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Hamilton DE, Albright J, Seth M, Painter I, Maynard C, Hira RS, Sukul D, Gurm HS. Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions. Eur Heart J 2024; 45:601-609. [PMID: 38233027 DOI: 10.1093/eurheartj/ehad836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 11/01/2023] [Accepted: 12/05/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND AND AIMS Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural variables to accurately predict common post-PCI complications. METHODS A group of 66 adults underwent a semiquantitative survey assessing a preferred list of outcomes and model display. The machine learning cohort included 107 793 patients undergoing PCI procedures performed at 48 hospitals in Michigan between 1 April 2018 and 31 December 2021 in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) registry separated into training and validation cohorts. External validation was conducted in the Cardiac Care Outcomes Assessment Program database of 56 583 procedures in 33 hospitals in Washington. RESULTS Overall rate of in-hospital mortality was 1.85% (n = 1999), acute kidney injury 2.51% (n = 2519), new-onset dialysis 0.44% (n = 462), stroke 0.41% (n = 447), major bleeding 0.89% (n = 942), and transfusion 2.41% (n = 2592). The model demonstrated robust discrimination and calibration for mortality {area under the receiver-operating characteristic curve [AUC]: 0.930 [95% confidence interval (CI) 0.920-0.940]}, acute kidney injury [AUC: 0.893 (95% CI 0.883-0.903)], dialysis [AUC: 0.951 (95% CI 0.939-0.964)], stroke [AUC: 0.751 (95%CI 0.714-0.787)], transfusion [AUC: 0.917 (95% CI 0.907-0.925)], and major bleeding [AUC: 0.887 (95% CI 0.870-0.905)]. Similar discrimination was noted in the external validation population. Survey subjects preferred a comprehensive list of individually reported post-procedure outcomes. CONCLUSIONS Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions.
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Affiliation(s)
- David E Hamilton
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, 1500 East Medical Center Dr., Ann Arbor, MI 48109-5853, USA
| | - Jeremy Albright
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, 1500 East Medical Center Dr., Ann Arbor, MI 48109-5853, USA
| | - Milan Seth
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, 1500 East Medical Center Dr., Ann Arbor, MI 48109-5853, USA
| | - Ian Painter
- Foundation for Health Care Quality, Seattle, WA, USA
| | - Charles Maynard
- Foundation for Health Care Quality, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ravi S Hira
- Foundation for Health Care Quality, Seattle, WA, USA
- Pulse Heart Institute and Multicare Health System, Tacoma, WA, USA
| | - Devraj Sukul
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, 1500 East Medical Center Dr., Ann Arbor, MI 48109-5853, USA
| | - Hitinder S Gurm
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, 1500 East Medical Center Dr., Ann Arbor, MI 48109-5853, USA
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Miao S, Pan C, Li D, Shen S, Wen A. Endorsement of the TRIPOD statement and the reporting of studies developing contrast-induced nephropathy prediction models for the coronary angiography/percutaneous coronary intervention population: a cross-sectional study. BMJ Open 2022; 12:e052568. [PMID: 35190425 PMCID: PMC8862501 DOI: 10.1136/bmjopen-2021-052568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE Clear and specific reporting of a research paper is essential for its validity and applicability. Some studies have revealed that the reporting of studies based on the clinical prediction models was generally insufficient based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. However, the reporting of studies on contrast-induced nephropathy (CIN) prediction models in the coronary angiography (CAG)/percutaneous coronary intervention (PCI) population has not been thoroughly assessed. Thus, the aim is to evaluate the reporting of the studies on CIN prediction models for the CAG/PCI population using the TRIPOD checklist. DESIGN A cross-sectional study. METHODS PubMed and Embase were systematically searched from inception to 30 September 2021. Only the studies on the development of CIN prediction models for the CAG/PCI population were included. The data were extracted into a standardised spreadsheet designed in accordance with the 'TRIPOD Adherence Assessment Form'. The overall completeness of reporting of each model and each TRIPOD item were evaluated, and the reporting before and after the publication of the TRIPOD statement was compared. The linear relationship between model performance and TRIPOD adherence was also assessed. RESULTS We identified 36 studies that developed CIN prediction models for the CAG/PCI population. Median TRIPOD checklist adherence was 60% (34%-77%), and no significant improvement was found since the publication of the TRIPOD checklist (p=0.770). There was a significant difference in adherence to individual TRIPOD items, ranging from 0% to 100%. Moreover, most studies did not specify critical information within the Methods section. Only 5 studies (14%) explained how they arrived at the study size, and only 13 studies (36%) described how to handle missing data. In the Statistical analysis section, how the continuous predictors were modelled, the cut-points of categorical or categorised predictors, and the methods to choose the cut-points were only reported in 7 (19%), 6 (17%) and 1 (3%) of the studies, respectively. Nevertheless, no relationship was found between model performance and TRIPOD adherence in both the development and validation datasets (r=-0.260 and r=-0.069, respectively). CONCLUSIONS The reporting of CIN prediction models for the CAG/PCI population still needs to be improved based on the TRIPOD checklist. In order to promote further external validation and clinical application of the prediction models, more information should be provided in future studies.
