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Objectives: Health information for 17 109 people living with hemophilia A (PLwHA) is contained within the ATHNdataset. We aimed to evaluate the real-world effectiveness of damoctocog alfa pegol (BAY 94-9027, Jivi®) for hemophilia A., Methods: The ATHNdataset was queried for PLwHA receiving damoctocog alfa pegol between January 1, 2010 and April 30, 2022. Data captured via patient charts were analyzed., Results: At data cutoff, 205 PLwHA were treated with damoctocog alfa pegol: 150 (73.2%) severe (1 female [0.5%]) and 55 (26.8%) mild/moderate (3 [1.5%] female). In total, 32/205 (25.9%) PLwHA received on-demand treatment; 172 (83.9%) received prophylaxis-161 (93.6%) continuous prophylaxis. Documented bleed rates were available for 187 (91.2%) PLwHA, including those on prophylaxis and on-demand regimens, with 150 (80.2%) treated for > 12 months. Overall annualized bleeding rates and proportion of PLwHA with zero bleeds, receiving prophylaxis during the observation period, were mean (SD) 0.26 (1.03) and 138/157 (87.9%), respectively. No new or recurring inhibitors were reported., Conclusion: A low number of bleeds were observed with damoctocog alfa pegol in the real world in both male and female PLwHA. Data should be interpreted with caution owing to limitations of real-world studies and insubstantial data for female PLwHA., (© 2024 The Author(s). European Journal of Haematology published by John Wiley & Sons Ltd.)

This study addresses the challenge of leveraging free-text descriptions in Electronic Health Records (EHR) for clinical research and healthcare improvement. Despite the potential of this data, its direct interpretation by computers is limited. Semantic annotation emerges as a method to make EHR free text machine-interpretable but struggles with specific domain ontologies and faces heightened difficulties in psychiatry. To tackle these challenges, this study proposes a system based on unsupervised learning techniques to extract entities and their relationships, aligning them with a domain ontology. The effectiveness of this system has been validated within PsyCARE project by analyzing 60 patient discharge summaries.

Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therapeutic strategies and better understanding the TRS etiology. The aim of this study was to explore, with machine learning methods, the impact of demographic and clinical patient characteristics on TRS prediction, for already established risk factors and unexplored ones. This was a retrospective study of 500 patients admitted during 2020 to the University Hospital Group for Paris Psychiatry. We hypothesized potential TRS risk factors. The selected features were coded into structured variables in a new dataset, by processing patients discharge summaries and medical narratives with natural-language processing methods. We compared three machine learning models (XGBoost, logistic elastic net regression, logistic regression without regularization) for predicting TRS outcome. We analysed feature impact on the models, suggesting the following factors as markers of a high-risk TRS profile: early age at first contact with psychiatry, antipsychotic treatment interruptions due to non-adherence, absence of positive symptoms at baseline, educational problems and adolescence mental disorders in the personal psychiatric history. Specifically, we found a significant association with TRS outcome for age at first contact with psychiatry and medication non-adherence. Our findings on TRS risk factors are consistent with the review of the literature and suggest potential in using early pathophysiologic features for TRS prediction. Results were encouraging with the use of natural-langage processing techniques to leverage raw data provided by discharge summaries, combined with machine leaning models. These findings are a promising step for helping clinicians adapt their guidelines to early detection of TRS., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Aims: The global older population is growing rapidly, and the rise in polypharmacy has increased potentially inappropriate medication (PIM) encounters. PIMs pose health risks, but detecting them automatically in large medical databases is complex. This review aimed to uncover PIM prevalence in individuals aged 65 years or older using health databases and emphasized the risk of underestimating PIM prevalence due to underutilization of detection tools., Methods: This study conducted a broad search on the Medline database to identify articles about the prevalence of PIMs in older adults using various databases. Articles published between January 2010 and June 2023 were included, and specific criteria were applied for study selection. Two literature reviews conducted before our study period were integrated to obtain a perspective from the 1990s to the present day. The selected papers were analysed for variables including database type, screening method, adaptations and PIM prevalence. The study categorized databases and original screening tools for clarity, examined adaptations and assessed concordance among different screening methods., Results: This study encompassed 48 manuscripts, covering 58 sample evaluations. The mean prevalence of PIMs within the general population aged over 65 years was 27.8%. Relevant heterogeneity emerged in both the utilized databases and the detection methods. Adaptation of original screening tools was observed in 86.2% (50/58) of cases. Half of the original screening tools used for assessing PIMs belonged to the simple category. About a third of the studies employed less than half of the original criteria after adaptation. Only three studies used over 75% of the original criteria and more than 50 criteria., Conclusions: This extensive review highlights PIM prevalence among the older adults, emphasizing method intricacies and the potential for underestimation due to data limitations and algorithm adjustments. The findings call for enhanced methodologies, transparent algorithms and a deeper understanding of intricate rules' impact on public health implications., (© 2024 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Aims: To describe the key elements of the interprofessional decision-making process in health, based on published scientific studies. To describe the authors, reviews and subject matter of those publications., Design: Scoping review of the literature., Data Sources: MEDLINE, APA Psycinfo OpenGrey, Lissa and Cochrane databases were searched in December 2019 and January 2023., Review Methods: References were considered eligible if they (i) were written in French or English, (ii) concerned health, (iii) studied a clinical decision-making process, (iv) were performed in an interprofessional context. 'PRISMA-scoping review' guidelines were respected. The eligible studies were analysed and classified by an inductive approach RESULTS: We identified 1429 sources of information, 145 of which were retained for the analysis. Based on these studies, we identified five key elements of interprofessional decision-making in health. The process was found to be influenced by group dynamics, the available information and consideration of the unique characteristics of the patient. An organizational framework and specific training favoured improvements in the process., Conclusion: Decision-making can be based on a willingness of the healthcare organization to promote models based on more shared leadership and to work on professional roles and values. It also requires healthcare professionals trained in the entire continuum of collaborative practices, to meet the unique needs of each patient. Finally, it appears essential to favour the sharing of multiple sources of accessible and structured information. Tools for knowledge formalization should help to optimize interprofessional decision-making in health., Impact: The quality of a team decision-making is critical to the quality of care. Interprofessional decision-making can be structured and improved through different levels of action. These improvements could benefit to patients and healthcare professionals in every settings of care involving care collaboration., Impact Statement: Interprofessional decision-making in health is an essential lever of quality of care, especially for the most complex patients which are a contemporary challenge. This scoping review article offers a synthesis of a large corpus of data published to date about the interprofessional clinical decision-making process in healthcare. It has the potential to provide a global vision, practical data and a list of references to facilitate the work of healthcare teams, organizations and teachers ready to initiate a change., (© 2023 The Authors. Journal of Advanced Nursing published by John Wiley & Sons Ltd.)

