Jing Huang

Background: Delirium is a frequent postoperative complication among patients who have undergone cardiac surgery and is associated with prolonged hospitalization, cognitive decline, and increased mortality. Early prediction of delirium is therefore critical for initiating timely interventions. Objective: This study proposes the development and validation of a machine learning–based model to predict postoperative delirium in patients undergoing cardiac surgery during intensive care unit (ICU) care, facilitating the early detection of individuals at high risk of delirium and supporting clinicians in the deployment of targeted preventive strategies. Methods: This study extracted data on postoperative cardiac surgery patients who remained in the ICU for more than 24 hours from the Medical Information Mart for Intensive Care IV version 2.0 (MIMIC-IV 2.0) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV 2.0 cohort was randomly divided into a training set and an internal validation set in a 7:3 ratio, whereas the eICU-CRD functioned as an independent validation cohort. We used data from the first 24 hours of ICU monitoring to model the likelihood of delirium over the entire ICU admission period. Delirium was identified by a positive Confusion Assessment Method for the Intensive Care Unit evaluation (ie, score ≥4). We built predictive models by using logistic regression, support vector classifier, extreme gradient boosting (XGB), and random forest classifiers. Their performance was assessed via the area under the receiver operating characteristic curve, accuracy, sensitivity, positive predictive value, negative predictive value, and F1-score. Results: The analysis involved 2124 patients from the MIMIC-IV 2.0 database and 2406 from the eICU-CRD. A set of 57variables was selected to construct the predictive models. Among the various machine learning models tested, the XGB model demonstrated the best performance for delirium prediction during internal validation. As for external validation, the model achieved an area under the receiver operating characteristic curve of 0.75, indicating strong discriminatory ability. The most important predictive features identified by the model included hospital length of stay, minimum Glasgow Coma Scale score, mean blood pressure, Sequential Organ Failure Assessment score, weight, urine output, heart rate, and age. Conclusions: The XGB model with strong predictive capability for ICU delirium after cardiac surgery was developed and externally validated. This model offers essential technical support for building real-time delirium alert systems and enables ongoing risk stratification and evidence-based decision-making within the ICU environment.

Background: Delirium is a prevalent phenomenon among patients admitted to the geriatric intensive care unit (ICU) and can adversely impact prognosis and augment the risk of complications. Objective: We aimed to construct and validate a predictive model for postoperative delirium state in older ICU patients, providing timely and effective early identification of high-risk individuals and assisting clinicians in decision-making. Methods: The data from patients admitted to the ICU for over 24 hours were extracted from the Medical Information Marketplace for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were split (7:3) into training and internal validation sets, while the eICU-CRD data served as an external validation set. Delirium predictions were conducted for the subsequent prediction windows (12 h, 24 h, 48 h, and whole stay time) using data from the first 24 hours post admission. The corresponding feature variables were subjected to Boruta feature selection, and the prediction models were constructed using logistic regression, support vector classifier, random forest classifier, and extreme gradient boosting (XGB). Subsequently, model performance was evaluated using areas under the receiver operating characteristic curves (AUCs), Brier scores, and decision curve analysis, and external validation. Results: The MIMIC-IV and eICU-CRD datasets comprised 6129 and 709 patients, respectively, who were included in the analysis. Fifty-four features were selected to construct the predictive model. Regarding internal validation, the XGB model demonstrated the most effective prediction of delirium across different prediction windows. The AUCs for the 4 prediction windows (12 h, 24 h, 48 h, and whole stay time) were 0.848 (95% CI 0.826-0.869), 0.852 (95% CI 0.831-0.872), 0.851 (95% CI 0.831-0.871), and 0.844 (95% CI 0.823-0.863), respectively, and those of the external validation set were 0.777 (95% CI 0.726-0.825), 0.761 (95% CI 0.710-0.808), 0.753 (95% CI 0.704-0.798), and 0.737 (95% CI 0.695-0.777), respectively. Furthermore, the XGB model demonstrated the most accurate calibration across all prediction windows, with values of 0.129, 0.136, 0.144, and 0.148, respectively. Additionally, decision curve analysis revealed that the XGB model outperformed the other models in terms of net gain for the majority of threshold probability values. The 6 most significant predictive features identified were the first day’s delirium assessment results, type of first care unit, minimum Glasgow Coma Scale (GCS) score, Acute Physiology Score III, acetaminophen, and nonsteroidal anti-inflammatory drugs. Conclusions: The high-performance XGB model for predicting postoperative delirium state in older adult ICU patients has been successfully developed and validated. The model predicts the delirium state at 12 h, 24 h, 48 h, and whole stay time after the first day of hospitalization within the ICU. This enables physicians to identify high-risk patients early, thus facilitating the optimization of personalized management strategies and care plans.

