Issues and place of the data sciences for reusing clinical big data : a case-based study

La dématérialisation des données de santé a permis depuis plusieurs années de constituer un véritable gisement de données provenant de tous les domaines de la santé. Ces données ont pour caractéristiques d’être très hétérogènes et d’être produites à différentes échelles et dans différents domaines. Leur réutilisation dans le cadre de la recherche clinique, de la santé publique ou encore de la prise en charge des patients implique de développer des approches adaptées reposant sur les méthodes issues de la science des données. L’objectif de cette thèse est d’évaluer au travers de trois cas d’usage, quels sont les enjeux actuels ainsi que la place des data sciences pour l’exploitation des données massives en santé. La démarche utilisée pour répondre à cet objectif consiste dans une première partie à exposer les caractéristiques des données massives en santé et les aspects techniques liés à leur réutilisation. La seconde partie expose les aspects organisationnels permettant l’exploitation et le partage des données massives en santé. La troisième partie décrit les grandes approches méthodologiques en science des données appliquées actuellement au domaine de la santé. Enfin, la quatrième partie illustre au travers de trois exemples l’apport de ces méthodes dans les champs suivant : la surveillance syndromique, la pharmacovigilance et la recherche clinique. Nous discutons enfin les limites et enjeux de la science des données dans le cadre de la réutilisation des données massives en santé.The dematerialization of health data, which started several years ago, now generates na huge amount of data produced by all actors of health. These data have the characteristics of being very heterogeneous and of being produced at different scales and in different domains. Their reuse in the context of clinical research, public health or patient care involves developing appropriate approaches based on methods from data science. The aim of this thesis is to evaluate, through three use cases, what are the current issues as well as the place of data sciences regarding the reuse of massive health data. To meet this objective, the first section exposes the characteristics of health big data and the technical aspects related to their reuse. The second section presents the organizational aspects for the exploitation and sharing of health big data. The third section describes the main methodological approaches in data sciences currently applied in the field of health. Finally, the fourth section illustrates, through three use cases, the contribution of these methods in the following fields: syndromic surveillance, pharmacovigilance and clinical research. Finally, we discuss the limits and challenges of data science in the context of health big data

Enjeux et place des data sciences dans le champ de la réutilisation secondaire des données massives cliniques : une approche basée sur des cas d’usage

The dematerialization of health data, which started several years ago, now generates na huge amount of data produced by all actors of health. These data have the characteristics of being very heterogeneous and of being produced at different scales and in different domains. Their reuse in the context of clinical research, public health or patient care involves developing appropriate approaches based on methods from data science. The aim of this thesis is to evaluate, through three use cases, what are the current issues as well as the place of data sciences regarding the reuse of massive health data. To meet this objective, the first section exposes the characteristics of health big data and the technical aspects related to their reuse. The second section presents the organizational aspects for the exploitation and sharing of health big data. The third section describes the main methodological approaches in data sciences currently applied in the field of health. Finally, the fourth section illustrates, through three use cases, the contribution of these methods in the following fields: syndromic surveillance, pharmacovigilance and clinical research. Finally, we discuss the limits and challenges of data science in the context of health big data.La dématérialisation des données de santé a permis depuis plusieurs années de constituer un véritable gisement de données provenant de tous les domaines de la santé. Ces données ont pour caractéristiques d’être très hétérogènes et d’être produites à différentes échelles et dans différents domaines. Leur réutilisation dans le cadre de la recherche clinique, de la santé publique ou encore de la prise en charge des patients implique de développer des approches adaptées reposant sur les méthodes issues de la science des données. L’objectif de cette thèse est d’évaluer au travers de trois cas d’usage, quels sont les enjeux actuels ainsi que la place des data sciences pour l’exploitation des données massives en santé. La démarche utilisée pour répondre à cet objectif consiste dans une première partie à exposer les caractéristiques des données massives en santé et les aspects techniques liés à leur réutilisation. La seconde partie expose les aspects organisationnels permettant l’exploitation et le partage des données massives en santé. La troisième partie décrit les grandes approches méthodologiques en science des données appliquées actuellement au domaine de la santé. Enfin, la quatrième partie illustre au travers de trois exemples l’apport de ces méthodes dans les champs suivant : la surveillance syndromique, la pharmacovigilance et la recherche clinique. Nous discutons enfin les limites et enjeux de la science des données dans le cadre de la réutilisation des données massives en santé

