{"title":"结肠镜检查期间2型糖尿病患者低血糖风险的预测模型:一项回顾性队列研究","authors":"Haiyan Yang, Linlin Zhang, Huiling Liu, Shuqiao Hu, Qiuping Yang, Jianan Wu, Mingming Xu, Shufang Chu","doi":"10.1111/jan.16806","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aims</h3>\n \n <p>To identify factors influencing hypoglycaemia in patients with type 2 diabetes mellitus (T2DM) undergoing colonoscopy and to construct a predictive model for assessing hypoglycaemia risk.</p>\n </section>\n \n <section>\n \n <h3> Design</h3>\n \n <p>A retrospective cohort study.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We retrospectively collected data on 598 T2DM patients who underwent colonoscopy and randomised them into a developmental cohort and a validation cohort in a 7:3 ratio. We used multivariate logistic regression to develop a predictive model for hypoglycaemia during colonoscopy and identify independent predictors in pre- and post-colonoscopy hypoglycaemia groups.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We identified 112 of 598 (18.7%) T2DM patients who experienced hypoglycaemia during the peri-colonoscopy period: 43 pre-colonoscopy, 61 post-colonoscopy and 8 at both junctures. Ultimately, five predictors—insulin, SGLT2 inhibitors, fasting after colonoscopy, fasting C-peptide and estimated glomerular filtration rate (eGFR)—were integrated into the predictive model. The AUC for predicting hypoglycaemia was 0.78 (95% CI, 0.71–0.84) and 0.82 (95% CI, 0.74–0.90) in the development and validation cohort, respectively. Variables associated with pre-colonoscopy hypoglycaemia included SGLT2 inhibitors, fasting C-peptide and eGFR, whereas the post-colonoscopy hypoglycaemia group was associated with metformin, duration of diabetes, fasting C-peptide and fasting after the examination.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This study successfully developed and validated a predictive model for assessing hypoglycaemia risk in T2DM patients during peri-colonoscopy.</p>\n </section>\n \n <section>\n \n <h3> Implications for the Profession and/or Patient Care</h3>\n \n <p>Early identification of patients at high risk for peri-colonoscopy hypoglycaemia allows nurses to implement personalised preventive strategies. The predictive model enables clinical nurses to deliver tailored interventions based on individual risk factors, potentially reducing hypoglycaemia-related complications and improving patient safety outcomes.</p>\n </section>\n \n <section>\n \n <h3> Impact</h3>\n \n <p>This study provides nurses with a validated risk prediction tool for identifying high-risk type 2 diabetes patients during colonoscopy, enabling targeted blood glucose monitoring protocols and preventive interventions in clinical practice.</p>\n </section>\n \n <section>\n \n <h3> Reporting Method</h3>\n \n <p>This study follows the STROBE guidelines for reporting cohort studies.</p>\n </section>\n \n <section>\n \n <h3> Patient or Public Contribution</h3>\n \n <p>Diabetes patients contributed electronic health record datasets.</p>\n </section>\n </div>","PeriodicalId":54897,"journal":{"name":"Journal of Advanced Nursing","volume":"81 11","pages":"7633-7644"},"PeriodicalIF":3.4000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jan.16806","citationCount":"0","resultStr":"{\"title\":\"Predictive Model for Hypoglycaemia Risk in Type 2 Diabetes Mellitus Patients During the Peri-Colonoscopy Period: A Retrospective Cohort Study\",\"authors\":\"Haiyan Yang, Linlin Zhang, Huiling Liu, Shuqiao Hu, Qiuping Yang, Jianan Wu, Mingming Xu, Shufang Chu\",\"doi\":\"10.1111/jan.16806\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aims</h3>\\n \\n <p>To identify factors influencing hypoglycaemia in patients with type 2 diabetes mellitus (T2DM) undergoing colonoscopy and to construct a predictive model for assessing hypoglycaemia risk.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Design</h3>\\n \\n <p>A retrospective cohort study.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We retrospectively collected data on 598 T2DM patients who underwent colonoscopy and randomised them into a developmental cohort and a validation cohort in a 7:3 ratio. We used multivariate logistic regression to develop a predictive model for hypoglycaemia during colonoscopy and identify independent predictors in pre- and post-colonoscopy hypoglycaemia groups.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>We identified 112 of 598 (18.7%) T2DM patients who experienced hypoglycaemia during the peri-colonoscopy period: 43 pre-colonoscopy, 61 post-colonoscopy and 8 at both junctures. Ultimately, five predictors—insulin, SGLT2 inhibitors, fasting after colonoscopy, fasting C-peptide and estimated glomerular filtration rate (eGFR)—were integrated into the predictive model. The AUC for predicting hypoglycaemia was 0.78 (95% CI, 0.71–0.84) and 0.82 (95% CI, 0.74–0.90) in the development and validation cohort, respectively. Variables associated with pre-colonoscopy hypoglycaemia included SGLT2 inhibitors, fasting C-peptide and eGFR, whereas the post-colonoscopy hypoglycaemia group was associated with metformin, duration of diabetes, fasting C-peptide and fasting after the examination.