Optimal Indicator of Death for Using Real-World Cancer Patients' Data From the Healthcare System - Jang, Suk-Chan; Kwon, Sun-Hong; Min, Serim; Jo, Ae-Ryeo; Lee, Eui-Kyung; Nam, Jin Hyun (Frontiers in Pharmacology, 2022)
Background: Information on patient 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. Materials and methods: Data of newly diagnosed patients with cancer between 2002 and 2005 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 (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. Results: 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 (92) 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 cancer except for thyroid cancer. Conclusions: 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.