关键词: 心脑血管疾病, 队列研究, 危险因素, 预测模型, 航天, 职业人群

Abstract: Objective  To develop and validate a 10-year prediction model for the incidence risk of cardiovascular diseases (CVD) among the aerospace occupational population, providing a quantitative tool for early CVD prevention and control in this occupational group. Methods  We included CVD-free employees from Shanghai Academy of Spaceflight Technology who underwent physical examinations at Aerospace Wuxi Health Management Center in 2014. Annual physical examination was conducted, with follow up through April 5, 2025. The data were randomly divided into a training set and a validation set at a ratio of 7∶3. A total of 236 physical examination indicators were screened using the LASSO-penalized Cox algorithm. The model was constructed using Cox regression, and a nomogram was drawn. The discrimination was evaluated using the Harrell C statistic and the area under the receiver operating characteristic（ROC） curve (AUC), the calibration was evaluated using the calibration curve, and the DeLong's test was used to compare the performance differences with classic domestic and international models. Results  A total of 13 303 participants was included, with a median baseline age of 32 (28,43) years. The model incorporated 8 risk factors: a history of diabetes, a history of hypertension, a family history of hypertension,total cholesterol (TC)/high density lipoprotein -cholesterol(HDL-C), age, systolic blood pressure, waist circumference, and γ-glutamyltransferase. The model demonstrated adequate discrimination ［Harrell C: 0.898 vs 0.874; AUC (95% CI): 0.911 (0.889-0.931) vs 0.890 (0.854-0.923)］ and calibration in both the training set and the validation set, outperforming the classic models (P<0.05). Conclusion  A model constructed using readily accessible physical examination indicators can accurately predict the 10-year CVD incidence risk among the aerospace occupational population. This provides a scientific basis for early identification of high-risk individuals within this population, development of occupation-specific strategies, and implementation of preventive measures, thereby filling the gap in CVD risk assessment tools tailored to aerospace occupations. 

Key words: cardiovascular diseases, cohort study, risk factors, prediction model, aerospace, occupational population