Citations

Background: The Society for Cardiovascular Angiography and Interventions (SCAI) staging system is a crucial tool for categorizing cardiogenic shock (CS) and guiding interventions. SCAI staging is traditionally static and may not capture dynamic changes. We evaluated the impact of axillary mechanical circulatory support and SCAI stage transition in patients with CS supported until heart transplantation. Methods: A retrospective study was conducted on patients with Impella support as bridge to transplant. Hemodynamic and vasoactive support parameters were collected and SCAI stage recorded at baseline and after 72 hours. Results: 36 patients were included in the analysis. The median age was 61 (52 – 68) with an EF of 19% (15-26%) (Figure 1). Before Impella placement, the distribution of patients across SCAI stages was as follows: 19 patients in SCAI B, 8 patients in SCAI C, and 9 patients in SCAI D. After 72 hours of Impella support, 7 patients initially classified as SCAI C transitioned to SCAI B with one deteriorating to SCAI D (Figure 2). Patients in SCAI D showed notable improvement, with 8 out of 9 patients transitioning to SCAI B. For patients initially SCAI B, RA changed from 10 to 11 mmHg, and PCWP decreased from 23 to 17 mmHg. The Fick CI increased from 2.53 to 3.02 L/min/m 2 (p=0.04), and the vasoactive inotropic score (VIS) decreased from 7.5-5.0 (p=0.05). In patients initially classified as SCAI C, RA pressure decreased from 13 to 8 mmHg, and PCWP reduced from 32 to 30 mmHg. The Fick CI improved from 1.79 to 2.61 L/min/m 2 (p<0.001), while the VIS decreased from 9.2 to 6.3 (p=0.004). For those initially in SCAI D, RA pressure reduced from 8 to 5 mmHg, and PCWP decreased from 27 to 18 mmHg. The Fick CI increased from 1.55 to 2.67 L/min/m 2 (p=0.002), and the VIS was notably reduced from 4.75 to 3.8 (p=0.36). All 36 patients survived to heart transplantation without major pre-transplant complications. Conclusion: SCAI stage C-E mortality ranges from 25 – 50% or worse. Implementing Impella 5.5 support reduced SCAI severity and may be associated with improved survival in HFCS. We demonstrated lower VIS after Impella 5.5 and a 100% event free survival to heart transplantation despite a 25% baseline SCAI D cohort. Larger data should be evaluated to assess this trend.

Introduction/Background: Patients with heart failure and those waiting for a heart transplant often experience a significant decrease in their ability to perform daily activities and exercise, leading to a lower quality of life. Although exercise-based cardiac rehabilitation (EBCR) is a possible treatment option, its effectiveness has not been well measured, resulting in limited adoption. Research Questions/Hypothesis: Could physical rehabilitation before receiving a heart transplant improve functional status and better outcomes? Goals/Aims: Our objective is to evaluate the effect of pre-transplant physical therapy on the standardized improvement of AM-PAC scores, measured from the earliest pre-transplant assessment to the most recent pre-transplant evaluation. Our second objective is to assess the impact of the latest Pre-Transplant AM-PAC Score on the number of hospital-free days following transplantation. Methods: We conducted a retrospective study on 91 heart transplant patients who received Impella support from January 2019 to April 2023, approved by the Mayo Clinic Institutional Review Board. We collected data on demographics, AMPAC scores, and outcomes at three time points. Continuous variables were compared using the T-test or Mann-Whitney test, and categorical variables using the Chi-square or Fisher's exact test. We used a linear regression model to investigate the relationship between rehabilitation duration and AMPAC scores. Quantile regression was preferred due to the non-normal distribution of the outcome, hospital-free days. Results: We conducted a linear regression analysis and found a statistically significant association between increased hours spent in PT and greater improvement. After one hour of physical therapy, there was an average increase of 1.07 (95% CI 0.1, 2; p=0.03) in AMPAC scoore. We also compared the change in the AMPAC score with hospital-free days, and it was statistically significant in quantile regression analysis, indicating an increase of 0.28 (95% CI 0.2, 0.55; P=0.03) points in the AMPAC Score, corresponding to an additional hospital-free day. Conclusion: Increased hours spent in physical therapy show a significant positive association with greater improvement in standardized AM-PAC Mobility scores and hospital-free days. These findings encourage increasing uptake and support of cardiac rehabilitation programs.