BACKGROUND Delirium is a frequent postoperative complication among patients who have undergone cardiac surgery and is associated with prolonged hospitalization, cognitive decline, and increased mortality. Early prediction of delirium is therefore critical for initiating timely interventions. OBJECTIVE This study proposes the development and validation of a machine learning–based model to predict postoperative delirium in patients undergoing cardiac surgery during intensive care unit (ICU) care, facilitating the early detection of individuals at high risk of delirium and supporting clinicians in the deployment of targeted preventive strategies. METHODS This study extracted data on postoperative cardiac surgery patients who remained in the ICU for more than 24 hours from the Medical Information Mart for Intensive Care IV version 2.0 (MIMIC-IV 2.0) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV 2.0 cohort was randomly divided into a training set and an internal validation set in a 7:3 ratio, whereas the eICU-CRD functioned as an independent validation cohort. We used data from the first 24 hours of ICU monitoring to model the likelihood of delirium over the entire ICU admission period. Delirium was identified by a positive Confusion Assessment Method for the Intensive Care Unit evaluation (ie, score ≥4). We built predictive models by using logistic regression, support vector classifier, extreme gradient boosting (XGB), and random forest classifiers. Their performance was assessed via the area under the receiver operating characteristic curve, accuracy, sensitivity, positive predictive value, negative predictive value, and F1-score. RESULTS The analysis involved 2124 patients from the MIMIC-IV 2.0 database and 2406 from the eICU-CRD. A set of 57variables was selected to construct the predictive models. Among the various machine learning models tested, the XGB model demonstrated the best performance for delirium prediction during internal validation. As for external validation, the model achieved an area under the receiver operating characteristic curve of 0.75, indicating strong discriminatory ability. The most important predictive features identified by the model included hospital length of stay, minimum Glasgow Coma Scale score, mean blood pressure, Sequential Organ Failure Assessment score, weight, urine output, heart rate, and age. CONCLUSIONS The XGB model with strong predictive capability for ICU delirium after cardiac surgery was developed and externally validated. This model offers essential technical support for building real-time delirium alert systems and enables ongoing risk stratification and evidence-based decision-making within the ICU environment.

BACKGROUND Delirium is a prevalent phenomenon among patients admitted to the geriatric intensive care unit (ICU) and can adversely impact prognosis and augment the risk of complications. OBJECTIVE We aimed to construct and validate a predictive model for postoperative delirium state in older ICU patients, providing timely and effective early identification of high-risk individuals and assisting clinicians in decision-making. METHODS The data from patients admitted to the ICU for over 24 hours were extracted from the Medical Information Marketplace for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were split (7:3) into training and internal validation sets, while the eICU-CRD data served as an external validation set. Delirium predictions were conducted for the subsequent prediction windows (12 h, 24 h, 48 h, and whole stay time) using data from the first 24 hours post admission. The corresponding feature variables were subjected to Boruta feature selection, and the prediction models were constructed using logistic regression, support vector classifier, random forest classifier, and extreme gradient boosting (XGB). Subsequently, model performance was evaluated using areas under the receiver operating characteristic curves (AUCs), Brier scores, and decision curve analysis, and external validation. RESULTS The MIMIC-IV and eICU-CRD datasets comprised 6129 and 709 patients, respectively, who were included in the analysis. Fifty-four features were selected to construct the predictive model. Regarding internal validation, the XGB model demonstrated the most effective prediction of delirium across different prediction windows. The AUCs for the 4 prediction windows (12 h, 24 h, 48 h, and whole stay time) were 0.848 (95% CI 0.826-0.869), 0.852 (95% CI 0.831-0.872), 0.851 (95% CI 0.831-0.871), and 0.844 (95% CI 0.823-0.863), respectively, and those of the external validation set were 0.777 (95% CI 0.726-0.825), 0.761 (95% CI 0.710-0.808), 0.753 (95% CI 0.704-0.798), and 0.737 (95% CI 0.695-0.777), respectively. Furthermore, the XGB model demonstrated the most accurate calibration across all prediction windows, with values of 0.129, 0.136, 0.144, and 0.148, respectively. Additionally, decision curve analysis revealed that the XGB model outperformed the other models in terms of net gain for the majority of threshold probability values. The 6 most significant predictive features identified were the first day’s delirium assessment results, type of first care unit, minimum Glasgow Coma Scale (GCS) score, Acute Physiology Score III, acetaminophen, and nonsteroidal anti-inflammatory drugs. CONCLUSIONS The high-performance XGB model for predicting postoperative delirium state in older adult ICU patients has been successfully developed and validated. The model predicts the delirium state at 12 h, 24 h, 48 h, and whole stay time after the first day of hospitalization within the ICU. This enables physicians to identify high-risk patients early, thus facilitating the optimization of personalized management strategies and care plans.