使用常规收集的行政数据开发和验证乳腺癌复发的病例发现算法。
Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data.
机构信息
Department of Surgery, Foothills Medical Centre, University of Calgary, 3280 Hospital Drive NW, Calgary, Alberta, T2N4Z6, Canada.
Department of Community Health Science, Foothills Medical Centre, University of Calgary, 3280 Hospital Drive NW, Calgary, Alberta, T2N4Z6, Canada.
出版信息
BMC Cancer. 2019 Mar 8;19(1):210. doi: 10.1186/s12885-019-5432-8.
BACKGROUND
Recurrence is not explicitly documented in cancer registry data that are widely used for research. Patterns of events after initial treatment such as oncology visits, re-operation, and receipt of subsequent chemotherapy or radiation may indicate recurrence. This study aimed to develop and validate algorithms for identifying breast cancer recurrence using routinely collected administrative data.
METHODS
The study cohort included all young (≤ 40 years) breast cancer patients (2007-2010), and all patients receiving neoadjuvant chemotherapy (2012-2014) in Alberta, Canada. Health events (including mastectomy, chemotherapy, radiation, biopsy and specialist visits) were obtained from provincial administrative data. The algorithms were developed using classification and regression tree (CART) models and validated against primary chart review.
RESULTS
Among 598 patients, 121 (20.2%) had recurrence after a median follow-up of 4 years. The high sensitivity algorithm achieved 94.2% (95% CI: 90.1-98.4%) sensitivity, 93.7% (91.5-95.9%) specificity, 79.2% (72.5-85.8%) positive predictive value (PPV), and 98.5% (97.3-99.6%) negative predictive value (NPV). The high PPV algorithm had 75.2% (67.5-82.9%) sensitivity, 98.3% (97.2-99.5%) specificity, 91.9% (86.6-97.3%) PPV, and 94% (91.9-96.1%) NPV. Combining high PPV and high sensitivity algorithms with additional (7.5%) chart review to resolve discordant cases resulted in 94.2% (90.1-98.4%) sensitivity, 98.3% (97.2-99.5%) specificity, 93.4% (89.1-97.8%) PPV, and 98.5% (97.4-99.6%) NPV.
CONCLUSION
The proposed algorithms based on routinely collected administrative data achieved favorably high validity for identifying breast cancer recurrences in a universal healthcare system in Canada.
背景
广泛用于研究的癌症登记数据并未明确记录复发情况。初始治疗后（如肿瘤就诊、再次手术以及接受后续化疗或放疗）的事件模式可能表明出现了复发。本研究旨在开发和验证使用常规收集的行政数据识别乳腺癌复发的算法。
方法
研究队列包括所有年轻（≤40 岁）乳腺癌患者（2007-2010 年）和所有在加拿大艾伯塔省接受新辅助化疗的患者（2012-2014 年）。健康事件（包括乳房切除术、化疗、放疗、活检和专家就诊）从省级行政数据中获得。该算法是使用分类和回归树（CART）模型开发的，并通过与原始图表审查进行验证。
结果
在 598 名患者中，121 名（20.2%）在中位随访 4 年后复发。高灵敏度算法的敏感性为 94.2%（95%CI：90.1-98.4%），特异性为 93.7%（91.5-95.9%），阳性预测值（PPV）为 79.2%（72.5-85.8%），阴性预测值（NPV）为 98.5%（97.3-99.6%）。高 PPV 算法的敏感性为 75.2%（67.5-82.9%），特异性为 98.3%（97.2-99.5%），PPV 为 91.9%（86.6-97.3%），NPV 为 94%（91.9-96.1%）。将高 PPV 和高灵敏度算法与额外的（7.5%）图表审查相结合，以解决不一致的病例，从而得到 94.2%（90.1-98.4%）的敏感性，98.3%（97.2-99.5%）的特异性，93.4%（89.1-97.8%）的 PPV 和 98.5%（97.4-99.6%）的 NPV。
结论
基于常规收集的行政数据提出的算法在加拿大的全民医疗保健系统中识别乳腺癌复发具有较高的有效性。
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