Background: In the landscape of organ transplantation, heart transplantation stands as a life-saving procedure for patients with end-stage heart failure. Comprehensive analyses of transplant programs are imperative to optimize outcomes and understand the complexities. Hypothesis: This study hypothesizes that an increasing trend of heart transplant service and short-term mechanical circulatory support (STMCS) is the bridge to transplant; the UNOS (United Network for Organ Sharing) guideline positively impacts STMCS utilization. Even with the standard of care, there might be a disparity in receiving care in terms of gender and race. Goal/ Aims: By examining this center's collective experience, this research provides valuable insights into the evolving landscape of heart transplantation, ultimately contributing to advancements in patient care and transplant policy. Methods: This retrospective cohort study includes heart transplant patients in a single large transplant center. For statistical analysis, Cox regression, Mann-Whitney test, Kruskal Wallis, and Chi-Square test were used in IBM SPSS 25. Result: Trends: This retrospective study covering 2010 to 2022 comprehensively analyzed 1513 heart transplant patients. The cohort consisted of 1071 (70.78%) males and 442 (29.21%) females, 31.9% were bridged through STMCS. 76.7% of Caucasian and 16.1% were African American.The mean utilization of STMCS was 20.08% before the UNOS policy change, increasing to 53.81% after that (p < 0.001). Disparity Analysis: There was a statistically significant difference in STMCS utilization between white and black patients (p= 0.008) but no difference in between obese and non-obese cohorts (P=0.362).Wait time was similar among the genders (p=0.161) and different races (p=0.302). Survival Analysis: There is a significant survival discrepancy between patients bridged through MCS and those without STMCS(p< 0.001). However, yearly trend analysis showed an evolving survival trajectory favoring STMCS users. There are statistically significant survival differences among whites and blacks (P =0.008) but similar among the genders (p=0.589). Summary: The study reveals significant changes in the trend of transplants regarding STMCS utilization since the UNOS guideline. There are substantial differences in survival between STMCS vs. non-STMCS groups and among the races.There was a significant difference in STMCS utilization among the races.

Abstract Aims Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU. Methods We screened 6008 Mayo Clinic CICU patients with an admission diagnosis of HF from 2007 to 2018 and included those without missing values for common laboratory tests. Consensus k-means clustering was performed based on 10 common admission laboratory values (potassium, chloride, anion gap, blood urea nitrogen, haemoglobin, red blood cell distribution width, mean corpuscular volume, platelet count, white blood cell count and neutrophil-to-lymphocyte ratio). In-hospital mortality was evaluated using logistic regression, and 1 year mortality was evaluated using Cox proportional hazard models after multivariable adjustment. Results Among 4877 CICU patients with HF who had complete admission laboratory data (mean age 69.4 years, 38.4% females), we identified five clusters with divergent demographics, comorbidities, laboratory values, admission diagnoses and use of critical care therapies. We labelled these clusters based on the characteristic laboratory profile of each group: uncomplicated (25.7%), iron-deficient (14.5%), cardiorenal (18.4%), inflamed (22.3%) and hypoperfused (19.2%). In-hospital mortality occurred in 10.7% and differed between the phenotypes: uncomplicated, 2.7% (reference); iron-deficient, 8.1% [adjusted odds ratio (OR) 2.18 (1.38–3.48), P &lt; 0.001]; cardiorenal, 10.3% [adjusted OR 2.11 (1.37–3.32), P &lt; 0.001]; inflamed, 12.5% [adjusted OR 1.79 (1.18–2.76), P = 0.007]; and hypoperfused, 21.9% [adjusted OR 4.32 (2.89–6.62), P &lt; 0.001]. These differences in mortality between phenotypes were consistent when patients were stratified based on demographics, aetiology, admission diagnoses, mortality risk scores, shock severity and systolic function. One-year mortality occurred in 31.5% and differed between the phenotypes: uncomplicated, 11.9% (reference); inflamed, 26.8% [adjusted hazard ratio (HR) 1.56 (1.27–1.92), P &lt; 0.001]; iron-deficient, 33.8% [adjusted HR 2.47 (2.00–3.04), P &lt; 0.001]; cardiorenal, 41.2% [adjusted HR 2.41 (1.97–2.95), P &lt; 0.001]; and hypoperfused, 52.3% [adjusted HR 3.43 (2.82–4.18), P &lt; 0.001]. Similar findings were observed for post-discharge 1 year mortality. Conclusions Unsupervised machine learning clustering can identify multiple distinct clinical HF phenotypes within the CICU population that display differing mortality profiles both in-hospital and at 1 year. Mortality was lowest for the uncomplicated HF phenotype and highest for the hypoperfused phenotype. The inflamed phenotype had comparatively higher in-hospital mortality yet lower post-discharge mortality, suggesting divergent short-term and long-term prognosis.

