Are we ready to predict late effects? A systematic review of clinically useful prediction models.
BACKGROUND
After completing treatment for cancer, survivors may experience late effects: consequences of treatment that persist or arise after a latent period.


OBJECTIVE
To identify and describe all models that predict the risk of late effects and could be used in clinical practice.


METHODS
We searched Medline through April 2014.


METHODS
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.


METHODS
Three authors independently extracted data pertaining to patient characteristics, late effects, the prediction model and model evaluation.


RESULTS
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.


CONCLUSIONS
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.
Memorial Sloan-Kettering Cancer Center, New York, NY, United States. Electronic address: salzt@mskcc.org.
Talya
T
Salz
Memorial Sloan-Kettering Cancer Center, New York, NY, United States.
Shrujal S
SS
Baxi
Memorial Sloan-Kettering Cancer Center, New York, NY, United States.
Nirupa
N
Raghunathan
Harvard School of Public Health, Boston, MA, United States.
Erin E
EE
Onstad
National Institute of Health, Bethesda, MD, United States.
Andrew N
AN
Freedman
Memorial Sloan-Kettering Cancer Center, New York, NY, United States.
Chaya S
CS
Moskowitz
Danish Cancer Society Research Center, Copenhagen, Denmark.
Susanne Oksbjerg
SO
Dalton
Memorial Sloan-Kettering Cancer Center, New York, NY, United States.
Karyn A
KA
Goodman
Danish Cancer Society Research Center, Copenhagen, Denmark.
Christoffer
C
Johansen
Memorial Sloan-Kettering Cancer Center, New York, NY, United States.
Matthew J
MJ
Matasar
Rigshospitalet, Copenhagen, Denmark.
Peter
P
de Nully Brown
Memorial Sloan-Kettering Cancer Center, New York, NY, United States.
Kevin C
KC
Oeffinger
Memorial Sloan-Kettering Cancer Center, New York, NY, United States.
Andrew J
AJ
Vickers
D003661:Decision Support Techniques; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000401:mortality / Q000503:physiopathology*; D017741:Survivors
Decision support techniques; Neoplasms; Risk; Secondary prevention; Survivors