Search

Background: To examine the association of ultrasensitive cTnI (cardiac troponin I) with incident cardiovascular disease events (CVDs) in the primary prevention setting. Methods: cTnI was analyzed in the baseline plasma (2008–2012) of CVD-free volunteers from the Paris Prospective Study III using a novel ultrasensitive immunoassay (Simoa Troponin-I 2.0 Kit, Quanterix, Lexington) with a limit of detection of 0.013 pg/mL. Incident CVD hospitalizations (coronary heart disease, stroke, cardiac arrhythmias, deep venous thrombosis or pulmonary embolism, heart failure, or arterial aneurysm) were validated by critical review of the hospital records. Hazard ratios were estimated per log-transformed SD increase of cTnI in Cox models using age as the time scale. Results: The study population includes 9503 participants (40% women) aged 59.6 (6.3) years. cTnI was detected in 99.6% of the participants (median value=0.63 pg/mL, interquartile range, 0.39–1.09). After a median follow-up of 8.34 years (interquartile range, 8.0–10.07), 516 participants suffered 612 events. In fully adjusted analysis, higher cTnI (per 1 SD increase of log cTnI) was significantly associated with CVD events combined (hazard ratio, 1.18 [1.08–1.30]). Among all single risk factors, cTnI had the highest discrimination capacity for incident CVD events (C index=0.6349). Adding log cTnI to the SCORE 2 (Systematic Coronary Risk Evaluation) risk improved moderately discriminatory capacity (C index 0.698 versus 0.685; bootstrapped C index difference: 0.0135 [95% CI, 0.0131–0.0138]), and reclassification of the participants (categorical net reclassification index, 0.0628 [95% CI, 0.023–0.102]). Findings were consistent using the US pooled cohort risk equation. Conclusions: Ultrasensitive cTnI is an independent marker of CVD events in the primary prevention setting.

BACKGROUND Most researches considering relationships between violence and health focus on associations between a type of violence and a specific medical diagnosis. Little is known about the functional consequences of violence, directly assessed as a primary outcome. Yet, WHO acknowledged the importance of considering a functional approach of health (2001) when releasing the International classification of functioning, disabilty and health (ICF). The evaluation of victims’ functional impairment relies both on objective, subjective and contextual elements, as when they report symptoms of fear, pain or the circumstances of the assault. Using scales to quantify these subjective elements should facilitate their interpretation in a judicial context. OBJECTIVE To 1) elaborate a typology of violence based on medical observations reported and available through electronical medical records (EHRs) and 2) based on this typology, to characterize and 3) assess 6 scales measuring subjective elements of functional impairment among victims of violence. METHODS We conducted a retrospective, monocentric study included assaults’ survivors examined in a French department of forensic medicine over 12 months. Typology was built based on the mining of EHRs and the use of pattern recognitions algorithms. The optimal number of types of violence was searched requiring a approach based on robustness and stability of clusters, using a sample/re-sample and multi-metric scheme. We then paired patients based on their belonging to the same homogeneous profile and evaluated the intra-rater and inter-rater reproducibility of the scales. RESULTS We found five remarkably stable profiles of situations of violence, consistent with clinical practice. All pain, fear and life-threat scales were significantly associated with higher functional impairment, suggesting that their measure contributes to the global assessment of functional impairment. Intra and inter reproducibility of scales among the same situations of violence profiles were measured from mild to good (coefficient of concordance 0.46 to 0.66 and 0.43 to 0.66, respectively). Victims reporting intimate partner violence showed higher scores in fear and perception of a life threat both during assault and medical interview while victims reporting battery by multiple, unknown assailants presented higher scores only in perception of a life threat during assault. CONCLUSIONS We could elaborate a well defined 5-profile typology of violence as encountered in medical practice, based on observations and robust EHRs mining techniques. These profiles provide a more comprehensive characterization of real-life situations of violence, which means they could be use as a typology for further research on violence rather than only focussing on a specific type of violence. Pain, fear and life-threat scales were related to functional impairment in accordance with expert knowledge, and showed in real life conditions a fair reproducibility for similar situations of violence. Subjective elements related to functional impairment in assault survivors can be quantified using Likert scales during medical interviews.

International audience; With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children’s hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems.

