GP.6 Improving Triaging of EEG Referrals for Rule out Infantile Spasms (ITERIS)

• Published in: Canadian journal of neurological sciences Vol. 48; no. s3; p. S13
• Main Authors: Djordjevic, D, Tracey, J, Alqahtani, M, Boyd, J, Go, C
• Format: Journal Article
• Language: English
• Published: New York, USA Cambridge University Press 01.11.2021
• Subjects: Grand Plenary; Machine learning; Platform Presentations
• ISSN: 0317-1671; 2057-0155
• DOI: 10.1017/cjn.2021.262

Background: Infantile spasms (IS) is a devastating pediatric seizure disorder for which EEG referrals are prioritized at the Hospital for Sick Children, representing a resource challenge. The goal of this study was to improve the triaging system for these referrals. Methods: Part 1: descriptive analysis was performed retrospectively on EEG referrals. Part 2: prospective questionnaires were used to determine relative risk of various predictive factors. Part 3: electronic referral form was amended to include 5 positive predictive factors. A triage point system was tested by assigning EEGs as high risk (3 days), standard risk (1 week), or low risk (2 weeks). A machine learning model was developed. Results: Most EEG referrals were from community pediatricians with a low yield of IS diagnoses. Using the 5 predictive factors, the proposed triage system accurately diagnosed all IS within 3 days. No abnormal EEGs were missed in the low-risk category. The machine learning model had over 90% predictive accuracy and will be prospectively tested. Conclusions: Improving EEG triaging for IS may be possible to prioritize higher risk patients. Machine Learning techniques can potentially be applied to help with predictions. We hope that our findings will ultimately improve resource utilization and patient care.