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Jiang X, Li Z, Pan C, Fang H, Xu W, Chen Z, Zhu J, He L, Fang M, Chen C. The role of serum magnesium in the prediction of acute kidney injury after total aortic arch replacement: A prospective observational study. J Med Biochem 2024; 43:574-586. [PMID: 39139155 PMCID: PMC11318877 DOI: 10.5937/jomb0-48779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/21/2024] [Indexed: 08/15/2024] Open
Abstract
Background Considerable morbidity and death are associated with acute kidney damage (AKI) following total aortic arch replacement (TAAR). The relationship between AKI following TAAR and serum magnesium levels remains unknown. The intention of this research was to access the predictive value of serum magnesium levels on admission to the Cardiovascular Surgical Intensive Care Unit (CSICU) for AKI in patients receiving TAAR. Methods From May 2018 to January 2020, a prospective, observational study was performed in the Guangdong Provincial People's Hospital CSICU. Patients accepting TAAR admitted to the CSICU were studied. The Kidney Disease: Improving Global Outcomes (KDIGO) definition of serum creatinine was used to define AKI, and KDIGO stages two or three were used to characterize severe AKI. Multivariable logistic regression and area under the curve receiver-operator characteristic curve (AUC-ROC) analysis were conducted to assess the predictive capability of the serum magnesium for AKI detection. Finally, the prediction model for AKI was established and internally validated. Results Of the 396 enrolled patients, AKI occurred in 315 (79.5%) patients, including 154 (38.8%) patients with severe AKI. Serum magnesium levels were independently related to the postoperative AKI and severe AKI (both, P < 0.001), and AUC-ROCs for predicting AKI and severe AKI were 0.707 and 0.695, respectively. Across increasing quartiles of serum magnesium, the multivariable-adjusted odds ratios (95% confidence intervals) of postoperative AKI were 1.00 (reference), 1.04 (0.50-2.82), 1.20 (0.56-2.56), and 6.19 (2.02-23.91) (P for Trend < 0.001). When serum magnesium was included to a baseline model with established risk factors, AUC-ROC (0.833 vs 0.808, P = 0.050), reclassification (P < 0.001), and discrimination (P = 0.002) were further improved. Conclusions Serum magnesium levels on admission are an independent predictor of AKI. In TAAR patients, elevated serum magnesium levels were linked to an increased risk of AKI. In addition, the established risk factor model for AKI can be considerably improved by the addition of serum magnesium in TAAR patients hospitalized in the CSICU.
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Affiliation(s)
- Xinyi Jiang
- South China University of Technology, School of Medicine, Guangzhou, Guangdong Province, China
| | - Ziyun Li
- Guangdong Medical University, Maoming Clinical College, Maoming, Guangdong Province, China
| | - Chixing Pan
- Guangdong Medical University, Maoming Clinical College, Maoming, Guangdong Province, China
| | - Heng Fang
- Southern Medical University, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Department of Critical Care Medicine, Guangzhou, Guangdong Province, China
| | - Wang Xu
- Southern Medical University, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Department of Intensive Care Unit of Cardiac Surgery, Guangzhou, Guangdong Province, China
| | - Zeling Chen
- Southern Medical University, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Department of Intensive Care Unit of Cardiac Surgery, Guangzhou, Guangdong Province, China
| | - Junjiang Zhu
- Southern Medical University, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Department of Intensive Care Unit of Cardiac Surgery, Guangzhou, Guangdong Province, China
| | - Linling He
- Shenzhen People's Hospital, Department of Critical Care Medicine, Shenzhen, Guangdong Province, China
| | - Miaoxian Fang
- Southern Medical University, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Department of Intensive Care Unit of Cardiac Surgery, Guangzhou, Guangdong Province, China
| | - Chunbo Chen
- South China University of Technology, School of Medicine, Guangzhou, Guangdong Province, China
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Xu W, Ouyang X, Lin Y, Lai X, Zhu J, Chen Z, Liu X, Jiang X, Chen C. Prediction of acute kidney injury after cardiac surgery with fibrinogen-to-albumin ratio: a prospective observational study. Front Cardiovasc Med 2024; 11:1336269. [PMID: 38476379 PMCID: PMC10927956 DOI: 10.3389/fcvm.2024.1336269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/25/2024] [Indexed: 03/14/2024] Open
