TY - JOUR
T1 - Prediction models for overall survival and all-cause mortality risk in older adults with cancer
T2 - a systematic review
AU - Duquenne, Pauline
AU - Liposits, Gabor
AU - Vonnes, Cassandra O.
AU - Navarrete, Erna
AU - Serrano, Adolfo Gonzalez
AU - Canoui-Poitrine, Florence
AU - Marinho, Joana
AU - Akagündüz, Baran
AU - Haase, Kristen R.
AU - Verduzco-Aguirre, Haydee C.
AU - Li, Juan
AU - Eochagáin, Colm Mac
AU - Soto-Perez-de-Celis, Enrique
AU - Ayala, Ana Patricia
AU - Baltussen, Joosje C.
AU - Kantilal, Kavita
AU - Kantilal, Kumud
AU - Wing-Lok, Chan
AU - de Acha, Andrea Perez
AU - Meckstroth, Shelby
AU - Perez, Ana Cristina Torres
AU - Güven, Deniz Can
AU - Zhao, Yue
AU - Puts, Martine
AU - Beauplet, Bérengère
AU - Lund, Jennifer L.
AU - Pilleron, Sophie
AU - Young International Society of Geriatric Oncology and Nursing, Allied Health and Scientists interest groups
N1 - Copyright © 2026 The Author(s). Published by Elsevier Ltd.. All rights reserved.
PY - 2026/3/21
Y1 - 2026/3/21
N2 - Mortality risk prediction models can support decision making in older adults with cancer; however, existing models are associated with a high risk of bias. This systematic review assessed published prediction models for overall and all-cause mortality in adults with cancer aged 65 years or older. We searched for publications in Ovid Embase, Ovid Medline, Cochrane CENTRAL, and EBSCO CINAHL on Nov 25, 2022, and updated the search on Feb 24, 2024. We included 250 studies, of which 182 (72·8%) reported both model development and internal validation. 176 (70·4%) of 250 models predicted overall survival; 40 (16·0%) models focused on lung cancer and 30 (12·0%) models on colorectal cancer. 43 (17·2%) models were specifically developed for older adults; 138 (55·2%) models did not incorporate geriatric variables such as comorbidities, nutrition, and cognition. Risk of bias was high in all models, largely owing to inappropriate handling of continuous predictors, univariable selection of predictors, and inadequate control for overfitting. These limitations preclude clinical use. Future models predicting overall and all-cause mortality in older adults with cancer should adhere to existing methodological guidelines and incorporate geriatric domains.
AB - Mortality risk prediction models can support decision making in older adults with cancer; however, existing models are associated with a high risk of bias. This systematic review assessed published prediction models for overall and all-cause mortality in adults with cancer aged 65 years or older. We searched for publications in Ovid Embase, Ovid Medline, Cochrane CENTRAL, and EBSCO CINAHL on Nov 25, 2022, and updated the search on Feb 24, 2024. We included 250 studies, of which 182 (72·8%) reported both model development and internal validation. 176 (70·4%) of 250 models predicted overall survival; 40 (16·0%) models focused on lung cancer and 30 (12·0%) models on colorectal cancer. 43 (17·2%) models were specifically developed for older adults; 138 (55·2%) models did not incorporate geriatric variables such as comorbidities, nutrition, and cognition. Risk of bias was high in all models, largely owing to inappropriate handling of continuous predictors, univariable selection of predictors, and inadequate control for overfitting. These limitations preclude clinical use. Future models predicting overall and all-cause mortality in older adults with cancer should adhere to existing methodological guidelines and incorporate geriatric domains.
KW - Humans
KW - Aged
KW - Neoplasms/mortality
KW - Risk Assessment/methods
KW - Aged, 80 and over
UR - https://www.scopus.com/pages/publications/105033482627
UR - https://pubmed.ncbi.nlm.nih.gov/41875911/
U2 - 10.1016/j.lanhl.2026.100829
DO - 10.1016/j.lanhl.2026.100829
M3 - Review article
C2 - 41875911
AN - SCOPUS:105033482627
SN - 2666-7568
VL - 7
JO - The Lancet Healthy Longevity
JF - The Lancet Healthy Longevity
IS - 3
M1 - 100829
ER -