Prediction model using readily available clinical data for colorectal cancer in a chinese population

In China, health screening has become common, although colonoscopy is not always available or acceptable. We sought to develop a prediction model of colorectal cancer (CRC) for health screening population based on readily available clinical data to reduce labor and economic costs.

We conducted a cross-sectional study based on a health screening population in Karamay Central Hospital. By collecting clinical data and basic information from participants, we identified independent risk factors and established a prediction model of CRC. Internal and external validation, calibration plot, and decision curve analysis were employed to test discriminating ability, calibration ability, and clinical practicability.

Independent risk factors of CRC, which were readily available in primary public health institutions, included high-density lipoprotein cholesterol, male sex, total cholesterol, advanced age, and hemoglobin. These factors were successfully incorporated into the prediction model (AUC 0.740, 95% CI 0.713-0.767). The model demonstrated a high degree of discrimination and calibration, in addition to a high degree of clinical practicability in high-risk people.

The prediction model exhibits good discrimination and calibration and is pragmatic for CRC screening in rural areas and primary public health institutions.