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

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. 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 anti-depressant 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.

Opioid use and subsequent delirium risk in patients with advanced cancer in palliative care: a multicenter registry study.

The prevalent use of opioids for pain management in patients with advanced cancer underscores the need for research on their neuropsychiatric impacts, particularly delirium. Therefore, we aimed to investigate the potential association between opioid use and the risk of delirium in patients with advanced cancer admitted to the acute palliative care unit. We conducted a retrospective observational study utilizing a multicenter, patient-based registry cohort by collecting the data from January 1, 2019, to December 31, 2020, in South Korea. All data regarding exposures, outcomes, and covariates were obtained through retrospective chart reviews by a team of specialized medical professionals with expertise in oncology. Full unmatched and 1:1 propensity-score matched cohorts were formed, and stratification analysis was conducted. The primary outcome, delirium, was defined and diagnosed by the DSM-IV. Of the 2,066 patients with advanced cancer, we identified 42.8% (mean [SD] age, 64.4 [13.3] years; 60.8% male) non-opioid users and 57.2% (62.8 [12.5] years; 55.9% male) opioid users, respectively. Opioid use was significantly associated with an increased occurrence of delirium in patients with advanced cancer (OR, 2.02 [95% CI 1.22-3.35]). The risk of delirium in patients with advanced cancer showed increasing trends in a dose-dependent manner. High-dose opioid users showed an increased risk of delirium in patients with advanced cancer compared to non-opioid users (low-dose user: OR, 2.21 [95% CI 1.27-3.84]; high-dose user: OR, 5.75 [95% CI 2.81-11.77]; ratio of OR, 2.60 [95% CI 1.05-6.44]). Patients with old age, male sex, absence of chemotherapy during hospitalization, and non-obese status were more susceptible to increased risk of delirium in patients with cancer. In this multicenter patient-based registry cohort study, we found a significant, dose-dependent association between opioid use and increased risk of delirium in patients with advanced cancer. We also identified specific patient groups more susceptible to delirium. These findings highlight the importance of opioid prescription in these patients with advanced cancer, balancing effective doses for pain management and adverse dose-inducing delirium.

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort.

This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significant features that influenced the model. A multicenter, patient-based registry cohort study in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified by reviewing the medical records based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The study dataset included 165 patients with delirium among 2314 patients with advanced cancer admitted to the acute palliative care unit. Seven machine learning models, including extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting, logistic regression, support vector machine, and random forest, were evaluated to predict delirium in patients with advanced cancer admitted to the acute palliative care unit. An ensemble approach was adopted to determine the optimal model. For k-fold cross-validation, the combination of extreme gradient boosting and random forest provided the best performance, achieving the following accuracy metrics: 68.83% sensitivity, 70.85% specificity, 69.84% balanced accuracy, and 74.55% area under the receiver operating characteristic curve. The performance of the isolated testing dataset was also validated, and the machine learning model was successfully deployed on a public website ( http://ai-wm.khu.ac.kr/Delirium/ ) to provide public access to delirium prediction results in patients with advanced cancer. Furthermore, using feature importance analysis, sex was determined to be the top contributor in predicting delirium, followed by a history of delirium, chemotherapy, smoking status, alcohol consumption, and living with family. Based on a large-scale, multicenter, patient-based registry cohort, a machine learning prediction model for delirium in patients with advanced cancer was developed in South Korea. We believe that this model will assist healthcare providers in treating patients with delirium and advanced cancer.