TreatmentPatterns
收藏资源简介：
人们越来越有兴趣使用现实世界的数据来说明在现实生活中如何治疗特定医疗状况的患者。对当前治疗实践的洞察力有助于改善和定制患者护理，但通常由于缺乏数据互操作性和所需资源的高水平而受到阻碍。我们旨在提供一种简单的工具来克服这些障碍，以支持针对各种医疗状况的治疗模式的标准化开发和分析。方法: 我们正式定义了构建治疗途径的过程，并在开源R包处理模式 (https://github.com/mi-erasmusmc/处理模式) 中实现了这一过程，以实现对治疗模式的可重复和及时的分析。结果: 开发的软件包支持对感兴趣的研究人群的治疗模式进行分析。我们通过分析荷兰综合初级保健信息 (IPCI) 数据库中三种常见慢性疾病 (II型糖尿病，高血压和抑郁症) 的治疗模式来证明该软件包的功能。结论: 治疗模式是一种工具，可以使对治疗模式的分析更容易获得，更标准化，并且对解释更友好。我们希望它有助于跨疾病领域有关现实世界治疗模式的知识的积累。我们鼓励研究人员根据他们的研究需求进一步调整和添加自定义分析到R包。
There is growing interest in using real-world data to demonstrate how patients with specific medical conditions receive treatment in real-life clinical scenarios. Insights into current treatment practices can help improve and personalize patient care, but such efforts are often hindered by a lack of data interoperability and high resource requirements. We aim to provide a straightforward tool to overcome these barriers, supporting the standardized development and analysis of treatment patterns across diverse medical conditions. Methods: We formally define the workflow for constructing treatment pathways, and implement this workflow in the open-source R package *TreatmentPatterns* (https://github.com/mi-erasmusmc/TreatmentPatterns) to enable reproducible and timely analysis of treatment patterns. Results: The developed package supports analysis of treatment patterns for targeted study populations. We validate the package's functionality by analyzing treatment patterns of three common chronic diseases (type 2 diabetes mellitus, hypertension, and depression) using the Dutch Integrated Primary Care Information (IPCI) database. Conclusions: TreatmentPatterns is a tool that makes treatment pattern analysis more accessible, standardized, and interpretable. We anticipate that it will facilitate the accumulation of cross-disease domain knowledge regarding real-world treatment patterns. We encourage researchers to further adapt the R package and incorporate custom analyses based on their specific research needs.
