Data Dictionary Research Articles

Proposed framework for medication delivery system in the Jordanian public health sector

BackgroundIn Jordan, the confluence of traffic congestion and overcrowding in public hospitals poses a significant challenge for patients to collect their medications timely. This challenge was further intensified during the COVID-19 pandemic. Recognizing this issue, the Ministry of Health (MOH) and Electronic Health Solutions (EHS) intend to establish a Medication Delivery System (MDS), designed to provide patients with home delivery of medications and ensure proper treatment. This paper outlines a comprehensive framework to guide requirements engineers in devising an effective MDS framework, with a focus on expediting the development and testing processes and mitigating the risks associated with constructing such a system.MethodThe proposed methodology entails a robust, structured approach to requirements development for an MDS that integrates an electronic health record system, billing system, pharmacy application, the patient-oriented My Hakeem app, and a delivery tracking system. The requirements elicitation and analysis processes were undertaken by a multidisciplinary committee from MOH and EHS teams, ensuring a diverse understanding of stakeholder needs and expectations. The requirement specifications were meticulously documented via a data dictionary, unified modeling language (UML), and context diagrams. The quality and accuracy of the requirements were verified through an extensive validation process, involving thorough review by various EHS teams and the MOH committee.ResultsThe MDS was implemented across numerous MOH facilities within a timeline that was a third of the original projection, leveraging the same level of resources and expertise. Post the requirements development phase, there were no changes requested by any stakeholders, indicating a high level of requirement accuracy and satisfaction.ConclusionThe study illustrates that our proposed methodology significantly results in a comprehensive, well-documented, and validated set of requirements, which streamlines the development and testing phases of the project and effectively eliminates requirement errors at an early stage of the requirements development process.

Implementación de un sistema de gestión de la información para la Administración de Casa de Paz

This paper describes the implementation of an information management system for the Administration of Casas de Paz through the Access software with the aim of having better control with the data of this ministry. The program was developed together with the different diagrams that are a support for its development, in turn the data dictionary was made to facilitate the use of the program. By means of the Data System, better control will be obtained regarding attendance, where they are located, in turn to facilitate the location of a new member in the church and having the information of the leaders of each one of the Casas de Paz. ILIA: Investigaciones Latinoamericanas en Ingeniería y Arquitectura, No. 01, 2024: 46-51.

A clinical outcomes data archive for a comprehensive fetal diagnosis and treatment center.

Data on near and long-term outcomes are critical for the care of all maternal-fetal patients presenting to a fetal center. This is important since physiologic and neurodevelopmental attributes do not manifest until childhood when multi-level factors influence health outcomes. Electronic health records (EHR) documentation structures are not designed for acquisition of key attributes, and changes over time and between-clinician differences can affect resultant output. Therefore, EHR derived datasets have limited ability to accurately characterize the clinical presentation and care trajectory of patients with congenital anomalies. Moreover, the fetus lacks a digital identity and requires relinking attributes documented in the maternal chart to those in the pediatric EHR. This conundrum amplifies in the setting of multiple gestation, returning maternal patients, and pregnancies with fetal demise. Current data systems result in incomplete abstraction of variables that may confound, mediate, or moderate critical associations. Our objective was to develop and implement a prospective data capture platform to transform EHR data into an analytic-grade database for multi-purpose use. A unified platform for longitudinal follow-up of maternal-child dyads named the Clinical Outcomes Data Archive (CODA) was constructed. CODA was designed using a data dictionary based on multidisciplinary input, a relational identity for each patient, fetus, and pregnancy, and a process by which EHR-sourced and chart-abstracted data are validated. Descriptive analyses were performed for data acquired between July 2022 - July 2023, and a comparison of studies before and after implementation of CODA is presented. 5,394,106 data points were validated for 7,662 patients across 12 conditions. 2% of data points were found to be unreliable or undocumented. 91% of data points were sourced from the EHR. 85% of condition-specific variables required manual chart abstraction. The study conducted with CODA contributed to 18 studies. CODA successfully merges EHR-sourced and manually abstracted documentation for longitudinal study of the maternal-child dyad.

