Heterogeneity of right ventricular echocardiographic parameters in systemic lupus erythematosus among four clinical subgroups, as stratified by clinical organ involvement in observational cohort.

BACKGROUND: Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Cardiac involvement in SLE is rare but plays an important prognostic role. The degree of cardiac involvement according to SLE subsets defined by non-cardiac manifestations is unknown. The objective of this study was to identify differences in transthoracic echocardiography (TTE) parameters associated with different SLE subgroups. METHODS: One hundred eighty-one patients who fulfilled the 2019 American College of Rheumatology/EULAR classification criteria for SLE and underwent baseline TTE were included in this cross-sectional study. We defined four subsets of SLE based on the predominant clinical manifestations. A multivariate multinomial regression analysis was performed to determine whether TTE parameters differed between groups. RESULTS: Four clinical subsets were defined according to non-cardiac clinical manifestations: group A (n=37 patients) showed features of mixed connective tissue disease, group B (n=76 patients) had primarily cutaneous involvement, group C (n=18) exhibited prominent serositis and group D (n=50) had severe, multi-organ involvement, including notable renal disease. Forty TTE parameters were assessed between groups. Per multivariate multinomial regression analysis, there were statistically significant differences in early diastolic tricuspid annular velocity (RV-Ea, p<0.0001), RV S' wave (p=0.0031) and RV end-diastolic diameter (p=0.0419) between the groups. Group B (primarily cutaneous involvement) had the lowest degree of RV dysfunction. CONCLUSION: When defining clinical phenotypes of SLE based on organ involvement, we found four distinct subgroups which showed notable differences in RV function on TTE. Risk-stratifying patients by clinical phenotype could help better tailor cardiac follow-up in this population.

Safety of subcutaneous versus intravenous ceftriaxone administration in older patients: A retrospective study.

BACKGROUND: Antibiotics play a central role in infection management. In older patients, antibiotics are frequently administered subcutaneously. Ceftriaxone pharmacokinetics after subcutaneous administration is well documented, but little data are available on its safety. METHODS: We compared the occurrence of adverse events associated with ceftriaxone administered subcutaneously versus intravenously in ≥75-year-old patients. We used data from a single-center, retrospective, clinical-administrative database to compare the occurrence of adverse events at day 14 and outcome at day 21 in older patients who received ceftriaxone via the subcutaneous route or the intravenous route at Rennes University Hospital, France, from May 2020 to February 2023. RESULTS: The subcutaneous and intravenous groups included 402 and 3387 patients, respectively. Patients in the subcutaneous group were older and more likely to receive palliative care. At least one adverse event was reported for 18% and 40% of patients in the subcutaneous and intravenous group, respectively (RR = 2.21). Mortality at day 21 was higher in the subcutaneous route group, which could be linked to between-group differences in clinical and demographic features. CONCLUSIONS: In ≥75-year-old patients, ceftriaxone administered by the subcutaneous route is associated with less-adverse events than by the intravenous route. The subcutaneous route, which is easier to use, has a place in infection management in geriatric settings.

Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models.

BACKGROUND: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identification pipeline to other clinical centers. METHODS: We proposed an automated annotation process for French clinical de-identification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods. RESULTS: A French de-identification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets. We evaluated a Bi-LSTM + CRF sequence labeling architecture, combined with Flair + FastText word embeddings, on a test set of manually annotated clinical reports. The model outperformed the other tested models with a significant F1 score of 96,96%, demonstrating the effectiveness of our automatic approach for deidentifying sensitive information. CONCLUSIONS: This study provides an automatic de-identification pipeline for clinical notes, which can facilitate the reuse of EHRs for secondary purposes such as clinical research. Our study highlights the importance of using advanced NLP techniques for effective de-identification, as well as the need for innovative solutions such as distant supervision to overcome the challenge of limited annotated data in the medical domain.

Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography.

Aims: Patients presenting symptoms of heart failure with preserved ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large university hospital centre using machine learning. Methods and results: We explored the use of clinical variables from electronic health records in addition to echocardiography to identify different phenotypes of patients with HFpEF. The proposed methodology identifies four phenotypic clusters based on both clinical and echocardiographic characteristics, which have differing prognoses (death and cardiovascular hospitalization). Conclusion: This work demonstrated that artificial intelligence-derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.

