Department of Epidemiology and Data Science, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands (J.L., M.L., P.M.B.).
Department of Vascular Medicine, Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands (F.M.).
Ann Intern Med. 2022 Jul;175(7):1010-1018. doi: 10.7326/M22-0276. Epub 2022 Jun 14.
Whereas diagnostic tests help detect the cause of signs and symptoms, prognostic tests assist in evaluating the probable course of the disease and future outcome. Studies to evaluate prognostic tests are longitudinal, which introduces sources of bias different from those for diagnostic accuracy studies. At present, systematic reviews of prognostic tests often use the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to assess risk of bias and applicability of included studies because no equivalent instrument exists for prognostic accuracy studies. QUAPAS (Quality Assessment of Prognostic Accuracy Studies) is an adaptation of QUADAS-2 for prognostic accuracy studies. Questions likely to identify bias were evaluated in parallel and collated from QUIPS (Quality in Prognosis Studies) and PROBAST (Prediction Model Risk of Bias Assessment Tool) and paired to the corresponding question (or domain) in QUADAS-2. A steering group conducted and reviewed 3 rounds of modifications before arriving at the final set of domains and signaling questions. QUAPAS follows the same steps as QUADAS-2: Specify the review question, tailor the tool, draw a flow diagram, judge risk of bias, and identify applicability concerns. Risk of bias is judged across the following 5 domains: participants, index test, outcome, flow and timing, and analysis. Signaling questions assist the final judgment for each domain. Applicability concerns are assessed for the first 4 domains. The authors used QUAPAS in parallel with QUADAS-2 and QUIPS in a systematic review of prognostic accuracy studies. QUAPAS improved the assessment of the flow and timing domain and flagged a study at risk of bias in the new analysis domain. Judgment of risk of bias in the analysis domain was challenging because of sparse reporting of statistical methods.
虽然诊断性测试有助于发现症状和体征的病因,但预后性测试有助于评估疾病的可能病程和未来结局。评估预后性测试的研究是纵向研究,这引入了与诊断准确性研究不同的偏倚来源。目前,预后准确性研究的系统评价通常使用 QUADAS-2(诊断准确性研究的质量评估)工具来评估偏倚风险和纳入研究的适用性,因为不存在用于预后准确性研究的等效工具。QUAPAS(预后准确性研究的质量评估)是 QUADAS-2 的改编版,用于预后准确性研究。可能识别偏倚的问题是从 QUIPS(预后研究的质量)和 PROBAST(预测模型偏倚风险评估工具)中平行评估并整理出来的,并与 QUADAS-2 中的相应问题(或域)配对。一个指导小组在达成最终的域和信号问题集之前进行了 3 轮修改并进行了审查。QUAPAS 遵循与 QUADAS-2 相同的步骤:明确审查问题、调整工具、绘制流程图、判断偏倚风险和识别适用性问题。偏倚风险在以下 5 个领域进行判断:参与者、指标测试、结局、流程和时间以及分析。信号问题有助于对每个域的最终判断。适用性问题在头 4 个域进行评估。作者在预后准确性研究的系统评价中,与 QUADAS-2 和 QUIPS 一起平行使用了 QUAPAS。QUAPAS 改善了对流程和时间域的评估,并在新的分析域中标记了一项存在偏倚风险的研究。由于对统计方法的报告稀疏,分析域中的偏倚风险判断具有挑战性。