Abstract Background Stiff left atrial syndrome is a well-established cause of heart failure symptoms. A parallel entity involving the right atrium (RA) has not previously been described. We present a case of refractory right heart failure (RHF) 12 years following orthotopic heart transplantation. Case summary Patient underwent annuloplasty ring placement for severe tricuspid regurgitation in 2018 and kidney transplantation in 2020. The use of multimodality imaging and a multidisciplinary approach suggested a stiff RA as a potential etiology to refractory symptoms. Redo-heart and kidney transplantation in March 2021 led to the resolution of symptoms without recurrence. Discussion We propose stiff right atrial syndrome that may need to be considered in the setting of refractory RHF primarily suggested by significant right atrial enlargement and restrictive physiology.

ABSTRACT Purpose: The aim of the study is to evaluate whether serial assessment of shock severity can improve prognostication in intensive care unit (ICU) patients. Materials and Methods: This is a retrospective cohort of 21,461 ICU patient admissions from 2014 to 2018. We assigned the Society for Cardiovascular Angiography and Interventions (SCAI) Shock Stage in each 4-h block during the first 24 h of ICU admission; shock was defined as SCAI Shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: The admission SCAI Shock stages were as follows: A, 39.0%; B, 27.0%; C, 28.9%; D, 2.6%; and E, 2.5%. The SCAI Shock stage subsequently increased in 30.6%, and late-onset shock developed in 30.4%. In-hospital mortality was higher in patients who had shock on admission (11.9%) or late-onset shock (7.3%) versus no shock (4.3%). Persistence of shock predicted higher mortality (adjusted OR = 1.09; 95% CI = 1.06–1.13, for each ICU block with shock). The mean SCAI Shock stage had higher discrimination for in-hospital mortality than the admission or maximum SCAI Shock stage. Dynamic modeling of the SCAI Shock classification improved discrimination for in-hospital mortality (C-statistic = 0.64–0.71). Conclusions: Serial application of the SCAI Shock classification provides improved mortality risk stratification compared with a single assessment on admission, facilitating dynamic prognostication.

Introduction: A single assessment of the SCAI shock classification robustly predicts mortality, and a repeat assessment may improve prognostication. Hypothesis: We hypothesized that frequent serial assignment of the SCAI shock stage could further improve risk stratification. Methods: Unique AHA Level 1 cardiac intensive care unit (CICU) admissions at a single center from 2015 to 2018 were reviewed retrospectively. Time-stamped electronic health record data were used to assign the SCAI shock stage in each 4-hour block during the first 24 hours of CICU admission. Shock was defined as SCAI shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: Among 2,918 CICU patients, 1,537 (52.7%) met criteria for shock during >=1 block and 266 (9.1%) died in hospital. The SCAI shock stage on admission was: A, 37.6%; B, 31.5%; C, 25.9%; D, 1.8%; E, 3.3%. Patients with worsening SCAI shock stage after admission (first 4 hours) were at higher risk of mortality ( Figure A ), as were patients who met SCAI criteria for shock (particularly those with shock on admission, Figure B ). The pattern was consistent in patients with acute coronary syndromes, heart failure, or cardiac arrest (who had very high mortality). Each higher admission (aOR 1.36, 95% CI 1.18-1.56, AUC 0.70), maximum (aOR 1.59, 95% CI 1.37-1.85, AUC 0.73) and 24 hour mean (aOR 2.42, 95% CI 1.99-2.95, AUC 0.78) SCAI shock stage were incrementally associated with increasing in-hospital mortality. Discrimination was highest for the mean SCAI shock stage (p <0.05). Each additional 4 hour block meeting SCAI criteria for shock was associated with higher mortality (aOR 1.15, 95% CI 1.07-1.24). Conclusions: Dynamic assessment of shock using serial SCAI shock classification assignment can improve mortality risk stratification in CICU patients. Cumulative time in shock was a strong mortality risk indicator, highlighting the area under the curve of shock severity as an important determinant of outcomes.

