Faculty Bibliography

BACKGROUND:Targeted temperature management (TTM) has been recommended after cardiac arrest (CA), however the specific temperature targets and cooling methods (intravascular cooling (IVC) versus surface cooling (SC)) remain uncertain. METHODS:PUBMED and EMBASE were searched until October 8, 2022 for randomized clinical trials (RCTs) investigating the efficacy of TTM after CA. The randomized treatment arms were categorized into the following 6 groups: 31..C to 33..C IVC, 31..C to 33..C SC, 34..C to 36..C IVC, 34..C to 36..C SC, strict normothermia or fever prevention (Strict NT or FP), and standard of care without TTM (No-TTM). The primary outcome was neurological recovery. P-score was used to rank the treatments, where a larger value indicates better performance. RESULTS:We identified 15 RCTs, involving 5,218 patients with CA. Compared to No-TTM as the reference, the other therapeutic options significantly improved neurological outcomes (vs No-TTM; 31..C to 33.. C IVC/UNASSIGNED:RR = 0.67, 95% CI 0.54 to 0.83; 31..C to 33..C SC RR = 0.73, 95% CI 0.61 to 0.87; 34..C to 36.. C IVC/UNASSIGNED:RR = 0.66, 95% CI 0.51 to 0.86; 34..C to 36..C SC: RR = 0.73, 0.59 to 0.90; Strict NT or FP: RR = 0.75, 95% CI 0.62 to 0.90). Overall, 31-33..C IVC had the highest probability to be the best therapeutic option to improve outcomes (the ranking P-score of 0.836). As a subgroup analysis, the ranking P-score showed that IVC might be a better cooling method compared to SC (IVC vs SC P-score: 0.960 vs 0.670). CONCLUSIONS:Hypothermia (31..C to 36..C IVC and SC) and active normothermia (Strict-NT and Strict-FP) were associated with better neurological outcomes compared to No-TTM, with IVC having a greater probability of being the better cooling method than SC.

Oxygen supplementation has been a mainstay in the management of patients with acute cardiac disease. While hypoxia is known to be detrimental, the adverse effects of artificially high oxygen levels (hyperoxia) have only recently been recognized. Hyperoxia may induce harmful hemodynamic effects, including peripheral and coronary vasoconstriction, and direct cellular toxicity through the production of reactive oxygen species. In addition, emerging evidence has shown that hyperoxia is associated with adverse clinical outcomes. Thus, it is essential for the cardiac intensive care unit (CICU) clinician to understand the available evidence and titrate oxygen therapies to specific goals. This review summarizes the pathophysiology of oxygen within the cardiovascular system and the association between supplemental oxygen and hyperoxia in patients with common CICU diagnoses, including acute myocardial infarction, heart failure, shock, cardiac arrest, pulmonary hypertension, and respiratory failure. Finally, we highlight lessons learned from available trials, gaps in knowledge, and future directions.

AIMS/OBJECTIVE:The aims of the Critical Care Cardiology Trials Network (CCCTN) are to develop a registry to investigate the epidemiology of cardiac critical illness and to establish a multicenter research network to conduct randomized clinical trials (RCTs) in patients with cardiac critical illness. METHODS AND RESULTS/RESULTS:The CCCTN was founded in 2017 with 16 centers and has grown to a research network of over 40 academic and clinical centers in the United States and Canada. Each center enters data for consecutive cardiac intensive care unit (CICU) admissions for at least two months of each calendar year. More than 20 000 unique CICU admissions are now included in the CCCTN Registry. To date, scientific observations from the CCCTN Registry include description of variations in care, the epidemiology and outcomes of all CICU patients, as well as subsets of patients with specific disease states, such as shock, heart failure, renal dysfunction, and respiratory failure. The CCCTN has also characterized utilization patterns, including use of mechanical circulatory support in response to changes in the heart transplantation allocation system, and the use and impact of multidisciplinary shock teams. Over years of multicenter collaboration, the CCCTN has established a robust research network to facilitate multicenter registry-based randomized trials in patients with cardiac critical illness. CONCLUSIONS:The CCCTN is a large, prospective registry dedicated to describing processes-of-care and expanding clinical knowledge in cardiac critical illness. The CCCTN will serve as an investigational platform from which to conduct randomized controlled trials in this important patient population.

