Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol
Introduction Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associ...
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BMJ Publishing Group
2025-01-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/1/e092594.full |
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| author | Jiawen Deng Hemang Yadav Kiyan Heybati |
| author_facet | Jiawen Deng Hemang Yadav Kiyan Heybati |
| author_sort | Jiawen Deng |
| collection | DOAJ |
| description | Introduction Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimising sedation strategies and preventing adverse outcomes. Machine-learning (ML) models offer a promising approach for predicting individualised patient risks of propofol-associated hypertriglyceridemia.Methods and analysis We propose the development of an ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. The study will use retrospective data from four Mayo Clinic sites. Nested cross validation (CV) will be employed, with a tenfold inner CV loop for model tuning and selection as well as an outer loop using leave-one-site-out CV for external validation. Feature selection will be conducted using Boruta and least absolute shrinkage and selection operator-penalised logistic regression. Data preprocessing steps include missing data imputation, feature scaling and dimensionality reduction techniques. Six ML algorithms will be tuned and evaluated. Bayesian optimisation will be used for hyperparameter selection. Global model explainability will be assessed using permutation importance, and local model explainability will be assessed using SHapley Additive exPlanations.Ethics and dissemination The proposed ML model aims to provide a reliable and interpretable tool for clinicians to predict the risk of propofol-associated hypertriglyceridemia in ICU patients. The final model will be deployed in a web-based clinical risk calculator. The model development process and performance measures obtained during nested CV will be described in a study publication to be disseminated in a peer-reviewed journal. The proposed study has received ethics approval from the Mayo Clinic Institutional Review Board (IRB #23–0 07 416). |
| format | Article |
| id | doaj-art-4d99354a195a4c55b6dbcfe9edce43a1 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-4d99354a195a4c55b6dbcfe9edce43a12025-01-23T05:15:08ZengBMJ Publishing GroupBMJ Open2044-60552025-01-0115110.1136/bmjopen-2024-092594Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocolJiawen Deng0Hemang Yadav1Kiyan Heybati21 Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada3 Division of Pulmonary and Critical Care, Mayo Clinic, Rochester, Minnesota, USA2 Alix School of Medicine, Mayo Clinic, Rochester, Minnesota, USAIntroduction Propofol is a widely used sedative-hypnotic agent for critically ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimising sedation strategies and preventing adverse outcomes. Machine-learning (ML) models offer a promising approach for predicting individualised patient risks of propofol-associated hypertriglyceridemia.Methods and analysis We propose the development of an ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. The study will use retrospective data from four Mayo Clinic sites. Nested cross validation (CV) will be employed, with a tenfold inner CV loop for model tuning and selection as well as an outer loop using leave-one-site-out CV for external validation. Feature selection will be conducted using Boruta and least absolute shrinkage and selection operator-penalised logistic regression. Data preprocessing steps include missing data imputation, feature scaling and dimensionality reduction techniques. Six ML algorithms will be tuned and evaluated. Bayesian optimisation will be used for hyperparameter selection. Global model explainability will be assessed using permutation importance, and local model explainability will be assessed using SHapley Additive exPlanations.Ethics and dissemination The proposed ML model aims to provide a reliable and interpretable tool for clinicians to predict the risk of propofol-associated hypertriglyceridemia in ICU patients. The final model will be deployed in a web-based clinical risk calculator. The model development process and performance measures obtained during nested CV will be described in a study publication to be disseminated in a peer-reviewed journal. The proposed study has received ethics approval from the Mayo Clinic Institutional Review Board (IRB #23–0 07 416).https://bmjopen.bmj.com/content/15/1/e092594.full |
| spellingShingle | Jiawen Deng Hemang Yadav Kiyan Heybati Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol BMJ Open |
| title | Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol |
| title_full | Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol |
| title_fullStr | Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol |
| title_full_unstemmed | Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol |
| title_short | Development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data: a protocol |
| title_sort | development and validation of machine learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data a protocol |
| url | https://bmjopen.bmj.com/content/15/1/e092594.full |
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