Microcirculation and macrocirculation in cardiac surgical patients

Background. The aim of our study was to investigate the relationship between microcirculatory alterations after open cardiac surgery, macrohemodynamics, and global indices of organ perfusion. Methods. Patients' microcirculation was assessed with near-infrared spectroscopy (NIRS) and the vascular occlusion technique (VOT). Results. 23 patients undergoing open cardiac surgery (11 male/12 female, median age 68 (range 28-82) years, EuroSCORE 6 (1-12)) were enrolled in the study. For pooled data, CI correlated with the tissue oxygen consumption rate as well as the reperfusion rate (r=0.56, P<0.001 and r=0.58, P<0.001, resp.). In addition, both total oxygen delivery (DO 2, mL/min per m 2) and total oxygen consumption (VO 2, mL/min per m 2) also correlated with the tissue oxygen consumption rate and the reperfusion rate. The tissue oxygen saturation of the thenar postoperatively correlated with the peak lactate levels during the six hour monitoring period (r=0.50, P<0.05). The tissue oxygen consumption rate (/min) and the reperfusion rate (/min), as derived from the VOT, were higher in survivors compared to nonsurvivors for pooled data [23 (4-54) versus 20 (8-38) P<0.05 ] and [424 (27-1215) versus 197 (57-632) P<0.01 ], respectively. Conclusion. Microcirculatory alterations after open cardiac surgery are related to macrohemodynamics and global indices of organ perfusion. © 2012 Elli-Sophia Tripodaki et al

A Novel Risk Score Predicts Early Right Ventricular Failure after Lvad: A Derivation-validation Multicenter Study

Introduction: Right ventricular failure (RVF) after LVAD implantation is associated with increased morbidity and mortality. Despite several RVF predictive models, poor performance in external validation cohorts has limited their widespread clinical adoption. Objective: To develop a novel RVF predictive model, ascertain its performance in an independent validation cohort, and develop an RVF risk score. Methods: Consecutive LVAD patients were prospectively enrolled at the Utah Transplantation Affiliated Hospitals (U.T.A.H) Cardiac Transplant Program (n=477, Derivation cohort). LVAD patients from Inova Heart & Vascular Institute and Henry Ford Medical Center formed the external dataset (n=321, Validation cohort). The primary outcome was early RVF, defined as the need for RVAD or intravenous inotropes for \u3e14 days. The secondary outcome was 3-year all-cause mortality. Multivariable logistic regression analysis was used to develop a predictive model. An RVF risk score was developed using weighted points based on the β-regression coefficients of the multivariable predictors. Results: The study included 798 patients, with a mean age of 56y, 84% male, and 30% INTERMACS Profile 1-2. Compared to the derivation cohort, the validation cohort had a higher proportion of African-Americans (37% vs 7%; p35mg/dL, PA pulse pressure/PCWP \u3e0.5). The model had a c-statistic of 0.73 ([95% CI:0.67-0.79]; p Conclusion: We propose a novel scoring system to predict post-LVAD RVF, achieving high discriminative performance after being tested in distinct and highly heterogeneous populations. This simple predictive tool could impact patient selection and perioperative management of LVAD patients

Predicting Right Ventricular Failure Following Left Ventricular Assist Device Support: A Derivation-Validation Multicenter Risk Score

Purpose: Despite several models predicting right ventricular failure (RVF) after durable left ventricular assist device (LVAD) support, poor performance when externally validated has limited their widespread use. We sought to derive a predictive model for RVF after LVAD implantation, and ascertain its performance in an independent cohort. Methods: End-stage heart failure (HF) patients requiring continuous-flow LVAD were prospectively enrolled at one US program (n=477, derivation cohort), with two other US medical centers forming the validation cohort (n=321). The primary outcome was RVF incidence, defined as the need for right ventricular assist device or inotropes for \u3e14 days. Multivariable logistic regression in the derivation set yielded a RVF predictive model, which was subsequently applied to the validation cohort, and a risk score was ultimately developed. Results: Derivation cohort included patients less likely to be African-Americans (7% vs 37%; p\u3c0.001), Hispanics (7% vs 30%; p\u3c0.001), have a remote history of hypertension (49% vs 60%; p=0.002) or be bridged with short-term MCS (8% vs 16%; p=0.001), compared to the validation set. RVF incidence was 16% in the derivation and 36% in the validation cohort (p\u3c0.001). Multivariable analysis identified 7 variables (Figure) as predictive of RVF, with the model achieving a C statistic of 0.734 (95% CI=0.674-0.794) in the derivation and 0.709 (95% CI=0.651-0.767) in the heterogeneous validation cohort. Patients were stratified into 3 RVF risk groups (all comparisons; p\u3c0.001) (Figure). Conclusion: We propose a novel scoring system to predict post-LVAD RVF, achieving high discriminative performance in distinct, heterogeneous LVAD cohorts

Multicenter Development and Validation of a Machine Learning Model to Predict Myocardial Recovery During LVAD Support: The UCAR Score

Purpose: Although significant cardiac reverse remodeling is a prerequisite for a left ventricular (LV) assist device (LVAD) patient to be considered for device weaning, multiple factors including patient goals, physician comfort, and center experience, weigh in on this complex decision. Existing predictive models defining recovery as device withdrawal, entail the above-mentioned confounders, and may under detect patients that could benefit from a targeted bridge to recovery strategy. We sought to derive and validate a predictive tool to identify patients prone to reverse remodel, independent of the complex decision to remove a durable, surgically deployed device. Methods: Heart failure patients (N=782) requiring LVAD were enrolled at one (n=537) and five US programs (n=245). Baseline characteristics were recorded. The primary outcome was responder incidence, defined as follow-up LV ejection fraction ≥40% and LV end-diastolic diameter ≤6 cm within one year on LVAD support. Bootstrap imputation and lasso variable selection techniques were used to derive a predictive model which was then validated using our multicenter dataset. A predictive calculator was developed, and patients were classified into groups with varying potential for reverse remodeling. Results: Patients were predominantly white (84%), male (82%), aged 56±1 years. Overall, 14.8% patients were identified as responders. Nine preoperative variables associated with reverse remodeling were included in the multivariate model achieving an optimism corrected C-statistic of 0.77 (95% CI: 0.71-0.82) (Figure). Conclusion: The UCAR calculator is a machine learning-based multicenter and validated risk tool, implementing routine clinical data, that effectively stratifies patients into groups with varying potential for reverse remodeling. This tool can be useful in selecting patients to implement diagnostic and therapeutic protocols that can promote reverse remodeling and myocardial recovery