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

Niklas Giesa, Stefan Haufe, Mario Menk, Björn Weiß, Claudia D Spies, Sophie K Piper, Felix Balzer and Sebastian D Boie

PLOS Digital Health, 2024, vol. 3, issue 8, 1-21

Abstract: 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. 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. 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. 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 (American Society of Anesthesiologists physical status classification system), the intraoperative amount of given remifentanil, and the postoperative Aldrete score. The best model used pre-, intra-, and postoperative data. The non-linear boosted trees 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. 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.Author summary: Currently, the pathophysiology of postoperative delirium (POD) is unknown. Hence, there is no dedicated medication for treatment. Many patients who experience POD suffer from chronic mental disorders causing pressure on related family members, clinicians, and the health system. With our study, we want to detect suspected POD before onset trying to give decision support to health professionals. Vulnerable patients could be transferred to a higher level of care mitigating the risk of severe outcomes such as long-term cognitive decline. We also provide insides into clinical parameters—recorded before, during, and after the surgery—that could be adapted for reducing POD risk. Our work is openly available, developed for clinical implementation, and could be transferred to other clinical institutions.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000414

DOI: 10.1371/journal.pdig.0000414

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