• Arisa Kawashima, Taiki Furukawa, Takahiro Imaizumi, Akemi Morohashi, Mariko Hara, Satomi Yamada, Masayo Hama, Aya Kawaguchi, and Kazuki Sato.
• Division of Integrated Health Sciences (A.K. K.S.), Department of Nursing for Advanced Practice, Nagoya University Graduate School of Medicine, Nagoya, Japan; Department of Social Science (A.K.), Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan.. Electronic address: kawashima.arisa.c2@s.mail.nagoya-u.ac.jp.
• J Pain Symptom Manage. 2024 Apr 1; 67 (4): 306316.e6306-316.e6.

ContextEarly palliative care is recommended within eight-week of diagnosing advanced cancer. Although guidelines suggest routine screening to identify cancer patients who could benefit from palliative care, implementing screening can be challenging due to understaffing and time constraints.ObjectivesTo develop and evaluate machine learning models for predicting specialist palliative care needs in advanced cancer patients undergoing chemotherapy, and to investigate if predictive models could substitute screening tools.MethodsWe conducted a retrospective cohort study using supervised machine learning. The study included patients aged 18 or older, diagnosed with metastatic or stage IV cancer, who underwent chemotherapy and distress screening at a designated cancer hospital in Japan from April 1, 2018, to March 31, 2023. Specialist palliative care needs were assessed based on distress screening scores and expert evaluations. Data sources were hospital's cancer registry, health claims database, and nursing admission records. The predictive model was developed using XGBoost, a machine learning algorithm.ResultsOut of the 1878 included patients, 561 were analyzed. Among them, 114 (20.3%) exhibited needs for specialist palliative care. After under-sampling to address data imbalance, the models achieved an Area Under the Curve (AUC) of 0.89 with 95.8% sensitivity and a specificity of 71.9%. After feature selection, the model retained five variables, including the patient-reported pain score, and showcased an 0.82 AUC.ConclusionOur models could forecast specialist palliative care needs for advanced cancer patients on chemotherapy. Using five variables as predictors could replace screening tools and has the potential to contribute to earlier palliative care.Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

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