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

Autor: Akira Nozaki, Yasushi Okuno, Yosuke Yamamoto, Akira Yoshioka, Mika Baba, Tatsuya Morita, Shuji Hiramoto, Yoshitaka Nishikawa, Masahiko Nakatsui, Masashi Kanai, Yu Uneno, Kei Taneishi, Shigemi Matsumoto, Teruko Tomono, Kazuya Okamoto, Tomohiro Kuroda, Manabu Muto, Daisuke Yamaguchi
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Male
Medical Doctors
Neutrophils
Health Care Providers
Cancer Treatment
lcsh:Medicine
Bioinformatics
Biochemistry
White Blood Cells
0302 clinical medicine
Mathematical and Statistical Techniques
Animal Cells
Neoplasms
Medicine and Health Sciences
030212 general & internal medicine
Prospective Studies
Prospective cohort study
lcsh:Science
Multidisciplinary
Cross-Over Studies
Pharmaceutics
Area under the curve
Middle Aged
Prognosis
C-Reactive Proteins
Professions
Oncology
030220 oncology & carcinogenesis
Cohort
Physical Sciences
Female
Cellular Types
Statistics (Mathematics)
Research Article
Clinical Oncology
medicine.medical_specialty
Immune Cells
Immunology
Antineoplastic Agents
Research and Analysis Methods
03 medical and health sciences
Cancer Chemotherapy
Drug Therapy
Diagnostic Medicine
Internal medicine
Albumins
Physicians
medicine
Humans
Chemotherapy
Time point
Statistical Methods
Aged
Blood Cells
business.industry
Model selection
lcsh:R
Cancer
Biology and Life Sciences
Proteins
Cell Biology
Models
Theoretical
medicine.disease
Crossover study
Health Care
People and Places
Population Groupings
lcsh:Q
Clinical Medicine
business
Predictive modelling
Mathematics
Forecasting
Zdroj: PLoS ONE, Vol 12, Iss 8, p e0183291 (2017)
PLoS ONE
ISSN: 1932-6203
Popis: 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 (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. 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.
Databáze: OpenAIRE
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