Contribution à la prévention des risques liés à l’anesthésie par la valorisation des informations hospitalières au sein d’un entrepôt de données

Introduction Hospital Information Systems (HIS) manage and register every day millions of data related to patient care: biological results, vital signs, drugs administrations, care process... These data are stored by operational applications provide remote access and a comprehensive picture of Electronic Health Record. These data may also be used to answer to others purposes as clinical research or public health, particularly when integrated in a data warehouse. Some studies highlighted a statistical link between the compliance of quality indicators related to anesthesia procedure and patient outcome during the hospital stay. In the University Hospital of Lille, the quality indicators, as well as the patient comorbidities during the post-operative period could be assessed with data collected by applications of the HIS. The main objective of the work is to integrate data collected by operational applications in order to realize clinical research studies.Methods First, the data quality of information registered by the operational applications is evaluated with methods … by the literature or developed in this work. Then, data quality problems highlighted by the evaluation are managed during the integration step of the ETL process. New data are computed and aggregated in order to dispose of indicators of quality of care. Finally, two studies bring out the usability of the system.Results Pertinent data from the HIS have been integrated in an anesthesia data warehouse. This system stores data about the hospital stay and interventions (drug administrations, vital signs …) since 2010. Aggregated data have been developed and used in two clinical research studies. The first study highlighted statistical link between the induction and patient outcome. The second study evaluated the compliance of quality indicators of ventilation and the impact on comorbity.Discussion The data warehouse and the cleaning and integration methods developed as part of this work allow performing statistical analysis on more than 200 000 interventions. This system can be implemented with other applications used in the CHRU of Lille but also with Anesthesia Information Management Systems used by other hospitals.Introduction Le Système d'Information Hospitalier (SIH) exploite et enregistre chaque jours des millions d'informations liées à la prise en charge des patients : résultats d'analyses biologiques, mesures de paramètres physiologiques, administrations de médicaments, parcours dans les unités de soins, etc... Ces données sont traitées par des applications opérationnelles dont l'objectif est d'assurer un accès distant et une vision complète du dossier médical des patients au personnel médical. Ces données sont maintenant aussi utilisées pour répondre à d'autres objectifs comme la recherche clinique ou la santé publique, en particulier en les intégrant dans un entrepôt de données. La principale difficulté de ce type de projet est d'exploiter des données dans un autre but que celui pour lequel elles ont été enregistrées. Plusieurs études ont mis en évidence un lien statistique entre le respect d'indicateurs de qualité de prise en charge de l'anesthésie et le devenir du patient au cours du séjour hospitalier. Au CHRU de Lille, ces indicateurs de qualité, ainsi que les comorbidités du patient lors de la période post-opératoire pourraient être calculés grâce aux données recueillies par plusieurs applications du SIH. L'objectif de se travail est d'intégrer les données enregistrées par ces applications opérationnelles afin de pouvoir réaliser des études de recherche clinique.Méthode Dans un premier temps, la qualité des données enregistrées dans les systèmes sources est évaluée grâce aux méthodes présentées par la littérature ou développées dans le cadre ce projet. Puis, les problèmes de qualité mis en évidence sont traités lors de la phase d'intégration dans l'entrepôt de données. De nouvelles données sont calculées et agrégées afin de proposer des indicateurs de qualité de prise en charge. Enfin, deux études de cas permettent de tester l'utilisation du système développée.Résultats Les données pertinentes des applications du SIH ont été intégrées au sein d'un entrepôt de données d'anesthésie. Celui-ci répertorie les informations liées aux séjours hospitaliers et aux interventions réalisées depuis 2010 (médicaments administrées, étapes de l'intervention, mesures, parcours dans les unités de soins, ...) enregistrées par les applications sources. Des données agrégées ont été calculées et ont permis de mener deux études recherche clinique. La première étude a permis de mettre en évidence un lien statistique entre l'hypotension liée à l'induction de l'anesthésie et le devenir du patient. Des facteurs prédictifs de cette hypotension ont également étaient établis. La seconde étude a évalué le respect d'indicateurs de ventilation du patient et l'impact sur les comorbidités du système respiratoire.Discussion The data warehouse L'entrepôt de données développé dans le cadre de ce travail, et les méthodes d'intégration et de nettoyage de données mises en places permettent de conduire des analyses statistiques rétrospectives sur plus de 200 000 interventions. Le système pourra être étendu à d'autres systèmes sources au sein du CHRU de Lille mais également aux feuilles d'anesthésie utilisées par d'autres structures de soins

Good practices for clinical data warehouse implementation: a case study in France

Real World Data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern Clinical Data Warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multi-level governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multi-centric data reuses as well as innovations in routine care.Comment: 16 page

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Contributing to preventing anesthesia adverse events through the reuse of hospital information in a data warehouse

