Ensuring test evaluation research is applicable in practice: investigating the effects of routine data on the validity of test accuracy meta-analyses

诊断是一个困难的过程。向医生提出患病的病人通常会经历一个过程，包括被询问、观察、检查，甚至可能进行血液或影像学“检查”。所问的每个问题或所做的每个观察，要么本身就是一个诊断测试，要么就是其中的一部分，是得出诊断的必要部分。但有些测试比其他测试更好，重要的是，可能没有测试是100%准确的。有时，检查结果可能表明病人健康正常，而实际上他们患有疾病，或者在健康正常的情况下患有疾病。这种情况发生在所有测试中，诊断测试准确性研究的目的是评估这种情况发生的频率，换句话说，确定测试的准确性。从本质上讲，当临床医生决定诊断时，他们是在有意识地或以其他方式调用一个概率过程，在这个过程中，多个测试相结合，患者的诊断应该是考虑到所有测试结果的最可能的诊断。然而，为了使这一过程真正对患者有益，临床医生需要在诊断过程开始之前了解每项检查的准确性以及患者患病的可能性。这就是那些实践循证医学的人的困难所在。虽然许多测试的准确性已被研究估计，但个别测试的准确性在不同研究之间可能有很大差异。这种变化可能取决于谁在应用测试，它是如何应用的，它是应用于哪个病人，最重要的是，在研究中如何测量准确性。当有几个研究时，有一些方法可以让我们把它们的结果结合起来。这些方法也可以帮助确定测试准确性变化的真正原因。然而，总的来说，这些研究报告的数据质量不足，不足以使这种分析成为可能或全面。此外，从以前的工作中，我们已经能够证明，在某些情况下，研究报告的测试准确性在某些患者环境中实际上是不可能的。这给医生带来了一个问题。如果不同的研究结果差异很大，而且他们自己的实践几乎不可能实现，他们怎么知道应该使用哪种测试的准确性估计值呢？我们已经开始开发一些方法，使人们能够确定一项测试研究的结果是否可能准确地代表医生的一般实践。这意味着医生可以毫无保留地自信地将这项研究应用到自己的实践中。然而，有时研究并不能反映实践中看到的不同临床环境，需要更具体的解决方案。这可以通过从医生自己的设置中收集常规数据并使用它来确定测试准确性的可行值范围来完成。以目前的形式，这种方法被用来排除那些“最不可能”为医生在自己的实践中得出对测试准确性的合理估计的研究。目前，这两种方法都处于开发阶段，但有可能在现实世界中实施，并用于改善诊断。提高诊断性能对患者有明显的好处，包括减少不必要治疗的患者数量和增加适当治疗的患者数量。这项研究的目的之一是试点将这种方法整合到一般实践中，以帮助诊断感染。这也可以通过减少不适当的抗生素处方数量来帮助减少抗生素耐药性的可能性。然而，在此之前，需要对这些方法进行充分调查，以确定它们的效用和局限性。其他方法可能会给患者带来更大的好处，对这些方法进行评估，将是拟议研究的重点。

Diagnosis is a difficult process. A patient who presents to their doctor ill will often undergo a process which involves being asked questions, observed, examined and perhaps even having blood or imaging 'tests'. Each question asked or observation made is either a diagnostic test in its own right or part of one and is a necessary part of arriving at a diagnosis.But some tests are better than others and importantly probably no test is 100% accurate. Sometimes a test result may suggest a patient has normal health when they actually have disease or have disease when they have normal health. This happens to all tests and diagnostic test accuracy research is aimed at evaluating how often this happens, in other words, determining how accurate tests are.Essentially when a clinician decides upon a diagnosis they are consciously or otherwise invoking a probabilistic process where multiple tests are combined and the patient's diagnosis should be the one most probable given the combination of all the test results. However, for this process to be truly beneficial to the patient the clinician needs to know the accuracy of each of these tests and how likely the patient has disease before the diagnostic process has even started.This is where the difficulty lies for those who practise evidence-based medicine. Although the accuracy of many tests has been estimated by research studies, for individual tests the accuracy may vary significantly between studies. This variation may depend on who is applying the test, how it is being applied, which patient it is being applied to and most significantly of all, how the accuracy was measured in the study. When there are several studies there are methods which allow us to combine their results. These methods may also help determine the real reasons why the test's accuracy varies. However, in general, the studies report insufficient data of sufficient quality to enable such analyses to be either possible or comprehensive. Furthermore, from previous work, we have been able to demonstrate that in some cases the test accuracy reported by a study may be virtually impossible in some patient settings. This creates a problem for the doctor. How do they know which estimate of a test's accuracy to use if it varies greatly between studies and risks being nearly impossible for their own practice?We have already begun to develop methods which make it possible to determine whether results from a test study are likely to accurately represent a doctor's practice in general. This would mean that a doctor could confidently apply the research to their own practice without reservation. However, sometimes the research is not reflective of the different clinical settings seen in practice and a more specific solution is required. This may be done by collecting routine data from the doctor's own setting and using it to determine a feasible range of values for the test's accuracy. This method, in its current form, is used to exclude the studies 'least likely' to derive a plausible estimate of a test's accuracy for the doctor in their own practice.At the moment both methods are in development but potentially could be implemented into the real-world and used to improve diagnosis. There are clear patient benefits to improving diagnostic performance including reducing the number of patients treated unnecessarily and increasing the number treated appropriately. One of the aims of this research is to pilot integrating this method into General Practice to help diagnose infection. This could also help reduce the potential for antibiotic resistance by reducing the number of antibiotics prescribed inappropriately.However, before this is done the methods need to be fully investigated to determine their utility and limitations. It may be that other approaches afford greater patient benefit, and an evaluation of these with the methods already described, will be the focus of the proposed research.