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

Published:November 02, 2018DOI:


      • We developed a prognostic index that required only laboratory data and vital signs.
      • Survival of advanced cancer patients can be predicted without a physician's assessments.
      • Our prognostic index was more accurate than existing predictive tools.
      • Our prognostic index could minimize attrition rates of clinical trials.
      • Fractional polynomials model is a promising way to develop prognostic indexes.



      There have been no reports about predicting survival of patients with advanced cancer constructed entirely with objective variables. We aimed to develop a prognostic model based on laboratory findings and vital signs using a fractional polynomial (FP) model.


      A multicentre prospective cohort study was conducted at 58 specialist palliative care services in Japan from September 2012 to April 2014. Eligible patients were older than 20 years and had advanced cancer. We developed models for predicting 7-day, 14-day, 30-day, 56-day and 90-day survival by using the FP modelling method.


      Data from 1039 patients were analysed to develop each prognostic model (Objective Prognostic Index for advanced cancer [OPI-AC]). All models included the heart rate, urea and albumin, while some models included the respiratory rate, creatinine, C-reactive protein, lymphocyte count, neutrophil count, total bilirubin, lactate dehydrogenase and platelet/lymphocyte ratio. The area under the curve was 0.77, 0.81, 0.90, 0.90 and 0.92 for the 7-day, 14-day, 30-day, 56-day and 90-day model, respectively. The accuracy of the OPI-AC predicting 30-day, 56-day and 90-day survival was significantly higher than that of the Palliative Prognostic Score or the Prognosis in Palliative Care Study model, which are based on a combination of symptoms and physician estimation.


      We developed highly accurate prognostic indexes for predicting the survival of patients with advanced cancer from objective variables alone, which may be useful for end-of-life management. The FP modelling method could be promising for developing other prognostic models in future research.


