Bernstein J
Department of Orthopedic Surgery, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, USA.
Med Decis Making. 1997 Jan-Mar;17(1):103-6. doi: 10.1177/0272989X9701700112.
Test-indication curves (TICs) are tools for determining whether a test is indicated for a given patient. They apply the threshold approach of Pauker and Kassirer in graphic form. These curves are composed of two parts: the raw curve, which plots posttest probability versus pretest probability (given values for specificity and sensitivity); and the final curve, in which three straight lines are added to the raw curve by the clinician to generate a TIC for a given treatment threshold. In the final curve, the complete range of pretest probability is segregated into three zones, corresponding to the three groups described by Pauker and Kassirer: those patients in whom disease is assumed to be present and who are thus best treated empirically; at the other extreme, those who require neither testing nor treatment; and, finally, those in the middle, for whom the test is indicated, since the decision to treat would be based on the test result. Thus the clinician could consult the TIC and determine with certainty whether the test should be employed for a given patient. It also could be modified with ease for a different patient, with a different set of threshold values. TICs provide a complete, visual interpretation of a test's diagnostic power, in the context of a given treatment threshold. They foster an intuitive comprehension of Pauker and Kassirer's method, and offer the clinician a facile means to prove that a test is indicated in a given setting. By promoting the use of exactly those tests that are indicated, TICs can help spare the patient the cost, burden, and risk of unnecessary testing, and help spare the physician the cost, burden, and risk of interpreting inconclusive test results.
试验指征曲线（TICs）是用于确定某项检测是否适用于特定患者的工具。它们以图形形式应用了帕克（Pauker）和卡西勒（Kassirer）的阈值方法。这些曲线由两部分组成：原始曲线，其绘制了检测后概率与检测前概率（给定特异性和敏感性值）；以及最终曲线，临床医生在原始曲线上添加三条直线以生成针对给定治疗阈值的TIC。在最终曲线中，检测前概率的整个范围被划分为三个区域，对应于帕克和卡西勒所描述的三组患者：那些被假定患有疾病且因此最好接受经验性治疗的患者；在另一个极端，那些既不需要检测也不需要治疗的患者；最后，处于中间的那些患者，检测对他们是适用的，因为治疗决策将基于检测结果。因此，临床医生可以参考TIC并确定是否应对特定患者进行检测。它也可以轻松地针对不同患者、不同阈值集进行修改。TIC在给定治疗阈值的背景下，对检测的诊断能力提供了完整的可视化解释。它们促进了对帕克和卡西勒方法的直观理解，并为临床医生提供了一种简便的方法来证明在给定情况下某项检测是适用的。通过促进恰当地使用那些适用的检测，TIC可以帮助患者避免不必要检测的费用、负担和风险，并帮助医生避免解读不确定检测结果的费用、负担和风险。
