Xu Yuan, Kong Shiying, Cheung Winson Y, Quan May Lynn, Nakoneshny Steven C, Dort Joseph C
Department of Surgery, Foothills Medical Centre, University of Calgary, Calgary, Alberta, Canada.
Department of Community Health Science, Foothills Medical Centre, University of Calgary, Calgary, Alberta, Canada.
Head Neck. 2019 Jul;41(7):2291-2298. doi: 10.1002/hed.25682. Epub 2019 Feb 1.
Second event (recurrence or second primary cancer)-free survival is an important indicator for assessing treatment efficacy. However, second events are not explicitly documented in administrative data such as cancer registries. Thus, validated algorithms using administrative data are needed to identify second events of oropharyngeal cancers.
The algorithms were developed using classification and regression tree models. Data from chart review served as the reference standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated.
The high-sensitivity algorithm achieved 87.9% (95% confidence interval: 82.2%-93.6%) sensitivity, 84.5% (81.1%-87.8%) specificity, 61.2% (54.1%-68.4%) PPV, 96.2% (94.2%-98.1%) NPV, and 85.2% (82.3%-88.1%) accuracy. The high-PPV algorithm obtained 52.4% (43.6%-61.2%) sensitivity, 99.1% (98.2%-100.0%) specificity, 94.2% (88.7%-99.7%) PPV, 88.2% (85.3%-91.0%) NPV, and 88.9% (86.3%-91.5%) accuracy.
The validity of the algorithms for identifying second events following primary treatment of oropharyngeal cancers was acceptable.
无二次事件（复发或第二原发性癌症）生存是评估治疗效果的重要指标。然而，癌症登记等管理数据中并未明确记录二次事件。因此，需要使用管理数据的经过验证的算法来识别口咽癌的二次事件。
使用分类和回归树模型开发算法。病历审查数据作为参考标准。计算敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和准确性。
高敏感性算法的敏感性为87.9%（95%置信区间：82.2%-93.6%），特异性为84.5%（81.1%-87.8%），PPV为61.2%（54.1%-68.4%），NPV为96.2%（94.2%-98.1%），准确性为85.2%（82.3%-88.1%）。高PPV算法的敏感性为52.4%（43.6%-61.2%），特异性为99.1%（98.2%-100.0%），PPV为94.2%（88.7%-99.7%），NPV为88.2%（85.3%-91.0%），准确性为88.9%（86.3%-91.5%）。
用于识别口咽癌初次治疗后二次事件的算法有效性是可接受的。