IDI Search: a metadata search app for exploring New Zealand’s administrative linked data

How can we lower barriers to reuse of linked administrative data and improve quality at the same time? Linked data resources are expensive and complex, but accompanying readily-accessible metadata supports data reuse and quality control. However, metadata systems that are unsystematic or non-automated can lead to inconsistencies, making metadata use difficult even for experienced analysts. Our national statistics office (NSO) maintains a research database (RD) of administrative datasets linkable at the individual level. With the NSO’s agreement, we extracted schema information (e.g. tables and variables) from the RD and obtained data dictionaries. R was used to extract information (e.g., descriptions, human-friendly names, codings) from the data dictionaries, which identified errors and inconsistencies that the NSO then fixed. A metadata database was collated using schema and data dictionary information about variables and datasets in the RD. Finally, a public web app was developed to enable exploration of the meta database by searching specific terms or navigating the hierarchical relationships. We now work with the NSO data team to re-extract variables and add or update dictionaries ahead of the regular data updates. Metadata coverage is displayed on the app and used by our NSO to improve metadata quality and coverage. Our scripting workflow demonstrates the utility of automation in picking up errors and inconsistencies quickly, and before they can propagate by copy and paste. The web app, available to existing and new users, is now listed by the NSO as a “go-to” resource for using the linked administrative data resources.

Exploring methods for generating synthetic data in Scotland to improve access to public sector data for research

Objective and ApproachWe are working to improve the economic, social and environmental wellbeing in Scotland by enabling access to and linkage of public-sector data for research in the public good. Our objective was to examine how synthetic data can support this aim by allowing researchers to test approaches and write code while awaiting the necessary permissions to use the real data. We have explored generating synthetic data from metadata and from actual data. ResultsThrough engagement, researchers expressed interest in low fidelity data, which minimises privacy and information governance risks. We have created low fidelity data from information provided by data controllers in publicly-available data dictionaries. We have also created more high-fidelity data based on actual data, using the synthpop tool. We are carrying out further work to engage with data controllers about the level of fidelity that is acceptable to them, as well as the information that needs to be included in the data dictionary to meet this in an automated way, and how we will share datasets with researchers. A key part of these considerations is the ability to link synthetic datasets and the impact that has on the fidelity of the data. Conclusions and ImplicationsWe will share our approaches, the limitations we have found in using metadata to create synthetic data, difficulties encountered in using the synthpop tool, engagement we have had from controllers and researchers, and the proposed way forward for synthetic data in Scotland.

Development of a dictionary of information items inspired by common data models to support health research data access requests

A network of organizations collaborates to provide services to facilitate multi-regional research across Canada. To streamline the data access process, the network is developing a dictionary of information items (InfoItems), providing a label and definition that represent a semantic sourced from an ontology (e.g., “human birth date” from a demography ontology). This allows for a standardized and shareable means to express data requirements. Inspired by health-research common data models (CDMs) like those from the Observational Medical Outcomes Partnership (OMOP) and the Canadian Network for Observational Drug Effects (CNODES), approximately 60 InfoItems were developed by the network. As a pilot exercise, four data centres mapped their data assets to the InfoItems to ensure they are relevant to the Canadian health data context. This has laid the foundation for InfoItems as a basic data harmonization mechanism. The new InfoItems-based data dictionary promotes the standardization of data asset contents across the network’s data centres. Researchers can use the dictionary to specify a project’s data requirements, establishing details like data availability across multiple regions, for inclusion in their data access request. The InfoItems dictionary is pivotal in simplifying data access requests, promoting multi-regional research in Canada. This resource can simultaneously safeguard the quality of analytical results and optimize data extraction into existing data models (e.g., health-research CDMs). Ongoing improvements in this area aim to enhance user experience and facilitate quality research.

