Feature statistics for the prediction of postoperative delirium in the recovery room

Background: Postoperative delirium (POD) contributes to severe outcomes
such as death or development of dementia. Thus, it is desirable to
identify vulnerable patients in advance during the perioperative phase.
Previous studies mainly investigated risk factors for delirium during
hospitalization and further used a linear logistic regression (LR)
approach with time-invariant data. Studies have not investigated patients’
fluctuating conditions to support POD precautions. Objective: In this
single-center study, we aimed to predict POD in a recovery room setting
with a non-linear machine learning (ML) technique using pre-, intra-, and
postoperative data. Methods: The target variable POD was defined with the
Nursing Screening Delirium Scale (Nu-DESC) ≥ 1. Feature selection was
conducted based on robust univariate test statistics and L1
regularization. Non-linear multi-layer perceptron (MLP) as well as
tree-based models were trained and evaluated – with the receiver operating
characteristics curve (AUROC), the area under precision recall curve
(AUPRC), and additional metrics – against LR and published models on
bootstrapped testing data. Results:The prevalence of POD was 8.2% in a
sample of 73,181 surgeries performed between 2017 and 2020. Significant
univariate impact factors were the preoperative ASA status, the
intraoperative amount of given remifentanil, and the postoperative Aldrete
score. The best model used pre-, intra-, and postoperative data. The
tree-based model achieved a mean AUROC of 0.854 and a mean AUPRC of 0.418
outperforming linear LR, well as best applied and retrained baseline
models. Conclusions: Overall, non-linear machine learning models using
data from multiple perioperative time phases were superior to traditional
ones in predicting POD in the recovery room. Class imbalance was seen as a
main impediment for model application in clinical practice.