Cancer Res Treat.  2024 Oct;56(4):1277-1287. 10.4143/crt.2023.1243.

Development of a Prediction Model for Delirium in Hospitalized Patients with Advanced Cancer

• Jung EH
• Yoo SH
• Lee SW
• Kang B
• Kim YJ

• KMID: 2560261
• DOI: http://doi.org/10.4143/crt.2023.1243

Abstract

Purpose
Delirium is a common neurocognitive disorder in patients with advanced cancer and is associated with poor clinical outcomes. As a potentially reversible phenomenon, early recognition of delirium by identifying the risk factors demands attention. We aimed to develop a model to predict the occurrence of delirium in hospitalized patients with advanced cancer.
Materials and Methods
This retrospective study included patients with advanced cancer admitted to the oncology ward of four tertiary cancer centers in Korea for supportive cares and excluded those discharged due to death. The primary endpoint was occurrence of delirium. Sociodemographic characteristics, clinical characteristics, laboratory findings, and concomitant medication were investigated for associating variables. The predictive model developed using multivariate logistic regression was internally validated by bootstrapping.
Results
From January 2019 to December 2020, 2,152 patients were enrolled. The median age of patients was 64 years, and 58.4% were male. A total of 127 patients (5.9%) developed delirium during hospitalization. In multivariate logistic regression, age, body mass index, hearing impairment, previous delirium history, length of hospitalization, chemotherapy during hospitalization, blood urea nitrogen and calcium levels, and concomitant antidepressant use were significantly associated with the occurrence of delirium. The predictive model combining all four categorized variables showed the best performance among the developed models (area under the curve 0.831, sensitivity 80.3%, and specificity 72.0%). The calibration plot showed optimal agreement between predicted and actual probabilities through internal validation of the final model.
Conclusion
We proposed a successful predictive model for the risk of delirium in hospitalized patients with advanced cancer.

Keyword

Delirium; Neoplasms; Palliative care; Predictive model

Figure

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  Fig. 1. Receiver operating characteristic curves of predictive models according to variables. (A) Sociodemographic+clinical characteristics. (B) Sociodemographic+clinical+laboratory characteristics. (C) Sociodemographic+clinical+concomitant medication characteristics. (D) Sociodemographic+clinical+laboratory+concomitant medication characteristics. AUROC, area under the receiver operating characteristic curve; CI, confidence interval.

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  Fig. 2. Calibration plot for predictive model of delirium occurrence using bootstrapping method.

Reference

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