Transparency, reproducibility, and replicability of pharmacoepidemiology studies in a distributed network environment.

PURPOSE: Our objective is to describe how the U.S. Food and Drug Administration (FDA)'s Sentinel System implements best practices to ensure trust in drug safety studies using real-world data from disparate sources. METHODS: We present a stepwise schematic for Sentinel's data harmonization, data quality check, query design and implementation, and reporting practices, and describe approaches to enhancing the transparency, reproducibility, and replicability of studies at each step. CONCLUSIONS: Each Sentinel data partner converts its source data into the Sentinel Common Data Model. The transformed data undergoes rigorous quality checks before it can be used for Sentinel queries. The Sentinel Common Data Model framework, data transformation codes for several data sources, and data quality assurance packages are publicly available. Designed to run against the Sentinel Common Data Model, Sentinel's querying system comprises a suite of pre-tested, parametrizable computer programs that allow users to perform sophisticated descriptive and inferential analysis without having to exchange individual-level data across sites. Detailed documentation of capabilities of the programs as well as the codes and information required to execute them are publicly available on the Sentinel website. Sentinel also provides public trainings and online resources to facilitate use of its data model and querying system. Its study specifications conform to established reporting frameworks aimed at facilitating reproducibility and replicability of real-world data studies. Reports from Sentinel queries and associated design and analytic specifications are available for download on the Sentinel website. Sentinel is an example of how real-world data can be used to generate regulatory-grade evidence at scale using a transparent, reproducible, and replicable process.

Essential data variables for a minimum dataset for head and neck cancer trials and clinical research: HNCIG consensus recommendations and database.

The Head and Neck Cancer International Group (HNCIG) has undertaken an international modified Delphi process to reach consensus on the essential data variables to be included in a minimum database for HNC research. Endorsed by 19 research organisations representing 34 countries, these recommendations provide the framework to facilitate and harmonise data collection and sharing for HNC research. These variables have also been incorporated into a ready to use downloadable HNCIG minimum database, available from the HNCIG website.

Data Checks Before Registering Study Protocols for Health Care Database Analyses.

This Viewpoint discusses the challenges involved with secondary health care data collection vs primary data collection and provides a list of suggested data checks before registration of a study protocol using secondary data.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

EMDB-the Electron Microscopy Data Bank.

The Electron Microscopy Data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein Data Bank consortium (wwPDB; wwpdb.org) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

Promoting Data Harmonization of COVID-19 Research in Pregnant and Pediatric Populations.

This Viewpoint investigates the use of common data elements to promote data harmonization in COVID-19­related studies of pediatric and pregnant populations.

Trend of incompleteness of maternal schooling and race/skin color variables held on the Brazilian Live Birth Information System, 2012-2020.

OBJECTIVE: to analyze the trend of incompleteness of the maternal schooling and race/skin color variables held on the Brazilian Live Birth Information System (SINASC) between 2012 and 2020. METHODS: this was an ecological time series study of the incompleteness of maternal schooling and race/skin color data for Brazil, its regions and Federative Units, by means of joinpoint regression and calculation of annual percentage change (APC) and average annual percentage change. RESULTS: a total of 26,112,301 births were registered in Brazil in the period; incompleteness of maternal schooling data decreased for Brazil (APC = -8.1%) and the Southeast (APC = -19.5%) and Midwest (APC = -17.6%) regions; as for race/skin color, there was a downward trend for Brazil (APC = -8.2%) and all regions, except the Northeast region, while nine Federative Units and the Federal District showed a stationary trend. CONCLUSION: there was an improvement in filling out these variables on the SINASC, but with regional disparities, mainly for race/skin color.

Validity of diagnoses of respiratory diseases recorded in a Japanese administrative database.