AIMS/OBJECTIVE:Contemporary cardiac intensive care unit (CICU) outcomes remain highly heterogeneous. As such, a risk-stratification tool using readily available lab data at time of CICU admission may help inform clinical decision-making. METHODS AND RESULTS/RESULTS:The primary derivation cohort included 4352 consecutive CICU admissions across 25 tertiary care CICUs included in the Critical Care Cardiology Trials Network (CCCTN) Registry. Candidate lab indicators were assessed using multivariable logistic regression. An integer risk score incorporating the top independent lab indicators associated with in-hospital mortality was developed. External validation was performed in a separate CICU cohort of 9716 patients from the Mayo Clinic (Rochester, MN, USA). On multivariable analysis, lower pH [odds ratio (OR) 1.96, 95% confidence interval (CI) 1.72-2.24], higher lactate (OR 1.40, 95% CI 1.22-1.62), lower estimated glomerular filtration rate (OR 1.26, 95% CI 1.10-1.45), and lower platelets (OR 1.18, 95% CI 1.05-1.32) were the top four independent lab indicators associated with higher in-hospital mortality. Incorporated into the CCCTN Lab-Based Risk Score, these four lab indicators identified a 20-fold gradient in mortality risk with very good discrimination (C-index 0.82, 95% CI 0.80-0.84) in the derivation cohort. Validation of the risk score in a separate cohort of 3888 patients from the Registry demonstrated good performance (C-index of 0.82; 95% CI 0.80-0.84). Performance remained consistent in the external validation cohort (C-index 0.79, 95% CI 0.77-0.80). Calibration was very good in both validation cohorts (r = 0.99). CONCLUSION/CONCLUSIONS:A simple integer risk score utilizing readily available lab indicators at time of CICU admission may accurately stratify in-hospital mortality risk.

Background Several studies have shown improved outcomes in closed compared with open medical and surgical intensive care units. However, very little is known about the ideal organizational structure in the modern cardiac intensive care unit (CICU). Methods and Results We retrospectively reviewed consecutive unique admissions (n=3996) to our tertiary care CICU from September 2013 to October 2017. The aim of our study was to assess for differences in clinical outcomes between an open compared with a closed CICU. We used multivariable logistic regression adjusting for demographics, comorbidities, and severity of illness. The primary outcome was in-hospital mortality. We identified 2226 patients in the open unit and 1770 in the closed CICU. The unadjusted in-hospital mortality in the open compared with closed unit was 9.6% and 8.9%, respectively (P=0.42). After multivariable adjustment, admission to the closed unit was associated with a lower in-hospital mortality (odds ratio [OR], 0.69; 95% CI: 0.53-0.90, P=0.007) and CICU mortality (OR, 0.70; 95% CI, 0.52-0.94, P=0.02). In subgroup analysis, admissions for cardiac arrest (OR, 0.42; 95% CI, 0.20-0.88, P=0.02) and respiratory insufficiency (OR, 0.43; 95% CI, 0.22-0.82, P=0.01) were also associated with a lower in-hospital mortality in the closed unit. We did not find a difference in CICU length of stay or total hospital charges (P>0.05). Conclusions We found an association between lower in-hospital and CICU mortality after the transition to a closed CICU. These results may help guide the ongoing redesign in other tertiary care CICUs.

BACKGROUND:The benefit of red blood cell (RBC) transfusion in anemic critically-ill patients with cardiovascular disease is uncertain, as is the optimal threshold at which RBC transfusion should be considered. We sought to examine the association between RBC transfusion and mortality stratified by nadir Hgb level and admission diagnosis among cardiac intensive care unit (CICU) patients. METHODS:Retrospective single-center cohort of 11,754 CICU patients admitted between 2007 and 2018. The association between RBC transfusion and hospital mortality at each nadir Hgb (<8 g/dL, 8-9.9 g/dL, ≥10 g/dL) was assessed using multivariable logistic regression adjusted for the propensity to receive RBC transfusion. RESULTS:The study population had a mean age of 68±15 years, including 38% females; 1,134 (11.4%) received RBC transfusion. Admission diagnoses included: acute coronary syndrome , 42%; heart failure, 50%; cardiac arrest , 12%; and cardiogenic shock , 12%. Patients who received RBC transfusion had higher crude hospital mortality (19% vs. 8%, P<.001). RBC transfusion was associated with lower adjusted hospital mortality in patients with nadir Hgb <8 g/dL after propensity adjustment, including subgroups with acute coronary syndrome, cardiac arrest, or cardiogenic shock (all P <.01). RBC transfusion was not associated with lower adjusted hospital mortality in any subgroup of patients with nadir Hgb ≥8 g/dL. CONCLUSIONS:These observational data suggest the use of a Hgb threshold <8 g/dL for RBC transfusion in most CICU patients, although we could not exclude a potential benefit of RBC transfusion at a nadir Hgb of 8 to 9.9 g/dL; we did not observe any benefit from RBC transfusion at a nadir Hgb ≥10 g/dL.