Abstract
Background The occurrence of acute kidney injury (AKI) following cardiac surgery is common and linked to unfavorable consequences while identifying it in its early stages remains a challenge. The aim of this research was to examine whether the fibrinogen-to-albumin ratio (FAR), an innovative inflammation-related risk indicator, has the ability to predict the development of AKI in individuals after cardiac surgery. Methods Patients who underwent cardiac surgery from February 2023 to March 2023 and were admitted to the Cardiac Surgery Intensive Care Unit of a tertiary teaching hospital were included in this prospective observational study. AKI was defined according to the KDIGO criteria. To assess the diagnostic value of the FAR in predicting AKI, calculations were performed for the area under the receiver operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results Of the 260 enrolled patients, 85 developed AKI with an incidence of 32.7%. Based on the multivariate logistic analyses, FAR at admission [odds ratio (OR), 1.197; 95% confidence interval (CI), 1.064-1.347, p = 0.003] was an independent risk factor for AKI. The receiver operating characteristic (ROC) curve indicated that FAR on admission was a significant predictor of AKI [AUC, 0.685, 95% CI: 0.616-0.754]. Although the AUC-ROC of the prediction model was not substantially improved by adding FAR, continuous NRI and IDI were significantly improved. Conclusions FAR is independently associated with the occurrence of AKI after cardiac surgery and can significantly improve AKI prediction over the clinical prediction model.
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Affiliation(s)
- Wang Xu
- Department of Intensive Care Unit of Cardiac Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xin Ouyang
- Department of Intensive Care Unit of Cardiac Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Yingxin Lin
- Department of Intensive Care Unit of Cardiac Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
- Peking University Shenzhen Hospital, Shenzhen, China
| | - Xue Lai
- Day Surgery Center, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Junjiang Zhu
- Department of Intensive Care Unit of Cardiac Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Zeling Chen
- Department of Intensive Care Unit of Cardiac Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xiaolong Liu
- Department of Intensive Care Unit of Cardiac Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xinyi Jiang
- Department of Intensive Care Unit of Cardiac Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Chunbo Chen
- Department of Intensive Care Unit of Cardiac Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
- Department of Critical Care Medicine, Shenzhen People’s Hospital, Shenzhen, Guangdong Province, China
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Li ZT, Huang DB, Zhao JF, Li H, Fu SQ, Wang W. Comparison of various surrogate markers for venous congestion in predicting acute kidney injury following cardiac surgery: A cohort study. J Crit Care 2024; 79:154441. [PMID: 37812993 DOI: 10.1016/j.jcrc.2023.154441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/10/2023] [Accepted: 09/27/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Venous congestion has been demonstrated to increase the risk of acute kidney injury (AKI) after cardiac surgery. Although many surrogate markers for venous congestion are currently used in clinical settings, there is no consensus on which marker is most effective in predicting AKI. METHODS We evaluated various markers of venous congestion, including central venous pressure (CVP), inferior vena cava (IVC) diameter, portal pulsatility fraction (PPF), hepatic vein flow pattern (HVF), intra-renal venous flow pattern (IRVF), and venous excess ultrasound grading score (VExUS) in adult patients undergoing cardiac surgery to compare their ability in predicting AKI. RESULTS Among the 230 patients enrolled in our study, 53 (23.0%) developed AKI, and 11 (4.8%) required continuous renal replacement therapy (CRRT). Our multivariate logistic analysis revealed that IRVF, PPF, HVF, and CVP were significantly associated with AKI, with IRVF being the strongest predictor (odds ratio [OR] 2.27; 95% confidence interval [CI], 1.38-3.73). However, we did not observe any association between these markers and CRRT. CONCLUSION Venous congestion is associated with AKI after cardiac surgery, but not necessarily with CRRT. Among the markers tested, IRVF exhibits the strongest correlation with AKI.