BACKGROUND: Validating the information recorded in administrative databases is essential. However, no study has comprehensively validated the accuracy of Japanese Diagnosis Procedure Combination (DPC) data on various respiratory diseases. Therefore, this study aimed to evaluate the validity of diagnoses of respiratory diseases in the DPC database. METHODS: We conducted chart reviews of 400 patients hospitalized in the departments of respiratory medicine in two acute-care hospitals in Tokyo, between April 1, 2019 and March 31, 2021, and used them as reference standards. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DPC data on 25 respiratory diseases were determined. RESULTS: Sensitivity ranged from 22.2% (aspiration pneumonia) to 100% (chronic eosinophilic pneumonia and malignant pleural mesothelioma) and was <50% for eight diseases, while specificity was >90% for all diseases. PPV ranged from 40.0% (aspiration pneumonia) to 100% (coronavirus disease 2019, bronchiectasis, chronic eosinophilic pneumonia, pulmonary hypertension, squamous cell carcinoma, small cell carcinoma, lung cancer of other histological types, and malignant pleural mesothelioma) and was >80% for 16 diseases. Except for chronic obstructive pulmonary disease (82.9%) and interstitial pneumonia (other than idiopathic pulmonary fibrosis) (85.4%), NPV was >90% for all diseases. These validity indices were similar in both hospitals. CONCLUSIONS: The validity of diagnoses of respiratory diseases in the DPC database was high in general, thereby providing an important basis for future studies.

CAS(ME)3: A Third Generation Facial Spontaneous Micro-Expression Database With Depth Information and High Ecological Validity.

Micro-expression (ME) is a significant non-verbal communication clue that reveals one person's genuine emotional state. The development of micro-expression analysis (MEA) has just gained attention in the last decade. However, the small sample size problem constrains the use of deep learning on MEA. Besides, ME samples distribute in six different databases, leading to database bias. Moreover, the ME database development is complicated. In this article, we introduce a large-scale spontaneous ME database: CAS(ME) 3. The contribution of this article is summarized as follows: (1) CAS(ME) 3 offers around 80 hours of videos with over 8,000,000 frames, including manually labeled 1,109 MEs and 3,490 macro-expressions. Such a large sample size allows effective MEA method validation while avoiding database bias. (2) Inspired by psychological experiments, CAS(ME) 3 provides the depth information as an additional modality unprecedentedly, contributing to multi-modal MEA. (3) For the first time, CAS(ME) 3 elicits ME with high ecological validity using the mock crime paradigm, along with physiological and voice signals, contributing to practical MEA. (4) Besides, CAS(ME) 3 provides 1,508 unlabeled videos with more than 4,000,000 frames, i.e., a data platform for unsupervised MEA methods. (5) Finally, we demonstrate the effectiveness of depth information by the proposed depth flow algorithm and RGB-D information.

Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos.

OBJECTIVE: To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the secondary objective was to identify limitations that may impact clinical use. DESIGN: Retrospective study. SETTING: Consortium of 11 assisted reproductive technology centers in the United States. PATIENT(S): Static images of 5,923 transferred blastocysts and 2,614 nontransferred aneuploid blastocysts. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Prediction of clinical pregnancy (fetal heartbeat). RESULT(S): The area under the curve of the AI model ranged from 0.6 to 0.7 and outperformed manual morphology grading overall and on a per-site basis. A bootstrapped study predicted improved pregnancy rates between +5% and +12% per site using AI compared with manual grading using an inverted microscope. One site that used a low-magnification stereo zoom microscope did not show predicted improvement with the AI. Visualization techniques and attribution algorithms revealed that the features learned by the AI model largely overlap with the features of manual grading systems. Two sources of bias relating to the type of microscope and presence of embryo holding micropipettes were identified and mitigated. The analysis of AI scores in relation to pregnancy rates showed that score differences of ≥0.1 (10%) correspond with improved pregnancy rates, whereas score differences of <0.1 may not be clinically meaningful. CONCLUSION(S): This study demonstrates the potential of AI for ranking blastocyst stage embryos and highlights potential limitations related to image quality, bias, and granularity of scores.

Evaluating the use of a two-step age-based cutoff for the UICC/AJCC TNM staging system in patients with papillary or follicular thyroid cancer.

