Article ; Online: Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units.
Revista espanola de cardiologia (English ed.)
2024
Abstract: Introduction and objectives: Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a ... ...
| Abstract | Introduction and objectives: Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predictive model for delirium for patients in the CICU. Methods: This study included consecutive patients admitted to the CICU of the Samsung Medical Center. To assess the candidate variables for the model: we applied the following machine learning methods: random forest, extreme gradient boosting, partial least squares, and Plmnet-elastic.net. After selecting relevant variables, we performed a logistic regression analysis to derive the model formula. Internal validation was conducted using 100-repeated hold-out validation. Results: We analyzed 2774 patients, 677 (24.4%) of whom developed delirium in the CICU. Machine learning-based models showed good predictive performance. Clinically significant and frequently important predictors were selected to construct a delirium prediction scoring model for CICU patients. The model included albumin level, international normalized ratio, blood urea nitrogen, white blood cell count, C-reactive protein level, age, heart rate, and mechanical ventilation. The model had an area under the receiver operating characteristics curve (AUROC) of 0.861 (95%CI, 0.843-0.879). Similar results were obtained in internal validation with 100-repeated cross-validation (AUROC, 0.854; 95%CI, 0.826-0.883). Conclusions: Using variables frequently ranked as highly important in four machine learning methods, we created a novel delirium prediction model. This model could serve as a useful and simple tool for risk stratification for the occurrence of delirium at the patient's bedside in the CICU. |
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| Language | Spanish |
| Publishing date | 2024-01-16 |
| Publishing country | Spain |
| Document type | Journal Article |
| ZDB-ID | 2592481-3 |
| ISSN | 1885-5857 ; 1885-5857 |
| ISSN (online) | 1885-5857 |
| ISSN | 1885-5857 |
| DOI | 10.1016/j.rec.2023.12.007 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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