1
|
Fu G, Bai S. Preoperative serum selenium predicts acute kidney injury after adult cardiac surgery. BMC Cardiovasc Disord 2024; 24:159. [PMID: 38486133 PMCID: PMC10941384 DOI: 10.1186/s12872-024-03825-y] [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: 01/13/2024] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND The relationship between serum selenium (Se) and acute kidney injury after adult cardiac surgery (CSA-AKI) remains controversial. This study aimed to investigate the association of preoperative Se level with incident CSA-AKI. METHOD AND RESULTS A retrospective cohort study was conducted on patients who underwent cardiac surgery. The primary outcome was incident CSA-AKI. Multivariable logistic regression models and natural cubic splines were used to estimate the association of Se levels and primary outcome. A total of 453 patient with a mean age of 62.97 years were included. Among all patients, 159 (35.1%) incident cases of CSA-AKI were identified. The level of preoperative Se concentration in patients with CSA-AKI was significant lower than that in patients without CSA-AKI. The higher preoperative Se level was significantly associated with decreased risk of CSA-AKI (adjusted OR 0.91, 95% CI: 0.87-0.99). Dose-response relationship curve revealed a nearly L-shape correlation between serum Se selenium levels and incident CSA-AKI. CONCLUSION Our study suggested that a higher level of serum Se was significantly associated with lower risk of CSA-AKI. Further prospective studies are needed to clarify the causal relationship between serum Se level and incident CSA-AKI.
Collapse
Affiliation(s)
- Guowei Fu
- Department of Anesthesiology, Changzhou Second People's Hospital, No.29, Xinglong Lane, Changzhou, 213003, China
| | - Shuying Bai
- Department of Anesthesiology, Changzhou Second People's Hospital, No.29, Xinglong Lane, Changzhou, 213003, China.
| |
Collapse
|
2
|
Fan J, Liu J, Wang G, Liu R. Dynamic Changes in the Renal Function of Acute Myocardial Infarction Patients with Reduced eGFR After Emergency Percutaneous Coronary Intervention. Int Heart J 2023; 64:798-806. [PMID: 37704408 DOI: 10.1536/ihj.23-102] [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] [Indexed: 09/15/2023]
Abstract
Renal dysfunction greatly influences decision-making for emergency percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI). This observational study investigated renal function changes and risk factors for renal injury in patients with AMI with reduced estimated glomerular filtration rate (eGFR) who underwent emergency PCI. The study included 85 patients with AMI with decreased eGFR who underwent emergency PCI, categorized into stage 2, 3, and 4 chronic kidney disease groups. Baseline data, laboratory indicators, coronary characteristics, and serum creatinine concentration were monitored at multiple time points. Renal injury was defined using two criteria: an increase in serum creatinine level by 0.3 mg/dL or a 50% increase from baseline. During the 1-year follow-up, renal injury incidence varied from 1.18% to 15.29%. The pattern showed an increasing trend in the 1st week after PCI, peaking at 1 week, followed by a decrease at 3 months, and another increase at one year. Low basal eGFR, high contrast agent dosage, and diabetes were associated with renal injury according to logistic regression analysis. The eGFR cutoff value of 35.475 mL/minute·1.73 m2 had a sensitivity of 83.05% and specificity of 57.69% for predicting renal injury based on receiver operating characteristic curve analysis. In summary, patients with AMI with basal eGFR lower than 35.475 mL/minute·1.73 m2 have a higher risk of renal injury after PCI. These findings emphasize the importance of assessing renal function and considering associated risk factors when deciding on emergency PCI for AMI with reduced eGFR.
Collapse
Affiliation(s)
- Jihong Fan
- Department of Cardiology, Beijing Friendship Hospital Affiliated with Capital Medical University
| | - Jianghong Liu
- Department of Cardiology, Beijing Friendship Hospital Affiliated with Capital Medical University
| | - Gang Wang
- Department of Cardiology, Beijing Friendship Hospital Affiliated with Capital Medical University
| | - Ruifeng Liu
- Department of Cardiology, Beijing Friendship Hospital Affiliated with Capital Medical University
| |
Collapse
|
3
|
Feng Y, Wang AY, Jun M, Pu L, Weisbord SD, Bellomo R, Hong D, Gallagher M. Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis. JAMA Netw Open 2023; 6:e2313359. [PMID: 37184837 DOI: 10.1001/jamanetworkopen.2023.13359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
Importance Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes. Objective To systematically review published AKI prediction models across all clinical subsettings. Data Sources MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models. Study Selection All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs. Data Extraction and Synthesis Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias. Main Outcomes and Measures C statistic was used to measure the discrimination of prediction models. Results Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool. Conclusions and Relevance In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.
