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Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model
OBJECTIVE: This study aimed at evaluating the efficacy of the questionnaire-based prediction model in an independent prospective cohort. METHODS: A cluster-randomized controlled trial was conducted in Changsha, Harbin, Luoshan, and Sheyang in eastern China in 2015−2017. A total of 182 villages/commu...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666781/ https://www.ncbi.nlm.nih.gov/pubmed/33223755 http://dx.doi.org/10.21147/j.issn.1000-9604.2020.05.05 |
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author | Cao, Maomao Li, He Sun, Dianqin Lei, Lin Ren, Jiansong Shi, Jufang Li, Ni Peng, Ji Chen, Wanqing |
author_facet | Cao, Maomao Li, He Sun, Dianqin Lei, Lin Ren, Jiansong Shi, Jufang Li, Ni Peng, Ji Chen, Wanqing |
author_sort | Cao, Maomao |
collection | PubMed |
description | OBJECTIVE: This study aimed at evaluating the efficacy of the questionnaire-based prediction model in an independent prospective cohort. METHODS: A cluster-randomized controlled trial was conducted in Changsha, Harbin, Luoshan, and Sheyang in eastern China in 2015−2017. A total of 182 villages/communities were regarded as clusters, and allocated to screening arm or control arm randomly. Face-to-face interview through a questionnaire interview, including of relevant risk factors of gastric cancer, was administered for each subject. Participants were further classified into high-risk or low-risk groups based on their exposure to risk factors. All participants were followed up until December 31, 2019. Cumulative incidence rates from gastric cancer between high-risk and low-risk groups were calculated and compared using the log-rank test. Cox proportional hazard regression models were applied to estimate hazard ratio (HR) and 95% confidence interval (95% CI). RESULTS: Totally, 89,914 residents were recruited with a mean follow-up of 3.47 years. And 42,015 (46.73%) individuals were classified into high-risk group and 47,899 (53.27%) subjects were categorized into low-risk group. Gastric cancer was diagnosed in 131 participants, of which 91 were in high-risk group. Compared with the low-risk participants, high-risk individuals were more likely to develop gastric cancer (adjusted HR=2.15, 95% CI, 1.23−3.76). The sensitivity of the questionnaire-based model was estimated at 61.82% (95% CI, 47.71−74.28) in a general population. CONCLUSIONS: Our questionnaire-based model is effective at identifying high-risk individuals for gastric cancer. |
format | Online Article Text |
id | pubmed-7666781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-76667812020-11-19 Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model Cao, Maomao Li, He Sun, Dianqin Lei, Lin Ren, Jiansong Shi, Jufang Li, Ni Peng, Ji Chen, Wanqing Chin J Cancer Res Original Article OBJECTIVE: This study aimed at evaluating the efficacy of the questionnaire-based prediction model in an independent prospective cohort. METHODS: A cluster-randomized controlled trial was conducted in Changsha, Harbin, Luoshan, and Sheyang in eastern China in 2015−2017. A total of 182 villages/communities were regarded as clusters, and allocated to screening arm or control arm randomly. Face-to-face interview through a questionnaire interview, including of relevant risk factors of gastric cancer, was administered for each subject. Participants were further classified into high-risk or low-risk groups based on their exposure to risk factors. All participants were followed up until December 31, 2019. Cumulative incidence rates from gastric cancer between high-risk and low-risk groups were calculated and compared using the log-rank test. Cox proportional hazard regression models were applied to estimate hazard ratio (HR) and 95% confidence interval (95% CI). RESULTS: Totally, 89,914 residents were recruited with a mean follow-up of 3.47 years. And 42,015 (46.73%) individuals were classified into high-risk group and 47,899 (53.27%) subjects were categorized into low-risk group. Gastric cancer was diagnosed in 131 participants, of which 91 were in high-risk group. Compared with the low-risk participants, high-risk individuals were more likely to develop gastric cancer (adjusted HR=2.15, 95% CI, 1.23−3.76). The sensitivity of the questionnaire-based model was estimated at 61.82% (95% CI, 47.71−74.28) in a general population. CONCLUSIONS: Our questionnaire-based model is effective at identifying high-risk individuals for gastric cancer. AME Publishing Company 2020-10-31 /pmc/articles/PMC7666781/ /pubmed/33223755 http://dx.doi.org/10.21147/j.issn.1000-9604.2020.05.05 Text en Copyright © 2020 Chinese Journal of Cancer Research. All rights reserved. http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
spellingShingle | Original Article Cao, Maomao Li, He Sun, Dianqin Lei, Lin Ren, Jiansong Shi, Jufang Li, Ni Peng, Ji Chen, Wanqing Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model |
title | Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model |
title_full | Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model |
title_fullStr | Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model |
title_full_unstemmed | Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model |
title_short | Classifying risk level of gastric cancer: Evaluation of questionnaire-based prediction model |
title_sort | classifying risk level of gastric cancer: evaluation of questionnaire-based prediction model |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666781/ https://www.ncbi.nlm.nih.gov/pubmed/33223755 http://dx.doi.org/10.21147/j.issn.1000-9604.2020.05.05 |
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