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Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction.评估数据漂移对临床脓毒症预测中使用的机器学习模型性能的影响。
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The impact of lookback windows on the prevalence and incidence of chronic diseases among people living with HIV: an exploration in administrative health data in Canada.回顾期对加拿大 HIV 感染者中慢性病的患病率和发病率的影响:基于行政健康数据的探索。
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Data variability across Canadian administrative health databases: Differences in content, coding, and completeness.加拿大行政健康数据库中的数据变异性:内容、编码和完整性方面的差异。
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基于常规收集的健康数据报告算法的多辖区可行性评估的最低要素:加拿大健康数据研究网络建议

Minimum elements for reporting a multi-jurisdiction feasibility assessment of algorithms based on routinely collected health data: Health Data Research Network Canada recommendations.

作者信息

Hamm Naomi C, Bartholomew Sharon, Zhao Yinshan, Peterson Sandra, Al-Azazi Saeed, McGrail Kimberlyn, Lix Lisa M

机构信息

Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.

Centre for Surveillance and Applied Research, Public Health Agency of Canada, Ottawa, Canada.

出版信息

Int J Popul Data Sci. 2025 Jan 28;10(2):2466. doi: 10.23889/ijpds.v10i2.2466. eCollection 2025.

DOI:10.23889/ijpds.v10i2.2466
PMID:40129910
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11931606/
Abstract

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.

摘要

背景

利用常规收集的健康数据进行研究和监测依赖于算法或定义来确定疾病病例或健康指标。每当由于缺乏参考标准而无法进行算法验证研究时,算法可行性研究可用于创建和评估适用于多个群体或辖区的算法。公布进行可行性研究所用的方法对于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《加强流行病学观察性研究报告规范》(STROBE)和《利用常规收集的健康数据进行研究的报告规范》(RECORD)。这些指南可能需要增加一些要素,以涵盖多辖区算法可行性研究的特定方面,并确保其可重复性。本文旨在确定报告可行性研究的最低要素,以确保可重复性和透明度。

方法

由加拿大健康数据研究网络(HDRN)的算法与统一数据工作组(AHD-WG)成立了一个由四人组成的小组委员会,成员在常规收集的健康数据、多辖区健康研究以及算法开发与实施方面具有专业知识。该小组委员会审查了STROBE和RECORD指南中的项目,并根据已发表的可行性研究对这些项目进行了评估。小组委员会成员通过名义群体技术达成共识,确定了STROBE或RECORD指南中未包含的确保可行性研究透明报告的项目。AHD-WG对这些额外的推荐要素进行了审查和批准。

结果

确定了11个新的推荐要素:标题和摘要部分1个,引言部分1个,方法部分5个,结果部分4个。推荐要素主要涉及报告辖区数据变异性、数据统一方法和算法实施技术。

意义

实施这些推荐要素以及RECORD指南,旨在鼓励支持可重复性的方法的一致发表,同时提高算法在国内和国际研究中的可比性及其应用。