of multifactorial and heterogeneous diseases, such as the dry eye (DE). This crowdsourced cross-sectional study explored a novel smartphone-based digital phenotyping strategy to stratify and visualize the heterogenous DE symptoms into distinct subgroups. Multidimensional integrative data were collected from 3,593 participants between November 2016 and September 2019. Dimension reduction via Uniform Manifold Approximation and Projection stratified the collected data into seven clusters of symptomatic DE. Symptom profiles and risk factors in each cluster were identified by hierarchical heatmaps and multivariate logistic regressions. Stratified DE subgroups were visualized by chord diagrams, co-occurrence networks, and Circos plot analyses to improve interpretability. Maximum blink interval was reduced in clusters 1, 2, and 5 compared to non-symptomatic DE. Clusters 1 and 5 had severe DE symptoms. A data-driven multidimensional analysis with digital phenotyping may establish predictive, preventive, personalized, and participatory medicine.

Poor Separation of Clinical Symptom Profiles by DSM-5 Disorder Criteria

Assessment of mental illness typically relies on a disorder classification system that is considered to be at odds with the vast disorder comorbidity and symptom heterogeneity that exists within and across patients. Patients with the same disorder diagnosis exhibit diverse symptom profiles and comorbidities creating numerous clinical and research challenges. Here we provide a quantitative analysis of the symptom heterogeneity and disorder comorbidity across a sample of 107,349 adult individuals (aged 18–85 years) from 8 English-speaking countries. Data were acquired using the Mental Health Quotient, an anonymous, online, self-report tool that comprehensively evaluates symptom profiles across 10 common mental health disorders. Dissimilarity of symptom profiles within and between disorders was then computed. We found a continuum of symptom prevalence rather than a clear separation of normal and disordered. While 58.7% of those with 5 or more clinically significant symptoms did not map to the diagnostic criteria of any of the 10 DSM-5 disorders studied, those with symptom profiles that mapped to at least one disorder had, on average, 20 clinically significant symptoms. Within this group, the heterogeneity of symptom profiles was almost as high within a disorder label as between 2 disorder labels and not separable from randomly selected groups of individuals with at least one of any of the 10 disorders. Overall, these results quantify the scale of misalignment between clinical symptom profiles and DSM-5 disorder labels and demonstrate that DSM-5 disorder criteria do not separate individuals from random when the complete mental health symptom profile of an individual is considered. Greater emphasis on empirical, disorder agnostic approaches to symptom profiling would help overcome existing challenges with heterogeneity and comorbidity, aiding clinical and research outcomes.

Healthcare workers benefit from second dose of COVID-19 mRNA vaccine: Effects of partial and full vaccination on sick leave duration and symptoms

Importance: In addition to morbidity and mortality of individuals, COVID-19 can affect staffing among organizations. It is important to determine whether vaccination can mitigate this burden. Objective: This study examined the association between COVID-19 vaccination status and time until return to work among 952 healthcare workers (HCW) who tested positive for COVID-19. Design: Data were collected prospectively between December 2020 and July 2021. HCW who tested positive for COVID-19 completed an initial interview and were followed until they returned to work. Setting: An academic campus in Southern California consisting of two large hospitals and multiple outpatient clinics and other facilities. Participants: Clinical and nonclinical HCW who tested positive for COVID-19 during the study period (N=952, mean age=39.2 years, 69% female, 45% Hispanic, 14% white, 14% Asian/Pacific Islander, 5% African American, and 21% other race/ethnicity). Exposure: COVID-19 vaccination status (unvaccinated, partially vaccinated, or fully vaccinated) Main Outcome Measures: Days until return to work, presenting symptom Results: Return-to-work time for fully vaccinated HCWs (mean=10.9 days) was significantly shorter than that of partially vaccinated HCWs (15.5 days), which in turn was significantly shorter than that of unvaccinated HCWs (18.0 days). Fully vaccinated HCWs also showed milder symptom profiles compared to partially vaccinated and unvaccinated HCWs. Conclusions and Relevance: COVID-19 vaccination has the potential to prevent long absences from work and the adverse financial, staffing, and managerial consequences of these long absences.