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        • Degner L.F.
        • Kristjanson L.J.
        • Bowman D.
        • Sloan J.A.
        • Carriere K.C.
        • O'Neil J.
        • et al.
        Information needs and decisional preferences in women with breast cancer.
        J Am Med Assoc. 1997; 277: 1485-1492
        • Steinhauser K.E.
        • Christakis N.A.
        • Clipp E.C.
        • McNeilly M.
        • McIntyre L.
        • Tulsky J.A.
        Factors considered important at the end of life by patients, family, physicians, and other care providers.
        JAMA  J Am Med Assoc. 2000; 284: 2476-2482
        • Kirk P.
        • Kirk I.
        • Kristjanson L.J.
        What do patients receiving palliative care for cancer and their families want to be told? A Canadian and Australian qualitative study.
        BMJ (Clin Res Ed). 2004; 328: 1343
        • Simmons C.P.L.
        • McMillan D.C.
        • McWilliams K.
        • Sande T.A.
        • Fearon K.C.
        • Tuck S.
        • et al.
        Prognostic tools in patients with advanced cancer: a systematic review.
        J Pain Symptom Manag. 2017; 53 (e10): 962-970
        • 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: 128-133
        • 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 Manag. 1999; 17: 240-247
        • 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): d4920
        • Maltoni M.
        • Scarpi E.
        • Pittureri C.
        • Martini F.
        • Montanari L.
        • Amaducci E.
        • et al.
        Prospective comparison of prognostic scores in palliative care cancer populations.
        Oncol. 2012; 17: 446-454
        • Glare P.
        • Virik K.
        • Jones M.
        • Hudson M.
        • Eychmuller S.
        • Simes J.
        • et al.
        A systematic review of physicians' survival predictions in terminally ill cancer patients.
        BMJ (Clin Res Ed). 2003; 327: 195-198
        • Vigano A.
        • Dorgan M.
        • Buckingham J.
        • Bruera E.
        • Suarez-Almazor M.E.
        Survival prediction in terminal cancer patients: a systematic review of the medical literature.
        Palliat Med. 2000; 14: 363-374
        • Hui D.
        • Park M.
        • Liu D.
        • Paiva C.E.
        • Suh S.-Y.
        • Morita T.
        • et al.
        Clinician prediction of survival versus the Palliative Prognostic Score: which approach is more accurate?.
        Eur J Cancer. 2016; 64: 89-95
        • 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 Manag. 2018; 55: 792-797
        • Reid V.L.
        • McDonald R.
        • Nwosu A.C.
        • Mason S.R.
        • Probert C.
        • Ellershaw J.E.
        • et al.
        A systematically structured review of biomarkers of dying in cancer patients in the last months of life; an exploration of the biology of dying.
        PLoS One. 2017; 12e0175123
        • Taylor P.
        • Crouch S.
        • Howell D.A.
        • Dowding D.W.
        • Johnson M.J.
        Change in physiological variables in the last 2 weeks of life: an observational study of hospital in-patients with cancer.
        Palliat Med. 2015; 29: 120-127
        • Hui D.
        • dos Santos R.
        • Chisholm G.
        • Bansal S.
        • Silva T.B.
        • Kilgore K.
        • et al.
        Clinical signs of impending death in cancer patients.
        Oncol. 2014; 19: 681-687
        • Hui D.
        • Dos Santos R.
        • Chisholm G.
        • Bansal S.
        • Souza Crovador C.
        • Bruera E.
        Bedside clinical signs associated with impending death in patients with advanced cancer: preliminary findings of a prospective, longitudinal cohort study.
        Cancer. 2015; 121: 960-967
        • Hui D.
        • Hess K.
        • dos Santos R.
        • Chisholm G.
        • Bruera E.
        A diagnostic model for impending death in cancer patients: preliminary report.
        Cancer. 2015; 121: 3914-3921
        • Collins G.S.
        • Reitsma J.B.
        • Altman D.G.
        • Moons K.G.M.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.
        Ann Intern Med. 2015; 162: 55
        • Chen Y.-T.
        • Ho C.-T.
        • Hsu H.-S.
        • Huang P.-T.
        • Lin C.-Y.
        • Liu C.-S.
        • et al.
        Objective palliative prognostic score among patients with advanced cancer.
        J Pain Symptom Manag. 2015; 49: 690-696
        • Uneno Y.
        • Taneishi K.
        • Kanai M.
        • Okamoto K.
        • Yamamoto Y.
        • Yoshioka A.
        • et al.
        Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: a multicenter case crossover study.
        PLoS One. 2017; 12e0183291
        • 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: 1618-1629
        • Templeton A.J.
        • McNamara M.G.
        • eruga B.
        • Vera-Badillo F.E.
        • Aneja P.
        • Ocana A.
        • et al.
        Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis.
        JNCI J Natl Cancer Inst. 2014; 106 (dju124): dju124
        • Zhang C.
        • Wang H.
        • Ning Z.
        • Xu L.
        • Zhuang L.
        • Wang P.
        • et al.
        Prognostic nutritional index serves as a predicative marker of survival and associates with systemic inflammatory response in metastatic intrahepatic cholangiocarcinoma.
        OncoTargets Ther. 2016; 9: 6417-6423
        • Templeton A.J.
        • Ace O.
        • McNamara M.G.
        • Al-Mubarak M.
        • Vera-Badillo F.E.
        • Hermanns T.
        • et al.
        Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis.
        Cancer Epidemiol Biomark Prev. 2014; 23: 1204-1212
        • Royston P.
        • Altman D.G.
        Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling.
        Appl Stat. 1994; 43: 429-467
        • Buck K.
        • Vrieling A.
        • Zaineddin A.K.
        • Becker S.
        • Hüsing A.
        • Kaaks R.
        • et al.
        Serum enterolactone and prognosis of postmenopausal breast cancer.
        J Clin Oncol. 2011; 29: 3730-3738
        • Gémes K.
        • Malmo V.
        • Laugsand L.E.
        • Loennechen J.P.
        • Ellekjaer H.
        • László K.D.
        • et al.
        Does moderate drinking increase the risk of atrial fibrillation? The Norwegian HUNT (Nord-Trøndelag health) study.
        J Am Heart Assoc. 2017; 6e007094
        • Cheon S.
        • Agarwal A.
        • Popovic M.
        • Milakovic M.
        • Lam M.
        • Fu W.
        • et al.
        The accuracy of clinicians' predictions of survival in advanced cancer: a review.
        Ann Palliat Med. 2016; 5: 22-29
        • Mori M.
        • Morita T.
        • Igarashi N.
        • Shima Y.
        • Miyashita M.
        Communication about the impending death of patients with cancer to the family: a nationwide survey.
        BMJ Support Palliat Care. 2018; (bmjspcare-2017-001460)
        • Hui D.
        • De La Cruz M.
        • Mori M.
        • Parsons H.A.
        • Kwon J.H.
        • Torres-Vigil I.
        • et al.
        Concepts and definitions for “supportive care,” “best supportive care,” “palliative care,” and “hospice care” in the published literature, dictionaries, and textbooks.
        Support Care Cancer. 2013; 21: 659-685