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Affiliation(s)
- Simeng Miao
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Pharmacy, Shanxi Cancer Hospital, Taiyuan, Shanxi, China
| | - Chen Pan
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Dandan Li
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Su Shen
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Aiping Wen
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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The Pathophysiology and the Management of Radiocontrast-Induced Nephropathy. Diagnostics (Basel) 2022; 12:diagnostics12010180. [PMID: 35054347 PMCID: PMC8774832 DOI: 10.3390/diagnostics12010180] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/29/2021] [Accepted: 01/10/2022] [Indexed: 12/12/2022] Open
Abstract
Contrast-induced nephropathy (CIN) is an impairment of renal function that occurs after the administration of an iodinated contrast medium (CM). Kidney dysfunction in CIN is considered transient and reversible in most cases. However, it is the third most common cause of hospital-acquired acute kidney injury and is associated with increased morbidity and mortality, especially in high-risk patients. Diagnostic and interventional procedures that require intravascular CM are being used with increasing frequency, especially among the elderly, who can be particularly susceptible to CIN due to multiple comorbidities. Therefore, identifying the exact mechanisms of CIN and its associated risk factors is crucial not only to provide optimal preventive management for at-risk patients, but also to increase the feasibility of diagnostic and interventional procedure that use CM. CM induces kidney injury by impairing renal hemodynamics and increasing the generation of reactive oxygen species, in addition to direct cytotoxicity. Periprocedural hydration is the most widely accepted preventive strategy to date. Here, we review the latest research results on the pathophysiology and management of CIN.
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The global incidence and mortality of contrast-associated acute kidney injury following coronary angiography: a meta-analysis of 1.2 million patients. J Nephrol 2021; 34:1479-1489. [PMID: 34076881 PMCID: PMC8494686 DOI: 10.1007/s40620-021-01021-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/05/2021] [Indexed: 10/25/2022]
Abstract
BACKGROUND Contrast-associated acute kidney injury (CA-AKI) is a common complication after coronary angiography (CAG), which brings a poor prognosis. But up to now, there were fewer studies to discuss the incidence of CA-AKI comprehensively. We comprehensively explore the incidence of CA-AKI after coronary angiography. METHODS We searched Medline, Embase, and Cochrane Database of Systematic Reviews (to 30th June 2019). We evaluated the world's incidence of the CA-AKI, and associated mortality, and to described geographic variations according to countries, regions, and economies. CA-AKI was defined as an increase in serum creatinine ≥ 0.5 mg/dl or ≥ 25% within 72 h. Random effects model meta-analyses and meta-regressions was performed to derive the sources of heterogeneity. RESULTS A total of 134 articles (1,211,106 participants) were included in our meta-analysis. Most studies originated from China, Japan, Turkey and United States, from upper middle income and high income countries. The pooled incidence of CA-AKI after coronary angiography was 12.8% (95% CI 11.7-13.9%), and the CA-AKI associated mortality was 20.2% (95% CI 10.7-29.7%). The incidence of CA-AKI and the CA-AKI associated mortality were not declined over time (Incidence rate change: 0.23% 95% CI - 0.050 to 0.510 p = 0.617; Mortality rate change: - 1.05% 95% CI - 3.070 to 0.970 p = 0.308, respectively). CONCLUSION CA-AKI was a universal complication in many regions, and the burden of CA-AKI remains severe. In clinical practice, physicians should pay more attention to the occurrence and active prevention and treatment of CA-AKI.
