Jentzer, Jacob C.

BACKGROUND: Percutaneous microaxial ventricular assist devices (pVADs) have the potential to reduce mortality of patients with cardiogenic shock (CS). However, the association between the distribution of pVAD-performing centers and outcomes of CS has not been explored. METHODS: This observational study included Medicare fee-for-service beneficiaries aged 65 to 99 years treated with pVAD for CS from 2016 to 2020. It examined the associations between patient outcomes and 2 exposure variables: hospitals’ procedure volumes of pVAD and patient-hospital distances (in quintiles [Qn]). We developed Cox proportional hazards regression for 180-day mortality and heart failure readmission rates, and multivariable logistic regression for in-hospital outcomes, adjusting for patient demographics, comorbidities, concomitant treatments, and hospital characteristics, including CS volume, teaching status, and the ability to perform extracorporeal membrane oxygenation. RESULTS: A total of 6637 patients with CS underwent pVAD at 1041 hospitals, with the annualized hospital volume ranging widely from 0.3 to 55.6 cases/year. Patients treated at higher-volume centers experienced lower 180-day mortality compared with those treated at lower-volume centers (Qn1=reference; Qn2: adjusted hazard ratio [aHR], 0.88 [95% CI, 0.79–0.97]; Qn3: aHR, 0.88 [95% CI, 0.79–0.98]; Qn4: aHR, 0.88 [95% CI, 0.78–0.99]; Qn5: aHR, 0.84 [95% CI, 0.74–0.95]; P for trend, 0.026), while we found no evidence that patient-hospital distances were associated with mortality (Qn1=reference; Qn2: aHR, 0.99 [95% CI, 0.89–1.09]; Qn3: aHR, 0.94 [95% CI, 0.85–1.04]; Qn4: aHR, 1.01 [95% CI, 0.92–1.11]; Qn5: aHR, 0.91 [95% CI, 0.82–1.01]; P for trend, 0.160). We found no evidence that the hospital volume and patient-hospital distances were associated with in-hospital bleeding, intracranial hemorrhage, or renal replacement therapy initiation. CONCLUSIONS: Hospital volume was more strongly associated with mortality than patient-hospital distances, suggesting that rational distribution of pVAD-performing centers while ensuring adequate procedure volumes may optimize patient mortality.

Abstract Background Pulmonary Hypertension (PH) is a diverse condition associated with elevated mortality. We sought to determine the association of Transthoracic Echocardiography (TTE) parameters with in-hospital mortality in patients with PH admitted to Cardiac Intensive Care Unit (CICU). Methods We included 1 Clinic CICU admissions from 2007 to 2018, with available TTE parameters within the first day of CICU admission who had PH defined as an estimated right ventricular systolic pressure (RVSP) ≥36 mmHg. The primary outcome was in-hospital mortality, evaluated using logistic regression. Results We included 3085 unique CICU patients with PH, predominantly WHO group 2 and 3. Median age was 73.7 (63.8, 82.4) years, and 1343 (43.5%) were females. Heart failure (65.6%) and respiratory failure (34.0%) were the most common admission diagnoses. The median RVSP was 47 (41, 56) mmHg, and 1314 (42.6%) had RVSP ≥50 mmHg. A total of 337 (10.9%) patients died during hospitalization. In-hospital deaths had higher RVSP (51 versus 47 mmHg, p <0.001), reflecting higher right atrial (RA) pressure (14 versus 10 mmHg, p <0.001) (Figure). In-hospital mortality increased with worse TTE parameters reflecting Right Ventricle-Pulmonary Artery (RV-PA) coupling, such as Tricuspid Annular Plane Systolic Excursion/ Right Ventricular Systolic Pressure (RVSP), Tricuspid Annular Systolic Velocity (TASV)/RVSP and TASV/ Tricuspid Regurgitation velocity ratios. Conclusion In CICU patients with PH, we have identified the specific Doppler TTE parameters with strong associations with outcomes. Specifically, RA pressure and parameters of RV-PA coupling had the highest discrimination for in-hospital mortality. Comprehensive Doppler TTE is valuable for identification of high-risk PH patients to facilitate further investigation plans, management, and prognostication. Figure: In-hospital mortality in association with right ventricular (RV) function and right atrial pressure (RAP).In-hospital mortality in association wit