Collapse
Affiliation(s)
- Yunlin Feng
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Amanda Y Wang
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- Concord Clinical School, University of Sydney, Sydney, Australia
- The Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia
| | - Min Jun
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Lei Pu
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Steven D Weisbord
- Renal Section, Medicine Service, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Renal-Electrolyte Division, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Rinaldo Bellomo
- Department of Critical Care, University of Melbourne, Melbourne, Australia
| | - Daqing Hong
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Martin Gallagher
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, Australia
| |
Collapse
|
4
|
Sůva M, Kala P, Poloczek M, Kaňovský J, Štípal R, Radvan M, Hlasensky J, Hudec M, Brázdil V, Řehořová J. Contrast-induced acute kidney injury and its contemporary prevention. Front Cardiovasc Med 2022; 9:1073072. [PMID: 36561776 PMCID: PMC9763312 DOI: 10.3389/fcvm.2022.1073072] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
The complexity and application range of interventional and diagnostic procedures using contrast media (CM) have recently increased. This allows more patients to undergo procedures that involve CM administration. However, the intrinsic CM toxicity leads to the risk of contrast-induced acute kidney injury (CI-AKI). At present, effective therapy of CI-AKI is rather limited. Effective prevention of CI-AKI therefore becomes crucially important. This review presents an in-depth discussion of CI-AKI incidence, pathogenesis, risk prediction, current preventive strategies, and novel treatment possibilities. The review also discusses the difference between CI-AKI incidence following intraarterial and intravenous CM administration. Factors contributing to the development of CI-AKI are considered in conjunction with the mechanism of acute kidney damage. The need for ultimate risk estimation and the prediction of CI-AKI is stressed. Possibilities of CI-AKI prevention is evaluated within the spectrum of existing preventive measures aimed at reducing kidney injury. In particular, the review discusses intravenous hydration regimes and pre-treatment with statins and N-acetylcysteine. The review further focuses on emerging alternative imaging technologies, alternative intravascular diagnostic and interventional procedures, and new methods for intravenous hydration guidance; it discusses the applicability of those techniques in complex procedures and their feasibility in current practise. We put emphasis on contemporary interventional cardiology imaging methods, with a brief discussion of CI-AKI in non-vascular and non-cardiologic imaging and interventional studies.
Collapse
Affiliation(s)
- Marek Sůva
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Petr Kala
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia,*Correspondence: Petr Kala,
| | - Martin Poloczek
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Jan Kaňovský
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Roman Štípal
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Martin Radvan
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Jiří Hlasensky
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Martin Hudec
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Vojtěch Brázdil
- Department of Internal Medicine and Cardiology, University Hospital, Brno, Czechia,Department of Internal Medicine and Cardiology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Jitka Řehořová
- Department of Internal Medicine and Gastroenterology, University Hospital, Brno, Czechia
| |
Collapse
|
5
|
Lu J, Qi Z, Liu J, Liu P, Li T, Duan M, Li A. Nomogram Prediction Model of Serum Chloride and Sodium Ions on the Risk of Acute Kidney Injury in Critically Ill Patients. Infect Drug Resist 2022; 15:4785-4798. [PMID: 36045875 PMCID: PMC9420741 DOI: 10.2147/idr.s376168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/17/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose This study aims to investigate the effect of serum chloride and sodium ions on AKI occurrence in ICU patients, and further constructs a prediction model containing these factors to explore the predictive value of these ions in AKI. Methods The clinical information of patients admitted to ICU of Beijing Friendship Hospital Affiliated to Capital Medical University was collected for retrospective analysis. Logistic regression analysis was used to analyzing the influencing factors. A nomogram for predicting AKI risk was constructed with R software and validated by repeated sampling. Afterwards, the effectiveness and accuracy of the model were tested and evaluated. Results A total of 446 cases met the requirements of this study, of which 178 developed AKI during their stay in ICU, with an incidence rate of 39.9%. Hypernatremia, heart failure, sepsis, APACHE II score, and initial creatinine value and BE value at ICU admission before the diagnosis of AKI were identified as independent risk factors for developing AKI during ICU stay. These predictors were incorporated into the nomogram of AKI risk in critically ill patients, which was constructed by using R software. Receiver operating characteristic curve analysis was further used and showed that the area under the curve of the model was 0.7934 (95% CI 0.742–0.8447), indicating that the model had an ideal value. Finally, further evaluated its clinical effectiveness. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model owned a certain clinical effectiveness. Conclusion The nomogram based on hypernatremia, heart failure, sepsis, APACHE II score, and initial creatinine and BE value in ICU can predict the individualized risk of AKI with satisfactory distinguishability and accuracy.
Collapse
Affiliation(s)
- Jiaqi Lu
- Department of Critical Care Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Zhili Qi
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Jingyuan Liu
- Department of Critical Care Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Pei Liu
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Tian Li
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Ang Li
- Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|