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使用仿真模型评估达到 2018 年 FDA 血糖仪性能指导标准所需的精度和偏差规范。

Evaluation of Precision and Bias Specifications Required to Achieve the 2018 FDA Guidance Criteria for Glucose Meter Performance Using Simulation Models.

机构信息

Department of Pathology and Laboratory Medicine, Saskatchewan Health Authority, Saskatoon, Saskatchewan, Canada.

出版信息

J Diabetes Sci Technol. 2020 May;14(3):513-518. doi: 10.1177/1932296819889639. Epub 2019 Nov 21.

Abstract

BACKGROUND

The objective of this study was to estimate the combinations of total bias and total imprecision required for devices to meet the Food and Drug Administration (FDA) specifications using Monte Carlo simulation rather than collection and analysis of experimental data.

METHODS

A model Gaussian distribution of true-glucose values was altered by adding bias and imprecision to create measured-glucose values affected by analytic error. The fraction of measured-glucose values that met the 2018 FDA criteria for blood glucose monitoring system (BGMS) or self-monitoring blood glucose (SMBG) devices was determined as a function of bias and imprecision.

RESULTS

The BGMS model determined that a maximum total imprecision of 6% was required with no bias, and with a total bias of +10 mg/dL the total imprecision allowed was reduced to 5% to achieve the 95% FDA performance expectation: 95% of results ≥75 mg/dL within ±12% and 95% of results <75 mg/dL within ±12 mg/dL. The SMBG model determined that a maximum total imprecision of 6% was required at no bias, and with a total bias of +10 mg/dL the total imprecision allowed was reduced to 4% to achieve the 98% FDA expectation: 98% of results ±75 mg/dL within ±15% and 98% of results <75 mg/dL within ±15 mg/dL.

CONCLUSIONS

The 2018 FDA guidance criteria require strict conditions for glucose meter clinical trials to achieve <10 mg/dL total bias and total imprecision of <5%. Total imprecision and bias values assessed in models in this study represent the cumulative imprecision and bias errors for the glucose meters, the reference method, and preanalytic processes.

摘要

背景

本研究旨在通过蒙特卡罗模拟而不是收集和分析实验数据来估计设备达到食品和药物管理局 (FDA) 规范所需的总偏差和总不精密度的组合。

方法

通过向真实葡萄糖值的模型高斯分布添加偏差和不精密度来创建受分析误差影响的测量葡萄糖值,从而创建测量葡萄糖值。作为偏差和不精密度的函数,确定符合 2018 年 FDA 血糖仪(BGMS)或自我监测血糖(SMBG)设备标准的测量葡萄糖值的分数。

结果

BGMS 模型确定,最大总不精密度为 6%且无偏差,总偏差为+10mg/dL 时,允许的总不精密度降低至 5%,以达到 95%的 FDA 性能预期:95%的结果≥75mg/dL,偏差在±12%以内,95%的结果<75mg/dL,偏差在±12mg/dL 以内。SMBG 模型确定,最大总不精密度为 6%且无偏差,总偏差为+10mg/dL 时,允许的总不精密度降低至 4%,以达到 98%的 FDA 预期:98%的结果±75mg/dL,偏差在±15%以内,98%的结果<75mg/dL,偏差在±15mg/dL 以内。

结论

2018 年 FDA 指导准则要求血糖仪临床试验的总偏差和总不精密度<10mg/dL,<5%。本研究模型中评估的总不精密度和偏差值代表了血糖仪、参考方法和分析前过程的累积不精密度和偏差误差。

相似文献

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Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study.血糖监测准确性的临床影响:一项计算机模拟研究。
J Diabetes Sci Technol. 2017 Nov;11(6):1187-1195. doi: 10.1177/1932296817710474. Epub 2017 Jun 1.

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