Data Harmonization Research Articles

Advances in the Clinical Application of High-throughput Proteomics.

High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

Evaluating the harmonization potential of oral health-related questionnaires in national longitudinal birth and child cohort surveys.

Effective use of longitudinal study data is challenging because of divergences in the construct definitions and measurement approaches over time, between studies and across disciplines. One approach to overcome these challenges is data harmonization. Data harmonization is a practice used to improve variable comparability and reduce heterogeneity across studies. This study describes the process used to evaluate the harmonization potential of oral health-related variables across each survey wave. National child cohort surveys with similar themes/objectives conducted in the last two decades were selected. The Maelstrom Research Guidelines were followed for harmonization potential evaluation. Seven nationally representative child cohort surveys were included and questionnaires examined from 50 survey waves. Questionnaires were classified into three domains and fifteen constructs and summarized by age groups. A DataSchema (a list of core variables representing the suitable version of the oral health outcomes and risk factors) was compiled comprising 42 variables. For each study wave, the potential (or not) to generate each DataSchema variable was evaluated. Of the 2100 harmonization status assessments, 543 (26%) were complete. Approximately 50% of the DataSchema variables can be generated across at least four cohort surveys while only 10% (n = 4) variables can be generated across all surveys. For each survey, the DataSchema variables that can be generated ranged between 26% and 76%. Data harmonization can improve the comparability of variables both within and across surveys. For future cohort surveys, the authors advocate more consistency and standardization in survey questionnaires within and between surveys.

Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans.

We develop a data harmonization approach for C. elegans volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID (wormid.org). We use this repository to train three existing automated cell identification algorithms to, for the first time, enable accuracy in neural identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to (a) study neuroanatomical organization and neural activity across diverse experimental paradigms, (b) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (c) inform models of neurobiological development and function.

Data Model for the Comprehensive Management of Biobanks and Its Contribution to Personalized Medicine.

Biobanks are infrastructures essential for research involving multi-disciplinary teams and an increasing number of stakeholders. In the field of personalized medicine, biobanks play a key role through the provision of well-characterized and annotated samples protecting at the same time the right of donors. The Andalusian Public Health System Biobank (SSPA Biobank) has implemented a global information management system made up of different modules that allow for the recording, traceability and monitoring of all the information associated with the biobank operations. The data model, designed in a standardized and normalized way according to international initiatives on data harmonization, integrates the information necessary to guarantee the quality of results from research, benefiting researchers, clinicians and donors.

Recommendations for data collection in cohort studies of preterm born individuals - The RECAP Preterm Core Dataset.

Preterm birth (before 37 completed weeks of gestation) is associated with an increased risk of adverse health and developmental outcomes relative to birth at term. Existing guidelines for data collection in cohort studies of individuals born preterm are either limited in scope, have not been developed using formal consensus methodology, or did not involve a range of stakeholders in their development. Recommendations meeting these criteria would facilitate data pooling and harmonisation across studies. To develop a Core Dataset for use in longitudinal cohort studies of individuals born preterm. This work was carried out as part of the RECAP Preterm project. A systematic review of variables included in existing core outcome sets was combined with a scoping exercise conducted with experts on preterm birth. The results were used to generate a draft core dataset. A modified Delphi process was implemented using two stages with three rounds each. Three stakeholder groups participated: RECAP Preterm project partners; external experts in the field; people with lived experience of preterm birth. The Delphi used a 9-point Likert scale. Higher values indicated greater importance for inclusion. Participants also suggested additional variables they considered important for inclusion which were voted on in later rounds. An initial list of 140 data items was generated. Ninety-six participants across 22 countries participated in the Delphi, of which 29% were individuals with lived experience of preterm birth. Consensus was reached on 160 data items covering Antenatal and Birth Information, Neonatal Care, Mortality, Administrative Information, Organisational Level Information, Socio-economic and Demographic information, Physical Health, Education and Learning, Neurodevelopmental Outcomes, Social, Lifestyle and Leisure, Healthcare Utilisation and Quality of Life. This core dataset includes 160 data items covering antenatal care through outcomes in adulthood. Its use will guide data collection in new studies and facilitate pooling and harmonisation of existing data internationally.

