Contrast Volume to Glomerular Filtration Ratio and Acute Kidney Injury among ST-Segment Elevation Myocardial Infarction Patients Treated with Primary Percutaneous Coronary Intervention

2019 ◽  
Vol 10 (2) ◽  
pp. 108-115
Author(s):  
David Zahler ◽  
Keren-Lee Rozenfeld ◽  
Ilan Merdler ◽  
Yogev Peri ◽  
Yacov Shacham
Keyword(s):  
Myocardial Infarction ◽  
Acute Kidney Injury ◽  
Percutaneous Coronary Intervention ◽  
Logistic Regression ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Primary Pci ◽  
Optimal Cutoff ◽  
Cutoff Value ◽  
Percutaneous Coronary

Introduction: The ratio of contrast media volume to glomerular filtration rate (contrast/GFR) has been shown to correlate with the occurrence of contrast-induced acute kidney injury (CI-AKI) in unselected patient populations who underwent percutaneous coronary intervention (PCI). Objective: We evaluated the possible utilization of this marker and optimal cutoff among ST-elevation myocardial infarction (STEMI) patients undergoing primary PCI. Methods: We retrospectively included 419 patients with STEMI treated with primary PCI. The occurrence of CI-AKI was defined by the KDIGO criteria as an increase in serum creatinine of ≥0.3 mg/dL within 48 h following PCI. A receiver-operator characteristic (ROC) curve was used to identify the optimal cutoff value of contrast/GFR ratio to predict CI-AKI. This value was then assessed using multivariable logistic regression. Results: The overall incidence of CI-AKI was 9%. The contrast/GFR ratio was significantly higher among patients with CI-AKI (2.7 ± 1.2 vs. 1.9 ± 0.9; p < 0.001). According to the ROC curve analysis, the optimal cutoff value of contrast/GFR ratio to predict AKI was measured as ≥2.13, with 70% sensitivity and 60% specificity (AUC 0.65, 95% CI 0.56–0.74; p = 0.002). In a multivariate logistic regression model, contrast/GFR ratio ≥2.13 was independently associated with CI-AKI (OR 2.46, 95% CI 1.09–5.57; p = 0.03). Conclusions: Among STEMI patients undergoing primary PCI, contrast/GFR ratio ≥2.13 was independently associated with CI-AKI.

scholarly journals Machine learning prediction model of acute kidney injury after percutaneous coronary intervention

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Toshiki Kuno ◽  
Takahisa Mikami ◽  
Yuki Sahashi ◽  
Yohei Numasawa ◽  
Masahiro Suzuki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Kidney Injury ◽  
Percutaneous Coronary Intervention ◽  
Logistic Regression ◽  
Risk Model ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Percutaneous Coronary ◽  
St Elevation

AbstractAcute kidney injury (AKI) after percutaneous coronary intervention (PCI) is associated with a significant risk of morbidity and mortality. The traditional risk model provided by the National Cardiovascular Data Registry (NCDR) is useful for predicting the preprocedural risk of AKI, although the scoring system requires a number of clinical contents. We sought to examine whether machine learning (ML) techniques could predict AKI with fewer NCDR-AKI risk model variables within a comparable PCI database in Japan. We evaluated 19,222 consecutive patients undergoing PCI between 2008 and 2019 in a Japanese multicenter registry. AKI was defined as an absolute or a relative increase in serum creatinine of 0.3 mg/dL or 50%. The data were split into training (N = 16,644; 2008–2017) and testing datasets (N = 2578; 2017–2019). The area under the curve (AUC) was calculated using the light gradient boosting model (GBM) with selected variables by Lasso and SHapley Additive exPlanations (SHAP) methods among 12 traditional variables, excluding the use of an intra-aortic balloon pump, since its use was considered operator-dependent. The incidence of AKI was 9.4% in the cohort. Lasso and SHAP methods demonstrated that seven variables (age, eGFR, preprocedural hemoglobin, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction/unstable angina, heart failure symptoms, and cardiogenic shock) were pertinent. AUC calculated by the light GBM with seven variables had a performance similar to that of the conventional logistic regression prediction model that included 12 variables (light GBM, AUC [training/testing datasets]: 0.779/0.772; logistic regression, AUC [training/testing datasets]: 0.797/0.755). The AKI risk model after PCI using ML enabled adequate risk quantification with fewer variables. ML techniques may aid in enhancing the international use of validated risk models.

