Jentzer, Jacob C

Background: Exposure to hyperoxia, a high arterial partial pressure of oxygen (PaO2), may be associated with worse outcomes in patients receiving extracorporeal membrane oxygenator (ECMO) support. We examined hyperoxia in the Extracorporeal Life Support Organization Registry among patients receiving venoarterial ECMO for cardiogenic shock. Methods: We included Extracorporeal Life Support Organization Registry patients from 2010 to 2020 who received venoarterial ECMO for cardiogenic shock, excluding extracorporeal CPR. Patients were grouped based on PaO2 after 24 hours of ECMO: normoxia (PaO2 60–150 mmHg), mild hyperoxia (PaO2 151–300 mmHg), and severe hyperoxia (PaO2 >300 mmHg). In-hospital mortality was evaluated using multivariable logistic regression. Results: Among 9959 patients, 3005 (30.2%) patients had mild hyperoxia and 1972 (19.8%) had severe hyperoxia. In-hospital mortality increased across groups: normoxia, 47.8%; mild hyperoxia, 55.6% (adjusted odds ratio, 1.37 [95% CI, 1.23–1.53]; P <0.001); severe hyperoxia, 65.4% (adjusted odds ratio, 2.20 [95% CI, 1.92–2.52]; P <0.001). A higher PaO2 was incrementally associated with increased in-hospital mortality (adjusted odds ratio, 1.14 per 50 mmHg higher [95% CI, 1.12–1.16]; P <0.001). Patients with a higher PaO2 had increased in-hospital mortality in each subgroup and when stratified by ventilator settings, airway pressures, acid-base status, and other clinical variables. In the random forest model, PaO2 was the second strongest predictor of in-hospital mortality, after older age. Conclusions: Exposure to hyperoxia during venoarterial ECMO support for cardiogenic shock is strongly associated with increased in-hospital mortality, independent from hemodynamic and ventilatory status. Until clinical trial data are available, we suggest targeting a normal PaO2 and avoiding hyperoxia in CS patients receiving venoarterial ECMO.

Background Sepsis is associated with an elevated risk of late cardiovascular events among hospital survivors. Methods and Results We included OptumLabs Data Warehouse patients from 2009 to 2019 who survived a medical/nonsurgical hospitalization lasting at least 2 nights. The association between sepsis during hospitalization, based on explicit and implicit discharge International Classification of Diseases, Ninth Revision ( ICD‐9 )/ Tenth Revision ( ICD‐10 ) diagnosis codes, with subsequent death and rehospitalization was analyzed using Kaplan–Meier survival analysis and multivariable Cox proportional‐hazards models. The study population included 2 258 464 survivors of nonsurgical hospitalization (5 396 051 total patient‐years of follow‐up). A total of 808 673 (35.8%) patients had a sepsis hospitalization, including implicit sepsis only in 448 644, explicit sepsis only in 124 841, and both in 235 188. Patients with sepsis during hospitalization had an elevated risk of all‐cause mortality (adjusted hazard ratio [HR], 1.27 [95% CI, 1.25–1.28]; P <0.001), all‐cause rehospitalization (adjusted HR, 1.38 [95% CI, 1.37–1.39]; P <0.001), and cardiovascular hospitalization (adjusted HR, 1.43 [95% CI, 1.41–1.44]; P <0.001), especially heart failure hospitalization (adjusted HR, 1.51 [95% CI, 1.49–1.53]). Patients with implicit sepsis had higher risk than those with explicit sepsis. A sensitivity analysis using the first hospitalization yielded concordant results for cardiovascular hospitalization (adjusted HR, 1.78 [95% CI, 1.76–1.78]; P <0.001), as did a propensity‐weighted analysis (adjusted HR, 1.52 [95% CI, 1.50–1.54]; P <0.001). Conclusions Survivors of sepsis hospitalization are at elevated risk of early and late post‐discharge death as well as cardiovascular and non‐cardiovascular rehospitalization. This hazard spans the spectrum of cardiovascular events and may suggest that sepsis is an important cardiovascular risk factor.

Background Limited data exist regarding urine output (UO) as a prognostic marker in out-of-hospital-cardiac-arrest (OHCA) survivors undergoing targeted temperature management (TTM). Methods We included 247 comatose adult patients who underwent TTM after OHCA between 2007 and 2017, excluding patients with end-stage renal disease. Three groups were defined based on mean hourly UO during the first 24 h: Group 1 (<0.5 mL/kg/h, n = 73), Group 2 (0.5−1 mL/kg/h, n = 81) and Group 3 (>1 mL/kg/h, n = 93). Serum creatinine was used to classify acute kidney injury (AKI). The primary and secondary outcomes respectively were in-hospital mortality and favorable neurological outcome at hospital discharge (modified Rankin Scale [mRS]<3). Results In-hospital mortality decreased incrementally as UO increased (adjusted OR 0.9 per 0.1 mL/kg/h higher; p = 0.002). UO < 0.5 mL/kg/h was strongly associated with higher in-hospital mortality (adjusted OR 4.2 [1.6−10.8], p = 0.003) and less favorable neurological outcomes (adjusted OR 0.4 [0.2−0.8], p = 0.007). Even among patients without AKI, lower UO portended higher mortality (40% vs 15% vs 9% for UO groups 1, 2, and 3 respectively, p < 0.001). Conclusion Higher UO is incrementally associated with lower in-hospital mortality and better neurological outcomes. Oliguria may be a more sensitive early prognostic marker than creatinine-based AKI after OHCA.

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.