Introduction Le Système d'Information Hospitalier (SIH) exploite et enregistre chaque jours des millions d'informations liées à la prise en charge des patients : résultats d'analyses biologiques, mesures de paramètres physiologiques, administrations de médicaments, parcours dans les unités de soins, etc... Ces données sont traitées par des applications opérationnelles dont l'objectif est d'assurer un accès distant et une vision complète du dossier médical des patients au personnel médical. Ces données sont maintenant aussi utilisées pour répondre à d'autres objectifs comme la recherche clinique ou la santé publique, en particulier en les intégrant dans un entrepôt de données. La principale difficulté de ce type de projet est d'exploiter des données dans un autre but que celui pour lequel elles ont été enregistrées. Plusieurs études ont mis en évidence un lien statistique entre le respect d'indicateurs de qualité de prise en charge de l'anesthésie et le devenir du patient au cours du séjour hospitalier. Au CHRU de Lille, ces indicateurs de qualité, ainsi que les comorbidités du patient lors de la période post-opératoire pourraient être calculés grâce aux données recueillies par plusieurs applications du SIH. L'objectif de se travail est d'intégrer les données enregistrées par ces applications opérationnelles afin de pouvoir réaliser des études de recherche clinique.Méthode Dans un premier temps, la qualité des données enregistrées dans les systèmes sources est évaluée grâce aux méthodes présentées par la littérature ou développées dans le cadre ce projet. Puis, les problèmes de qualité mis en évidence sont traités lors de la phase d'intégration dans l'entrepôt de données. De nouvelles données sont calculées et agrégées afin de proposer des indicateurs de qualité de prise en charge. Enfin, deux études de cas permettent de tester l'utilisation du système développée.Résultats Les données pertinentes des applications du SIH ont été intégrées au sein d'un entrepôt de données d'anesthésie. Celui-ci répertorie les informations liées aux séjours hospitaliers et aux interventions réalisées depuis 2010 (médicaments administrées, étapes de l'intervention, mesures, parcours dans les unités de soins, ...) enregistrées par les applications sources. Des données agrégées ont été calculées et ont permis de mener deux études recherche clinique. La première étude a permis de mettre en évidence un lien statistique entre l'hypotension liée à l'induction de l'anesthésie et le devenir du patient. Des facteurs prédictifs de cette hypotension ont également étaient établis. La seconde étude a évalué le respect d'indicateurs de ventilation du patient et l'impact sur les comorbidités du système respiratoire.Discussion The data warehouse L'entrepôt de données développé dans le cadre de ce travail, et les méthodes d'intégration et de nettoyage de données mises en places permettent de conduire des analyses statistiques rétrospectives sur plus de 200 000 interventions. Le système pourra être étendu à d'autres systèmes sources au sein du CHRU de Lille mais également aux feuilles d'anesthésie utilisées par d'autres structures de soins.Introduction Hospital Information Systems (HIS) manage and register every day millions of data related to patient care: biological results, vital signs, drugs administrations, care process... These data are stored by operational applications provide remote access and a comprehensive picture of Electronic Health Record. These data may also be used to answer to others purposes as clinical research or public health, particularly when integrated in a data warehouse. Some studies highlighted a statistical link between the compliance of quality indicators related to anesthesia procedure and patient outcome during the hospital stay. In the University Hospital of Lille, the quality indicators, as well as the patient comorbidities during the post-operative period could be assessed with data collected by applications of the HIS. The main objective of the work is to integrate data collected by operational applications in order to realize clinical research studies.Methods First, the data quality of information registered by the operational applications is evaluated with methods … by the literature or developed in this work. Then, data quality problems highlighted by the evaluation are managed during the integration step of the ETL process. New data are computed and aggregated in order to dispose of indicators of quality of care. Finally, two studies bring out the usability of the system.Results Pertinent data from the HIS have been integrated in an anesthesia data warehouse. This system stores data about the hospital stay and interventions (drug administrations, vital signs …) since 2010. Aggregated data have been developed and used in two clinical research studies. The first study highlighted statistical link between the induction and patient outcome. The second study evaluated the compliance of quality indicators of ventilation and the impact on comorbity.Discussion The data warehouse and the cleaning and integration methods developed as part of this work allow performing statistical analysis on more than 200 000 interventions. This system can be implemented with other applications used in the CHRU of Lille but also with Anesthesia Information Management Systems used by other hospitals

Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study

BackgroundIn the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. ObjectiveThe objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. MethodsWe transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. ResultsWith an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a maximum duration of 1 minute. ConclusionsThe resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field

Automated Data Aggregation for Time-Series Analysis: Study Case on Anaesthesia Data Warehouse

