{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:00:19Z","timestamp":1768568419152,"version":"3.49.0"},"posted":{"date-parts":[[2024,8,19]]},"group-title":"Intensive Care and Critical Care Medicine","reference-count":48,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T00:00:00Z","timestamp":1724025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,1,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Introduction<\/jats:title>\n                  <jats:p>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 optimizing sedation strategies and preventing adverse outcomes. Machine learning (ML) models offer a promising approach for predicting individualized patient risks of propofol-associated hypertriglyceridemia.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods and analysis<\/jats:title>\n                  <jats:p>We propose the development of a ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. The study will utilize retrospective data from four Mayo Clinic sites. Nested cross-validation (CV) will be employed, with a 10-fold 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 LASSO-penalized logistic regression. Data preprocessing steps include missing data imputation, feature scaling, and dimensionality reduction techniques. Six ML algorithms will be tuned and evaluated. Bayesian optimization 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 (SHAP).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Ethics and dissemination<\/jats:title>\n                  <jats:p>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 cross-validation 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 #23-007416).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Strengths and limitations of this study<\/jats:title>\n                  <jats:list list-type=\"simple\">\n                    <jats:list-item>\n                      <jats:label>-<\/jats:label>\n                      <jats:p>Robust external validation using a nested cross-validation (CV) framework will help assess the generalizability of models produced from the modeling pipeline across different hospital settings.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:label>-<\/jats:label>\n                      <jats:p>A diverse set of machine learning (ML) algorithms and advanced hyperparameter tuning techniques will be employed to identify the most optimal model configuration.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:label>-<\/jats:label>\n                      <jats:p>Integration of feature explainability will enhance the clinical applicability of the ML models by providing transparency in predictions, which can improve clinician trust and encourage adoption.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:label>-<\/jats:label>\n                      <jats:p>Reliance on retrospective data may introduce biases due to inconsistent or erroneous data collection, and the computational intensity of the validation approach may limit replication and future model expansion in resource-constrained settings.<\/jats:p>\n                    <\/jats:list-item>\n                  <\/jats:list>\n                <\/jats:sec>","DOI":"10.1101\/2024.08.17.24312159","type":"posted-content","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T14:35:14Z","timestamp":1724078114000},"source":"Crossref","is-referenced-by-count":1,"title":["Protocol for the development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8274-6468","authenticated-orcid":false,"given":"Jiawen","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiyan","family":"Heybati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1889-2524","authenticated-orcid":false,"given":"Hemang","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025010811401935000_2024.08.17.24312159v2.1","doi-asserted-by":"publisher","DOI":"10.1007\/s40262-018-0672-3"},{"key":"2025010811401935000_2024.08.17.24312159v2.2","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000003299"},{"key":"2025010811401935000_2024.08.17.24312159v2.3","doi-asserted-by":"publisher","DOI":"10.1111\/j.1527-3458.2008.00043.x"},{"key":"2025010811401935000_2024.08.17.24312159v2.4","doi-asserted-by":"publisher","DOI":"10.1592\/phco.2005.25.10.1348"},{"key":"2025010811401935000_2024.08.17.24312159v2.5","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202205-403OC"},{"key":"2025010811401935000_2024.08.17.24312159v2.6","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-012-2688-8"},{"key":"2025010811401935000_2024.08.17.24312159v2.7","doi-asserted-by":"publisher","DOI":"10.1097\/MJT.0000000000000461"},{"key":"2025010811401935000_2024.08.17.24312159v2.8","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.25057"},{"key":"2025010811401935000_2024.08.17.24312159v2.9","doi-asserted-by":"publisher","DOI":"10.3949\/ccjm.91a.23070"},{"key":"2025010811401935000_2024.08.17.24312159v2.10","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2017.0387"},{"key":"2025010811401935000_2024.08.17.24312159v2.11","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378"},{"key":"2025010811401935000_2024.08.17.24312159v2.12","doi-asserted-by":"publisher","DOI":"10.2196\/49023"},{"key":"2025010811401935000_2024.08.17.24312159v2.13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115222"},{"key":"2025010811401935000_2024.08.17.24312159v2.14","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.220232"},{"key":"2025010811401935000_2024.08.17.24312159v2.15","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v036.i11"},{"key":"2025010811401935000_2024.08.17.24312159v2.16","doi-asserted-by":"crossref","unstructured":"Muthukrishnan R , Rohini R. LASSO: A feature selection technique in predictive modeling for machine learning. 