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Affiliation(s)
- Zhi-Tao Li
- Department of Surgical Intensive Care Unit, The First Affiliated Hospital, Zhejiang University, School of Medicine, China
| | - Da-Bing Huang
- Department of Surgical Intensive Care Unit, The First Affiliated Hospital, Zhejiang University, School of Medicine, China
| | - Jian-Feng Zhao
- Department of Surgical Intensive Care Unit, The First Affiliated Hospital, Zhejiang University, School of Medicine, China
| | - Hui Li
- Department of Surgical Intensive Care Unit, The First Affiliated Hospital, Zhejiang University, School of Medicine, China
| | - Shui-Qiao Fu
- Department of Surgical Intensive Care Unit, The First Affiliated Hospital, Zhejiang University, School of Medicine, China.
| | - Wei Wang
- Department of Surgical Intensive Care Unit, The First Affiliated Hospital, Zhejiang University, School of Medicine, China.
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Hu J, Xu J, Li M, Jiang Z, Mao J, Feng L, Miao K, Li H, Chen J, Bai Z, Li X, Lu G, Li Y. Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study. EClinicalMedicine 2024; 68:102409. [PMID: 38273888 PMCID: PMC10809096 DOI: 10.1016/j.eclinm.2023.102409] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/19/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
Background Acute kidney injury (AKI) is a common and serious organ dysfunction in critically ill children. Early identification and prediction of AKI are of great significance. However, current AKI criteria are insufficiently sensitive and specific, and AKI heterogeneity limits the clinical value of AKI biomarkers. This study aimed to establish and validate an explainable prediction model based on the machine learning (ML) approach for AKI, and assess its prognostic implications in children admitted to the pediatric intensive care unit (PICU). Methods This multicenter prospective study in China was conducted on critically ill children for the derivation and validation of the prediction model. The derivation cohort, consisting of 957 children admitted to four independent PICUs from September 2020 to January 2021, was separated for training and internal validation, and an external data set of 866 children admitted from February 2021 to February 2022 was employed for external validation. AKI was defined based on serum creatinine and urine output using the Kidney Disease: Improving Global Outcome (KDIGO) criteria. With 33 medical characteristics easily obtained or evaluated during the first 24 h after PICU admission, 11 ML algorithms were used to construct prediction models. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley Additive exPlanation method was used to rank the feature importance and explain the final model. A probability threshold for the final model was identified for AKI prediction and subgrouping. Clinical outcomes were evaluated in various subgroups determined by a combination of the final model and KDIGO criteria. Findings The random forest (RF) model performed best in discriminative ability among the 11 ML models. After reducing features according to feature importance rank, an explainable final RF model was established with 8 features. The final model could accurately predict AKI in both internal (AUC = 0.929) and external (AUC = 0.910) validations, and has been translated into a convenient tool to facilitate its utility in clinical settings. Critically ill children with a probability exceeding or equal to the threshold in the final model had a higher risk of death and multiple organ dysfunctions, regardless of whether they met the KDIGO criteria for AKI. Interpretation Our explainable ML model was not only successfully developed to accurately predict AKI but was also highly relevant to adverse outcomes in individual children at an early stage of PICU admission, and it mitigated the concern of the "black-box" issue with an undirect interpretation of the ML technique. Funding The National Natural Science Foundation of China, Jiangsu Province Science and Technology Support Program, Key talent of women's and children's health of Jiangsu Province, and Postgraduate Research & Practice Innovation Program of Jiangsu Province.
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Affiliation(s)
- Junlong Hu
- Department of Nephrology and Immunology, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Jing Xu
- Department of Nephrology and Immunology, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Min Li
- Pediatric Intensive Care Unit, Anhui Provincial Children’s Hospital, Hefei, Anhui province, China
| | - Zhen Jiang
- Pediatric Intensive Care Unit, Xuzhou Children’s Hospital, Xuzhou, Jiangsu province, China
| | - Jie Mao
- Department of Nephrology and Immunology, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Lian Feng
- Department of Nephrology and Immunology, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Kexin Miao
- Department of Nephrology and Immunology, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Huiwen Li
- Department of Nephrology and Immunology, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Jiao Chen
- Pediatric Intensive Care Unit, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Zhenjiang Bai
- Pediatric Intensive Care Unit, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Xiaozhong Li
- Department of Nephrology and Immunology, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
| | - Guoping Lu
- Pediatric Intensive Care Unit, Children’s Hospital of Fudan University, Shanghai, China
| | - Yanhong Li
- Department of Nephrology and Immunology, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
- Institute of Pediatric Research, Children’s Hospital of Soochow University, Suzhou, Jiangsu province, China
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