Lijie Liu, Jin Li, Liting Hu, Xiaowei Cai, Xiaoyan Li, Yang Bai
{"title":"Development and Validation of a Prediction Model for Enteral Feeding Intolerance in Critical Ill Patients: A Retrospective Cohort Study.","authors":"Lijie Liu, Jin Li, Liting Hu, Xiaowei Cai, Xiaoyan Li, Yang Bai","doi":"10.1111/jocn.17660","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>To construct and validate a prediction model for enteral feeding intolerance in critically ill patients during the first 7 days of enteral feeding.</p><p><strong>Design: </strong>A retrospective cohort study.</p><p><strong>Methods: </strong>We reviewed the medical records of two intensive care units from January 2015 to August 2023, to develop a prediction model by univariate analysis and logistic regression analysis. Model's performance was evaluated through discrimination, calibration and decision curve analysis.</p><p><strong>Results: </strong>This study involved a total of 471 patients, with an enteral feeding intolerance incidence rate of 35.7%. The prediction model comprised six variables, namely neurological disease, chronic gastrointestinal disease, Acute Physiological and Chronic Health Assessment II score, sedatives, acid suppressants and serum albumin. The model showed robust discrimination, calibration and clinical net benefit, indicating significant potential for practical application with readily available variables.</p><p><strong>Conclusions: </strong>The model demonstrated strong predictive performance in assessing the risk of enteral feeding intolerance during the early stage of nutrition initiation.</p><p><strong>Implications for the profession and/or patient care: </strong>Enhancing clinicians' capacity to reduce the incidence of enteral feeding intolerance and improve patient outcomes.</p><p><strong>Impact: </strong>The prediction model shows a good capacity to discriminate critically ill patients at risk of enteral feeding intolerance, is helpful to provide personalised care.</p><p><strong>Reporting method: </strong>TRIPOD + AI checklist.</p><p><strong>Patient or public contribution: </strong>No patient or public contribution.</p><p><strong>Trial registration: </strong>https://www.chictr.org.cn/ ChiCTR2400090757.</p>","PeriodicalId":50236,"journal":{"name":"Journal of Clinical Nursing","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jocn.17660","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 0
Abstract
Aim: To construct and validate a prediction model for enteral feeding intolerance in critically ill patients during the first 7 days of enteral feeding.
Design: A retrospective cohort study.
Methods: We reviewed the medical records of two intensive care units from January 2015 to August 2023, to develop a prediction model by univariate analysis and logistic regression analysis. Model's performance was evaluated through discrimination, calibration and decision curve analysis.
Results: This study involved a total of 471 patients, with an enteral feeding intolerance incidence rate of 35.7%. The prediction model comprised six variables, namely neurological disease, chronic gastrointestinal disease, Acute Physiological and Chronic Health Assessment II score, sedatives, acid suppressants and serum albumin. The model showed robust discrimination, calibration and clinical net benefit, indicating significant potential for practical application with readily available variables.
Conclusions: The model demonstrated strong predictive performance in assessing the risk of enteral feeding intolerance during the early stage of nutrition initiation.
Implications for the profession and/or patient care: Enhancing clinicians' capacity to reduce the incidence of enteral feeding intolerance and improve patient outcomes.
Impact: The prediction model shows a good capacity to discriminate critically ill patients at risk of enteral feeding intolerance, is helpful to provide personalised care.
Reporting method: TRIPOD + AI checklist.
Patient or public contribution: No patient or public contribution.
期刊介绍：
The Journal of Clinical Nursing (JCN) is an international, peer reviewed, scientific journal that seeks to promote the development and exchange of knowledge that is directly relevant to all spheres of nursing practice. The primary aim is to promote a high standard of clinically related scholarship which advances and supports the practice and discipline of nursing. The Journal also aims to promote the international exchange of ideas and experience that draws from the different cultures in which practice takes place. Further, JCN seeks to enrich insight into clinical need and the implications for nursing intervention and models of service delivery. Emphasis is placed on promoting critical debate on the art and science of nursing practice.
JCN is essential reading for anyone involved in nursing practice, whether clinicians, researchers, educators, managers, policy makers, or students. The development of clinical practice and the changing patterns of inter-professional working are also central to JCN''s scope of interest. Contributions are welcomed from other health professionals on issues that have a direct impact on nursing practice.
We publish high quality papers from across the methodological spectrum that make an important and novel contribution to the field of clinical nursing (regardless of where care is provided), and which demonstrate clinical application and international relevance.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
