Diepen, Sean van

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.

Background: Patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS) often require vasoactive medications to maintain organ perfusion. However, there are limited data on the real-world use of vasoactive medications in this population. Methods: We used the Vizient® Clinical Data Base to identify patients aged ≥18 years who were admitted between 2015-2019 with AMI-CS and required mechanical ventilation. We report the proportion of patients receiving vasoactive medications by day as well as site-level variability for vasoactive medication strategies using the median odds ratio (MOR), which quantifies the odds that a patient receives a strategy at two randomly selected sites after adjusting for covariates. Results: We identified 10,758 patients from 160 centers admitted with AMI-CS. The median age was 65.8 years, 29.8% were female, 83.6% received vasoactive medications in the first 5 days, and 56.9% received temporary mechanical support. The most used vasoactive medication on day 1 was norepinephrine (48.2%), followed by epinephrine (31.5%), and dopamine (16.9%) ( Figure ). On day 1, 65.9% and 36.2% of patients receiving vasoactive medications were prescribed ≥2 or ≥3 different medications, respectively, including 111 different combinations. Among patients receiving inotropes (N=4,485), there was a nearly 3-fold difference in milrinone use across sites after accounting for differences in patient characteristics (MOR 2.89; 95% confidence interval [CI]: 2.39-3.38). For patients receiving either norepinephrine, dopamine, or epinephrine on day 1 (N=3,224), there was a nearly 2-fold difference in the use of norepinephrine as a first-line vasopressor across sites (MOR 1.84; 95% CI: 1.59-2.06). Conclusion: Norepinephrine is the most commonly used vasoactive medication in patients with AMI-CS. However, there is substantial site-level variability in vasoactive medication strategies, which require further study.

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 Invasive haemodynamic assessment with a pulmonary artery catheter is often used to guide the management of patients with cardiogenic shock (CS) and may provide important prognostic information. We aimed to assess prognostic associations and relationships to end-organ dysfunction of presenting haemodynamic parameters in CS. Methods and results The Critical Care Cardiology Trials Network is an investigator-initiated multicenter registry of cardiac intensive care units (CICUs) in North America coordinated by the TIMI Study Group. Patients with CS (2018–2022) who underwent invasive haemodynamic assessment within 24 h of CICU admission were included. Associations of haemodynamic parameters with in-hospital mortality were assessed using logistic regression, and associations with presenting serum lactate were assessed using least squares means regression. Sensitivity analyses were performed excluding patients on temporary mechanical circulatory support and adjusted for vasoactive-inotropic score. Among the 3603 admissions with CS, 1473 had haemodynamic data collected within 24 h of CICU admission. The median cardiac index was 1.9 (25th–75th percentile, 1.6–2.4) L/min/m2 and mean arterial pressure (MAP) was 74 (66–86) mmHg. Parameters associated with mortality included low MAP, low systolic blood pressure, low systemic vascular resistance, elevated right atrial pressure (RAP), elevated RAP/pulmonary capillary wedge pressure ratio, and low pulmonary artery pulsatility index. These associations were generally consistent when controlling for the intensity of background pharmacologic and mechanical haemodynamic support. These parameters were also associated with higher presenting serum lactate. Conclusion In a contemporary CS population, presenting haemodynamic parameters reflecting decreased systemic arterial tone and right ventricular dysfunction are associated with adverse outcomes and systemic hypoperfusion.

BACKGROUND: Cardiovascular and critical care professional societies recommend incorporating family engagement practices into routine clinical care. However, little is known about current family engagement practices in contemporary cardiac intensive care units (CICUs). METHODS: We implemented a validated 12-item family engagement practice survey among site investigators participating in the Critical Care Cardiology Trials Network, a collaborative network of CICUs in North America. The survey includes 9 items assessing specific engagement practices, 1 item about other family-centered care practices, and 2 open-ended questions on strategies and barriers concerning family engagement practice. We developed an engagement practice score by assigning 1 point for each family engagement practice partially or fully adopted at each site (max score 9). We assessed for relationships between the engagement practice score and CICU demographics. RESULTS: All sites (N=39; 100%) completed the survey. The most common family engagement practices were open visitation (95%), information and support to families (85%), structured care conferences (n=82%), and family participation in rounds (77%). The median engagement practice score was 5 (interquartile range, 4). There were no differences in engagement practice scores by geographic region or CICU type. The most commonly used strategies to promote family engagement were family presence during rounds (41%), communication (28%), and family meetings (28%). The most common barriers to family engagement were COVID-related visitation policies (38%) and resource limitations (13%). CONCLUSIONS: Family engagement practices are routinely performed in many CICUs; however, considerable variability exists. There is a need for strategies to address the variability of family engagement practices in CICUs.

