Risk Prediction Model for Colorectal Cancer: National Health Insurance Corporation Study, Korea

Abstract

PurposeIncidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection.MethodsGender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30–80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics.ResultsAge, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women.ConclusionsColorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

Demographic and Pathological Predictors of Colorectal Cancer Survival in Competing Risk Model, Using Generalized Weibull Distribution

“…While an Iranian study showed that family history of cancer increased the risk of CRC (33), Weibull model did not detect any relation between risk of mortality and family history of CRC and this is similar to an Asian study which could not find any relation with survival of CRC and family history (34). Although in Iran there is evidence to support the screening of average risk individuals, including person with family history of colorectal cancer (35), it is still controversial and needs to more research on the Iranian population.…”

Risk prediction models for colorectal cancer: A Scoping review

“…Socio-demographic characteristics of the study populations A majority of the risk prediction models have been developed in United States of America [18,22,33,38,39,42,44,45,48,49,50,51,53,54] followed by Korea [14,17,20,35]. Three models have been developed in China [16,23,36] and Japan [40] while, Netherlands [25,26], Germany [21], Taiwan [32,46] and United Kingdom [31,34] have developed two models each.…”

“…However, 11 models are specific for females [20,22,35,42,45,50] while 10 models are specific for males [20,22,35,45,48]. Although many models have not restricted application to a specific age limit, 11 models have been developed from populations aged 30 years and above [15,20,42,43,50], 10 from populations aged 50 years and above [18,25,26,45,51] and seven from populations aged 40 years and above [14,19,23,24,36,38,52]. In addition, one model has used a population above 20 years [35] while another, populations lower than 80 years [34].…”

“…The questionnaires range from those with only one or two simple questions to those with detailed questions on factors such as diet, physical activity, alcohol and smoking habits and past medical facts. Three purely genetic models have used only a blood test for genetic biomarkers [32,34,39], while thirteen models have used data from a questionnaire and the results of blood tests for genetic biomarkers and other biochemical tests [16,17,20,27,31,35,47]. One model has obtained data both from a questionnaire and medical records [15].…”

“…A majority of the studies have used multiple logistic regression to identify the risk predictors [16,27,28,30,33,34,35,41,43,47,50,52] followed by the allocation of the risk points, based on the values of beta-coefficients [14,15,18,21,23,24,36,37,39,45,46,48]. Fourteen models have incorporated Cox proportional hazards regression to develop the risk model and the score [17,19,20,22,38,40]. One model [30] was developed from meta-analysis of various studies, one [31] used risk modelling software in a simulated population and one [26] used pure bivariate analysis.…”