New indices of left ventricular function: let's move from ejection fraction to more physiological parameters

International audienceThe assessment of myocardial function in the context of valvular heart disease (VHD) remains highly challenging. The myocardium deforms simultaneously in three-dimensions, and global left ventricular (LV) function parameters such as volume and ejection fraction (EF) may remain compensated despite alterations in myocardial deformation properties. In VHD, the decline in myocardial deformation parameters precedes the onset of symptoms and portends a poor outcome. Nevertheless, it has not been demonstrated that LV global longitudinal strain (GLS) has independent prognostic value in patients with VHD2 and GLS does not figure in current recommendations for the management of these patient

Drug-Drug Interactions in Elderly Patients with Potentially Inappropriate Medications in Primary Care, Nursing Home and Hospital Settings: A Systematic Review and a Preliminary Study

International audienceDrug-drug interactions (DDI) occurring with potentially inappropriate medications (PIM) are additional risk factors that may increase the inappropriate character of PIM. The aim of this study was (1) to describe the prevalence and severity of DDI in patients with PIM and (2) to evaluate the DDI specifically regarding PIM. This systematic review is based on a search carried out on PubMed and Web-of-Science from inception to June 30, 2020. We extracted data of original studies that assessed the prevalence of both DDI and PIM in elderly patients in primary care, nursing home and hospital settings. Four hundred and forty unique studies were identified: 91 were included in the qualitative analysis and 66 were included in the quantitative analysis. The prevalence of PIM in primary care, nursing home and hospital were 19.1% (95% confidence intervals (CI): 15.1-23.0%), 29.7% (95% CI: 27.8-31.6%) and 44.6% (95% CI: 28.3-60.9%), respectively. Clinically significant severe risk-rated DDI averaged 28.9% (95% CI: 17.2-40.6), in a hospital setting; and were approximately 7-to-9 lower in primary care and nursing home, respectively. Surprisingly, only four of these studies investigated DDI involving specifically PIM. Hence, given the high prevalence of severe DDI in patients with PIM, further investigations should be carried out on DDI involving specifically PIM which may increase their inappropriate character, and the risk of adverse drug reactions

Drug-Drug Interactions with Oral Anticoagulants as Potentially Inappropriate Medications: Prevalence and Outcomes in Elderly Patients in Primary Care and Hospital Settings

International audienceDirect oral anticoagulants and vitamin K antagonists are considered as potentially inappropriate medications (PIM) in several situations according to Beers Criteria. Drug-drug interactions (DDI) occurring specifically with these oral anticoagulants considered PIM (PIM-DDI) is an issue since it could enhance their inappropriate character and lead to adverse drug events, such as bleeding events. The aim of this study was (1) to describe the prevalence of oral anticoagulants as PIM, DDI and PIM-DDI in elderly patients in primary care and during hospitalization and (2) to evaluate their potential impact on the clinical outcomes by predicting hospitalization for bleeding events using machine learning methods. This retrospective study based on the linkage between a primary care database and a hospital data warehouse allowed us to display the oral anticoagulant treatment pathway. The prevalence of PIM was similar between primary care and hospital setting (22.9% and 20.9%), whereas the prevalence of DDI and PIM-DDI were slightly higher during hospitalization (47.2% vs. 58.9% and 19.5% vs. 23.5%). Concerning mechanisms, combined with CYP3A4-P-gp interactions as PIM-DDI, were among the most prevalent in patients with bleeding events. Although PIM, DDI and PIM-DDI did not appeared as major predictors of bleeding events, they should be considered since they are the only factors that can be optimized by pharmacist and clinicians

