Jentzer, Jacob C.

Introduction: Hyperkalemia has been associated with increased mortality in cardiac intensive care unit (CICU) patients. An artificial intelligence (AI) augmented electrocardiogram (ECG) can predict hyperkalemia, and other AI-ECG algorithms have predicted mortality in CICU patients. Hypothesis: We hypothesized that the AI-ECG hyperkalemia algorithm could stratify mortality risk beyond the serum potassium measurement. Methods: We included 11 234 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who had both a 12 lead ECG and blood potassium (K) level obtained at admission with K>=5 mEq/L defining hyperkalemia. ECGs underwent AI evaluation for probability of hyperkalemia (probability >=0.5 defined as positive). Hospital mortality was analyzed using logistic regression and survival to one year was estimated using Kaplan-Meier and Cox analysis. Results: The mean age was 70 years, 37.8% were females, and 92.4% were white. Chronic kidney disease was present in 20%. The mean K value was 4.2 mEq/L, and 13% (n=1460) had hyperkalemia. AI-ECG predicted hyperkalemia in 34% (n=3820). In-hospital mortality was higher in patients with laboratory and/or AI-ECG hyperkalemia, and these patients had lower survival out to 1 year. Mortality was lowest in patients with normal K and negative AI-ECG and highest in patients with both laboratory-confirmed hyperkalemia and AI-ECG predicted hyperkalemia. Patients with isolated laboratory hyperkalemia (false-negative AI-ECG) had higher mortality than those with isolated AI-ECG hyperkalemia (false-positive AI-ECG). Conclusions: AI-ECG probability of hyperkalemia, even with a normal laboratory K, was associated with higher short-term and long-term mortality in CICU patients. The laboratory K value and AI-ECG probability of hyperkalemia are complementary risk factors. The AI algorithm can detect prognostically important ECG abnormalities which may reflect the effects of K levels on the myocardium.

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: Up to 70% of patients with cardiogenic shock (CS) will survive hospitalization, yet studies on the outcomes of CS survivors are scarce. Hypothesis: We hypothesized that CS survivors would be at high risk of rehospitalization, particularly CS without acute myocardial infarction (AMI) who may have chronic heart failure (HF). Methods: We used the Nationwide Readmissions Database (NRD) to examine adult patients who were discharged alive after an index CS admission from 2016-2019. We compared demographic and clinical features during the index admission and the readmission mortality rate between the AMI-CS versus non-AMI-CS groups. We analyzed the cumulative hazard for all-cause readmission within 90 days after index hospitalization using Kaplan-Meier curves. Results: We included 134,793 survivors of CS hospitalization from 2016 to 2019 who were discharged to home or a long-term facility, including 43.9% with AMI-CS and 56.1% with non-AMI-CS. The demographics and clinical features differed between the AMI-CS and non-AMI-CS groups, with more HF and non-cardiac comorbidities in the non-AMI-CS group. The non-AMI-CS patients were more likely to be readmitted after discharge, with a higher 90-day readmission rate compared to the AMI-CS group (31.6% vs 25.9%, p <0.001; Figure). During the readmission, the non-AMI-CS group had a higher rate of repeat CS presentation compared to the AMI-CS group (8.9% vs.. 6.1%, p < 0.001). In-hospital mortality during readmission was higher in the non-AMI-CS group (8.6% vs.7.2%, p < 0.001). Conclusions: Nearly one-third of CS survivors require readmission within 90 days after their index hospitalization. Non-AMI-CS patients have a higher re-admission rate after their index hospitalization and worse outcomes during readmission. This analysis identifies an unmet need and an important opportunity to improve long-term outcomes for CS survivors.

