Doctors’ predictions were used when available and nurses’ predictions were used when no doctors’ predictions were provided. To maximize the available data for comparisons with prognostic tools a hierarchical approach was taken to produce a clinical prediction of survival (CPS). The attending doctor and nurse estimated survival of study participants independently. This report describes the evaluation of these prognostic scores in a cohort of advanced incurable cancer patients and compares their performance against clinicians’ own predictions of survival. PaP and FPN, both require blood test results (like PiPS-B). PPI and PPS can both be calculated without the need for a blood test (like PiPS-A). In addition to validating PiPS, the PiPS2 study also evaluated four other prognostic models: Palliative Prognostic Index (PPI), Palliative Performance Scale (PPS), Palliative Prognostic (PaP) score, and Feliu Prognostic Nomogram (FPN). However, only the PiPS-B risk categories were found to be as accurate as an agreed multi-professional survival estimate. The primary analysis demonstrated that all of the models (PiPS-A14, PiPS-A56, PiPS-B14 and PiPS-B56) had excellent discrimination and were well-calibrated. The PiPS2 study was a prospective multi-centre validation of various prognostic tools including the PiPS-A and PiPS-B 14-day and 56-day models and the corresponding risk categories. PiPS-A and PiPS-B risk categories (predicted survival of “days”, “weeks” or “years”) were found to be as accurate as an agreed multi-professional (doctor and nurse) estimate of survival. Prognostic models were developed to predict 14-day and 56-day survival in either patients for whom blood results were not (PiPS-A) or were (PiPS-B) available. The Prognosis in Palliative Care Study (PiPS) was a multi-centre prospective study to develop and validate a prognostic tool for cancer palliative care. The performance of relatively few of these tools has been compared against clinicians’ own predictions of survival. A number of such prognostic tools have been developed for use in patients with advanced cancer. For this reason physicians are encouraged to supplement their clinical intuition with validated prognostic algorithms. However, although clinicians’ estimates are frequently better than patients’ own predictions, they still tend to be inaccurate. Patients expect their physicians to provide them with honest accurate and realistic estimates of survival. Patients’ understanding about their prognoses is often inaccurate and over-optimistic. Prognostic information is essential for informing decision-making at the end of life. Omar, Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing 6 Finlay, Data curation, Formal analysis, Investigation, Writing – review & editing, 1 and R. Buckle, Data curation, Formal analysis, Investigation, Writing – review & editing, 1 D. Spencer, Data curation, Formal analysis, Writing – original draft, Writing – review & editing, 2, 3 P. Keeley, Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing, 5 K. Griffiths, Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing, 2, 3 V. Todd, Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing, 2, 3, 4 J. Kalpakidou, Data curation, Project administration, Supervision, Writing – review & editing, 1 C. Vickerstaff, Formal analysis, Project administration, Writing – original draft, Writing – review & editing, 1 A. Stone, Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing, 1, * V.
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