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Kristić I, Crnčević N, Runjić F, Čapkun V, Polašek O, Matetic A, Vrsalovic M. ACEF performed better than other risk scores in non-ST-elevation acute coronary syndrome during long term follow-up. BMC Cardiovasc Disord 2021; 21:70. [PMID: 33535979 PMCID: PMC7860189 DOI: 10.1186/s12872-020-01841-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 12/27/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Risk stratification of patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) is an important clinical method, but long-term studies on patients subjected to all-treatment strategies are lacking. Therefore, the aim was to compare several established risk scores in the all-treatment NSTE-ACS cohort during long-term follow-up. METHODS Consecutive patients (n = 276) with NSTE-ACS undergoing coronary angiography were recruited between September 2012 and May 2015. Six risk scores for all patients were calculated, namely GRACE 2.0, ACEF, SYNTAX, Clinical SYNTAX, SYNTAX II PCI and SYNTAX II CABG. The primary end-point was Major Adverse Cardiovascular Events (MACE) which was a composite of cardiac death, nonfatal myocardial infarction, ischemic stroke or urgent coronary revascularization. RESULTS During a median follow-up of 33 months, 64 MACE outcomes were recorded (23.2%). There was no difference between risk score categories, except in the highest risk group of ACEF and SYNTAX II PCI scores which exhibited significantly more MACE (51.6%, N = 33 and 45.3%, N = 29, P = 0.024, respectively). In the multivariate Cox regression analysis of individual variables, only age and atrial fibrillation were significant predictors for MACE (HR 1.03, 95% CI 1.00-1.05, P = 0.023 and HR 2.02, 95% CI 1.04-3.89, P = 0.037, respectively). Furthermore, multivariate analysis of the risk scores showed significant prediction of MACE only with ACEF score (HR 2.16, 95% CI 1.36-3.44, P = 0.001). The overall performance of GRACE, SYNTAX, Clinical SYNTAX and SYNTAX II CABG was poor with AUC values of 0.596, 0.507, 0.530 and 0.582, respectively, while ACEF and SYNTAX II PCI showed the best absolute AUC values for MACE (0.630 and 0.626, respectively). CONCLUSIONS ACEF risk score showed better discrimination than other risk scores in NSTE-ACS patients undergoing all-treatment strategies over long-term follow-up and it could represent a fast and user-friendly tool to stratify NSTE-ACS patients.
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Affiliation(s)
- Ivica Kristić
- Department of Cardiology, University Hospital of Split, Split, Croatia
| | - Nikola Crnčević
- Department of Cardiology, University Hospital of Split, Split, Croatia
| | - Frane Runjić
- Department of Cardiology, University Hospital of Split, Split, Croatia
| | - Vesna Čapkun
- University Department of Health Studies, University of Split, Split, Croatia
| | - Ozren Polašek
- Department of Public Health, School of Medicine, University of Split, Split, Croatia
| | - Andrija Matetic
- Department of Cardiology, University Hospital of Split, Split, Croatia
| | - Mislav Vrsalovic
- Department of Cardiology, Sestre Milosrdnice University Hospital Center, Vinogradska cesta 29, 10000, Zagreb, Croatia.
- University of Zagreb School of Medicine, Zagreb, Croatia.