Introduction: An elevated shock index (SI) predicts worse outcomes in multiple clinical arenas. We aimed to determine whether the SI can aid in mortality risk stratification in unselected CICU patients. Methods: We included admissions to the Mayo Clinic from 2007 to 2015 and stratified them based on admission SI, defined as heart rate divided by systolic blood pressure. The primary outcome was in-hospital mortality, and predictors of in-hospital mortality were analyzed using multivariable logistic regression. Results: We included 9,939 unique CICU patients with available data for SI. The mean age was 69 years old (37% female). Median HR, SBP, and shock index for our cohort were 79 beats per minute (BPM), 121 mmHg, and 0.65 BPM/mmHg, respectively. Patients were grouped by SI as follows: < 0.6, 3,973 (40%); 0.6-0.99, 4,810 (48%); and ≥ 1.0, 1,156 (12%). After multivariable adjustment, both HR (adjusted OR 1.06 per 10 BPM higher; CI 1.02-1.10; p-value 0.005) and SBP (adjusted OR 0.94 per 10 mmHg higher; CI 0.90-0.97; p-value < 0.001) remained associated with higher in-hospital mortality. As SI increased there was an incremental increase in in-hospital mortality (adjusted OR 1.07 per 0.1 BPM/mmHg higher, CI 1.04-1.10, p-Value < 0.001). A higher SI was associated with increased mortality across all examined admission diagnoses, including acute coronary syndromes, heart failure, and cardiogenic shock. Conclusions: The SI is a simple and universally available bedside marker that can be used at the time of admission to predict in-hospital mortality in CICU patients across a variety of cardiac conditions.

Abstract Aims An elevated risk of adverse events persists for years in cardiogenic shock (CS) survivors with high mortality rate and physical/mental disability. This study aims to link clinical CS-survivor phenotypes with distinct late host-response patterns at intensive care unit (ICU) discharge and long-term outcomes using model-based clustering. Methods and results In the original prospective, observational, international French and European Outcome Registry in Intensive Care Units (FROG-ICU) study, ICU patients with CS on admission were identified (N = 228). Among them, 173 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct CS-survivor phenotypes at ICU discharge using 15 readily available clinical and laboratory variables. The primary endpoint was 1 year of mortality after ICU discharge. Secondary endpoints were readmission and physical/mental disability [short form-36 questionnaire (SF-36) score] within 1 year after ICU discharge. Two distinct phenotypes at ICU discharge were identified (A and B). Patients in Phenotype B (38%) were more anaemic and had higher circulating levels of lactate, sustained kidney injury, and persistent elevation in plasma markers of inflammation, myocardial fibrosis, and endothelial dysfunction compared with Phenotype A. They had also a higher rate of non-ischaemic origin of CS and right ventricular dysfunction on admission. CS survivors in Phenotype B had higher 1 year of mortality compared with Phenotype A (P = 0.045, Kaplan–Meier analysis). When adjusted for traditional risk factors (i.e. age, severity of illness, and duration of ICU stay), Phenotype B was independently associated with 1 year of mortality [adjusted hazard ratio = 2.83 (95% confidence interval 1.21–6.60); P = 0.016]. There was a significantly lower physical quality of life in Phenotype B patients at 3 months (i.e. SF-36 physical component score). Conclusions A phenotype with sustained inflammation, myocardial fibrosis, and endothelial dysfunction at ICU discharge was identified from readily available data and was independently associated with poor long-term outcomes in CS survivors.

Background Shock and preshock are defined on the basis of the presence of hypotension, hypoperfusion, or both. We sought to determine the hemodynamic underpinnings of shock and preshock noninvasively using transthoracic echocardiography (TTE). Methods and Results We included Mayo Clinic cardiac intensive care unit patients from 2007 to 2015 with TTE within 1 day of admission. Hypotension and hypoperfusion at the time of cardiac intensive care unit admission were used to define 4 groups. TTE findings were evaluated across these groups, and in‐hospital mortality was evaluated according to TTE findings in each group. We included 5375 patients with a median age of 69.2 years (36.8% women). The median left ventricular ejection fraction was 50%. Groups based on hypotension and hypoperfusion were assigned as follows: no hypotension or hypoperfusion, 59.7%; isolated hypotension, 15.3%; isolated hypoperfusion, 16.4%; and both hypotension and hypoperfusion, 8.7%. Most TTE variables of interest varied across these groups, with worse biventricular function, lower forward flow, and higher filling pressures as the degree of hemodynamic compromise increased. In‐hospital mortality occurred in 8.2%, and inpatient deaths had more TTE parameter abnormalities. In‐hospital mortality increased with the degree of hemodynamic compromise, and a marked gradient in in‐hospital mortality was observed when the clinical classification of shock and preshock was combined with TTE findings reflecting worse biventricular function, lower forward flow, or higher filling pressures. Conclusions Substantial differences in cardiac function are observed between cardiac intensive care unit patients with preshock and shock using TTE, and the combination of the clinical and TTE hemodynamic assessment provides robust mortality risk stratification.

