Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, The Netherlands
Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
BMJ Open. 2020 Nov 3;10(11):e040988. doi: 10.1136/bmjopen-2020-040988.
This protocol outlines a diagnostic individual patient data (IPD) meta-analysis aimed at developing simple prediction models based on readily available signs, symptoms and blood tests to accurately predict acute bacterial rhinosinusitis and CT-confirmed (fluid level or total opacification in any sinus) acute rhinosinusitis (ARS) in adults presenting to primary care with clinically diagnosed ARS, target conditions associated with antibiotic benefit.
The systematic searches of PubMed and Embase of a review on the accuracy of signs and symptoms for diagnosing ARS in ambulatory care will be updated to April 2020 to identify relevant studies. Authors of eligible studies will be contacted and invited to provide IPD. Methodological quality of the studies will be assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Candidate predictor selection will be based on knowledge from existing literature, clinical reasoning and availability. Multivariable logistic regression analyses will be used to develop prediction models aimed at calculating absolute risk estimates. Large unexplained between-study heterogeneity in predictive accuracy of the models will be explored and may lead to either model adjustment or derivation of separate context-specific models. Calibration and discrimination will be evaluated to assess the models' performance. Bootstrap resampling techniques will be used to assess internal validation and to inform on possible adjustment for overfitting. In addition, we aim to perform internal-external cross-validation procedures.
In this IPD meta-analysis, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act does not apply, and official ethical approval is not required. Findings will be published in international peer-reviewed journals and presented at scientific conferences.
PROSPERO CRD42020175659.
本方案概述了一项针对个体患者数据（IPD）的诊断性荟萃分析，旨在开发基于易于获得的体征、症状和血液检测的简单预测模型，以准确预测在初级保健机构就诊的成人临床诊断为急性鼻-鼻窦炎（ARS）且伴有疑似抗生素获益相关病症的急性细菌性鼻-鼻窦炎（ARS）和 CT 确诊（任何窦腔中有液体水平或完全混浊）的急性鼻-鼻窦炎（ARS）。
将对在门诊环境中诊断 ARS 的体征和症状准确性的综述进行系统的 PubMed 和 Embase 检索，并更新至 2020 年 4 月，以确定相关研究。将联系符合条件研究的作者，并邀请他们提供 IPD。使用诊断准确性研究质量评估工具 2 评估研究的方法学质量。候选预测因子的选择将基于现有文献、临床推理和可用性的知识。将使用多变量逻辑回归分析来开发旨在计算绝对风险估计的预测模型。将探索模型预测准确性方面存在的大量未解释的异质性，并可能导致模型调整或推导特定于背景的模型。将评估校准和区分度，以评估模型的性能。将使用自举重采样技术进行内部验证，并告知可能存在的过度拟合调整。此外，我们旨在进行内部-外部交叉验证程序。
在这项 IPD 荟萃分析中，不会使用可识别的患者数据。因此，不适用《涉及人类受试者的医学研究法》，也不需要正式的伦理批准。研究结果将发表在国际同行评议期刊上，并在科学会议上展示。
PROSPERO 注册号：PROSPERO CRD42020175659。
