| Autor: |
Xu, Wei, Mesa Eguiagaray, Ines, Kirkpatrick, Theresa, Devlin, Jennifer, Brogan, Stephanie, Turner, Patricia, Macdonald, Chloe, Thornton, Michelle J, Zhang, Xiaomeng, He, Yazhou, Li, Xue, Timofeeva, Maria, Farrington, Susan M, Din, Farhat V N, Dunlop, Malcolm G, Theodoratou, Evropi |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Zdroj: |
Xu, W, Mesa Eguiagaray, I, Kirkpatrick, T, Devlin, J, Brogan, S, Turner, P, Macdonald, C, Thornton, M J, Zhang, X, He, Y, Li, X, Timofeeva, M, Farrington, S M, Din, F V N, Dunlop, M G & Theodoratou, E 2023, ' Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms ', Journal of personalized medicine . https://doi.org/10.3390/jpm13071065 |
| DOI: |
10.3390/jpm13071065 |
| Popis: |
We aimed to develop and validate prediction models incorporating demographics, clinical fea-tures, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n=1686/ Controls: n=963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (change of bowel habit, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predic-tors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LAS-SO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models’ calibration and discrimination were evaluated through Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models’ prediction performance was: model A (corrected C-statistic=0.765); model B (corrected C-statistic=0.753); model C (corrected C-statistic=0.764); model D [corrected C-statistic=0.752). Models A and C that integrated wGRS with demographic and clinical predictors had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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