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Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention

OBJECTIVE: To establish a nomogram model to predict the risk of contrast-induced acute kidney injury (CI-AKI) by analyzing the risk factors of CI-AKI and to evaluate its effectiveness. METHODS: Retrospectively analyze the clinical data of non-ST-elevation acute coronary syndrome (NSTE-ACS) patients...

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Autores principales: Ma, Kai, Li, Jing, Shen, Guoqi, Zheng, Di, Xuan, Yongli, Lu, Yuan, Li, Wenhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8801515/
https://www.ncbi.nlm.nih.gov/pubmed/35115770
http://dx.doi.org/10.2147/CIA.S349159
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author Ma, Kai
Li, Jing
Shen, Guoqi
Zheng, Di
Xuan, Yongli
Lu, Yuan
Li, Wenhua
author_facet Ma, Kai
Li, Jing
Shen, Guoqi
Zheng, Di
Xuan, Yongli
Lu, Yuan
Li, Wenhua
author_sort Ma, Kai
collection PubMed
description OBJECTIVE: To establish a nomogram model to predict the risk of contrast-induced acute kidney injury (CI-AKI) by analyzing the risk factors of CI-AKI and to evaluate its effectiveness. METHODS: Retrospectively analyze the clinical data of non-ST-elevation acute coronary syndrome (NSTE-ACS) patients who underwent percutaneous coronary intervention (PCI) in our cardiology department from September 2018 to June 2021. Of these, patients who underwent PCI in an earlier period formed the training cohort (70%; n = 809) for nomogram development, and those who underwent PCI thereafter formed the validation cohort (30%; n = 347) to confirm the model’s performance. The independent risk factors of CI-AKI were determined by LASSO regression and multivariable logistic regression analysis. By using R software from which nomogram models were subsequently generated. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot, and decision curve analysis (DCA), respectively. RESULTS: The nomogram consisted of six variables: age >75, left ventricular ejection fraction, diabetes mellitus, fibrinogen-to-albumin ratio, high-sensitive C-reactive protein, and lymphocyte count. The C-index of the nomogram is 0.835 (95% CI: 0.800–0.871) in the training cohort and 0.767 (95% CI: 0.711–0.824) in the validation cohort, respectively. The calibration plots exhibited that the nomogram was in good agreement between prediction and observation in the training and validation cohorts. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility. CONCLUSION: The nomogram model established has a good degree of differentiation and accuracy, which is intuitively and individually to screen high-risk groups and has a certain predictive value for the occurrence of CI-AKI in NSTE-ACS patients after PCI.
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spelling pubmed-88015152022-02-02 Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention Ma, Kai Li, Jing Shen, Guoqi Zheng, Di Xuan, Yongli Lu, Yuan Li, Wenhua Clin Interv Aging Original Research OBJECTIVE: To establish a nomogram model to predict the risk of contrast-induced acute kidney injury (CI-AKI) by analyzing the risk factors of CI-AKI and to evaluate its effectiveness. METHODS: Retrospectively analyze the clinical data of non-ST-elevation acute coronary syndrome (NSTE-ACS) patients who underwent percutaneous coronary intervention (PCI) in our cardiology department from September 2018 to June 2021. Of these, patients who underwent PCI in an earlier period formed the training cohort (70%; n = 809) for nomogram development, and those who underwent PCI thereafter formed the validation cohort (30%; n = 347) to confirm the model’s performance. The independent risk factors of CI-AKI were determined by LASSO regression and multivariable logistic regression analysis. By using R software from which nomogram models were subsequently generated. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot, and decision curve analysis (DCA), respectively. RESULTS: The nomogram consisted of six variables: age >75, left ventricular ejection fraction, diabetes mellitus, fibrinogen-to-albumin ratio, high-sensitive C-reactive protein, and lymphocyte count. The C-index of the nomogram is 0.835 (95% CI: 0.800–0.871) in the training cohort and 0.767 (95% CI: 0.711–0.824) in the validation cohort, respectively. The calibration plots exhibited that the nomogram was in good agreement between prediction and observation in the training and validation cohorts. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility. CONCLUSION: The nomogram model established has a good degree of differentiation and accuracy, which is intuitively and individually to screen high-risk groups and has a certain predictive value for the occurrence of CI-AKI in NSTE-ACS patients after PCI. Dove 2022-01-26 /pmc/articles/PMC8801515/ /pubmed/35115770 http://dx.doi.org/10.2147/CIA.S349159 Text en © 2022 Ma et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Ma, Kai
Li, Jing
Shen, Guoqi
Zheng, Di
Xuan, Yongli
Lu, Yuan
Li, Wenhua
Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention
title Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention
title_full Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention
title_fullStr Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention
title_full_unstemmed Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention
title_short Development and Validation of a Risk Nomogram Model for Predicting Contrast-Induced Acute Kidney Injury in Patients with Non-ST-Elevation Acute Coronary Syndrome Undergoing Primary Percutaneous Coronary Intervention
title_sort development and validation of a risk nomogram model for predicting contrast-induced acute kidney injury in patients with non-st-elevation acute coronary syndrome undergoing primary percutaneous coronary intervention
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8801515/
https://www.ncbi.nlm.nih.gov/pubmed/35115770
http://dx.doi.org/10.2147/CIA.S349159
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