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不断增加的疾病流行率、假阴性漏报和诊断不确定性对 2019 冠状病毒病(COVID-19)检测性能的影响。

The Impact of Increasing Disease Prevalence, False Omissions, and Diagnostic Uncertainty on Coronavirus Disease 2019 (COVID-19) Test Performance.

机构信息

From the Department of Pathology and Laboratory Medicine, POCT•CTR, School of Medicine, University of California, Davis.

出版信息

Arch Pathol Lab Med. 2021 Jul 1;145(7):797-813. doi: 10.5858/arpa.2020-0716-SA.

Abstract

CONTEXT.—: Coronavirus disease 2019 (COVID-19) test performance depends on predictive values in settings of increasing disease prevalence. Geospatially distributed diagnostics with minimal uncertainty facilitate efficient point-of-need strategies.

OBJECTIVES.—: To use original mathematics to interpret COVID-19 test metrics; assess US Food and Drug Administration Emergency Use Authorizations and Health Canada targets; compare predictive values for multiplex, antigen, polymerase chain reaction kit, point-of-care antibody, and home tests; enhance test performance; and improve decision-making.

DESIGN.—: PubMed/newsprint-generated articles documenting prevalence. Mathematica and open access software helped perform recursive calculations, graph multivariate relationships, and visualize performance by comparing predictive value geometric mean-squared patterns.

RESULTS.—: Tiered sensitivity/specificity comprised: T1, 90%, 95%; T2, 95%, 97.5%; and T3, 100%, ≥99%. Tier 1 false negatives exceeded true negatives at >90.5% prevalence; false positives exceeded true positives at <5.3% prevalence. High-sensitivity/specificity tests reduced false negatives and false positives, yielding superior predictive values. Recursive testing improved predictive values. Visual logistics facilitated test comparisons. Antigen test quality fell off as prevalence increased. Multiplex severe acute respiratory syndrome (SARS)-CoV-2)influenza A/Brespiratory syncytial virus testing performed reasonably well compared with tier 3. Tier 3 performance with a tier 2 confidence band lower limit will generate excellent performance and reliability.

CONCLUSIONS.—: The overriding principle is to select the best combined performance and reliability pattern for the prevalence bracket. Some public health professionals recommend repetitive testing to compensate for low sensitivity. More logically, improved COVID-19 assays with less uncertainty conserve resources. Multiplex differentiation of COVID-19 from influenza A/B-respiratory syncytial virus represents an effective strategy if seasonal flu surges next year.

摘要

背景

新型冠状病毒病 2019(COVID-19)检测的性能取决于在疾病流行率不断增加的情况下预测值。具有最小不确定性的地理分布式诊断有助于实现高效的按需策略。

目的

使用原始数学方法解释 COVID-19 检测指标;评估美国食品和药物管理局的紧急使用授权和加拿大卫生部的目标;比较多重、抗原、聚合酶链反应试剂盒、即时护理抗体和家庭检测的预测值;提高检测性能;并改善决策。

设计

PubMed/新闻纸生成的文章记录了流行率。Mathematica 和开放访问软件有助于进行递归计算、绘制多元关系图,并通过比较预测值几何平均值平方模式来可视化性能。

结果

分层敏感性/特异性包括:T1,90%,95%;T2,95%,97.5%;T3,100%,≥99%。T1 假阴性在 >90.5%的流行率时超过真阴性;T1 假阳性在 <5.3%的流行率时超过真阳性。高敏感性/特异性测试减少了假阴性和假阳性,从而产生了更好的预测值。递归测试提高了预测值。可视化物流促进了测试比较。随着流行率的增加,抗原测试的质量下降。与 T3 相比,多重严重急性呼吸综合征(SARS)-CoV-2)流感 A/B呼吸道合胞病毒检测表现相当好。具有 T2 置信带下限的 T3 性能将产生出色的性能和可靠性。

结论

首要原则是为流行率区间选择最佳的综合性能和可靠性模式。一些公共卫生专业人员建议重复测试以弥补低敏感性。更合理的是,具有较少不确定性的改进 COVID-19 检测可以节省资源。如果明年季节性流感激增,多重区分 COVID-19 与流感 A/B-呼吸道合胞病毒将是一种有效的策略。

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