External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank

Abstract

Background: In China, colorectal cancer (CRC) incidence and mortality have been steadily increasing over the last decades. Risk models to predict incident CRC have been developed in various populations, but they have not been systematically externally validated in a Chinese population. This study aimed to assess the performance of risk scores in predicting CRC using the China Kadoorie Biobank (CKB), one of the largest and geographically diverse prospective cohort studies in China.

Methods: Nine models were externally validated in 512,415 participants in CKB and included 2976 cases of CRC. Model discrimination was assessed, overall and by sex, age, site, and geographic location, using the area under the receiver operating characteristic curve (AUC). Model discrimination of these nine models was compared to a model using age alone. Calibration was assessed for five models, and they were re-calibrated in CKB.

Results: The three models with the highest discrimination (Ma (Cox model) AUC 0.70 [95% CI 0.69-0.71]; Aleksandrova 0.70 [0.69-0.71]; Hong 0.69 [0.67-0.71]) included the variables age, smoking, and alcohol. These models performed significantly better than using a model based on age alone (AUC of 0.65 [95% CI 0.64-0.66]). Model discrimination was generally higher in younger participants, males, urban environments, and for colon cancer. The two models (Guo and Chen) developed in Chinese populations did not perform better than the others. Among the 10% of participants with the highest risk, the three best performing models identified 24-26% of participants that went on to develop CRC.

Conclusions: Several risk models based on easily obtainable demographic and modifiable lifestyle factor have good discrimination in a Chinese population. The three best performing models have a higher discrimination than using a model based on age alone.

Keywords: Cancer epidemiology; Colorectal cancer; External validation; Risk prediction models.

Fig. 1

PRISMA flow diagram of the updated systematic review of risk prediction models for CRC

Fig. 2

Model discrimination for 10-year risk of developing colorectal cancer. Area under the receiver operating characteristic curve for the risk models in A males and females, B males, and C females

Fig. 3

Model discrimination for 10-year risk of developing site-specific colorectal cancer. Area under the receiver operating characteristic curve for predicting A colon cancer, B rectal cancer, C right-sided colon cancer, and D left-sided colon cancer

Fig. 4

Model discrimination for 10-year risk of developing colorectal cancer by age and geographic location. Area under the receiver operating characteristic curve for the risk models in A younger participants (age < 56 years), B older participants (age ≥ 56), C participants from urban settings, and D participants from rural settings

Fig. 5

Model calibration curves of observed and expected 10-year risk of colorectal cancer in men and women. A Driver risk score, B Guo risk score, C Hong risk score, D Imperiale risk score, and E Ma Point risk score