ArticleMilitary Medical Research2024
Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).
Article in Military Medical Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Identifying, handling and impact of immortal time bias on addressing treatment effects in observational studies using routinely collected data.BMC medical research methodology · 2025Pooled it
- Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review.The British journal of nutrition · 2025Pooled it
- Single-cell mitophagy signature-based artificial intelligence model enhances prediction of prognosis and immunotherapy response in non-small-cell lung cancer.Respiratory research · 2026Article
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- The national and provincial disease and indirect economic burden of high alcohol use in China from 1990 to 2023 with forecasting to 2050.Military Medical Research · 2026Article
- Development and validation of case-ascertainment algorithms for hypertensive disorders of pregnancy using longitudinal electronic health records data.Journal of clinical epidemiology · 2025Article
- Evaluating the agreement between sensitivity and primary analyses in observational studies using routinely collected healthcare data: a meta-epidemiology study.BMC medicine · 2025Article
- Methods for identifying health status from routinely collected health data: An overview.Integrative medicine research · 2025Article
- Air pollution and risk of 32 health conditions: outcome-wide analyses in a population-based prospective cohort in Southwest China.BMC medicine · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundIn recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked.
methodsWe systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it.
resultsA standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm's suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance.
conclusionsThe establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.