瑞典结直肠癌登记册验证--重点关注组织病理学、并发症和复发情况

IF 3.4 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Örvar Arnarson, Peter Moberger, Filip Sköldberg, Kenneth Smedh, Helgi Birgisson, Ingvar Syk
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引用次数: 0

摘要

背景:目前迫切需要对医疗系统的质量进行评估,以改进和提供高质量的医疗服务。临床登记已成为绩效衡量、改进和临床研究的重要平台。因此,登记数据的质量至关重要。本研究旨在评估瑞典结直肠癌登记处(SCRCR)数据的有效性:方法:采用两阶段聚类抽样法随机抽取了瑞典 12 家医院的 700 名患者,并按比例分配到三个不同的医院类别中。通过重新抽取2015年向SCRCR报告的患者医疗档案中的数据来评估有效性。我们选择了组织病理学、术后并发症和 3 年随访数据进行验证。重新摘录的数据被定义为源数据,有效性被定义为SRCRC数据集中与源数据一致的病例比例。有效性表示为两个数据集中非遗漏数据的完全一致百分比,科恩卡帕系数(κ)用于衡量一致性的强度:结果:分类组织病理学变量的中位一致性为 93.4%(κ = 0.83)。一般术后并发症变量显示出很大的一致性(84.3%,κ = 0.61)。同样,总体癌症复发变量几乎完全一致(95.7%,κ = 0.86),而局部复发和远处复发的特定变量仅显示出中等和一般的一致性(分别为85.9%和89.1%,κ = 0.58和0.34):SCRCR数据的验证结果表明,病理数据和复发率的有效性较高,而复发的详细数据则不尽如人意。术后并发症数据的可靠性较低,但严重并发症(3b 级或以上)的发生率和 Clavien-Dindo 分级是可靠的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Validation of the Swedish Colorectal Cancer Register – With Focus on Histopathology, Complications and Recurrences
Background: There is an urgent need to evaluate the quality of healthcare systems to improve and deliver high-quality care. Clinical registries have become important platforms for performance measurements, improvements, and clinical research. Hence, the quality of data in registries is crucial. This study aimed to assess the validity of data in the Swedish Colorectal Cancer Register (SCRCR).
Methods: Seven hundred patients from 12 hospitals were randomly selected and proportionally distributed among three different hospital categories in Sweden using two-stage cluster sampling. Validity was assessed by re-abstracting data from the medical files of patients reported to the SCRCR in 2015. Data on histopathology, postoperative complications, and a 3-year follow-up were selected for validation. Re-abstracted data were defined as source data, and validity was defined as the proportion of cases in the SRCRC dataset that agreed with the source data. Validity was expressed as the percentage of exact agreement of non-missing data in both data sets, and Cohen´s kappa coefficient (κ) was used to measure the strength of the agreement.
Results: The median agreement of the categorical histopathology variables was 93.4% (κ = 0.83). The general postoperative complication variable showed substantial agreement (84.3%, κ = 0.61). Likewise, the variable for overall cancer recurrence showed an almost perfect agreement (95.7%, κ = 0.86), whereas specific variables for local recurrence and distant recurrence displayed only moderate and fair agreement (85.9% and 89.1%, κ = 0.58 and 0.34, respectively).
Conclusion: Validation of the SCRCR data showed high validity of pathology data and recurrence rates, whereas detailed data on recurrence were not as good. Data on postoperative complications were less reliable, although the incidence and Clavien–Dindo grading of severe complications (grade 3b or higher) were reliable.

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来源期刊
Clinical Epidemiology
Clinical Epidemiology Medicine-Epidemiology
CiteScore
6.30
自引率
5.10%
发文量
169
审稿时长
16 weeks
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.
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