Jared A. Sninsky , J. Vincent Toups , Cary C. Cotton , Anne F. Peery , Shifali Arora
{"title":"通过电子病历预测模型识别肠镜检查门诊患者肠道准备不足的情况","authors":"Jared A. Sninsky , J. Vincent Toups , Cary C. Cotton , Anne F. Peery , Shifali Arora","doi":"10.1016/j.tige.2023.12.008","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Aims</h3><p><span><span><span>Inadequate bowel preparation during </span>colonoscopy is associated with decreased </span>adenoma detection, increased costs, and patient procedural risks. The aim of this study was to develop a prediction model for identifying patients at high risk of inadequate bowel preparation for potential clinical integration into the </span>electronic medical record (EMR).</p></div><div><h3>Methods</h3><p>A retrospective study was conducted using outpatient screening/surveillance colonoscopies at the University of North Carolina from 2017 to 2022. Data were extracted from the EMRs of <em>Epic</em> and <em>ProVation</em><span>, including demographic, socioeconomic, and clinical variables. Logistic regression, LASSO regression, and gradient boosting machine models were evaluated and validated in a held-out testing set.</span></p></div><div><h3>Results</h3><p>The dataset included 23,456 colonoscopies, of which 6.25% had inadequate bowel preparation. The reduced LASSO regression model demonstrated an area under the curve of 0.65 (95% CI 0.63-0.67) in the held-out testing set. The relative risk of inadequate bowel prep in the high-risk group determined by the model was 2.42 (95% CI 2.07-2.82) compared with patients identified as low risk. The model calibration in the testing set revealed that among patients categorized as having 0%-11%, 11%-22%, and 22%-33% predicted risk of inadequate prep, the respective proportions of patients with inadequate prep were 5.5%, 19.3%, and 33.3%. Using the reduced LASSO model, a rudimentary code for a potential Epic FHIR application called <em>PrepPredict</em> was developed.</p></div><div><h3>Conclusion</h3><p>This study developed a prediction model for inadequate bowel preparation with the potential to integrate into the EMR for clinical use and optimize bowel preparation to improve patient care.</p></div>","PeriodicalId":36169,"journal":{"name":"Techniques and Innovations in Gastrointestinal Endoscopy","volume":"26 2","pages":"Pages 130-137"},"PeriodicalIF":1.2000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Electronic Medical Record Prediction Model to Identify Inadequate Bowel Preparation in Patients at Outpatient Colonoscopy\",\"authors\":\"Jared A. Sninsky , J. Vincent Toups , Cary C. Cotton , Anne F. Peery , Shifali Arora\",\"doi\":\"10.1016/j.tige.2023.12.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and Aims</h3><p><span><span><span>Inadequate bowel preparation during </span>colonoscopy is associated with decreased </span>adenoma detection, increased costs, and patient procedural risks. The aim of this study was to develop a prediction model for identifying patients at high risk of inadequate bowel preparation for potential clinical integration into the </span>electronic medical record (EMR).</p></div><div><h3>Methods</h3><p>A retrospective study was conducted using outpatient screening/surveillance colonoscopies at the University of North Carolina from 2017 to 2022. Data were extracted from the EMRs of <em>Epic</em> and <em>ProVation</em><span>, including demographic, socioeconomic, and clinical variables. Logistic regression, LASSO regression, and gradient boosting machine models were evaluated and validated in a held-out testing set.</span></p></div><div><h3>Results</h3><p>The dataset included 23,456 colonoscopies, of which 6.25% had inadequate bowel preparation. The reduced LASSO regression model demonstrated an area under the curve of 0.65 (95% CI 0.63-0.67) in the held-out testing set. The relative risk of inadequate bowel prep in the high-risk group determined by the model was 2.42 (95% CI 2.07-2.82) compared with patients identified as low risk. The model calibration in the testing set revealed that among patients categorized as having 0%-11%, 11%-22%, and 22%-33% predicted risk of inadequate prep, the respective proportions of patients with inadequate prep were 5.5%, 19.3%, and 33.3%. Using the reduced LASSO model, a rudimentary code for a potential Epic FHIR application called <em>PrepPredict</em> was developed.</p></div><div><h3>Conclusion</h3><p>This study developed a prediction model for inadequate bowel preparation with the potential to integrate into the EMR for clinical use and optimize bowel preparation to improve patient care.</p></div>\",\"PeriodicalId\":36169,\"journal\":{\"name\":\"Techniques and Innovations in Gastrointestinal Endoscopy\",\"volume\":\"26 2\",\"pages\":\"Pages 130-137\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Techniques and Innovations in Gastrointestinal Endoscopy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590030723000843\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Techniques and Innovations in Gastrointestinal Endoscopy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590030723000843","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
An Electronic Medical Record Prediction Model to Identify Inadequate Bowel Preparation in Patients at Outpatient Colonoscopy
Background and Aims
Inadequate bowel preparation during colonoscopy is associated with decreased adenoma detection, increased costs, and patient procedural risks. The aim of this study was to develop a prediction model for identifying patients at high risk of inadequate bowel preparation for potential clinical integration into the electronic medical record (EMR).
Methods
A retrospective study was conducted using outpatient screening/surveillance colonoscopies at the University of North Carolina from 2017 to 2022. Data were extracted from the EMRs of Epic and ProVation, including demographic, socioeconomic, and clinical variables. Logistic regression, LASSO regression, and gradient boosting machine models were evaluated and validated in a held-out testing set.
Results
The dataset included 23,456 colonoscopies, of which 6.25% had inadequate bowel preparation. The reduced LASSO regression model demonstrated an area under the curve of 0.65 (95% CI 0.63-0.67) in the held-out testing set. The relative risk of inadequate bowel prep in the high-risk group determined by the model was 2.42 (95% CI 2.07-2.82) compared with patients identified as low risk. The model calibration in the testing set revealed that among patients categorized as having 0%-11%, 11%-22%, and 22%-33% predicted risk of inadequate prep, the respective proportions of patients with inadequate prep were 5.5%, 19.3%, and 33.3%. Using the reduced LASSO model, a rudimentary code for a potential Epic FHIR application called PrepPredict was developed.
Conclusion
This study developed a prediction model for inadequate bowel preparation with the potential to integrate into the EMR for clinical use and optimize bowel preparation to improve patient care.