Prognostic value and predictors of the alteration of the diffusing capacity of the lungs for carbon monoxide in systemic lupus erythematosus.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by heterogeneous manifestations and severity, with frequent lung involvement. Among pulmonary function tests (PFT), the measure of the diffusing capacity of the lungs for carbon monoxide (DLCO) is a noninvasive and sensitive tool assessing pulmonary microcirculation. Asymptomatic and isolated DLCO alteration has been frequently reported in SLE, but its clinical relevance has not been established. METHODS: This retrospective study focused on 232 SLE patients fulfilling the 2019 EULAR/ACR classification criteria for SLE. Data were collected from the patient's medical record, including demographic, clinical, and immunological characteristics while DLCO was measured when performing PFT as part of routine patient follow-up. RESULTS: At the end of follow-up, DLCO alteration (<70% of predicted value) was measured at least once in 154 patients (66.4%), and was associated with a history of smoking as well as interstitial lung disease (ILD), but was also associated with renal and neurological involvement. History of smoking, detection of anti-nucleosome autoantibodies and clinical lymphadenopathy at diagnosis were independent predictors of DLCO alteration, while early cutaneous involvement with photosensitivity was a protective factor. DLCO alteration, at baseline or anytime during follow-up was predictive of admission in intensive care unit and/or of all-cause death, both mainly due to severe disease flares and premature cardiovascular complications. CONCLUSION: This study suggests a link between DLCO alteration and disease damage, potentially related to SLE vasculopathy, and prognostic value of DLCO on death or ICU admission in SLE.

Leveraging Clinical Data Warehouses to Measure Impact of Update Prescription Guidelines of Polyvalent Immunoglobulins in 2018 in France.

In France and in other countries, we observed a significant growth in human polyvalent immunoglobulins (PvIg) usage. PvIg is manufactured from plasma collected from numeral donors, and its production is complex. Supply tensions have been observed for several years, and it is necessary to limit their consumption. Therefore, French Health Authority (FHA) provided guidelines in June 2018 to restrict their usage. This research aims to assess the guidelines' impact of the FHA on the use of PvIg. We analyzed data from Rennes University Hospital, where all PvIg prescriptions are reported electronically with quantity, rhythm, and indication. From the clinical data warehouses of RUH, we extracted comorbidities and lab results to evaluate the more complex guidelines. We globally noticed a reduction in the consumption of PvIg after the guidelines. Compliance with the recommended quantities and rhythms have also been observed. By combining two sources of data, we have been able to show an impact of FHA's guidelines on the consumption of PvIg.

Amoxicillin-Induced Neurotoxicity: Contribution of a Healthcare Data Warehouse to the Determination of a Toxic Concentration Threshold.

BACKGROUND: Amoxicillin (AMX)-induced neurotoxicity is well described and may be associated with AMX overexposure. No neurotoxic concentration threshold has been determined thus far. A better knowledge of maximum tolerable AMX concentrations is of importance to improve the safety of high doses of AMX. METHODS: We conducted a retrospective study using the local hospital data warehouse EhOP® to generate a specific query related to AMX neurotoxicity symptomatology. All patient medical reports containing a mention of neurotoxicity clinical symptoms coupled with AMX plasma concentration measurements were explored. Patients were classified into two groups according to the imputability of AMX in the onset of their neurotoxicity, on the basis of chronological and semiological criteria. A receiver-operating characteristic curve was performed to identify an AMX neurotoxic steady-state concentration (Css) threshold. RESULTS: The query identified 101 patients among 2054 patients benefiting from AMX TDM. Patients received a median daily dose of 9 g AMX, with a median creatinine clearance of 51 mL/min. A total of 17 of the 101 patients exhibited neurotoxicity attributed to AMX. The mean Css was higher for patients with neurotoxicity attributed to AMX (118 ± 62 mg/L) than those without 74 ± 48 mg/L (p = 0.002). A threshold AMX concentration of 109.7 mg/L predicted the occurrence of neurotoxicity. CONCLUSIONS: This study identified, for the first time, an AMX Css threshold of 109.7 mg/L associated with an excess risk of neurotoxicity. This approach needs to be confirmed by a prospective study with systematic neurological evaluation and TDM.

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

Defining 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.

Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study.

BACKGROUND: Disease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In France, existing clinic-based disease surveillance systems produce gastroenteritis activity information that lags real time by 1 to 3 weeks. This temporal data gap prevents public health officials from having a timely epidemiological characterization of this disease at any point in time and thus leads to the design of interventions that do not take into consideration the most recent changes in dynamics. OBJECTIVE: The goal of this study was to evaluate the feasibility of using internet search query trends and electronic health records to predict acute gastroenteritis (AG) incidence rates in near real time, at the national and regional scales, and for long-term forecasts (up to 10 weeks). METHODS: We present 2 different approaches (linear and nonlinear) that produce real-time estimates, short-term forecasts, and long-term forecasts of AG activity at 2 different spatial scales in France (national and regional). Both approaches leverage disparate data sources that include disease-related internet search activity, electronic health record data, and historical disease activity. RESULTS: Our results suggest that all data sources contribute to improving gastroenteritis surveillance for long-term forecasts with the prominent predictive power of historical data owing to the strong seasonal dynamics of this disease. CONCLUSIONS: The methods we developed could help reduce the impact of the AG peak by making it possible to anticipate increased activity by up to 10 weeks.

