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Prediction models for overall survival and all-cause mortality risk in older adults with cancer: a systematic review.

The lancet. Healthy longevity 2026 Vol.7(3) p. 100829

Duquenne P, Liposits G, Vonnes CO, Navarrete E, Serrano AG, Canoui-Poitrine F, Marinho J, Akagündüz B, Haase KR, Verduzco-Aguirre HC, Li J, Eochagáin CM, Soto-Perez-de-Celis E, Ayala AP, Baltussen JC, Kantilal K, Kantilal K, Wing-Lok C, de Acha AP, Meckstroth S, Perez ACT, Güven DC, Zhao Y, Puts M, Beauplet B, Lund JL, Pilleron S

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Mortality risk prediction models can support decision making in older adults with cancer; however, existing models are associated with a high risk of bias.

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • 연구 설계 systematic review

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BibTeX ↓ RIS ↓
APA Duquenne P, Liposits G, et al. (2026). Prediction models for overall survival and all-cause mortality risk in older adults with cancer: a systematic review.. The lancet. Healthy longevity, 7(3), 100829. https://doi.org/10.1016/j.lanhl.2026.100829
MLA Duquenne P, et al.. "Prediction models for overall survival and all-cause mortality risk in older adults with cancer: a systematic review.." The lancet. Healthy longevity, vol. 7, no. 3, 2026, pp. 100829.
PMID 41875911

Abstract

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.

MeSH Terms

Humans; Aged; Neoplasms; Risk Assessment; Aged, 80 and over