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Objective To identify predictors of 30-day all-cause mortality for patients with cardiogenic shock secondary to acute coronary syndrome (ACS-CS) who require short-term mechanical circulatory support (ST-MCS). Background ACS-CS mortality is high. ST-MCS is an attractive treatment option for hemodynamic support and stabilization of deteriorating patients. Mortality prediction modeling for ACS-CS patients requiring ST-MCS has not been well-defined. Methods The Utah Cardiac Recovery (UCAR) Shock database was used to identify patients admitted with ACS-CS requiring ST-MCS devices between May 2008 and August 2018. Pre-ST-MCS clinical, laboratory, echocardiographic, and angiographic data were collected. The primary endpoint was 30-day all-cause mortality. A weighted score comprising of pre-ST-MCS variables independently associated with 30-day all-cause mortality was derived and internally validated. Results A total of 159 patients (mean age, 61 years; 78% male) were included. Thirty-day all-cause mortality was 49%. Multivariable analysis resulted in four independent predictors of 30-day all-cause mortality: age, lactate, SCAI CS classification, and acute kidney injury. The model had good calibration and discrimination (area under the receiver operating characteristics curve 0.80). A predictive score (ranging 0-4) comprised of age ≥ 60 years, pre-ST-MCS lactate ≥2.5 mmol/L, AKI at time of ST-MCS implementation, and SCAI CS stage E effectively risk stratified our patient population. Conclusion The ACS-MCS score is a simple and practical predictive score to risk-stratify CS secondary to ACS patients based on their mortality risk. Effective mortality risk assessment for ACS-CS patients could have implications on patient selection for available therapeutic strategy options.

Purpose Diabetes mellitus (DM) is prevalent among advanced heart failure (HF) patients, however its effect on the potential of left ventricular assist device (LVAD)-mediated cardiac recovery has not been described. Methods Consecutive advanced chronic HF patients (N=425) requiring durable continuous-flow LVAD were prospectively evaluated. After excluding patients with acute HF etiologies or post-LVAD follow up Results Baseline characteristics of the 2 groups are shown in the Table. Cardiac functional and structural improvement, as evidenced by relative LVEF and LVEDD changes, was more prominent in non-DM compared to DM patients, and in well- compared to not well-controlled DM patients (Figure). Overall, DM patients were less likely to experience cardiac recovery (8.4% vs 17.5%; p=0.032), while on LVAD support. This remained significant in a multivariate logistic regression after controlling for potential confounders. Conclusion The presence of DM, and notably not well-controlled DM, appears to negatively affect the potential for LVAD-induced myocardial recovery. Further research is needed to investigate the dynamic cardiac metabolism in HF with DM.

Purpose The variability of left ventricular structural and functional improvement following continuous flow left ventricular assist device (LVAD) implantation is poorly understood. Methods The study consecutively enrolled 358 patients with confirmed chronic heart failure who required a durable LVAD across four study sites. Echocardiograms were performed preceding LVAD implantation and serially up to a year after. Patients were categorized by the degree of structural (left ventricular internal diastolic diameter, LVIDd) and functional (left ventricular ejection fraction, LVEF) change after LVAD. Results In the cohort, 34 patients (10%) experienced improvement of the LVEF ≥40% and LVIDd ≤6.0cm and were termed responders. The rest of the patients were distinguished in 3 groups based on the absolute change (Δ) in LVEF during follow up: Partial Responders had Δ-LVEF ≥10% (n=57, 16%), Borderline Responders Δ-LVEF 5-10% (n=55, 15%), Non-Responders Δ-LVEF Conclusion Reverse left ventricular remodeling associated with durable LVAD support is not an all or none phenomenon and manifests in a continuous spectrum. Defining four stages across this continuum can inform clinical management and facilitate advancing the field of myocardial plasticity by informing the design of future investigations.

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 >14 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 Conclusion We propose a novel scoring system to predict post-LVAD RVF, achieving high discriminative performance in distinct, heterogeneous LVAD cohorts.

Purpose The use of temporary mechanical circulatory support (tMCS) as a bridge to transplant (BTT) has increased after the 2018 UNOS heart allocation policy. To understand the influence of these policy changes on sex equity, we examined outcomes based on sex in these high-risk patients. Methods UNOS registry was queried for adults listed for heart transplant (HTx) with a tMCS device (IABP+ ECMO+ percutaneous LVAD+ surgical temporary LVAD) 1-year before and after the October 2018 policy change. We examined association of sex with use of tMCS, time to transplant, waitlist mortality or delisting due to worsening clinical status and 1-year post-transplant survival. Results Of 8,015 patients listed for HTx, 31.8% were women. In the pre-policy change era, 290 of 4,027 (7.2%) were listed with tMCS vs 649 of 3,988 (16.3%) in the post change era (p Conclusion In the current and previous heart allocation policy era, the rate of Women listed for HTx with tMCS appears to be lower compared to Men. The reasons for these inequities of care needs to be further determined. In patients who were listed with tMCS, we did not identify sex differences in waitlist mortality, transplant rates, or survival after transplant.

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 >14 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%; p 35mg/dL, PA pulse pressure 0.5). 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.