Construction and validation of a risk prediction model for postoperative delirium in patients with off‑pump coronary artery bypass grafting.

Abstract

Compared with cardiopulmonary bypass surgery, off-pump coronary artery bypass grafting (OPCABG) reduces trauma to the body. However, there is still a risk of neurological complications, including postoperative delirium (POD). To date, few studies have been conducted on the risk of POD in OPCABG patients, and no standardized prediction model has been established. Thus, this study sought to analyze the factors influencing POD in OPCABG patients and to construct a risk prediction model. A total of 1,258 patients with OPCABG were enrolled and divided into the training set for model construction (944 cases) and the test set for model validation (314 cases). A risk prediction model for POD in OPCABG patients was established by least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, and a nomogram was drawn. The discrimination and calibration degree of the model was evaluated by the receiver operator characteristic (ROC) curve and calibration curve. Eight variables [i.e., age, tissue oxygen saturation, mean arterial pressure (MAP), carotid stenosis, the anterior-posterior diameter of the aortic sinus, ventricular septum thickness, left ventricular ejection fraction (LVEF), and Mini-Mental State Examination (MMSE) scores] were screen out by the LASSO regression and multivariate logistic regression, and the model was constructed. The area under the ROC curve of the training set was 0.702 [95% confidence interval (CI): 0.662-0.743], and that of the test set was 0.658 (95% CI: 0.585-0.730). The results of the Hosmer-Lemeshow goodness-of-fit test showed that the predicted POD risk of OPCABG patients in the training and test sets was consistent with the actual POD risk (χ2=5.154, P=0.74). The occurrence of POD in OPCABG patients is related to age, tissue oxygen saturation, MAP, carotid artery stenosis, the anterior-posterior diameter of aortic sinus, ventricular septal thickness, LVEF, and MMSE scores. The prediction model constructed with the above variables had high predictive performance, and thus may be helpful in the early identification of such patients.