Abstract Background Greater vasopressor requirements predict higher mortality in patients with cardiogenic shock (CS).(1) The extent to which CS phenotype modifies the relationship between vasopressor dose and mortality risk is unknown. Purpose We investigated the association between in-hospital mortality and vasopressor dosing (expressed as norepinephrine equivalents [NEE]) during the first 24 hours of hospitalization across distinct CS phenotypes. Methods We included patients with CS admitted to our cardiac intensive care unit (CICU) between 2007 and 2015. Peak combined vasopressor doses during the first 24 hours of hospitalization were recorded as NEE. High dose vasopressor (HDV) use was defined as peak 24 hour NEE >0.3 mcg/kg/min. Unsupervised machine learning was used to assign patients to one of three phenotypes (non-congested, cardiorenal, and haemometabolic), as reported previously by our group and others.(2,3) The primary outcome was in-hospital mortality. Multivariable logistic regression was used to adjust for relevant covariates. Results A total of 1078 patients with CS (median age 69 years, 37% female, 59% with acute myocardial infarction) were admitted to our cardiac intensive care unit (CICU) between 2017 and 2015. The number of patients assigned to each CS phenotype were: haemometabolic (n=325, 30%), cardiorenal (n=314, 29%), non-congested (439, 41%). There was a strong incremental association between in-hospital mortality and NEE (Fig. 1, adjusted OR 1.104 per 0.1 mcg/kg/min, 95% CI 1.134–1.231, P<0.001), and patients receiving HDV (n=234, 22%) had significantly higher in-hospital mortality (57% vs 28%, p<0.001). Among patients who did not receive HDV, mortality was higher in haemometabolic CS (49%) compared to 24% in cardiorenal CS and 21% in non-congested CS (adjusted OR 3.58 relative to non-congested CS, 95% CI 2.46-5.23, p<0.001, Fig. 1). Among patients receiving HDV, hemometabolic CS was also associated with higher mortality (68%) than those with cardiorenal (46.2%) or non-congested CS (39.3%), (adjusted OR 3.26 relative to non-congested CS, 95% CI 1.75-6.18, P<0.001, Fig. 1). Conclusion Integration of CS phenotype with vasopressor requirements may provide incremental prognostic information in CS during the first 24 hours of admission, with haemometabolic CS portending a greater mortality risk even at lower NEE. Patients with HDV (particularly those requiring >=1 mcg/kg/min NEE) had high mortality irrespective of CS phenotype.Figure 1

Abstract Introduction Non-cardiovascular end-organ dysfunction is an important determinant of outcomes in cardiac intensive care unit (CICU) patients. We examined the association between in-hospital mortality with the severity and extent of admission liver function test (LFT) abnormalities in a heterogeneous CICU population. Purpose We hypothesized that a greater severity or extent of LFT abnormalities would be associated with higher in-hospital mortality. Methods We included consecutive unique adult CICU patients with available data for one or more of the admission LFT values of interest. Admission laboratory values were defined as those closest to CICU admission. We used the NIH Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 to categorize the severity of each LFT abnormality from Grade 0 (normal) to Grade 4 (life-threatening). We defined the extent of LFT abnormality as the number of individual LFTs meeting criteria for CTCAE Grade 1 or greater. The primary outcome of interest was all-cause in-hospital mortality. Odds ratio (OR) and 95% confidence interval (CI) values for in-hospital mortality were estimated using logistic regression, before and after multivariable adjustment. Results Of 12,428 unique CICU patients, 5,144 were excluded due to a lack of available data for any admission LFT values. The remaining 7,284 patients comprised the final study population. Among patients with available data for each individual LFT, abnormal values were present in: AST, 2,743/5,733 (47.8%); ALT 1,576/5,512 (28.6%); ALK 611/3,684 (16.6%); and TB 898/5,155 (17.4%). A total of 864 (11.9%) patients died during hospitalization. In-hospital mortality was higher for patients with one or more LFTs meeting criteria for CTCAE Grade 1 or greater (17.9% vs. 6.4%, adjusted OR 1.54 [1.27-1.86], p <0.001). A stepwise increase in in-hospital mortality was observed with increasing CTCAE grade, both overall (adjusted OR 1.25 per each higher CTCAE grade [1.16-1.34], p <0.001); (Figure 1A), and for each individual LFT (Figure 1B). Conclusion Cardiohepatic syndrome is an important predictor of prognosis in CICU patients, as the extent and severity of LFT abnormalities are strongly associated with in-hospital mortality. This is the first large-scale study to examine the association between the magnitude of hepatic biomarker derangement with in-hospital mortality in unselected CICU patients. Patients with markedly elevated LFT values exhibited the highest risk of in-hospital mortality. This analysis advocates for the inclusion of commonly obtained LFTs in future risk-prediction tools for enhanced prognostication.

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 < 0.001]; cardiorenal, 10.3% [adjusted OR 2.11 (1.37–3.32), P < 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 < 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 < 0.001]; iron-deficient, 33.8% [adjusted HR 2.47 (2.00–3.04), P < 0.001]; cardiorenal, 41.2% [adjusted HR 2.41 (1.97–2.95), P < 0.001]; and hypoperfused, 52.3% [adjusted HR 3.43 (2.82–4.18), P < 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.

