Aims
The goal of the study was to examine the prognostic impact, haemodynamic and clinical features associated with lung congestion in patients with chronic heart failure (HF).
Methods and results
HF patients (n = 186) and HF‐free controls (n = 21) underwent right heart catheterization, echocardiography, pulmonary function testing and chest radiography that was blindly scored for the presence and severity of lung oedema. Lung congestion correlated directly with pulmonary vascular resistance (PVR, P = 0.004) and inversely with pulmonary artery (PA) compliance (P < 0.001) and the diffusion limit for carbon monoxide (DLCO, P = 0.009). Compared with dry lung HF, wet lung HF patients (congestion score > median) had 25% lower PA compliance and 25–35% higher PVR, transpulmonary gradients and PA pressures (40 vs. 32 mmHg, P < 0.001) despite marginally higher PA wedge pressure (PAWP; 22 vs. 19 mmHg, P = 0.002). Wet lung HF patients displayed more right ventricular (RV) dilatation and dysfunction, more restrictive ventilation and greater reduction of DLCO. The strongest correlates of lung congestion were NT‐proBNP, haemoglobin, albumin, and glomerular filtration, all surpassing PAWP. After a median of 333 days (interquartile range 80–875), 59 patients (32%) died. Lung congestion was associated with reduced survival (P < 0.0001), even after adjusting for PAWP, NT‐proBNP, anaemia, CAD and renal dysfunction.
Conclusion
Interstitial lung oedema is associated with pulmonary vascular disease, RV overload and dysfunction and increased mortality in HF. These data reinforce the importance of aggressive decongestion in HF and suggest that novel agents aimed at reducing lung water may help to deter progression of pulmonary vascular disease and biventricular HF.
Background:
The Society of Thoracic Surgeons (STS) scores are used to screen patients for transcatheter aortic valve replacement (TAVR). The STS scores were also used to risk stratify patients in major TAVR trials. This study evaluates the reclassification of predicted risk of mortality by the currently available online STS score calculator compared with the 2008 STS risk model in patients undergoing TAVR.
Methods and Results:
All patients who underwent TAVR from 2006 to 2016 were included in the study. The STS scores for all included patients were calculated by applying the 2008 STS risk model and again using the current STS online calculator. Among 1209 patients who underwent TAVR, 30-day mortality was 27 (2.2%). The overall predicted risk of mortality estimated by using the current online STS risk calculator was significantly lower than the 2008 STS risk model (6.3±4.4 vs 7.3±4.9;
P
<0.001). A total of 235 (19%) patients were reclassified into a lower risk category per the current STS risk model. In a multivariable logistic regression analysis, patients with persistent atrial fibrillation (odds ratio, 1.4; 95% CI, 1.0–1.9;
P
=0.03), chronic heart failure (odds ratio, 6.0; 95% CI, 3.8–10.1;
P
<0.001), and New York Heart Association class IV heart failure (odds ratio, 2.4; 95% CI, 1.3–4.4;
P
=0.007) were more likely to be reclassified into a lower risk category per the current STS risk model.
Conclusions:
The current STS calculation method produces significantly lower predicted risk of mortality than the 2008 calculator, more pronounced in patients with certain comorbid conditions. These results should be considered while evaluating data from prior studies of TAVR.
Background: Monitored anesthesia care (MAC) has become more widely used during transcatheter aortic valve replacement (TAVR) to avoid the complications of general anesthesia (GA).
Methods:We included consecutive patients who underwent transfemoral-TAVR at our institution between January 2012 and April 2017. We compared outcomes with GA versus MAC.Results: Of 998 patients, MAC was used in 43.9%. MAC was associated with shorter
In the current era, cardiovascular intensive care units care for more complex patients who are far sicker than historical post-myocardial infarction patients, and sedation has become a common intervention in these units. Current sedation best practices derive mainly from non-cardiac units which limits their generalization to the critically ill cardiac patient. Thus, a great variability in sedation protocols, especially the selection of sedative agents, is commonly seen in daily practice across cardiac units. We present an updated review on sedation in cardiovascular critical care medicine with emphasis on the hemodynamic impact. The goal of this review is to generate a general sedation algorithm specific for the cardiac patient.
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