Volume 24, Issue 2 pp. 179-188
Original article

A mortality risk prediction model for older adults with lymph node-positive colon cancer

M.L. Jorgensen BAppSc (Sp Path) Hons, PhD

Corresponding Author

M.L. Jorgensen BAppSc (Sp Path) Hons, PhD

Research Fellow

Cancer Epidemiology and Services Research (CESR), Sydney School of Public Health, Sydney Medical School, University of Sydney, Sydney, NSW, Australia

Correspondence address: Mikaela L. Jorgensen, Cancer Epidemiology and Services Research (CESR), Sydney School of Public Health, Sydney Medical School, University of Sydney, Level 6, Lifehouse (C39Z), 119-143 Missenden Road, Camperdown, Sydney, NSW 2050, Australia (e-mail: [email protected]).Search for more papers by this author
J.M. Young MBBS, MPH, PhD, FAFPHM

J.M. Young MBBS, MPH, PhD, FAFPHM

Professor in Cancer Epidemiology

Cancer Epidemiology and Services Research (CESR), Sydney School of Public Health, Sydney Medical School, University of Sydney, Sydney, NSW, Australia

Surgical Outcomes Research Centre (SOuRCe), Sydney Local Health District and University of Sydney, Sydney, NSW, Australia

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T.A. Dobbins BMath, PhD

T.A. Dobbins BMath, PhD

Senior Lecturer

Cancer Epidemiology and Services Research (CESR), Sydney School of Public Health, Sydney Medical School, University of Sydney, Sydney, NSW, Australia

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M.J. Solomon MBBCh, BAO, MSc (Clin Epi), FRACS, FRCSI

M.J. Solomon MBBCh, BAO, MSc (Clin Epi), FRACS, FRCSI

Clinical Professor of Surgery

Surgical Outcomes Research Centre (SOuRCe), Sydney Local Health District and University of Sydney, Sydney, NSW, Australia

Discipline of Surgery, University of Sydney, Sydney, NSW, Australia

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First published: 09 February 2015
Citations: 10

Abstract

Clinicians are less likely to recommend adjuvant chemotherapy for older adults based on their age alone. This study aimed to develop a mortality risk model to assist treatment decision making by identifying patients who are unlikely to live to benefit from chemotherapy. All lymph node-positive colon cancer patients ≥65 years who received surgery in New South Wales, Australia in 2007/2008 were identified using a linked population-based dataset (n = 1550). A model predicting 1-year all-cause mortality was built using multilevel logistic regression. Risk scores derived from model factors were summed for each patient. One-year mortality was 11.5%. The risk model consisted of 14 factors, including comorbidities, hospital admission factors and other markers of frailty or health status. People with a total score of 0, 1 or 2 were considered at low risk (predicted 1-year mortality of 2.9%), those scoring 3 to 8 at medium risk (7.4% mortality) and those scoring 9 or above at high risk (24.7% mortality). The model had good discrimination (area under the receiver operating characteristic curve = 0.788, 95% confidence interval: 0.752–0.825) and calibration (P = 0.46). The risk model accurately predicts mortality for this cohort and could be useful in shifting the emphasis in chemotherapy decision making from chronological age to the identification of those of any age who will benefit.

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