2016 IEEE International Conference on Advances in Computer Applications (ICACA). IEEE 2016.","DOI":"10.1109\/ICACA.2016.7887916"},{"key":"2025010811401935000_2024.08.17.24312159v2.17","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4780030207"},{"key":"2025010811401935000_2024.08.17.24312159v2.18","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4"},{"key":"2025010811401935000_2024.08.17.24312159v2.19","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202407-781OC"},{"key":"2025010811401935000_2024.08.17.24312159v2.20","doi-asserted-by":"crossref","unstructured":"Ashenden SK , Bartosik A , Agapow P-M , et al. Chapter 2 - Introduction to artificial intelligence and machine learning. In: Ashenden SK , ed. The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry. Cambridge, MA, USA: Academic Press 2021:15\u201326.","DOI":"10.1016\/B978-0-12-820045-2.00003-9"},{"key":"2025010811401935000_2024.08.17.24312159v2.21","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v045.i03"},{"key":"2025010811401935000_2024.08.17.24312159v2.22","doi-asserted-by":"publisher","DOI":"10.1080\/10705510802339072"},{"key":"2025010811401935000_2024.08.17.24312159v2.23","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4067"},{"key":"2025010811401935000_2024.08.17.24312159v2.24","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2401.13796"},{"key":"2025010811401935000_2024.08.17.24312159v2.25","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/52.3-4.591"},{"key":"2025010811401935000_2024.08.17.24312159v2.26","doi-asserted-by":"publisher","DOI":"10.5812\/ijem.3505"},{"key":"2025010811401935000_2024.08.17.24312159v2.27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109924"},{"key":"2025010811401935000_2024.08.17.24312159v2.28","doi-asserted-by":"crossref","unstructured":"Cabello-Solorzano K , Ortigosa de Araujo I , Pe\u00f1a M , et al. The impact of data normalization on the accuracy of machine learning algorithms: A comparative analysis. Lecture Notes in Networks and Systems. Cham: Springer Nature Switzerland 2023:344\u201353.","DOI":"10.1007\/978-3-031-42536-3_33"},{"key":"2025010811401935000_2024.08.17.24312159v2.29","unstructured":"James G , Witten D , Hastie T , et al. An Introduction to Statistical Learning: with Applications in R. New York, NY: Springer Science & Business Media 2013."},{"key":"2025010811401935000_2024.08.17.24312159v2.30","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2015.0202"},{"key":"2025010811401935000_2024.08.17.24312159v2.31","unstructured":"Ke G , Meng Q , Finley T , et al. LightGBM: A highly efficient gradient boosting decision tree. In: von Luxburg U , Guyon I , Bengio S , et al. , eds. NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc. 2017:3149\u201357."},{"key":"2025010811401935000_2024.08.17.24312159v2.32","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"2025010811401935000_2024.08.17.24312159v2.33","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1505.01866"},{"key":"2025010811401935000_2024.08.17.24312159v2.34","doi-asserted-by":"crossref","unstructured":"Chen T , Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM 2016.","DOI":"10.1145\/2939672.2939785"},{"key":"2025010811401935000_2024.08.17.24312159v2.35","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1502.01852"},{"key":"2025010811401935000_2024.08.17.24312159v2.36","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOS1000. 1102.4807"},{"key":"2025010811401935000_2024.08.17.24312159v2.37","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1502.03167"},{"key":"2025010811401935000_2024.08.17.24312159v2.38","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1711.05101"},{"key":"2025010811401935000_2024.08.17.24312159v2.39","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"2025010811401935000_2024.08.17.24312159v2.40","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1206.2944"},{"key":"2025010811401935000_2024.08.17.24312159v2.41","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2104.10201"},{"key":"2025010811401935000_2024.08.17.24312159v2.42","doi-asserted-by":"crossref","unstructured":"Niculescu-Mizil A , Caruana R. Predicting good probabilities with supervised learning. Proceedings of the 22nd international conference on Machine learning - ICML \u201805. New York, New York, USA: ACM Press 2005.","DOI":"10.1145\/1102351.1102430"},{"key":"2025010811401935000_2024.08.17.24312159v2.43","first-page":"61","article-title":"Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods","volume":"10","year":"1999","journal-title":"ADVANCES IN LARGE MARGIN CLASSIFIERS"},{"key":"2025010811401935000_2024.08.17.24312159v2.44","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3"},{"key":"2025010811401935000_2024.08.17.24312159v2.45","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181c30fb2"},{"key":"2025010811401935000_2024.08.17.24312159v2.46","doi-asserted-by":"publisher","DOI":"10.1023\/a:1010933404324"},{"key":"2025010811401935000_2024.08.17.24312159v2.47","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1705.07874"},{"key":"2025010811401935000_2024.08.17.24312159v2.48","doi-asserted-by":"crossref","unstructured":"Fang ML , Dhami DS , Kersting K. DP-CTGAN: Differentially private medical data generation using CTGANs. Artificial Intelligence in Medicine. Cham: Springer International Publishing 2022:178\u201388.","DOI":"10.1007\/978-3-031-09342-5_17"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2024.08.17.24312159","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:09:33Z","timestamp":1768507773000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2024.08.17.24312159"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,19]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1101\/2024.08.17.24312159","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.1136\/bmjopen-2024-092594","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2024,8,19]]},"subtype":"preprint"}}