Pre-Procedural Blood Glucose Levels

Alpert, Martin A.; Carlino, Carl

doi:10.1016/j.jacc.2009.10.066

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…Recent studies have suggested a potential and independent prognostic role of preprocedural blood glucose levels (BGLs), irrespective of diabetes mellitus, in patients undergoing PCI; both hyperglycemia and hypoglycemia significantly correlated with the incidence of periprocedural myocardial infarction (PMI) [ 5 ], contrast-induced acute kidney injury (CI-AKI) [ 5 – 8 ], and in-stent restenosis [ 5 , 9 , 10 ]. Potential pathophysiologic mechanisms include endothelial dysfunction and oxidative stress caused by abnormal BGLs that may increase both myocardial damage and renal toxicity of contrast media during PCI [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Glycemic Variability Assessed by Continuous Glucose Monitoring and Short-Term Outcome in Diabetic Patients Undergoing Percutaneous Coronary Intervention: An Observational Pilot Study

Nusca

Pantano

Melfi

et al. 2015

Journal of Diabetes Research

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Poor glycemic control is associated with unfavorable outcome in patients undergoing percutaneous coronary intervention (PCI), irrespective of diabetes mellitus. However a complete assessment of glycemic status may not be fully described by glycated hemoglobin or fasting blood glucose levels, whereas daily glycemic fluctuations may influence cardiovascular risk and have even more deleterious effects than sustained hyperglycemia. Thus, this paper investigated the effectiveness of a continuous glucose monitoring (CGM), registering the mean level of glycemic values but also the extent of glucose excursions during coronary revascularization, in detecting periprocedural outcome such as renal or myocardial damage, assessed by serum creatinine, neutrophil gelatinase-associated lipocalin (NGAL), and troponin I levels. High glycemic variability (GV) has been associated with worse postprocedural creatinine and NGAL variations. Moreover, GV, and predominantly hypoglycemic variations, has been observed to increase in patients with periprocedural myocardial infarction. Thus, our study investigated the usefulness of CGM in the setting of PCI where an optimal glycemic control should be achieved in order to prevent complications and improve outcome.

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Section: Introductionmentioning

confidence: 99%

Glycemic Variability Assessed by Continuous Glucose Monitoring and Short-Term Outcome in Diabetic Patients Undergoing Percutaneous Coronary Intervention: An Observational Pilot Study

Nusca

Pantano

Melfi

et al. 2015

Journal of Diabetes Research

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Pre-Procedural Hyperglycemia Increases the Risk of Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography: A Systematic Review and Meta-Analysis

Kewcharoen

Trongtorsak

et al. 2020

Cardiovascular Revascularization Medicine

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Preprocedural Prediction Model for Contrast‐Induced Nephropathy Patients

Guan

et al. 2017

JAHA

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BackgroundSeveral models have been developed for prediction of contrast‐induced nephropathy (CIN); however, they only contain patients receiving intra‐arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure‐related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media.Methods and ResultsA total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5‐fold cross‐validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver‐operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level.ConclusionsThe newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN.

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Pre-Procedural Blood Glucose Levels

Cited by 6 publications

References 25 publications

Glycemic Variability Assessed by Continuous Glucose Monitoring and Short-Term Outcome in Diabetic Patients Undergoing Percutaneous Coronary Intervention: An Observational Pilot Study

Glycemic Variability Assessed by Continuous Glucose Monitoring and Short-Term Outcome in Diabetic Patients Undergoing Percutaneous Coronary Intervention: An Observational Pilot Study

Pre-Procedural Hyperglycemia Increases the Risk of Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography: A Systematic Review and Meta-Analysis

Preprocedural Prediction Model for Contrast‐Induced Nephropathy Patients

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