BACKGROUND Although sudden cardiac death (SCD) is recognized as a high-priority public health topic, reliable estimates of the incidence of SCD or, more broadly, out-of-hospital cardiac arrest (OHCA), in the population are scarce, especially in the European Union.OBJECTIVES The study objective was to determine the incidence of SCD and OHCA in the European Union.METHODS The study examined 4 large (ie, >2 million inhabitants) European population-based prospective registries collecting emergency medical services (EMS)-attended (ie, with attempted resuscitation) OHCA and SCD (OHCA without obvious extracardiac causes) for >5 consecutive years from January 2012 to December 2017 in the Paris region (France), the North Holland region (the Netherlands), the Stockholm region (Sweden), and in all of Denmark.RESULTS The average annual incidence of SCD in the 4 registries ranged from 36.8 per 100,000 (95% CI: 23.5-50.1 per 100,000) to 39.7 per 100,000 (95% CI: 32.6-46.8 per 100,000). When extrapolating to each European country and accounting for age and sex, this yields to 249,538 SCD cases per year (95% CI: 155,377-343,719 SCD cases per year). The average annual incidence of OHCA in the 4 registries ranged from 47.8 per 100,000 (95% CI: 21.2-74.4 per 100,000) to 57.9 per 100,000 (95% CI: 19.6-96.3 per 100,000), corresponding to 343,496 OHCA cases per year (95% CI: 216,472-464,922 OHCA cases per year) in the European Union. Incidence rates of SCD and OHCA increased with age and were systematically higher in men compared with women.CONCLUSIONS By combining data from 4 large, population-based registries with at least 5 years of data collection, this study provided an estimate of the incidence of SCD and OHCA in the European Union. (C) 2022 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation.

UNSTRUCTURED Not applicable

International audience; PurposeCompliance to the Surviving Sepsis Campaign (SSC) guidelines is limited. This is known to be associated with increased mortality. The aim of this retrospective cohort study was to identify among the SCC guidelines the optimal bundle of recommendations that minimize 28-day mortality.MethodsWe used a training cohort to identify, using a least absolute shrinkage and selection operator penalized machine learning model, this bundle. Patients with sepsis/septic shock admitted to the intensive care unit (ICU) were extracted from two US databases, the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training and internal validation cohorts) and the eICU Collaborative Research Database (eICU-CRD) (external validation cohort). In the validation cohorts, we defined a bundle group that includes patients who were treated with at least all the recommendations selected in our bundle and a no-bundle group that includes patients in whom at least one recommendation from our bundle was omitted.ResultsAll-cause 28-day mortality was the primary outcome measure. A total of 42,735 patients were included. Six recommendations (antimicrobials, balanced crystalloid, insulin therapy, corticosteroids, vasopressin, and bicarbonate therapy) were identified from the training cohort to be included in our bundle. In the propensity score-(PS)-matched internal validation cohort, the bundle group was associated with a lower mortality (OR 0.41 [0.33–0.53]; p

Background Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. Objective With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. Methods We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. Results A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.01) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. Conclusions Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.

International audience; BackgroundPrior research suggests that psychiatric disorders could be linked to increased mortality among patients with COVID-19. However, whether all or specific psychiatric disorders are intrinsic risk factors of death in COVID-19 or whether these associations reflect the greater prevalence of medical risk factors in people with psychiatric disorders has yet to be evaluated.MethodsWe performed an observational, multicenter, retrospective cohort study to examine the association between psychiatric disorders and mortality among patients hospitalized for laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals.ResultsOf 15,168 adult patients, 857 (5.7%) had an ICD-10 diagnosis of psychiatric disorder. Over a mean follow-up period of 14.6 days (SD = 17.9), 326 of 857 (38.0%) patients with a diagnosis of psychiatric disorder died compared with 1276 of 14,311 (8.9%) patients without such a diagnosis (odds ratio 6.27, 95% CI 5.40–7.28, p < .01). When adjusting for age, sex, hospital, current smoking status, and medications according to compassionate use or as part of a clinical trial, this association remained significant (adjusted odds ratio 3.27, 95% CI 2.78–3.85, p < .01). However, additional adjustments for obesity and number of medical conditions resulted in a nonsignificant association (adjusted odds ratio 1.02, 95% CI 0.84–1.23, p = .86). Exploratory analyses after the same adjustments suggested that a diagnosis of mood disorders was significantly associated with reduced mortality, which might be explained by the use of antidepressants.ConclusionsThese findings suggest that the increased risk of COVID-19–related mortality in individuals with psychiatric disorders hospitalized for COVID-19 might be explained by the greater number of medical conditions and the higher prevalence of obesity in this population and not by the underlying psychiatric disease.

