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The use of simple maths with the likelihood ratio strategy fits in nicely with our clinical views. By making the most out of the entire range of diagnostic test results (i.e., several levels, each with its own likelihood ratio, rather than a single cut-off point and a single ratio) and by permitting us to keep track of the likelihood that a patient has the target disorder at each point along the diagnostic sequence, this strategy allows us to place patients at an extremely high or an extremely low likelihood of disease. Thus, the numbers of patients with ultimately false-positive results (who suffer the slings of labelling and the arrows of needless therapy) and of those with ultimately false-negative results (who therefore miss their chance for diagnosis and, possibly, efficacious therapy) will be dramatically reduced. The following guidelines will be useful in interpreting signs, symptoms and laboratory tests with the likelihood ratio strategy: Seek out, and demand from the clinical or laboratory experts who ought to know, the likelihood ratios for key symptoms and signs, and several levels (rather than just the positive and negative results) of diagnostic test results. Identify, when feasible, the logical sequence of diagnostic tests. Estimate the pretest probability of disease for the patient, and, using either the nomogram or the conversion formulas, apply the likelihood ratio that corresponds to the first diagnostic test result. While remembering that the resulting post-test probability or odds from the first test becomes the pretest probability or odds for the next diagnostic test, repeat the process for all the pertinent symptoms, signs and laboratory studies that pertain to the target disorder. However, these combinations may not be independent, and convergent diagnostic tests, if treated as independent, will combine to overestimate the final post-test probability of disease. You are now far more sophisticated in interpreting diagnostic tests than most of your teachers. In the last part of our series we will show you some rather complex strategies that combine diagnosis and therapy, quantify our as yet nonquantified ideas about use, and require the use of at least a hand calculator.
将似然比策略与简单数学方法相结合,与我们的临床观点非常契合。通过充分利用诊断测试结果的整个范围(即几个水平,每个水平都有其自身的似然比,而不是单一的临界点和单一的比率),并允许我们追踪患者在诊断序列中每个点患有目标疾病的可能性,这种策略使我们能够将患者置于疾病可能性极高或极低的状态。因此,最终出现假阳性结果(遭受标签的困扰和不必要治疗的负担)的患者数量以及最终出现假阴性结果(从而错过诊断机会以及可能的有效治疗)的患者数量将大幅减少。以下指南对于使用似然比策略解释体征、症状和实验室检查将很有帮助:向应该了解情况的临床或实验室专家询问关键症状和体征的似然比,以及诊断测试结果的几个水平(而不仅仅是阳性和阴性结果)。在可行的情况下,确定诊断测试的逻辑顺序。估计患者疾病的验前概率,并使用列线图或转换公式,应用与第一个诊断测试结果相对应的似然比。在记住第一次测试得出的验后概率或比值成为下一次诊断测试的验前概率或比值的同时,对与目标疾病相关的所有相关症状、体征和实验室研究重复此过程。然而,这些组合可能并非相互独立,并且如果将聚合诊断测试视为独立的,将会高估疾病的最终验后概率。在解释诊断测试方面,你现在比大多数老师都要老练得多。在我们系列的最后一部分,我们将向你展示一些相当复杂的策略,这些策略将诊断与治疗相结合,量化我们关于使用的尚未量化的想法,并且至少需要使用手持计算器。