Phenotyping of Heart Failure with Preserved Ejection Faction Using Health Electronic Records and Echocardiography

International audiencePatients suffering from heart failure (HF) symptoms and a normal left ventricular ejection fraction (LVEF 50%) present very different clinical phenotypes that could influence their survival. This study aims to identify phenotypes of this type of HF by using the medical information database from Rennes University Hospital Center. We present a preliminary work, where we explore the use of clinical variables from health electronic records (HER) in addition to echocardiography to identify several phenotypes of patients suffering from heart failure with preserved ejection fraction. The proposed methodology identifies 4 clusters with various characteristics (both clinical and echocardiographic) that are linked to survival (death, surgery, hospitalization). In the future, this work could be deployed as a tool for the physician to assess risks and contribute to support better care for patients

Prescreening in oncology trials using medical records. Natural language processing applied on lung cancer multidisciplinary team meeting reports

International audienceDefining profiles of patients that could benefit from relevant anti-cancer treatments is essential. An increasing number of specific criteria are necessary to be eligible to specific anti-cancer therapies. This study aimed to develop an automated algorithm able to detect patient and tumor characteristics to reduce the time-consuming prescreening for trial inclusions without delay. Hence, 640 anonymized multidisciplinary team meetings (MTM) reports concerning lung cancers from one French teaching hospital data warehouse between 2018 and 2020 were annotated. To automate the extraction of eight major eligibility criteria, corresponding to 52 classes, regular expressions were implemented. The RegEx’s evaluation gave a F1-score of 93% in average, a positive predictive value (precision) of 98% and sensitivity (recall) of 92%. However, in MTM, fill rates variabilities among patient and tumor information remained important (from 31% to 100%). Genetic mutations and rearrangement test results were the least reported characteristics and also the hardest to automatically extract. To ease prescreening in clinical trials, the PreScIOUs study demonstrated the additional value of rule based and machine learning based methods applied on lung cancer MTM reports

Machine learning is the key to diagnose COVID-19: a proof-of-concept study

International audienceThe reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model’s performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis

Supervised Learning for the ICD-10 Coding of French Clinical Narratives

International audienceAutomatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we present a corpus of French clinical narratives annotated with the ICD-10 codes. Then, we propose automatic methods based on neural network approaches for the automatic detection of the ICD-10 codes. The results show that we need 1) more examples per class given the number of classes to assign, and 2) a better word/concept vector representation of documents in order to accurately assign codes

Prevalence and nature of statin drug-drug interactions in a university hospital by electronic health record mining

International audienceAim: Our aim was to describe prevalence, nature, and level of severity of potential statin drug-drug interactions in a university hospital.Methods: In a cross-sectional study, statin drug-drug interactions were screened from medical record of 10,506 in-patients treated stored in the clinical data warehouse “eHOP.” We screened drug-drug interactions using Theriaque and Micromedex drug databases.Results: A total of 22.5% of patients were exposed to at least one statin drug-drug interaction. Given their lipophilicity and CYP3A4 metabolic pathway, atorvastatin and simvastatin presented a higher prevalence of drug-drug interactions while fluvastatin presented the lowest prevalence. Up to 1% of the patients was exposed to a contraindicated drug-drug interaction, the most frequent drug-drug interaction involving influx-transporter (i.e., OATP1B1) interactions between simvastatin or rosuvastatin with cyclosporin. The second most frequent contraindicated drug-drug interaction involved CYP3A4 interaction between atorvastatin or simvastatin with either posaconazole or erythromycin. Furthermore, our analysis showed some discrepancies between Theriaque and Micromedex in the prevalence and the nature of drug-drug interactions.Conclusions: Different drug-drug interaction profiles were observed between statins with a higher prevalence of CYP3A4-based interactions for lipophilic statins. Analyzing the three most frequent DDIs, the more significant DDIs (level 1: contraindication) were reported for transporter-based DDI involving OATP1B1 influx transporter. These points are of concern to improve prescriptions of statins