Feasibility of automated surveillance of implantable devices in orthopaedics via clinical data warehouse: the Studio study.

BACKGROUND

Total hip, knee and shoulder arthroplasties (THKSA) are increasing due to expanding demands in ageing population. Material surveillance is important to prevent severe complications involving implantable medical devices (IMD) by taking appropriate preventive measures. Automating the analysis of patient and IMD features could benefit physicians and public health policies, allowing early issue detection and decision support. The study aimed to demonstrate the feasibility of automated cohorting of patients with a first arthroplasty in two hospital data warehouses (HDW) in France.

METHODS

The study included adult patients with an arthroplasty between 2010 and 2019 identified by 2 data sources: hospital discharge and pharmacy. Selection was based on the health insurance thesaurus of IMDs in the pharmacy database: 1,523 distinct IMD references for primary THSKA. In the hospital discharge database, 22 distinct procedures for native joint replacement allowing a matching between IMD and surgical procedure of each patient selected. A program to automate information extraction was implemented in the 1st hospital data warehouse using natural language processing (NLP) on pharmacy labels, then it was then applied to the 2nd hospital.

RESULTS

The e-cohort was built with a first arthroplasty for THKSA performed in 7,587 patients with a mean age of 67.4 years, and a sex ratio of 0.75. The cohort involved 4,113 hip, 2,630 knee and 844 shoulder surgical patients. Obesity, cardio-vascular diseases and hypertension were the most frequent medical conditions.

DISCUSSION

The implementation of an e-cohort for material surveillance will be easily workable over HDWs France wild. Using NLP as no international IMD mapping exists to study IMD, our approach aims to close the gap between conventional epidemiological cohorting tools and bigdata approach.

CONCLUSION

This pilot study demonstrated the feasibility of an e-cohort of orthopaedic devices using clinical data warehouses. The IMD and patient features could be studied with intra-hospital follow-up and will help analysing the infectious and unsealing complications.