The Discover In-Hospital Cardiac Arrest (Discover IHCA) Study: An Investigation of Hospital Practices After In-Hospital Cardiac Arrest.

In-hospital cardiac arrest (IHCA) is a significant public health burden. Rates of return of spontaneous circulation (ROSC) have been improving, but the best way to care for patients after the initial resuscitation remains poorly understood, and improvements in survival to discharge are stagnant. Existing North American cardiac arrest databases lack comprehensive data on the post-resuscitation period, and we do not know current post-IHCA practice patterns. To address this gap, we developed the Discover In-Hospital Cardiac Arrest (Discover IHCA) study, which will thoroughly evaluate current post-IHCA care practices across a diverse cohort. Our study collects granular data on post-IHCA treatment practices, focusing on temperature control and prognostication, with the objective of describing variation in current post-IHCA practice. This is a multicenter, prospectively collected, observational cohort study of patients who have suffered IHCA and have been successfully resuscitated (achieved ROSC). There are 24 enrolling hospital systems (23 in the United States) with 69 individual enrolling hospitals (39 in the United States). We developed a standardized data dictionary, and data collection began in October 2023, with a projected 1000 total enrollments. Discover IHCA is endorsed by the Society of Critical Care Medicine. The study collects data on patient characteristics including pre-arrest frailty, arrest characteristics, and detailed information on post-arrest practices and outcomes. Data collection on post-IHCA practice was structured around current American Heart Association and European Resuscitation Council guidelines. Among other data elements, the study captures post-arrest temperature control interventions and post-arrest prognostication methods. Analysis will evaluate variations in practice and their association with mortality and neurologic function. We expect this study, Discover IHCA, to identify variability in practice and outcomes following IHCA, and be a vital resource for future investigations into best-practice for managing patients after IHCA.

Challenges of Integrating ICD 11 into Automatic Alerting Systems.

Automatic alerting systems (AASs) can identify adverse health events but emergency communication relies on human operators and natural languages. For complete automation, we need to code the diversity of adverse events in a granularity that supports optimal dispatches. Hence, AAs shall integrate with the International Classification of Diseases (ICD). The ICD-11 coding system includes chapters for external causes of injury. However, ICD-11 supports coding injury incidents in electronic health records (EHRs) after they have occurred, while disregarding integrating real-time injury reporting within its framework. We explore the potential challenges associated with integrating ICD-11 into AAS by analyzing external causes of morbidity or mortality and the dimensions of external causes as potential areas of integration. We recognize the themes: (i) incident of injury, (ii) mode of transport, (iii) indoor location, (iv) outdoor location, and (v) type of building, and identify four challenges: (i) conceptual differences between the two systems, (ii) injury identification, (iii) presence of entities below the shoreline in ICD-11, and (iv) lack of specificity in certain ICD-11 codes related to AASs. For easy integration of ICD-11 into AASs, we recommend an AAS data dictionary and propose ICD-11 updates related to external causes of injury.

Pericardiocentesis, Chest Tube Insertion, and Needle Thoracostomy During Resuscitation of Nontraumatic Adult In-Hospital Cardiac Arrest: A Retrospective Cohort Study.

