Development and Validation of a Risk Prediction Model for Contrast-induced Acute Kidney Injury after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction
Background Early reperfusion therapy for acute myocardial infarction (AMI) is an effective approach to reduce mortality in AMI patients. Percutaneous coronary intervention (PCI) is one of the reperfusion therapy modalities, and contrast-induced acute kidney injury (CI-AKI) after PCI has become one o...
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| Format: | Article |
| Language: | zho |
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Chinese General Practice Publishing House Co., Ltd
2023-10-01
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| Series: | Zhongguo quanke yixue |
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| Online Access: | https://www.chinagp.net/fileup/1007-9572/PDF/20230139.pdf |
| _version_ | 1797216638448173056 |
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| author | WANG Zhen, SHEN Guoqi, LI Yanan, ZHU Yinghua, QIU Hang, ZHENG Di, XU Tongda, LI Wenhua |
| author_facet | WANG Zhen, SHEN Guoqi, LI Yanan, ZHU Yinghua, QIU Hang, ZHENG Di, XU Tongda, LI Wenhua |
| author_sort | WANG Zhen, SHEN Guoqi, LI Yanan, ZHU Yinghua, QIU Hang, ZHENG Di, XU Tongda, LI Wenhua |
| collection | DOAJ |
| description | Background Early reperfusion therapy for acute myocardial infarction (AMI) is an effective approach to reduce mortality in AMI patients. Percutaneous coronary intervention (PCI) is one of the reperfusion therapy modalities, and contrast-induced acute kidney injury (CI-AKI) after PCI has become one of the common causes of AKI. Objective To investigate the risk factors for the development of CI-AKI in AMI patients after PCI, establish a risk prediction model for CI-AKI based on risk factors and evaluate its validity. Methods The clinical data of 1 274 patients who attended the Affiliated Hospital of Xuzhou Medical University diagnosed of AMI and treated with PCI were collected consecutively from 2019 to 2021. According to the chronological order of admission, the included patients were divided into the training group (January 2019 to March 2021, 900 cases) and validation group (April 2021 to December 2021, 374 cases) in a ratio of approximately 7∶3; and divided into the CI-AKI and non-CI-AKI groups according to the diagnostic criteria of CI-AKI. Independent risk factors were screened using univariable Logistic regression analysis, Lasso regression, cross-validation, multivariable Logistic regression analysis, and a nomogram for predicting the risk of CI-AKI was plotted. Their discriminatory power, calibration ability, and clinical application value were evaluated by calculating concordance statistic (C-statistic), plotting calibration curve and decision curve. Results Among the 900 patients in the training group, 109 patients (12.1%) developed CI-AKI after PCI; among the 374 patients in the validation group, 27 patients (7.2%) developed CI-AKI. Multivariable Logistic regression analysis showed that LVEF〔OR=0.903, 95%CI (0.873, 0.934) 〕, platelet distribution width〔OR=1.158, 95%CI (1.053, 1.274) 〕, MPVLR〔OR=1.047, 95%CI (1.016, 1.079) 〕, NHR〔OR=1.072, 95%CI (1.021, 1.124) 〕, Scr〔OR=1.006, 95%CI (1.002, 1.011) 〕, and diuretics〔OR=2.321, 95%CI (1.452, 3.709) 〕 were independent influencing factors for CI-AKI after PCI in AMI patients (P<0.05). A prediction model containing 6 risk factors of LVEF, platelet distribution width, MPVLR, NHR, Scr and diuretics was constructed and a nomogram for predicting the risk of CI-AKI was plotted. The C-statistic was 0.794〔95%CI (0.766, 0.820) 〕 for the training group and 0.799〔95%CI (0.774, 0.855) 〕 for the validation group, and the calibration plots showed good consistency between the predicted and actual results; the decision curve and clinical impact curve showed clinical application value of nomogram. Conclusion The CI-AKI risk prediction model including LVEF, platelet distribution width, MPVLR, NHR, Scr, and diuretics has good discrimination and accuracy, which can intuitively and independently screen high-risk population and has high predictive value for the development of CI-AKI after PCI in AMI patients. |
| first_indexed | 2024-04-24T11:49:09Z |
| format | Article |
| id | doaj.art-720f501eb7a64868bae0d777be004681 |
| institution | Directory Open Access Journal |
| issn | 1007-9572 |
| language | zho |
| last_indexed | 2024-04-24T11:49:09Z |
| publishDate | 2023-10-01 |
| publisher | Chinese General Practice Publishing House Co., Ltd |
| record_format | Article |
| series | Zhongguo quanke yixue |
