Pilot Study of an e-Cohort to Monitor Adverse Event for Patient with Hip Prostheses from Clinical Data Warehouse.

Hip arthroplasty represents a large proportion of orthopaedic activity, constantly increasing. Automating monitoring from clinical data warehouses is an opportunity to dynamically monitor devices and patient outcomes allowing improve clinical practices. Our objective was to assess quantitative and qualitative concordance between claim data and device supply data in order to create an e-cohort of patients undergoing a hip replacement. We performed a single-centre cohort pilot study, from one clinical data warehouse of a French University Hospital, from January 1, 2010 to December 31, 2019. We included all adult patients undergoing a hip arthroplasty, and with at least one hip medical device provided. Patients younger than 18 years or opposed to the reuse of their data were excluded from the analysis. Our primary outcome was the percentage of hospital stays with both hip arthroplasty and hip device provided. The patient and stay characteristics assessed in this study were: age, sex, length of stay, surgery procedure (replacement, repositioning, change, or reconstruction), medical motif for surgery (osteoarthritis, fracture, cancer, infection, or other) and device provided (head, stem, shell, or other). We found 3,380 stays and 2,934 patients, 96.4% of them had both a hip surgery procedure and a hip device provided. These data from different sources are close enough to be integrated in a common clinical data warehouse.

Role of real-world digital data for orthopedic implant automated surveillance: a systematic review.

INTRODUCTION: Data collection automation through the reuse of real-world digital data from clinical data warehouses (CDW) could represent a great opportunity to improve medical device monitoring. For instance, this approach is starting to be used for the design of automated decision support systems for joint replacement monitoring. However, a number of obstacles remains, such as data quality and interoperability through the use of common and regularly updated terminologies, and the use of a Unique Device Identifier (UDI). AREAS COVERED: To present the existing models of automated surveillance of orthopedic devices, a systematic review of initiatives using real-world digital health data to monitor joint replacement surgery was performed following the PRISMA 2020 guidelines. The main objective was to identify the data sources, the target populations, the population size, the device location, and the main results of studies on such initiatives. EXPERT OPINION: Analysis of the identified studies showed that real-world digital data offer many opportunities for improving the automation of monitoring in orthopedics. The contribution of real-world data, especially through natural language processing, UDI use in CDW and the integration of device databases, is needed for automated and more robust health surveillance.

Prescreening in Oncology Using Data Sciences: The PreScIOUS Study.

The development of precision medicine in oncology to define profiles of patients who could benefit from specific and relevant anti-cancer therapies is essential. An increasing number of specific eligibility criteria are necessary to be eligible to targeted therapies. This study aimed to develop an automated algorithm based on natural language processing to detect patients and tumor characteristics to reduce the time-consuming prescreening for trial inclusions. Hence, 640 anonymized multidisciplinary team meeting (MTM) reports concerning lung cancer were extracted from one teaching hospital data warehouse in France and annotated. To automate the extraction of 52 bioclinical information corresponding to 8 major eligibility criteria, regular expressions were implemented and evaluated. The performance parameters were satisfying: macroaverage F1-score 93%; rates reached 98% for precision and 92% for recall. In MTM, fill rates variabilities among patients and tumors information remained important (from 31.4% to 100%). The least reported characteristics and the most difficult to automatically collect were genetic mutations and rearrangement test results.

Comparison of Knowledge Databases to Be Used in Automated Monitoring of Orthopedic Medical Devices.

Surveillance and traceability of medical devices (MD) is a challenge in health care systems. In the perspective of reusing EHR data to automate the monitoring of medical devices, we carried out a comparison of the main MD knowledge bases (MD-KDB) currently available in France. Four MD-KDBs (ANSM, Gudid, Exhausmed and CIOdm) were compared quantitatively and through an example of a shoulder prosthesis. The number of MDs registered differs from one MD-KDB to another. Domain terminologies used in MD-KDBs differ in terms of granularity and in the ease of querying. Waiting EUDAMED, the European MD-KDB, it seems necessary so far to use and combine information coming from several MD-KDBs to address MD monitoring.

[ANCA-vasculitis with renal impairment treated by intravenous versus oral cyclophosphamide: Multicentric analysis of relapse free survival (VaReCyS)]. / Vascularites associées aux ANCA avec atteinte rénale recevant un traitement d'induction par cyclophosphamide oral versus intraveineux : étude rétrospective multicentrique de la survie sans rechute (VaReCyS).

INTRODUCTION: ANCA-vasculitis are associated with high morbidity and mortality. Large use of cyclophosphamide as induction immunosuppressive therapy is limited by its side effects. All recent literature trends in decreasing cumulative dose while optimizing maintenance therapy. METHODS: This retrospective multicentric analysis included ANCA-vasculitis patients with renal impairment and de novo diagnose followed in Rennes and Vannes hospitals for 2 years minimum. The primary endpoint was to analyze relapse free survival comparing oral and intravenous administration of cyclophosphamide. RESULTS: From 01/01/2003 to 01/03/2016, 91 patients were included (45 oral and 46 intravenous group). Patients in oral group were 10 years younger (P<0,001), with higher maintenance therapy (P<0,001) and steroids (P<0,001) duration. With a Cox model adjusted on age, steroid and maintenance therapy duration, oral cyclophosphamide showed no benefice in decreasing relapse free survival (OR 0,80; 95%IC 0,38-1,66; P=0,55). No difference was observed on either mortality or renal survival. Oral group at 1-year trends to achieve more leucopenia (40 vs 24%) and infection (30 vs 22%) episodes, but less hospitalization (40 vs 65%), without reaching statistical significance. CONCLUSION: In this retrospective multicentric analysis, oral cyclophosphamide induction was not associated with better relapse free survival after adjustment with age, steroid and maintenance therapy duration. Maintenance therapy duration is believed to better prevent ANCA-vasculitis relapse.

Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data.

Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compared statistics (Logistic Regression) and machine learning algorithms (Gradient Boosting, Random Forest, and Neural Network) for predicting the risk of all-cause, 30-day hospital readmission using data from the clinical data warehouse of Rennes and from other sources. The dataset included hospital stays based on the criteria of the French national methodology for the 30-day readmission rate (i.e., patients older than 18 years, geolocation, no iterative stays, and no hospitalization for palliative care), with a similar pre-processing for all algorithms. We calculated the area under the ROC curve (AUC) for 30-day readmission prediction by each model. In total, we included 259114 hospital stays, with a readmission rate of 8.8%. The AUC was 0.61 for the Logistic Regression, 0.69 for the Gradient Boosting, 0.69 for the Random Forest, and 0.62 for the Neural Network model. We obtained the best performance and reproducibility to predict readmissions with Random Forest, and found that the algorithms performed better when data came from different sources.