基于常规收集的健康数据对算法进行多管辖区可行性评估的最低报告要素:加拿大健康数据研究网络建议。

IF 1.6 Q3 HEALTH CARE SCIENCES & SERVICES
International Journal of Population Data Science Pub Date : 2025-01-28 eCollection Date: 2025-01-01 DOI:10.23889/ijpds.v10i2.2466
Naomi C Hamm, Sharon Bartholomew, Yinshan Zhao, Sandra Peterson, Saeed Al-Azazi, Kimberlyn McGrail, Lisa M Lix
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引用次数: 0

摘要

背景:使用常规收集的健康数据进行研究和监测,依靠算法或定义来确定疾病病例或健康措施。当算法验证研究由于无法获得参考标准而无法进行时,算法可行性研究可用于创建和评估算法,以便在多个人群或司法管辖区中使用。公布用于进行可行性研究的方法对可重复性和透明度至关重要。适用于可行性研究的现有指南包括《加强报告流行病学观察性研究》(STROBE)和《报告使用常规收集的观察性健康数据进行的研究》(RECORD)指南。这些准则可能受益于捕获多管辖算法可行性研究的特定方面并确保其可重复性的其他要素。本文的目的是确定可行性研究报告的最低要素,以确保可重复性和透明度。方法:健康数据研究网络(HDRN)加拿大算法和协调数据工作组(AHD-WG)成立了一个由四名具有常规收集卫生数据、多司法管辖区卫生研究和算法开发和实施方面专业知识的小组委员会。小组委员会审查了STROBE和RECORD指南中的项目,并根据已发表的可行性研究报告对这些项目进行了评估。确保STROBE或RECORD指南中未包含的可行性研究报告透明的项目由小组委员会成员使用名义小组技术协商一致确定。AHD-WG审查并批准了这些额外的建议要素。结果:确定了11个新的推荐元素:1个用于标题和摘要,1个用于介绍,5个用于方法,4个用于结果部分。推荐的元素主要涉及报告管辖数据的可变性、数据协调方法和算法实现技术。意义:实施这些推荐的要素,与RECORD指南一起,旨在鼓励支持可重复性的方法的一致发表,并增加算法及其在国内和国际研究中的使用的可比性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Minimum elements for reporting a multi-jurisdiction feasibility assessment of algorithms based on routinely collected health data: Health Data Research Network Canada recommendations.

Background: Research and surveillance using routinely collected health data rely on algorithms or definitions to ascertain disease cases or health measures. Whenever algorithm validation studies are not possible due to the unavailability of a reference standard, algorithm feasibility studies can be used to create and assess algorithms for use in more than one population or jurisdiction. Publication of the methods used to conduct feasibility studies is critical for reproducibility and transparency. Existing guidelines applicable to feasibility studies include the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) and REporting of studies Conducted using Observational Routinely collected health Data (RECORD) guidelines. These guidelines may benefit from additional elements that capture aspects particular to multi-jurisdiction algorithm feasibility studies and ensure their reproducibility. The aim of this paper is to identify the minimum elements for reporting feasibility studies to ensure reproducibility and transparency.

Methods: A subcommittee of four individuals with expertise in routinely collected health data, multi-jurisdiction health research, and algorithm development and implementation was formed from Health Data Research Network (HDRN) Canada's Algorithms and Harmonized Data Working Group (AHD-WG). The subcommittee reviewed items within the STROBE and RECORD guidelines and evaluated these items against published feasibility studies. Items to ensure transparent reporting of feasibility studies not contained within STROBE or RECORD guidelines were identified through consensus by subcommittee members using the Nominal Group Technique. The AHD-WG reviewed and approved these additional recommended elements.

Results: Eleven new recommended elements were identified: one element for the title and abstract, one for the introduction, five for the methods, and four for the results sections. Recommended elements primarily addressed reporting jurisdictional data variabilities, data harmonization methods, and algorithm implementation techniques.

Significance: Implementation of these recommended elements, alongside the RECORD guidelines, is intended to encourage consistent publication of methods that support reproducibility, as well as increase comparability of algorithms and their use in national and international studies.

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CiteScore
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0.00%
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