Armenian conference dedicated to the 850th death anniversary of St. Nerses the Gracious

Conference section 1 Opening of the conference with speeches by official persons Boghos Levon Zekiyan, Archeparch of Istanbul and Turkey for the Catholic Armenians - Saint Nerses Shnorhali as a Unique Figure in Christian Thought And Praxis: Shnorhali’s Pioneering Vision of Christian Oecumenism Claude Mutafian, Dr.hist. (University of Paris) -- Mleh, a Successful Armenian Prince (1169- 1175) Andris Priede, Dr.hist.eccl. (Faculty of Theology, University of Latvia) - Marginal Forms of Armenian Monasticism in 13th Century Valda Salmiņa, M.A. - St. Nerses Shnorhali’s historical poem “Lament of Edessa” Conference section 2 Vahan S. Hovhanessian, Bishop PhD (Karekin the First Research Center) -Analysis of the Four Gospels attributed to St. Nerses the Graceful Abraham Terian, Prof. emeritus (St. Nersess Armenian Seminary) - Shnorhali’s Commentary on the Beatitudes Elizabete Taivāne, Dr.theol. (Faculty of Theology, University of Latvia) -Christology of St. Nerses Shnorhali at the Crossroads of Eastern and Western Theology: The Edifying Details Conference section 3 Ojārs Spārītis, Dr.habil.art. (Latvian Academy of Arts) - View on Sacral Architecture of Central Armenia Haig Utidjian, PhD (CESEM, New University of Lisbon) - The music of the Šnorhali Corpus: challenges and rewards Arusyak Tamrazyan, PhD (Matenadaran, Research Institute of Ancient manuscripts) - Mystical symbolism in melismatic performance: the odes by St Nersēs th

Electronic health records identify timely trends in childhood mental health conditions

Abstract Background Electronic health records (EHRs) data provide an opportunity to collect patient information rapidly, efficiently and at scale. National collaborative research networks, such as PEDSnet, aggregate EHRs data across institutions, enabling rapid identification of pediatric disease cohorts and generating new knowledge for medical conditions. To date, aggregation of EHR data has had limited applications in advancing our understanding of mental health (MH) conditions, in part due to the limited research in clinical informatics, necessary for the translation of EHR data to child mental health research. Methods In this cohort study, a comprehensive EHR-based typology was developed by an interdisciplinary team, with expertise in informatics and child and adolescent psychiatry, to query aggregated, standardized EHR data for the full spectrum of MH conditions (disorders/symptoms and exposure to adverse childhood experiences (ACEs), across 13 years (2010–2023), from 9 PEDSnet centers. Patients with and without MH disorders/symptoms (without ACEs), were compared by age, gender, race/ethnicity, insurance, and chronic physical conditions. Patients with ACEs alone were compared with those that also had MH disorders/symptoms. Prevalence estimates for patients with 1+ disorder/symptoms and for specific disorders/symptoms and exposure to ACEs were calculated, as well as risk for developing MH disorder/symptoms. Results The EHR study data set included 7,852,081 patients < 21 years of age, of which 52.1% were male. Of this group, 1,552,726 (19.8%), without exposure to ACEs, had a lifetime MH disorders/symptoms, 56.5% being male. Annual prevalence estimates of MH disorders/symptoms (without exposure to ACEs) rose from 10.6% to 2010 to 15.1% in 2023, a 44% relative increase, peaking to 15.4% in 2019, prior to the Covid-19 pandemic. MH categories with the largest increases between 2010 and 2023 were exposure to ACEs (1.7, 95% CI 1.6–1.8), anxiety disorders (2.8, 95% CI 2.8–2.9), eating/feeding disorders (2.1, 95% CI 2.1–2.2), gender dysphoria/sexual dysfunction (43.6, 95% CI 35.8–53.0), and intentional self-harm/suicidality (3.3, 95% CI 3.2–3.5). White youths had the highest rates in most categories, except for disruptive behavior disorders, elimination disorders, psychotic disorders, and standalone symptoms which Black youths had higher rates. Median age of detection was 8.1 years (IQR 3.5–13.5) with all standalone symptoms recorded earlier than the corresponding MH disorder categories. Conclusions These results support EHRs’ capability in capturing the full spectrum of MH disorders/symptoms and exposure to ACEs, identifying the proportion of patients and groups at risk, and detecting trends throughout a 13-year period that included the Covid-19 pandemic. Standardized EHR data, which capture MH conditions is critical for health systems to examine past and current trends for future surveillance. Our publicly available EHR-mental health typology codes can be used in other studies to further advance research in this area

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network

Introduction Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved.Methods and analysis The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0–17 years only (component A), three centres conduct surveillance in young adults aged 18–44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression.Ethics and dissemination The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA