Analysis of a Unique Postmarket Surveillance Dataset That a Glucose Test-Strip Demonstrates no Evidence of Interference and Robust Clinical Accuracy Irrespective of the Prescription Medication Status of a Large Cohort of Patients With Diabetes. (January 2021) - Grady, Mike; Cameron, Hilary; Phillips, Stuart; Smith, Gillian; Setford, Steven
Background: Despite a marked increase in polypharmacy in patients with diabetes there have been no thorough evaluations of the impact of polypharmacy on the accuracy of any current blood glucose monitoring (BGM) system. This study evaluated the accuracy of a BGM test-strip with respect to polypharmacy using a large clinical registry dataset. Methods: Medication profiles were analyzed for 830 subjects (334 with type 1 [T1D] and 496 with type 2 diabetes [T2D]) attending three hospitals. Blood samples were analyzed to determine clinical accuracy of the BGM test-strip compared to a laboratory comparator. Results: Across the 830 subjects, 473 different medications (41 diabetes and 432 nondiabetes) were recorded. Patients took on average 6.5 ( n = 1-23) individual medications and 4 ( n = 1-11) unique classes of medication. Clinical accuracy to EN ISO 15197:2015 criteria was met irrespective of increasing average number of individual medication, categorized from 1 to 4, 5 to 8, 9 to 12, and >12 taken per subject (97.7%, 98.4%, 98.1%, and 98.5%, respectively). Clinical accuracy to EN ISO 15197:2015 criteria was also met across 15 classes of medication using the combined dataset (98.1%; 13 003/13 253). Surveillance error grid analysis showed 98.8% (13 079/13 232) of readings presented no clinical risk. No individual class or combination of medication classes impacted clinical accuracy of the BGM test-strip. Conclusions: This comprehensive analysis for this specific test-strip platform demonstrated no evidence of interference and robust clinical accuracy of this test strip, irrespective of the prescription medication status of patients with diabetes.