Charles Vesteghem, Martin Bøgsted, Deirdre Cronin-Fenton, Laurids Østergaard Poulsen
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All methods relied on time intervals between two consecutive drug administrations to determine if they belonged to the same treatment line. MedOnc, a local dataset from the Department of Oncology, Aalborg University Hospital was used as a reference. To evaluate the performance of each method, F1-scores were calculated after matching the lines identified in both datasets. We used three different matching strategies: stringent matching, loose matching, and matching based on line numbers, controlling for overfitting.<br/><strong>Results:</strong> Overall, the two new approaches outperformed the simpler and best performing of the two existing methods, with F1-scores of 0.47 and 0.45 vs 0.44 for stringent matching and 0.84 and 0.83 vs 0.82 for loose matching. Nevertheless, only one of the new methods outperformed the existing simpler method when matching on the number of lines (0.73 vs 0.72). Large differences were seen by cancer site, especially for the stringent and line number matchings. Performances were relatively stable by calendar year.<br/><strong>Conclusion:</strong> The high F1-scores for the new methods confirm that they should be generally preferred to reconstruct systemic anticancer treatment trajectories using the DNPR.<br/><br/>","PeriodicalId":10362,"journal":{"name":"Clinical Epidemiology","volume":"27 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Extracting Systemic Anticancer Treatment Lines from the Danish National Patient Registry for Solid Tumour Patients Treated in the North Denmark Region Between 2009 and 2019\",\"authors\":\"Charles Vesteghem, Martin Bøgsted, Deirdre Cronin-Fenton, Laurids Østergaard Poulsen\",\"doi\":\"10.2147/clep.s442591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<strong>Background:</strong> Reconstructing patient treatment trajectories is important to generate real-world evidence for epidemiological studies. 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引用次数: 0
摘要
背景:重建患者的治疗轨迹对于为流行病学研究提供真实世界的证据非常重要。丹麦国家患者登记处(Danish National Patient Registry,DNPR)包含药物处方信息,因此可用于重建治疗轨迹。我们的目的是评估和改进现有的两种重建系统性抗癌治疗轨迹的方法:本研究基于 2009 年至 2019 年期间在北丹麦地区接受治疗的 8738 名连续实体瘤患者的数据。文献中的两种方法和两种新方法被应用于 DNPR 数据。所有方法都依赖于两次连续给药之间的时间间隔来确定它们是否属于同一治疗线。奥尔堡大学医院肿瘤部的本地数据集 MedOnc 被用作参考。为了评估每种方法的性能,我们在对两个数据集中识别出的治疗线进行匹配后计算了 F1 分数。我们使用了三种不同的匹配策略:严格匹配、宽松匹配和基于线号的匹配,并控制了过拟合:总体而言,两种新方法的性能优于现有两种方法中最简单、性能最好的方法,严格匹配的 F1 分数分别为 0.47 和 0.45,而松散匹配的 F1 分数分别为 0.84 和 0.83,而松散匹配的 F1 分数为 0.82。然而,在行数匹配方面,只有一种新方法优于现有的简单方法(0.73 对 0.72)。癌症部位的差异很大,特别是严格匹配和行数匹配。不同日历年的性能相对稳定:结论:新方法的高 F1 分数证实,在使用 DNPR 重建全身抗癌治疗轨迹时,一般应首选新方法。
Extracting Systemic Anticancer Treatment Lines from the Danish National Patient Registry for Solid Tumour Patients Treated in the North Denmark Region Between 2009 and 2019
Background: Reconstructing patient treatment trajectories is important to generate real-world evidence for epidemiological studies. The Danish National Patient Registry (DNPR) contains information about drug prescriptions and could therefore be used to reconstruct treatment trajectories. We aimed to evaluate and enhance two existing methods to reconstruct systemic anticancer treatment trajectories. Methods: This study was based on data from 8738 consecutive patients with solid tumors treated in the North Denmark Region between 2009 and 2019. Two approaches found in the literature as well as two new approaches were applied to the DNPR data. All methods relied on time intervals between two consecutive drug administrations to determine if they belonged to the same treatment line. MedOnc, a local dataset from the Department of Oncology, Aalborg University Hospital was used as a reference. To evaluate the performance of each method, F1-scores were calculated after matching the lines identified in both datasets. We used three different matching strategies: stringent matching, loose matching, and matching based on line numbers, controlling for overfitting. Results: Overall, the two new approaches outperformed the simpler and best performing of the two existing methods, with F1-scores of 0.47 and 0.45 vs 0.44 for stringent matching and 0.84 and 0.83 vs 0.82 for loose matching. Nevertheless, only one of the new methods outperformed the existing simpler method when matching on the number of lines (0.73 vs 0.72). Large differences were seen by cancer site, especially for the stringent and line number matchings. Performances were relatively stable by calendar year. Conclusion: The high F1-scores for the new methods confirm that they should be generally preferred to reconstruct systemic anticancer treatment trajectories using the DNPR.
期刊介绍:
Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment.
Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews.
Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews.
When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes.
The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.