scholarly journals Contrast Volume/Raw eGFR Ratio for Predicting Contrast-Induced Acute Kidney Injury in Patients Undergoing Percutaneous Coronary Intervention for Myocardial Infarction

2015 ◽  
Vol 5 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Hoon Suk Park ◽  
Chan Joon Kim ◽  
Jeong-Eun Yi ◽  
Byung-Hee Hwang ◽  
Tae-Hoon Kim ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Kidney Injury ◽  
Percutaneous Coronary Intervention ◽  
Roc Curve ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Contrast Volume ◽  
Percutaneous Coronary

Background: Considering that contrast medium is excreted through the whole kidney in a similar manner to drug excretion, the use of raw estimated glomerular filtration rate (eGFR) rather than body surface area (BSA)-normalized eGFR is thought to be more appropriate for evaluating the risk of contrast-induced acute kidney injury (CI-AKI). Methods: This study included 2,189 myocardial infarction patients treated with percutaneous coronary intervention. Logistic regression analysis was performed to identify the independent risk factors. We used receiver-operating characteristic (ROC) curves to compare the ratios of contrast volume (CV) to eGFR with and without BSA normalization in predicting CI-AKI. Results: The area under the curve (AUC) of the ROC curve for the model including all the significant variables such as diabetes mellitus, left ventricular ejection fraction, preprocedural glucose, and the CV/raw modification of diet in renal disease (MDRD) eGFR ratio was 0.768 [95% confidence interval (CI), 0.720-0.816; p < 0.001]. When the CV/raw MDRD eGFR ratio was used as a single risk value, the AUC of the ROC curve was 0.650 (95% CI, 0.590-0.711; p < 0.001). When the CV/MDRD eGFR ratio with BSA normalization ratio was used, the AUC of the ROC curve further decreased to 0.635 (95% CI, 0.574-0.696; p < 0.001). The difference between the two AUCs was significant (p = 0.002). Conclusions: Raw eGFR is a better predictor for CI-AKI than BSA-normalized eGFR.

scholarly journals Editor’s Choice- What do small serum creatinine changes tell us about outcomes after acute myocardial infarction?

2017 ◽  
Vol 7 (8) ◽  
pp. 739-742 ◽  
Author(s):  
Johann Auer ◽  
Frederik H Verbrugge ◽  
Gudrun Lamm
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Acute Kidney Injury ◽  
Percutaneous Coronary Intervention ◽  
Serum Creatinine ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Adverse Outcomes ◽  
Hospital Length Of Stay ◽  
Percutaneous Coronary

Acute kidney injury (AKI), mostly defined as a rise in serum creatinine concentration of more than 0.5 mg/dl, is a common, serious, and potentially preventable complication of percutaneous coronary intervention and is associated with adverse outcomes including an increased risk of inhospital mortality. Recent data from the National Cardiovascular Data Registry/Cath-PCI registry including 985,737 consecutive patients undergoing percutaneous coronary intervention suggest that approximately 7% experienced AKI with a reported incidence of 3–19%. In patients undergoing primary percutaneous coronary intervention for acute myocardial infarction (AMI), AKI occurs more frequently with rates up to 20% depending on patient and procedural characteristics. However, varying definitions of AKI limit comparisons of AKI rates across different studies. Recently, most studies have adopted the Acute Kidney Injury Network (AKIN) criteria for definition and classification of AKI. Beyond the AKIN criteria for AKI, other classifications such as the risk, injury, failure, loss and end-stage kidney disease (RIFLE) and kidney disease: improving global outcomes (KDIGO) criteria are used to define AKI. Notably, even small increases in serum creatinine beyond AKI may be associated with adverse outcomes including increased hospital length of stay and excess. Acute kidney injury (AKI) is a serious and potentially preventable complication of percutaneous coronary intervention (PCI). Worsening renal function is associated with adverse outcomes including a higher rate of in-hospital mortality. In patients undergoing primary PCI for acute myocardial infarction (AMI), AKI occurs up to 20% of such individuals. Varying definitions of AKI limit comparisons of AKI rates across different studies. Additionally, even small increases in serum creatinine beyond lavels meeting AKI definitions may be associated with adverse outcomes including increased hospital length of stay.

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