Automated machine learning‐based model for the prediction of delirium in patients after surgery for degenerative spinal disease

Abstract

Objective This study used machine learning algorithms to identify critical variables and predict postoperative delirium (POD) in patients with degenerative spinal disease. Methods We included 663 patients who underwent surgery for degenerative spinal disease and received general anesthesia. The LASSO method was used to screen essential features associated with POD. Clinical characteristics, preoperative laboratory parameters, and intraoperative variables were reviewed and were used to construct nine machine learning models including a training set and validation set (80% of participants), and were then evaluated in the rest of the study sample (20% of participants). The area under the receiver‐operating characteristic curve (AUROC) and Brier scores were used to compare the prediction performances of different models. The eXtreme Gradient Boosting algorithms (XGBOOST) model was used to predict POD. The SHapley Additive exPlanations (SHAP) package was used to interpret the XGBOOST model. Data of 49 patients were prospectively collected for model validation. Results The XGBOOST model outperformed the other classifier models in the training set (area under the curve [AUC]: 92.8%, 95% confidence interval [CI]: 90.7%–95.0%), validation set (AUC: 87.0%, 95% CI: 80.7%–93.3%). This model also achieved the lowest Brier Score. Twelve vital variables, including age, serum albumin, the admission‐to‐surgery time interval, C‐reactive protein level, hypertension, intraoperative blood loss, intraoperative minimum blood pressure, cardiovascular‐cerebrovascular disease, smoking, alcohol consumption, pulmonary disease, and admission‐intraoperative maximum blood pressure difference, were selected. The XGBOOST model performed well in the prospective cohort (accuracy: 85.71%). Conclusion A machine learning model and a web predictor for delirium after surgery for the degenerative spinal disease were successfully developed to demonstrate the extent of POD risk during the perioperative period, which could guide appropriate preventive measures for high‐risk patients.

Temporal Generalizability of Machine Learning Models for Predicting Postoperative Delirium Using Electronic Health Record Data: Model Development and Validation Study

“…The extreme gradient boosting (XGBoost) effectively captures nonlinear relationships and interactions between predictors and outcomes [39]. Based on these characteristics, previous studies have reported that XGBoost exhibits a high level of accuracy in predicting postoperative delirium [11][12][13][14][15]. Therefore, we used XGBoost, which is a decision tree ensemble learning method, as a complex machine learning model.…”

“…Recently, machine learning models have garnered attention in the medical field for their high performance in predicting adverse events . Thus, machine learning techniques have been recognized as promising tools for the prediction of postoperative delirium [9][10][11][12][13][14][15][16][17].…”

Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department

“…[10][11][12] There is a need to identify an optimal screening strategy for delirium beyond cognitive assessment because, until we have it, delirium will likely remain an elusive diagnosis. In recent years, numerous studies have developed models in the hospital setting for estimating the risk of delirium in postoperative patients [13][14][15][16][17][18][19][20][21][22] and ICU patients, [23][24][25][26][27][28] but limited work has focused on the ED patient population. 3,29,30 Our objective was to leverage electronic health records to identify a clinically valuable risk estimation model for positive delirium screens in patients admitted from the ED to inpatient units.…”

“…There is a need to identify an optimal screening strategy for delirium beyond cognitive assessment because, until we have it, delirium will likely remain an elusive diagnosis. In recent years, numerous studies have developed models in the hospital setting for estimating the risk of delirium in postoperative patients 13–22 and ICU patients, 23–28 but limited work has focused on the ED patient population 3,29,30 …”

Machine learning for prediction of delirium in patients with extensive burns after surgery

“…Currently, machine learning models are widely used in medical fields, such as data mining, medical diagnosis, and disease risk prediction, 16 which have good predictive performance 17 . Risk factors for delirium have been used to develop predictive models for delirium in different populations, but no predictive models for delirium in patients with extensive burns have been developed 18,19 . It is difficult to achieve cross‐population usage between existing predictive models 18 .…”