BACKGROUND Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. OBJECTIVE With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. METHODS We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. RESULTS A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. CONCLUSIONS Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.

Objectives We aimed to develop and evaluate an algorithm for automatically screening citations when updating living network meta-analysis (NMA). Study Design and Setting Our algorithm learns from the initial screening of citations conducted when creating an NMA to automatically identify eligible citations (i.e., needing full-text consideration) when updating the NMA. We evaluated our algorithm on four NMAs from different medical domains. For each NMA we constructed sets of initially screened citations and citations to screen during an update that took place 2 years after the conduct of the NMA. We encoded free text of citations (title and abstract) using word embeddings. On top of this vectorized representation, we fitted a logistic regression model to the set of initially screened citations to predict the eligibility of citations screened during an update. Results Our algorithm achieved 100% sensitivity on two NMAs (100% [95% confidence interval 93–100] and 100% [40–100] sensitivity), and 94% (81–99) and 97% (86–100) on the remaining two others. For all NMAs, our algorithm would have spared to manually screen 1,345 of 2,530 citations, decreasing the workload by 53% (51–55), while missing 3 of 124 eligible citations (2% [1–7]), none of which were finally included in the NMAs after full-text consideration. Conclusion For updating an NMA after 2 years, our algorithm considerably diminished the workload required for screening, and the number of missed eligible citations remained low.

Background Information related to patient medication is crucial for health care; however, up to 80% of the information resides solely in unstructured text. Manual extraction is difficult and time-consuming, and there is not a lot of research on natural language processing extracting medical information from unstructured text from French corpora. Objective We aimed to develop a system to extract medication-related information from clinical text written in French. Methods We developed a hybrid system combining an expert rule–based system, contextual word embedding (embedding for language model) trained on clinical notes, and a deep recurrent neural network (bidirectional long short term memory–conditional random field). The task consisted of extracting drug mentions and their related information (eg, dosage, frequency, duration, route, condition). We manually annotated 320 clinical notes from a French clinical data warehouse to train and evaluate the model. We compared the performance of our approach to those of standard approaches: rule-based or machine learning only and classic word embeddings. We evaluated the models using token-level recall, precision, and F-measure. Results The overall F-measure was 89.9% (precision 90.8; recall: 89.2) when combining expert rules and contextualized embeddings, compared to 88.1% (precision 89.5; recall 87.2) without expert rules or contextualized embeddings. The F-measures for each category were 95.3% for medication name, 64.4% for drug class mentions, 95.3% for dosage, 92.2% for frequency, 78.8% for duration, and 62.2% for condition of the intake. Conclusions Associating expert rules, deep contextualized embedding, and deep neural networks improved medication information extraction. Our results revealed a synergy when associating expert knowledge and latent knowledge.

