• Masanori Mori, Takuhiro Yamaguchi, Isseki Maeda, Yutaka Hatano, Shih-Wei Chiu, Takashi Yamaguchi, Kengo Imai, Naosuke Yokomichi, Hiroyuki Otani, Jun Hamano, Satoru Tsuneto, David Hui, Tatsuya Morita, and EASED collaborators.
• Division of Palliative and Supportive Care, Seirei Mikatahara General Hospital, Hamamatsu, Japan.
• J Palliat Med. 2025 Feb 10.

AbstractBackground: Accurate prediction of next-day survival in imminently dying patients is crucial for facilitating timely end-of-life decisions. Objectives: To develop diagnostic models for predicting next-day survival in cancer patients with a Palliative Performance Scale (PPS) score of ≤20. Design: A multicenter, prospective, observational study. Setting/subjects: We enrolled advanced cancer patients at 23 palliative care units across Japan. Measurements: Clinical signs of impending death were recorded daily after patients' PPS scores decreased to ≤20, continuing until death or for up to 14 days. The developed models included the prediction of one-day survival-decision tree (P1d-Survival-DT), based on recursive partitioning analysis, the P1d-Survival-organ system score, which utilized a scoring system across four clinical systems (nervous/cardiovascular/respiratory/musculoskeletal), and the early signs model that focused on the absence of two early signs (altered consciousness and liquid dysphagia). Results: Of the 1896 patients included in the study, 1396 (74%) reached PPS ≤20. The average age was 73 ± 12 years, with 49% being female. The P1d-Survival-DT model showed next-day survival rates of 91.6% for patients with a response to verbal stimuli and no peripheral cyanosis, and 37.1% for those with no response to verbal stimuli and respiration with mandibular movement. The P1d-Survival-organ system score model revealed a 95.9% survival rate for score = 0, decreasing progressively to 46.7% for score = 4. The early signs model predicted a 95.2% survival rate in patients with normal consciousness and no liquid dysphagia. Conclusions: This study successfully developed three distinct models to predict next-day survival in cancer patients with PPS ≤20, offering vital tools for informed decision making in palliative care settings.

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