In-hospital cardiac arrest (IHCA) is a significant public health burden. Rates of return of spontaneous circulation (ROSC) have been improving, but the best way to care for patients after the initial resuscitation remains poorly understood, and improvements in survival to discharge are stagnant. Existing North American cardiac arrest databases lack comprehensive data on the postresuscitation period, and we do not know current post-IHCA practice patterns. To address this gap, we developed the Discover IHCA study, which will thoroughly evaluate current post-IHCA care practices across a diverse cohort. Our study collects granular data on post-IHCA treatment practices, focusing on temperature control and prognostication, with the objective of describing variation in current post-IHCA practices. This is a multicenter, prospectively collected, observational cohort study of patients who have suffered IHCA and have been successfully resuscitated (achieved ROSC). There are 24 enrolling hospital systems (23 in the United States) with 69 individuals enrolling in hospitals (39 in the United States). We developed a standardized data dictionary, and data collection began in October 2023, with a projected 1000 total enrollments. Discover IHCA is endorsed by the Society of Critical Care Medicine. The study collects data on patient characteristics, including prearrest frailty, arrest characteristics, and detailed information on postarrest practices and outcomes. Data collection on post-IHCA practice was structured around current American Heart Association and European Resuscitation Council guidelines. Among other data elements, the study captures postarrest temperature control interventions and postarrest prognostication methods. The majority of participating hospital systems are large, academic, tertiary care centers serving urban populations. The analysis will evaluate variations in practice and their association with mortality and neurologic function. We expect this study, Discover IHCA, to identify variability in practice and outcomes following IHCA and be a vital resource for future investigations into best practices for managing patients after IHCA.

Collaborative data sharing: The International Acute Myeloid Leukemia Consortium (INTERACT).

169 Background: Outcomes for children and adolescents/young adults (AYAs) with acute myeloid leukemia (AML) remain suboptimal and are decidedly poor for patients with subtypes defined by high-risk features, including canonical genetic alterations and relapsed/refractory presentation. Due to their rarity, these subtypes are difficult to study within the existing clinical trials paradigm. Data sharing efforts aim to increase our understanding of rare cancer subtypes by pooling data from multiple sources. The INTERnational Acute myeloid leukemia ConsorTium (INTERACT) was established to pool and share data on pediatric and AYA patients with AML. Methods: In 2019, a data dictionary was created through an iterative, consensus-driven process that analyzed pre-existing clinical trials case report forms as the initial basis. By developing this standardized representation for data on patients with AML, data could be harmonized and integrated. A memorandum of understanding was signed in 2021, formally establishing INTERACT. An executive committee was convened to establish scientific priorities, facilitate ongoing data dictionary work, usher agreements with data contributors and review data requests submitted by investigators. In January 2024, the initial set of AML data was released on the Pediatric Cancer Data Commons (PCDC) data portal. Results: As of April 2024, the data dictionary includes 157 standardized data elements that were used to harmonize clinical data on 3,413 patients who participated in seven clinical trials conducted by four pediatric oncology cooperative groups across the United States, Europe, and Asia. Elements in the data dictionary include demographics, initial disease characteristics, genetic alterations, and survival status. Aggregate data can be freely explored using the publicly-available PCDC data portal (portal.pedscommons.org) and investigators seeking line-level access to data may submit a project request to the INTERACT executive committee for consideration. Conclusions: International collaborative data-sharing efforts will advance our understanding of pediatric and AYA AML by reducing barriers that constrain our study of AML to smaller cohorts of data. We hope that an early outcome of data sharing will be to increase our understanding of optimal treatments for patients with relapsed/refractory disease. Future goals of INTERACT include identifying data from additional studies to include, establishing relationships with new data contributors, and establishing working groups to advance the consortium’s scientific and clinical trials goals.

Demographic and Prognostic Factors of the Columnar Cell Variant of Papillary Thyroid Carcinoma: A National Cancer Database Study.