Abstract Background Impella is an advanced ventricular assist device frequently used as a bridge to heart transplantation. The association of Impella with increased rates of gout flares has not been studied. Our primary aim is to determine the rates of gout flares in patients on Impella support. Methodology A retrospective study was conducted between January 2017 and September 2022 involving all patients who underwent heart transplantation. The cohort was divided into groups based on Impella support for statistical analysis. In patients receiving Impella support, outcome measures were compared based on the development of gout flares. Results 213 patients were analyzed, with 19.71% supported by Impella. Impella and non-Impella groups had similar age, race, and BMI, but more males were in the Impella group. Gout and chronic kidney disease were more prevalent in Impella-supported patients, while coronary artery disease was less common. The incidence of gout flare was significantly higher in Impella patients (31% vs. 5.3%). The Impella group had an adjusted odds ratio for in-hospital gout flare of 8.069 (95% CI = 2.13–35.08, P = 0.003). Impella-supported patients with gout flares had longer intensive care unit and hospital stays but no difference in mortality or other outcomes. Conclusion Our study is the first to identify the potential association between Impella support and increased rates of gout flares in hospitalized patients. Future studies are required to confirm this association and further elucidate the biological pathways. It is imperative to consider introducing appropriate measures to prevent and promptly manage gout flares in Impella-supported patients.

Background One‐time assessment of the Society for Cardiovascular Angiography and Interventions (SCAI) shock classification robustly predicts mortality in the cardiac intensive care unit (CICU). We sought to determine whether serial SCAI shock classification could improve risk stratification. Methods and Results Unique admissions to a single academic level 1 CICU from 2015 to 2018 were included in this retrospective cohort study. Electronic health record data were used to assign the SCAI shock stage during 4‐hour blocks of the first 24 hours of CICU admission. Shock was defined as hypoperfusion (SCAI shock stage C, D, or E). In‐hospital death was evaluated using logistic regression. Among 2918 unique CICU patients, 1537 (52.7%) met criteria for shock during ≥1 block, and 266 (9.1%) died in the hospital. The SCAI shock stage on admission was: A, 37.6%; B, 31.5%; C, 25.9%; D, 1.8%; and E, 3.3%. Patients who met SCAI criteria for shock on admission (first 4 hours) and those with worsening SCAI shock stage after admission were at higher risk for in‐hospital death. Each higher admission (adjusted odds ratio, 1.36 [95% CI, 1.18–1.56]; area under the receiver operating characteristic curve, 0.70), maximum (adjusted odds ratio, 1.59 [95% CI, 1.37–1.85]; area under the receiver operating characteristic curve, 0.73) and mean (adjusted odds ratio, 2.42 [95% CI, 1.99–2.95]; area under the receiver operating characteristic curve, 0.78) SCAI shock stage was incrementally associated with a higher in‐hospital mortality rate. Discrimination was highest for the mean SCAI shock stage ( P <0.05). Each additional 4‐hour block meeting SCAI criteria for shock predicted a higher mortality rate (adjusted odds ratio, 1.15 [95% CI, 1.07–1.24]). Conclusions Dynamic assessment of shock using serial SCAI shock classification assignment can improve mortality risk stratification in CICU patients by quantifying the magnitude and duration of shock.