Prediction model for delirium in patients with cardiovascular surgery: development and validation

Background: The aim of this study was to construct a nomogram model for discriminating the risk of delirium in patients undergoing cardiovascular surgery. Methods: From January 2017 to June 2020, we collected data from 838 patients who underwent cardiovascular surgery at the Affiliated Hospital of Nantong University. Patients were randomly divided into a training set and a validation set at a 5:5 ratio. A nomogram model was established based on logistic regression. Discrimination and calibration were used to evaluate the predictive performance of the model. Results: The incidence of delirium was 48.3%. A total of 389 patients were in the modelling group, and 449 patients were in the verification group. Logistic regression analysis showed that CPB duration (OR = 1.004, 95% CI: 1.001-1.008, P = 0.018), postoperative serum sodium (OR = 1.112, 95% CI: 1.049-1.178, P < 0.001), age (OR = 1.027, 95% CI: 1.0061.048, P = 0.011), and postoperative MV (OR = 1.019, 95% CI: 1.008-1.030, P < 0.001) were independent risk factors. The results showed that AUCROC was 0.712 and that the 95% CI was 0.661-0.762. The Hosmer-Lemeshow goodness of fit test showed that the predicted results of the model were in good agreement with the actual situation (chi(2) = 6.200, P = 0.625). The results of verification showed that the AUCROC was 0.705, and the 95% CI was 0.657-0.752. The Hosmer-Lemeshow goodness of fit test results were chi(2) = 8.653 and P = 0.372, indicating that the predictive effect of the model is good. Conclusions: The establishment of the model provides accurate and objective assessment tools for medical staff to start preventing postoperative delirium in a purposeful and focused manner when a patient enters the CSICU after surgery.

Automated summary

The aim of this study was to construct a nomogram model for discriminating the risk of delirium in patients undergoing cardiovascular surgery. Logistic regression analysis identified CPB duration, postoperative serum sodium, age, and postoperative MV as independent risk factors for delirium. The established model showed good predictive performance and can assist medical staff in preventing postoperative delirium.