Application of a Monte Carlo simulation model to estimate clinical risk associated with the analytic performance of point of care INR devices
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BACKGROUND: In 2016 FDA proposed performance expectations for POCT INR devices in response to a post market risk analysis of serious clinical and patient selfmonitoring adverse events: 95% of all INR results should fall within ±0.4 for INR <2; ± 20% for INR ≥2 to 3.5; ± 20% for INR ≥3.5 to 4.5 and ± 25% for INR ≥4.5. OBJECTIVE: To estimate the clinical risk of warfarin dosing error as a consequence of POCT INR assay inaccuracy and imprecision at FDA performance goals. METHOD: INR values (n= 53, 535) were obtained from community adult patients in the Saskatoon Health Region (SHR). Monte Carlo simulation models were used to assess the influence of analytical bias and imprecision on INR values by evaluating the fraction of warfarin-dose-categories according to the SHR algorithm that were unchanged or changed by ≥1, ≥2 or ≥3 dose categories. RESULTS: Simulations used a bias of ±0.4 to ±0.8 combined with 3% imprecision and predicted that 45% to 75% of results would have ≥1 category warfarin dosing error, and 1% to 18% of results would have ≥2 category errors. If INR imprecision was increased to 10%, then the model predicted that 45% to 75% of results continue with ≥1 category warfarin dose error but the fraction with ≥2 category error would increase to 2% to 24%. CONCLUSIONS: Simulation models demonstrated the extent of one category and two category treatment errors for POCT INR assays is highly dependent on method bias and only partially affected by method imprecision ≤10%.