Empowering healthcare with NLP-driven deep learning unveiling biomedical materials through text mining

This study presents a comprehensive approach to automated biomedical materials discovery in healthcare applications by integrating text mining and deep learning techniques. The research methodology encompasses two main components: exploration of key research questions through graphical representation and evaluation of model performance using precision, recall, and F1-score metrics. The identification of pertinent research questions is visualized using bar charts, offering insights into the distribution of studies across domains such as data harmonization, heterogeneity, industrial textual data, and sequential data performance. The precision comparison chart highlights the strengths and weaknesses of different models, with model 1 demonstrating notable precision. The recall comparison chart emphasizes model 2’s outstanding performance in capturing relevant information, while the f1-score comparison chart showcases the balanced metrics of model 2 and 4. These visual analyses contribute to a nuanced understanding of the research landscape and guide the development of the proposed NLP-ML pipeline. The study’s findings underscore the significance of addressing data harmonization challenges and extracting insights from industrial textual data in advancing biomedical materials discovery. Overall, this research amalgamates exploratory data analysis and quantitative model evaluation to contribute to the evolving field of text mining and deep learning applications in biomedical material discovery for healthcare applications.

Biological Sample Collection to Advance Research and Treatment: A Fight Osteosarcoma Through European Research and Euro Ewing Consortium Statement.

Osteosarcoma and Ewing sarcoma are bone tumors mostly diagnosed in children, adolescents, and young adults. Despite multimodal therapy, morbidity is high and survival rates remain low, especially in the metastatic disease setting. Trials investigating targeted therapies and immunotherapies have not been groundbreaking. Better understanding of biological subgroups, the role of the tumor immune microenvironment, factors that promote metastasis, and clinical biomarkers of prognosis and drug response are required to make progress. A prerequisite to achieve desired success is a thorough, systematic, and clinically linked biological analysis of patient samples, but disease rarity and tissue processing challenges such as logistics and infrastructure have contributed to a lack of relevant samples for clinical care and research. There is a need for a Europe-wide framework to be implemented for the adequate and minimal sampling, processing, storage, and analysis of patient samples. Two international panels of scientists, clinicians, and patient and parent advocates have formed the Fight Osteosarcoma Through European Research consortium and the Euro Ewing Consortium. The consortia shared their expertise and institutional practices to formulate new guidelines. We report new reference standards for adequate and minimally required sampling (time points, diagnostic samples, and liquid biopsy tubes), handling, and biobanking to enable advanced biological studies in bone sarcoma. We describe standards for analysis and annotation to drive collaboration and data harmonization with practical, legal, and ethical considerations. This position paper provides comprehensive guidelines that should become the new standards of care that will accelerate scientific progress, promote collaboration, and improve outcomes.

Improving efficiency and quality of operational industrial production assets information management in customer–vendor interaction

Industrial assets management has a key role in ensuring continuity of industrial production via purchasing, warehousing and maintenance of devices and spare parts used in manufacturing, but it suffers from data and information accessibility, completeness, consistency, interoperability, and timeliness challenges in the customer - equipment vendors interaction and information sharing. Current practices in devices and spare parts purchasing, warehousing and maintenance include a lot of manual information search, validation and update work done at the manufacturing companies to maintain high quality master data records for the production assets management. This article addresses the challenge of improving efficiency and quality of operational production assets information management in industry customer company interaction with their production equipment vendors, with a design science research approach. The research was executed in collaboration between research organizations, forest industry companies, industrial production equipment vendors and information technology (IT) companies on two parallel and complementary research tracks, which are described with their key research results. The contributions are an ex-ante evaluated digital marketplace concept with preliminary requirements, and an ex-post evaluated semi-automated master data harmonization tool, as artefacts for improving efficiency and quality of production assets information management in industrial customer–vendors interaction. As theoretical contribution, the article provides a synthesis of the research results in the framework of information systems (IS) design theory as a nascent and novel design theory on designing IS artifacts for improving efficiency and quality of production assets information management, both in single industrial customer company and ecosystem settings. The contributions provide a starting point for further research and development of a digital marketplace for improving production assets information management in industrial customers – vendors interaction, potentially also applicable for similar ecosystems in other continuous production process -based manufacturing industries.

