Development and validation of a postoperative delirium risk prediction model for non-cardiac surgery in elderly patients: The PNDABLE Study
Authors: Lin Xu , Tian Na , Wang Yuanlong , Hua Shuhui , Kong Jian , Xu Shanling , Lin Yanan , Li Chuan , Wang Bin , Bi Yanlin 
Published: September 2023

Abstract
Background
To develop and validate a postoperative delirium (POD) risk prediction preoperative model for elderly patients undergoing non-cardiac surgery.
Methods
This study selected 663 elderly patients undergoing non-cardiac elective surgery under general anesthesia for tracheal intubation in general surgery, orthopedics, urology, hepatobiliary and pancreatic surgery in our hospital from September 1st, 2020 to June 1st, 2022. Simple random sampling method was used according to 7: 3. The proportions divided the patients into the development group 464 cases and the validation group 199 cases. The clinical data of the patients before, during and after the operation were collected, and the occurrence of POD within 1 to 7 days after the operation (or before discharge) was followed up. This study innovatively included the Pittsburgh Sleep Quality Index (PSQI) and the Numerical Pain Score (NRS), two convenient and easy scales for clinical work, to explore the relationship between sleep quality and postoperative pain and POD. Univariate and multivariate Logistic regression analysis was used to analyze stepwise regression to screen independent risk factors for POD. Construct a clinical prediction model based on the stepwise regression results of multivariate Logistic regression analysis of the development group, draw a nomogram, draw a receiver operating curve (ROC curve), calculate the area under the curve (AUC), and finally use the validation group to verify the prediction model, to evaluate the effectiveness of the POD prediction model. At the same time, the calibration curve is used to visualize the results of the goodness of fit test, which can more intuitively show the degree of fit between the clinical prediction situation and the actual situation.
Results
A total of 663 elderly patients were enrolled in this study, and 131 (19.76%) patients developed POD. The incidence of POD in each department was not statistically significant. Multivariate logistic regression analysis showed that advanced age, low Mini-mental State Examination (MMSE) score, diabetes history, low years of education, high sleep quality index, high ASA classification, long anesthesia time and high NRS score were independent risk factors for non-cardiac POD. Use the selected independent risk factors to construct a predictive model. The formula Z = 8.293 + 0.102×age-1.214×MMSE score + 1.285×with or without diabetes history − 0.304×years of education + 0.602×PSQI + 1.893× ASA grade + 0.027 × anesthesia time + 1.297 × NRS score. Conducive to the validation group to evaluate the prediction model, the validation group AUC is 0.939 (95% CI 0.894–0.969), the sensitivity is 94.44%, and the specificity is 85.09%
Conclusion
The clinical prediction model constructed based on these independent risk factors has better predictive performance, which can provide reference for the early screening and prevention of POD in clinical work.
Trial registration:
ChiCTR2000033439 Retrospectively registered (date of registration: 06/01/2020)