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Lun Z, Lei L, Zhou D, Ying M, Liu L, Chen G, Liu J, He Y, Li H, Huang Z, Yang Y, Ye J, Liu Y. A comparison between two different definitions of contrast-associated acute kidney injury for long-term mortality in patients with diabetes undergoing coronary angiography: a prospective cohort study. BMC Cardiovasc Disord 2020; 20:485. [PMID: 33198639 PMCID: PMC7670693 DOI: 10.1186/s12872-020-01778-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 11/09/2020] [Indexed: 01/22/2023] Open
Abstract
Background The definitions of contrast-associated acute kidney injury (CA-AKI) are diverse and have different predictive effects for prognosis, which are adverse for clinical practice. Few articles have discussed the relationship between these definitions and long-term prognosis in patients with diabetes. Methods A total of 1154 diabetic patients who were undergoing coronary angiography (CAG) were included in this study. Two definitions of CA-AKI were used: CA-AKIA was defined as an increase ≥ 0.3 mg/dl or > 50% in serum creatinine (SCr) from baseline within 72 h after CAG, and CA-AKIB was defined as an increase ≥ 0.5 mg/dl or > 25% in SCr from baseline within 72 h after CAG. We used Cox regression to evaluate the association of these two CA-AKI definitions with long-term mortality and calculate the population attributable risks (PARs) of different definitions for long-term prognosis. Results During the median follow-up period of 7.4 (6.2–8.2) years, the overall long-term mortality was 18.84%, and the long-term mortality in patients with CA-AKI according to both CA-AKIA and CA-AKIB criteria were 36.73% and 28.86%, respectively. We found that CA-AKIA (HR: 2.349, 95% CI 1.570–3.517, p = 0.001) and CA-AKIB (HR: 1.608, 95% CI 1.106–2.339, p = 0.013) were associated with long-term mortality. The PARs were the highest for CA-AKIA (31.14%), followed by CA-AKIB (14.93%). Conclusions CA-AKI is a common complication in diabetic patients receiving CAG. The two CA-AKI definitions are significantly associated with a poor long-term prognosis, and CA-AKIA, with the highest PAR, needs more clinical attention.
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Affiliation(s)
- Zhubin Lun
- The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, 523808, China.,Department of Cardiology, Dongguan TCM Hospital, Dongguan, 523000, China.,Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Li Lei
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Dianhua Zhou
- Department of Cardiology, Dongguan TCM Hospital, Dongguan, 523000, China
| | - Ming Ying
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Liwei Liu
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Guanzhong Chen
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Jin Liu
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yibo He
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Huanqiang Li
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Zhidong Huang
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yongquan Yang
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Jianfeng Ye
- Department of Cardiology, Dongguan TCM Hospital, Dongguan, 523000, China.
| | - Yong Liu
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, The Affiliated Guangdong Provincial People's Hospital of South China University of Technology, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
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Jesus-Silva SGD, Chaves AE, Maciel CAA, Scotini EEF, Mesquita PGM, De Moraes Silva MA, Cardoso RS. Evaluation of prediction score of contrast-induced nephropathy in inpatients undergone to digital or CT angiography. REVISTA CIÊNCIAS EM SAÚDE 2020. [DOI: 10.21876/rcshci.v10i3.974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Objectives: To assess the incidence of contrast-induced nephropathy (CIN) and determine the Mehran Score's (MS) ability to predict CIN in patients undergoing digital angiography or computed tomography angiography. Methods: 252 medical records of inpatients who underwent DA or CTA over 28 months in a quaternary hospital were reviewed. CIN was defined as serum creatinine> 0.5 mg / dL or > 25% increase in baseline creatinine, 48 h after administration of iodinated contrast. The ROC curve and the area under the curve (AUC) were used as a score test. Results: The majority (159; 63.1%) were male, and the average age was 60.4 years. Anemia, diabetes mellitus, and age > 75 years were the most prevalent factors. The incidence of CIN was 17.8% (n = 45). There was a decrease in the mean values of creatinine pre and post among patients who did not suffer CIN (1.38 ± 1.22 vs 1.19 ± 0.89; t = 3.433; p = 0.0007), while among patients who suffering CIN, the mean increase was 1.03 mg / dL (1.43 ± 1.48 vs 2.46 ± 2.35 mg / dL; t = 5.44; p = 0.117). The ROC curve analysis identified a low correlation between MS and the occurrence of CIN (AUC = 0.506). Conclusion: The incidence of CIN in hospitalized patients undergoing angiography or computed tomography angiography was high. The EM did not allow the prediction of NIC.