ObjectiveTo assess the clinical effectiveness of oral hydroxychloroquine (HCQ) with or without azithromycin (AZI) in preventing death or leading to hospital discharge.DesignRetrospective cohort study.SettingAn analysis of data from electronic medical records and administrative claim data from the French Assistance Publique - Hôpitaux de Paris (AP-HP) data warehouse, in 39 public hospitals, Ile-de-France, France.ParticipantsAll adult inpatients with at least one PCR-documented SARS-CoV-2 RNA from a nasopharyngeal sample between February 1st, 2020 and April 6th, 2020 were eligible for analysis. The study population was restricted to patients who did not receive COVID-19 treatments assessed in ongoing trials, including antivirals and immunosuppressive drugs. End of follow-up was defined as the date of death, discharge home, day 28 after admission, whichever occurred first, or administrative censoring on May 4, 2020.InterventionPatients were further classified into 3 groups: (i) receiving HCQ alone, (ii) receiving HCQ together with AZI, and (iii) receiving neither HCQ nor AZI. Exposure to a HCQ/AZI combination was defined as a simultaneous prescription of the 2 treatments (more or less one day).Main outcome measuresThe primary outcome was all-cause 28-day mortality as a time-to-event endpoint under a competing risks survival analysis framework. The secondary outcome was 28-day discharge home. Augmented inverse probability of treatment weighted (AIPTW) estimates of the average treatment effect (ATE) were computed to account for confounding.ResultsA total of 4,642 patients (mean age: 66.1 ± 18; males: 2,738 (59%)) were included, of whom 623 (13.4%) received HCQ alone, 227 (5.9%) received HCQ plus AZI, and 3,792 (81.7%) neither drug. Patients receiving ‘HCQ alone’ or ‘HCQ plus AZI’ were more likely younger, males, current smokers and overall presented with slightly more co-morbidities (obesity, diabetes, any chronic pulmonary diseases, liver diseases), while no major difference was apparent in biological parameters. After accounting for confounding, no statistically significant difference was observed between the ‘HCQ’ and ‘Neither drug’ groups for 28-day mortality: AIPTW absolute difference in ATE was +1.24% (−5.63 to 8.12), ratio in ATE 1.05 (0.77 to 1.33). 28-day discharge rates were statistically significantly higher in the ‘HCQ’ group: AIPTW absolute difference in ATE (+11.1% [3.30 to 18.9]), ratio in ATE (1.25 [1.07 to 1.42]). As for the ‘HCQ+AZI’ vs neither drug, trends for significant differences and ratios in AIPTW ATE were found suggesting higher mortality rates in the former group (difference in ATE +9.83% [-0.51 to 20.17], ratio in ATE 1.40 [0.98 to 1.81];p=0.062).ConclusionsUsing a large non-selected population of inpatients hospitalized for COVID-19 infection in 39 hospitals in France and robust methodological approaches, we found no evidence for efficacy of HCQ or HCQ combined with AZI on 28-day mortality. Our results suggested a possible excess risk of mortality associated with HCQ combined with AZI, but not with HCQ alone. Significantly higher rates of discharge home were observed in patients treated by HCQ, a novel finding warranting further confirmation in replicative studies. Altogether, our findings further support the need to complete currently undergoing randomized clinical trials.WHAT THIS PAPER ADDS?What is already known on this subject-The use of Hydroxychloroquine (HCQ) or HCQ with azithromycin (AZI) has been associated with viral load reduction at 6 days in COVID-19 infected patients-No difference between HCQ and no-HCQ groups in terms of risk of death or need for mechanical ventilation was found in two large cohorts of hospitalized COVID-19 infected patientsWhat this study adds-Using a large non-selected population of inpatients hospitalized for COVID-19 infection in 39 hospitals in France and robust methodological approaches, we found no evidence for efficacy of HCQ on 28-day mortality-Our results suggest an excess risk of mortality in patients treated by a combination of HCQ and AZI, but not with HCQ alone-Significantly higher rates of discharge home were observed in patients treated by HCQ, a novel finding warranting further confirmation in replicative studies

