Literature DB >> 30391780

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

Jun Hamano1, Ayano Takeuchi2, Takuhiro Yamaguchi3, Mika Baba4, Kengo Imai5, Masayuki Ikenaga6, Yoshihisa Matsumoto7, Ryuichi Sekine8, Takashi Yamaguchi9, Takeshi Hirohashi10, Tsukasa Tajima11, Ryohei Tatara12, Hiroaki Watanabe13, Hiroyuki Otani14, Hiroka Nagaoka15, Masanori Mori16, Yo Tei17, Shuji Hiramoto18, Tatsuya Morita19.   

Abstract

INTRODUCTION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Fractional polynomial model; Laboratory findings; Prognostic index; Vital signs

Mesh:

Year:  2018        PMID: 30391780     DOI: 10.1016/j.ejca.2018.09.037

Source DB:  PubMed          Journal:  Eur J Cancer        ISSN: 0959-8049            Impact factor:   9.162


  4 in total

1.  Oral dryness and moisture degree at the lingual but not buccal mucosa predict prognosis in end-of-life cancer patients.

Authors:  Maiko Shimosato; Naoki Sakane
Journal:  Support Care Cancer       Date:  2021-04-14       Impact factor: 3.603

2.  Collapse of Fluid Balance and Association with Survival in Patients with Advanced Cancer Admitted to a Palliative Care Unit: Preliminary Findings.

Authors:  Koji Amano; Diane Liu; Eduardo Bruera; David Hui
Journal:  J Palliat Med       Date:  2019-10-29       Impact factor: 2.947

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

Authors:  Patrick Stone; Anastasia Kalpakidou; Chris Todd; Jane Griffiths; Vaughan Keeley; Karen Spencer; Peter Buckle; Dori-Anne Finlay; Victoria Vickerstaff; Rumana Z Omar
Journal:  Health Technol Assess       Date:  2021-05       Impact factor: 4.014

4.  Prognostication of the Last Days of Life.

Authors:  Masanori Mori; Tatsuya Morita; Eduardo Bruera; David Hui
Journal:  Cancer Res Treat       Date:  2022-03-30       Impact factor: 5.036

  4 in total

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