External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank
Abstract Background In China, colorectal cancer (CRC) incidence and mortality have been steadily increasing over the last decades. Risk models to predict incident CRC have been developed in various populations, but they have not been systematically externally validated in a Chinese population. This...
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-09-01
|
| Series: | BMC Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12916-022-02488-w |
| _version_ | 1798002960188309504 |
|---|---|
| author | Roxanna E. Abhari Blake Thomson Ling Yang Iona Millwood Yu Guo Xiaoming Yang Jun Lv Daniel Avery Pei Pei Peng Wen Canqing Yu Yiping Chen Junshi Chen Liming Li Zhengming Chen Christiana Kartsonaki |
| author_facet | Roxanna E. Abhari Blake Thomson Ling Yang Iona Millwood Yu Guo Xiaoming Yang Jun Lv Daniel Avery Pei Pei Peng Wen Canqing Yu Yiping Chen Junshi Chen Liming Li Zhengming Chen Christiana Kartsonaki |
| author_sort | Roxanna E. Abhari |
| collection | DOAJ |
| description | Abstract Background In China, colorectal cancer (CRC) incidence and mortality have been steadily increasing over the last decades. Risk models to predict incident CRC have been developed in various populations, but they have not been systematically externally validated in a Chinese population. This study aimed to assess the performance of risk scores in predicting CRC using the China Kadoorie Biobank (CKB), one of the largest and geographically diverse prospective cohort studies in China. Methods Nine models were externally validated in 512,415 participants in CKB and included 2976 cases of CRC. Model discrimination was assessed, overall and by sex, age, site, and geographic location, using the area under the receiver operating characteristic curve (AUC). Model discrimination of these nine models was compared to a model using age alone. Calibration was assessed for five models, and they were re-calibrated in CKB. Results The three models with the highest discrimination (Ma (Cox model) AUC 0.70 [95% CI 0.69–0.71]; Aleksandrova 0.70 [0.69–0.71]; Hong 0.69 [0.67–0.71]) included the variables age, smoking, and alcohol. These models performed significantly better than using a model based on age alone (AUC of 0.65 [95% CI 0.64–0.66]). Model discrimination was generally higher in younger participants, males, urban environments, and for colon cancer. The two models (Guo and Chen) developed in Chinese populations did not perform better than the others. Among the 10% of participants with the highest risk, the three best performing models identified 24–26% of participants that went on to develop CRC. Conclusions Several risk models based on easily obtainable demographic and modifiable lifestyle factor have good discrimination in a Chinese population. The three best performing models have a higher discrimination than using a model based on age alone. |
| first_indexed | 2024-04-11T12:00:42Z |
| format | Article |
| id | doaj.art-3d0366e121194c0ab4b9e078d06af315 |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-04-11T12:00:42Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-3d0366e121194c0ab4b9e078d06af3152022-12-22T04:24:52ZengBMCBMC Medicine1741-70152022-09-0120111410.1186/s12916-022-02488-wExternal validation of models for predicting risk of colorectal cancer using the China Kadoorie BiobankRoxanna E. Abhari0Blake Thomson1Ling Yang2Iona Millwood3Yu Guo4Xiaoming Yang5Jun Lv6Daniel Avery7Pei Pei8Peng Wen9Canqing Yu10Yiping Chen11Junshi Chen12Liming Li13Zhengming Chen14Christiana Kartsonaki15Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of OxfordDepartment of Surveillance and Health Equity Science, American Cancer SocietyClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of OxfordClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of OxfordFuwai Hospital, Chinese Academy of Medical SciencesClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of OxfordDepartment of Epidemiology and Biostatistics, School of Public Health, Peking UniversityClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of OxfordChinese Academy of Medical SciencesMaiji CDCDepartment of Epidemiology and Biostatistics, School of Public Health, Peking UniversityClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of OxfordNational Center for Food Safety Risk AssessmentDepartment of Epidemiology and Biostatistics, School of Public Health, Peking UniversityClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of OxfordClinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of OxfordAbstract Background In China, colorectal cancer (CRC) incidence and mortality have been steadily increasing over the last decades. Risk models to predict incident CRC have been developed in various populations, but they have not been systematically externally validated in a Chinese population. This study aimed to assess the performance of risk scores in predicting CRC using the China Kadoorie Biobank (CKB), one of the largest and geographically diverse prospective cohort studies in China. Methods Nine models were externally validated in 512,415 participants in CKB and included 2976 cases of CRC. Model discrimination was assessed, overall and by sex, age, site, and geographic location, using the area under the receiver operating characteristic curve (AUC). Model discrimination of these nine models was compared to a model using age alone. Calibration was assessed for five models, and they were re-calibrated in CKB. Results The three models with the highest discrimination (Ma (Cox model) AUC 0.70 [95% CI 0.69–0.71]; Aleksandrova 0.70 [0.69–0.71]; Hong 0.69 [0.67–0.71]) included the variables age, smoking, and alcohol. These models performed significantly better than using a model based on age alone (AUC of 0.65 [95% CI 0.64–0.66]). Model discrimination was generally higher in younger participants, males, urban environments, and for colon cancer. The two models (Guo and Chen) developed in Chinese populations did not perform better than the others. Among the 10% of participants with the highest risk, the three best performing models identified 24–26% of participants that went on to develop CRC. Conclusions Several risk models based on easily obtainable demographic and modifiable lifestyle factor have good discrimination in a Chinese population. The three best performing models have a higher discrimination than using a model based on age alone.https://doi.org/10.1186/s12916-022-02488-wCancer epidemiologyColorectal cancerRisk prediction modelsExternal validation |
| spellingShingle | Roxanna E. Abhari Blake Thomson Ling Yang Iona Millwood Yu Guo Xiaoming Yang Jun Lv Daniel Avery Pei Pei Peng Wen Canqing Yu Yiping Chen Junshi Chen Liming Li Zhengming Chen Christiana Kartsonaki External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank BMC Medicine Cancer epidemiology Colorectal cancer Risk prediction models External validation |
| title | External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank |
| title_full | External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank |
| title_fullStr | External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank |
| title_full_unstemmed | External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank |
| title_short | External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank |
| title_sort | external validation of models for predicting risk of colorectal cancer using the china kadoorie biobank |
| topic | Cancer epidemiology Colorectal cancer Risk prediction models External validation |
| url | https://doi.org/10.1186/s12916-022-02488-w |
| work_keys_str_mv | AT roxannaeabhari externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT blakethomson externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT lingyang externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT ionamillwood externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT yuguo externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT xiaomingyang externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT junlv externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT danielavery externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT peipei externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT pengwen externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT canqingyu externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT yipingchen externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT junshichen externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT limingli externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT zhengmingchen externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank AT christianakartsonaki externalvalidationofmodelsforpredictingriskofcolorectalcancerusingthechinakadooriebiobank |