Objective The columnar cell variant of papillary thyroid carcinoma (PTC-CC) is a rare, malignant tumor of the thyroid gland. This study uses the National Cancer Database (NCDB) to analyze demographic and prognostic factors affecting the overall survival rates of PTC-CC. Methods From 2004 to 2020, 7,079 patients diagnosed with columnar cell papillary thyroid carcinoma were identified in the NCDB. Patient demographics were reviewed based on categories listed in the NCDB participant user file data dictionary. Kaplan-Meier curves, log-rank tests, and multivariable Cox hazard regression models were used to analyze the significance of demographic and prognostic factors on overall survival rates of PTC-CC. Results Multivariate analysis demonstrated each five-year increment in age was associated with a 30% increase in mortality (hazard ratio (HR) = 1.30, 95% confidence interval (CI): 1.25-1.36, P < 0.001). Charlson-Deyo scores displayed similar incremental increases, such that patients with a score ≥ 3 had a 154% increase in mortality risk relative to a score of 0 (HR = 2.54; 95% CI: 1.75-3.68, P < 0.001). Black individuals had a 70% increase in mortality compared to White individuals (HR = 1.70, 95% CI: 1.25-2.30, P < 0.001), while all Other races had the highest 10-year survival rate of 92.7%. Females had a significant 37% decrease in mortality compared to males (HR = 0.63, 95% CI: 0.54-0.73, P < 0.001). Patients in the lowest income quartiles were found to have a significant increase in mortality compared to the highest income group (HR = 0.54; 95% CI: 0.41-0.71, P < 0.001). Survival rates were negatively correlated with NCDB Analytic Staging increases. Conclusion In general, age, sex, race, education, income, comorbidities, and cancer staging were found to be predictive factors of overall survival rates of PTC-CC. However, insurance status and education levels did not result in significant differences.

The Ugandan sickle Pan-African research consortium registry: design, development, and lessons

BackgroundSub-Saharan Africa bears the highest burden of sickle cell disease (SCD) globally with Nigeria, Democratic Republic of Congo, Tanzania, Uganda being the most affected countries. Uganda reports approximately 20,000 SCD births annually, constituting 6.67% of reported global SCD births. Despite this, there is a paucity of comprehensive data on SCD from the African continent. SCD registries offer a promising avenue for conducting prospective studies, elucidating disease severity patterns, and evaluating the intricate interplay of social, environmental, and genetic factors. This paper describes the establishment of the Sickle Pan Africa Research Consortium (SPARCo) Uganda registry, encompassing its design, development, data collection, and key insights learned, aligning with collaborative efforts in Nigeria, Tanzania, and Ghana SPARCo registries.MethodsThe registry was created using pre-existing case report forms harmonized from the SPARCo data dictionary and ontology to fit Uganda clinical needs. The case report forms were developed with SCD data elements of interest including demographics, consent, baseline, clinical, laboratory and others. That data was then parsed into a customized REDCap database, configured to suit the optimized ontologies and support retrieval aggregations and analyses. Patients were enrolled from one national referral and three regional referral hospitals in Uganda.ResultsA nationwide electronic patient-consented registry for SCD was established from four regional hospitals. A total of 5,655 patients were enrolled from Mulago National Referral Hospital (58%), Jinja Regional Referral (14.4%), Mbale Regional Referral (16.9%), and Lira Regional Referral (10.7%) hospitals between June 2022 and October 2023.ConclusionUganda has been able to develop a SCD registry consistent with data from Tanzania, Nigeria and Ghana. Our findings demonstrate that it’s feasible to develop longitudinal SCD registries in sub-Saharan Africa. These registries will be crucial for facilitating a range of studies, including the analysis of SCD clinical phenotypes and patient outcomes, newborn screening, and evaluation of hydroxyurea use, among others. This initiative underscores the potential for developing comprehensive disease registries in resource-limited settings, fostering collaborative, data-driven research efforts aimed at addressing the multifaceted challenges of SCD in Africa.

Pooled Cohort Profile: ReCoDID Consortium's Harmonized Acute Febrile Illness Arbovirus Meta-Cohort.

Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease).

Blood Cancer Network Ireland (BCNI) and National Cancer Registry Ireland (NCRI) collaboration: challenges and utility of an Enhanced Blood Cancer Outcomes Registry (EBCOR) pilot.

