Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function
BackgroundThe study aimed to develop a risk prediction model through screening preoperative risk factors for acute kidney injury (AKI) after heart valve replacement in patients with normal renal function.MethodsA total of 608 patients with normal renal function who underwent heart valve replacement...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1422870/full |
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| author | Xiaofan Huang Xiaofan Huang Xiangyu Sun Jiangang Song Yongqiang Wang Jindong Liu Jindong Liu Yu Zhang Yu Zhang |
| author_facet | Xiaofan Huang Xiaofan Huang Xiangyu Sun Jiangang Song Yongqiang Wang Jindong Liu Jindong Liu Yu Zhang Yu Zhang |
| author_sort | Xiaofan Huang |
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| description | BackgroundThe study aimed to develop a risk prediction model through screening preoperative risk factors for acute kidney injury (AKI) after heart valve replacement in patients with normal renal function.MethodsA total of 608 patients with normal renal function who underwent heart valve replacement from November 2013 to June 2022 were analyzed retrospectively. The Lasso regression was used to preliminarily screen potential risk factors, which were entered into the multivariable logistic regression analysis to identify preoperative independent risk factors for postoperative AKI. Based on the results, a risk prediction model was developed, and traditional and dynamic nomograms were constructed. The risk prediction model was evaluated using receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA).Results220 patients (36.2%) developed AKI after surgery. Current smoker, hypertension, heart failure, previous myocardial infarction, cerebrovascular disease, CysC, and NT-proBNP were selected as independent risk factors for AKI. A risk prediction model, a traditional and a dynamic nomogram were developed based on the above factors. The area under the curve (AUC) of the ROC for predicting the risk of postoperative AKI was 0.803 (95% CI 0.769–0.836), with sensitivity and specificity of 84.9% and 63.4%, respectively. The calibration curve slope was close to 1, and the DCA showed that the model produced better clinical benefits when the probability threshold was set at 10%–82%.ConclusionsWe developed a preoperative risk prediction model for AKI after heart valve replacement in patients with normal renal function, which demonstrated satisfactory discrimination and calibration. |
| format | Article |
| id | doaj-art-9ba4128c04334cc1978bd32f462d9454 |
| institution | Kabale University |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-9ba4128c04334cc1978bd32f462d94542025-02-10T06:48:25ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-02-011210.3389/fcvm.2025.14228701422870Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal functionXiaofan Huang0Xiaofan Huang1Xiangyu Sun2Jiangang Song3Yongqiang Wang4Jindong Liu5Jindong Liu6Yu Zhang7Yu Zhang8Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, ChinaDepartment of Anesthesiology, Shuguang Hospital Affiliated with Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Anesthesiology, Shuguang Hospital Affiliated with Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Anesthesiology, Shuguang Hospital Affiliated with Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Anesthesiology, Shuguang Hospital Affiliated with Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, ChinaJiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaDepartment of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, ChinaJiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, ChinaBackgroundThe study aimed to develop a risk prediction model through screening preoperative risk factors for acute kidney injury (AKI) after heart valve replacement in patients with normal renal function.MethodsA total of 608 patients with normal renal function who underwent heart valve replacement from November 2013 to June 2022 were analyzed retrospectively. The Lasso regression was used to preliminarily screen potential risk factors, which were entered into the multivariable logistic regression analysis to identify preoperative independent risk factors for postoperative AKI. Based on the results, a risk prediction model was developed, and traditional and dynamic nomograms were constructed. The risk prediction model was evaluated using receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA).Results220 patients (36.2%) developed AKI after surgery. Current smoker, hypertension, heart failure, previous myocardial infarction, cerebrovascular disease, CysC, and NT-proBNP were selected as independent risk factors for AKI. A risk prediction model, a traditional and a dynamic nomogram were developed based on the above factors. The area under the curve (AUC) of the ROC for predicting the risk of postoperative AKI was 0.803 (95% CI 0.769–0.836), with sensitivity and specificity of 84.9% and 63.4%, respectively. The calibration curve slope was close to 1, and the DCA showed that the model produced better clinical benefits when the probability threshold was set at 10%–82%.ConclusionsWe developed a preoperative risk prediction model for AKI after heart valve replacement in patients with normal renal function, which demonstrated satisfactory discrimination and calibration.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1422870/fullacute kidney injurydynamic nomogramheart valve replacementnormal renal functionprediction model |
| spellingShingle | Xiaofan Huang Xiaofan Huang Xiangyu Sun Jiangang Song Yongqiang Wang Jindong Liu Jindong Liu Yu Zhang Yu Zhang Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function Frontiers in Cardiovascular Medicine acute kidney injury dynamic nomogram heart valve replacement normal renal function prediction model |
| title | Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function |
| title_full | Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function |
| title_fullStr | Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function |
| title_full_unstemmed | Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function |
| title_short | Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function |
| title_sort | risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function |
| topic | acute kidney injury dynamic nomogram heart valve replacement normal renal function prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1422870/full |
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