Key Points Measuring blood potassium has always required access to blood. The surface electrocardiogram, analyzed using an artificial intelligence algorithm, can detect hyperkalemia bloodlessly. The artificial intelligence-analyzed electrocardiogram exhibited a high negative predictive value but substantially lower positive predictive value. Background Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results The ED cohort ( N =40,128) had a mean age of 60 years, 48% were male, and 1% ( N =351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort ( N =2636) had a mean age of 65 years, 60% were male, and 3% ( N =87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

Abstract Funding Acknowledgements None. Background Hyperoxemia (a high PaO2, i.e. &gt;200-300 mmHg) has been associated with adverse outcomes in general critical care settings as well as in acute cardiovascular care conditions. The association between hyperoxemia and mortality has not been characterized in the heterogeneous population admitted to the cardiac intensive care unit (CICU). We aimed to examine the association between hyperoxemia on CICU admission with in-hospital mortality. Methods We retrospectively analyzed Mayo Clinic CICU patients from January 2007 to April 2018. PaO2 closest on admission was analyzed as a continuous variable (per 100 mmHg) and categorized as: 60-100 mmHg (51.9%), 101-150 mmHg (28.6%), 151-200 mmHg (10.6%), 201-300 mmHg (6.4%), and &gt;300 mmHg (2.5%). Those without PaO2 on admission and those with hypoxemia were excluded. The primary outcome was all-cause in-hospital mortality evaluated using logistic regression, after adjusting for age, Charlson Comorbidity Index, M-CARS, and use of IMV and NIPPV. Results A total of 3,368 patients were included (median 70.3 years, 39.4% women). Admission diagnoses included ACS (37.0%, with 19.8% STEMI), HF (67.8%), shock (33.0%) and cardiac arrest (25.3%). Median admission PaO2 was 99 (IQR 78, 136) mmHg, with no difference in median PaO2 between hospital survivors and inpatient deaths (99 versus 98, p = 0.38). A J-shaped relationship was observed between admission PaO2 and in-hospital mortality (Figure A), with slightly higher mortality below 100 mmHg and incrementally higher mortality above this threshold (esp. at &gt;200 mmHg). The incremental association between higher PaO2 and mortality (adjusted OR 1.17 per 100 mmHg higher, 95% CI 1.01-1.34, P = 0.03) was focused in the highest PaO2 group (i.e., &gt;300 mmHg; Figure B). Conclusion Hyperoxia (esp. PaO2 &gt;300 mmHg) on CICU admission is associated with increased in-hospital mortality, demonstrating a J-shaped association with an inflection point of lowest mortality around 100 mmHg. Further studies are warranted to assess if specific oxygen targets influence outcomes in critically ill cardiac patients.

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Aarhus University Dagmar Marshalls Fond. Background The assessment of left ventricular (LV) function through transthoracic echocardiography (TTE) is essential for guiding diagnosis and treatment in cases of cardiogenic shock (CS). Myocardial global longitudinal strain (GLS) provides an objective and reproducible measure of systolic function, independently predicting outcomes in acute heart failure, regardless of left ventricular ejection fraction (LVEF). However, whether assessment of GLS can predict mortality in patients with CS remains unknown. Purpose In this study, we aimed to evaluate the prognostic significance of GLS in patients with CS. Methods Data from cardiac intensive care unit (CICU) patients admitted between 2007 and 2018 with CS who received vasoactive agents and/or mechanical circulatory support were analysed. GLS (as absolute values) and biplane LVEF were measured via TTE performed within 24 hours of CICU admission. Patients with CS were categorised based on the GLS quartile: mild (GLS &gt;9.7%), moderate (7.0% &lt; GLS &lt;9.6%), severe (5.0% &lt; GLS &lt; 6.9%), and very severe (GLS ≤ 4.9%). CICU and in-hospital mortality were compared across GLS categories, and the relationship between GLS and mortality was assessed using multivariable logistic regression. Results The study included 626 patients with a median age of 68.6 years, a median LVEF of 31% (IQR 24 to 40%), and a median GLS of 7.0% (IQR 5.0 to 9.7%). In-hospital mortality was 29.1%. As GLS deteriorated, there was an increasing need for vasopressors (P&lt;0.001). Patients with progressively lower GLS exhibited significantly higher CICU and in-hospital mortality across the GLS quartiles (Figure A; P&lt;0.001). Patients in the severe (adjusted OR 2.67; p&lt;0.001) and very severe category (adjusted OR 3.54; P&lt;0.001) had significantly increased risk of in-hospital death compared with mild category. In multivariable analysis, every 1% decrease in GLS corresponded to a 14% higher risk of in-hospital mortality (P&lt;0.001). Finally, GLS outperformed LVEF for discrimination of in-hospital mortality (P=0.002), and when both measurements were included in the same multivariable model, only GLS remained significant. When patients were divided according to whether they had GLS and/or LVEF below the median value, patients with low GLS had higher mortality irrespective of the LVEF (Figure B). Conclusion In patients with CS, GLS assessment surpasses LVEF in its prognostic value and improves risk stratification. Routine evaluation of GLS should be considered for patients with CS.Figure AFigure B