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>This study successfully developed and validated a predictive model for assessing hypoglycaemia risk in T2DM patients during peri-colonoscopy.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Implications for the Profession and/or Patient Care</h3>\\n \\n <p>Early identification of patients at high risk for peri-colonoscopy hypoglycaemia allows nurses to implement personalised preventive strategies. The predictive model enables clinical nurses to deliver tailored interventions based on individual risk factors, potentially reducing hypoglycaemia-related complications and improving patient safety outcomes.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Impact</h3>\\n \\n <p>This study provides nurses with a validated risk prediction tool for identifying high-risk type 2 diabetes patients during colonoscopy, enabling targeted blood glucose monitoring protocols and preventive interventions in clinical practice.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Reporting Method</h3>\\n \\n <p>This study follows the STROBE guidelines for reporting cohort studies.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Patient or Public Contribution</h3>\\n \\n <p>Diabetes patients contributed electronic health record datasets.</p>\\n </section>\\n </div>\",\"PeriodicalId\":54897,\"journal\":{\"name\":\"Journal of Advanced Nursing\",\"volume\":\"81 11\",\"pages\":\"7633-7644\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-02-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jan.16806\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Nursing\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jan.16806\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NURSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Nursing","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jan.16806","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
Predictive Model for Hypoglycaemia Risk in Type 2 Diabetes Mellitus Patients During the Peri-Colonoscopy Period: A Retrospective Cohort Study
Aims
To identify factors influencing hypoglycaemia in patients with type 2 diabetes mellitus (T2DM) undergoing colonoscopy and to construct a predictive model for assessing hypoglycaemia risk.
Design
A retrospective cohort study.
Methods
We retrospectively collected data on 598 T2DM patients who underwent colonoscopy and randomised them into a developmental cohort and a validation cohort in a 7:3 ratio. We used multivariate logistic regression to develop a predictive model for hypoglycaemia during colonoscopy and identify independent predictors in pre- and post-colonoscopy hypoglycaemia groups.
Results
We identified 112 of 598 (18.7%) T2DM patients who experienced hypoglycaemia during the peri-colonoscopy period: 43 pre-colonoscopy, 61 post-colonoscopy and 8 at both junctures. Ultimately, five predictors—insulin, SGLT2 inhibitors, fasting after colonoscopy, fasting C-peptide and estimated glomerular filtration rate (eGFR)—were integrated into the predictive model. The AUC for predicting hypoglycaemia was 0.78 (95% CI, 0.71–0.84) and 0.82 (95% CI, 0.74–0.90) in the development and validation cohort, respectively. Variables associated with pre-colonoscopy hypoglycaemia included SGLT2 inhibitors, fasting C-peptide and eGFR, whereas the post-colonoscopy hypoglycaemia group was associated with metformin, duration of diabetes, fasting C-peptide and fasting after the examination.
Conclusion
This study successfully developed and validated a predictive model for assessing hypoglycaemia risk in T2DM patients during peri-colonoscopy.
Implications for the Profession and/or Patient Care
Early identification of patients at high risk for peri-colonoscopy hypoglycaemia allows nurses to implement personalised preventive strategies. The predictive model enables clinical nurses to deliver tailored interventions based on individual risk factors, potentially reducing hypoglycaemia-related complications and improving patient safety outcomes.
Impact
This study provides nurses with a validated risk prediction tool for identifying high-risk type 2 diabetes patients during colonoscopy, enabling targeted blood glucose monitoring protocols and preventive interventions in clinical practice.
Reporting Method
This study follows the STROBE guidelines for reporting cohort studies.
Patient or Public Contribution
Diabetes patients contributed electronic health record datasets.
期刊介绍:
The Journal of Advanced Nursing (JAN) contributes to the advancement of evidence-based nursing, midwifery and healthcare by disseminating high quality research and scholarship of contemporary relevance and with potential to advance knowledge for practice, education, management or policy.
All JAN papers are required to have a sound scientific, evidential, theoretical or philosophical base and to be critical, questioning and scholarly in approach. As an international journal, JAN promotes diversity of research and scholarship in terms of culture, paradigm and healthcare context. For JAN’s worldwide readership, authors are expected to make clear the wider international relevance of their work and to demonstrate sensitivity to cultural considerations and differences.