Introduction Une maladie emergente pose des problemes specifiques pour les outils informatiques. L’informatique biomedicale repose en grande partie sur les donnees structurees qui requierent l’existence de donnees ou de modeles de connaissances. Cependant, une nouvelle maladie ne peut avoir de modele de connaissances preexistant. Au cours d’une epidemie de maladie emergente, le traitement automatique de la langue (TAL) peut permettre la conversion rapide de donnees textuelles non structurees en un nouveau modele de connaissances. Bien que cette idee ait deja ete suggeree, il n’y avait pas eu jusqu’a present d’opportunite pour la tester en temps reel. La pandemie actuelle de COVID-19 en est une. L’objectif de cette etude etait de montrer la valeur ajoutee de l’extraction par TAL de l’information clinique presente dans les textes pour repondre aux questions posees dans le cadre d’une maladie emergente. Methodes Nous avons explore les effets a long-terme des traitements par inhibiteurs calciques sur le devenir des patients hypertendus, hospitalises pour une infection COVID-19. Dans l’entrepot de donnees de sante de l’AP-HP, nous avons compare deux sources differentes d’information : les donnees structurees (codes diagnostics CIM10, resultats biologiques, prescriptions medicamenteuses) et les donnees extraites des textes cliniques par TAL. Resultats Dans cette etude multicentrique sur les 39 hopitaux de l’AP-HP, le TAL a permis d’augmenter suffisamment la puissance statistique pour rendre significatif un resultat de risque relatif ajuste alors qu’il ne l’etait pas avec les donnees structurees uniquement ( Fig. 1 ). En comparant aux donnees structurees, le nombre de patients incluables dans l’etude a ete multiplie par 2,95, la quantite d’information sur les medicaments par 7,2 et les informations phenotypiques par 11,9. Conclusion Dans notre etude, l’utilisation d’inhibiteurs calciques etait associee a une diminution de la mortalite intra-hospitaliere chez les patients avec une infection COVID-19. Ces resultats ont ete obtenus en adaptant rapidement des pipelines TAL au domaine d’une nouvelle maladie. Ce pipeline d’extraction etait suffisamment performant pour extraire des informations utiles. Quand ces informations ont ete utilisees pour enrichir les donnees structurees deja disponibles, l’echantillon de l’etude a pu etre suffisamment augmente pour voir apparaitre un effet de traitement qui n’etait jusqu’alors pas detectable.

Introduction Les formes sévères d’infection à COVID-19 sont liées à une importante réponse inflammatoire. Certains biomédicaments (BM) sont en cours d’évaluation dans des essais thérapeutiques avec pour rationnel cet orage cytokinique. À l’inverse, nous pouvons nous interroger sur le risque d’infections à COVID-19 chez les patients sous BM au long cours. L’objectif principal de notre étude était de déterminer l’impact de la prise d’un BM sur le taux d’hospitalisation, de passage en réanimation (ICU) et de décès chez les patients sous BM atteints d’une infection à COVID-19. Matériels et méthodes Étude de cohorte rétrospective multicentrique à partir des données médico-administratives de l’Entrepôt de Données de Santé (EDS) de l’APHP. L’ensemble des patients recevant un BM (anti-TNF, anti-IL-12/23, anti-IL-17 ou anti-intégrine) étaient inclus. Les événements d’intérêt étaient la survenue d’une hospitalisation, d’un séjour en ICU ou d’un décès dans le cadre d’une infection à COVID-19 (confirmée par RT-PCR ou TDM thoracique) entre le 01/02 et le 22/04/20. Le risque d’hospitalisation/ICU/décès était évalué selon la méthodologie du Ratio de Morbidité/Mortalité Standardisé (SMR) en calculant le rapport entre le nombre observé et le nombre attendu. Le nombre attendu d’hospitalisation/ICU/décès était calculé en appliquant le taux d’hospitalisation/ICU/décès à COVID-19 de la population d’Ile-de-France (par tranche d’âge de 20 ans et par sexe) au nombre de personnes sous BM dans la sous-classe correspondante de la même source de données (données EDS de l’APHP). Résultats Un total de7808 patients (âge médian 45 ans, 51 % de femmes) étaient inclus ; dont 48 avec un diagnostic d’infection à COVID-19 : 19 (40 %) hospitalisés, 4 (8 %) en ICU et 1 décès. En comparaison avec la population d’Ile-de-France, les taux d’hospitalisation et d’ICU étaient significativement élevés chez les patients sous BM avec SMR 2,19, IC95 % 1,32-3,42, p

