Abstract
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.
| Original language | English |
|---|---|
| Journal | European journal of cancer (Oxford, England : 1990) |
| Volume | 51 |
| Issue number | 6 |
| Pages (from-to) | 758-66 |
| Number of pages | 9 |
| ISSN | 0959-8049 |
| DOIs | |
| Publication status | Published - Apr 2015 |
Keywords
- Decision Support Techniques
- Humans
- Models, Statistical
- Neoplasms
- Survivors
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