Muir Maxwell Epilepsy Centre, University of Edinburgh Centre for Clinical Brain Sciences, Edinburgh, UK.
Department of Clinical Neurosciences, Western General Hospital, Edinburgh, UK.
BMJ Open. 2018 Jun 30;8(6):e020824. doi: 10.1136/bmjopen-2017-020824.
In an increasingly digital age for healthcare around the world, administrative data have become rich and accessible tools for potentially identifying and monitoring population trends in diseases including epilepsy. However, it remains unclear (1) how accurate administrative data are at identifying epilepsy within a population and (2) the optimal algorithms needed for administrative data to correctly identify people with epilepsy within a population. To address this knowledge gap, we will conduct a novel systematic review of all identified studies validating administrative healthcare data in epilepsy identification. We provide here a protocol that will outline the methods and analyses planned for the systematic review.
The systematic review described in this protocol will be conducted to follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE and Embase will be searched for studies validating administrative data in epilepsy published from 1975 to current (01 June 2018). Included studies will validate the International Classification of Disease (ICD), Ninth Revision (ICD-9) onwards (ie, ICD-9 code 345 and ICD-10 codes G40-G41) as well as other non-ICD disease classification systems used, such as Read Codes in the UK. The primary outcome will be providing pooled estimates of accuracy for identifying epilepsy within the administrative databases validated using sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curves. Heterogeneity will be assessed using the I statistic and descriptive analyses used where this is present. The secondary outcome will be the optimal administrative data algorithms for correctly identifying epilepsy. These will be identified using multivariable logistic regression models. 95% confidence intervals will be quoted throughout. We will make an assessment of risk of bias, quality of evidence, and completeness of reporting for included studies.
Ethical approval is not required as primary data will not be collected. Results will be disseminated in peer-reviewed journals, conference presentations and in press releases.
CRD42017081212.
在全球医疗保健日益数字化的时代，行政数据已成为识别和监测包括癫痫在内的疾病人群趋势的丰富且可利用的工具。然而，目前仍不清楚（1）行政数据在人群中识别癫痫的准确性如何，以及（2）人群中正确识别癫痫患者所需的最佳行政数据算法。为了解决这一知识空白，我们将对所有已确定的验证癫痫行政医疗数据识别的研究进行一项新的系统综述。我们在此提供一个方案，概述系统综述计划的方法和分析。
本方案中描述的系统综述将遵循系统评价和荟萃分析的首选报告项目（PRISMA）指南进行。将从 1975 年到当前（2018 年 6 月 1 日）检索 MEDLINE 和 Embase 中发表的验证癫痫行政数据的研究。纳入的研究将验证国际疾病分类（ICD），第九修订版（ICD-9）及以后（即 ICD-9 代码 345 和 ICD-10 代码 G40-G41）以及其他非 ICD 疾病分类系统，如英国的 Read 代码。主要结果将是使用敏感性、特异性、阳性和阴性预测值以及接收者操作特征曲线下的面积，提供使用经过验证的行政数据库识别癫痫的汇总准确性估计。使用 I 统计量评估异质性，并在存在异质性时使用描述性分析。次要结果将是正确识别癫痫的最佳行政数据算法。这些将使用多变量逻辑回归模型确定。将报告 95%置信区间。我们将对纳入研究的偏倚风险、证据质量和报告完整性进行评估。
由于不会收集原始数据，因此不需要伦理批准。研究结果将发表在同行评议的期刊、会议演讲和新闻稿中。
PROSPERO 注册：CRD42017081212。