The Blood Cancer Network Ireland and National Cancer Registry Ireland worked to create an Enhanced Blood Cancer Outcomes Registry (EBCOR). Enhanced data in acute myeloid leukaemia (AML) included an extensive data dictionary, bespoke software and longitudinal follow-up. To demonstrate the utility of the database, we applied the data to examine a clinically relevant question: Charlson comorbidity index (CCI) usefulness in predicting AML patients' survival. A software designer and consultant haematologists in Cork University Hospital worked together to standardise a data dictionary, train registrars and populate a database. One hundred and forty-one AML patients underwent enhanced data registration. Comorbidities identified by chart review were used to examine the capability of the CCI and age at diagnosis to predict mortality using Kaplan-Meier curves, Cox regression and receiver operating characteristic curves. In regression analysis, a dose-response relationship was observed; patients in the highest CCI tertile displayed a greater risk (HR = 4.90; 95% CI 2.79-8.63) of mortality compared to subjects in tertile 2 (HR = 2.74; 95% CI 1.64-4.57) and tertile 1 (reference). This relationship was attenuated in an analysis which adjusted for age at diagnosis. The area under the curve (AUC) for the CCI was 0.76 (95% CI 0.68-0.84) while the AUC for age at diagnosis was 0.84 (95% CI 0.78-0.90). Results suggest that the CCI provides no additional prognostic information beyond that obtained from age alone at AML diagnosis and that an EBCOR can provide a rich database for cancer outcomes research, including predictive models and resource allocation.

Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost

Abstract Because of electrification conditions, key components of battery swapping systems (BSSs) for electric heavy trucks are always damaged by electric erosion, which presents challenges to the safety and efficiency of high-intensity transportation. Due to the special working conditions of a BSS, the fault diagnosis of its driving gear encounters several issues, including reciprocation motion, low and fluctuating speed, and complicated noises. To solve these problems, audio features, including Mel-frequency cepstral coefficients and Gammatone cepstral coefficients, are extracted from the vibration signals. Then, these features are utilized to construct an original dictionary. Next, based on data augmentation and dictionary learning, a robust dictionary is generated from the original dictionary. Finally, with the robust dictionary, sparse representation-based classification is integrated into AdaBoost to achieve accurate fault diagnosis for the driving gear in BSS. The effectiveness of the fault diagnosis scheme is validated based on the monitoring data of the BSS, and the accuracy of fault diagnosis is 99.17%.

FHIR Implementation Guide for Stroke: A dual focus on the patient’s clinical pathway and value-based healthcare

BackgroundStroke management requires a coordinated strategy, adhering to clinical pathways (CP) and value-based healthcare (VBHC) principles from onset to rehabilitation. However, the discrepancies between these pathways and actual patient experiences highlight the need for ongoing monitoring and addressing interoperability issues across multiple institutions in stroke care. To address this, the Fast Healthcare Interoperability Resource (FHIR) Implementation Guide (IG) standardizes the information exchange among these systems, considering a specific context of use. ObjectiveDevelop an FHIR IG for stroke care rooted in established stroke CP and VBHC principles. MethodWe represented the stroke patient journey by considering the core stroke CP, the International Consortium for Health Outcomes Measurement (ICHOM) dataset for stroke, and a Brazilian case study using the Business Process Model and Notation (BPMN). Next, we developed a data dictionary that aligns variables with existing FHIR resources and adapts profiling from the Brazilian National Health Data Network (BNHDN). ResultsOur BPMN model encompassed three critical phases that represent the entire patient journey from symptom onset to rehabilitation. The stroke data dictionary included 81 variables, which were expressed as questionnaires, profiles, and extensions. The FHIR IG comprised nine pages: Home, Stroke-CP, Data Dictionary, FHIR, ICHOM, Artifacts, Examples, Downloads, and Security. We developed 96 artifacts, including 7 questionnaires, 27 profiles with corresponding example instances, 3 extensions, 18 value sets, and 14 code systems pertinent to ICHOM outcome measures. ConclusionThe FHIR IG for stroke in this study represents a significant advancement in healthcare interoperability, streamlining the tracking of patient outcomes for quality enhancement, facilitating informed treatment choices, and enabling the development of dashboards to promote collaborative excellence in patient care.