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study.

BACKGROUND: Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. OBJECTIVE: The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. METHODS: Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. RESULTS: The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of -3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of -2 weeks. The most correlated query from Google Trends was the "Dengue" topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of -3 weeks. CONCLUSIONS: Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories.

Linking Biomedical Data Warehouse Records With the National Mortality Database in France: Large-scale Matching Algorithm.

BACKGROUND: Often missing from or uncertain in a biomedical data warehouse (BDW), vital status after discharge is central to the value of a BDW in medical research. The French National Mortality Database (FNMD) offers open-source nominative records of every death. Matching large-scale BDWs records with the FNMD combines multiple challenges: absence of unique common identifiers between the 2 databases, names changing over life, clerical errors, and the exponential growth of the number of comparisons to compute. OBJECTIVE: We aimed to develop a new algorithm for matching BDW records to the FNMD and evaluated its performance. METHODS: We developed a deterministic algorithm based on advanced data cleaning and knowledge of the naming system and the Damerau-Levenshtein distance (DLD). The algorithm's performance was independently assessed using BDW data of 3 university hospitals: Lille, Nantes, and Rennes. Specificity was evaluated with living patients on January 1, 2016 (ie, patients with at least 1 hospital encounter before and after this date). Sensitivity was evaluated with patients recorded as deceased between January 1, 2001, and December 31, 2020. The DLD-based algorithm was compared to a direct matching algorithm with minimal data cleaning as a reference. RESULTS: All centers combined, sensitivity was 11% higher for the DLD-based algorithm (93.3%, 95% CI 92.8-93.9) than for the direct algorithm (82.7%, 95% CI 81.8-83.6; P<.001). Sensitivity was superior for men at 2 centers (Nantes: 87%, 95% CI 85.1-89 vs 83.6%, 95% CI 81.4-85.8; P=.006; Rennes: 98.6%, 95% CI 98.1-99.2 vs 96%, 95% CI 94.9-97.1; P<.001) and for patients born in France at all centers (Nantes: 85.8%, 95% CI 84.3-87.3 vs 74.9%, 95% CI 72.8-77.0; P<.001). The DLD-based algorithm revealed significant differences in sensitivity among centers (Nantes, 85.3% vs Lille and Rennes, 97.3%, P<.001). Specificity was >98% in all subgroups. Our algorithm matched tens of millions of death records from BDWs, with parallel computing capabilities and low RAM requirements. We used the Inseehop open-source R script for this measurement. CONCLUSIONS: Overall, sensitivity/recall was 11% higher using the DLD-based algorithm than that using the direct algorithm. This shows the importance of advanced data cleaning and knowledge of a naming system through DLD use. Statistically significant differences in sensitivity between groups could be found and must be considered when performing an analysis to avoid differential biases. Our algorithm, originally conceived for linking a BDW with the FNMD, can be used to match any large-scale databases. While matching operations using names are considered sensitive computational operations, the Inseehop package released here is easy to run on premises, thereby facilitating compliance with cybersecurity local framework. The use of an advanced deterministic matching algorithm such as the DLD-based algorithm is an insightful example of combining open-source external data to improve the usage value of BDWs.

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

BACKGROUND: 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.

Real-world safety profiles of pirfenidone and nintedanib in idiopathic pulmonary fibrosis patients.

INTRODUCTION: While pirfenidone and nintedanib have greatly influenced the treatment of idiopathic pulmonary fibrosis (IPF), both drugs have significant early adverse drug reactions (ADRs) and almost nothing is known of their rare and delayed ADRs. We collected and analyzed pirfenidone- or nintedanib-related ADRs identified in a French rare lung disease center, recorded their profiles and identified potential safety signals. METHODS: We analyzed the medical records of IPF patients treated with pirfenidone or nintedanib between January 2011 and January 2020 at the Rennes University Hospital to estimate the incidence of serious and non-serious ADRs cases due to each drug and the incidence of ADRs involving the cardiovascular, hepatobiliary, gastro-intestinal, dermatological, and metabolic/nutritional systems. RESULTS: The 176 patients included 115 (65%) initially treated with pirfenidone and 61 (35%) given nintedanib. ADRs occurred in 78.3% of those given pirfenidone and in 70.5% of those given nintedanib. The incidence of first serious ADRs cases was about 33 per 100 person-years (100 PY) for both drugs; first non-serious pirfenidone ADRs cases were 102 per 100 PY and 130 per 100 PY for nintedanib. The incidence involving each organ system were quite similar, except for the gastro-intestinal and skin disorders. Cardiovascular disorders occurred in about 10 cases per 100 PY in both pirfenidone and nintedanib patients. DISCUSSION: Most ADRs were consistent with the expected antifibrotic drug safety profiles. As arterial and venous thromboembolic events are rare, it is important to assess the risk associated with using antifibrotics by a dedicated pharmacoepidemiological study.

