Esophageal adenocarcinoma (EAC) is characterized by rapidly increasing incidence and poor prognosis, stressing the need for preventive and early detection strategies. We used data from a nationwide population-based case-control study, which included 189 incident cases of EAC and 820 age- and sex-matched control participants, from 1995 through 1997 in Sweden. We developed risk prediction models based on unconditional logistic regression. Candidate predictors included established and readily identifiable risk factors for EAC. The performance of model was assessed by the area under receiver operating characteristic curve (AUC) with cross-validation. The final model could explain 94% of all case patients with EAC (94% population attributable risk) and included terms for gastro-esophageal reflux symptoms or use of antireflux medication, body mass index (BMI), tobacco smoking, duration of living with a partner, previous diagnoses of esophagitis and diaphragmatic hernia and previous surgery for esophagitis, diaphragmatic hernia or severe reflux or gastric or duodenal ulcer. The AUC was 0.84 (95% confidence interval [CI] 0.81–0.87) and slightly lower after cross-validation. A simpler model, based only on reflux symptoms or use of antireflux medication, BMI and tobacco smoking could explain 91% of the case patients with EAC and had an AUC of 0.82 (95% CI 0.78–0.85). These EAC prediction models showed good discriminative accuracy, but need to be validated in other populations. These models have the potential for future use in identifying individuals with high absolute risk of EAC in the population, who may be considered for endoscopic screening and targeted prevention.What's new?
Many patients diagnosed with esophageal adenocarcinoma (EAC) are in advanced stages of disease, greatly reducing their chances of survival. Since the absolute risk in the population is low, however, universal endoscopic screening is impractical, necessitating the development of novel preventive and early-detection strategies. Here, prediction models based on EAC risk factors were tested for their ability to identify individuals at increased risk of the disease. The most refined model successfully identified 94 percent of patients with EAC. Such prediction models could facilitate EAC prevention and detection.