Literature DB >> 34018486

Prognostic models of survival in patients with advanced incurable cancer: the PiPS2 observational study.

Patrick Stone1, Anastasia Kalpakidou1, Chris Todd2, Jane Griffiths2, Vaughan Keeley3, Karen Spencer2, Peter Buckle1, Dori-Anne Finlay1, Victoria Vickerstaff1, Rumana Z Omar4.   

Abstract

BACKGROUND: The Prognosis in Palliative care Study (PiPS) prognostic survival models predict survival in patients with incurable cancer. PiPS-A (Prognosis in Palliative care Study - All), which involved clinical observations only, and PiPS-B (Prognosis in Palliative care Study - Blood), which additionally required blood test results, consist of 14- and 56-day models that combine to create survival risk categories: 'days', 'weeks' and 'months+'.
OBJECTIVES: The primary objectives were to compare PIPS-B risk categories against agreed multiprofessional estimates of survival and to validate PiPS-A and PiPS-B. The secondary objectives were to validate other prognostic models, to assess the acceptability of the models to patients, carers and health-care professionals and to identify barriers to and facilitators of clinical use.
DESIGN: This was a national, multicentre, prospective, observational, cohort study with a nested qualitative substudy using interviews with patients, carers and health-care professionals.
SETTING: Community, hospital and hospice palliative care services across England and Wales. PARTICIPANTS: For the validation study, the participants were adults with incurable cancer, with or without capacity to consent, who had been recently referred to palliative care services and had sufficient English language. For the qualitative substudy, a subset of participants in the validation study took part, along with informal carers, patients who declined to participate in the main study and health-care professionals. MAIN OUTCOME MEASURES: For the validation study, the primary outcomes were survival, clinical prediction of survival and PiPS-B risk category predictions. The secondary outcomes were predictions of PiPS-A and other prognostic models. For the qualitative substudy, the main outcomes were participants' views about prognostication and the use of prognostic models.
RESULTS: For the validation study, 1833 participants were recruited. PiPS-B risk categories were as accurate as agreed multiprofessional estimates of survival (61%; p = 0.851). Discrimination of the PiPS-B 14-day model (c-statistic 0.837, 95% confidence interval 0.810 to 0.863) and the PiPS-B 56-day model (c-statistic 0.810, 95% confidence interval 0.788 to 0.832) was excellent. The PiPS-B 14-day model showed some overfitting (calibration in the large -0.202, 95% confidence interval -0.364 to -0.039; calibration slope 0.840, 95% confidence interval 0.730 to 0.950). The PiPS-B 56-day model was well-calibrated (calibration in the large 0.152, 95% confidence interval 0.030 to 0.273; calibration slope 0.914, 95% confidence interval 0.808 to 1.02). PiPS-A risk categories were less accurate than agreed multiprofessional estimates of survival (p < 0.001). The PiPS-A 14-day model (c-statistic 0.825, 95% confidence interval 0.803 to 0.848; calibration in the large -0.037, 95% confidence interval -0.168 to 0.095; calibration slope 0.981, 95% confidence interval 0.872 to 1.09) and the PiPS-A 56-day model (c-statistic 0.776, 95% confidence interval 0.755 to 0.797; calibration in the large 0.109, 95% confidence interval 0.002 to 0.215; calibration slope 0.946, 95% confidence interval 0.842 to 1.05) had excellent or reasonably good discrimination and calibration. Other prognostic models were also validated. Where comparisons were possible, the other prognostic models performed less well than PiPS-B. For the qualitative substudy, 32 health-care professionals, 29 patients and 20 carers were interviewed. The majority of patients and carers expressed a desire for prognostic information and said that PiPS could be helpful. Health-care professionals said that PiPS was user friendly and may be helpful for decision-making and care-planning. The need for a blood test for PiPS-B was considered a limitation. LIMITATIONS: The results may not be generalisable to other populations.
CONCLUSIONS: PiPS-B risk categories are as accurate as agreed multiprofessional estimates of survival. PiPS-A categories are less accurate. Patients, carers and health-care professionals regard PiPS as potentially helpful in clinical practice. FUTURE WORK: A study to evaluate the impact of introducing PiPS into routine clinical practice is needed. TRIAL REGISTRATION: Current Controlled Trials ISRCTN13688211. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 28. See the NIHR Journals Library website for further project information.

Entities:  

Keywords:  CAREGIVERS; DECISION-MAKING; HOSPICES; NEOPLASMS; PALLIATIVE CARE; PROGNOSIS; PROSPECTIVE STUDIES; QUALITATIVE RESEARCH

Mesh:

Year:  2021        PMID: 34018486      PMCID: PMC8182445          DOI: 10.3310/hta25280

Source DB:  PubMed          Journal:  Health Technol Assess        ISSN: 1366-5278            Impact factor:   4.014


  54 in total

1.  Palliative performance scale (PPS): a new tool.

Authors:  F Anderson; G M Downing; J Hill; L Casorso; N Lerch
Journal:  J Palliat Care       Date:  1996       Impact factor: 2.250

2.  A combination of routine laboratory findings and vital signs can predict survival of advanced cancer patients without physician evaluation: a fractional polynomial model.

Authors:  Jun Hamano; Ayano Takeuchi; Takuhiro Yamaguchi; Mika Baba; Kengo Imai; Masayuki Ikenaga; Yoshihisa Matsumoto; Ryuichi Sekine; Takashi Yamaguchi; Takeshi Hirohashi; Tsukasa Tajima; Ryohei Tatara; Hiroaki Watanabe; Hiroyuki Otani; Hiroka Nagaoka; Masanori Mori; Yo Tei; Shuji Hiramoto; Tatsuya Morita
Journal:  Eur J Cancer       Date:  2018-11-02       Impact factor: 9.162

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Authors:  M E Miller; C D Langefeld; W M Tierney; S L Hui; C J McDonald
Journal:  Med Decis Making       Date:  1993 Jan-Mar       Impact factor: 2.583

4.  Clarifying confusion: the confusion assessment method. A new method for detection of delirium.

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Journal:  Ann Intern Med       Date:  1990-12-15       Impact factor: 25.391

5.  Preparing for the end of life: preferences of patients, families, physicians, and other care providers.

Authors:  K E Steinhauser; N A Christakis; E C Clipp; M McNeilly; S Grambow; J Parker; J A Tulsky
Journal:  J Pain Symptom Manage       Date:  2001-09       Impact factor: 3.612

6.  Validation of the palliative performance scale in the acute tertiary care hospital setting.

Authors:  Oludamilola Olajide; Laura Hanson; Barbara M Usher; Bahjat F Qaqish; Robert Schwartz; Stephen Bernard
Journal:  J Palliat Med       Date:  2007-02       Impact factor: 2.947

7.  Validation of the prognosis in palliative care study predictor models in terminal cancer patients.

Authors:  Eun-Shin Kim; Jung-Kwon Lee; Mi-Hyun Kim; Hye-Mi Noh; Yeong-Hyeon Jin
Journal:  Korean J Fam Med       Date:  2014-11-21

8.  Developing and evaluating complex interventions: the new Medical Research Council guidance.

Authors:  Peter Craig; Paul Dieppe; Sally Macintyre; Susan Michie; Irwin Nazareth; Mark Petticrew
Journal:  BMJ       Date:  2008-09-29

9.  The Prognosis in Palliative care Study II (PiPS2): study protocol for a multi-centre, prospective, observational, cohort study.

Authors:  Anastasia K Kalpakidou; Chris Todd; Vaughan Keeley; Jane Griffiths; Karen Spencer; Victoria Vickerstaff; Rumana Z Omar; Patrick Stone
Journal:  BMC Palliat Care       Date:  2018-08-13       Impact factor: 3.234

10.  Factors affecting recruitment to an observational multicentre palliative care study.

Authors:  Patrick C Stone; Bridget Gwilliam; Vaughan Keeley; Chris Todd; Laura C Kelly; Stephen Barclay
Journal:  BMJ Support Palliat Care       Date:  2013-01-07       Impact factor: 3.568

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Journal:  Cancers (Basel)       Date:  2022-05-19       Impact factor: 6.575

2.  The accuracy of clinician predictions of survival in the Prognosis in Palliative care Study II (PiPS2): A prospective observational study.

Authors:  Patrick C Stone; Christina Chu; Chris Todd; Jane Griffiths; Anastasia Kalpakidou; Vaughan Keeley; Rumana Z Omar; Victoria Vickerstaff
Journal:  PLoS One       Date:  2022-04-14       Impact factor: 3.752

3.  Prognostication in palliative radiotherapy-ProPaRT: Accuracy of prognostic scores.

Authors:  Marco Maltoni; Emanuela Scarpi; Monia Dall'Agata; Simona Micheletti; Maria Caterina Pallotti; Martina Pieri; Marianna Ricci; Antonino Romeo; Maria Valentina Tenti; Luca Tontini; Romina Rossi
Journal:  Front Oncol       Date:  2022-08-16       Impact factor: 5.738

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