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Should we maintain the 95 percent reference intervals in the era of wellness testing? A concept paper.

机译:我们是否应该在健康测试时代保持95%的参考间隔?概念文件。

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摘要

The reference interval is probably the most widely used decision-making tool in clinical practice, with a modern use aiming at identifying wellness during health check and screening. Its use as a diagnostic tool is much less recognised and may be obsolete. The present study investigates the consequences of the new practice for the interpretation of prospective value, negative vs. positive, the probability of confirming wellness, and number of false results based on selected strategy for reference interval establishment. Calculations assumed normalised Gaussian-distributed reference intervals with analytical variation set to zero and absolute accuracy. Also assumed is the independency of tests. Probability for no values outside reference intervals in healthy subjects was calculated from the formula p(no) outside=(1 - p(single)) and according to the formula for repeated testing: p(one) outside =n x p(single) (1 - p(single))n-1 etc. Here n is the number of tests performed and p(single) is the probability of one result outside reference limits with the general formula p(i) outside n-i=k x p(single)i (1- p(single))n-i, with k being the binominal coefficient and i the number outside the reference intervals. Use of the 99.9 centile for health checks will increase the probability for no false from 60% to 99% for 10 tests, and from 46% to 98% for 15 tests. The probability for one false-positive result in 10 tests in a panel can be reduced from 32% to 1% if the 99.9% centile is substituted for the 95% centile. For two in 10 tests, the probability can be reduced from 8% to below 0.1%. In both cases, selection of the 99.9% centile improves the diagnostic accuracy. Reference intervals are needed as a "true" negative reference for absence of disease, and should cover the 99.9% centile of the reference distribution of an analyte to avoid false positives. For this new use, it is critical that reference persons are absolutely normal without clinical, genetic and biochemical signs of the condition being investigated. However, reference intervals cannot substitute clinical decision limits for diagnosis and medical intervention.
机译:参考区间可能是临床实践中使用最广泛的决策工具,其现代用途旨在在健康检查和筛查中确定健康状况。很少将其用作诊断工具,并且可能已过时。本研究调查了新方法对预期值,负值与正值的解释,确认健康的可能性以及基于选定的参考区间建立策略的错误结果数量的后果。计算假定归一化的高斯分布参考区间,且分析变化设置为零且绝对精度。还假设了测试的独立性。根据公式p(no)outside =(1--p(single))并根据重复测试的公式,计算出健康受试者在参考区间以外没有值的概率:p(one)outside = nxp(single)(1 -p(single))n-1等。这里n是执行的测试数,p(single)是一个结果的参考概率超出参考极限的概率,且通式p(i)在ni = kxp(single)i( 1-p(single))ni,其中k是二项式系数,i是参考区间之外的数字。使用99.9个百分位进行健康检查会将10次测试无误的可能性从60%增加到99%,而15次测试从46%增加到98%。如果将99.9%的百分位数替换为95%的百分位数,则小组中10个测试中一个假阳性结果的可能性可以从32%降低到1%。对于十分之二的测试,概率可以从8%降低到0.1%以下。在这两种情况下,选择99.9%的百分位都可以提高诊断准确性。需要参考间隔作为不存在疾病的“真实”阴性参考,并应覆盖分析物参考分布的99.9%,以避免假阳性。对于这种新用途,至关重要的是参考人员绝对正常,没有要研究的疾病的临床,遗传和生化迹象。但是,参考间隔不能代替临床决策限制进行诊断和医学干预。

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