Literature DB >> 30936377

Accuracy and Prognostic Significance of Oncologists' Estimates and Scenarios for Survival Time in Advanced Gastric Cancer.

Anuradha Vasista1, Martin Stockler2, Andrew Martin2, Nick Pavlakis3, Katrin Sjoquist2,4, David Goldstein5, Sanjeev Gill6, Vikram Jain7, Geoffrey Liu8, George Kannourakis9, Yeul Hong Kim10, Louise Nott11, Stephanie Snow12, Matthew Burge13, Dean Harris14, Derek Jonker15, Yu Jo Chua16, Richard Epstein17, Antony Bonaventura18, Belinda Kiely2.   

Abstract

BACKGROUND: Worst-case, typical, and best-case scenarios for survival, based on simple multiples of an individual's expected survival time (EST), estimated by their oncologist, are a useful way of formulating and explaining prognosis. We aimed to determine the accuracy and prognostic significance of oncologists' estimates of EST, and the accuracy of the resulting scenarios for survival time, in advanced gastric cancer.
MATERIALS AND METHODS: Sixty-six oncologists estimated the EST at baseline for each of the 152 participants they enrolled in the INTEGRATE trial. We hypothesized that oncologists' estimates of EST would be unbiased (∼50% would be longer or shorter than the observed survival time [OST]); imprecise (<33% within 0.67-1.33 times the OST); independently predictive of overall survival (OS); and accurate at deriving scenarios for survival time with approximately 10% of patients dying within a quarter of their EST (worst-case scenario), 50% living within half to double their EST (typical scenario), and 10% living three or more times their EST (best-case scenario).
RESULTS: Oncologists' estimates of EST were unbiased (45% were shorter than the OST, 55% were longer); imprecise (29% were within 0.67-1.33 times observed); moderately discriminative (Harrell's C-statistic 0.62, p = .001); and an independently significant predictor of OS (hazard ratio, 0.89; 95% confidence interval, 0.83-0.95; p = .001) in a Cox model including performance status, number of metastatic sites, neutrophil-to-lymphocyte ratio ≥3, treatment group, age, and health-related quality of life (EORTC-QLQC30 physical function score). Scenarios for survival time derived from oncologists' estimates were remarkably accurate: 9% of patients died within a quarter of their EST, 57% lived within half to double their EST, and 12% lived three times their EST or longer.
CONCLUSION: Oncologists' estimates of EST were unbiased, imprecise, moderately discriminative, and independently significant predictors of OS. Simple multiples of the EST accurately estimated worst-case, typical, and best-case scenarios for survival time in advanced gastric cancer. IMPLICATIONS FOR PRACTICE: Results of this study demonstrate that oncologists' estimates of expected survival time for their patients with advanced gastric cancer were unbiased, imprecise, moderately discriminative, and independently significant predictors of overall survival. Simple multiples of the expected survival time accurately estimated worst-case, typical, and best-case scenarios for survival time in advanced gastric cancer. © AlphaMed Press 2019.

Entities:  

Keywords:  Estimating survival times; Prognosis in gastric cancer

Mesh:

Substances:

Year:  2019        PMID: 30936377      PMCID: PMC6853097          DOI: 10.1634/theoncologist.2018-0613

Source DB:  PubMed          Journal:  Oncologist        ISSN: 1083-7159


  15 in total

1.  Terminal cancer. duration and prediction of survival time.

Authors:  J Llobera; M Esteva; J Rifà; E Benito; J Terrasa; C Rojas; O Pons; G Catalán; A Avellà
Journal:  Eur J Cancer       Date:  2000-10       Impact factor: 9.162

Review 2.  How long have I got? Estimating typical, best-case, and worst-case scenarios for patients starting first-line chemotherapy for metastatic breast cancer: a systematic review of recent randomized trials.

Authors:  Belinda E Kiely; Yu Yang Soon; Martin H N Tattersall; Martin R Stockler
Journal:  J Clin Oncol       Date:  2010-12-28       Impact factor: 44.544

3.  Can oncologists predict survival for patients with progressive disease after standard chemotherapies?

Authors:  T K Taniyama; K Hashimoto; N Katsumata; A Hirakawa; K Yonemori; M Yunokawa; C Shimizu; K Tamura; M Ando; Y Fujiwara
Journal:  Curr Oncol       Date:  2014-04       Impact factor: 3.677

Review 4.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Authors:  F E Harrell; K L Lee; D B Mark
Journal:  Stat Med       Date:  1996-02-28       Impact factor: 2.373

5.  Regorafenib for the Treatment of Advanced Gastric Cancer (INTEGRATE): A Multinational Placebo-Controlled Phase II Trial.

