Jentzer, Jacob C.

Abstract Aims We sought to determine whether the Diamond–Forrester classification using non-invasive haemodynamic measurements by 2-D and Doppler echocardiography would predict hospital mortality in cardiac intensive care unit (CICU) patients. Methods and results We retrospectively analysed unique patients admitted to the CICU at Mayo Clinic Rochester from 2007 to 2018. Doppler-derived cardiac index (CI) and ratio of mitral valve E velocity to medial mitral annulus e′ velocity (E/e′ ratio) were used to classify patients into four profiles: Profile I (warm/dry), Profile II (warm/wet), Profile III (cold/dry), and Profile IV (cold/wet). Logistic regression was used to determine predictors of hospital mortality, and Cox proportional-hazards analysis was used to determine predictors of mortality during one year of follow-up. We included 4563 patients with a mean age of 68.3 ± 14.3 years, including 36.2% female patients. The distribution of each profile was as follows: I, 47.4%; II, 36.2%; III, 7.9%; IV, 8.5%. A total of 5.8% patients died during hospitalization, and 18.1% died by 1 year. Patients with either low CI or elevated E/e′ ratio had higher in-hospital and 1 year mortality. Patients with elevated E/e′ ratio (i.e. Profiles II and IV) had an increased risk of death during hospitalization and at 1 year after multivariate adjustment (adjusted hazard ratio 1.72 and 2.17 for 1 year mortality, respectively, compared with Profile I, P < 0.01). Conclusions Simple Doppler echocardiographic assessment can be used to identify haemodynamic profiles defined by the Diamond–Forester classification in patients admitted in CICU. These profiles predict outcomes and may be used to guide therapy in critically ill patients.

AbstractRisk stratification dates to the dawn of the cardiac intensive care unit (CICU). As the CICU has evolved from a dedicated unit caring for patients with acute myocardial infarction to a complex healthcare environment encompassing a broad array of acute and chronic cardiovascular pathology, an expanding array of risk scores are available that can be applied to CICU patients. Most of these scores were designed for use either in patients with a specific acute cardiovascular diagnosis or unselected critically ill patients, and risk scores developed in other populations often underperform in the CICU. More recently, risk scores have been developed specific to the CICU population, demonstrating improved performance. All existing risk scores have relevant limitations, both in terms of performance and applicability to patient care. Risk scores have been predominantly developed to predict short-term mortality, either by quantifying severity of illness or by incorporating other risk factors for mortality. It is essential to distinguish mortality risk attributable to severity of illness, which may be modifiable through intervention, from mortality risk attributable to non-modifiable risk factors. This review discusses established risk scores applicable to the CICU population, details how risk score performance is characterized, describes how new risk scores can be developed, explains how the information provided by risk scores can be used in clinical practice, and highlights how novel risk stratification approaches can be developed.

Introduction: Renal dysfunction, as defined by elevated serum creatinine, is associated with decreased survival among patients resuscitated after out-of-hospital cardiac arrest (OHCA). Less is known about other measures of renal function and outcomes after OHCA. Hypothesis: BUN and urine output will predict mortality after OHCA. Methods: Retrospective single-center cohort study of 276 adult OHCA patients treated with targeted temperature management after OHCA between December 2005 and September 2016. Fluid balance and urine output were recorded for the first 6, 12 and 24 hours. Initial laboratory values were determined from electronic records. Hospital survivors and non-survivors were compared using the Wilcoxon test, and multivariate analysis was performed to determine predictors of hospital death. Results: Mean age was 63±12 years, and 204 (73%) were male; shockable rhythm occurred in 241 (87%). Inpatient death occurred in 111 (40%). Hospital survivors had lower initial BUN (13 vs. 20 mg/dL) and creatinine (1.1 vs. 1.5 mg/dL); all p <.001. Hospital survivors had higher (p <.001) urine output during the first 6 hours (1,091 vs. 559 ml), 12 hours (1,583 vs. 843 ml), and 24 hours (2,215 vs. 1,294 ml). Higher initial BUN (OR 1.12, 95% CI 1.07-1.18) and creatinine (OR 3.036, 95% CI 1.65-5.59) predicted hospital mortality; all p <.001. Urine output during the first 24 hours was associated with hospital mortality (OR 0.57 per each 1 L, 95% CI 0.45-0.72, p <.001; AUROC 0.73, optimal cut-off 1 L). Patients with 24-hour urine output <1 L were at increased risk of hospital death (66.3% vs. 26.0%, unadjusted OR 5.61, 95% CI 3.27-9.63, p <.001). Initial BUN (adjusted OR 1.09, 95% CI 1.01-1.16, p=.02) and urine output during the first 24 hours (adjusted OR 0.71 per each 1 L; 95% CI 0.52-0.97, p=.03) remained predictive of hospital mortality after adjustment for patient characteristics and illness severity. Conclusions: Lower 24-hour urine output and higher initial BUN were associated with higher hospital mortality among patients resuscitated from OHCA, even after adjustment for illness severity. Serum creatinine was not a significant predictor of mortality. This emphasizes the importance of low urine output as a prognostic marker and therapeutic target after OHCA.

