Rasmussen Linda Aagaard, Christensen Niels Lyhne, Winther-Larsen Anne, Dalton Susanne Oksbjerg, Virgilsen Line Flytkjær, Jensen Henry, Vedsted Peter
Research Unit for General Practice, Aarhus, Denmark.
Department of Pulmonary Medicine and Allergy, Aarhus University Hospital, Aarhus, Denmark.
Clin Epidemiol. 2023 Mar 1;15:251-261. doi: 10.2147/CLEP.S396738. eCollection 2023.
Recurrence of cancer is not routinely registered in Danish national health registers. This study aimed to develop and validate a register-based algorithm to identify patients diagnosed with recurrent lung cancer and to estimate the accuracy of the identified diagnosis date.
Patients with early-stage lung cancer treated with surgery were included in the study. Recurrence indicators were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Information from CT scans and medical records served as the gold standard to assess the accuracy of the algorithm.
The final population consisted of 217 patients; 72 (33%) had recurrence according to the gold standard. The median follow-up time since primary lung cancer diagnosis was 29 months (interquartile interval: 18-46). The algorithm for identifying a recurrence reached a sensitivity of 83.3% (95% CI: 72.7-91.1), a specificity of 93.8% (95% CI: 88.5-97.1), and a positive predictive value of 87.0% (95% CI: 76.7-93.9). The algorithm identified 70% of the recurrences within 60 days of the recurrence date registered by the gold standard method. The positive predictive value of the algorithm decreased to 70% when the algorithm was simulated in a population with a recurrence rate of 15%.
The proposed algorithm demonstrated good performance in a population with 33% recurrences over a median of 29 months. It can be used to identify patients diagnosed with recurrent lung cancer, and it may be a valuable tool for future research in this field. However, a lower positive predictive value is seen when applying the algorithm in populations with low recurrence rates.
丹麦国家卫生登记册中未常规登记癌症复发情况。本研究旨在开发并验证一种基于登记册的算法，以识别被诊断为复发性肺癌的患者，并估计所确定诊断日期的准确性。
本研究纳入接受手术治疗的早期肺癌患者。复发指标为丹麦国家患者登记册中记录的诊断和手术操作代码，以及丹麦国家病理登记册中记录的病理结果。来自CT扫描和病历的信息作为评估该算法准确性的金标准。
最终研究人群包括217名患者；根据金标准，72名（33%）患者出现复发。自原发性肺癌诊断后的中位随访时间为29个月（四分位间距：18 - 46个月）。识别复发的算法灵敏度达到83.3%（95%置信区间：72.7 - 91.1），特异度为93.8%（95%置信区间：88.5 - 97.1），阳性预测值为87.0%（95%置信区间：76.7 - 93.9）。该算法在金标准方法登记的复发日期60天内识别出70%的复发情况。当在复发率为15%的人群中模拟该算法时，其阳性预测值降至70%。
所提出的算法在中位时间为29个月、复发率为33%的人群中表现良好。它可用于识别被诊断为复发性肺癌的患者，可能是该领域未来研究的一个有价值的工具。然而，在复发率低的人群中应用该算法时，阳性预测值较低。