Introduction Les formes sévères d’infection à SARS-CoV-2 sont liées à une importante réponse inflammatoire. Certains biomédicaments (BM) sont en cours d’évaluation dans des essais thérapeutiques avec pour rationnel cet orage cytokinique. À l’inverse, nous pouvons nous interroger sur le risque d’infection à SARS-CoV-2 chez les patients au long cours sous BM. L’objectif principal de notre étude était de déterminer l’impact de la prise d’un biomédicament sur le taux d’hospitalisation, de passage en réanimation (ICU) et de décès chez les patients sous BM atteints d’une infection à SARS-CoV-2. Matériel et méthodes Étude de cohorte rétrospective multicentrique à partir des données médico-administratives de l’Entrepôt de Données de Santé (EDS) de l’APHP. L’ensemble des patients recevant un BM (anti-TNF, anti-IL-12/23, anti-IL-17 ou anti-intégrine) était inclus. Les événements d’intérêt étaient la survenue d’une hospitalisation, d’un séjour en ICU ou d’un décès dans le cadre d’une infection à SARS-CoV-2 (confirmée par RT-PCR ou TDM thoracique) entre le 01/02 et le 22/04/20. Le risque d’hospitalisation/ICU/décès était évalué selon la méthodologie du Ratio de Morbidité/Mortalité Standardisé (SMR) en calculant le rapport entre le nombre observé et le nombre attendu. Le nombre attendu d’hospitalisation/ICU/décès était calculé en appliquant le taux d’hospitalisation/ICU/décès à SARS-CoV-2 de la population d’Ile-de-France (par tranche âge de 20 ans et sexe) au nombre de personnes sous BM dans la sous-classe correspondante de la même source de données (données EDS de l’AP–HP). Résultats Un total de 7 808 patients (âge médian 45 ans, 51 % de femmes) étaient inclus, dont 48 avec un diagnostic d’infection à SARS-CoV-2, 19 (40 %) hospitalisés, 4 (8 %) en ICU et 1 décès. En comparaison avec la population d’Ile-de-France, les taux d’hospitalisation et d’ICU étaient significativement élevés chez les patients sous BM avec SR = 2,19, 95 %CI 1,32–3,42, p

BACKGROUND Information related to patient medication is crucial for health care; however, up to 80% of the information resides solely in unstructured text. Manual extraction is difficult and time-consuming, and there is not a lot of research on natural language processing extracting medical information from unstructured text from French corpora. OBJECTIVE We aimed to develop a system to extract medication-related information from clinical text written in French. METHODS We developed a hybrid system combining an expert rule–based system, contextual word embedding (embedding for language model) trained on clinical notes, and a deep recurrent neural network (bidirectional long short term memory–conditional random field). The task consisted of extracting drug mentions and their related information (eg, dosage, frequency, duration, route, condition). We manually annotated 320 clinical notes from a French clinical data warehouse to train and evaluate the model. We compared the performance of our approach to those of standard approaches: rule-based or machine learning only and classic word embeddings. We evaluated the models using token-level recall, precision, and F-measure. RESULTS The overall F-measure was 89.9% (precision 90.8; recall: 89.2) when combining expert rules and contextualized embeddings, compared to 88.1% (precision 89.5; recall 87.2) without expert rules or contextualized embeddings. The F-measures for each category were 95.3% for medication name, 64.4% for drug class mentions, 95.3% for dosage, 92.2% for frequency, 78.8% for duration, and 62.2% for condition of the intake. CONCLUSIONS Associating expert rules, deep contextualized embedding, and deep neural networks improved medication information extraction. Our results revealed a synergy when associating expert knowledge and latent knowledge.

Objective We aimed to enhance the performance of a supervised model for clinical named-entity recognition (NER) using medical terminologies. In order to evaluate our system in French, we built a corpus for 5 types of clinical entities. Methods We used a terminology-based system as baseline, built upon UMLS and SNOMED. Then, we evaluated a biGRU-CRF, and a hybrid system using the prediction of the terminology-based system as feature for the biGRU-CRF. In French, we built APcNER, a corpus of 147 documents annotated for 5 entities (Drug names, Signs or symptoms, Diseases or disorders, Diagnostic procedures or lab tests and Therapeutic procedures). We evaluated each NER systems using exact and partial match definition of F-measure for NER. The APcNER contains 4,837 entities, which took 28 h to annotate. The inter-annotator agreement as measured by Cohen’s Kappa was substantial for non-exact match (Κ = 0.61) and moderate considering exact match (Κ = 0.42). In English, we evaluated the NER systems on the i2b2-2009 Medication Challenge for Drug name recognition, which contained 8,573 entities for 268 documents, and i2b2-small a version reduced to match APcNER number of entities. Results For drug name recognition on both i2b2-2009 and APcNER, the biGRU-CRF performed better that the terminology-based system, with an exact-match F-measure of 91.1% versus 73% and 81.9% versus 75% respectively. For i2b2-small and APcNER, the hybrid system outperformed the biGRU-CRF, with an exact-match F-measure of 87.8% versus 85.6% and 86.4% versus 81.9% respectively. On APcNER corpus, the micro-average F-measure of the hybrid system on the 5 entities was 69.5% in exact match and 84.1% in non-exact match. Conclusion APcNER is a French corpus for clinical-NER of five types of entities which covers a large variety of document types. The extension of the supervised model with terminology has allowed an easy increase in performance, especially for rare entities, and established near state of the art results on the i2b2-2009 corpus.

