Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World Interrogation Network (DARWIN EU).

Purpose: The generation of representative disease phenotypes is important for ensuring the reliability of the findings of observational studies. The aim of this manuscript is to outline a reproducible framework for reliable and traceable phenotype generation based on real world data for use in the Data Analysis and Real-World Interrogation Network (DARWIN EU). We illustrate the use of this framework by generating phenotypes for two diseases: pancreatic cancer and systemic lupus erythematosus (SLE).

Disproportionality Analysis and Characterisation of Medication Errors in EudraVigilance: Exploring Findings on Sexes and Age Groups.

Background: While medication errors (MEs) have been studied in the European Medicines Agency's EudraVigilance, extensive characterisation and signal detection based on sexes and age groups have not been attempted.

Objectives: The aim of this study was to characterise all ME-related individual case safety reports in EudraVigilance and explore notable signals of disproportionate reporting (SDRs) among sexes and age groups for the 30 most frequently reported drugs.

Methods: Individual case safety reports were used from EudraVigilance reported between 2002 and 2021.

Annotation-preserving machine translation of English corpora to validate Dutch clinical concept extraction tools.

Objective: To explore the feasibility of validating Dutch concept extraction tools using annotated corpora translated from English, focusing on preserving annotations during translation and addressing the scarcity of non-English annotated clinical corpora.

Materials And Methods: Three annotated corpora were standardized and translated from English to Dutch using 2 machine translation services, Google Translate and OpenAI GPT-4, with annotations preserved through a proposed method of embedding annotations in the text before translation. The performance of 2 concept extraction tools, MedSpaCy and MedCAT, was assessed across the corpora in both Dutch and English.

Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data.

Objective: Observational studies using electronic health record (EHR) databases often face challenges due to unspecific clinical codes that can obscure detailed medical information, hindering precise data analysis. In this study, we aimed to assess the feasibility of refining these unspecific condition codes into more specific codes in a Dutch general practitioner (GP) EHR database by leveraging the available clinical free text.

Methods: We utilized three approaches for text classification-search queries, semi-supervised learning, and supervised learning-to improve the specificity of ten unspecific International Classification of Primary Care (ICPC-1) codes.

Comparing penalization methods for linear models on large observational health data.

Objective: This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation.

Materials And Methods: We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases.

Prediction of 30-day, 90-day, and 1-year mortality after colorectal cancer surgery using a data-driven approach.

Purpose: To develop prediction models for short-term mortality risk assessment following colorectal cancer surgery.

Methods: Data was harmonized from four Danish observational health databases into the Observational Medical Outcomes Partnership Common Data Model. With a data-driven approach using the Least Absolute Shrinkage and Selection Operator logistic regression on preoperative data, we developed 30-day, 90-day, and 1-year mortality prediction models.

Real-world treatment trajectories of adults with newly diagnosed asthma or COPD.

Background: There is a lack of knowledge on how patients with asthma or chronic obstructive pulmonary disease (COPD) are globally treated in the real world, especially with regard to the initial pharmacological treatment of newly diagnosed patients and the different treatment trajectories. This knowledge is important to monitor and improve clinical practice.

Methods: This retrospective cohort study aims to characterise treatments using data from four claims (drug dispensing) and four electronic health record (EHR; drug prescriptions) databases across six countries and three continents, encompassing 1.

Risk of Arterial and Venous Thrombotic Events Among Patients with COVID-19: A Multi-National Collaboration of Regulatory Agencies from Canada, Europe, and United States.

Purpose: Few studies have examined how the absolute risk of thromboembolism with COVID-19 has evolved over time across different countries. Researchers from the European Medicines Agency, Health Canada, and the United States (US) Food and Drug Administration established a collaboration to evaluate the absolute risk of arterial (ATE) and venous thromboembolism (VTE) in the 90 days after diagnosis of COVID-19 in the ambulatory (eg, outpatient, emergency department, nursing facility) setting from seven countries across North America (Canada, US) and Europe (England, Germany, Italy, Netherlands, and Spain) within periods before and during COVID-19 vaccine availability.

Patients And Methods: We conducted cohort studies of patients initially diagnosed with COVID-19 in the ambulatory setting from the seven specified countries.

Advancing the use of real world evidence in health technology assessment: insights from a multi-stakeholder workshop.

Real-world evidence (RWE) in health technology assessment (HTA) holds significant potential for informing healthcare decision-making. A multistakeholder workshop was organised by the European Health Data and Evidence Network (EHDEN) and the GetReal Institute to explore the status, challenges, and opportunities in incorporating RWE into HTA, with a focus on learning from regulatory initiatives such as the European Medicines Agency (EMA) Data Analysis and Real World Interrogation Network (DARWIN EU). The workshop gathered key stakeholders from regulatory agencies, HTA organizations, academia, and industry for three panel discussions on RWE and HTA integration.

Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research.

The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared.

Blood pressure measurements for diagnosing hypertension in primary care: room for improvement.

Background: In the adult population, about 50% have hypertension, a risk factor for cardiovascular disease and subsequent premature death. Little is known about the quality of the methods used to diagnose hypertension in primary care.

Objectives: The objective was to assess the frequency of use of recognized methods to establish a diagnosis of hypertension, and specifically for OBPM, whether three distinct measurements were taken, and how correctly the blood pressure levels were interpreted.

Attention-based neural networks for clinical prediction modelling on electronic health records.

Background: Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechanism and compare against logistic regression and XGBoost using discrimination, calibration and clinical utility.

Parasitic infections related to anti-type 2 immunity monoclonal antibodies: a disproportionality analysis in the food and drug administration's adverse event reporting system (FAERS).

Monoclonal antibodies (mAbs) targeting immunoglobulin E (IgE) [omalizumab], type 2 (T2) cytokine interleukin (IL) 5 [mepolizumab, reslizumab], IL-4 Receptor (R) α [dupilumab], and IL-5R [benralizumab]), improve quality of life in patients with T2-driven inflammatory diseases. However, there is a concern for an increased risk of helminth infections. The aim was to explore safety signals of parasitic infections for omalizumab, mepolizumab, reslizumab, dupilumab, and benralizumab.

European Health Data & Evidence Network-learnings from building out a standardized international health data network.

Objective: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful.

Materials And Methods: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data.

Drug Utilisation Patterns of Alternatives to Ranitidine-Containing Medicines in Patients Treated with Ranitidine: A Network Analysis of Data from Six European National Databases.

Introduction: Ranitidine, a histamine H-receptor antagonist (HRA), is indicated in the management of gastric acid-related disorders. In 2020, the European Medicines Agency (EMA) recommended suspension of all ranitidine-containing medicines in the European Union (EU) due to the presence of N-nitrosodimethylamine (NDMA) impurities, which were considered to be carcinogenic. The aim of this study was to investigate the impact of regulatory intervention on use patterns of ranitidine-containing medicines and their therapeutic alternatives.

IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model.

Purpose: Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM).

Materials And Methods: We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data.

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study.

Introduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made.

The added value of text from Dutch general practitioner notes in predictive modeling.

Objective: This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting.

Materials And Methods: We trained and validated prediction models for 4 common clinical prediction problems using various sparse text representations, common prediction algorithms, and observational GP electronic health record (EHR) data. We trained and validated 84 models internally and externally on data from different EHR systems.

The DELPHI Library: Improving Model Validation, Transparency and Dissemination Through a Centralised Library of Prediction Models.

The Deposit, Evaluate and Lookup Predictive Healthcare Information (DELPHI) library provides a centralised location for the depositing, exploring and analysing of patient-level prediction models that are compatible with data mapped to the observational medical outcomes partnership common data model.