Domesticating the Global War on Terrorism in West Africa: Implications for Sub-Regional Security

Objective: The objective of this study is to the recent incidences of terror attacks in the West African sub-region that have only overwhelmed national governments concerned. Using the case studies of Burkina Faso, Mali, Nigeria, and Niger, the paper interrogates the recent scourge of terrorism in these countries within the context of sub-regional security. Theoretical Framework: The structural conflict theory, which argues that conflict is built into the systems and structures of society, is employed in the work. This is based on the radical transformative agenda promoted by globalization and accentuated by the violent intra-state conflicts in Africa, especially following the restructuring of the international security architecture after the end of the Cold War in 1989. Method: The methodology adopted for this research utilizes the qualitative research design using the case study approach. Secondary data is engaged for this study, alongside thematic analysis for the harmonization of data. Results and Discussion: The study discovered that the activities of domestic terror groups is hinged on the unresolved rhetoric of globalization. The study concludes that until there are institutional measures to strengthen the fragile politico-economic structures hurriedly erected after prolonged dictatorships in the countries concerned, the solution to the threat of terrorism will remain a tantalizing mirage. It canvasses for the creation of a civil-military alliance in the war on terror, and the strengthening of democratic institutions to bring about good governance which will make states better equipped to curb terrorism. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the fields of national security, strategic studies and even institutional democracy. These implications could encompass the protection of lives and property within the state, protection of the state’s territorial integrity and the consolidation of democracy by state governments. Originality/Value: This study contributes to the literature by highlighting the fragilitiy of democracy in West African states as a consequence of prolonged terrorist attacks that are yet to be adequately catered to by civilian governments. The relevance and value of this research are evidenced by it practical applicability across the fields of policy and strategic studies, and political science.

Cross study analyses of SEND data: toxicity profile classification.

A SEND toxicology data transformation, harmonization, and analysis platform were created to improve the identification of unique findings related to the intended target, species, and duration of dosing using data from multiple studies. The lack of a standardized digital format for data analysis had impeded large-scale analysis of in vivo toxicology studies. The CDISC SEND standard enables the analysis of data from multiple studies performed by different laboratories. This work describes methods to analyze data and automate cross-study analysis of toxicology studies. Cross-study analysis can be used to understand a single compound's toxicity profile across all studies performed and/or to evaluate on-target versus off-target toxicity for multiple compounds intended for the same pharmacological target. This work involved development of data harmonization/transformation strategies to enable cross-study analysis of both numerical and categorical SEND data. Four de-identified SEND datasets from the BioCelerate database were used for the analyses. Toxicity profiles for key organ systems were developed for liver, kidney, male reproductive tract, endocrine system, and hematopoietic system using SEND domains. A cross-study analysis dashboard with a built-in user-defined scoring system was created for custom analyses, including visualizations to evaluate data at the organ system level and drill down into individual animal data. This data analysis provides the tools for scientists to compare toxicity profiles across multiple studies using SEND. A cross-study analysis of 2 different compounds intended for the same pharmacological target is described and the analyses indicate potential on-target effects to liver, kidney, and hematopoietic systems.

Streamlining intersectoral provision of real-world health data: a service platform for improved clinical research and patient care.

Obtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities - including hospitals, outpatient clinics, and physician practices - the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites. We investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term. We have developed the pre-built packages "ResearchData-to-FHIR," "FHIR-to-OMOP," and "Addons," which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use. Our development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

Deployment and validation of the CLL treatment infection model adjoined to an EHR system

Research algorithms are seldom externally validated or integrated into clinical practice, leaving unknown challenges in deployment. In such efforts, one needs to address challenges related to data harmonization, the performance of an algorithm in unforeseen missingness, automation and monitoring of predictions, and legal frameworks. We here describe the deployment of a high-dimensional data-driven decision support model into an EHR and derive practical guidelines informed by this deployment that includes the necessary processes, stakeholders and design requirements for a successful deployment. For this, we describe our deployment of the chronic lymphocytic leukemia (CLL) treatment infection model (CLL-TIM) as a stand-alone platform adjoined to an EPIC-based Danish Electronic Health Record (EHR), with the presentation of personalized predictions in a clinical context. CLL-TIM is an 84-variable data-driven prognostic model utilizing 7-year medical patient records and predicts the 2-year risk composite outcome of infection and/or treatment post-CLL diagnosis. As an independent validation cohort for this deployment, we used a retrospective population-based cohort of patients diagnosed with CLL from 2018 onwards (n = 1480). Unexpectedly high levels of missingness for key CLL-TIM variables were exhibited upon deployment. High dimensionality, with the handling of missingness, and predictive confidence were critical design elements that enabled trustworthy predictions and thus serves as a priority for prognostic models seeking deployment in new EHRs. Our setup for deployment, including automation and monitoring into EHR that meets Medical Device Regulations, may be used as step-by-step guidelines for others aiming at designing and deploying research algorithms into clinical practice.

