Suppr超能文献

在初级保健中识别最受益于抗生素的急性鼻-鼻窦炎成人患者:使用多变量风险预测模型的个体患者数据荟萃分析方案。

Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling.

机构信息

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

出版信息

BMJ Open. 2021 Jul 1;11(7):e047186. doi: 10.1136/bmjopen-2020-047186.

Abstract

INTRODUCTION

Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials.

METHODS AND ANALYSIS

This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8-15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal-external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk.

ETHICS AND DISSEMINATION

In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals.

PROSPERO REGISTRATION NUMBER

CRD42020220108.

摘要

简介

急性鼻-鼻窦炎(ARS)是导致患者看医生的主要原因,也是成年人中超剂量使用抗生素的主要病症之一。为了减少不适当的处方,我们旨在通过将多变量风险预测方法应用于多个随机安慰剂对照试验的个体患者数据(IPD),预测个体成年 ARS 患者接受抗生素治疗的绝对获益。

方法和分析

这是对 2008 年关于成人临床诊断 ARS 抗生素的 IPD 荟萃分析的更新和重新分析。首先,将对 2008 年以来发表的相关研究进行审查 2018 年 Cochrane 抗生素治疗 ARS 综述的参考文献。其次,将对 Cochrane 综述的 CENTRAL、MEDLINE 和 Embase 的系统搜索更新至 2020 年 9 月 1 日。使用 Cochrane 偏倚风险工具 2 评估合格研究的方法学质量。主要结局是 8-15 天的治愈。将使用基于回归的方法根据相关预测因素和治疗来建模基于治疗的治愈风险,同时考虑聚类。该模型允许作为治疗和个体患者特征的函数进行风险预测,从而深入了解个体化的绝对获益。候选预测因子将基于文献、临床推理和可用性。将评估校准和区分度以评估模型性能。将使用重采样技术评估内部验证。此外,将使用内部-外部交叉验证程序来了解研究间差异并估计样本外模型性能。其次,我们将研究治疗效果作为结局风险的函数的可能异质性。

伦理和传播

在这项研究中,不会使用可识别的患者数据。因此,《涉及人类受试者的医学研究法》(WMO)不适用,也不需要官方伦理批准。结果将提交给国际同行评议期刊发表。

PROSPERO 注册号:CRD42020220108。

相似文献

3
Antibiotics for clinically diagnosed acute rhinosinusitis in adults.用于成人临床诊断为急性鼻-鼻窦炎的抗生素
Cochrane Database Syst Rev. 2012 Oct 17;10:CD006089. doi: 10.1002/14651858.CD006089.pub4.
7
Antibiotics for acute maxillary sinusitis in adults.成人急性上颌窦炎的抗生素治疗
Cochrane Database Syst Rev. 2014 Feb 11(2):CD000243. doi: 10.1002/14651858.CD000243.pub3.
9
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.

本文引用的文献

10
Antibiotics for acute rhinosinusitis in adults.成人急性鼻窦炎的抗生素治疗
Cochrane Database Syst Rev. 2018 Sep 10;9(9):CD006089. doi: 10.1002/14651858.CD006089.pub5.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验