Book Music Representation for Temporal Data, as a Part of the Feature Extraction Process: A Novel Approach to Improve the Handling of Time-Dependent Data in Secondary Use of Healthcare Structured Data

International audienceBook music is extensively used in street organs. It consists of thick cardboard, containing perforated holes specifying the musical notes. We propose to represent clinical time-dependent data in a tabular form inspired from this principle. The sheet represents a statistical individual, each row represents a binary time-dependent variable, and each hole denotes the "true" value. Data from electronic health records or nationwide medical-administrative databases can then be represented: demographics, patient flow, drugs, laboratory results, diagnoses, and procedures. This data representation is suitable for survival analysis (e.g., Cox model with repeated outcomes and changing covariates) and different types of temporal association rules. Quantitative continuous variables can be discretized, as in clinical studies. The "book music" approach could become an intermediary step in feature extraction from structured data. It would enable to better account for time in analyses, notably for historical cohort analyses based on healthcare data reuse

Book Music Representation for Temporal Data, as a Part of the Feature Extraction Process: A Novel Approach to Improve the Handling of Time-Dependent Data in Secondary Use of Healthcare Structured Data

International audienceBook music is extensively used in street organs. It consists of thick cardboard, containing perforated holes specifying the musical notes. We propose to represent clinical time-dependent data in a tabular form inspired from this principle. The sheet represents a statistical individual, each row represents a binary time-dependent variable, and each hole denotes the "true" value. Data from electronic health records or nationwide medical-administrative databases can then be represented: demographics, patient flow, drugs, laboratory results, diagnoses, and procedures. This data representation is suitable for survival analysis (e.g., Cox model with repeated outcomes and changing covariates) and different types of temporal association rules. Quantitative continuous variables can be discretized, as in clinical studies. The "book music" approach could become an intermediary step in feature extraction from structured data. It would enable to better account for time in analyses, notably for historical cohort analyses based on healthcare data reuse

What Is a Chronic Disease? A Contribution Based on the Secondary Use of 161 Million Discharge Records

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Heimdall, a Computer Program for Electronic Health Records Data Visualization

International audienceIntroduction - Electronic health records (EHR) comprehend structured and unstructured data, that are usually time dependent, enabling the use of timelines. However, it is often difficult to display all data without inducing information overload. In both clinical usual care and medical research, users should be able to quickly find relevant information, with minimal cognitive overhead. Our goal was to devise simple visualization techniques for handling medical data in both contexts. Methods - An abstraction layer for structured EHR data was devised after an informal literature review and discussions between authors. The "Heimdall" prototype was developed. Two experts evaluated the tool by answering 5 questions on 24 clinical cases. Results - Temporal data was abstracted in three simple types: events, states and measures, with appropriate visual representations for each type. Heimdall can load and display complex heterogeneous structured temporal data in a straightforward way. The main view can display events, states and measures along a shared timeline. Users can summarize data using temporal, hierarchical compression and filters. Default and custom views can be used to work in problem- oriented ways. The evaluation found conclusive results. Conclusion - The "Heimdall" prototype provides a comprehensive and efficient graphical interface for EHR data visualization. It is open source, can be used with an R package, and is available at https://koromix.dev/files/R

Santé au travail et Système national des données de santé

International audienceLe Système national de données de santé (SNDS) repose sur l’enregistrement et la mise à disposition des données sur les prestations en nature (soins, médicaments, transports…) et espèce (indemnité journalière, rente, capital…) de l’ensemble des régimes d’assurance maladie. Il intègre des informations de santé, des données sur l’activité des établissements hospitaliers, ainsi que sur les causes de décès. L’intégration de données sur le handicap issu des différentes maisons départementales des personnes handicapées est prévue très prochainement. À terme, il est également prévu que des données issues des dossiers médicaux en santé (DMST) au travail soient intégrées. Les modalités d’accès aux données du SNDS s’élargissent et deviennent plus flexibles à mesure que le système se développe, avec encore très récemment l’adoption des méthodologies de référence MR-007 et MR-008 par la Commission nationale de l’informatique et des libertés (CNIL). Ainsi, les centres régionaux de pathologies professionnelles et environnementales (CRPPE) peuvent maintenant bénéficier d’un accès permanent et complet au SNDS. De plus les services de prévention et de santé au travail (SPST) ont la possibilité d’utiliser le numéro d’inscription au répertoire national d’identification des personnes physiques (NIR) comme identifiant de santé des personnes pour la prise en charge des salariés suivis. Dès lors, l’occasion de réaliser des études épidémiologiques à grande échelle s’ouvre pour l’ensemble des acteurs de la santé au travail, sans avoir à attendre l’intégration d’informations du DMST dans le SNDS, dont la nature reste encore à déterminer. Les données recueillies par les SPST concernant les expositions professionnelles sont une mine d’or d’informations pour étudier des thématiques aussi diverses que la grossesse et la périnatalité, les accidents du travail, l’impact de nouvelles technologiques en santé sur le maintien en emploi. Le croisement avec des outils spécialisés comme TOXILIST pourrait permettre d’étudier des problématiques très spécifiques comme celle de l’impact de l’exposition à des perturbateurs endocriniens potentiels sur des populations humaines, avec là encore des retombées majeures en matière de santé publique. Mais ces projets se confrontent encore à des difficultés : hétérogénéité dans la qualité des informations saisies dans le DMST, manque d’interopérabilité des systèmes informatiques, périmètre restreint des données du SNDS, manque d’implication des employeurs dans la traçabilité des expositions professionnelles. Ces freins ne doivent pas empêcher les acteurs de la santé au travail de se saisir de cet outil. Nous présenterons donc les modalités permettant d’apparier les données des SPST au SNDS sur projet

Mortality risk and antibiotic use for COVID-19 in hospitalized patients over 80

International audienceIntroduction: Patients over 80 years of age are more prone to develop severe symptoms and die from COVID-19. Antibiotics were massively prescribed in the first days of the pandemic without evidence of super infection. Antibiotics may increase the risk of mortality in cases of viral pneumonia. With age and antibiotic use, the microbiota becomes altered and less protective effect against lethal viral pneumonia. Thus we assessed whether it is safe to prescribe antibiotics for COVID-19 pneumonia to patients over 80 years of age. Method: We conducted a retrospective monocentric study in a 1240-bed university hospital. Our inclusion criteria were patients aged >= 80 years, hospitalized in a COVID-19 unit, with either a positive SARS-CoV-2 RT-PCR from a nasopharyngeal swab or a CT scan within 72 h after or prior to hospitalization in the unit suggestive of infection. Results: We included 101 patients who received antibiotics and 48 who did not. The demographics in the two groups were similar. Overall mortality was higher for the group that received antibiotics than for the other group (36.6% vs 14.6%,). According to univariate COX analysis, the risk of mortality was higher (HR = 1.98 [0.926; 4.23]) but non-significantly for the antibiotic group. In multivariate analysis, independent risk factors of mortality were an increased leukocyte count and decreased oxygen saturation (HR = 1.097 [1.022; 1.178] and HR = 0.927 [0.891; 0.964], respectively). Conclusion: This study raises questions about the interest of antibiotic therapy, its efficacy, and its effect on COVID-19 and encourages further research

Standardized Description of the Feature Extraction Process to Transform Raw Data Into Meaningful Information for Enhancing Data Reuse: Consensus Study

International audienceBackground Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm. Objective The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse. Methods This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM). Results We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. “Track” is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). “Feature” is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables “TRACK” and “FEATURE” to store variables obtained in feature extraction and extend the OMOP CDM. Conclusions We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies