Skip to main content
NCBI home page
Medline Book to support NIHPA logoLink to Medline Book to support NIHPA
. 2021 May;25(28):1–118. doi: 10.3310/hta25280

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

Patrick Stone, Anastasia Kalpakidou, Chris Todd, Jane Griffiths, Vaughan Keeley, Karen Spencer, Peter Buckle, Dori-Anne Finlay, Victoria Vickerstaff, Rumana Z Omar
PMCID: PMC8182445  PMID: 34018486

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.

Plain language summary

A prognosis is a prediction about how long someone will live after a diagnosis of illness. The Prognosis in Palliative care Study (PiPS) tools [PiPS-A (Prognosis in Palliative care Study – All) and PiPS-B (Prognosis in Palliative care Study – Blood), respectively] were designed to predict survival in patients with incurable cancer. Previously, they were found to be as accurate as health-care professionals. The purpose of this study was to find out whether PiPS was more accurate at prognosticating than health-care professionals, to evaluate other prognostic tools and to ask patients, their carers and health-care professionals what they thought about using them. We studied 1833 patients with advanced cancer and calculated their PiPS score and other prognostic scores. We asked health-care professionals to estimate how long the patients would live. We then followed up the patients to find out how long they actually lived and if the predictions made by health-care professionals were as accurate as the predictions made by the prognostic tools. We interviewed patients, their carers and health-care professionals to ask them what they thought about using these prognostic tools. We found that PiPS-B was as accurate as the combined wisdom of a doctor and a nurse at predicting whether patients would live for ‘days’, ‘weeks’ or ‘months+’. We found that PiPS-A predictions were not as accurate as predictions made by health-care professionals. We found that (where direct comparisons could be made) PiPS-B was better than other prognostic tools. Finally, we found that patients, carers and health-care professionals thought that PiPS tools could be helpful in clinical practice because they would be less subjective than clinicians’ intuition. This means that PiPS-B could be considered as a tool to support clinician predictions of survival and may lead to patients and families being able to take more control at the end of their lives. Further research will be required to investigate whether or not this approach actually leads to improvements in care.

Full text of this article can be found in Bookshelf.

References

  1. Adams E, Boulton M, Watson E. The information needs of partners and family members of cancer patients: a systematic literature review. Patient Educ Couns 2009;77:179–86. https://doi.org/10.1016/j.pec.2009.03.027 doi: 10.1016/j.pec.2009.03.027. [DOI] [PubMed]
  2. Degner LF, Kristjanson LJ, Bowman D, Sloan JA, Carriere KC, O’Neil J, et al. Information needs and decisional preferences in women with breast cancer. JAMA 1997;277:1485–92. https://doi.org/10.1001/jama.1997.03540420081039 doi: 10.1001/jama.1997.03540420081039. [DOI] [PubMed]
  3. Steinhauser KE, Christakis NA, Clipp EC, McNeilly M, Grambow S, Parker J, Tulsky JA. Preparing for the end of life: preferences of patients, families, physicians, and other care providers. J Pain Symptom Manage 2001;22:727–37. https://doi.org/10.1016/S0885-3924(01)00334-7 doi: 10.1016/S0885-3924(01)00334-7. [DOI] [PubMed]
  4. Kirk P, Kirk I, Kristjanson LJ. What do patients receiving palliative care for cancer and their families want to be told? A Canadian and Australian qualitative study. BMJ 2004;328:1343. https://doi.org/10.1136/bmj.38103.423576.55 doi: 10.1136/bmj.38103.423576.55. [DOI] [PMC free article] [PubMed]
  5. Kutner JS, Steiner JF, Corbett KK, Jahnigen DW, Barton PL. Information needs in terminal illness. Soc Sci Med 1999;48:1341–52. https://doi.org/10.1016/S0277-9536(98)00453-5 doi: 10.1016/S0277-9536(98)00453-5. [DOI] [PubMed]
  6. Glare P, Virik K, Jones M, Hudson M, Eychmuller S, Simes J, Christakis N. A systematic review of physicians’ survival predictions in terminally ill cancer patients. BMJ 2003;327:195–8. https://doi.org/10.1136/bmj.327.7408.195 doi: 10.1136/bmj.327.7408.195. [DOI]
  7. Neuberger J, Guthrie C, Aaronovitch D, Hameed K, Bonser T, Pentregarh H, et al. More Care, Less Pathway: A Review of the Liverpool Care Pathway. London: Department of Health and Social Care; 2013.
  8. Ellershaw J, Foster A, Murphy D, Shea T, Overill S. Developing an integrated care pathway for the dying patient. Eur J Palliati Care 1997;4:203–7.
  9. Department of Health and Social Care (DHSC). End of Life Care Strategy – Promoting High Quality Care for All Adults at the End of Life. London: DHSC; 2008. URL: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/136431/End_of_life_strategy.pdf (accessed 28 July 2020).
  10. Thomas K. Gold Standards Framework Prognostic Indicator Guidance (PIG). URL: www.goldstandardsframework.org.uk/PIG (accessed 28 January 2020).
  11. Gwilliam B, Keeley V, Todd C, Gittins M, Roberts C, Kelly L, et al. Development of prognosis in palliative care study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study. BMJ 2011;343:d4920. https://doi.org/10.1136/bmj.d4920 doi: 10.1136/bmj.d4920. [DOI] [PMC free article] [PubMed]
  12. National Institute for Health Research (NIHR). Prognostic Models for People with Advanced Cancer. Southampton: NIHR; 2013. URL: https://fundingawards.nihr.ac.uk/award/13/20/01? (accessed August 2020).
  13. Stone PC, Lund S. Predicting prognosis in patients with advanced cancer. Ann Oncol 2007;18:971–6. https://doi.org/10.1093/annonc/mdl343 doi: 10.1093/annonc/mdl343. [DOI] [PubMed]
  14. Lund S, Stone P. Predicting survival in advanced cancer patients. Eur J Pall Care 2013;20:58–61.
  15. Morita T, Tsunoda J, Inoue S, Chihara S. The Palliative Prognostic Index: a scoring system for survival prediction of terminally ill cancer patients. Support Care Cancer 1999;7:128–33. https://doi.org/10.1007/s005200050242 doi: 10.1007/s005200050242. [DOI] [PubMed]
  16. Anderson F, Downing GM, Hill J, Casorso L, Lerch N. Palliative Performance Scale (PPS): a new tool. J Palliat Care 1996;12:5–11. https://doi.org/10.1177/082585979601200102 doi: 10.1177/082585979601200102. [DOI] [PubMed]
  17. Pirovano M, Maltoni M, Nanni O, Marinari M, Indelli M, Zaninetta G, et al. A new palliative prognostic score: a first step for the staging of terminally ill cancer patients. Italian Multicenter and Study Group on Palliative Care. J Pain Symptom Manage 1999;17:231–9. https://doi.org/10.1016/S0885-3924(98)00145-6 doi: 10.1016/S0885-3924(98)00145-6. [DOI] [PubMed]
  18. Feliu J, Jiménez-Gordo AM, Madero R, Rodríguez-Aizcorbe JR, Espinosa E, Castro J, et al. Development and validation of a prognostic nomogram for terminally ill cancer patients. J Natl Cancer Inst 2011;103:1613–20. https://doi.org/10.1093/jnci/djr388 doi: 10.1093/jnci/djr388. [DOI] [PubMed]
  19. Stone CA, Tiernan E, Dooley BA. Prospective validation of the palliative prognostic index in patients with cancer. J Pain Symptom Manage 2008;35:617–22. https://doi.org/10.1016/j.jpainsymman.2007.07.006 doi: 10.1016/j.jpainsymman.2007.07.006. [DOI] [PubMed]
  20. Lau F, Downing M, Lesperance M, Karlson N, Kuziemsky C, Yang J. Using the Palliative Performance Scale to provide meaningful survival estimates. J Pain Symptom Manage 2009;38:134–44. https://doi.org/10.1016/j.jpainsymman.2008.05.017 doi: 10.1016/j.jpainsymman.2008.05.017. [DOI] [PubMed]
  21. Olajide O, Hanson L, Usher BM, Qaqish BF, Schwartz R, Bernard S. Validation of the palliative performance scale in the acute tertiary care hospital setting. J Palliat Med 2007;10:111–17. https://doi.org/10.1089/jpm.2006.0125 doi: 10.1089/jpm.2006.0125. [DOI] [PubMed]
  22. Maltoni M, Nanni O, Pirovano M, Scarpi E, Indelli M, Martini C, et al. Successful validation of the palliative prognostic score in terminally ill cancer patients. Italian Multicenter Study Group on Palliative Care. J Pain Symptom Manage 1999;17:240–7. https://doi.org/10.1016/S0885-3924(98)00146-8 doi: 10.1016/S0885-3924(98)00146-8. [DOI] [PubMed]
  23. Glare P, Virik K. Independent prospective validation of the PaP score in terminally ill patients referred to a hospital-based palliative medicine consultation service. J Pain Symptom Manage 2001;22:891–8. https://doi.org/10.1016/S0885-3924(01)00341-4 doi: 10.1016/S0885-3924(01)00341-4. [DOI] [PubMed]
  24. Glare PA, Eychmueller S, McMahon P. Diagnostic accuracy of the palliative prognostic score in hospitalized patients with advanced cancer. J Clin Oncol 2004;22:4823–8. https://doi.org/10.1200/JCO.2004.12.056 doi: 10.1200/JCO.2004.12.056. [DOI] [PubMed]
  25. Naylor C, Cerqueira L, Costa-Paiva LH, Costa JV, Conde DM, Pinto-Neto AM. Survival of women with cancer in palliative care: use of the palliative prognostic score in a population of Brazilian women. J Pain Symptom Manage 2010;39:69–75. https://doi.org/10.1016/j.jpainsymman.2009.05.020 doi: 10.1016/j.jpainsymman.2009.05.020. [DOI] [PubMed]
  26. Tarumi Y, Watanabe SM, Lau F, Yang J, Quan H, Sawchuk L, et al. Evaluation of the Palliative Prognostic Score (PaP) and routinely collected clinical data in prognostication of survival for patients referred to a palliative care consultation service in an acute care hospital. J Pain Symptom Manage 2011;42:419–31. https://doi.org/10.1016/j.jpainsymman.2010.12.013 doi: 10.1016/j.jpainsymman.2010.12.013. [DOI] [PubMed]
  27. Hui D, Park M, Liu D, Paiva CE, Suh SY, Morita T, Bruera E. Clinician prediction of survival versus the Palliative Prognostic Score: which approach is more accurate? Eur J Cancer 2016;64:89–95. https://doi.org/10.1016/j.ejca.2016.05.009 doi: 10.1016/j.ejca.2016.05.009. [DOI] [PMC free article] [PubMed]
  28. Gwilliam B, Keeley V, Todd C, Roberts C, Gittens M, Kelly L, et al. Prognosticating in patients with advanced cancer – observational study comparing the accuracy of clinicians’ and patients’ estimates of survival. Ann Oncol 2013;24:482–8. https://doi.org/10.1093/annonc/mds341 doi: 10.1093/annonc/mds341. [DOI] [PubMed]
  29. Kalpakidou AK, Todd C, Keeley V, Griffiths J, Spencer K, Vickerstaff V, et al. The Prognosis in Palliative care Study II (PiPS2): study protocol for a multi-centre, prospective, observational, cohort study. BMC Palliat Care 2018;17:101. https://doi.org/10.1186/s12904-018-0352-y doi: 10.1186/s12904-018-0352-y. [DOI] [PMC free article] [PubMed]
  30. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M, Medical Research Council Guidance. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ 2008;337:a1655. https://doi.org/10.1136/bmj.a1655 doi: 10.1136/bmj.a1655. [DOI] [PMC free article] [PubMed]
  31. Department of Health and Social Care (DHSC). Mental Capacity Act. London: DHSC; 2005.
  32. Royal College of General Practitioners. Mental Capacity Act Toolkit for Adults in England and Wales. 2011. URL: www.rcgp.org.uk/-/media/Files/CIRC/Learning-disabilities/Learning-disabilities-resources/CIRC_Mental-Capacity-Act-Toolkit-2011.ashx?la=en (accessed 20 January 2020).
  33. Hodkinson HM. Evaluation of a mental test score for assessment of mental impairment in the elderly. Age Ageing 1972;1:233–8. https://doi.org/10.1093/ageing/1.4.233 doi: 10.1093/ageing/1.4.233. [DOI] [PubMed]
  34. Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, Carbone PP. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol 1982;5:649–55. https://doi.org/10.1097/00000421-198212000-00014 doi: 10.1097/00000421-198212000-00014. [DOI] [PubMed]
  35. Karnofsky D, Burchenal J. The Clinical Evaluation of Chemotherapeutic Agents in Cancer. In MacLeod CM, editor. Evaluation of Chemotherapeutic Agents. New York, NY: Columbia University Press; 1949. p. 196.
  36. Machin D, Campbell MJ, Tan SB, Tan SH. Sample Size Tables for Clinical Studies. 3rd edn. Oxford: Wiley-Blackwell; 2009. https://doi.org/10.1002/9781444300710 doi: 10.1002/9781444300710. [DOI]
  37. Harrell F. Regression Modelling Strategies with Applications to Linear Models, Logistic Regression and Survival Analysis. New York, NY: Springer Publishing; 2002. p. 92. https://doi.org/10.1007/978-1-4757-3462-1 doi: 10.1007/978-1-4757-3462-1. [DOI]
  38. Adams G, Gulliford MC, Ukoumunne OC, Eldridge S, Chinn S, Campbell MJ. Patterns of intra-cluster correlation from primary care research to inform study design and analysis. J Clin Epidemiol 2004;57:785–94. https://doi.org/10.1016/j.jclinepi.2003.12.013 doi: 10.1016/j.jclinepi.2003.12.013. [DOI] [PubMed]
  39. Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux PJ, et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA 2017;318:1377–84. https://doi.org/10.1001/jama.2017.12126 doi: 10.1001/jama.2017.12126. [DOI] [PubMed]
  40. Miller ME, Langefeld CD, Tierney WM, Hui SL, McDonald CJ. Validation of probabilistic predictions. Med Decis Making 1993;13:49–58. https://doi.org/10.1177/0272989X9301300107 doi: 10.1177/0272989X9301300107. [DOI] [PubMed]
  41. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010;21:128–38. https://doi.org/10.1097/EDE.0b013e3181c30fb2 doi: 10.1097/EDE.0b013e3181c30fb2. [DOI] [PMC free article] [PubMed]
  42. Steyerberg E. Clinical Prediction Models: A Practical Approach to Development, Validation and Updating. Rotterdam: Springer Publishing; 2009.
  43. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ 1995;310:170. https://doi.org/10.1136/bmj.310.6973.170 doi: 10.1136/bmj.310.6973.170. [DOI] [PMC free article] [PubMed]
  44. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med 2011;30:377–99. https://doi.org/10.1002/sim.4067 doi: 10.1002/sim.4067. [DOI] [PubMed]
  45. Walshe C, Todd C, Caress AL, Chew-Graham C. Judgements about fellow professionals and the management of patients receiving palliative care in primary care: a qualitative study. Br J Gen Pract 2008;58:264–72. https://doi.org/10.3399/bjgp08X279652 doi: 10.3399/bjgp08X279652. [DOI] [PMC free article] [PubMed]
  46. McNamara B. Fragile Lives: Death, Dying and Care. Sydney, NSW: Allen & Unwin; 2001.
  47. Chapple A, Ziebland S. Prostate cancer: embodied experience and perceptions of masculinity. Sociol Health Illn 2002;24:820–41. https://doi.org/10.1111/1467-9566.00320 doi: 10.1111/1467-9566.00320. [DOI]
  48. Sinding C, Wiernikowski J. Disruption foreclosed: older women’s cancer narratives. Health 2008;12(3):389–411. https://doi.org/10.1177/1363459308090055 doi: 10.1177/1363459308090055. [DOI] [PubMed]
  49. Bury M. Chronic illness as biographical disruption. Sociol Health Illn 1982;4:167–82. https://doi.org/10.1111/1467-9566.ep11339939 doi: 10.1111/1467-9566.ep11339939. [DOI] [PubMed]
  50. Higginson IJ, Evans CJ, Grande G, Preston N, Morgan M, McCrone P, et al. Evaluating complex interventions in end of life care: the MORECare statement on good practice generated by a synthesis of transparent expert consultations and systematic reviews. BMC Med 2013;11:111. https://doi.org/10.1186/1741-7015-11-111 doi: 10.1186/1741-7015-11-111. [DOI] [PMC free article] [PubMed]
  51. Ritchie J, Lewis J. Designing and Selecting Samples. In Ritchie J, Lewis J, Elam G, editors. Qualitative Research Practice: A Guide for Social Science Students and Researchers. Newbury Park, CA: SAGE Publications Ltd; 2003. pp. 77–108.
  52. Srivastava A, Thompson S. Framework analysis: a qualitative methodology for applied policy research. J Admin Govern 2009;4:72–9.
  53. Bernard H, Ryan G. Analysing Qualitative Data; Systematic Approaches. Newbury Park, CA; SAGE Publications Ltd; 2010.
  54. Ritchie J, Spencer L. Qualitative Data Analysis for Applied Policy. In Bryman A, Burgess R, editors. Analysing Qualitative Data. Oxon: Routledge; 1994. pp. 173–94. https://doi.org/10.4324/9780203413081_chapter_9 doi: 10.4324/9780203413081_chapter_9. [DOI]
  55. Hosmer Jr D, Lemeshow S, Sturdivant R. Applied Logistic Regression. New York, NY: John Wiley & Sons; 2013. https://doi.org/10.1002/9781118548387 doi: 10.1002/9781118548387. [DOI]
  56. Stone PC, Kalpakidou A, Todd C, Griffiths J, Keeley V, Spencer K, et al. The Prognosis in Palliative care Study II (PiPS2): a prospective observational validation study of a prognostic tool with an embedded qualitative evaluation. PLOS ONE 2021;16:e0249297. https://doi.org/10.1371/journal.pone.0249297 doi: 10.1371/journal.pone.0249297. [DOI] [PMC free article] [PubMed]
  57. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York, NY: John Wiley & Sons; 1987. https://doi.org/10.1002/9780470316696 doi: 10.1002/9780470316696. [DOI]
  58. Li K-H, Meng X-L, Raghunathan TE, Rubin DB. Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica 1991;1:65–92.
  59. Stone P, Vickerstaff V, Kalpakidou A, Todd C, Griffiths J, Keeley V, et al. Prognostic tools or clinical predictions: Which are better in palliative care? PLOS ONE 2021;16:e0249763. https://doi.org/10.1371/journal.pone.0249763 doi: 10.1371/journal.pone.0249763. [DOI] [PMC free article] [PubMed]
  60. Murray S, Kendall M, Boyd K, Sheikh A. Illness trajectories and palliative care. BMJ 2005;330:1007–11. https://doi.org/10.1136/bmj.330.7498.1007 doi: 10.1136/bmj.330.7498.1007. [DOI] [PMC free article] [PubMed]
  61. Seow H, O’Leary E, Perez R, Tanuseputro P. Access to palliative care by disease trajectory: a population-based cohort of Ontario decedents. BMJ Open 2018;8:e021147. https://doi.org/10.1136/bmjopen-2017-021147 doi: 10.1136/bmjopen-2017-021147. [DOI] [PMC free article] [PubMed]
  62. Stone P, Gwilliam B, Keeley V, Todd C, Kelly L, Barclay S. Factors affecting recruitment to an observational multicentre palliative care study. BMJ Support Palliat Care 2013;3:318–23. https://doi.org/10.1136/bmjspcare-2012-000396 doi: 10.1136/bmjspcare-2012-000396. [DOI] [PMC free article] [PubMed]
  63. Baba M, Maeda I, Morita T, Hisanaga T, Ishihara T, Iwashita T, et al. Independent validation of the modified prognosis palliative care study predictor models in three palliative care settings. J Pain Symptom Manage 2015;49:853–60. https://doi.org/10.1016/j.jpainsymman.2014.10.010 doi: 10.1016/j.jpainsymman.2014.10.010. [DOI] [PubMed]
  64. Chow E, Harth T, Hruby G, Finkelstein J, Wu J, Danjoux C. How accurate are physicians’ clinical predictions of survival and the available prognostic tools in estimating survival times in terminally ill cancer patients? A systematic review. Clin Oncol (R Coll Radiol) 2001;13:209–18. https://doi.org/10.1007/s001740170078 doi: 10.1007/s001740170078. [DOI] [PubMed]
  65. Baba M, Maeda I, Morita T, Inoue S, Ikenaga M, Matsumoto Y, et al. Survival prediction for advanced cancer patients in the real world: a comparison of the Palliative Prognostic Score, Delirium-Palliative Prognostic Score, Palliative Prognostic Index and modified Prognosis in Palliative Care Study predictor model. Eur J Cancer 2015;51:1618–29. https://doi.org/10.1016/j.ejca.2015.04.025 doi: 10.1016/j.ejca.2015.04.025. [DOI] [PubMed]
  66. Hamano J, Takeuchi A, Yamaguchi T, Baba M, Imai K, Ikenaga M, et al. A combination of routine laboratory findings and vital signs can predict survival of advanced cancer patients without physician evaluation: a fractional polynomial model. Eur J Cancer 2018;105:50–60. https://doi.org/10.1016/j.ejca.2018.09.037 doi: 10.1016/j.ejca.2018.09.037. [DOI] [PubMed]
  67. Kim ES, Lee JK, Kim MH, Noh HM, Jin YH. Validation of the prognosis in palliative care study predictor models in terminal cancer patients. Korean J Fam Med 2014;35:283–94. https://doi.org/10.4082/kjfm.2014.35.6.283 doi: 10.4082/kjfm.2014.35.6.283. [DOI] [PMC free article] [PubMed]
  68. Ermacora P, Mazzer M, Isola M, Pascoletti G, Gregoraci G, Basile D, et al. Prognostic evaluation in palliative care: final results from a prospective cohort study. Support Care Cancer 2019;27:2095–102. https://doi.org/10.1007/s00520-018-4463-z doi: 10.1007/s00520-018-4463-z. [DOI] [PubMed]
  69. Maltoni M, Scarpi E, Pittureri C, Martini F, Montanari L, Amaducci E, et al. Prospective comparison of prognostic scores in palliative care cancer populations. Oncologist 2012;17:446–54. https://doi.org/10.1634/theoncologist.2011-0397 doi: 10.1634/theoncologist.2011-0397. [DOI] [PMC free article] [PubMed]
  70. Mendis R, Soo WK, Zannino D, Michael N, Spruyt O. Multidisciplinary prognostication using the Palliative Prognostic Score in an Australian cancer center. Palliat Care 2015;9:7–14. https://doi.org/10.4137/PCRT.S24411 doi: 10.4137/PCRT.S24411. [DOI] [PMC free article] [PubMed]
  71. Numico G, Occelli M, Russi EG, Silvestris N, Pasero R, Fea E, et al. Survival prediction and frequency of anticancer treatment in cancer patients hospitalized due to acute conditions. Role of clinical parameters and PaP score. Support Care Cancer 2011;19:1823–30. https://doi.org/10.1007/s00520-010-1024-5 doi: 10.1007/s00520-010-1024-5. [DOI] [PubMed]
  72. Stiel S, Bertram L, Neuhaus S, Nauck F, Ostgathe C, Elsner F, Radbruch L. Evaluation and comparison of two prognostic scores and the physicians’ estimate of survival in terminally ill patients. Support Care Cancer 2010;18:43–9. https://doi.org/10.1007/s00520-009-0628-0 doi: 10.1007/s00520-009-0628-0. [DOI] [PMC free article] [PubMed]
  73. Tavares T, Oliveira M, Gonçalves J, Trocado V, Perpétuo J, Azevedo A, et al. Predicting prognosis in patients with advanced cancer: a prospective study. Palliat Med 2018;32:413–16. https://doi.org/10.1177/0269216317705788 doi: 10.1177/0269216317705788. [DOI] [PubMed]
  74. Farinholt P, Park M, Guo Y, Bruera E, Hui D. A comparison of the accuracy of clinician prediction of survival versus the Palliative Prognostic Index. J Pain Symptom Manage 2018;55:792–7. https://doi.org/10.1016/j.jpainsymman.2017.11.028 doi: 10.1016/j.jpainsymman.2017.11.028. [DOI] [PMC free article] [PubMed]
  75. K Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI. Clarifying confusion: the confusion assessment method. A new method for detection of delirium. Ann Intern Med 1990;113:941–8. https://doi.org/10.7326/0003-4819-113-12-941 doi: 10.7326/0003-4819-113-12-941. [DOI] [PubMed]
  76. Victoria Hospice Society. Palliative Performace Scale version 2. URL: www.victoriahospice.org/health-professionals/clinical-tools (accessed August 2020).

RESOURCES