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Kidney Week

Abstract: FR-PO003

Developing a Kidney Genetics Registry Within an Electronic Medical Record

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Gupta, Asheeta A., The Royal Children's Hospital Melbourne, Parkville, Victoria, Australia
  • Mcdonnell, Ciara, The Royal Children's Hospital Melbourne, Parkville, Victoria, Australia
  • Shanks, Josiah, The Royal Children's Hospital Melbourne, Parkville, Victoria, Australia
  • Butler, Grainne H., The Royal Children's Hospital Melbourne, Parkville, Victoria, Australia
  • Jayasinghe, Kushani C., Monash Health, Clayton, Victoria, Australia
  • Quinlan, Catherine, The Royal Children's Hospital Melbourne, Parkville, Victoria, Australia
Background

Dynamic patient registries within the electronic medical record (EMR) can provide security in a clinical setting by using existing tools to automate database compilation, reducing the time burden required to populate information whilst maintaining the accuracy of patient lists and data entry.
A kidney genetics registry within the EMR facilitates decision-making that directly affects clinical care whilst concurrently contributing to a better understanding of disease processes and therapeutic approaches. We present our approach to the development of an EMR-embedded kidney registry in a tertiary pediatric setting highlighting the implementation challenges of amalgamating clinical data from multiple platforms.

Methods

An encounter-based EPIC registry was designed and created in collaboration with the Centre of Health Analytics for all patients referred to the Kidney Genetics clinic at the Royal Children’s Hospital, Melbourne from February 2016 to present. Metrics for data capture included demographic information, growth parameters, clinical diagnoses, non-genetic diagnostic test results (including imaging and histopathology), and surveillance (including clinical parameters, hematological and biochemical test results). Triggers to warrant genetic re-evaluation or clinical intervention were included. A subsequent validation study was performed with a manually collated cohort of patients.

Results

The complete registry consisted of 516 patients (median age at presentation of 24 months). Challenges included the identification of appropriate patients, inclusion of genetic results from external laboratories, manual validation of non-machine-readable genetic reports, and the concurrent use of paper-based family genetic files. Sequencing was undertaken in the form of a clinical exome or whole exome sequencing with or without microarray depending on clinical indication. A genetic diagnosis was found in 88/212 (41.5%), variants of uncertain significance in 16/212 (7.5%), and incidental findings in 2/212 (0.94%).

Conclusion

Obtaining a genetic diagnosis can instigate a precision medicine approach, aiding risk stratification, tailored surveillance, initiation of disease-modifying therapies, and transplant planning. Combining genetic results with real-time monitoring of patients has the potential to streamline and automate this process.