Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis
Abstract Objective This study aims to establish the predictive model of carotid plaque formation and carotid plaque location by retrospectively analyzing the clinical data of subjects with carotid plaque formation and normal people, and to provide technical support for screening patients with caroti...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-022-02806-3 |
| _version_ | 1818498012354183168 |
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| author | Jian Hu Fan Su Xia Ren Lei Cao Yumei Zhou Yuhan Fu Grace Tatenda Mingfei Jiang Huan Wu Yufeng Wen |
| author_facet | Jian Hu Fan Su Xia Ren Lei Cao Yumei Zhou Yuhan Fu Grace Tatenda Mingfei Jiang Huan Wu Yufeng Wen |
| author_sort | Jian Hu |
| collection | DOAJ |
| description | Abstract Objective This study aims to establish the predictive model of carotid plaque formation and carotid plaque location by retrospectively analyzing the clinical data of subjects with carotid plaque formation and normal people, and to provide technical support for screening patients with carotid plaque. Methods There were 4300 subjects in the ultrasound department of Maanshan People's Hospital collected from December 2013 to December 2018. We used demographic and biochemical data from 3700 subjects to establish predictive models for carotid plaque and its location. The leave-one-out cross-validated classification, 600 external data validation, and area under the receiver operating characteristic curve (AUC) were used to verify the accuracy, sensitivity, specificity, and application value of the model. Results There were significant difference of age (F = − 34.049, p < 0.01), hypertension (χ 2 = 191.067, p < 0.01), smoking (χ 2 = 4.762, p < 0.05) and alcohol (χ 2 = 8.306, p < 0.01), Body mass index (F = 15.322, p < 0.01), High-density lipoprotein (HDL) (F = 13.840, p < 0.01), Lipoprotein a (Lp a) (F = 52.074, p < 0.01), Blood Urea Nitrogen (F = 2.679, p < 0.01) among five groups. Prediction models were built: carotid plaque prediction model (Model CP); Prediction model of left carotid plaque only (Model CP Left); Prediction model of right carotid plaque only (Model CP Right). Prediction model of bilateral carotid plaque (Model CP Both). Model CP (Wilks' lambda = 0.597, p < 0.001, accuracy = 78.50%, sensitivity = 78.07%, specificity = 79.07%, AUC = 0.917). Model CP Left (Wilks' lambda = 0.605, p < 0.001, accuracy = 79.00%, sensitivity = 86.17%, specificity = 72.70%, AUC = 0.880). Model CP Right (Wilks' lambda = 0.555, p < 0.001, accuracy = 83.00%, sensitivity = 81.82%, specificity = 84.44%, AUC = 0.880). Model CP Both (Wilks' lambda = 0.651, p < 0.001, accuracy = 82.30%, sensitivity = 89.50%, specificity = 72.70%, AUC = 0.880). Conclusion Demographic characteristics and blood biochemical indexes were used to establish the carotid plaque and its location discriminant models based on Fisher discriminant analysis (FDA), which has high application value in community screening. |
| first_indexed | 2024-12-10T18:52:30Z |
| format | Article |
| id | doaj.art-5c44e3753cf74e218d43c89b2bd15af7 |
| institution | Directory Open Access Journal |
| issn | 1471-2261 |
| language | English |
| last_indexed | 2024-12-10T18:52:30Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj.art-5c44e3753cf74e218d43c89b2bd15af72022-12-22T01:37:15ZengBMCBMC Cardiovascular Disorders1471-22612022-08-012211810.1186/s12872-022-02806-3Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysisJian Hu0Fan Su1Xia Ren2Lei Cao3Yumei Zhou4Yuhan Fu5Grace Tatenda6Mingfei Jiang7Huan Wu8Yufeng Wen9School of Public Health, Wannan Medical CollegeSchool of Public Health, Wannan Medical CollegeSchool of Public Health, Wannan Medical CollegeSchool of Public Health, Wannan Medical CollegeSchool of Public Health, Wannan Medical CollegeSchool of Public Health, Wannan Medical CollegeSchool of Public Health, Wannan Medical CollegeSchool of Clinical Medicine, Wannan Medical CollegeSchool of Laboratory Medicine, Wannan Medical CollegeSchool of Public Health, Wannan Medical CollegeAbstract Objective This study aims to establish the predictive model of carotid plaque formation and carotid plaque location by retrospectively analyzing the clinical data of subjects with carotid plaque formation and normal people, and to provide technical support for screening patients with carotid plaque. Methods There were 4300 subjects in the ultrasound department of Maanshan People's Hospital collected from December 2013 to December 2018. We used demographic and biochemical data from 3700 subjects to establish predictive models for carotid plaque and its location. The leave-one-out cross-validated classification, 600 external data validation, and area under the receiver operating characteristic curve (AUC) were used to verify the accuracy, sensitivity, specificity, and application value of the model. Results There were significant difference of age (F = − 34.049, p < 0.01), hypertension (χ 2 = 191.067, p < 0.01), smoking (χ 2 = 4.762, p < 0.05) and alcohol (χ 2 = 8.306, p < 0.01), Body mass index (F = 15.322, p < 0.01), High-density lipoprotein (HDL) (F = 13.840, p < 0.01), Lipoprotein a (Lp a) (F = 52.074, p < 0.01), Blood Urea Nitrogen (F = 2.679, p < 0.01) among five groups. Prediction models were built: carotid plaque prediction model (Model CP); Prediction model of left carotid plaque only (Model CP Left); Prediction model of right carotid plaque only (Model CP Right). Prediction model of bilateral carotid plaque (Model CP Both). Model CP (Wilks' lambda = 0.597, p < 0.001, accuracy = 78.50%, sensitivity = 78.07%, specificity = 79.07%, AUC = 0.917). Model CP Left (Wilks' lambda = 0.605, p < 0.001, accuracy = 79.00%, sensitivity = 86.17%, specificity = 72.70%, AUC = 0.880). Model CP Right (Wilks' lambda = 0.555, p < 0.001, accuracy = 83.00%, sensitivity = 81.82%, specificity = 84.44%, AUC = 0.880). Model CP Both (Wilks' lambda = 0.651, p < 0.001, accuracy = 82.30%, sensitivity = 89.50%, specificity = 72.70%, AUC = 0.880). Conclusion Demographic characteristics and blood biochemical indexes were used to establish the carotid plaque and its location discriminant models based on Fisher discriminant analysis (FDA), which has high application value in community screening.https://doi.org/10.1186/s12872-022-02806-3Carotid plaqueCarotid plaque locationPrediction modelFisher discriminant analysis |
| spellingShingle | Jian Hu Fan Su Xia Ren Lei Cao Yumei Zhou Yuhan Fu Grace Tatenda Mingfei Jiang Huan Wu Yufeng Wen Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis BMC Cardiovascular Disorders Carotid plaque Carotid plaque location Prediction model Fisher discriminant analysis |
| title | Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis |
| title_full | Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis |
| title_fullStr | Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis |
| title_full_unstemmed | Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis |
| title_short | Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis |
| title_sort | prediction of carotid plaque by blood biochemical indices and related factors based on fisher discriminant analysis |
| topic | Carotid plaque Carotid plaque location Prediction model Fisher discriminant analysis |
| url | https://doi.org/10.1186/s12872-022-02806-3 |
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