BACKGROUND: The joint Union International Contre le Cancer and American Joint Committee on Cancer (UICC/AJCC) Tumor, Node, Metastasis (TNM) staging system for differentiated thyroid cancer (DTC) involves a single age cutoff as a prognostic criterion. Because a single cutoff is a dichotomization of what might be a sliding scale, using multiple age cutoffs might result into a better stage definition. The aim of our study was to investigate if using a two-step age-based cutoff would improve the TNM staging system regarding disease-specific survival (DSS). METHODS: We retrospectively studied two cohorts of adult DTC patients from The Netherlands and Germany. DSS was analyzed for papillary (PTC) and follicular thyroid cancer (FTC) separately, investigating several two-step age-based cutoffs for those with distant metastases; below lower threshold classified as stage I, between lower and upper threshold as stage II, and above upper threshold as stage IV. RESULTS: We included 3074 DTC patients (77% PTC). For PTC, an age cutoff of 45 with 50 years had the best statistical model performance, while this was 25 with 40 years for FTC. However, differences with the optimal single age cutoffs of 50 years for PTC and 40 years for FTC were small. CONCLUSIONS: The optimal two-step age-based cutoff to predict DSS is 45 with 50 years for PTC and 25 with 40 years for FTC, rather than 55 years currently used for DTC. Although these two-step age-based cutoffs were marginally better from a statistical point of view, from a clinical point of view, the recently defined optimal single age cutoffs of 50 years for PTC and 40 years for FTC might be preferable.

Overview of Current Herb-Drug Interaction Databases.

An HERB-Drug Interaction (HDI) database is a structured data collection method for HDI information extracted from scattered literatures for quick retrieval. Our review summarized the ten currently available HDI databases, including those databases comprising HDI on the market. A detailed comparison on the scope of monographs, including the nature of content extracted from the original literature and user interfaces of these databases, was performed, and the number of references of fifty popular herbs in each HDI database was counted and presented in a heatmap to give users an intuitive understanding of the focuses of different HDI databases. Since it is well known that the development and maintenance of databases need continuous investment of capital and manpower, the sustainability of these databases was also reviewed and compared. Recently, artificial intelligence (AI) technologies, especially Natural Language Processing (NLP), have been applied to screen specific topics from massive articles and automatically identify the names of drugs and herbs in the literature. However, its application on the labor-intensive extraction and evaluation of HDI-related experimental conditions and results from literature remains limited due to the scarcity of these HDI data and the lack of well-established annotated datasets for these specific NLP recognition tasks. In view of the difficulties faced by current HDI databases and potential expansion of AI application in HDI database development, we propose a standardized format for data reporting and use of Concept Unique Identifier (CUI) for medical terms in the literature to accelerate the structured data collection. SIGNIFICANCE STATEMENT: The worldwide popularity of botanical and/or traditional medicine products has raised safety concerns due to potential HDI. However, the publicly available HDI databases are mostly outdated or incomplete. Through our review of the currently available HDI databases, a clear understanding of the key issues could be obtained and possible solutions to overcome the labour-intensive extraction as well as professional evaluation of information in HDI database development are proposed.

The limitations of hospital and law enforcement databases in characterizing the epidemiology of firearm injury.

BACKGROUND: Current data on the epidemiology of firearm injury in the United States are incomplete. Common sources include hospital, law enforcement, consumer, and public health databases, but each database has limitations that exclude injury subgroups. By integrating hospital (inpatient and outpatient) and law enforcement databases, we hypothesized that a more accurate depiction of the totality of firearm injury in our region could be achieved. METHODS: We constructed a collaborative firearm injury database consisting of all patients admitted as inpatients to the regional level 1 trauma hospital (inpatient registry), patients treated and released from the emergency department (ED), and subjects encountering local law enforcement as a result of firearm injury in Jefferson County, Kentucky. Injuries recorded from January 1, 2016, to December 31, 2020, were analyzed. Outcomes, demographics, and injury detection rates from individual databases were compared with those of the combined collaborative database and compared using χ2 testing across databases. RESULTS: The inpatient registry (n = 1,441) and ED database (n = 1,109) were combined, resulting in 2,550 incidents in the hospital database. The law enforcement database consisted of 2,665 patient incidents, with 2,008 incidents in common with the hospital database and 657 unique incidents. The merged collaborative database consisted of 3,207 incidents. In comparison with the collaborative database, the inpatient, total hospital (inpatient and ED), and law enforcement databases failed to include 55%, 20%, and 17% of all injuries, respectively. The hospital captured nearly 94% of survivors but less than 40% of nonsurvivors. Law enforcement captured 93% of nonsurvivors but missed 20% of survivors. Mortality (11-26%) and injury incidence were markedly different across the databases. DISCUSSION: The utilization of trauma registry or law enforcement databases alone do not accurately reflect the epidemiology of firearm injury and may misrepresent areas in need of greater injury prevention efforts. LEVEL OF EVIDENCE: Epidemiological, level IV.

