- 著者
- Makoto Hirata Akiko Nagai Yoichiro Kamatani Toshiharu Ninomiya Akiko Tamakoshi Zentaro Yamagata Michiaki Kubo Kaori Muto Yutaka Kiyohara Taisei Mushiroda Yoshinori Murakami Koichiro Yuji Yoichi Furukawa Hitoshi Zembutsu Toshihiro Tanaka Yozo Ohnishi Yusuke Nakamura BioBank Japan Cooperative Hospital Group Koichi Matsuda
- 出版者
- 日本疫学会
- 雑誌
- Journal of Epidemiology (ISSN:09175040)
- 巻号頁・発行日
- vol.27, no.Supplement_III, pp.S22-S28, 2017 (Released:2017-04-14)
- 参考文献数
- 34
- 被引用文献数
- 36
Background: We established a patient-oriented biobank, BioBank Japan, with information on approximately 200,000 patients, suffering from any of 47 common diseases. This follow-up survey focused on 32 diseases, potentially associated with poor vital prognosis, and collected patient survival information, including cause of death. We performed a survival analysis for all subjects to get an overview of BioBank Japan follow-up data.Methods: A total of 141,612 participants were included. The survival data were last updated in 2014. Kaplan–Meier survival analysis was performed after categorizing subjects according to sex, age group, and disease status. Relative survival rates were estimated using a survival-rate table of the Japanese general population.Results: Of 141,612 subjects (56.48% male) with 1,087,434 person-years and a 97.0% follow-up rate, 35,482 patients died during follow-up. Mean age at enrollment was 64.24 years for male subjects and 63.98 years for female subjects. The 5-year and 10-year relative survival rates for all subjects were 0.944 and 0.911, respectively, with a median follow-up duration of 8.40 years. Patients with pancreatic cancer had the least favorable prognosis (10-year relative survival: 0.184) and patients with dyslipidemia had the most favorable prognosis (1.013). The most common cause of death was malignant neoplasms. A number of subjects died from diseases other than their registered disease(s).Conclusions: This is the first report to perform follow-up survival analysis across various common diseases. Further studies should use detailed clinical and genomic information to identify predictors of mortality in patients with common diseases, contributing to the implementation of personalized medicine.