[论文解读] Developing an Evidence-Based Framework for Grading and Assessment of Predictive Tools for Clinical Decision Support

Mohamed Khalifa, Farah Magrabi+1 more|arXiv (Cornell University)|Jan 1, 2019

Clinical practice guidelines implementation参考文献 171被引用 2

一句话总结

本文提出了GRASP框架——一种标准化、基于证据的系统，用于从三个维度对临床预测工具进行分级与评估：评估阶段、证据等级和证据方向。该研究评估了五个工具，发现奥利佛膝关节规则（Ottawa Knee Rule）因具有显著的实施后影响而获得最高评分，而LACE指数则因缺乏实施后的证据支持而获得最低评分。

ABSTRACT

Background: Clinical predictive tools quantify contributions of relevant patient characteristics to derive likelihood of diseases or predict clinical outcomes. When selecting a predictive tool, for implementation at clinical practice or for recommendation in clinical guidelines, clinicians are challenged with an overwhelming and ever growing number of tools, most of which have never been implemented or assessed for comparative effectiveness. Objective: To develop a comprehensive framework to Grade and Assess Predictive tools (GRASP), and provide clinicians with a standardised, evidence based system to support their search for and selection of effective tools. Methods: A focused review of literature was conducted to extract criteria along which tools should be evaluated. An initial framework was designed and applied to assess and grade five tools: LACE Index, Centor Score, Wells Criteria, Modified Early Warning Score, and Ottawa knee rule. After peer review, by expert clinicians and healthcare researchers, the framework was revised and the grading of the tools was updated. Results: GRASP framework grades predictive tools based on published evidence across three dimensions: 1) Phase of evaluation; 2) Level of evidence; and 3) Direction of evidence. The final grade of a tool is based on the highest phase of evaluation, supported by the highest level of positive evidence, or mixed evidence that supports positive conclusion. Discussion and Conclusion: the GRASP framework builds on well established models and widely accepted concepts to provide standardised assessment and evidence based grading of predictive tools. Unlike other methods, GRASP is based on the critical appraisal of published evidence reporting the predictive tools predictive performance before implementation, potential effect and usability during implementation, and their post implementation impact.

研究动机与目标

• 解决临床预测工具评估缺乏标准化、基于证据的方法的问题。
• 为临床医生和指南制定者提供选择有效、可靠预测工具的支持。
• 提供一个系统性框架，根据预实施、实施中和实施后各阶段的已发表证据对工具进行分级。
• 减少临床实践中对主观或经验性工具选择的依赖。
• 通过透明、可复现的方法，实现对预测工具的基准比较评估。

提出的方法

• GRASP框架通过聚焦文献综述开发，以提取预测工具的评估标准。
• 初步框架被用于评估五个预测工具：LACE指数、Centor评分、Wells标准、改良早期预警评分（MEWS）和奥利佛膝关节规则。
• 该框架从三个维度评估工具：评估阶段（预实施、实施中、后实施）、证据等级（如随机对照试验、队列研究）以及证据方向（正面、负面或混合）。
• 通过临床医生和研究人员的专家同行评审，对框架进行优化并修订工具评分。
• 最终评分基于最强正面或混合证据所支持的最高评估阶段，确定工具的总体评分。
• 该框架设计为在线平台实施，以支持对证据和评分的实时访问。

实验结果

研究问题

• RQ1 如何利用已发表证据，对所有实施阶段的预测工具进行系统化评估与分级？
• RQ2 哪些标准应定义临床预测工具在真实世界环境中的质量和可靠性？
• RQ3 预测工具的实施后影响在多大程度上影响其基于证据的总体评分？
• RQ4 现有工具在实施后在临床结局、效率或成本效益方面是否表现出可测量的改善？
• RQ5 标准化、基于证据的评分系统在多大程度上能改善临床决策和指南制定？

主要发现

• 奥利佛膝关节规则因实施后产生积极影响而获得最高的GRASP评分，包括使急诊科患者停留时间减少33.1分钟，以及每位患者节省80美元成本。
• LACE指数获得最低评分，因其仅获得预实施阶段的证据支持，显示中等预测性能（AUC 0.72–0.84），但缺乏实施后的验证。
• MEWS表现出强劲的预测性能（住院死亡率AUC为0.89），并具有积极的实施后结果，包括严重不良事件减少和生命体征记录改善。
• Wells标准在实施后显著降低了不必要CTPA扫描比例（从30.7%降至17.4%），并降低了不必要影像检查的发生率。
• 可用性测试表明，Centor评分和Wells标准均被临床医生认为易于使用且有帮助，且未影响临床判断。
• GRASP框架成功实现了对所有评估阶段工具的系统化、透明化和基于证据的分级，凸显了外部验证和实施后研究的不足。