Literature DB >> 20805456

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

Lisa Martin1, Sharon Watanabe, Robin Fainsinger, Francis Lau, Sunita Ghosh, Hue Quan, Marlis Atkins, Konrad Fassbender, G Michael Downing, Vickie Baracos.   

Abstract

PURPOSE: To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. PATIENTS AND METHODS: A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0.
RESULTS: A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS.
CONCLUSION: We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

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Year:  2010        PMID: 20805456     DOI: 10.1200/JCO.2009.27.1916

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  37 in total

1.  Prospective comparison of prognostic scores in palliative care cancer populations.

Authors:  Marco Maltoni; Emanuela Scarpi; Cristina Pittureri; Francesca Martini; Luigi Montanari; Elena Amaducci; Stefania Derni; Laura Fabbri; Marta Rosati; Dino Amadori; Oriana Nanni
Journal:  Oncologist       Date:  2012-02-29

2.  Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.

Authors:  Xinsong Du; Jae Min; Chintan P Shah; Rohit Bishnoi; William R Hogan; Dominick J Lemas
Journal:  Int J Med Inform       Date:  2020-04-15       Impact factor: 4.046

3.  Predicting life expectancy in patients with metastatic cancer receiving palliative radiotherapy: the TEACHH model.

Authors:  Monica S Krishnan; Zachary Epstein-Peterson; Yu-Hui Chen; Yolanda D Tseng; Alexi A Wright; Jennifer S Temel; Paul Catalano; Tracy A Balboni
Journal:  Cancer       Date:  2013-10-02       Impact factor: 6.860

4.  Prevalence of Malnutrition in Older Hospitalized Cancer Patients: A Multicenter and Multiregional Study.

Authors:  C A D'Almeida; W A F Peres; N B de Pinho; R B Martucci; V D Rodrigues; A Ramalho
Journal:  J Nutr Health Aging       Date:  2020       Impact factor: 4.075

5.  Clinical significance of weight changes at diagnosis in solid tumours.

Authors:  Niamh O'Donoghue; Shiva Shrotriya; Aynur Aktas; Barbara Hullihen; Serkan Ayvaz; Bassam Estfan; Declan Walsh
Journal:  Support Care Cancer       Date:  2018-11-29       Impact factor: 3.603

6.  The relationship between nutritional status, inflammatory markers and survival in patients with advanced cancer: a prospective cohort study.

Authors:  Cindy S Y Tan; Jane A Read; Viet H Phan; Philip J Beale; Jennifer K Peat; Stephen J Clarke
Journal:  Support Care Cancer       Date:  2014-08-13       Impact factor: 3.603

7.  Prevalence and Survival Impact of Pretreatment Cancer-Associated Weight Loss: A Tool for Guiding Early Palliative Care.

Authors:  Bhavani S Gannavarapu; Steven K M Lau; Kristen Carter; Nathan A Cannon; Ang Gao; Chul Ahn; Jeffrey J Meyer; David J Sher; Aminah Jatoi; Rodney Infante; Puneeth Iyengar
Journal:  J Oncol Pract       Date:  2018-02-21       Impact factor: 3.840

8.  The TEACHH model to predict life expectancy in patients presenting for palliative spine radiotherapy: external validation and comparison with alternate models.

Authors:  Maryam Dosani; Scott Tyldesley; Brendan Bakos; Jeremy Hamm; Tim Kong; Sarah Lucas; Jordan Wong; Mitchell Liu; Sarah Hamilton
Journal:  Support Care Cancer       Date:  2018-02-01       Impact factor: 3.603

Review 9.  Understanding the mechanisms and treatment options in cancer cachexia.

Authors:  Kenneth Fearon; Jann Arends; Vickie Baracos
Journal:  Nat Rev Clin Oncol       Date:  2012-12-04       Impact factor: 66.675

10.  Activities, function, and health-related quality of life (HRQOL) of older adults with cancer.

Authors:  Mackenzi Pergolotti; Allison M Deal; Grant R Williams; Ashley L Bryant; Jeannette T Bensen; Hyman B Muss; Bryce B Reeve
Journal:  J Geriatr Oncol       Date:  2017-03-09       Impact factor: 3.599

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