Uneno Yu, Taneishi Kei, Kanai Masashi, Okamoto Kazuya, Yamamoto Yosuke, Yoshioka Akira, Hiramoto Shuji, Nozaki Akira, Nishikawa Yoshitaka, Yamaguchi Daisuke, Tomono Teruko, Nakatsui Masahiko, Baba Mika, Morita Tatsuya, Matsumoto Shigemi, Kuroda Tomohiro, Okuno Yasushi, Muto Manabu
Department of Clinical Oncology, Kyoto University Hospital, Kyoto city, Japan.
RIKEN Advanced Institute for Computational Science, Kobe city, Japan.
PLoS One. 2017 Aug 24;12(8):e0183291. doi: 10.1371/journal.pone.0183291. eCollection 2017.
We aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data.
Between April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (40C3 = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings.
A prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1-6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models.
By applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy.
我们旨在通过应用时间序列真实世界大数据，开发一种适用于接受化疗的癌症患者治疗过程中任何时间点的预后预测模型。
2004年4月至2014年9月期间，4997例接受全身化疗的癌症患者在京都大学医院的前瞻性队列数据库中进行了登记。其中，2693例有死亡记录的患者符合纳入标准，并被分为训练队列（n = 1341）和测试队列（n = 1352）。总共3471521条在115738个时间点的实验室数据，代表了在死亡事件前1年监测的40项实验室指标[如白细胞计数和白蛋白（Alb）水平]，用于构建预后预测模型。在训练队列中生成了所有可能的由40项实验室指标中的三项不同指标组成的预测模型（40C3 = 9880），并在测试队列中进行模型选择。所选模型的适用性在来自三个独立机构的验证队列中进行了外部验证。
基于对1 - 6个月内死亡事件的强大预测能力，选择了一个利用白蛋白、乳酸脱氢酶和中性粒细胞的预后预测模型，并开发了一组对应于1、2、3、4、5和6个月的六个预测模型。曲线下面积（AUC）范围从1个月模型的0.852到6个月模型的0.713。外部验证支持了这些模型的性能。
通过应用时间序列真实世界大数据，我们成功地为接受化疗的癌症患者开发了一组六个适应性预后预测模型。