BACKGROUND:Ventricular arrhythmias (VAs) and electrical storm (ES) are recognized complications following left ventricular assist device (LVAD) implantation; however, their association with long term-outcomes remains poorly understood. OBJECTIVE:The purpose of this study was to describe the clinical impact of ES in a population of patients undergoing LVAD implantation at a quaternary care center in the United States. METHODS:This was an observational retrospective study of patients undergoing LVAD implantation from 2009 to 2020 at Duke University Hospital. The incidence of ES (≥3 sustained VA episodes over a 24-hour period without an identifiable reversible cause) was determined from patient records. Risk factors for ES were identified using multivariable Cox proportional hazards modeling. RESULTS:Among 730 patients undergoing LVAD implant, 78 (10.7%) developed ES at a median of 269 (interquartile range [IQR] 7-766) days following surgery. Twenty-seven patients (34.6%) developed ES within 30 days, while 51 (65.4%) presented with ES at a median 639 (IQR 281-1017) days after implant. Following ES, 41% of patients died within 1 year. Patients who developed ES were more likely to have a history of VAs, ventricular tachycardia ablation, antiarrhythmic drug use, and perioperative mechanical circulatory support around the time of LVAD implant than patients without ES. CONCLUSION:ES occurs in 1 in 10 patients after LVAD and is associated with higher mortality. Risk factors for ES include a history of VAs, VT ablation, antiarrhythmic drug use, and perioperative mechanical circulatory support. Optimal management of ES surrounding LVAD implant, including escalation of medical therapy, catheter ablation, or adjunctive sympatholytic therapies, remains uncertain.

OBJECTIVES:Cardiogenic shock presents with variable severity. Categorizing cardiogenic shock into clinical stages may improve risk stratification and patient selection for therapies. We sought to determine whether a structured implementation of the 2019 Society for Cardiovascular Angiography and Interventions clinical cardiogenic shock staging criteria that is ascertainable in clinical registries discriminates mortality in a contemporary population with or at-risk for cardiogenic shock. DESIGN:We developed a pragmatic application of the Society for Cardiovascular Angiography and Interventions cardiogenic shock staging criteria-A (at-risk), B (beginning), C (classic cardiogenic shock), D (deteriorating), or E (extremis)-and examined outcomes by stage. SETTING:The Critical Care Cardiology Trials Network is an investigator-initiated multicenter research collaboration coordinated by the TIMI Study Group (Boston, MA). Consecutive admissions with or at-risk for cardiogenic shock during two annual 2-month collection periods (2017-2019) were analyzed. PATIENTS:Patients with or at-risk for cardiogenic shock. MEASUREMENTS AND MAIN RESULTS:Of 8,240 CICU admissions reviewed, 1,991 (24%) had or were at-risk for cardiogenic shock. Distributions across the five stages were as follows: A: 33%; B: 7%; C: 16%; D: 23%; and E: 21%. Overall in-hospital mortality among patients with established cardiogenic shock was 39%; however, mortality varied from only 15.8% to 32.1% to 62.5% across stages C, D, and E (Cochran-Armitage ptrend < 0.0001). The Society for Cardiovascular Angiography and Interventions stages improved mortality prediction beyond the Sequential Organ Failure Assessment and Intra-Aortic Balloon Pumpin Cardiogenic Shock II scores. CONCLUSIONS:Although overall mortality in cardiogenic shock remains high, it varies considerably based on clinical stage, identifying stage C as relatively lower risk. We demonstrate a pragmatic adaptation of the Society for Cardiovascular Angiography and Interventions cardiogenic shock stages that effectively stratifies mortality risk and could be leveraged for future clinical research.

In this observational study, we compared the prognostic ability of an electronic health record (EHR)-derived risk score, the Rothman Index (RI), automatically derived on admission, to the first 24-hour Sequential Organ Failure Assessment (SOFA) score for outcome prediction in the modern cardiac intensive care unit (CICU). We found that while the 24-hour SOFA score provided modestly superior discrimination for both in-hospital and CICU mortality, the RI upon CICU admission had better calibration for both outcomes. Given the ubiquitous nature of EHR utilization in the United States, the RI may become an important tool to rapidly risk stratify CICU patients within the ICU and improve resource allocation.