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Zhang F, Lu Z, Wang F. Advances in the pathogenesis and prevention of contrast-induced nephropathy. Life Sci 2020; 259:118379. [PMID: 32890604 DOI: 10.1016/j.lfs.2020.118379] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/18/2020] [Accepted: 08/31/2020] [Indexed: 12/11/2022]
Abstract
With the increasing application of medical imaging contrast materials, contrast-induced nephropathy has become one of the leading causes of iatrogenic renal insufficiency. The underlying mechanism is associated with renal medullary hypoxia, direct toxicity of contrast agents, oxidative stress, apoptosis, immune/inflammation and epigenetic regulation in contrast-induced nephropathy. Up to date, there is no effective therapy for contrast-induced nephropathy, and thus risk predication and effective preventive strategies are keys to reduce the occurrence of contrast-induced nephropathy. It was found that the proper use of contrast medium, personalized hydration, and high-dose statins may reduce the occurrence of contrast-induced nephropathy, while antioxidants have not shown significant therapeutic benefits. Additionally, the role of remote ischemia preconditioning and vasodilators in the prevention of contrast-induced nephropathy needs further study. This review aims to discuss the incidence, pathogenesis, risk prediction, and preventive strategies for contrast-induced nephropathy.
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Affiliation(s)
- Fangfei Zhang
- Department of Nephrology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Zeyuan Lu
- Department of Nephrology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Feng Wang
- Department of Nephrology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China.
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De Filippo O, D'Ascenzo F, Piroli F, Budano C, De Ferrari GM. Sometimes neither water nor fire are more useful than friendship-a new risk score for prediction of contrast-induced nephropathy (CIN) and long-term adverse outcomes in patients undergoing coronary angiography. J Thorac Dis 2019; 11:2675-2679. [PMID: 31463091 DOI: 10.21037/jtd.2019.06.51] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Ovidio De Filippo
- Division of Cardiology, Department of Internal Medicine, Città Della Salute e Della Scienza di Torino, Turin, Italy
| | - Fabrizio D'Ascenzo
- Division of Cardiology, Department of Internal Medicine, Città Della Salute e Della Scienza di Torino, Turin, Italy
| | - Francesco Piroli
- Division of Cardiology, Department of Internal Medicine, Città Della Salute e Della Scienza di Torino, Turin, Italy
| | - Carlo Budano
- Division of Cardiology, Department of Internal Medicine, Città Della Salute e Della Scienza di Torino, Turin, Italy
| | - Gaetano Maria De Ferrari
- Division of Cardiology, Department of Internal Medicine, Città Della Salute e Della Scienza di Torino, Turin, Italy
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Ni Z, Liang Y, Xie N, Liu J, Sun G, Chen S, Ye J, He Y, Guo W, Tan N, Chen J, Liu Y, Chen Z, Wang S. Erratum to simple pre-procedure risk stratification tool for contrast-induced nephropathy. J Thorac Dis 2019; 11:E105. [PMID: 31463159 DOI: 10.21037/jtd.2019.06.57] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
[This corrects the article DOI: 10.21037/jtd.2019.04.69.].
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Affiliation(s)
- Zhonghan Ni
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Yan Liang
- Department of Cardiology, Maoming People's Hospital, Maoming 525000, China
| | - Nianjin Xie
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Jin Liu
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Guoli Sun
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Shiqun Chen
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Jianfeng Ye
- Department of Cardiology, Dongguan People's Hospital, Dongguan 523059, China
| | - Yibo He
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Wei Guo
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Ning Tan
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Jiyan Chen
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Yong Liu
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Zhujun Chen
- Department of Cardiology, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Shouhong Wang
- Department of Critical Care Medicine, Guangdong Geriatric Institute, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou 510080, China
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