Troponin I Levels and Postoperative Myocardial Infarction following Renal Transplantation

Abstract

Background: The relationship of routine postoperative troponin I (TnI) monitoring in kidney transplant recipients and in-hospital myocardial infarction (MI) is not known. Methods: This observational study evaluated the prevalence of abnormal postoperative TnI (Ortho Clinical Diagnostics assay) in 376 consecutive kidney or kidney/pancreas transplant recipients. In-hospital MI was adjudicated using the universal definition. Rates of death and coronary revascularizations at 1 year were studied. Logistic regression analysis was performed to identify independent predictors of abnormal TnI. Results: Ninety-five (25%) recipients had abnormal TnI (>0.04 ng/ml) following transplantation. Abnormal TnI levels were more common in older (mean age: 52.2 ± 13.4 vs. 48.3 ± 13.2 years, p = 0.01), diabetic (57.9 vs. 45.6%, p = 0.04), and prior coronary artery disease (31.6 vs. 20.3%, p = 0.02) patients. In-hospital MI occurred in 6 patients (1.6%). All subsequent in-hospital cardiovascular events occurred in the abnormal postoperative TnI group; most in those with TnI levels >1 ng/ml. Previous coronary artery disease was the only independent predictor of a postoperative TnI level >1 ng/ml in multivariate analysis (odds ratio 4.61, 95% confidence interval 1.49–14.32). At 1 year there was no significant difference in death (3.2 vs. 1.8%, p = 0.42) and borderline significant difference in coronary revascularization (5.3 vs. 1.4%, p = 0.049) in abnormal versus normal TnI groups. Conclusions: In-hospital MI was infrequent, but abnormal TnI highly prevalent following renal transplantation. Normal TnI levels following renal transplantation had a high negative predictive value in excluding patients likely to develop subsequent postoperative MI. The role of a higher TnI cut-off for screening for postoperative MI in high-risk subgroups deserves future prospective evaluation.

Cardiac Biomarkers Predicting MACE in Patients Undergoing Noncardiac Surgery: A Meta-Analysis

“…Study Characteristics: A total of 7,877 participants were recruited in the eligible articles Among them three articles (Oscarsson et al, 2009b; Stone et al, 2014; Kopec et al, 2017) reported two biomarkers, eleven studies detected an association between BNP/ NT-proBNP and MACE (Feringa et al, 2007; Rajagopalan et al, 2008, 2011; Oscarsson et al, 2009a,b; Vetrugno et al, 2012; Cagini et al, 2014; Stone et al, 2014; Kim et al, 2016; Long et al, 2016; Kopec et al, 2017), fourteen reported an association between cTn and MACE (Lopez-Jimenez et al, 1997; Oscarsson et al, 2004, 2009b; Gibson et al, 2006; Ausset et al, 2008; Dawson-Bowling et al, 2008; Chong et al, 2009; Winkel et al, 2010; Biccard et al, 2012; Shroff et al, 2012; Gillmann et al, 2014; Kopec et al, 2017; Golubović et al, 2018; Kim et al, 2018), and four revealed a relationship between hs-CRP and MACE (Owens et al, 2007; Martins et al, 2011; Scrutinio et al, 2011; Stone et al, 2014; Table 1). The quality evaluation according to NOS of each selected article was also shown in Table 1.…”

“…Though different study types may cause certain bias, we found no significant difference caused by study types in this meta-analysis. All the studies included in this meta-analysis were cohort studies, including 1 nested cohort study (Kopec et al, 2017), 5 retrospective studies (Shroff et al, 2012; Stone et al, 2014; Kim et al, 2016, 2018; Long et al, 2016) and others were prospective studies. Meanwhile, Kim reported NT-proBNP, Kopec reported NT-proBNP and cTnT, Long and Stone reported BNP, Shroff and Kim reported cTnI, and Stone also reported hs-CRP.…”

2014 ACC/AHA Guideline on Perioperative Cardiovascular Evaluation and Management of Patients Undergoing Noncardiac Surgery

“…204 Most studies have shown that a fixed perfusion defect, which reflects infarcted myocardium, has a low positive predictive value for perioperative cardiac events. However, patients with fixed defects have shown increased risk for long-term events relative to patients with a normal MPI test, which likely reflects ‡References 166,190,193,195,197,199,[202][203][204][205][206] …”

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

“…Regarding cardiac arrest, a number of biomarkers have been discovered in previous studies [11][12][13]. For example, myoglobin [12], creatine kinase MB isoenzyme (CK-MB) [14], and troponins [15] have been used as biomarkers to assess myocardial pain and diagnose postoperative myocardial infarction. Through transcriptomic analysis, thousands of genes, screened by differential gene expression analysis, have also been suggested as biological markers for cardiac arrest [16].…”