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152: A MEASURE FOR ESTIMATING THE MAGNITUDE OF UNNECESSARY OVERTREATMENT OVER TESTING AND OVER PREVENTING

机译:152:一种估计不必要的过度治疗过度测试和过度预防的幅度的措施

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摘要

There is not any summary measure in EBM for showing the magnitude of unnecessary medical interventions including overtreatment, over testing and over preventing. Based on the finding of a valid and reliable double blinded randomized controlled clinical trial (RCT) with a good external validity, two groups of patients may receive unnecessary overtreatment: the first group consists of patients who will not respond to medication or surgery. The second group consists of patients who respond to the placebo or sham surgery. Here we define an index to cover these two groups: Unnecessary Overtreatment Index (UOI). Based on the finding of RCTs, the UOI is defined as summation of two proportions: the proportion of patients who do not respond to medication/surgery and the proportion of patients who are improved by the placebo or sham surgery intervention as well. For example if the RCT shows that 30/100 of patients who received medication/surgery have improved and for the placebo intervention group only 20/100 patients have also improved, then the UOI is summation of (1–30/100) and 20/100. It means if a physician prescribes the medication/surgery to 100 patients then 90 of these patients will be treated unnecessarily. Seventy out of 90 are patients who will not be improved and 20 out of 90 are patients who would be improved even if they received placebo intervention. The 95% Confidence Interval can be calculated for UOI. An interesting point is the relation of UOI with Absolute Risk Reduction (ARR). Considering the definition of UOI and ARR, we will show that UOI is equal to “1- ARR. As in the example, ARR is 10/100 (30/100–20/100). Then, it can be stated that UOI=1–10%=90%. If the RCT is a diagnostic trial examining a diagnostic test, then we can define Unnecessary Overtesting Index and if the RCT examines a preventive intervention, we can define Unnecessary Over preventing Index. The utility of UOI in clinical practice will be discussed with demonstrating a few examples.
机译:EBM中没有任何汇总措施可用于显示不必要的医疗干预措施的数量,包括过度治疗,过度测试和过度预防。基于一项具有良好外部有效性的有效且可靠的双盲随机对照临床试验(RCT),两组患者可能会接受不必要的过度治疗:第一组包括对药物或手术无反应的患者。第二组由对安慰剂或假手术有反应的患者组成。在这里,我们定义了一个涵盖这两个组的索引:不必要的过度治疗指数(UOI)。基于RCT的发现,UOI被定义为两个比例的总和:对药物/手术无反应的患者比例以及通过安慰剂或假手术干预而改善的患者比例。例如,如果RCT显示接受药物/手术的患者中有30/100的患者有改善,而安慰剂干预组中只有20/100的患者也有所改善,则UOI为(1–30 / 100)和20 / 100这意味着,如果医师为100位患者开出药物/手术处方,那么其中90位患者将得到不必要的治疗。 90名患者中有70名患者将不会得到改善,而90名患者中有20名患者即使接受了安慰剂干预也将得到改善。可以为UOI计算95%的置信区间。有趣的一点是UOI与绝对风险降低(ARR)的关系。考虑到UOI和ARR的定义,我们将证明UOI等于“ 1- ARR”。如示例中所示,ARR为10/100(30 / 100–20 / 100)。然后,可以说UOI = 1–10%= 90%。如果RCT是检查诊断测试的诊断试验,则可以定义不必要的过度测试指数,如果RCT检查预防干预,则可以定义不必要的过度预防指数。将通过一些示例来讨论UOI在临床实践中的实用性。

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