Development and internal validation of an artificial intelligence-assisted bowel sounds auscultation system to predict early enteral nutrition-associated diarrhoea in acute pancreatitis: a prospective observational study.
Aims/Background An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (ENAD) in acute pancreatitis (AP) was developed utilising data obtained from bowel sounds auscultation. This model underwent validation through a single-centre, prospective observational s...
| Publicado en: | British Journal of Hospital Medicine (17508460) Vol. 85; no. 8; pp. 1 - 16 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Mark Allen Holdings Limited
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179360649&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179360649 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17508460 25KN jtl: British Journal of Hospital Medicine (17508460) issn: 17508460 maglogo: N pubinfo: dt: Aug2024 vid: 85 iid: 8 pid: 11383 pub: Mark Allen Holdings Limited artinfo: ui: 179360649 179360649 179360649 10.12968/hmed.2024.0120 179360649 ppf: 1 ppct: 15 formats: tig: atl: Development and internal validation of an artificial intelligence-assisted bowel sounds auscultation system to predict early enteral nutrition-associated diarrhoea in acute pancreatitis: a prospective observational study. aug: au: Liu, Chengcheng Wu, Li Xu, Rui Jiang, Zhiwei Xiao, Xiaoping Song, Nian Jin, Qianhong Dai, Zhengxiang affil: School of Nursing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China sug: subj: Pancreatitis Diagnosis Diarrhea Diagnosis Diarrhea Epidemiology Diarrhea Risk Factors Artificial Intelligence Gastrointestinal Motility Auscultation Methods Systems Development Internal Validity Prediction Models Enteral Nutrition Methods Decision Making, Clinical Risk Assessment Predictive Value of Tests Human Male Female Adult Middle Age Aged Prospective Studies Nonexperimental Studies Univariate Statistics Logistic Regression Algorithms Sensitivity and Specificity ROC Curve Confidence Intervals Construct Validity China Clinical Assessment Tools Descriptive Statistics Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Aims/Background An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (ENAD) in acute pancreatitis (AP) was developed utilising data obtained from bowel sounds auscultation. This model underwent validation through a single-centre, prospective observational study. The primary objective of the model was to enhance clinical decision-making by providing a more precise assessment of ENAD risk. Methods The study enrolled patients with AP who underwent early enteral nutrition (EN). Real-time collection and analysis of bowel sounds were conducted using an artificial intelligence bowel sounds auscultation system. Univariate analysis, multicollinearity analysis, and logistic regression analysis were employed to identify risk factors associated with ENAD. The random forest algorithm was utilised to establish the prediction model, and partial dependence plots were generated to analyse the impact of risk factors on ENAD risk. Validation of the model was performed using the optimal model Bootstrap resampling method. Predictive performance was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and an area under the receiver operating characteristic (ROC) curve. Results Among the 133 patients included in the study, the incidence of ENAD was 44.4%. Six risk factors were identified, and the model's accuracy was validated through Bootstrap iterations. The prediction accuracy of the model was 81.10%, with a sensitivity of 84.30% and a specificity of 77.80%. The positive predictive value was 82.60%, and the negative predictive value was 80.10%. The area under the ROC curve was 0.904 (95% confidence interval: 0.817–0.997). Conclusion The artificial intelligence bowel sounds auscultation system enhances the assessment of gastrointestinal function in AP patients undergoing EN and facilitates the construction of an ENAD predictive model. The model demonstrates good predictive efficacy, offering an objective basis for precise intervention timing in ENAD management. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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