Impact of nutrition‐related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol.
Background: This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the intensive care unit (ICU) using an advanced artificial intelligence (AI) model for mortality prediction. Methods: Data from ICU patients (December 2016...
| Publicado en: | Nutrition in Clinical Practice Vol. 40; no. 3; pp. 723 - 733 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2025
|
| 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=184927363&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184927363 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08845336 IKQ jtl: Nutrition in Clinical Practice issn: 08845336 maglogo: N pubinfo: dt: Jun2025 vid: 40 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 184927363 180444404 184927363 184927363 10.1002/ncp.11238 184927363 ppf: 723 ppct: 10 formats: tig: atl: Impact of nutrition‐related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol. aug: au: Park, Dong Jin Baik, Seung Min Lee, Hanyoung Park, Hoonsung Lee, Jae‐Myeong affil: Department of Laboratory Medicine, College of Medicine, Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, Korea sug: subj: Nutritional Assessment Trace Elements Blood Vitamins Blood Cholesterol Blood Serum Albumin Analysis Intensive Care Units Prediction Models Evaluation Multilayer Perceptrons Evaluation Boosting Machine Learning Algorithms Evaluation Critical Illness Mortality Critical Care Hospital Mortality Risk Factors Risk Assessment Human Inpatients Male Female Middle Age Aged Aged, 80 and Over APACHE (Acute Physiology and Chronic Health Evaluation) Questionnaires Critically Ill Patients Critical Illness Prognosis Retrospective Design ROC Curve Precision Age Factors Selenium Blood Lipoproteins, LDL Blood Zinc Blood Iron Blood Copper Blood Manganese Blood Cholecalciferol Blood Ascorbic Acid Blood Thiamine Blood Descriptive Statistics Data Analysis Software Hematologic Tests Comparative Studies Deep Learning Lipoproteins, HDL Cholesterol Blood Treatment Outcomes Biological Markers Blood Prediction Algorithms Evaluation Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the intensive care unit (ICU) using an advanced artificial intelligence (AI) model for mortality prediction. Methods: Data from ICU patients (December 2016 to December 2021), including serum levels of trace elements, vitamins, cholesterol, and prealbumin, were retrospectively analyzed using AI models. Models employed included category boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and multilayer perceptron (MLP). Performance was evaluated using area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1‐score. The performance was evaluated using 10‐fold crossvalidation. The SHapley Additive exPlanations (SHAP) method provided interpretability. Results: CatBoost emerged as the top‐performing individual AI model with an AUROC of 0.756, closely followed by LGBM, MLP, and XGBoost. Furthermore, the ensemble model combining these four models achieved the highest AUROC of 0.776 and more balanced metrics, outperforming all models. SHAP analysis indicated significant influences of prealbumin, Acute Physiology and Chronic Health Evaluation II score, and age on predictions. Notably, the ratios of selenium to age and low‐density lipoprotein to total cholesterol also had a notable impact on the models' output. Conclusion: The study underscores the critical role of nutrition‐related parameters in ICU patient outcomes. Advanced AI models, particularly in an ensemble approach, demonstrated improved predictive accuracy. SHAP analysis offered insights into specific factors influencing patient survival, highlighting the need for broader consideration of these biomarkers in critical care management. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|