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Establish a Nomogram of Cardiac Postoperative Cognitive Dysfunction
Naxin Xie1, Sunang Yan1, Xuejun Sun1
1Department of Cardiovascular Surgery, Fujian Medical University Affiliated First Quanzhou Hospital, Quanzhou, China.
Objectives:
Cardiac postoperative cognitive dysfunction (c-POCD) is a common complication. This article established a nomogram by analyzing preoperative and intraoperative data to help identify high-risk patients and take effective management for prevention of c-POCD in early stage.
Measurements And Main Results:
A total of 265 patients were enrolled in this study, 27 of whom were diagnosed as cardiac postoperative cognitive dysfunction (c-POCD). Patients were divided into a control group and c-POCD group. Univariate analysis suggested that gender, smoking, drinking history, hypertension, white blood cell (WBC) count, aspartate aminotransferase (AST), high-sensitivity troponin (hs-CRP), arrhythmia, left atrial diameter (LAD), cardiopulmonary bypass (CPB) time, and the ascending aortic block (AAB) time were correlated with postoperative cognitive dysfunction after cardiac surgery. Multivariate regression analysis indicated that CPB time (P = 0.0015, OR (95% CI) = 6.696 (2.068-21.675), hypertension (P = 0.0098, OR (95%CI) = 3.776 (1.377-10.356), WBC count (P = 0.0227, OR (95%CI ) = 3.358 (1.184-9.522), AST (P = 0.0128, OR (95%CI) = 3.966 (1.340-11.735), and arrhythmia (P = 0.0017, OR (95%CI) = 5.164 (1.855-14.371) were the independent risk factors of cognitive dysfunction after cardiac surgery and used to establish a nomogram for clinical use. The initial C-index of the nomogram was 0.8182 and good calibration. Corrected C-index value of 0.793 was reached after internal validation. The area under ROC curve of this model was 0.8188 (95%CI: 0.7185-0.9190). The positive odds ratio (PLR) was 1.21 (95%CI: 1.1-1.3), and the negative odds ratio (NLR) was 0.18 (95%CI: 0.03-1.3).
Conclusion:
This nomogram incorporating the CPB time, hypertension, WBC count, AST, and arrhythmia to predict the risk of c-POCD. The internal validation shows a good forecasting effect.
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