Development of an Early Prediction Model for Postoperative Delirium in Neurosurgical Patients Admitted to the ICU after Elective Craniotomy (E-PREPOD-NS): A Secondary Analysis of A Prospective Cohort Study

Abstract

Background: Postoperative delirium (POD) is a significant clinical problem in neurosurgical patients after intracranial surgery. Identification of high-risk patients may optimise individual perioperative management, but an adequate and simple risk model for use at super early phase after operation has not been developed.Methods: Adult patients were admitted to the ICU after elective intracranial surgery under general anaesthesia. The POD was diagnosed as Confusion Assessment Method for the ICU positive on postoperative day 1 to 3. Multivariate logistic regression analysis was used to develop the early prediction model (E-PREPOD-NS) and the final model was validated with 200 bootstrap samples.Results: Among 800 patients included in the study, POD occurred in 157 cases (19.6%). We identified nine variables independently associated with POD in the final E-PREPOD-NS model: age > 65 years [odds ratio (OR) = 3.336, 95% confidence interval (CI) = 1.765-6.305, 1 risk score point], education level < 9 years (OR = 2.528, 95% CI = 1.446-4.419, 1 point), history of smoking (OR = 2.582, 95% CI = 1.611-4.140, 1 point), history of diabetes (OR = 2.541, 95% CI = 1.201-5.377, 1 point), supra-tentorial lesions (OR = 3.424, 95% CI = 2.021-5.802, 1 point), anesthesia duration > 360 min (OR = 1.686, 95% CI = 1.062-2.674, 0.5 point), GCS <9 at ICU admission (OR = 6.059, 95% CI = 3.789-9.690, 1.5 points), metabolic acidosis (OR = 13.903, 95% CI = 6.248-30.938, 2.5 points), and positioning of neurosurgical drainage tube (OR = 1.924, 95% CI = 1.132-3.269, 0.5 point). The area under the receiver operator curve (AUROC) of the risk score for prediction of POD was 0.865 (95% CI = 0.835-0.895). After internal validation by bootstrap, the AUROC was 0.851 (95% CI = 0.791-0.912). The model showed good calibration (Hosmer-Lemeshow P = 0.593).Conclusions: The E-PREPOD-NS model based on nine perioperative risk factors can predict POD in patients admitted to the ICU after elective intracranial surgery with fairly good accuracy. External validation is needed before use in clinical practice.

Research progress on risk factors of delirium in burn patients: A narrative review

“…The education level is considered to be a proxy for the cognitive reserve ( 52 ). Patients with low educational attainment in multiple clinical settings are now more prone to delirium ( 53 – 55 ), consistent with the previous findings ( 52 ). In a multicenter epidemiological study of burn patients ( 56 ), it was found that elderly burn patients tend to live in rural areas and have low educational attainment.…”

Automated machine learning‐based model predicts postoperative delirium using readily extractable perioperative collected electronic data

“…At the same time, many disease prediction models are transformed into certain formulas, limiting the availability of prediction models. 11,12 Therefore, the model established in this study was transformed into a nomogram to increase the availability of the model further.…”

“…However, some prediction models for POD were based on a single statistical method, which may be limited in predictive performance. 11,12 Recently, it has been reported that using machine-learning techniques to establish various disease prediction models could improve the predictive performance of these models. 13,14 Thus, in the current study, we used machine-learning technology to extract the clinical data of 531 surgical patients who underwent general anesthesia before and on the first day after surgery and established four predictive models of POD using different methods.…”