This is a review of Deborah Allison's book, The Cinema of Michael Winterbottom (2013), which was published in Film International, Volume 11 Issue 5, DOI: 10.1386/fiin.11.5.77_5.

Collaborators : AP-HP / Université de Paris / INSERM COVID-19 research collaboration, AP-HP COVID CDR Initiative, “Entrepôt de Données de Santé” AP-HP Consortium: Pierre-Yves Ancel, Alain Bauchet, Nathanaël Beeker, Vincent Benoit, Mélodie Bernaux, Ali Bellamine, Romain Bey, Aurélie Bourmaud, Stéphane Breant, Anita Burgun, Fabrice Carrat, Charlotte Caucheteux, Julien Champ, Sylvie Cormont, Christel Daniel, Julien Dubiel, Catherine Ducloas, Loic Esteve, Marie Frank, Nicolas Garcelon, Alexandre Gramfort, Nicolas Griffon, Olivier Grisel, Martin Guilbaud, Claire Hassen-Khodja, François Hemery, Martin Hilka, Anne Sophie Jannot, Jerome Lambert, Richard Layese, Judith Leblanc, Léo Lebouter, Guillaume Lemaitre, Damien Leprovost, Ivan Lerner, Kankoe Levi Sallah, Aurélien Maire, Marie-France Mamzer, Patricia Martel, Arthur Mensch, Thomas Moreau, Antoine Neuraz, Nina Orlova, Nicolas Paris, Bastien Rance, Hélène Ravera, Antoine Rozes, Elisa Salamanca, Arnaud Sandrin, Patricia Serre, Xavier Tannier, Jean-Marc Treluyer, Damien Van Gysel, Gaël Varoquaux, Jill Jen Vie, Maxime Wack, Perceval Wajsburt, Demian Wassermann, Eric Zapletal; International audience; Several medications commonly used for a number of medical conditions share a property of functional inhibition of acid sphingomyelinase (ASM), or FIASMA. Preclinical and clinical evidence suggest that the (ASM)/ceramide system may be central to SARS-CoV-2 infection. We examined the potential usefulness of FIASMA use among patients hospitalized for severe COVID-19 in an observational multicenter study conducted at Greater Paris University hospitals. Of 2,846 adult patients hospitalized for severe COVID-19, 277 (9.7%) were taking a FIASMA medication at the time of their hospital admission. The primary endpoint was a composite of intubation and/or death. We compared this endpoint between patients taking vs. not taking a FIASMA medication in time-to-event analyses adjusted for sociodemographic characteristics and medical comorbidities. The primary analysis was a Cox regression model with inverse probability weighting (IPW). Over a mean follow-up of 9.2 days (SD=12.5), the primary endpoint occurred in 104 patients (37.5%) receiving a FIASMA medication, and 1,060 patients (41.4%) who did not. Despite being significantly and substantially associated with older age and greater medical severity, FIASMA medication use was significantly associated with reduced likelihood of intubation or death in both crude (HR=0.71; 95%CI=0.58-0.87; p

BackgroundPrevious studies have demonstrated that socioeconomic factors are associated with COVID-19 incidence. In this study, we analysed a broad range of socioeconomic indicators in relation to hospitalised cases in the Paris area.MethodsWe extracted 303 socioeconomic indicators from French census data for 855 residential units in Paris and assessed their association with COVID-19 hospitalisation risk.FindingsThe indicators most associated with hospitalisation risk were the third decile of population income (OR=9.10, 95% CI 4.98 to 18.39), followed by the primary residence rate (OR=5.87, 95% CI 3.46 to 10.61), rate of active workers in unskilled occupations (OR=5.04, 95% CI 3.03 to 8.85) and rate of women over 15 years old with no diploma (OR=5.04, 95% CI 3.03 to 8.85). Of note, population demographics were considerably less associated with hospitalisation risk. Among these indicators, the rate of women aged between 45 and 59 years (OR=2.17, 95% CI 1.40 to 3.44) exhibited the greatest level of association, whereas population density was not associated. Overall, 86% of COVID-19 hospitalised cases occurred within the 45% most deprived areas.InterpretationStudying a broad range of socioeconomic indicators using census data and hospitalisation data as a readily available and large resource can provide real-time indirect information on populations with a high incidence of COVID-19.

