Lash Timothy L, Riis Anders H, Ostenfeld Eva B, Erichsen Rune, Vyberg Mogens, Thorlacius-Ussing Ole
Department of Epidemiology, Rollins School of Public Health, Atlanta, GA; Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
Int J Cancer. 2015 May 1;136(9):2210-5. doi: 10.1002/ijc.29267. Epub 2014 Oct 28.
Colorectal cancer recurrences are difficult to ascertain accurately and efficiently. We developed and validated an algorithm to identify recurrences that uses Danish medical registries. The algorithm uses metastasis and chemotherapy codes in the Danish National Patient Registry and codes indicating cancer recurrence in the Danish Pathology Registry. We applied the algorithm to a cohort (n = 21,246) of colorectal cancer patients diagnosed 2001-2011 and followed through 2012. In a cohort (n = 355) of two groups of actively followed patients, we compared the imputed recurrence data with recurrences diagnosed by regular follow-up. We compared cumulative incidence curves of imputed recurrence in local and regional stage patients from the large cohort, and of imputed and diagnosed recurrences in the actively followed cohort. In the 355 members of the actively followed cohort, our algorithm correctly identified 60 of 63 recurrences [sensitivity = 95%; 95% confidence interval (CI) 87-99%] and misclassified only 10 of 292 without recurrence (specificity = 97%; 95% CI 94-98%). Cumulative incidence curves showed that members of the large cohort with regional disease had much higher incidence of imputed recurrence than those with local disease. In the actively followed cohort, the cumulative incidence of recurrence overlapped substantially when recurrence was imputed by our algorithm or using the follow-up data. Despite some limitations regarding ambiguous pathology codes, our algorithm showed excellent performance against actively followed recurrence data, and the expected relation between recurrence risk and cancer stage. It can be used in the Danish registries and adapted to similar registries elsewhere.
结直肠癌复发难以准确且高效地确定。我们开发并验证了一种利用丹麦医学登记处来识别复发情况的算法。该算法使用丹麦国家患者登记处中的转移和化疗编码以及丹麦病理学登记处中指示癌症复发的编码。我们将该算法应用于2001年至2011年诊断出的一组（n = 21,246）结直肠癌患者，并随访至2012年。在一组（n = 355）积极随访的两组患者中，我们将估算的复发数据与通过常规随访诊断出的复发情况进行了比较。我们比较了来自大型队列的局部和区域分期患者估算复发的累积发病率曲线，以及积极随访队列中估算和诊断出的复发情况的累积发病率曲线。在积极随访队列的355名成员中，我们的算法正确识别出63例复发中的60例[敏感性 = 95%；95%置信区间（CI）87 - 99%]，并且在292例无复发的患者中仅误分类了10例（特异性 = 97%；95% CI 94 - 98%）。累积发病率曲线显示，大型队列中患有区域疾病的成员估算复发的发生率远高于患有局部疾病的成员。在积极随访队列中，当通过我们的算法或使用随访数据估算复发情况时，复发的累积发病率有很大重叠。尽管在病理编码模糊方面存在一些局限性，但我们的算法在与积极随访的复发数据对比时表现出色，并且显示出复发风险与癌症分期之间的预期关系。它可用于丹麦的登记处，并可适用于其他地方的类似登记处。