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies.

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.

Strength together: examining risk and protective factors associated with dementia and cognitive impairment in Aboriginal and Torres Strait Islander peoples through harmonisation of landmark studies

BackgroundRates of dementia for Aboriginal and Torres Strait Islander peoples are three to five times greater compared to non-Indigenous Australians, with earlier age of onset. However, the risk and protective factors that drive these higher rates vary across existing cohort studies, with minimal findings on the role of vascular risk factors beyond stroke. Harmonisation of data across studies may offer greater insights through enhanced diversity and strengthened statistical capabilities. This study aims to combine three landmark cohort studies of Aboriginal and Torres Strait Islander participants to better understand the determinants of cognitive health and dementia.Methods/designThree cohort studies - the Kimberley Healthy Adults Project (KHAP, N = 363), Koori Growing Old Well Study (KGOWS, N = 336) and Torres Strait Dementia Prevalence Study (TSDPS, N = 274) - share a similar research methodology with demographic, medical history, psychosocial factors, cognitive tests and consensus clinical diagnoses of cognitive impairment and dementia. Associations between risk and protective factors of interest and the presence of dementia and/or cognitive impairment diagnoses will be evaluated by univariable and multivariable logistic regression in a harmonised cross-sectional cohort of 898 participants. Factors associated with incident dementia and/or cognitive impairment will be assessed in a subset of KHAP (n = 189) and KGOWS participants (n = 165) who were available in longitudinal follow-up, after exclusion of those with baseline dementia or cognitive impairment. Analyses in relation to outcome measure of death or dementia will be conducted to account for the competing risk of death. Logistic regression will be used to evaluate the association between the individual components of the 16-component Kimberley Indigenous Cognitive Assessment (KICA) tool and the presence of dementia and cognitive impairment determined by independent consensus diagnoses. Multivariable binary logistic regression will be used to adjust for the effect of confounding variables. Results will be reported as odds ratios (OR) with 95% confidence intervals (95% CI).DiscussionGreater understanding of risk and protective factors of dementia and cognitive impairment relevant to Aboriginal and Torres Strait Islander peoples may improve approaches across the life course to delay cognitive decline and reduce dementia risk.

Setting the Stage for Standardized Reporting of Clinical and Demographic Information in Laboratory-Based Studies of Hidradenitis Suppurativa

Hidradenitis suppurativa (HS) is a complex inflammatory skin condition affecting 0.1-4% of the population that leads to permanent scarring in the axilla, inframammary region, groin, and buttocks. Its complex pathogenesis involves genetics, innate and adaptive immunity, microbiota, and environmental stimuli. Specific populations have a higher incidence of HS, including females and Black individuals and those with associated comorbidities. HS registries and biobanks have set standards for the documentation of clinical data in the context of clinical trials and outcomes research, but collection, documentation, and reporting of these important clinical and demographic variables are uncommon in HS laboratory research studies. Standardization in the laboratory setting is needed because it helps to elucidate the factors that contribute mechanistically to HS symptoms and pathophysiology. The purpose of this article is to begin to set the stage for standardized reporting in the laboratory setting. We discuss how clinical guidelines can inform laboratory research studies, and we highlight what additional information is necessary for the use of samples in the wet laboratory and interpretation of associated mechanistic data. Through standardized data collection and reporting, data harmonization between research studies will transform our understanding of HS and lead to novel discoveries that will positively impact patient care.

