Influencing factors of and a nomogram model formulation for vulnerable carotid artery plaques in middle-aged and elderly patients
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1. Health Management Center, First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China;2. Department of Geriatrics, Taizhou People′s Hospital, Taizhou 225300, Jiangsu Province, China

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R543.4

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    Abstract:

    Objective To analyze the risk factors of the vulnerable carotid artery plaques in the middle-aged and elderly people undergoing physical examination and to formulate a predictive nomogram model. Methods A retrospective analysis was performed of the physical-examination data of 1 612 patients who underwent cervical vascular Doppler ultrasonography at Health Management Center of the First Affiliated Hospital of Soochow University from September 2021 to December 2021. According to ultrasonographic findings, the patients were divided into vulnerable plaque group (n=287) and non-vulnerable plaque group (n=1 325). Statistical analysis were done of the general data, results of routine blood test, biochemical and other laboratory indicators of the two groups. The risk factors for the occurrence of vulnerable plaques were screened with univariate analysis and multivariate logistic regression, and a predictive nomogram model for the occurrence of vulnerable plaques was formulated by combining the independent factors, and the predictiveness and discrimination of the model were verified. SPSS 26.0 and R Studio 4.0.2 were used for statistical analysis and graphics. Depending on the data type, t-test andχ2 test were used for comparison between groups. Results Compared with the non-vulnerable plaque group, the patients in vulnerable plaque group were older with a higher proportion of males, hypertension and diabetes history, the differences being statistically significant (P<0.05 for all). The vulnerable plaque group were higher than the non-vulnerable plaque group in neutrophil count, monocyte count, neutrophil to lymphocyte ratio, fasting plasma glucose and creatinine, but lower in platelet count, platelet-to-lymphocyte ratio, albumin, total cholesterol and low-density lipoprotein cholesterol, all with statistically significant difference (P<0.05 for all). Multivariate logistic regression analysis showed that age (OR=1.099,95%CI 1.078-1.120, P<0.001), hypertension (OR=1.848,95%CI 1.351-2.527, P<0.001), diabetes mellitus (OR=3.757,95%CI 2.614-5.400, P<0.001), and neutrophil count/lymphocyte count ratio (OR=2.025,95%CI 1.055-3.885, P=0.034) were independent risk factors of vulnerable carotid plaque. The above indicators were used to formulate a regression equation and draw a predictive nomogram for the occurrence of vulnerable plaque, and an internal verification of it was performed. With a parameter of 0.778 for C test, the calibration curve showed good consistency of the model with an area under the receiver operating characteristic curve of 0.781 (95%CI 0.752-0.809). Conclusion The predictive histogram model for the occurrence of vulnerable carotid plaque in middle-aged and elderly patients is good. The predictiveness and discrimination can be used to predict vulnerable carotid plaque in middle-aged and elderly people undergoing physical examination.

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History
  • Received:June 09,2022
  • Revised:
  • Adopted:
  • Online: January 09,2023
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