International audience; Introduction: Chlorpromazine has been suggested as being potentially useful in patients with coronavirus disease 2019 (COVID-19) on the grounds of its potential antiviral and anti-inflammatory effects.Objective: The aim of this study was to examine the association between chlorpromazine use and mortality among adult patients hospitalized for COVID-19.Methods: We conducted an observational, multicenter, retrospective study at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals. Study baseline was defined as the date of first prescription of chlorpromazine during hospitalization for COVID-19. The primary endpoint was death. Among patients who had not been hospitalized in intensive care units (ICUs), we compared this endpoint between those who received chlorpromazine and those who did not, in time-to-event analyses adjusted for patient characteristics, clinical markers of disease severity, and other psychotropic medications. The primary analysis used a Cox regression model with inverse probability weighting. Multiple sensitivity analyses were performed.Results: Of the 14,340 adult inpatients hospitalized outside ICUs for COVID-19, 55 patients (0.4%) received chlorpromazine. Over a mean follow-up of 14.3 days (standard deviation [SD] 18.2), death occurred in 13 patients (23.6%) who received chlorpromazine and 1289 patients (9.0%) who did not. In the primary analysis, there was no significant association between chlorpromazine use and mortality (hazard ratio [HR] 2.01, 95% confidence interval [CI] 0.75-5.40; p = 0.163). Sensitivity analyses included a Cox regression in a 1:5 ratio matched analytic sample that showed a similar result (HR 1.67, 95% CI 0.91-3.06; p = 0.100) and a multivariable Cox regression that indicated a significant positive association (HR 3.10, 95% CI 1.31-7.34; p = 0.010).Conclusion: Our results suggest that chlorpromazine prescribed at a mean daily dose of 70.8 mg (SD 65.3) was not associated with reduced mortality.

Purpose The role of angiotensin receptor blockers (ARB), angiotensin-converting enzyme inhibitors (ACEi), or other antihypertensive agents in the case of Covid-19 remains controversial. We aimed to investigate the association between antihypertensive agent exposure and in-hospital mortality in patients with Covid-19. Methods We performed a retrospective multicenter cohort study on patients hospitalized between February 1 and May 15, 2020. All patients had been followed up for at least 30 days. Results Of the 8078 hospitalized patients for Covid-19, 3686 (45.6%) had hypertension and were included in the study. In this population, the median age was 75.4 (IQR, 21.5) years and 57.1% were male. Overall in-hospital 30-day mortality was 23.1%. The main antihypertensive pharmacological classes used were calcium channel blockers (CCB) (n=1624, 44.1%), beta-blockers (n=1389, 37.7%), ARB (n=1154, 31.3%), and ACEi (n=998, 27.1%). The risk of mortality was lower in CCB (aOR, 0.83 [0.70–0.99]) and beta-blockers (aOR, 0.80 [0.67–0.95]) users and non-significant in ARB (aOR, 0.88 [0.72–1.06]) and ACEi (aOR, 0.83 [0.68–1.02]) users, compared to non-users. These results remain consistent for patients receiving CCB, beta-blocker, or ARB as monotherapies. Conclusion This large multicenter retrospective of Covid-19 patients with hypertension found a reduced mortality among CCB and beta-blockers users, suggesting a putative protective effect. Our findings did not show any association between the use of renin-angiotensin-aldosterone system inhibitors and the risk of in-hospital death. Although they need to be confirmed in further studies, these results support the continuation of antihypertensive agents in patients with Covid-19, in line with the current guidelines. Supplementary Information The online version contains supplementary material available at 10.1007/s10557-021-07155-5.

BackgroundA novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. ObjectiveThe aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). MethodsWe explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. ResultsIn this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. ConclusionsIn our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.