• J Palliat Med · Jul 2024

    Can We Make More Accurate Prognoses During Last Days of Life?

    • Sylvie Bouchard, Andreea Paula Iancu, Elena Neamt, François Collette, Sylvie Dufresne, Patricia Maureen Guercin, Suganthiny Jeyaganth, Desanka Kovacina, Taliá Malagón, Laurie Musgrave, Marilisa Romano, Jenny Wong, and Sybil Skinner-Robertson.
    • Montreal Institute for Palliative Care, Montreal, Quebec, Canada.
    • J Palliat Med. 2024 Jul 1; 27 (7): 895904895-904.

    AbstractBackground: Life expectancy prediction is important for end-of-life planning. Established methods (Palliative Performance Scale [PPS], Palliative Prognostic Index [PPI]) have been validated for intermediate- to long-term prognoses, but last-weeks-of-life prognosis has not been well studied. Patients admitted to a palliative care facility often have a life expectancy of less than three weeks. Reliable last-weeks-of-life prognostic tools are needed. Objective: To improve short-term survival prediction in terminally ill patients. Method: This prospective study included all patients admitted to a palliative care facility in Montreal, Canada, over one year. PPS and PPI were assessed until patients' death. Seven prognostic clinical signs of impending death (Short-Term Prognosis Signs [SPS]) were documented daily. Results: The analyses included 273 patients (76% cancer). The median survival time for a PPS ≤20% was 2.5 days, while for a PPS ≥50% it was 44.5 days, for a PPI >8 the median survival was 3.5 days and for a PPI ≤4 it was 38.5 days. Receiver operating characteristic curves showed a high accuracy in predicting survival. Median survival after the first occurrence of any SPS was below one week. Conclusions: This study demonstrated that the PPS and PPI perform well between one week and three months extending their usefulness to shorter term survival prediction. SPS items provided survival information during the last week of life. Using SPS along with PPS and PPI during the last weeks of life could enable a more precise short-term survival prediction across various end-of-life diagnoses. The translation of this research into clinical practice could lead to a better adapted treatment, the identification of a most appropriate care setting for patients, and improved communication of prognosis with patients and families.

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