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.

Methods
This open data repository presents descriptive statistics for inputs used by machine learning models from the manuscript "Predicting postoperative delirium assessed by the Nursing Screening Delirium Scale in the recovery room for non-cardiac surgeries without craniotomy: A retrospective study using a machine learning approach". Data were extracted from the clinical information systems (CIS) of three sides at our clinical center. Based on literature review and clinical expertise, a total of 549 clinical variables were identified with respect to different time phases in our source systems. From these variables, 375 were available for at least 10% of the included patients. Multiple values for one surgery recorded during one time phase were aggregated as 10th, 50th, and 90th percentiles as well as the median absolute deviation. For volumes and amounts of given medications, we calculated the sums.