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.
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 recei...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5570326?pdf=render |
| _version_ | 1818172658647302144 |
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| author | Yu Uneno Kei Taneishi Masashi Kanai Kazuya Okamoto Yosuke Yamamoto Akira Yoshioka Shuji Hiramoto Akira Nozaki Yoshitaka Nishikawa Daisuke Yamaguchi Teruko Tomono Masahiko Nakatsui Mika Baba Tatsuya Morita Shigemi Matsumoto Tomohiro Kuroda Yasushi Okuno Manabu Muto |
| author_facet | Yu Uneno Kei Taneishi Masashi Kanai Kazuya Okamoto Yosuke Yamamoto Akira Yoshioka Shuji Hiramoto Akira Nozaki Yoshitaka Nishikawa Daisuke Yamaguchi Teruko Tomono Masahiko Nakatsui Mika Baba Tatsuya Morita Shigemi Matsumoto Tomohiro Kuroda Yasushi Okuno Manabu Muto |
| author_sort | Yu Uneno |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T19:16:07Z |
| format | Article |
| id | doaj.art-d9f6fb20e126425d8c54d2662cae8d28 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T19:16:07Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d9f6fb20e126425d8c54d2662cae8d282022-12-22T00:53:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01128e018329110.1371/journal.pone.0183291Development 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.Yu UnenoKei TaneishiMasashi KanaiKazuya OkamotoYosuke YamamotoAkira YoshiokaShuji HiramotoAkira NozakiYoshitaka NishikawaDaisuke YamaguchiTeruko TomonoMasahiko NakatsuiMika BabaTatsuya MoritaShigemi MatsumotoTomohiro KurodaYasushi OkunoManabu MutoWe 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.http://europepmc.org/articles/PMC5570326?pdf=render |
| spellingShingle | Yu Uneno Kei Taneishi Masashi Kanai Kazuya Okamoto Yosuke Yamamoto Akira Yoshioka Shuji Hiramoto Akira Nozaki Yoshitaka Nishikawa Daisuke Yamaguchi Teruko Tomono Masahiko Nakatsui Mika Baba Tatsuya Morita Shigemi Matsumoto Tomohiro Kuroda Yasushi Okuno Manabu Muto 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. PLoS ONE |
| title | 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. |
| title_full | 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. |
| title_fullStr | 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. |
| title_full_unstemmed | 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. |
| title_short | 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. |
| title_sort | 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 |
| url | http://europepmc.org/articles/PMC5570326?pdf=render |
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