A decision tree prediction model for a short-term outcome of delirium in patients with advanced cancer receiving pharmacological interventions: A secondary analysis of a multicenter and prospective observational study (Phase-R)

Ken Kurisu, Shuji Inada, Isseki Maeda, Asao Ogawa, Satoru Iwase, Tatsuo Akechi, Tatsuya Morita, Shunsuke Oyamada, Takuhiro Yamaguchi, Kengo Imai, Rika Nakahara, Keisuke Kaneishi, Nobuhisa Nakajima, Masahiko Sumitani, Kazuhiro Yoshiuchi

Research output: Contribution to journalArticlepeer-review

Abstract

Objective There is no widely used prognostic model for delirium in patients with advanced cancer. The present study aimed to develop a decision tree prediction model for a short-term outcome. Method This is a secondary analysis of a multicenter and prospective observational study conducted at 9 psycho-oncology consultation services and 14 inpatient palliative care units in Japan. We used records of patients with advanced cancer receiving pharmacological interventions with a baseline Delirium Rating Scale Revised-98 (DRS-R98) severity score of ≥10. A DRS-R98 severity score of <10 on day 3 was defined as the study outcome. The dataset was randomly split into the training and test dataset. A decision tree model was developed using the training dataset and potential predictors. The area under the curve (AUC) of the receiver operating characteristic curve was measured both in 5-fold cross-validation and in the independent test dataset. Finally, the model was visualized using the whole dataset. Results Altogether, 668 records were included, of which 141 had a DRS-R98 severity score of <10 on day 3. The model achieved an average AUC of 0.698 in 5-fold cross-validation and 0.718 (95% confidence interval, 0.627-0.810) in the test dataset. The baseline DRS-R98 severity score (cutoff of 15), hypoxia, and dehydration were the important predictors, in this order. Significance of results We developed an easy-to-use prediction model for the short-term outcome of delirium in patients with advanced cancer receiving pharmacological interventions. The baseline severity of delirium and precipitating factors of delirium were important for prediction.

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalPalliative and Supportive Care
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Cancer
  • Delirium
  • Machine learning
  • Palliative care
  • Psycho-oncology

ASJC Scopus subject areas

  • Nursing(all)
  • Clinical Psychology
  • Psychiatry and Mental health

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