Application of Patient-Based Real-Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening

IF 2.6 4区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Journal of Clinical Laboratory Analysis Pub Date : 2024-03-11 DOI:10.1002/jcla.25019

Xuran Yang, Qianlan Chen, Zhifeng Pan, Jingmao Cheng, Wenting Zheng, Yingliang Liang, Hui Chen, Guanghui Chen, Wandang Wang

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引用次数: 0

Abstract

Background

Patient-based real-time quality control (PBRTQC) has gained attention because of its potential to continuously monitor the analytical quality in situations wherein internal quality control (IQC) is less effective. Therefore, we tried to investigate the application of PBRTQC method based on an artificial intelligence monitoring (AI-MA) platform in quality risk monitoring of Down syndrome (DS) serum screening.

Methods

The DS serum screening item determination data and relative IQC data from January 4 to September 7 in 2021 were collected. Then, PBRTQC exponentially weighted moving average (EWMA) and moving average (MA) procedures were built and optimized in the AI-MA platform. The efficiency of the EWMA and MA procedures with intelligent and traditional control rules were compared. Next, the optimal EWMA procedures that contributed to the quality assurance of serum screening were run and generated early warning cases were investigated.

Results

Optimal EWMA and MA procedures on the AI-MA platform were built. Comparison results showed the EWMA procedure with intelligent QC rules but not traditional quality rules contained the best efficiency. Based on the AI-MA platform, two early warning cases were generated by using the optimal EWMA procedure, which finally found were caused by instrument failure. Moreover, the EWMA procedure could truly reflect the detection accuracy and quality in situations wherein traditional IQC products were unstable or concentrations were inappropriate.

Conclusions

The EWMA procedure built by the AI-MA platform could be a good complementary control tool for the DS serum screening by truly and timely reflecting the detection quality risks.

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基于人工智能监测平台的患者实时质量控制在唐氏综合征血清筛查质量风险持续监测中的应用。

背景：基于患者的实时质量控制（PBRTQC）因其在内部质量控制（IQC）效果不佳的情况下持续监测分析质量的潜力而备受关注。因此，我们尝试研究基于人工智能监测（AI-MA）平台的 PBRTQC 方法在唐氏综合征（DS）血清筛查质量风险监测中的应用：方法：收集2021年1月4日至9月7日的DS血清筛查项目测定数据和相对IQC数据。然后，在 AI-MA 平台上构建并优化了 PBRTQC 指数加权移动平均（EWMA）和移动平均（MA）程序。比较了采用智能控制规则和传统控制规则的 EWMA 和 MA 程序的效率。接下来，运行了有助于保证血清筛查质量的最优 EWMA 程序，并对产生的预警案例进行了调查：结果：在 AI-MA 平台上建立了最优 EWMA 和 MA 程序。比较结果表明，采用智能质量控制规则而非传统质量规则的 EWMA 程序效率最高。基于 AI-MA 平台，使用最优 EWMA 程序生成了两个预警案例，最终发现这两个案例是由仪器故障引起的。此外，在传统 IQC 产品不稳定或浓度不合适的情况下，EWMA 程序能真实反映检测精度和质量：由 AI-MA 平台构建的 EWMA 程序可以真实、及时地反映 DS 血清筛查的检测质量风险，是一种很好的辅助控制工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Clinical Laboratory Analysis 医学-医学实验技术

CiteScore

5.60

自引率

7.40%

发文量

584

审稿时长

6-12 weeks

期刊介绍： Journal of Clinical Laboratory Analysis publishes original articles on newly developing modes of technology and laboratory assays, with emphasis on their application in current and future clinical laboratory testing. This includes reports from the following fields: immunochemistry and toxicology, hematology and hematopathology, immunopathology, molecular diagnostics, microbiology, genetic testing, immunohematology, and clinical chemistry.