Clinical nSights: A software platform to accelerate real world oncology analyses.

e23316 Background: Real world data analyses are critical to assess drug safety and effectiveness, including in patient populations underrepresented in clinical trials. Significant barriers to performing such analyses remain, including: (1) incomplete access to electronic health record (EHR) data due to patient privacy concerns, (2) lack of data harmonization within and across EHRs, (3) requirements for time- and labor-intensive data curation efforts, and (4) necessity of coding or data science expertise given the absence of user-friendly platforms. We present Clinical nSights, a software platform that addresses these challenges to enable researchers and clinicians to perform rapid, robust, and scalable real world oncology analyses. Methods: With input EHR data from multiple health systems, we developed intuitive user interfaces and a scalable researcher friendly programming environment for real world clinical analytics. We used (1) nCognito, a previously described HIPAA-compliant de-identification algorithm, to de-identify EHRs across data modalities; (2) the combination of a de novo knowledge graph, vector and distribution similarity metrics, and manual review to harmonize data types including but not limited to laboratory tests, disease codes, and drugs; and (3) a series of Bidirectional Encoder Representations from Transformers (BERT) models to perform augmented curation of variables of interest (e.g., cancer staging, tumor biomarkers) from unstructured clinical records. We developed a user interface for users to create and analyze defined patient cohorts. Results: Clinical nSights facilitates intuitive cohort creation via flexible specification of inclusion and exclusion criteria, including options to perform temporal linking between criteria and propensity score matching. The ability to specify criteria documented in de-identified clinical notes expands the repertoire of available features for cohort creation beyond those captured in structured EHR fields, including but not limited to cancer stage and tumor biomarker status (e.g., HER2 status, microsatellite instability). Data harmonization enables scalable longitudinal analyses of laboratory test data across cancer types, including changes in measurements in the years leading up to and following initial cancer diagnosis. Augmented curation improves the coverage of phenotypes including immune-related toxicities of checkpoint inhibitors and symptoms experienced by patients prior to a cancer diagnosis. Using the no-code interface, users can easily quantify and visualize outcomes such as overall survival and hospitalization rates. Conclusions: Clinical nSights addresses important challenges in the collection, curation, and analysis of real world oncology data. By enabling a broad range of users to rapidly generate and test hypotheses, this platform has the potential to significantly improve care delivery and patient outcomes.

Adoption of the International Cognition and Cancer Task Force recommendations for harmonizing studies of cancer-related cognitive impairment: A systematic review of published literature.

e24043 Background: This abstract presents a review of published research literature to determine research investigators’ rate of adoption of the International Cognition and Cancer Task Force (ICCTF) recommended criteria for defining cancer-related cognitive impairment (CRCI). Published in 2011, the ICCTF recommendations were intended to identify psychometric standards that would facilitate harmonization of neuropsychological test data across studies for improved accuracy of estimating prevalence of CRCI in cancer populations. The ICCTF recommended standards could help decipher variability or stability of CRCI prevalence across various cancer groups, such as individuals within a diagnostic classification (e.g., lung or breast cancer) or type of cancer treatment (e.g., chemotherapy, immunotherapy, or radiation therapy). However, since publication in 2011, the use of the ICCTF recommendations in CRCI research has not been examined. Methods: Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we restricted study selection to only published studies that examined CRCI using standardized neuropsychological tests (vs. self-report measures). We then identified studies that used the ICCFT recommendations based on the ICCTF psychometric definition of cognitive impairment: In a standard battery of neuropsychological tests, one neuropsychological test score >2 standard deviations (sd) below the mean of a healthy control sample, or 2 neuropsychological test scores of >1.5 sd’s below the control mean, is considered a case of cognitive impairment. Results: We screened 146 CRCI studies published since 2011, with 93 manuscripts that utilized neuropsychological tests to evaluate CRCI. Of those, 35 (37.6%) incorporated the ICCTF recommendations in their methodology and data analysis. Between 2011 and 2022, there was a gradual adoption of ICCTF recommended standard of defining CRCI, ranging from 0% of published studies in 2011 to 56.2% in 2022, but a decline to 40% in 2023. With respect to cancer populations, ICCTF guidelines were most often used in studies of individuals with breast cancer (n = 17;18%). Conclusions: This review demonstrates increased use of the ICCTF recommended definition of CRCI in empirical investigations since their 2011 introduction but a decline in 2023. Use of the ICCTF recommendations can improve accuracy of estimating CRCI prevalence across different cancer disease populations and/or groups of individuals undergoing different cancer therapies. Greater adoption of the ICCTF definition of CRCI is encouraged especially considering a 2023 drop in its use. Broadening the study of CRCI using ICCTF recommendations among cancer populations beyond breast cancer is encouraged to better understand the breadth of impact CRCI across the spectrum of disease.