Abstract Aims The incidence of respiratory failure and use of invasive or non-invasive mechanical ventilation (MV) in the cardiac intensive care units (CICUs) is increasing. While institutional MV volumes are associated with reduced mortality in medical and surgical ICUs, this volume–mortality relationship has not been characterized in the CICU. Methods and results National population-based data were used to identify patients admitted to CICUs (2005–2015) requiring MV in Canada. CICUs were categorized into low (≤100), intermediate (101–300), and high (&gt;300) volume centres based on spline knots identified in the association between annual MV volume and mortality. Outcomes of interest included all-cause in-hospital mortality, the proportion of patients requiring prolonged MV (&gt;96 h) and CICU length of stay (LOS). Among 47 173 CICU admissions requiring MV, 89.5% (42 200) required invasive MV. The median annual CICU MV volume was 43 (inter-hospital range 1–490). Compared to low-volume centres (35.9%), in-hospital mortality was lower in intermediate [29.2%, adjusted odds ratio (aOR) 0.84, 95% confidence interval (CI) 0.72–0.97, P = 0.019] and high-volume (18.2%; aOR 0.82, 95% CI 0.66–1.02, P = 0.076) centres. Prolonged MV was higher in low-volume (29.2%) compared to high-volume (14.8%, aOR 0.70, 95% CI 0.55–0.89, P = 0.003) and intermediate-volume (23.0%, aOR 0.85, 95% CI 0.68–1.06, P = 0.14] centres. Mortality and prolonged MV were lower in percutaneous coronary intervention (PCI)-capable and academic centres, but a shorter CICU LOS was observed only in subgroup of PCI-capable intermediate- and high-volume hospitals. Conclusions In a national dataset, we observed that higher CICU MV volumes were associated with lower incidence of in-hospital mortality, prolonged MV, and CICU LOS. Our data highlight the need for minimum MV volume benchmarks for CICUs caring for patients with respiratory failure.

Introduction: Lactate is a known prognostic marker in critically ill patients, including patients with shock and cardiac arrest (CA). We sought to describe the association between admission lactate and hospital mortality in cardiac intensive care unit (CICU) patients, particularly those with CA. Methods: We retrospectively evaluated adult patients admitted to a tertiary care CICU from January 1, 2007, to April 30, 2018, with measured lactate on admission. We examined hospital mortality as a function of admission lactate level in patients with and without CA. Multivariable logistic regression was used to determine predictors of hospital mortality in the overall cohort, after adjustment for clinical characteristics, therapies, and illness severity. Results: We included 3,042 patients with a median age of 70 years (IQR 60-80), including 40.5% (n=1,233) females. There were 789 patients (26.1%) with a diagnosis of CA. The median APACHE-4 predicted mortality was 24.2% (IQR 10.9-50.7), and 50.8% (n=1546) were treated with vasoactive infusions. The median lactate on admission was 1.8 mmol/L (IQR 1.1-3.0). CICU mortality occurred in 478 (15.7%) patients) and hospital mortality occurred in 706 (23.2%) patients. Hospital mortality rose progressively as a function of admission lactate ( Figure ). On univariable analysis, lactate was associated with increased hospital mortality among the overall cohort (OR 1.36, CI 1.31-1.42, P < .001; AUROC 0.71), patients with CA (OR 1.33 (95% CI 1.25-1.41, P < .01; AUROC 0.73), and patients without CA (OR 1.27, CI 1.21-1.34, P < .01; AUROC 0.64). On multivariable analysis, lactate was one of the most significant predictors of hospital mortality (adjusted OR 1.13 per mmol/L, 95% CI 1.08-1.18, P < .001). Conclusions: Admission lactate levels are strongly associated with increased hospital mortality among CICU patients, especially those with CA. The prognostic value of lactate levels may help inform clinicians caring for CICU patients.

