1.Influencing factors for poor prognosis in elderly patients with CHD complicated with left ventricular dysfunction after CABG
Yinhong ZHANG ; Liruo ZENG ; Lugang MEI ; Chen YANG ; Ping HU ; Xiaowu WANG
Chinese Journal of Geriatric Heart Brain and Vessel Diseases 2025;27(2):169-172
Objective To analyze the influencing factors for poor prognosis in elderly patients with CHD and left ventricular dysfunction(LVD)treated by CABG,and to construct a logistic predic-tion model.Methods A total of 199 elderly CHD patients with LVD undergoing CABG in Zhu-jiang Hospital from April 2020 to April 2023 were retrospectively enrolled.After 1 year of follow-up,according to whether MACCE occurred after surgery,they were divided into MACCE group(24 cases)and non-MACCE group(175 cases).The clinical data were compared between the two groups.Multivariate logistic regression analysis was used to analyze the influencing factors for poor prognosis,and a logistic prediction model was constructed.Results The MACCE group had significantly larger proportions of hypertension,diabetes,chronic kidney disease,NYHA gradeⅢand multi-vessel disease,and smaller proportion of non-cardiopulmonary bypass than the non-MACCE group(P<0.05,P<0.01).Multivariate logistic regression analysis showed that diabetes(OR=2.328,95%CI:1.469-3.690,P=0.000),NYH A grade(OR=2.181,95%CI:1.184-4.021,P=0.013),multi-vessel disease(OR=1.996,95%CI:1.187-3.355,P=0.009),and non-cardiopulmonary bypass(OR=0.660,95%CI:0.541-0.806,P=0.000)were independent influen-cing factors for poor prognosis in the patients after CABG.The AUC value of the constructed pre-diction model in predicting poor prognosis was 0.822(95%CI:0.721-0.923),with a sensitivity of 66.70%and a specificity of 80.60%.Conclusion Diabetes,NYHA grade,multi-vessel disease and non-cardiopulmonary bypass are independent influencing factors for poor prognosis in elderly CHD patients complicated with LVD after CABG.The constructed logistic prediction model has certain predictive value for poor prognosis in these elderly patients.
2.Influencing factors for poor prognosis in elderly patients with CHD complicated with left ventricular dysfunction after CABG
Yinhong ZHANG ; Liruo ZENG ; Lugang MEI ; Chen YANG ; Ping HU ; Xiaowu WANG
Chinese Journal of Geriatric Heart Brain and Vessel Diseases 2025;27(2):169-172
Objective To analyze the influencing factors for poor prognosis in elderly patients with CHD and left ventricular dysfunction(LVD)treated by CABG,and to construct a logistic predic-tion model.Methods A total of 199 elderly CHD patients with LVD undergoing CABG in Zhu-jiang Hospital from April 2020 to April 2023 were retrospectively enrolled.After 1 year of follow-up,according to whether MACCE occurred after surgery,they were divided into MACCE group(24 cases)and non-MACCE group(175 cases).The clinical data were compared between the two groups.Multivariate logistic regression analysis was used to analyze the influencing factors for poor prognosis,and a logistic prediction model was constructed.Results The MACCE group had significantly larger proportions of hypertension,diabetes,chronic kidney disease,NYHA gradeⅢand multi-vessel disease,and smaller proportion of non-cardiopulmonary bypass than the non-MACCE group(P<0.05,P<0.01).Multivariate logistic regression analysis showed that diabetes(OR=2.328,95%CI:1.469-3.690,P=0.000),NYH A grade(OR=2.181,95%CI:1.184-4.021,P=0.013),multi-vessel disease(OR=1.996,95%CI:1.187-3.355,P=0.009),and non-cardiopulmonary bypass(OR=0.660,95%CI:0.541-0.806,P=0.000)were independent influen-cing factors for poor prognosis in the patients after CABG.The AUC value of the constructed pre-diction model in predicting poor prognosis was 0.822(95%CI:0.721-0.923),with a sensitivity of 66.70%and a specificity of 80.60%.Conclusion Diabetes,NYHA grade,multi-vessel disease and non-cardiopulmonary bypass are independent influencing factors for poor prognosis in elderly CHD patients complicated with LVD after CABG.The constructed logistic prediction model has certain predictive value for poor prognosis in these elderly patients.

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