We developed a risk prediction model of ESCC and its precancerous lesions that has the potential to be applied in secondary prevention projects. The can help patients make preliminary judgements, and it can improve the detection rate of ESCC and precancerous lesions. Our results provide important information for further clarifying the impact of clinical symptoms on early warning and prognosis of EC.
Kunzmannn et al.  established a predictive model of oesophageal adenocarcinoma based on life risk factors through a prospective study. Age, sex, smoking, BMI, and history of oesophageal treatment were included in the final model (AUC = 0.80, 95%CI 0.77–0.82) used to predict patients with a higher absolute risk of oesophageal adenocarcinoma within 5 years. Ireland et al.  compared 120 Barrett oesophagus patients with 235 healthy individuals by age, sex, history of reflux, family history of reflux, history of hypertension, weekly alcoholic beverages, and BMI, and found significant differences; they established a Barrett oesophagus risk prediction model for the Australian population. The model's AUC was 0.82(95% CI 0.78–0.87), showing good discrimination.
In this study, the SDA and MDA models included the same risk factors age, cigarette smoking, alcohol drinking, pharyngeal foreign body sensation, swallowing obstruction, pain behind the sternum, and discomfort behind the breastbone. The MDA model's AUC indicated that it had high discrimination ability. Other prediction models have been based on patients' living habits and living environment as predictive factors, such as pesticide exposure and use coal or wood as the main cooking fuel. During the questionnaire, subjects could not accurately select answers, and because of economic and medical restrictions in China, the prevalence of a family history of EC in patients older than60 years of age cannot be accurately informed, making it difficult to apply prediction models. Our research model predictors can accurately reflect the patients and be used easily in screening practise. Compared with the hospital-based case-control design model, the main advantage of this study is that our prediction model was based on a large population-level screening procedure. Patients who participated in the screening were randomly selected from Nanchong City, Sichuan Province, a high-incidence area of EC. They were able to describe the distribution of EC and precancerous lesions in the general population. We used screening cases including severe dysplasia, CIS, and ESCC as the result of model construction. Early malignant lesions are the target of ESCC screening work . Compared with the model established by the case-control study with advanced ESCC as the outcome event, our model is more suitable for early detection of ESCC and its malignant lesions.
The present study showed that SDA and MDA are significantly related to smoking and drinking. Compared with non-smokers, smokers have a significantly increased risk of SDA and MDA, which is consistent with findings of previous case-control and cohort studies [34, 35]. Drinking alcohol produces carcinogenic metabolites, and heavy drinking especially increases the accumulation of such metabolites in the body, which in turn increases the risk of ESCC [36, 37].
The characteristics and criteria of early symptoms of EC and further clarifying the relationship between these symptoms and the course and pathological characteristics of EC are key to determining whether these symptoms can be used for early warning of EC. This strategy is also one of the most effective, low-cost, non-invasive, and easily accepted and popularised EC prevention and control methods in the absence of effective molecular markers for early warning and diagnosis of EC. Western literature reported that the main early warning symptoms of oesophageal adenocarcinoma are swallowing/choking .Current research on symptoms and tumours, lack of symptoms, and lack of awareness of the risk of alarm symptoms are risk factors for poor tumour prognosis . The most common presenting symptoms of EC are dysphagia and weight loss. Other symptoms include odynophagia, upper gastrointestinal bleeding, hoarseness, and respiratory symptoms.Based on large-scale screening tests, we summarised the warming symptoms of early oesophageal tumours. Although the value of each alarm symptom was relatively low, it may be possible to avoid unnecessary endoscopy by using various symptoms combined with patient risk factors to make a comprehensive judgement.
Some previous studies have also evaluated the value of age and warning symptoms in predicting cancer risk in patients with dyspepsia [20–22].These studies used age and warming symptoms to predict upper gastrointestinal cancer. Bai and colleagues studied the predictive value of alarm symptoms and age for upper gastrointestinal malignancies in China, and found that age or any alarm symptoms have limited value. In their study, alarm symptoms were highly specific, but the sensitivity was low. However, most of their discussions were based on the positive diagnostic likelihood ratio for each symptom, and they did not use all predictors to build models to predict the risk limits of upper gastrointestinal malignancies , Fransen and colleagues conducted a meta-analysis and found a limited diagnostic value for each alarm symptom. They recommended that the alarm symptoms may be related to other factors. Combined use may be a better tool for selecting high-risk patients. However, they could not test their hypothesis. Numans and colleagues used the calculated total score to establish a risk prediction model and showed that the classic warning symptoms through the risk prediction model are useful predictors of upper gastrointestinal malignancies. However, their model is somewhat complicated and contains multiple variables, so it is somewhat unstable. The above studies all predicted upper gastrointestinal tumours, which is not practical for high incidence areas of EC in China. In our risk prediction model, various symptoms and patients’ life habits can be used to diagnose EC.
Disease risk prediction models can provide personalised estimates of ESCC risk based on personal baseline data . The use of risk prediction models to distinguish high-risk groups from the general population will help formulate highly effective ESCC prevention and treatment strategies . The first method of using this model is that when the resources for EC are high and endoscopic screening is limited, it is urgent to identify more patients with early malignant lesions, such as the national population with ESCC in some high-risk areas of EC in China’s screening plan . A nomogram can be used as a predictor to assess the risk > 0.8, which can maximise the detection rate of malignant lesions. Compared with general screening, the detection rate of SDA and MDA lesions will increase. The second method is under the conditions of unlimited resources, such as the environment that usually exists in clinical and scientific research. Therefore, it is possible to select subjects with a risk > 0.5 for screening to ensure high sensitivity.
This study has a couple limitations. Although our study screened more than 6409 participants, the study population was still limited, and the screening population was divided according to whether patients participated in screening, causing biased selection. Multi-centre external verification and calibration are required in future studies. Because of regional differences in EC, validation studies conducted in other populations are also crucial for the generalisation of the model. Prospective collection of more malignant events and dynamic observation of exposure to predictive factors are also essential to improving these models. In conclusions, our EC high-risk population screening model constructed based on screening population information has good discriminatory and calibration capabilities, and it can be used to assess the risk of local EC. Screening high-risk populations may make EC screening more cost-effective. Additionally, the model can be applied to other Asian populations with similar socioeconomic or lifestyle characteristics, who may also benefit from our ESCC risk prediction model.