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

Direct 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.

Management of Pathogenic CDH1 Variant Carriers Within the FREGAT Network: A Multicentric Retrospective Study.

OBJECTIVE: To describe the management of pathogenic CDH1 variant carriers (pCDH1vc) within the FREGAT (FRench Eso-GAsTric tumor) network. Primary objective focused on clinical outcomes and pathological findings, Secondary objective was to identify risk factor predicting postoperative morbidity (POM). BACKGROUND: Prophylactic total gastrectomy (PTG) remains the recommended option for gastric cancer risk management in pCDH1vc with, however, endoscopic surveillance as an alternative. METHODS: A retrospective observational multicenter study was carried out between 2003 and 2021. Data were reported as median (interquartile range) or as counts (proportion). Usual tests were used for univariate analysis. Risk factors of overall and severe POM (ie, Clavien-Dindo grade 3 or more) were identified with a binary logistic regression. RESULTS: A total of 99 patients including 14 index cases were reported from 11 centers. Median survival among index cases was 12.0 (7.6-16.4) months with most of them having peritoneal carcinomatosis at diagnosis (71.4%). Among the remaining 85 patients, 77 underwent a PTG [median age=34.6 (23.7-46.2), American Society of Anesthesiologists score 1: 75%] mostly via a minimally invasive approach (51.9%). POM rate was 37.7% including 20.8% of severe POM, with age 40 years and above and low-volume centers as predictors ( P =0.030 and 0.038). After PTG, the cancer rate on specimen was 54.5% (n=42, all pT1a) of which 59.5% had no cancer detected on preoperative endoscopy (n=25). CONCLUSIONS: Among pCDH1vc, index cases carry a dismal prognosis. The risk of cancer among patients undergoing PTG remained high and unpredictable and has to be balanced with the morbidity and functional consequence of PTG.

How to Optimize Connection Between PACS and Clinical Data Warehouse: A Web Service Approach Based on Full Metadata Integration.

Clinical image data analysis is an active area of research. Integrating such data in a Clinical Data Warehouse (CDW) implies to unlock the PACS and RIS and to address interoperability and semantics issues. Based on specific functional and technical requirements, our goal was to propose a web service (I4DW) that allows users to query and access pixel data from a CDW by fully integrating and indexing imaging metadata. Here, we present the technical implementation of this workflow as well as the evaluation we carried out using a prostate cancer cohort use case. The query mechanism relies on a Dicom metadata hierarchy dynamically generated during the ETL Process. We evaluated the Dicom data transfer performance of I4DW, and found mean retrieval times of 5.94 seconds and 0.9 seconds to retrieve a complete DICOM series from the PACS and all metadata of a series. We could retrieve all patients and imaging tests of the prostate cancer cohort with a precision of 0.95 and a recall of 1. By leveraging the CMOVE method, our approach based on the Dicom protocol is scalable and domain-neutral. Future improvement will focus on performance optimization and de identification.

"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.

Book 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.

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

Patients 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.

Reliability of Drug-Drug Interaction Measurement on Real-Word Data: The ReMIAMes Project.

The ReMIAMes project proposes a methodological framework to provide a reliable and reproducible measurement of the frequency of drug-drug interactions (DDI) when performed on real-world data. This framework relies on (i) a fine-grained and contextualized definition of DDIs, (ii) a shared minimum information model to select the appropriate data for the correct interpretation of potential DDIs, (iii) an ontology-based inference module able to handle missing data to classify prescription lines with potential DDIs, (iv) a report generator giving the value of the measurement and explanations when potential false positive are detected due to a lack of available data. All the tools developed are intended to be publicly shared under open license.

Indexing Imaging Reports for Data Sharing: A Study of Mapping Using RadLex Playbook and LOINC.

New use cases and the need for quality control and imaging data sharing in health studies require the capacity to align them to reference terminologies. We are interested in mapping the local terminology used at our center to describe imaging procedures to reference terminologies for imaging procedures (RadLex Playbook and LOINC/RSNA Radiology Playbook). We performed a manual mapping of the 200 most frequent imaging report titles at our center (i.e. 73.2% of all imaging exams). The mapping method was based only on information explicitly stated in the titles. The results showed 57.5% and 68.8% of exact mapping to the RadLex and LOINC/RSNA Radiology Playbooks, respectively. We identified the reasons for the mapping failure and analyzed the issues encountered.