Jang Suk-Chan, Kwon Sun-Hong, Min Serim, Jo Ae-Ryeo, Lee Eui-Kyung, Nam Jin Hyun
School of Pharmacy, Sungkyunkwan University, Suwon, South Korea.
Divison of Big Data Science, Korea University Sejong Campus, Sejong, South Korea.
Front Pharmacol. 2022 Jun 16;13:906211. doi: 10.3389/fphar.2022.906211. eCollection 2022.
Information on patient's death is a major outcome of health-related research, but it is not always available in claim-based databases. Herein, we suggested the operational definition of death as an optimal indicator of real death and aim to examine its validity and application in patients with cancer. Data of newly diagnosed patients with cancer between 2006 and 2015 from the Korean National Health Insurance Service-National Sample Cohort data were used. Death indicators were operationally defined as follows: 1) in-hospital death (the result of treatment or disease diagnosis code from claims data), or 2) case wherein there are no claims within 365 days of the last claim. We estimated true-positive rates (TPR) and false-positive rates (FPR) for real death and operational definition of death in patients with high-, middle-, and low-mortality cancers. Kaplan-Meier survival curves and log-rank tests were conducted to determine whether real death and operational definition of death rates were consistent. A total of 40,970 patients with cancer were recruited for this study. Among them, 12,604 patients were officially reported as dead. These patients were stratified into high- (lung, liver, and pancreatic), middle- (stomach, skin, and kidney), and low- (thyroid) mortality groups consisting of 6,626 (death: 4,287), 7,282 (1,858), and 6,316 (93) patients, respectively. The TPR was 97.08% and the FPR was 0.98% in the high mortality group. In the case of the middle and low mortality groups, the TPR (FPR) was 95.86% (1.77%) and 97.85% (0.58%), respectively. The overall TPR and FPR were 96.68 and 1.27%. There was no significant difference between the real and operational definition of death in the log-rank test for all types of cancers except for thyroid cancer. Defining deaths operationally using in-hospital death data and periods after the last claim is a robust alternative to identifying mortality in patients with cancer. This optimal indicator of death will promote research using claim-based data lacking death information.
患者死亡信息是健康相关研究的一项主要成果，但在基于索赔的数据库中并非总能获取到。在此，我们提出将死亡的操作定义作为实际死亡的最佳指标，并旨在检验其在癌症患者中的有效性及应用情况。使用了韩国国民健康保险服务国家样本队列数据中2006年至2015年间新诊断癌症患者的数据。死亡指标的操作定义如下：1）院内死亡（来自索赔数据的治疗结果或疾病诊断代码），或2）最后一次索赔后365天内无索赔的情况。我们估计了高、中、低死亡率癌症患者实际死亡和死亡操作定义的真阳性率（TPR）和假阳性率（FPR）。进行了Kaplan-Meier生存曲线和对数秩检验，以确定实际死亡和死亡操作定义率是否一致。本研究共纳入40970例癌症患者。其中，12604例患者被正式报告死亡。这些患者被分为高死亡率组（肺癌、肝癌和胰腺癌）、中死亡率组（胃癌、皮肤癌和肾癌）和低死亡率组（甲状腺癌），分别有6626例（死亡：4287例）、7282例（1858例）和6316例（93例）患者。高死亡率组的TPR为97.08%，FPR为0.98%。在中、低死亡率组中，TPR（FPR）分别为95.86%（1.77%）和97.85%（0.58%）。总体TPR和FPR分别为96.68和1.27%。除甲状腺癌外，所有类型癌症的对数秩检验中，实际死亡和死亡操作定义之间无显著差异。使用院内死亡数据和最后一次索赔后的时间段对死亡进行操作定义，是识别癌症患者死亡率的一种可靠替代方法。这种最佳死亡指标将促进利用缺乏死亡信息的基于索赔的数据进行研究。