Background: Mortality remains high in patients with ST-segment–elevation myocardial infarction (STEMI) complicated by cardiogenic shock (CS), and early reperfusion has been shown to improve outcomes. We analyzed the association between first medical contact (FMC)-to-percutaneous coronary angiography time with mortality and major adverse cardiovascular events among patients with STEMI with and without CS. Methods: We performed a retrospective analysis of the Vancouver Coastal Health Authority STEMI registry, including all patients with STEMI who received primary percutaneous coronary angiography between January 1, 2010, and December 31, 2020, and stratified them by presence or absence of CS at hospital arrival. The primary outcome was in-hospital mortality, the secondary outcome was in-hospital major adverse cardiovascular events, defined as a composite of the first occurrence of mortality, cardiac arrest, heart failure, intracerebral hemorrhage, cerebrovascular accident, or reinfarction. Mixed effects logistic regression with restricted cubic splines was used to estimate the relationships between FMC-to-device time and the outcomes in the CS and non-CS groups. Results: 2929 patients were included, 9.4% (n=275) had CS. Median FMC-to-device time was 113.5 (interquartile range, 93.0–145.0) and 103.0 (interquartile range, 85.0–130.0) minutes for patients with CS and without CS, respectively. More patients with CS had FMC-to-device times above guideline recommendations (76.6% versus 54.1%, P <0.001). Between 60 and 90 minutes, for each 10-minute increase in FMC-to-device time, absolute mortality for patients with CS increased by 4% to 7%, whereas for patients without CS, it increased by <0.5%. Conclusions: Among patients with STEMI undergoing primary percutaneous coronary angiography, reperfusion delays among patients with CS are associated with significantly worse outcomes. Strategies to reduce FMC-to-device times for patients with STEMI presenting with CS are required.

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.

Background: Algorithmic application of the 2019 Society of Cardiovascular Angiography and Intervention (SCAI) shock stages effectively stratifies mortality risk for patients with cardiogenic shock. However, clinician assessment of SCAI staging may differ. Moreover, the implications of the 2022 SCAI criteria update remain incompletely defined. Methods: The Critical Care Cardiology Trials Network is a multicenter registry of cardiac intensive care units (CICUs). Between 2019 and 2021, participating centers (n=32) contributed at least a 2-month snapshot of consecutive medical CICU admissions. In-hospital mortality was assessed across 3 separate staging methods: clinician assessment, Critical Care Cardiology Trials Network algorithmic application of the 2019 SCAI criteria, and a revision of the Critical Care Cardiology Trials Network application using the 2022 SCAI criteria. Results: Of 9612 admissions, 1340 (13.9%) presented with cardiogenic shock with in-hospital mortality of 35.2%. Both clinician and algorithm-based staging using the 2019 SCAI criteria identified a stepwise gradient of mortality risk (stage C–E: 19.0% to 83.7% and 14.6% to 52.2%, respectively; P trend <0.001 for each). Clinician assignment of SCAI stages identified higher risk patients compared with algorithm-based assignment (stage D: 49.9% versus 29.3%; stage E: 83.7% versus 52.2%). Algorithmic application of the 2022 SCAI criteria, with incorporation of the vasoactive-inotropic score, more closely approximated clinician staging (mortality for stage C-E: 21.9% to 70.5%; P trend <0.001). Conclusions: Both clinician and algorithm-based application of the 2019 SCAI stages identify a stepwise gradient of mortality risk, although clinician-staging may better allocate higher risk patients into advanced SCAI stages. Updated algorithmic staging using the 2022 SCAI criteria and vasoactive-inotropic score further refines risk stratification.

Abstract Aims Shock severity predicts mortality in patients with cardiogenic shock (CS). We evaluated the association between pre-cannulation Society for Cardiovascular Angiography and Intervention (SCAI) shock classification and mortality among patients receiving venoarterial (VA) extracorporeal membrane oxygenation (ECMO) support for CS. Methods and results We included Extracorporeal Life Support Organization (ELSO) Registry patients from 2010 to 2020 who received VA ECMO for CS. SCAI shock stage was assigned based on hemodynamic support requirements prior to ECMO initiation. In-hospital mortality was analyzed using multivariable logistic regression. We included 12 106 unique VA ECMO patient runs with a median age of 57.9 (interquartile range: 46.8, 66.1) years and 31.8% were females; 3472 (28.7%) were post-cardiotomy. The distribution of SCAI shock stages at ECMO initiation was: B, 821 (6.8%); C, 7518 (62.1%); D, 2973 (24.6%); and E, 794 (6.6%). During the index hospitalization, 6681 (55.2%) patients died. In-hospital mortality increased incrementally with SCAI shock stage (adjusted OR: 1.24 per SCAI shock stage, 95% CI: 1.17–1.32, P &lt; 0.001): B, 47.5%; C, 52.8%; D, 60.8%; E, 65.1%. A higher SCAI shock stage was associated with increased in-hospital mortality in key subgroups, although the SCAI shock classification was only predictive of mortality in non-surgical (medical) CS and not in post-cardiotomy CS. Conclusion The severity of shock prior to cannulation is a strong predictor of in-hospital mortality in patients receiving VA ECMO for CS. Using the pre-cannulation SCAI shock classification as a risk stratification tool can help clinicians refine prognostication for ECMO recipients and guide future investigations to improve outcomes.