Patel, Parag C.

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.

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.

Abstract Aims We sought to investigate the outcomes of heart transplant patients supported with Impella 5.5 temporary mechanical circulatory support. Methods and results Patient demographics, perioperative data, hospital timeline, and haemodynamic parameters were followed during initial admission, Impella support, and post-transplant period. Vasoactive-inotropic score, primary graft failure, and complications were recorded. Between March 2020 and March 2021, 16 advanced heart failure patients underwent Impella 5.5 temporary left ventricular assist device support through axillary approach. Subsequently, all these patients had heart transplantation. All patients were either ambulatory or chair bound during their temporary mechanical circulatory support until heart transplantation. Patients were kept on Impella support median of 19 days (3–31) with the median lactate dehydrogenase level of 220 (149–430). All Impella devices were removed during heart transplantation. During Impella support, patients had improved renal function with median creatinine serum level of 1.55 mg/dL decreased to 1.25 (P = 0.007), pulmonary artery pulsatility index scores increased from 2.56 (0.86–10) to 4.2 (1.3–10) (P = 0.048), and right ventricular function improved (P = 0.003). Patients maintained improved renal function and favourable haemodynamics after their heart transplantation as well. All patients survived without any significant morbidity after their heart transplantation. Conclusions Impella 5.5 temporary left ventricular assist device optimizes care of heart transplant recipients providing superior haemodynamic support, mobility, improved renal function, pulmonary haemodynamics, and right ventricular function. Utilizing Impella 5.5 as a direct bridging strategy to heart transplantation resulted in excellent outcomes.

ABSTRACT Background: The Society for Cardiovascular Angiography and Intervention (SCAI) Shock Classification can define shock severity. We evaluated the vasoactive-inotropic score (VIS) combined with the SCAI Shock Classification for mortality risk stratification. Methods: This was a single-center retrospective cohort analysis including Mayo Clinic cardiac intensive care unit patients from 2007 to 2015. The peak VIS was calculated at 1 and 24 h after cardiac intensive care unit admission. In-hospital mortality was evaluated using multivariable logistic regression. Results: Of 9,916 included patients, vasoactive drugs were used in 875 (8.8%) within 1 h and 2,196 (22.1%) within 24 h. A total of 888 patients (9.0%) died during hospitalization. Patients who required vasoactive drugs within 1 h had higher in-hospital mortality (adjusted odds ratio [OR], 1.30; 95% confidence interval [CI], 1.03–1.65; P = 0.03) and in-hospital mortality rose with the VIS during the first 1 h (adjusted OR per 10 units, 1.22; 95% CI, 1.12–1.33; P < 0.001). The increase in VIS from 1 to 24 h was associated with higher in-hospital mortality (adjusted OR per 10 units, 1.16; 95% CI, 1.10–1.21; P < 0.001). These results were consistent in the 1,067 patients (10.9%) with cardiogenic shock. A gradient of in-hospital mortality was observed according to the VIS at 1 h and the increase in VIS from 1 to 24 h. Conclusions: Higher vasoactive drug requirements portend a higher risk of mortality, particularly a high VIS early after admission. The VIS provides incremental prognostic information beyond the SCAI Shock Classification, emphasizing the continuum of risk that exists across the spectrum of shock severity.

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

One of the signicant obstacles to donor heart utilization is that increasingly prolonged cold ischemic times are associated with worsened short- and long-term outcomes. As a result, most transplant centers aim to limit cold ischemic times to less than four hours. The TransMedics Organ Care System (OCS) (Trans Medics, Andover MA) is a portable ex vivo perfusion system designed to store a donor heart in a beating, metabolically active state, until it reaches the recipient, thereby allowing extended time outside the body while minimizing the effects of cold storage. We present a case of a heart transplant recipient supported with the Impella 5.5 as a bridge to transplantation who received a donation after circulatory death (DCD) heart supported by OCS. To our knowledge, this is the rst report of such a case.

AbstractIntroductionThe present study aimed to compare the clinical outcomes of heart transplant patients whose donor hearts were preserved with the SherpaPak controlled cold organ system versus the conventional ice storage technique.MethodsAll patients undergoing heart transplantation at our center between January 2019 and April 2021 were divided into two groups according to the technique used during donor heart preservation and transport. The first group consisted of 34 SherpaPak controlled temperature preservation patients, and the second group consisted of 47 patients where the conventional three bags and ice technique was utilized during organ transportation. The two groups were compared based on demographics, operative details, and postoperative outcomes.ResultsThere were no significant differences between the groups regarding Vasoactive Inotropic Score (VIS), Primary Graft Dysfunction (PGD), and the need for a transient pacer. However, the VIS, PGD, and pacing trends were lower in the SherpaPak patients even though the total ischemic and cardiopulmonary bypass times were significantly longer. Furthermore, SherpaPak patients exhibited a shorter stay in the ICU with no severe PGD and mortality.ConclusionThe SherpaPak donor heart preservation provides safe outcomes in heart transplant patients. Further research is needed to utilize this method for longer durations of ischemic time and expand travel distances for organ transportation.

Background Lactic acidosis is associated with mortality in patients with cardiogenic shock (CS). Elevated lactate levels and systemic acidemia (low blood pH) have both been proposed as drivers of death. We, therefore, analyzed the association of both high lactate concentrations and low blood pH with 30‐day mortality in patients with CS. Methods and Results This was a 2‐center historical cohort study of unselected patients with CS with available data for admission lactate level or blood pH. CS severity was graded using the Society for Cardiovascular Angiography and Intervention (SCAI) shock classification. All‐cause survival at 30 days was analyzed using Kaplan‐Meier curves and Cox proportional‐hazards analysis. There were 1814 patients with CS (mean age, 67.3 years; 68.5% men); 51.8% had myocardial infarction and 53.0% had cardiac arrest. The distribution of SCAI shock stages was B, 10.8%; C, 30.7%; D, 38.1%; and E, 18.7%. In both cohorts, higher lactate or lower pH predicted a higher risk of adjusted 30‐day mortality. Patients with a lactate ≥5 mmol/L or pH <7.2 were at increased risk of adjusted 30‐day mortality; patients with both lactate ≥5 mmol/L and pH <7.2 had the highest risk of adjusted 30‐day mortality. Patients in SCAI shock stages C, D, and E had higher 30‐day mortality in each SCAI shock stage if they had lactate ≥5 mmol/L or pH <7.2, particularly if they met both criteria. Conclusions Higher lactate and lower pH predict mortality in patients with cardiogenic shock beyond standard measures of shock severity. Severe lactic acidosis may serve as a risk modifier for the SCAI shock classification. Definitions of refractory or hemometabolic shock should include high lactate levels and low blood pH.