| spelling | doaj.art-720f501eb7a64868bae0d777be0046812024-04-09T08:42:49ZzhoChinese General Practice Publishing House Co., LtdZhongguo quanke yixue1007-95722023-10-0126293650365610.12114/j.issn.1007-9572.2023.0139Development and Validation of a Risk Prediction Model for Contrast-induced Acute Kidney Injury after Percutaneous Coronary Intervention in Patients with Acute Myocardial InfarctionWANG Zhen, SHEN Guoqi, LI Yanan, ZHU Yinghua, QIU Hang, ZHENG Di, XU Tongda, LI Wenhua01. Graduate School of Xuzhou Medical University, Xuzhou 221004, China;2. Department of Cardiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221004, ChinaBackground Early reperfusion therapy for acute myocardial infarction (AMI) is an effective approach to reduce mortality in AMI patients. Percutaneous coronary intervention (PCI) is one of the reperfusion therapy modalities, and contrast-induced acute kidney injury (CI-AKI) after PCI has become one of the common causes of AKI. Objective To investigate the risk factors for the development of CI-AKI in AMI patients after PCI, establish a risk prediction model for CI-AKI based on risk factors and evaluate its validity. Methods The clinical data of 1 274 patients who attended the Affiliated Hospital of Xuzhou Medical University diagnosed of AMI and treated with PCI were collected consecutively from 2019 to 2021. According to the chronological order of admission, the included patients were divided into the training group (January 2019 to March 2021, 900 cases) and validation group (April 2021 to December 2021, 374 cases) in a ratio of approximately 7∶3; and divided into the CI-AKI and non-CI-AKI groups according to the diagnostic criteria of CI-AKI. Independent risk factors were screened using univariable Logistic regression analysis, Lasso regression, cross-validation, multivariable Logistic regression analysis, and a nomogram for predicting the risk of CI-AKI was plotted. Their discriminatory power, calibration ability, and clinical application value were evaluated by calculating concordance statistic (C-statistic), plotting calibration curve and decision curve. Results Among the 900 patients in the training group, 109 patients (12.1%) developed CI-AKI after PCI; among the 374 patients in the validation group, 27 patients (7.2%) developed CI-AKI. Multivariable Logistic regression analysis showed that LVEF〔OR=0.903, 95%CI (0.873, 0.934) 〕, platelet distribution width〔OR=1.158, 95%CI (1.053, 1.274) 〕, MPVLR〔OR=1.047, 95%CI (1.016, 1.079) 〕, NHR〔OR=1.072, 95%CI (1.021, 1.124) 〕, Scr〔OR=1.006, 95%CI (1.002, 1.011) 〕, and diuretics〔OR=2.321, 95%CI (1.452, 3.709) 〕 were independent influencing factors for CI-AKI after PCI in AMI patients (P<0.05). A prediction model containing 6 risk factors of LVEF, platelet distribution width, MPVLR, NHR, Scr and diuretics was constructed and a nomogram for predicting the risk of CI-AKI was plotted. The C-statistic was 0.794〔95%CI (0.766, 0.820) 〕 for the training group and 0.799〔95%CI (0.774, 0.855) 〕 for the validation group, and the calibration plots showed good consistency between the predicted and actual results; the decision curve and clinical impact curve showed clinical application value of nomogram. Conclusion The CI-AKI risk prediction model including LVEF, platelet distribution width, MPVLR, NHR, Scr, and diuretics has good discrimination and accuracy, which can intuitively and independently screen high-risk population and has high predictive value for the development of CI-AKI after PCI in AMI patients.https://www.chinagp.net/fileup/1007-9572/PDF/20230139.pdfacute myocardial infarction|percutaneous coronary intervention|acute kidney injury|contrast-induced acute kidney injury|nomograms|risk prediction model |
| spellingShingle | WANG Zhen, SHEN Guoqi, LI Yanan, ZHU Yinghua, QIU Hang, ZHENG Di, XU Tongda, LI Wenhua Development and Validation of a Risk Prediction Model for Contrast-induced Acute Kidney Injury after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction Zhongguo quanke yixue acute myocardial infarction|percutaneous coronary intervention|acute kidney injury|contrast-induced acute kidney injury|nomograms|risk prediction model |
| title | Development and Validation of a Risk Prediction Model for Contrast-induced Acute Kidney Injury after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction |
| title_full | Development and Validation of a Risk Prediction Model for Contrast-induced Acute Kidney Injury after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction |
| title_fullStr | Development and Validation of a Risk Prediction Model for Contrast-induced Acute Kidney Injury after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction |
| title_full_unstemmed | Development and Validation of a Risk Prediction Model for Contrast-induced Acute Kidney Injury after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction |
| title_short | Development and Validation of a Risk Prediction Model for Contrast-induced Acute Kidney Injury after Percutaneous Coronary Intervention in Patients with Acute Myocardial Infarction |
| title_sort | development and validation of a risk prediction model for contrast induced acute kidney injury after percutaneous coronary intervention in patients with acute myocardial infarction |
| topic | acute myocardial infarction|percutaneous coronary intervention|acute kidney injury|contrast-induced acute kidney injury|nomograms|risk prediction model |
| url | https://www.chinagp.net/fileup/1007-9572/PDF/20230139.pdf |
| work_keys_str_mv | AT wangzhenshenguoqiliyananzhuyinghuaqiuhangzhengdixutongdaliwenhua developmentandvalidationofariskpredictionmodelforcontrastinducedacutekidneyinjuryafterpercutaneouscoronaryinterventioninpatientswithacutemyocardialinfarction |