BMC geriatrics · 2026
Development of a machine learning-based prediction model for hypothyroidism-associated delirium in elderly hypothyroid patients in the intensive care unit.
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Guan Binbin, Lin Lijing, Chen Zhou, Ma Li, Liu Libin
Аннотация
BACKGROUND: Patients with hypothyroidism admitted to the intensive care unit (ICU) frequently develop hypothyroidism-associated delirium (HAD), a condition strongly linked to adverse prognostic outcomes. The primary objective of this study was to develop a machine learning (ML) -based predictive model for the early identification of HAD.
MATERIALS AND METHODS: Patient data were retrieved from two non-overlapping datasets: Medical Information Mart for Intensive Care IV (MIMIC-IV) database and MIMIC-III database. Specifically, data from MIMIC-IV were split into a training set and an internal validation set, whereas MIMIC-III data served as an external validation set. Least Absolute Shrinkage and Selection Operator (LASSO) regression was utilized for feature variable selection, and predictive models were constructed using nine approaches. Model performance was assessed across discrimination, calibration, and clinical utility. SHAP (SHapley Additive exPlanations) was employed to visualize model characteristics and individual case predictions.
RESULTS: A model with 13 variables was built. Among all constructed models, the Gradient Boosting Machine (GBM) model demonstrated the optimal performance and was therefore selected as the final model (internal validation area under the receiver operating characteristic curve (AUROC)=0.806; external validation AUROC=0.788). Notably, the GBM model outperformed other approaches in HAD prediction. Key predictors included Glasgow Coma Scale (GCS), Sequential Organ Failure Assessment (SOFA), Sedatives, ICU Length of Stay (ICU_Day), Peripheral Oxygen Saturation SPO2, Calcium, Red Cell Distribution Width (RDW), and Mean Arterial Pressure(MAP). A user-friendly interface was developed for clinical use.
CONCLUSIONS: The establishment of this predictive model enables earlier HAD identification compared with traditional delirium assessment methods, and it is particularly applicable to patients for whom conventional delirium evaluation is challenging.
MATERIALS AND METHODS: Patient data were retrieved from two non-overlapping datasets: Medical Information Mart for Intensive Care IV (MIMIC-IV) database and MIMIC-III database. Specifically, data from MIMIC-IV were split into a training set and an internal validation set, whereas MIMIC-III data served as an external validation set. Least Absolute Shrinkage and Selection Operator (LASSO) regression was utilized for feature variable selection, and predictive models were constructed using nine approaches. Model performance was assessed across discrimination, calibration, and clinical utility. SHAP (SHapley Additive exPlanations) was employed to visualize model characteristics and individual case predictions.
RESULTS: A model with 13 variables was built. Among all constructed models, the Gradient Boosting Machine (GBM) model demonstrated the optimal performance and was therefore selected as the final model (internal validation area under the receiver operating characteristic curve (AUROC)=0.806; external validation AUROC=0.788). Notably, the GBM model outperformed other approaches in HAD prediction. Key predictors included Glasgow Coma Scale (GCS), Sequential Organ Failure Assessment (SOFA), Sedatives, ICU Length of Stay (ICU_Day), Peripheral Oxygen Saturation SPO2, Calcium, Red Cell Distribution Width (RDW), and Mean Arterial Pressure(MAP). A user-friendly interface was developed for clinical use.
CONCLUSIONS: The establishment of this predictive model enables earlier HAD identification compared with traditional delirium assessment methods, and it is particularly applicable to patients for whom conventional delirium evaluation is challenging.