Objectives: To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook., Methods: We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022., Results: We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model., Conclusions: In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

We present an ontology design pattern for modeling scientific experiments and examinations conducted in a clinical research study. Integrating heterogeneous data into a common ontological model is a challenge, redoubled if we want them to be explored later. In order to facilitate the development of dedicated ontological modules, this design pattern relies on invariants, is centered on the event of the experiment, and keeps the link to the original data.

The use of eCRFs is now commonplace in clinical research studies. We propose here an ontological model of these forms allowing to describe them, to express their granularity and to link them to the relevant entities of the study in which they are used. It has been developed in a psychiatry project but its generality may allow a wider application.

Building a timeline of psychiatric patient profiles can answer many valuable questions, such as how important medical events affect the progression of psychosis in patients. However, the majority of text information extraction and semantic annotation tools, as well as domain ontologies, are only available in English and cannot be easily extended to other languages, due to fundamental linguistic differences. In this paper, we describe a semantic annotation system based on an ontology developed in the PsyCARE framework. Our system is being manually evaluated by two annotators on 50 patient discharge summaries, showing promising results.

Objectives: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021., Methods: Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries., Results: A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, and the International Classification of Diseases., Conclusion: In the KRM best paper selection for 2021, the candidate best papers covered a wider spectrum of topics compared to the last year's significant focus on ontology curation. In particular, ontology development for specific domains (e.g., Alzheimer's disease, infectious diseases, bioethics) has received the most attention., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background: Although the drug is finished, identifiable, there is no universally accepted standard for naming them. The objective of this work is to evaluate qualitatively the HeTOP drug terminology server by two categories of students: (a) pharmacy students and (b) a control group., Methods: A formal evaluation was built to measure the perception of users about the HeTOP drug server, using the three mains questions about "teaching interest", "skill interest" (or competence) and "ergonomics"., Results: The three pharmacy student subgroups gave the best and the worst score to the same categories., Conclusion: All three criteria are rated above 6.5 out of 10. The HeTOP drug terminology server is freely available to "non drug" specialists (URL: www.hetop.eu/hetop/drugs/).

Biomedical ontologies define concepts having biomedical significance and the semantic relations among them. Developing high-quality and reusable ontologies in the biomedical domain is a challenging task. Pattern-based ontology design is considered a promising approach to overcome the challenges. Ontology Design Patterns (ODPs) are reusable modeling solutions to facilitate ontology development. This study relies on ODPs to semantically enrich biomedical ontologies by assigning logical definitions to ontological entities. Specifically, pattern-based logical definitions grounded on dispositions are given to prenatal disorders. The proposed approach is performed under the supervision of fetal domain experts.

Representing temporal information is a recurrent problem for biomedical ontologies. We propose a foundational ontology that combines the so-called three-dimensional and four-dimensional approaches in order to be able to track changes in an individual and to trace his or her medical history. This requires, on the one hand, associating with any representation of an individual the representation of his or her life course and, on the other hand, distinguishing the properties that characterize this individual from those that characterize his or her life course.

Biomedical research data reuse and sharing is essential for fostering research progress. To this aim, data producers need to master data management and reporting through standard and rich metadata, as encouraged by open data initiatives such as the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines. This helps data re-users to understand and reuse the shared data with confidence. Therefore, dedicated frameworks are required. The provenance reporting throughout a biomedical study lifecycle has been proposed as a way to increase confidence in data while reusing it. The Biomedical Study - Lifecycle Management (BMS-LM) data model has implemented provenance and lifecycle traceability for several multimodal-imaging techniques but this is not enough for data understanding while reusing it. Actually, in the large scope of biomedical research, a multitude of metadata sources, also called Knowledge Organization Systems (KOSs), are available for data annotation. In addition, data producers uses local terminologies or KOSs, containing vernacular terms for data reporting. The result is a set of heterogeneous KOSs (local and published) with different formats and levels of granularity. To manage the inherent heterogeneity, semantic interoperability is encouraged by the Research Data Management (RDM) community. Ontologies, and more specifically top ontologies such as BFO and DOLCE, make explicit the metadata semantics and enhance semantic interoperability. Based on the BMS-LM data model and the BFO top ontology, the BioMedical Study - Lifecycle Management (BMS-LM) core ontology is proposed together with an associated framework for semantic interoperability between heterogeneous KOSs. It is made of four ontological levels: top/core/domain/local and aims to build bridges between local and published KOSs. In this paper, the conversion of the BMS-LM data model to a core ontology is detailed. The implementation of its semantic interoperability in a specific domain context is explained and illustrated with examples from small animal preclinical research., (Copyright © 2022 Elsevier Inc. All rights reserved.)