Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study

被引：14

作者：

Uneno, Yu ^{[1
]}

Taneishi, Kei ^{[2
]}

Kanai, Masashi ^{[1
]}

Okamoto, Kazuya ^{[3
]}

Yamamoto, Yosuke ^{[4
,5
]}

Yoshioka, Akira ^{[6
]}

Hiramoto, Shuji ^{[7
]}

Nozaki, Akira ^{[8
]}

Nishikawa, Yoshitaka ^{[1
]}

Yamaguchi, Daisuke ^{[9
]}

Tomono, Teruko ^{[10
]}

Nakatsui, Masahiko ^{[11
]}

Baba, Mika ^{[12
]}

Morita, Tatsuya ^{[13
]}

Matsumoto, Shigemi ^{[1
]}

Kuroda, Tomohiro ^{[3
]}

Okuno, Yasushi ^{[2
,11
]}

Muto, Manabu ^{[1
]}

机构：

[1] Kyoto Univ Hosp, Dept Clin Oncol, Kyoto, Japan

[2] RIKEN Adv Inst Computat Sci, Kobe, Hyogo, Japan

[3] Kyoto Univ Hosp, Div Informat Technol & Adm Planning, Kyoto, Japan

[4] Kyoto Univ, Grad Sch Med, Sch Publ Hlth, Dept Healthcare Epidemiol, Kyoto, Japan

[5] Kyoto Univ Hosp, Inst Adv Clin & Translat Sci, Kyoto, Japan

[6] Mitsubishi Kyoto Hosp, Dept Palliat Care, Kyoto, Japan

[7] Mitsubishi Kyoto Hosp, Dept Clin Oncol, Kyoto, Japan

[8] Kyoto Min Iren Chuo Hosp, Dept Med Oncol, Kyoto, Japan

[9] Natl Canc Ctr Hosp East, Dept Gastrointestinal Oncol, Kashiwa, Chiba, Japan

[10] Kyoto Univ, Grad Sch Med, Dept Gastroenterol & Hepatol, Kyoto, Japan

[11] Kyoto Univ, Grad Sch Med, Dept Clin Syst Oncoinformat, Kyoto, Japan

[12] Suita Tokushukai Hosp, Dept Palliat Med, Suita, Osaka, Japan

[13] Seirei Mikatahara Gen Hosp, Palliat & Support Care Div, Hamamatsu, Shizuoka, Japan

来源：

PLOS ONE | 2017年 / 12卷 / 08期

关键词：

SURVIVAL PREDICTION; SCORE; SYSTEM; INDEX;

D O I：

10.1371/journal.pone.0183291

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Background 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. Methods 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 (C-40(3) = 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. Results 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. Conclusion 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.

引用

页数：13