Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study. (December 2022)

• Record Type: Journal Article
• Authors: Spiller, Tobias R.; Tufan, Ege; Petry, Heidi; Böttger, Sönke; Fuchs, Simon; Duek, Or; Ben-Zion, Ziv; Korem, Nachshon; Harpaz-Rotem, Ilan; von Känel, Roland; Ernst, Jutta
• Is Part Of: Journal of psychiatric research. Volume 156(2023)
• Page Start: 194
• Page End: 199
• Publication Date: 2022-12
• Subjects: Delirium -- Machine learning -- Prediction model -- Screening; Psychiatry -- Periodicals; Mental Disorders -- Periodicals; Maladies mentales -- Périodiques; Psychiatry; Electronic journals; Periodicals; 616.89005
• DOI: 10.1016/j.jpsychires.2022.10.018
• Languages: English
• ISSNs: 0022-3956

Abstract

Abstract: Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014, and December 31, 2018, at a single tertiary teaching hospital in Zurich, Switzerland. A total of 48, 840 patients met inclusion criteria. 18, 873 (38.6%) were excluded due to missing data. Mean age (SD) of the included 29, 967 patients was 71.1 (12.2) years and 12, 231 (40.8%) were women. Delirium was assessed with the Delirium Observation Scale (DOS) with a total score of 3 or greater indicating that a patient is at risk for delirium. Additional measures included structured data collected for nursing process planning and demographic characteristics. The performance of the machine learning models was assessed using the area under the receiver operating characteristic curve (AUC). The training set consisted of 21, 147 patients (mean age 71.1 (12.1) years; 8, 630 (40.8%) women|) including 233, 024 observations with 16, 167 (6.9%) positive DOS screens. The test set comprised 8, 820 patients (median age 71.1 (12.4) years; 3, 601 (40.8%) women) with 91, 026 observations with 5, 445 (6.0%) positive DOS screens. Overall, the gradient boosting machine model performed best with an AUC of 0.933 (95% CI, 0.929 - 0.936). In conclusion, machine learning models based only on structured nursing data can reliably predict patients at risk for delirium in an acute care setting. Prediction models, using existing data collection processes, could reduce the resources required for delirium screening procedures in clinical practice. Highlights: Delirium can be reliably predicted with machine-learning models using nursing data. Delirium prediction models with few predictors can perform as well as more complex ones. Despite the implementation of screening programs, delirium is often not diagnosed.