A Predictive Model for Upper Gastrointestinal Bleeding in Patients with Acute Myocardial Infarction Complicated by Cardiogenic Shock During Hospitalization

fei dongying zhouyufei zhaoyunqiang zhanghaiqing liangyu songRui Jing^*

• TEDA International Cardiovascular Hospital, Tianjin, China

Objective: To explore the current status and characteristics of upper gastrointestinal bleeding(UGIB) in patients with acute myocardial infarction complicated by cardiogenic shock (AMICS) following emergency percutaneous coronary intervention (PCI), and to develop and validate a predictive model based on baseline risk factors at the time of admission. Methods:We selected patients diagnosed with AMICS who underwent emergency PCI. Patients were categorized into the non-bleeding group and the bleeding group based on the occurrence of UGIB during hospitalization. Logistic regression analysis was employed to construct a predictive model for UGIB based on baseline risk factors at admission. Results: A total of 253 patients were included in the study, of whom 58 experienced UGIB, resulting in an incidence rate of 22.9%. Univariate analysis indicated 7 variables that show statistical differences. The LASSO regression identified 5 non-zero coefficient variables, and the VIF test excluded the collinearity relationships among these 5 variables. Multivariate logistic regression analysis identified 3 risk factors for UGIB following AMICS emergency PCI: SCAI-CS stage D+E, eGFR < 45 ml/(min·1.73 m2), and LVEF < 36% (P < 0.05). The predictive model based on multivariate logistic regression results, demonstrated good fit according to the Hosmer-Lemeshow test (χ² = 6.968, P = 0.324). The model achieved an AUC of 0.768 [95% CI(0.700 ,0.837)] in predicting UGIB in patients undergoing emergency PCI for AMICS, with a sensitivity of 87.9%[95% CI (0.700, 0.837)] and a specificity of 52.3% [95% CI (27.8%, 61.2%)] . The DeLong test indicated that the AUC of the predictive model was superior to that of any individual indicator (P < 0.05), and the DCA curve confirmed the model's clinical utility.Recent Prognosis: The mortality rate during hospitalization in the bleeding group was significantly higher than that in the non-bleeding group (63.8% vs. 29.7%, P < 0.05). Additionally, the duration of hospital stays for surviving patients in the bleeding group was significantly longer than that in the non-bleeding group [19(14,37) vs. 12(9,16), (P < 0.001)]. Conclusion: High baseline SCAI-CS stage, poor kidney function, and low LVEF are independent risk factors for UGIB during hospitalization. The constructed predictive model demonstrates high predictive efficacy.