OTHR-12. GENERATING A COMPREHENSIVE INTERACTIVE PEDIATRIC BRAIN TUMOR VISUALIZATION PORTAL FROM MOLECULAR AND CLINICAL DATA DERIVED FROM TWO MULTI-INSTITUTIONAL PROTOCOLS (SJMB03, SJYC07) AND NON-PROTOCOL PATIENTS TREATED AT ST JUDE CHILDREN’S RESEARCH HOSPITAL

Abstract BACKGROUND Pediatric central nervous system (CNS) tumors represent a diverse group of neoplasms with heterogeneous molecular and clinical characteristics. The need for well-organized, searchable datasets is paramount to enhance our understanding and improve the treatment approaches for these devastating diseases. To illustrate this, we have organized multifaceted data from patients into an interactive portal equipped with cutting-edge visualization tools. METHODS Clinical and molecular data from the SJMB03 and SJYC07 protocols and select published non-protocol St. Jude patients were collated and harmonized. A data dictionary was constructed to record demographic information, treatment details, histology, methylation classification, mutations, chromosomal aberrations, and outcomes. We implemented five visualization techniques - summary plots, sample view, scatter plots, sample matrix, and survival plots. These techniques are interconnected, enabling interaction and “crosstalk”. RESULTS Data from 710 patients with CNS tumors were incorporated: 386 Medulloblastomas (MB); 88 Atypical teratoid Rhabdoid Tumors (ATRT); 54 Ependymomas (EPN); 49 Pineoblastomas (PB); 28 Embryonal Tumors with Multilayered Rosettes (ETMR), 22 Infant-type hemispheric glioma (IHG), 16 High grade glioma (HGG), 13 Choroid Plexus Tumors (CPT); 11 CNS Sarcoma; 8 CNS tumor with BCOR internal tandem duplication (CNS BCOR); 6 CNS neuroblastoma FOXR2 activated (CNS NB FOXR2); 5 Low grade glioma (LGG); and 24 other entities or unclassified tumors. The portal features enabled the creation of custom cohorts. For example, patients with TP53 mutations (n=32; 15 MB, 7 CPT, 6 HGG, 1 CNS Sarcoma, 1 CNS NB FOXR2, 1 PB, 1 unclassified tumor) or DICER1 mutations (n=10; 7 PB, 2 CNS Sarcoma, and 1 ETMR) could be identified, cross-compared and analyzed adjusting for variables at users’ choice. CONCLUSION The integration of molecular and clinical data from multiple sources into an interactive visualization portal presents a comprehensive resource for research and clinical practice. Future work will focus on expanding the portal to integrate more cases/cohorts.

MDB-105. CONSTRUCTION OF AN INTERACTIVE MEDULLOBLASTOMA VISUALIZATION PORTAL FROM MOLECULAR AND CLINICAL DATA DERIVED FROM THREE MULTI-INSTITUTIONAL PROTOCOLS (ACNS0331, SJMB03, ACNS0332) TO COMPARE, CONTRAST, AND INVESTIGATE COHORTS AS A MEANS TO REFINE THERAPY