Abstract Background Vasoactive medications and Mechanical Circulatory Support (MCS) are both central to the contemporary management of cardiogenic shock (CS). There are limited data on the interaction between vasoactive medication dosing and MCS in patients with CS. Purpose We investigated the association between in-hospital mortality and vasoactive medication dosing among patients with CS who did and did not receive MCS during hospitalization. Methods We included patients with CS admitted to our cardiac intensive care unit (CICU) between 2007 and 2015. The peak combined vasoactive medication dose during the first 24 hours of CICU admission was recorded and converted to norepinephrine equivalents (NEE)(references 1 & 2). The primary outcome was in-hospital mortality. Multivariable logistic regression was used to adjust for relevant variables and calculate adjusted odds ratios (OR). Results Of 1078 patients with CS (median age 69.0 years [Interquartile Range 59.1-77.3], 36.8% female, 59% with acute myocardial infarction), 721 (66.9%) received vasoactive medications in the first 24 hours and 510 (47.3%) received MCS (87% intra-aortic balloon pump). Among those receiving vasoactive medications during the first 24 hours, the median NEE was 0.16 (Interquartile Range 0.06-0.40) mcg/kg/min. In hospital mortality was 34.4% overall, and was lower among those who received MCS (26.1% versus 41.9%, p&lt;0.001; adjusted OR 0.71 95% CI 0.50-0.99) and higher among those who received vasoactive medications (38.1% versus 26.9%, p&lt;0.001). A gradient of mortality was observed according to the use of vasoactive medications and MCS (Fig. 1). We found a strong incremental association between in-hospital mortality and NEE (adjusted OR 1.11 per 0.1 mcg/kg/min, 95% CI 1.06–1.16, P&lt;0.001; Fig. 2). Mortality was lower across the spectrum of NEE for patients who received MCS (Fig. 2). There was no statistically significant difference in the slope of the relationship between in-hospital mortality and NEE in patients who did and did not receive MCS (p = 0.86). Conclusion Increasing vasoactive medication requirement during the first 24 hours of CICU admission is incrementally associated with in-hospital mortality in CS, representing a simplified marker of shock severity. Patients who received MCS were at lower risk of mortality across the spectrum of vasopressor requirements, emphasizing the potential synergy between these two complementary approaches to hemodynamic support.Figure 1Figure 2

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.

Introduction: Venoarterial extracorporeal membrane oxygenation (VA ECMO) provides full hemodynamic support for cardiogenic shock (CS), but optimal timing of ECMO initiation remains uncertain. Hypothesis: We hypothesized that earlier initiation of ECMO would be associated with improved survival in CS patients. Methods: We analyzed adult patients with CS who received VA ECMO from the Extracorporeal Life Support Organization (ELSO) Registry from 2009 - 2019, excluding those cannulated following an operation. Multivariable logistic regression evaluated the association between time from admission to ECMO initiation and in-hospital mortality. Results: Among 8619 patients (median 56.7 years; 33.5% females), the duration from admission to ECMO initiation was 14 (5, 32) hours. Patients who had ECMO initiated within 24 hours (n = 5882 [68.2%]) differed from those who had ECMO initiated after 24 hours, with younger age, more preceding cardiac arrest, and worse acidosis. After adjustment, patients with ECMO initiated more than 24 hours after admission had higher risk of adjusted in-hospital mortality (adjusted OR 1.20, 95% CI 1.06-1.36, p = 0.004) (Figure 1). Each 12-hour increase in the time from admission to ECMO initiation was incrementally associated with higher adjusted in-hospital mortality (adjusted OR 1.06, 95% CI 1.03-1.10, p < 0.001). The association between longer time to ECMO and worse outcomes appeared stronger in patients with lower shock severity. Among patients who were cannulated within 24 hours, those cannulated during the same day shift had lower mortality than those who were admitted at night and cannulated the following day (adjusted OR adjusted OR 0.81, 95% CI 0.66-0.99, p = 0.04). Conclusions: Longer delays from admission to ECMO initiation were associated with higher mortality in a large-scale, international registry. Our analysis supports optimization of door-to-support time and the avoidance of inappropriately delayed ECMO initiation.