Systemic inflammation and oxidative stress contribute to acute kidney injury after transcatheter aortic valve implantation

Background: Acute kidney injury (AKI) is a frequent complication of transcatheter aortic valve implantation (TAVI) and has been linked to preexisting comorbidities, peri-procedural hypotension, and systemic inflammation. The extent of systemic inflammation after TAVI is not fully understood. Our aim was to characterize the inflammatory response after TAVI and evaluate its contribution to the mechanism of post-procedural AKI. Methods: One hundred and five consecutive patients undergoing TAVI at our institution were included. We analyzed the peri-procedural inflammatory and oxidative stress responses by measuring a range of biomarkers (including C-reactive protein [hsCRP], cytokine levels, and myeloperoxidase [MPO]), before TAVI and 6, 24, and 48 hours post-procedure. We correlated this with changes in renal function and patient and procedural characteristics. Results: We observed a significant increase in plasma levels of pro-inflammatory cytokines (hsCRP, interleukin 6, tumor necrosis factor alpha receptors) and markers of oxidative stress (MPO) after TAVI. The inflammatory response was significantly greater after trans-apical (TA) TAVI compared to trans-femoral (TF). This was associated with a higher incidence of AKI in the TA cohort compared to TF (44% vs. 8%, respectively, p < 0.0001). The incidence of AKI was significantly lower when N-acetylcysteine (NAC) was given peri-procedurally (12% vs. 38%, p < 0.005). In multivariate analysis, only the TA approach and no use of NAC before the procedure were independent predictors of AKI. Conclusions: TAVI creates a significant post-procedural inflammatory response, more so with the TA approach. Mechanisms of AKI after TAVI are complex. Inflammatory response, hypoperfusion, and oxidative stress may all play a part and are potential therapeutic targets to reduce/prevent AKI

Effects of nitric oxide synthase inhibition on Basal function and the force-frequency relationship in the normal and failing human heart in vivo.

BACKGROUND: Nitric oxide (NO) exerts autocrine/paracrine effects on cardiac function, including alterations of the inotropic state. In vitro studies suggest that NO modulates the myocardial force-frequency relationship. Basal left ventricular (LV) contractility is depressed and the force-frequency relationship is blunted in human heart failure, and it is speculated that an increase in NO production is involved. METHODS AND RESULTS: We compared the effects of intracoronary NO synthase inhibition with N(G)-monomethyl-L-arginine (L-NMMA; 25 micromol/min) on basal LV function and the response to incremental atrial pacing in patients with dilated cardiomyopathy (n=11; mean age, 51 years) and in control subjects with atypical chest pain and normal cardiac function (n=7; mean age, 54 years). In controls, L-NMMA significantly reduced basal LV dP/dt(max) (from 1826 to 1578 mm Hg/s; P<0.002), but had no effect on heart rate, mean aortic pressure, or right atrial pressure. Pacing-induced increases in LV dP/dt(max) were unaltered by L-NMMA. In patients with dilated cardiomyopathy, L-NMMA had no effect on baseline LV dP/dt(max) (from 1313 to 1337 mm Hg/s; P=NS). The blunted pacing-induced rise in LV dP/dt(max) in these patients was unaltered by L-NMMA. CONCLUSION: Endogenous NO has a small baseline positive inotropic effect in the normal human heart, which is lost in heart failure patients. NO does not significantly influence the force-frequency relationship in either the normal or failing human heart in vivo. Because this study was performed in patients with moderate heart failure, whether the findings apply to subjects with more severe heart failure requires further investigation

A machine learning algorithm to predict a culprit lesion after out of hospital cardiac arrest

Background: we aimed to develop a machine learning algorithm to predict the presence of a culprit lesion in patients with out-of-hospital cardiac arrest (OHCA). Methods: we used the King's Out-of-Hospital Cardiac Arrest Registry, a retrospective cohort of 398 patients admitted to King's College Hospital between May 2012 and December 2017. The primary outcome was the presence of a culprit coronary artery lesion, for which a gradient boosting model was optimized to predict. The algorithm was then validated in two independent European cohorts comprising 568 patients. Results: a culprit lesion was observed in 209/309 (67.4%) patients receiving early coronary angiography in the development, and 199/293 (67.9%) in the Ljubljana and 102/132 (61.1%) in the Bristol validation cohorts, respectively. The algorithm, which is presented as a web application, incorporates nine variables including age, a localizing feature on electrocardiogram (ECG) (≥2 mm of ST change in contiguous leads), regional wall motion abnormality, history of vascular disease and initial shockable rhythm. This model had an area under the curve (AUC) of 0.89 in the development and 0.83/0.81 in the validation cohorts with good calibration and outperforms the current gold standard-ECG alone (AUC: 0.69/0.67/0/67). Conclusions: a novel simple machine learning-derived algorithm can be applied to patients with OHCA, to predict a culprit coronary artery disease lesion with high accuracy.</p