Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease
ObjectiveTo analyze the risk factors of in-stent restenosis (ISR) after the first implantation of drug-eluting stent (DES) patients with coronary heart disease (CHD) and to establish a nomogram model to predict the risk of ISR.MethodsThis study retrospectively analyzed the clinical data of patients...
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| Format: | Article |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2023.1117915/full |
| _version_ | 1827994245956370432 |
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| author | Hongfei Xi Jiasi Liu Tao Xu Zhe Li Xuanting Mou Yu Jin Shudong Xia |
| author_facet | Hongfei Xi Jiasi Liu Tao Xu Zhe Li Xuanting Mou Yu Jin Shudong Xia |
| author_sort | Hongfei Xi |
| collection | DOAJ |
| description | ObjectiveTo analyze the risk factors of in-stent restenosis (ISR) after the first implantation of drug-eluting stent (DES) patients with coronary heart disease (CHD) and to establish a nomogram model to predict the risk of ISR.MethodsThis study retrospectively analyzed the clinical data of patients with CHD who underwent DES treatment for the first time at the Fourth Affiliated Hospital of Zhejiang University School of Medicine from January 2016 to June 2020. Patients were divided into an ISR group and a non-ISR (N-ISR) group according to the results of coronary angiography. The least absolute shrinkage and selection operator (LASSO) regression analysis was performed on the clinical variables to screen out the characteristic variables. Then we constructed the nomogram prediction model using conditional multivariate logistic regression analysis combined with the clinical variables selected in the LASSO regression analysis. Finally, the decision curve analysis, clinical impact curve, area under the receiver operating characteristic curve, and calibration curve were used to evaluate the nomogram prediction model's clinical applicability, validity, discrimination, and consistency. And we double-validate the prediction model using ten-fold cross-validation and bootstrap validation.ResultsIn this study, hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen were all predictive factors for ISR. We successfully constructed a nomogram prediction model using these variables to quantify the risk of ISR. The AUC value of the nomogram prediction model was 0.806 (95%CI: 0.739–0.873), indicating that the model had a good discriminative ability for ISR. The high quality of the calibration curve of the model demonstrated the strong consistency of the model. Moreover, the DCA and CIC curve showed the model's high clinical applicability and effectiveness.ConclusionsHypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen are important predictors for ISR. The nomogram prediction model can better identify the high-risk population of ISR and provide practical decision-making information for the follow-up intervention in the high-risk population. |
| first_indexed | 2024-04-10T04:37:36Z |
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| id | doaj.art-8ebc795c2f584709b5b9c1cea4970c07 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-10T04:37:36Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-8ebc795c2f584709b5b9c1cea4970c072023-03-09T13:54:42ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2023-03-011010.3389/fcvm.2023.11179151117915Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart diseaseHongfei Xi0Jiasi Liu1Tao Xu2Zhe Li3Xuanting Mou4Yu Jin5Shudong Xia6Department of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, ChinaDepartment of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, ChinaDepartment of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, ChinaDepartment of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, ChinaDepartment of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, ChinaDepartment of Cardiology, the Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, ChinaObjectiveTo analyze the risk factors of in-stent restenosis (ISR) after the first implantation of drug-eluting stent (DES) patients with coronary heart disease (CHD) and to establish a nomogram model to predict the risk of ISR.MethodsThis study retrospectively analyzed the clinical data of patients with CHD who underwent DES treatment for the first time at the Fourth Affiliated Hospital of Zhejiang University School of Medicine from January 2016 to June 2020. Patients were divided into an ISR group and a non-ISR (N-ISR) group according to the results of coronary angiography. The least absolute shrinkage and selection operator (LASSO) regression analysis was performed on the clinical variables to screen out the characteristic variables. Then we constructed the nomogram prediction model using conditional multivariate logistic regression analysis combined with the clinical variables selected in the LASSO regression analysis. Finally, the decision curve analysis, clinical impact curve, area under the receiver operating characteristic curve, and calibration curve were used to evaluate the nomogram prediction model's clinical applicability, validity, discrimination, and consistency. And we double-validate the prediction model using ten-fold cross-validation and bootstrap validation.ResultsIn this study, hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen were all predictive factors for ISR. We successfully constructed a nomogram prediction model using these variables to quantify the risk of ISR. The AUC value of the nomogram prediction model was 0.806 (95%CI: 0.739–0.873), indicating that the model had a good discriminative ability for ISR. The high quality of the calibration curve of the model demonstrated the strong consistency of the model. Moreover, the DCA and CIC curve showed the model's high clinical applicability and effectiveness.ConclusionsHypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen are important predictors for ISR. The nomogram prediction model can better identify the high-risk population of ISR and provide practical decision-making information for the follow-up intervention in the high-risk population.https://www.frontiersin.org/articles/10.3389/fcvm.2023.1117915/fulldrug-eluting stentin-stent restenosiscoronary heart diseasenomogramprediction model |
| spellingShingle | Hongfei Xi Jiasi Liu Tao Xu Zhe Li Xuanting Mou Yu Jin Shudong Xia Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease Frontiers in Cardiovascular Medicine drug-eluting stent in-stent restenosis coronary heart disease nomogram prediction model |
| title | Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease |
| title_full | Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease |
| title_fullStr | Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease |
| title_full_unstemmed | Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease |
| title_short | Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease |
| title_sort | risk investigation of in stent restenosis after initial implantation of intracoronary drug eluting stent in patients with coronary heart disease |
| topic | drug-eluting stent in-stent restenosis coronary heart disease nomogram prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2023.1117915/full |
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