A model to predict the risk of keratinocyte carcinomas

Whiteman, David C., Thompson, Bridie S., Thrift, Aaron P., Hughes, Maria-Celia, Muranushi, Chiho, Neale, Rachel E., Green, Adele C. and Olsen, Catherine M. (2016) A model to predict the risk of keratinocyte carcinomas. Journal of Investigative Dermatology, 136 6: 1247-1254. doi:10.1016/j.jid.2016.02.008


Author Whiteman, David C.
Thompson, Bridie S.
Thrift, Aaron P.
Hughes, Maria-Celia
Muranushi, Chiho
Neale, Rachel E.
Green, Adele C.
Olsen, Catherine M.
Title A model to predict the risk of keratinocyte carcinomas
Journal name Journal of Investigative Dermatology   Check publisher's open access policy
ISSN 0022-202X
1523-1747
Publication date 2016-06
Sub-type Article (original research)
DOI 10.1016/j.jid.2016.02.008
Open Access Status Not yet assessed
Volume 136
Issue 6
Start page 1247
End page 1254
Total pages 8
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Collection year 2017
Language eng
Abstract Basal cell and squamous cell carcinomas of the skin are the commonest cancers in humans, yet no validated tools exist to estimate future risks of developing keratinocyte carcinomas. To develop a prediction tool, we used baseline data from a prospective cohort study (n = 38,726) in Queensland, Australia, and used data linkage to capture all surgically excised keratinocyte carcinomas arising within the cohort. Predictive factors were identified through stepwise logistic regression models. In secondary analyses, we derived separate models within strata of prior skin cancer history, age, and sex. The primary model included terms for 10 items. Factors with the strongest effects were >20 prior skin cancers excised (odds ratio 8.57, 95% confidence interval [95% CI] 6.73–10.91), >50 skin lesions destroyed (odds ratio 3.37, 95% CI 2.85–3.99), age ≥ 70 years (odds ratio 3.47, 95% CI 2.53–4.77), and fair skin color (odds ratio 1.75, 95% CI 1.42–2.15). Discrimination in the validation dataset was high (area under the receiver operator characteristic curve 0.80, 95% CI 0.79–0.81) and the model appeared well calibrated. Among those reporting no prior history of skin cancer, a similar model with 10 factors predicted keratinocyte carcinoma events with reasonable discrimination (area under the receiver operator characteristic curve 0.72, 95% CI 0.70–0.75). Algorithms using self-reported patient data have high accuracy for predicting risks of keratinocyte carcinomas.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Public Health Publications
 
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