Linling Yu, Peijiang Lu, Meng Yang, Xiong Wang, Qi Liu, Pinpin Long, Ze-Min Fang, Zihao Zhang, Jinzhu Zhao, Qi Xiang, Canqing Yu, Huaidong Du, Ling Yang, Yiping Chen, Zhengming Chen, Jun Lv, Liming Li, Dianjianyi Sun, Wei Liu, Xin Yi, Ding-Sheng Jiang
A SEER-based prediction model was developed and temporally validated for six-month CSM in de novo metastatic GEJA. The model provided moderate discrimination, acceptable calibration, and clinically distinct population-level risk strata. Because validation was limited to a later SEER cohort, the model should be externally validated and locally recalibrated before clinical workflow use, and it must not be interpreted as evidence of treatment efficacy.
Aortic diseases are often clinically silent until advanced stages, and risk determinants beyond traditional cardiovascular factors remain incompletely characterised. Here, we investigate associations of 42 chronic conditions and multimorbidity with incident aortic disease in UK Biobank (UKB), with external validation in China Kadoorie Biobank (CKB). In UKB, 21 chronic conditions are associated with overall aortic disease after false discovery rate correction, including coronary heart disease, hypertension, atrial fibrillation, peripheral vascular disease, heart failure, and COPD; similar patterns are observed in CKB. Aortic atherosclerosis shows the broadest comorbidity profile, whereas dissection and aneurysm subtypes show narrower profiles. Higher multimorbidity burden is associated with higher incidence in both cohorts. In UKB, hypertension shows the largest estimated population-attributable fraction, with additional contributions from COPD and chronic kidney disease. Mendelian randomization supports selected associations, and machine-learning models show internal discriminatory performance. These findings support consideration of multimorbidity in aortic disease risk assessment.