AI-PBRTQC 与传统 PBRTQC 模型在识别质量风险方面的质量控制效果比较研究。

Xucai Dong, Xi Meng, Bin Li, Dongmei Wen, Xianfei Zeng
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摘要

引言我们比较了人工智能-基于患者的实时质量控制(AI-PBRTQC)和传统PBRTQC在实验室中的质量控制效率,为PBRTQC在临床实验室中的广泛应用创造有利条件:本研究将患者5个月内的总甲状腺素(TT4)、抗穆勒氏管激素(AMH)、丙氨酸氨基转移酶(ALT)、总胆固醇(TC)、尿素和白蛋白(ALB)数据分为两组:AI-PBRTQC 组和传统 PBRTQC 组。传统 PBRTQC 组采用 Box-Cox 变换法估算截断范围。而在 AI-PBRTQC 组中,PBRTQC 软件平台会智能选择截断范围。本研究采用错误检测率、假阳性率、假阴性率、直至错误检测的患者样本平均数量和曲线下面积来评估最佳 PBRTQC 模型。本研究通过分析质量风险案例,证明了 AI-PBRTQC 在识别质量风险方面的有效性:PBRTQC的最优参数设置方案为:TT4(78-186),λ=0.03;AMH(0.02-2.96),λ=0.02;ALT(10-25),λ=0.02;TC(2.84-5.87),λ=0.02;尿素(3.5-6.6),λ=0.02;ALB(43-52),λ=0.05:与传统的 PBRTQC 相比,AI-PBRTQC 组能更有效地识别质量风险。AI-PBRTQC 还能在少量样本中有效识别质量风险。AI-PBRTQC 可用于确定生化和免疫分析物的质量风险。AI-PBRTQC 可识别试剂校准、上机时间和品牌变更等质量风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative study on the quality control effectiveness of AI-PBRTQC and traditional PBRTQC model in identifying quality risks.

Introduction: We compared the quality control efficiency of artificial intelligence-patient-based real-time quality control (AI-PBRTQC) and traditional PBRTQC in laboratories to create favorable conditions for the broader application of PBRTQC in clinical laboratories.

Materials and methods: In the present study, the data of patients with total thyroxine (TT4), anti-Müllerian hormone (AMH), alanine aminotransferase (ALT), total cholesterol (TC), urea, and albumin (ALB) over five months were categorized into two groups: AI-PBRTQC group and traditional PBRTQC group. The Box-Cox transformation method estimated truncation ranges in the conventional PBRTQC group. In contrast, in the AI-PBRTQC group, the PBRTQC software platform intelligently selected the truncation ranges. We developed various validation models by incorporating different weighting factors, denoted as λ. Error detection, false positive rate, false negative rate, average number of the patient sample until error detection, and area under the curve were employed to evaluate the optimal PBRTQC model in this study. This study provides evidence of the effectiveness of AI-PBRTQC in identifying quality risks by analyzing quality risk cases.

Results: The optimal parameter setting scheme for PBRTQC is TT4 (78-186), λ = 0.03; AMH (0.02-2.96), λ = 0.02; ALT (10-25), λ = 0.02; TC (2.84-5.87), λ = 0.02; urea (3.5-6.6), λ = 0.02; ALB (43-52), λ = 0.05.

Conclusions: The AI-PBRTQC group was more efficient in identifying quality risks than the conventional PBRTQC. AI-PBRTQC can also effectively identify quality risks in a small number of samples. AI-PBRTQC can be used to determine quality risks in both biochemistry and immunology analytes. AI-PBRTQC identifies quality risks such as reagent calibration, onboard time, and brand changes.

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