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)

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.

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

“…The search strategy identified a total of 921 records; after duplicate removal and title and abstract screening, 114 full-text studies were retrieved, of which 3916–54 met the selection criteria for inclusion in the final analysis (figure 2).…”

“…Study characteristics are summarised in online supplemental table 1. Studies originated from the USA (n=12),17 19–23 25 41 43 50 51 54 Austria (n=9),24 28–31 33 39 47 48 China (n=6),26 32 35 49 52 53 Germany (n=3),37 45 46 South Korea (n=3),27 40 44 Canada (n=3),30 36 38 Brazil (n=1),16 Japan (n=1),34 Spain (n=1)18 and one study was labelled as international 42. Over the 6-year distribution of publications to June 2022, most studies were published in 2021 (n=10) and the first half of 2022 (n=12), indicating considerable growth in research in this area since the publication of previous reviews of studies published up to 2019 11 12.…”

“…Thirty of thirty-nine publications described the training and internal validation of a delirium model,17 18 21–26 30 32–41 43 44 46–54 with investigators of 6 of these studies (20%) externally validating their model in a subsequent paper 16 19 20 27 29 42. Investigators of three studies (10%) implemented and evaluated their model in real-time clinical workflows,2 8 31 45 but no publications described monitoring or optimising a deployed model.…”

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide

“…As a machine learning method, decision trees have demonstrated numerous applications in medicine and public health, particularly in addressing various issues within the field of cancer. 25 – 27 Such applications span an array of cancer types, including breast, 28 – 32 gastric, 33 , 34 thyroid, 35 prostate, 36 and colorectal cancer. 37 These studies have exhibited significant improvements in the accuracy of cancer diagnoses.…”

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

“…However, physicians may find it challenging to conduct daily assessments through questionnaires. Machine learning models have recently been introduced 9 – 14 . Machine learning models were previously used to predict delirium among patients after surgery for degenerative spinal disease 10 , patients admitted to the intensive care unit 11 , hospitalized patients without cognitive impairment 12 , patients admitted to the general ward 13 , and older patients after general surgery 14 .…”