Identifying people at higher risk of having squamous dysplasia, the precursor lesion for esophageal squamous cell carcinoma (ESCC), would allow targeted endoscopic screening.
We used multivariate logistic regression models to predict ESCC and dysplasia as outcomes. The ESCC model was based on data from the Golestan Case-Control Study (total n=871; cases=300), and the dysplasia model was based on data from a cohort of subjects from a GI clinic in Northeast Iran (total n=724; cases=26). In each of these analyses, we fit a model including all risk factors known in this region to be associated with ESCC. Individual risks were calculated using the linear combination of estimated regression coefficients and individual-specific values for covariates. We used cross-validation to determine the area under the curve (AUC) and to find the optimal cut points for each of the models.
The model had an area under the curve of 0.77 (95% CI: 0.74–0.80) to predict ESCC with 74% sensitivity and 70.4% specificity for the optimum cut point. The area under the curve was 0.71 (95% CI: 0.64–0.79) for dysplasia diagnosis, and the classification table optimized at 61.5% sensitivity and 69.5% specificity. In this population, the positive and negative predictive values for diagnosis of dysplasia were 6.8% and 97.8%, respectively.
Our models were able to discriminate between ESCC cases and controls in about 77%, and between individuals with and without squamous dysplasia in about 70% of the cases. Using risk factors to predict individual risk of ESCC or squamous dysplasia still has limited application in clinical practice, but such models may be suitable for selecting high risk individuals in research studies, or increasing the pretest probability for other screening strategies.