International audienceData stored in operational databases are not reusable directly. Aggregation modules are necessary to facilitate secondary use. They decrease volume of data while increasing the number of available information. In this paper, we present four automated engines of aggregation, integrated into an anaesthesia data warehouse. Four instances of clinical questions illustrate the use of those engines for various improvements of quality of care: duration of procedure, drug administration, assessment of hypotension and its related treatment

The Relationship between the Immigrant Rate and Health Status in the General Population in France

International audienceMostly studied at the individual level, the analysis of immigrants’ health status at a populational level may provide a different perspective to investigate, including social determinants as part of the explanation of the relationship between them and health status in France. We analyzed freely accessible databases curated by French public bodies. The dependent variables were death rate and mean age at death. Immigrant rate and covariates associated with either of the outcomes were explored in univariate and multivariate models. Linear models were used to explain the mean age at death, whereas tobit models were used to explain the death rate. The immigrant rate varied markedly from one department to another, as did healthcare accessibility, population’s age profile, and economic covariates. Considering univariate models, almost all the studied covariates were significantly associated with comes. The immigrant rate was associated with a lower death rate and a lower age at death. In multivariate models, the immigrant rate was no longer associated with age at death but was still negatively associated with the death rate. In France, the departments with a higher proportion of immigrants were those with a lower death rate, possibly because immigrants are attracted to economically thriving areas

Master’s Degree in Health Data Science: Implementation and Assessment After Five Years

International audienceHealth data science is an emerging discipline that bridges computer science, statistics and health domain knowledge. This consists of taking advantage of the large volume of data, often complex, to extract information to improve decision-making. We have created a Master’s degree in Health Data Science to meet the growing need for data scientists in companies and institutions. The training offers, over two years, courses covering computer science, mathematics and statistics, health and biology. With more than 60 professors and lecturers, a total of 835 hours of classes (not including the mandatory 5 months of internship per year), this curriculum has enrolled a total of 53 students today. The feedback from the students and alumni allowed us identifying new needs in terms of training, which may help us to adapt the program for the coming academic years. In particular, we will offer an additional module covering data management, from the edition of the clinical report form to the implementation of a data warehouse with an ETL process. Git and application lifecycle management will be included in programming courses or multidisciplinary projects

Visualization of medical concepts represented using word embeddings: a scoping review.

International audienceBackgroundAnalyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language processing (NLP), to learn dense and low-dimensional word representations from large unlabeled corpora that capture the implicit semantics of words. Models like Word2Vec, GloVe or FastText have been broadly applied and reviewed in the bioinformatics and healthcare fields, most often to embed clinical notes or activity and diagnostic codes. Visualization of the learned embeddings has been used in a subset of these works, whether for exploratory or evaluation purposes. However, visualization practices tend to be heterogeneous, and lack overall guidelines.ObjectiveThis scoping review aims to describe the methods and strategies used to visualize medical concepts represented using word embedding methods. We aim to understand the objectives of the visualizations and their limits.MethodsThis scoping review summarizes different methods used to visualize word embeddings in healthcare. We followed the methodology proposed by Arksey and O’Malley (Int J Soc Res Methodol 8:19–32, 2005) and by Levac et al. (Implement Sci 5:69, 2010) to better analyze the data and provide a synthesis of the literature on the matter.ResultsWe first obtained 471 unique articles from a search conducted in PubMed, MedRxiv and arXiv databases. 30 of these were effectively reviewed, based on our inclusion and exclusion criteria. 23 articles were excluded in the full review stage, resulting in the analysis of 7 papers that fully correspond to our inclusion criteria. Included papers pursued a variety of objectives and used distinct methods to evaluate their embeddings and to visualize them. Visualization also served heterogeneous purposes, being alternatively used as a way to explore the embeddings, to evaluate them or to merely illustrate properties otherwise formally assessed.ConclusionsVisualization helps to explore embedding results (further dimensionality reduction, synthetic representation). However, it does not exhaust the information conveyed by the embeddings nor constitute a self-sustaining evaluation method of their pertinence

Master’s Degree in Health Data Science: Implementation and Assessment After Five Years

International audienceHealth data science is an emerging discipline that bridges computer science, statistics and health domain knowledge. This consists of taking advantage of the large volume of data, often complex, to extract information to improve decision-making. We have created a Master’s degree in Health Data Science to meet the growing need for data scientists in companies and institutions. The training offers, over two years, courses covering computer science, mathematics and statistics, health and biology. With more than 60 professors and lecturers, a total of 835 hours of classes (not including the mandatory 5 months of internship per year), this curriculum has enrolled a total of 53 students today. The feedback from the students and alumni allowed us identifying new needs in terms of training, which may help us to adapt the program for the coming academic years. In particular, we will offer an additional module covering data management, from the edition of the clinical report form to the implementation of a data warehouse with an ETL process. Git and application lifecycle management will be included in programming courses or multidisciplinary projects