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Are we ready to predict late effects? A systematic review of clinically useful prediction models.

Publication ,  Journal Article
Salz, T; Baxi, SS; Raghunathan, N; Onstad, EE; Freedman, AN; Moskowitz, CS; Dalton, SO; Goodman, KA; Johansen, C; Matasar, MJ; Oeffinger, KC ...
Published in: Eur J Cancer
April 2015

BACKGROUND: After completing treatment for cancer, survivors may experience late effects: consequences of treatment that persist or arise after a latent period. PURPOSE: To identify and describe all models that predict the risk of late effects and could be used in clinical practice. DATA SOURCES: We searched Medline through April 2014. STUDY SELECTION: Studies describing models that (1) predicted the absolute risk of a late effect present at least 1 year post-treatment, and (2) could be used in a clinical setting. DATA EXTRACTION: Three authors independently extracted data pertaining to patient characteristics, late effects, the prediction model and model evaluation. DATA SYNTHESIS: Across 14 studies identified for review, nine late effects were predicted: erectile dysfunction and urinary incontinence after prostate cancer; arm lymphoedema, psychological morbidity, cardiomyopathy or heart failure and cardiac event after breast cancer; swallowing dysfunction after head and neck cancer; breast cancer after Hodgkin lymphoma and thyroid cancer after childhood cancer. Of these, four late effects are persistent effects of treatment and five appear after a latent period. Two studies were externally validated. Six studies were designed to inform decisions about treatment rather than survivorship care. Nomograms were the most common clinical output. CONCLUSION: Despite the call among survivorship experts for risk stratification, few published models are useful for risk-stratifying prevention, early detection or management of late effects. Few models address serious, modifiable late effects, limiting their utility. Cancer survivors would benefit from models focused on long-term, modifiable and serious late effects to inform the management of survivorship care.

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Published In

Eur J Cancer

DOI

EISSN

1879-0852

Publication Date

April 2015

Volume

51

Issue

6

Start / End Page

758 / 766

Location

England

Related Subject Headings

  • Survivors
  • Oncology & Carcinogenesis
  • Neoplasms
  • Models, Statistical
  • Humans
  • Decision Support Techniques
  • 3211 Oncology and carcinogenesis
  • 1117 Public Health and Health Services
  • 1112 Oncology and Carcinogenesis
 

Citation

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Salz, T., Baxi, S. S., Raghunathan, N., Onstad, E. E., Freedman, A. N., Moskowitz, C. S., … Vickers, A. J. (2015). Are we ready to predict late effects? A systematic review of clinically useful prediction models. Eur J Cancer, 51(6), 758–766. https://doi.org/10.1016/j.ejca.2015.02.002
Salz, Talya, Shrujal S. Baxi, Nirupa Raghunathan, Erin E. Onstad, Andrew N. Freedman, Chaya S. Moskowitz, Susanne Oksbjerg Dalton, et al. “Are we ready to predict late effects? A systematic review of clinically useful prediction models.Eur J Cancer 51, no. 6 (April 2015): 758–66. https://doi.org/10.1016/j.ejca.2015.02.002.
Salz T, Baxi SS, Raghunathan N, Onstad EE, Freedman AN, Moskowitz CS, et al. Are we ready to predict late effects? A systematic review of clinically useful prediction models. Eur J Cancer. 2015 Apr;51(6):758–66.
Salz, Talya, et al. “Are we ready to predict late effects? A systematic review of clinically useful prediction models.Eur J Cancer, vol. 51, no. 6, Apr. 2015, pp. 758–66. Pubmed, doi:10.1016/j.ejca.2015.02.002.
Salz T, Baxi SS, Raghunathan N, Onstad EE, Freedman AN, Moskowitz CS, Dalton SO, Goodman KA, Johansen C, Matasar MJ, de Nully Brown P, Oeffinger KC, Vickers AJ. Are we ready to predict late effects? A systematic review of clinically useful prediction models. Eur J Cancer. 2015 Apr;51(6):758–766.
Journal cover image

Published In

Eur J Cancer

DOI

EISSN

1879-0852

Publication Date

April 2015

Volume

51

Issue

6

Start / End Page

758 / 766

Location

England

Related Subject Headings

  • Survivors
  • Oncology & Carcinogenesis
  • Neoplasms
  • Models, Statistical
  • Humans
  • Decision Support Techniques
  • 3211 Oncology and carcinogenesis
  • 1117 Public Health and Health Services
  • 1112 Oncology and Carcinogenesis