Authors:  Nick Pavlakis; Katrin M Sjoquist; Andrew J Martin; Eric Tsobanis; Sonia Yip; Yoon-Koo Kang; Yung-Jue Bang; Thierry Alcindor; Christopher J O'Callaghan; Margot J Burnell; Niall C Tebbutt; Sun Young Rha; Jeeyun Lee; Jae-Yong Cho; Lara R Lipton; Mark Wong; Andrew Strickland; Jin Won Kim; John R Zalcberg; John Simes; David Goldstein
Journal:  J Clin Oncol       Date:  2016-06-20       Impact factor: 44.544

6.  Health-related quality of life associated with regorafenib treatment in refractory advanced gastric adenocarcinoma.

Authors:  Andrew J Martin; Emma Gibbs; Katrin Sjoquist; Nick Pavlakis; John Simes; Tim Price; Jenny Shannon; Sanjeev Gill; Vikram Jain; Geoffrey Liu; George Kannourakis; Yeul Hong Kim; Jin Won Kim; David Goldstein
Journal:  Gastric Cancer       Date:  2017-08-16       Impact factor: 7.370

7.  Using scenarios to explain life expectancy in advanced cancer: attitudes of people with a cancer experience.

Authors:  B E Kiely; G McCaughan; S Christodoulou; P J Beale; P Grimison; J Trotman; M H N Tattersall; M R Stockler
Journal:  Support Care Cancer       Date:  2012-06-21       Impact factor: 3.603

Review 8.  Chemotherapy for advanced gastric cancer.

Authors:  Anna Dorothea Wagner; Nicholas Lx Syn; Markus Moehler; Wilfried Grothe; Wei Peng Yong; Bee-Choo Tai; Jingshan Ho; Susanne Unverzagt
Journal:  Cochrane Database Syst Rev       Date:  2017-08-29

Review 9.  A systematic review of physicians' survival predictions in terminally ill cancer patients.

Authors:  Paul Glare; Kiran Virik; Mark Jones; Malcolm Hudson; Steffen Eychmuller; John Simes; Nicholas Christakis
Journal:  BMJ       Date:  2003-07-26

10.  Disarming the guarded prognosis: predicting survival in newly referred patients with incurable cancer.

Authors:  M R Stockler; M H N Tattersall; M J Boyer; S J Clarke; P J Beale; R J Simes
Journal:  Br J Cancer       Date:  2006-01-30       Impact factor: 7.640

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  5 in total

1.  Breast and Lung Effusion Survival Score Models: Improving Survival Prediction in Patients With Malignant Pleural Effusion and Metastasis.

Authors:  Sofia Molina; Gabriela Martinez-Zayas; Paula V Sainz; Cheuk H Leung; Liang Li; Horiana B Grosu; Roberto Adachi; David E Ost
Journal:  Chest       Date:  2021-05-11       Impact factor: 10.262

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.  Using three scenarios to explain life expectancy in advanced cancer: attitudes of patients, family members, and other healthcare professionals.

Authors:  Sharon H Nahm; Martin R Stockler; Andrew J Martin; Peter Grimison; Peter Fox; Rob Zielinski; Geoffrey At Hawson; Martin Hn Tattersall; Belinda E Kiely
Journal:  Support Care Cancer       Date:  2022-06-15       Impact factor: 3.359

4.  Voluntary assisted dying: estimating life expectancy to determine eligibility.

Authors:  Sharon H Nahm; Martin R Stockler; Belinda E Kiely
Journal:  Med J Aust       Date:  2022-07-24       Impact factor: 12.776

Review 5.  Prognosticating for Adult Patients With Advanced Incurable Cancer: a Needed Oncologist Skill.

Authors:  Christina Chu; Rebecca Anderson; Nicola White; Patrick Stone
Journal:  Curr Treat Options Oncol       Date:  2020-01-16
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