Department of Economics, Faculty of Arts, University of Calgary, 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada.
Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3D10 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada.
BMC Cancer. 2022 Mar 16;22(1):281. doi: 10.1186/s12885-022-09333-6.
In cancer survival analyses using population-based data, researchers face the challenge of ascertaining the timing of recurrence. We previously developed algorithms to identify recurrence of breast cancer. This is a follow-up study to detect the timing of recurrence.
Health events that signified recurrence and timing were obtained from routinely collected administrative data. The timing of recurrence was estimated by finding the timing of key indicator events using three different algorithms, respectively. For validation, we compared algorithm-estimated timing of recurrence with that obtained from chart-reviewed data. We further compared the results of cox regressions models (modeling recurrence-free survival) based on the algorithms versus chart review.
In total, 598 breast cancer patients were included. 121 (20.2%) had recurrence after a median follow-up of 4 years. Based on the high accuracy algorithm for identifying the presence of recurrence (with 94.2% sensitivity and 79.2% positive predictive value), the majority (64.5%) of the algorithm-estimated recurrence dates fell within 3 months of the corresponding chart review determined recurrence dates. The algorithm estimated and chart-reviewed data generated Kaplan-Meier (K-M) curves and Cox regression results for recurrence-free survival (hazard ratios and P-values) were very similar.
The proposed algorithms for identifying the timing of breast cancer recurrence achieved similar results to the chart review data and were potentially useful in survival analysis.
在使用基于人群的数据进行癌症生存分析时，研究人员面临确定复发时间的挑战。我们之前开发了用于识别乳腺癌复发的算法。这是一项后续研究，旨在检测复发的时间。
使用常规收集的行政数据获取表示复发和时间的健康事件。通过使用三种不同的算法分别找到关键指标事件的时间来估计复发的时间。为了验证，我们将算法估计的复发时间与从图表审查数据中获得的时间进行了比较。我们进一步比较了基于算法和图表审查的 Cox 回归模型（无复发生存率模型）的结果。
共纳入 598 例乳腺癌患者。中位随访 4 年后，有 121 例（20.2%）复发。基于用于识别复发存在的高准确性算法（灵敏度为 94.2%，阳性预测值为 79.2%），大多数（64.5%）算法估计的复发日期与相应图表审查确定的复发日期相差 3 个月以内。算法估计和图表审查数据生成了无复发生存率的 Kaplan-Meier（K-M）曲线和 Cox 回归结果（风险比和 P 值）非常相似。
用于确定乳腺癌复发时间的建议算法与图表审查数据产生了相似的结果，并且在生存分析中可能有用。