AbstractBackgroundRandomized studies of intra‐aortic balloon pump (IABP) in cardiogenic shock (CS) have only included on patients with acute coronary syndromes (ACS) without stratification according to shock severity. We examined the association between IABP and mortality in CS patients across the Society for Cardiovascular Angiography and Intervention (SCAI) shock stages.MethodsWe included cardiac intensive care unit patients admitted from 2007 to 2015 with CS from any etiology. In‐hospital mortality associated with IABP was examined in each SCAI shock stage. Multivariable logistic regression was performed using inverse probability of treatment weighting (IPTW) to determine the association between IABP and in‐hospital mortality.ResultsWe included 934 patients, with a mean age of 68 ± 14 years; 60% had ACS. The distribution of SCAI shock stages was: B, 41%; C, 13%; D, 38%; E, 8%. In‐hospital mortality was lower in the 39% of patients who received IABP (27% vs. 43%, adjusted OR with IABP after IPTW 0.53, 95% CI 0.40–0.72, p < .0001). IABP use was associated with lower crude in‐hospital mortality in each SCAI shock stage (all p < .05, except p = .08 in SCAI shock stage E). We did not observe any significant heterogeneity in the association between IABP use and in‐hospital mortality as a function of SCAI shock stage.ConclusionsPatients with CS who were selected to receive an IABP had lower in‐hospital mortality, without differences in this effect across the SCAI shock stages. Future studies should account for the severity and etiology of shock when evaluating the efficacy of IABP for CS.

Background: Post-arrest hypotension is common after out of hospital cardiac arrest (OHCA) and many patients resuscitated after OHCA will require vasopressors. We sought to determine the associations between echocardiographic parameters and vasopressor requirements in OHCA patients. Methods: We retrospectively analyzed adult patients with OHCA treated with targeted temperature management between December 2005 and September 2016 who underwent a transthoracic echocardiogram (TTE). Categorical variables were compared using 2-tailed Fisher’s exact and Pearson’s correlation coefficients and variance (r 2 ) values were used to assess relationships between continuous variables. Results: Among 217 included patients, the mean age was 62 ± 12 years, including 74% males. The arrest was witnessed in 90%, the initial rhythm was shockable in 88%, and 58% received bystander CPR. At the time of TTE, 41% of patients were receiving vasopressors; this group of patients was older, had greater severity of illness, higher inpatient mortality and left ventricular ejection fraction (LVEF) was modestly lower (36.8 ± 17.1% vs. 41.4 ± 16.4%, P = 0.04). Stroke volume, cardiac power output and left ventricular stroke work index correlated with number of vasopressors (Pearson r −0.24 to −0.34, all P < 0.002), but the correlation with LVEF was weak (Pearson r −0.13, P = 0.06). Conclusions: In patients after OHCA, left ventricular systolic dysfunction was associated with the need for vasopressors, and Doppler TTE hemodynamic parameters had higher correlation coefficients compared with vasopressor requirements than LVEF. This emphasizes the complex nature of shock after OHCA, including pathophysiologic processes not captured by TTE assessment alone.

Introduction: The Mayo Clinic Cardiac Intensive Care Unit Admission Risk Score (M-CARS) has been shown to predict hospital mortality in the CICU population better than conventional critical care scoring models. We tested the hypothesis that M-CARS could similarly be used to predict one-year mortality. Methods: We retrospectively reviewed adult CICU patients admitted from 2007 to 2018. M-CARS was calculated using admission data. Groups were compared using Wilcoxon test for continuous variables and chi-squared test for categorical variables. Results: This study included 12428 unique patients with a mean age of 67 ± 15 years (37% females), of whom 11279 (90.8%) survived hospitalization and 2839 patients (22.8%) died within 1 year of admission. The 1-year survival decreased incrementally as a function of increasing M-CARS ( Figure 1A , p<0.0001), and all components of M-CARS were significant predictors of 1-year mortality (p<0.001). 1690 hospital survivors (15.0%) died within 1 year. The 1-year survival decreased incrementally as a function of increasing M-CARS for scores <3 (all p<0.001); however, there was no further adverse 1-year survival for patients with M-CARS ≥3 ( Figure 1B, p=0.99). M-CARS components associated with 1-year mortality among hospital survivors included blood urea nitrogen, red blood cell distribution width, Braden score, and respiratory failure (all p<0.001), while cardiac arrest, cardiogenic shock, and anion gap were not. Conclusions: M-CARS accurately predicts 1-year mortality among CICU admissions, with a plateau effects at high M-CARS scores ≥3. Significant predictors of 1-year mortality among CICU survivors include frailty markers rather than admission severity.