Abstract Aims Cardiac point-of-care ultrasound (CV-POCUS) has become a fundamental part for the assessment of patients admitted to cardiac intensive care units (CICU). We sought to refine the practice of CV-POCUS by identifying 2D and Doppler-derived measurements from bedside transthoracic echocardiograms (TTEs) performed in the CICU that are associated with mortality. Methods and results We retrospectively included Mayo Clinic CICU patients admitted from 2007 to 2018 and assessed the TTEs performed within 1 day of CICU admission, including Doppler and 2D measurements of left and right ventricular function. Logistic regression and classification and regression tree (CART) analysis were used to determine the association between TTE variables with in-hospital mortality. A total of 6957 patients were included with a mean age of 68.0 ± 14.9 years (37.0% females). A total of 609 (8.8%) patients died in the hospital. Inpatient deaths group had worse biventricular systolic function [left ventricular ejection fraction (LVEF) 48.2 ± 16.0% vs. 38.7 ± 18.2%, P &lt; 0.0001], higher filling pressures, and lower forward flow. The strongest TTE predictors of hospital mortality were left ventricular outflow tract velocity–time integral [LVOT VTI, adjusted OR 0.912 per 1 cm higher, 95% confidence interval (CI) 0.883–0.942, P &lt; 0.0001] followed by medial mitral E/e′ ratio (adjusted OR 1.024 per 1 unit higher, 95% CI 1.010–1.039, P = 0.0011). Classification and regression tree analysis identified LVOT VTI &lt;16 cm as the most important TTE predictor of mortality. Conclusions Doppler-derived haemodynamic TTE parameters have a strong association with mortality in the CICU, particularly LVOT VTI &lt;16 cm or mitral E/e′ ratio &gt;15. The incorporation of these simplified Doppler-derived haemodynamics into admission CV-POCUS facilitates early risk stratification and strengthens the clinical yield of the ultrasound exam.

Background Cardiac arrest (CA) is associated with worse outcomes in patients with cardiogenic shock (CS). To better understand the contribution of CA on CS, we evaluated transthoracic echocardiography (TTE) parameters in CS patients with and without CA. Methods We retrospectively identified CS patients with a TTE performed near cardiac intensive care unit admission between 2007 to 2018. We compared TTE measurements of left ventricular (LV) and right ventricular (RV) function in patients with and without CA. The primary outcome was all-cause in-hospital mortality, as determined using multivariable logistic regression. Results We included 1085 patients, 35% of whom had CA. Median age was 70 years and 37% were females. CA patients had higher severity of illness, more invasive mechanical ventilation and greater vasopressor/inotrope use. In-hospital mortality was 31% and was higher in CA patients (45% vs. 23%, p <0.001). Although LV ejection fraction (LVEF) was similar (35% vs. 37%, p = 0.05), CA patients had lower cardiac index, mitral valve E wave peak velocity, E/A ratio and E/e' ratio. TTE variables that were associated with hospital mortality varied, among patients with CA, these included measures of RV pressure and function and among patients without CA, these included parameters reflecting LV systolic function. Conclusions Doppler assessments of RV systolic dysfunction were the strongest TTE predictors of hospital mortality in CS patients with CA, unlike CS patients without CA in whom LV systolic function was more important. This emphasizes the importance of RV assessment for mortality risk stratification after CA.

AbstractTargeted temperature management (TTM) has become a cornerstone in the treatment of comatose post-cardiac arrest patients over the last two decades. Belief in the efficacy of this intervention for improving neurologically intact survival was based on two trials from 2002, one truly randomized-controlled and one small quasi-randomized trial, without clear confirmation of that finding. Subsequent large randomized trials reported no difference in outcomes between TTM at 33 vs. 36°C and no benefit of TTM at 33°C as compared with fever control alone. Given that these results may help shape post-cardiac arrest patient care, we sought to review the history and rationale as well as trial evidence for TTM, critically review the TTM2 trial, and highlight gaps in knowledge and research needs for the future. Finally, we provide contemporary guidance for the use of TTM in daily clinical practice.

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.

AbstractBackgroundProposed phenotypes have recently been identified in cardiogenic shock (CS) populations using unsupervised machine learning clustering methods. We sought to validate these phenotypes in a mixed cardiac intensive care unit (CICU) population of patients with CS.MethodsWe included Mayo Clinic CICU patients admitted from 2007 to 2018 with CS. Agnostic K means clustering was used to assign patients to three clusters based on admission values of estimated glomerular filtration rate, bicarbonate, alanine aminotransferase, lactate, platelets, and white blood cell count. In‐hospital mortality and 1‐year mortality were analyzed using logistic regression and Cox proportional‐hazards models, respectively.ResultsWe included 1498 CS patients with a mean age of 67.8 ± 13.9 years, and 37.1% were females. The acute coronary syndrome was present in 57.3%, and cardiac arrest was present in 34.0%. Patients were assigned to clusters as follows: Cluster 1 (noncongested), 603 (40.2%); Cluster 2 (cardiorenal), 452 (30.2%); and Cluster 3 (hemometabolic), 443 (29.6%). Clinical, laboratory, and echocardiographic characteristics differed across clusters, with the greatest illness severity in Cluster 3. Cluster assignment was associated with in‐hospital mortality across subgroups. In‐hospital mortality was higher in Cluster 3 (adjusted odds ratio [OR]: 2.6 vs. Cluster 1 and adjusted OR: 2.0 vs. Cluster 2, both p < 0.001). Adjusted 1‐year mortality was incrementally higher in Cluster 3 versus Cluster 2 versus Cluster 1 (all p < 0.01).ConclusionsWe observed similar phenotypes in CICU patients with CS as previously reported, identifying a gradient in both in‐hospital and 1‐year mortality by cluster. Identifying these clinical phenotypes can improve mortality risk stratification for CS patients beyond standard measures.