Abstract BACKGROUND Medulloblastoma (MB) patients display a heterogeneous mix of molecular and clinical characteristics that influence tumor behavior and survival. We hypothesized that if these multiple characteristics could be incorporated into an organized, searchable, and interactive dataset, this would advance our understanding and improve treatment of this disease. Consequently, we organized multifaceted datasets into an interactive portal equipped with cutting-edge visualization tools. METHODS Clinical and molecular data from the ACNS0331, SJMB03, and ACNS0332 protocols were collated and harmonized. A data dictionary was built to capture demographics, treatment, histology, methylation classification, mutations, chromosomal copy number variations and outcomes. To display the data, a software framework was applied. Five visualization methods were implemented (summary plots, sample view, scatter plots, sample matrix, and survival plots) in an interconnected manner such that these “cross-talk” with each other. RESULTS Data from 898 patients with MB were incorporated: 131 WNT, 151 SHH, 220 G3, 396 G4. The portal features enabled the creation of custom cohorts that could be instantly queried by any of the dictionary terms. For example, cohorts could be customized to include only cases with a mutation of the user’s choice and analyzed by molecular group, gender, age at diagnosis, or treatment given. Survival of patients with SHH-MB harboring TP53 mutations could be compared to patients with WNT-MB harboring TP53 mutations. Moreover, the influence of MB subtype (e.g. G34_I-VIII), focal amplifications (MYC, MYCN) and whole chromosome aberration phenotype on survival could be queried across treatment regimens accounting for metastatic status and/or additional variables of the user’s choice. CONCLUSION The integration of molecular and clinical data from multiple trials into an interactive visualization data portal presents an unprecedented resource for research and clinical practice. This portal facilitates answering highly specific questions about mutational frequency and patient outcome by molecular and clinical risk factors.

A rapid method to census the seismic risk class of masonry buildings

Ministerial Decree 65/58 and its Guidelines, published in 2017, allow the seismic risk classification only of masonry buildings, associating with their vulnerability class their location within the four seismic zones into which Italy has been divided since 2003. Therefore, if the masonry type and structural weaknesses of a building are known, it is possible to assign the corresponding vulnerability class to it and to census, by means of a rapid onsite survey, the seismic risk class of a very large number of masonry buildings. Based on this assumption, a method has been developed for a single operator to rapidly determine the masonry type and structural weaknesses of a building. The survey was conducted by recording this information in alphanumeric tables divided into fields whose attributes were preliminarily chosen and inserted in data dictionaries that could be brought up with drop-down menus. At the end of the survey, the contents of the tables were transferred to a relational database, incorporated in a geographical information system, filtered and processed to obtain the vulnerability class of each building represented on the same basic numerical cartography used during the survey. By associating the seismic zone with the building, the corresponding class was calculated. The method made it possible to census the seismic risk class of 400 masonry buildings in a municipality with an extension of ca. 20 km2 in less than two working days. This highlights the possibility of extending it to larger areas to estimate, assess or manage the effects or the emergency phase of an earthquake.

Harmonizing data on correlates of sleep in children within and across neurodevelopmental disorders: lessons learned from an Ontario Brain Institute cross-program collaboration.

There is an increasing desire to study neurodevelopmental disorders (NDDs) together to understand commonalities to develop generic health promotion strategies and improve clinical treatment. Common data elements (CDEs) collected across studies involving children with NDDs afford an opportunity to answer clinically meaningful questions. We undertook a retrospective, secondary analysis of data pertaining to sleep in children with different NDDs collected through various research studies. The objective of this paper is to share lessons learned for data management, collation, and harmonization from a sleep study in children within and across NDDs from large, collaborative research networks in the Ontario Brain Institute (OBI). Three collaborative research networks contributed demographic data and data pertaining to sleep, internalizing symptoms, health-related quality of life, and severity of disorder for children with six different NDDs: autism spectrum disorder; attention deficit/hyperactivity disorder; obsessive compulsive disorder; intellectual disability; cerebral palsy; and epilepsy. Procedures for data harmonization, derivations, and merging were shared and examples pertaining to severity of disorder and sleep disturbances were described in detail. Important lessons emerged from data harmonizing procedures: prioritizing the collection of CDEs to ensure data completeness; ensuring unprocessed data are uploaded for harmonization in order to facilitate timely analytic procedures; the value of maintaining variable naming that is consistent with data dictionaries at time of project validation; and the value of regular meetings with the research networks to discuss and overcome challenges with data harmonization. Buy-in from all research networks involved at study inception and oversight from a centralized infrastructure (OBI) identified the importance of collaboration to collect CDEs and facilitate data harmonization to improve outcomes for children with NDDs.