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

Yoo, Shin Hye; Kang, Jiseung; Kim, Hyeon Jin; Lee, Si Won; Hong, Moonki; Jung, Eun Hee; Kim, Yu Jung; Yon, Dong Keon; Kang, Beodeul

doi:10.1038/s41598-024-56675-1

Cited by 1 publication

(1 citation statement)

References 39 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We excluded five patients with a hospital stay exceeding 3 months, six patients transferred to other departments, and three patients with terminal delirium, defined as delirium that occurred within 2 weeks of death. Our final sample consisted of 2314 patients with advanced cancer who were admitted to the APCU and who met all eligibility criteria 15 .…”

Section: Methodsmentioning

confidence: 99%

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

Kim,

Lee,

Woo

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%