Monitoring Variability of Laboratory Results in a Clinical Data Warehouse Using Automatic Dashboard.

Hospital laboratory results are a significant data source in Clinical Data Ware-houses (CDW). To ensure comparability across healthcare organizations and for use in research studies, the results need to be interoperable. The LOINC (Logical Observation Identifiers, Names, and Codes) terminology provides a unique identifier for local codes for lab tests, enabling interoperability. However, in real-world, events occur over time and can disrupt the distribution of lab result values. For example, new equipment may be added to the analysis pipeline, a machine may be replaced, formulas may evolve due to new scientific knowledge, and legacy terminologies may be adopted. This article proposes a pipeline for creating an automated dashboard to monitor these events and data quality. We used automatic change point detection methods such as PELT for event detection in lab results. For a given LOINC code, we create a dashboard that summarizes the number of local codes mapped, and the number of patients (by sex, age, and hospital service) associated with the code. Finally, the dashboard enables the visualization of time events that disrupt the signal distribution. The biologists were able to explain to us the changes for several biological assays.

How to Accurately Detect Renal Replacement Therapy Weaning in Intensive Care: Data Quality and Standardization Considerations for the OMOP Common Data Model.

This study assesses the effectiveness of the Observational Medical Outcomes Partnership common data model (OMOP CDM) in standardising Continuous Renal Replacement Therapy (CRRT) data from intensive care units (ICU) of two French university hospitals. Our objective was to extract and standardise data from various sources, enabling the development of predictive models for CRRT weaning that are agnostic to the data's origin. Data for 1,696 ICU stays from the two data sources were extracted, transformed, and loaded into the OMOP format after semantic alignment of 46 CRRT standard concepts. Although the OMOP CDM demonstrated potential in harmonising CRRT data, we encountered challenges related to data variability and the lack of standard concepts. Despite these challenges, our study supports the promise of the OMOP CDM for ICU data standardization, suggesting that further refinement and adaptation could significantly improve clinical decision making and patient outcomes in critical care settings.

Intensive Care Quality Indicators Dashboard Using Observational Medical Outcomes Partnership Common Data Model.

This paper presents the development of a visualization dashboard for quality indicators in intensive care units (ICUs), using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The dashboard enables the user to visualize quality indicator data using histograms, pie charts and tables. Our project uses the OMOP CDM, ensuring a seamless implementation of our dashboard across various hospitals. Future directions for our research include expanding the dashboard to incorporate additional quality indicators and evaluating clinicians' feedback on its effectiveness.

Applying Machine Learning for Prescriptive Support: A Use Case with Unfractionated Heparin in Intensive Care Units.

Continuous unfractionated heparin is widely used in intensive care, yet its complex pharmacokinetic properties complicate the determination of appropriate doses. To address this challenge, we developed machine learning models to predict over- and under-dosing, based on anti-Xa results, using a monocentric retrospective dataset. The random forest model achieved a mean AUROC of 0.80 [0.77-0.83], while the XGB model reached a mean AUROC of 0.80 [0.76-0.83]. Feature importance was employed to enhance the interpretability of the model, a critical factor for clinician acceptance. After prospective validation, machine learning models such as those developed in this study could be implemented within a computerized physician order entry (CPOE) as a clinical decision support system (CDSS).

Synthesizing Clinical Data Warehouse Content for Enhanced External Collaboration: A Preliminary Proposal.

This paper introduces a novel approach aimed at enhancing the accessibility of clinical data warehouses (CDWs) for external users, particularly researchers and biomedical companies interested in developing and testing their solutions. The primary focus is on proposing a clinical data catalogue designed to elucidate the contents of CDWs, facilitating biomedical project launch and completion. The catalogue is designed to address three fundamental inquiries that external users may have regarding CDWs: "What data is available, how much data is present, and how was it generated?" Additionally, the paper showcases a prototype of the catalogue through a visualization example, utilizing data from the CDW of Rennes University Hospital.

Secure Extraction of Personal Information from EHR by Federated Machine Learning.

Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges within the context of French EHRs. By utilizing a multilingual BERT model in an FL simulation involving 20 hospitals, each represented by a unique medical department or pole, we compared the performance of two setups: individual models, where each hospital uses only its own training and validation data without engaging in the FL process, and federated models, where multiple hospitals collaborate to train a global FL model. Our findings demonstrate that FL models not only preserve data confidentiality but also outperform the individual models. In fact, the Global FL model achieved an F1 score of 75,7%, slightly comparable to that of the Centralized approach at 78,5%. This research underscores the potential of FL in extracting PIIs from EHRs, encouraging its broader adoption in health data analysis.

Performance of a NLP Tool for Text Classification from Orthopaedic Operative Reports, Using Data from the Large Network of Clinical Data Warehouses of the West of France: The HACRO-HUGORTHO Project.

Electronic health data concerning implantable medical devices (IMD) opens opportunities for dynamic real-world monitoring to assess associated risks related to implanted materials. Due to population ageing and expanding demands, total hip, knee, and shoulder arthroplasties are increasing. Automating the collection and analysis of orthopedic device features could benefit physicians and public health policies enabling early issue detection, IMD monitoring and patient safety assessment. A machine learning tool using natural language processing (NLP) was developed for the automated extraction of operation information from medical reports in orthopedics. A corpus of 959 orthopaedic operative reports from 5 centres was manually annotated using the Prodigy software® with a strong inter-annotator agreement of 0.80. Data to extract concerned key clinical and procedure information (n= 9) selected by a multidisciplinary group based on the French health authority checklist. Performances parameters of the NLP model estimated an overall strong precision and recall of respectively 97.0 and 96.0 with a F1-score 96.3. Systematic monitoring of orthopedic devices could be ensured by an automated tool, leveraging clinical data warehouses. Traceability of medical devices with implantation modalities will allow detection of implant factors leading to complications. The evidence from real-world data could provide concrete and dynamic insights to surgeons and infectious disease specialists concerning implant follow-up, guiding therapeutic decision-making, and informing public health policymakers. The tool will be applied on clinical data warehouses to automate information extraction and presentation, providing feedback on mandatory information completion and contents of operative reports to support improvements, and thereafter implant research projects.

Tinzaparin, an alternative to subcutaneous unfractionated heparin, in patients with severe and end-stage renal impairment: a retrospective observational single-center study.

BACKGROUND: Tinzaparin could be easier to manage than unfractionated heparin in patients with severe renal impairment. However, clinical and pharmacologic data regarding its use in such patients are lacking. OBJECTIVES: The aims of this study were to determine, in patients with estimated glomerular filtration rate (eGFR) of <30 mL.min⁻1, tinzaparin pharmacokinetics (PK) parameters using a population PK approach and bleeding and thrombotic complications. METHODS: We performed a retrospective observational single-center study, including in-patients with eGFR of <30 mL.min⁻1 receiving prophylactic (4500 IU.d⁻1) or therapeutic (175 IU.kg⁻1.d⁻1) tinzaparin. Measured anti-Xa levels were analyzed using a nonlinear mixed-effects modeling approach. Individual predicted tinzaparin exposure markers at steady state were calculated for each patient and dosing regimen. The PK was also evaluated through Monte Carlo simulations based on the final covariate model parameter estimates. RESULTS: Over a 22-month period, 802 tinzaparin treatment periods in 623 patients were analyzed: two-thirds received a prophylactic dose, 66% had an eGFR of <20 mL.min⁻1, and 25% were on renal replacement therapy. In patients for whom anti-Xa measurements were performed (n = 199; 746 values), PK parameters, profiles, and maximum plasma concentrations were comparable with those in patients without renal impairment or in healthy volunteers. In the whole population, major bleeding occurred in 2.4% and 3.5% of patients receiving prophylactic and therapeutic doses over a median 9- and 7-day treatment period, respectively. No patients had thrombotic complications. CONCLUSION: Tinzaparin PK parameters and profiles were not affected by renal impairment. This suggests that tinzaparin, at therapeutic or prophylactic dose, could be an alternative to unfractionated heparin in hospitalized patients with severe renal impairment.

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.