A Guide to Industry Payments and an Algorithm for Their Management.

BACKGROUND: The Open Payments database was created to increase transparency of industry payment relationships within medicine. The current literature often examines only 1 year of the database. In this study, the authors use 5 years of data to show trends among industry payments to plastic surgeons from 2014 to 2018. In addition, the authors lay out the basics of conflict-of-interest reporting for the new plastic surgeon. Finally, the authors suggest an algorithm for the responsible management of industry relationships. METHODS: This study analyzed nonresearch payments made to plastic surgeons from January 1, 2014, to December 31, 2018. Descriptive statistics were calculated using R Statistical Software and visualized using Tableau. RESULTS: A total of 304,663 payments totaling $140,889,747 were made to 8148 plastic surgeons; 41 percent ($58.28 million) was paid to 50 plastic surgeons in the form of royalty or license payments. With royalties excluded, average and median payments were $276 and $25. The average yearly total per physician was $2028. Of the 14 payment categories, 95 percent of the total amount paid was attributable payments in one of six categories. Seven hundred thirty companies reported payments to plastic surgeons from 2014 to 2018; 15 companies (2 percent) were responsible for 80 percent ($66.34 million) of the total sum paid. Allergan was responsible for $24.45 million (29.6 percent) of this amount. CONCLUSIONS: Although discussions on the proper management of industry relationships continue to evolve, the data in this study illustrate the importance of managing industry relationships. The simple guidelines suggested create a basis for managing industry relationships in the career of the everyday plastic surgeon.

Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates.

The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging-derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.

Flimma: a federated and privacy-aware tool for differential gene expression analysis.

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

Comparison of prognosis between patients undergoing radical nephrectomy versus partial nephrectomy for renal cell carcinoma ≤7 cm T3aN0/xM0: Survival benefit is biased toward partial nephrectomy.

BACKGROUND: There is limited and controversial evidence on the prognosis of partial nephrectomy (PN) versus radical nephrectomy (RN) in patients with T3aN0/xM0 renal cell carcinoma (RCC) upstaged from clinical T1 RCC. In this study, we aimed to assess the prognosis difference following PN versus RN in patients with ≤7 cm T3aN0/xM0 RCC. METHODS: From the Surveillance, Epidemiology, and End Results database, a total of 3196 patients receiving treatment of PN/RN for ≤7 cm T3aN0/xM0 RCC with only extrarenal fat extension in 2010-2017 were identified. An inverse probability of treatment weighting (IPTW)-adjusted cause-specific Cox model with hazard ratio (HR) and 95% confidence interval (CI) was used for overall survival (OS) and cancer-specific survival (CSS) analyses. Sensitivity analysis was based on the propensity score matching of PN and RN groups and from the dataset of 2010-2013. RESULTS: A total of 872 patients underwent PN, compared with 2324 undergoing RN. After IPTW adjustment, there was no significant difference in preoperative baseline characteristics between the PN and RN cohorts. Patients who underwent RN had worse OS (HRIPTW-adjusted , 1.46; 95% CI, 1.16-1.84; p = 0.001) and comparable CSS (HRIPTW-adjusted , 1.03; 95% CI, 0.64-1.66; p = 0.890) than those receiving PN in all cohorts and subgroups with T3a RCC of ≤4 cm and perinephric fat extension. Further, in patients with 4-7 cm T3a RCC with perinephric-fat invasion and all sizes of T3a RCC with sinus/perisinus fat extension, PN led to comparable OS and CSS. Sensitivity analyses validated these results. CONCLUSION: PN provides comparable CSS and OS or even better OS than RN for patients with RCC ≤7 cm T3aN0/xM0. Although our study has some limitations, our results indicated that PN might oncologically safe for clinical T1 RCC, even confirmed a pathologically T3a upstaging post-PN.