PDF(Pubmed)
Abstract：
Background/Objectives: The recommendations included in medical guidelines (GLs) provide important help to medical professionals for making clinical decisions regarding the diagnosis and treatment of various diseases. However, there are no systematic methods to measure the helpfulness of GLs. Thus, we developed an objective assessment of GLs which indicates their helpfulness and quality. We hypothesized that a simple mathematical analysis of \'Recommendations\' and \'Evidence\' would suffice. Methods: As a proof of concept, a mathematical analysis was conducted on the \'2020 European Society of Cardiology Guidelines on Sports Cardiology and Exercise in Patients with Cardiovascular Disease Guideline\' (SCE-guideline). First, the frequencies of Classes of Recommendations (CLASS) and the Levels of Evidence (LEVEL) (n = 159) were analysed. Then, LEVEL areas under CLASS were calculated to form a certainty index (CI: -1 to +1). Results: The frequency of CLASS I (\'to do\') and CLASS III (\'not to do\') was relatively high in the SCE-guideline (52.2%). Yet, the most frequent LEVEL was C (41.2-83.8%), indicating only a relatively low quality of scientific evidence in the SCE-guideline. The SCE-guideline showed a relatively high CI (+0.57): 78.4% certainty and 21.6% uncertainty. Conclusions: The SCE-guideline provides substantial help in decision making through the recommendations (CLASS), while the supporting evidence (LEVEL) in most cases is of lower quality. This is what the newly introduced certainty index showed: a tool for \'quality control\' which can identify specific areas within GLs, and can promote the future improvement of GLs. The newly developed mathematical analysis can be used as a Guideline for the Guidelines, facilitating the assessment and comparison of the helpfulness and quality of GLs.
摘要：
背景/目标：医学指南（GL）中包含的建议为医学专业人员做出有关各种疾病的诊断和治疗的临床决策提供了重要帮助。然而,没有系统的方法来衡量GL的有用性。因此，我们对GL进行了客观评估，以表明其有用性和质量。我们假设对“建议”和“证据”进行简单的数学分析就足够了。方法：作为概念的证明，对《2020年欧洲心脏病学会心血管疾病患者运动心脏病学和运动指南》(SCE指南)进行了数学分析.首先,分析了建议类别(CLASS)和证据水平(LEVEL)(n=159)的频率。然后，计算CLASS下的水平面积以形成确定性指数(CI:-1至+1)。结果：SCE指南中I类（\'要做\'）和III类（\'不做\'）的频率相对较高（52.2％）。然而，最常见的水平是C（41.2-83.8%），表明SCE指南中的科学证据质量相对较低。SCE指南显示相对较高的CI（0.57）：78.4％的确定性和21.6％的不确定性。结论：SCE指南通过建议(CLASS)为决策提供了实质性帮助，而大多数情况下的支持证据(LEVEL)质量较低。这就是新引入的确定性指数所显示的：“质量控制”工具，可以识别GL中的特定区域，并可以促进未来GL的改进。新开发的数学分析可以作为指南的指南，促进对GL的帮助和质量的评估和比较。
