In this study, we developed and externally validated a clinical risk prediction model for BE based on existing data from a large, population-based study. We used a rigorous statistical approach to determine the most important panel of risk factors to predict the presence of BE in patients with GER symptoms. As recent epidemiological studies have shown that some risk factors, notably obesity, appear to have different associations with BE in men and women, we identified sex-specific predictors and then pooled these in an overall model. The final risk model included terms for age, sex, smoking status, BMI, highest level of education and frequency of use of acid suppressant medications. External validation of the model showed that it performed moderately well in discriminating between patients with nondysplastic BE complicating their GER (cases) and those with no BE (inflammation controls), and the predicted risk correlated well with the observed risk.
As BE is assumed to be an intermediate step in the development of EAC, screening to identify people with BE may be an effective strategy to prevent progression to cancer, at least in theory. Given the high prevalence of GER symptoms in the population and low prevalence of BE in these patients, endoscopic screening for BE in all patients with GER symptoms is not recommended in current international guidelines (13
). The American Gastroenterological Association guideline recommends screening in selective populations with multiple risk factors for EAC (age 50 years or older, male sex, white race, chronic GER, hiatal hernia, and elevated BMI) (14
). The evidence base underpinning these guidelines is not strong however, and adherence to the guidelines is likely to be incomplete. Data for Australia are limited, however a New Zealand study showed that approximately 50% of indications for endoscopy in 2003 were for heartburn or dyspepsia (i.e., to exclude BE/EAC), significantly higher than in 1997 (35
). In healthcare systems throughout the world, there is an increasing need for evidence based strategies including the need to establish an effective means of risk stratification for endoscopic BE screening among patients with GER symptoms.
Risk prediction can be used in clinical settings to stratify individuals into homogeneous risk groups. Risk prediction models are used in public health to quantify the probability of disease based on a combination of risk factors (36
). So far, risk prediction approaches have been used extensively for cardiovascular diseases (16
), and more recently, there has been a focus on deriving cancer risk prediction models (17
). These models can complement clinical assessment, but they also ensure that the decision making process is more uniform across different centers by moving away from using any individual clinician’s personal experience (37
Previous efforts to develop a risk model for BE focused on identifying BE among GER patients using gastrointestinal symptoms (27
). While these models performed well (AUCs > 0.70), they have not been externally validated. Our study utilized similar methods to derive a risk prediction model for BE using phenotypic and environmental risk factors, and tested its performance in an external population. The variables included in the model are all important and not necessarily causal correlates of BE and are supported by published findings from our own and other case-control and cohort studies of BE (10
). Furthermore, to encourage generalizability, we emphasized the use of information on risk factors that can be obtained by practitioners in the office setting during routine healthcare. Importantly, the discriminatory accuracy for our model (AUC=0.61 in the external validation dataset) compares favorably with cancer risk prediction models, such as the Gail (42
) model for Breast cancer risk (0.58) (43
), and the LLP (22
), Spitz (23
), and Bach (21
) lung cancer risk models (0.69, 0.69, and 0.62, respectively) (44
Applying this model to all patients with GER symptoms currently being considered for endoscopy, and using a threshold for making a decision has the potential to reduce the number of unnecessary endoscopies performed to exclude BE. This model makes explicit the proportion of BE cases who would be missed because their predicted risk lies below the threshold. Due to the case-control study design, we were unable to determine precisely the positive and negative predictive values for the model. However, if we assume 5% prevalence of BE among persons with GER symptoms, our best estimate is that 5–17% of persons referred for endoscopy will have BE (depending upon the cut point chosen for referral) and 95–100% of persons not referred will not have BE. Assuming 15% prevalence, 15–41% of persons referred for endoscopy will have BE and 85–100% of people not referred will not have BE. In general, determining an acceptable threshold involves a trade-off between sensitivity and specificity. In screening for a lethal cancer for example, high sensitivity is desirable, whereas for diseases with lower severity, a lower sensitivity can be tolerated. For our BE model, as the absolute risk of progression to cancer in BE patients is low (8
), a threshold whereby fewer investigations are performed at the risk of missing more BE cases (i.e., increased specificity and decreased sensitivity) may be desirable.
Our study had strengths and limitations. The large samples of patients newly diagnosed with BE in the two settings were recruited prospectively, and comparable, consistent and standardized criteria were used for histologic and endoscopic definitions of BE. Both the derivation sample and the validation sample only included patients who were selected for endoscopy; the large (but unknown) proportion of patients with GER symptoms not referred for screening had already been triaged away from endoscopy by clinicians using their own internal algorithms. Presumably, the clinicians had decided that those patients were at such low risk of significant pathology that there was no net benefit from undergoing endoscopic investigation. As such, it is likely that had those low risk patients been included in the two samples, our prediction models would have performed even better. While our modeling assumes that endoscopy is performed in the setting of GER symptoms solely to exclude BE, endoscopy may be undertaken for other indications in this clinical setting. If so, then this would tend to attenuate the predictive value of the models we have derived, since those patients being referred for other indications would presumably be at lower risk of BE than those being referred to confirm the clinical diagnosis.
A limitation of the Australian study was the relatively low rate of participation, raising concerns about possible biased selection of cases and controls. Because BE cases and inflammation controls were sampled from the same population, navigated the same clinical pathways and were recruited using identical methods, it is unlikely that systematically biased selection of one or other group explains our findings. Moreover, BE cases and inflammation controls were not informed about the hypotheses being tested, and so while biased recall of non-reflux exposures is possible, we consider the likelihood that this accounts for our observations as low. Although there were 108 dysplastic BE cases in our development dataset, we were unable to obtain a validation dataset with dysplastic BE cases. The best estimate for the performance of our model for predicting BE with dysplasia was an AUC estimate of 0.87 (95%CI 0.83–0.91) in the development dataset. Recently, central obesity has been found to be more strongly associated with BE than BMI, however measures of central obesity (e.g., waist-to-hip ratio) were not collected for this study. It is likely that adding such measures to the model, rather than BMI, may improve predictive accuracy. Finally, our sample was predominantly white (~97%) and thus the models may not be applicable to other ethnic groups.
This parsimonious model however could be considered as a starting point for further development, as a number of risk factors were not included and genetic information may also be important in predicting risk of BE. The inclusion of other environmental risk factors and the extension of the model to include biomarkers may go further to improving performance. However, a recent study has shown that breast cancer risk prediction does not improve significantly when genetic information is included in the risk model (45
In summary, we have derived and externally validated a risk prediction model which estimates the likelihood of undiagnosed BE in patients with GER symptoms being considered for upper gastrointestinal endoscopy. The prediction model has the potential to be a useful tool in the clinical setting for decisions regarding investigation and treatment of patients with GER.