A bird's-eye view of disparity from public historic cancer mortality data in the US: Advocacy for gastrointestinal cancers and for inter-agency data harmonization.

e13723 Background: We searched into public datasets released by CDC to answer a question raised during an NCI Health Disparity Workshop: "Can we have a bird’s-eye view of health disparity in cancer care in the US?" By looking into the historic data reported, we hope to identify not only health disparity reflected by the different cancer mortality rates for major cancer types, but also what might be missing from the data-reporting. Methods: We analyzed 2016-2020 US Cancer Statistics (USCS) cancer burden data to assess cancer disparity as reflected by rates of death in different races or ethnicities. We then looked into the recently available “Single Race 15” grouping for the underlying cause-of-death data in WONDER@CDC to count cancer deaths 2018-2021 in California comparing cancer mortality among 7 Asian minority groups, including Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and other Asians. We then matched census data for these Asian minority groups published by Census 2020. Results: We identified gastrointestinal (GI) cancers as an area of cancer health disparity. 5 GI cancers (colon and rectum, pancreas, liver and bile duct, stomach, and esophagus) contributed to a quarter of cancer deaths, and cancers in the pancreas, liver and bile duct, and esophagus still have very poor outcomes. Based on USCS data, Black non-Hispanic Americans are more likely to die from cancers of colon/rectum or pancreas. All minority races or ethnicities are more likely to die from cancers in liver and bile duct, or stomach than white non-Hispanic Americans. Cancers in colon and rectum, liver and bile duct, or stomach also cause high numbers of death in the under-50 US population. Since the 2022 AACR report on cancer in Asian Americans raised concerns on liver and stomach cancers, we counted cancer deaths in recent 4 years for different Asian groups in California that numbered about 7 million (alone or in combination) in 2020 Census. This includes 1.8 million Chinese, 1.7 million Filipino, 900k Asian Indian, 800k Vietnamese, 560k Korean, 470k Japanese, and other Asian ethnicities. Chinese has the highest numbers of deaths (2.8k) in 4 years from these 5 GI cancers followed by Filipino (2k), Vietnamese (1.2k), Korean (1.1k), Japanese (800), other Asians (1.1k), and Asian Indian (< 400). The lower cancer deaths in Asian Indians may be associated with their younger median age based on Census. With anecdotal stories suggesting immigrants may seek end-of-life care in their birth country to access more culture-concordant services, we looked into how cancer deaths abroad were reported. The data appeared missing. Conclusions: We identified gastrointestinal cancers as focuses for cancer health disparity advocacy. Harmonized consulate report of death abroad to include detailed race/ethnicity information will complete view of cancer mortality data in the US.

Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study

BackgroundEstablishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. MethodsIn this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. ResultsWe successfully applied our ETL tool and observed a complete coverage of the cohorts’ data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. ConclusionIn this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.

The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study

Aim: Data harmonization standardizes healthcare information, enhancing accessibility and interoperability, which is crucial for improving patient outcomes and driving medical research and innovation. It enables precise diagnoses and personalized treatments, and boosts AI model efficiency. However, significant challenges such as ethical concerns, technical barriers in the data lifecycle, AI biases, and varied regional regulations impede progress, underscoring the need for solutions like adopting universal standards such as HL7 FHIR, where the lack of generalized harmonization efforts is significant. Methods: We propose an advanced, holistic framework that utilizes FAIR-compliant reference ontologies (based on the FAIRplus and FAIR CookBook criteria) to make data findable, accessible, interoperable, and reusable enriched with terminologies from OHDSI (Observational Health Data Sciences and Informatics) vocabularies and word embeddings to identify lexical and conceptual overlaps across heterogeneous data models. Results: The proposed approach was applied to autoimmune diseases, cardiovascular diseases, and mental disorders using unstructured data from EU cohorts involving 7,551 patients with primary Sjogren’s Syndrome, 25,000 patients with cardiovascular diseases, and 3,500 patients with depression and anxiety. Metadata from these datasets were structured into dictionaries and linked with three newly developed reference ontologies (ROPSS, ROCVD, and ROMD), which are accessible on GitHub. These ontologies facilitated data interoperability across different systems and helped identify common terminologies with high precision within each domain. Conclusion: Through the proposed framework, we aim to urge the adoption of data harmonization as a priority, emphasizing the need for global cooperation, investment in technology and infrastructure, and adherence to ethical data usage practices toward a more efficient and patient-centered global healthcare system.