Murphree, Dennis H.

AbstractBackgroundNoncardiac organ failure has been associated with worse outcomes among a cardiac intensive care unit (CICU) population.HypothesisWe hypothesized that early organ failure based on the sequential organ failure assessment (SOFA) score would be associated with mortality in CICU patients.MethodsAdult CICU patients from 2007 to 2015 were reviewed. Organ failure was defined as any SOFA organ subscore ≥3 on the first CICU day. Organ failure was evaluated as a predictor of hospital mortality and postdischarge survival after adjustment for illness severity and comorbidities.ResultsWe included 10 004 patients with a mean age of 67 ± 15 years (37% female). Admission diagnoses included acute coronary syndrome in 43%, heart failure in 46%, cardiac arrest in 12%, and cardiogenic shock in 11%. Organ failure was present in 31%, including multiorgan failure in 12%. Hospital mortality was higher in patients with organ failure (22% vs 3%, adjusted OR 3.0, 95% CI 2.5‐3.7, P < .001). After adjustment, each failing organ system predicted twofold higher odds of hospital mortality (adjusted OR 1.9, 95% CI 1.1‐2.1, P < .001). Mortality risk was highest with cardiovascular, coagulation and liver failure. Among hospital survivors, organ failure was associated with higher adjusted postdischarge mortality risk (P < .001); multiorgan failure did not confer added long‐term mortality risk.ConclusionsEarly noncardiovascular organ failure, especially multiorgan failure, is associated with increased hospital mortality in CICU patients, and this risk continues after hospital discharge, emphasizing the need to promote early recognition of organ failure in CICU patients.

Background The cardiac intensive care unit ( CICU ) population is no longer composed of only patients with acute coronary syndromes, and includes those with acute heart failure and multiple comorbidities. We hypothesized that the GWTG ‐ HF (Get With The Guidelines–Heart Failure) risk score that predicts inpatient mortality in hospitalized patients with heart failure would predict mortality in CICU patients. Methods and Results We retrospectively analyzed CICU patients at a tertiary care hospital from 2007 to 2015. The GWTG ‐ HF risk score was calculated at CICU admission. As a secondary analysis, the EFFECT (Enhanced Feedback for Effective Cardiac Treatment) , OPTIMIZE‐HF (Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure), and ADHERE (Acute Decompensated Heart Failure National Registry) risk scores were calculated. Kaplan–Meier survival analysis and the area under the receiver operating characteristic curve value were determined for inpatient and 1‐year mortality. The GWTG ‐ HF risk score was calculated in 9532 (95%) patients, with a median value of 40 (interquartile range, 35–47). Inpatient mortality occurred in 824 (8.6%) patients, and 2075 (21.8%) patients died by 1 year. Patients who died in hospital had a significantly higher mean GWTG ‐ HF score (47.7 versus 40.2; P <0.001). Inpatient and 1‐year mortality increased in each GWTG ‐ HF risk score quartile ( P <0.0001). Discrimination of the GWTG ‐ HF , EFFECT, OPTIMIZE‐HF, and ADHERE risk scores was assessed using area under the receiver operating characteristic curve values for hospital mortality, and were similar for all risk scores (0.72–0.74; P >0.05). The Hosmer–Lemeshow statistic suggested poor calibration for hospital mortality by the GWTG ‐ HF risk score ( P <0.001). Conclusions The GWTG ‐ HF risk score and other heart failure prediction tools demonstrate good discrimination for inpatient and 1‐year mortality in a heterogeneous cohort of CICU patients. Our study emphasizes that prognostic variables overlap in cardiac patients, regardless of the admission diagnosis.

Background Abnormal serum sodium levels have been associated with higher mortality among patients with acute coronary syndromes and heart failure. We sought to describe the association between sodium levels and mortality among unselected cardiac intensive care unit ( CICU ) patients. Methods and Results We retrospectively reviewed consecutive adult patients admitted to our cardiac intensive care unit from 2007 to 2015. Hyponatremia and hypernatremia were defined as admission serum sodium <135 and >145 mE q/L, respectively. In‐hospital mortality was assessed by multivariable regression, and postdischarge mortality was evaluated by Cox proportional‐hazards analysis. We included 9676 patients with a mean age of 68±15 years (37.5% females). Hyponatremia occurred in 1706 (17.6%) patients, and hypernatremia occurred in 322 (3.3%) patients; these groups had higher illness severity and a greater number of comorbidities. Risk of hospital mortality was higher with hyponatremia (15.5% versus 7.5%; unadjusted odds ratio, 2.41; 95% CI , 2.06–2.82; P <0.001) or hypernatremia (17.7% versus 8.6%; unadjusted odds ratio, 2.82; 95% CI , 2.09–3.80; P <0.001), with a J‐shaped relationship between admission sodium and mortality. After multivariate adjustment, only hyponatremia was significantly associated with in‐hospital mortality (adjusted odds ratio, 1.42; 95% CI, 1.14–1.76; P =0.002). Among hospital survivors, risk of postdischarge mortality was higher in patients with hyponatremia (adjusted hazard ratio, 1.28; 95% CI , 1.17–1.41; P <0.001) or hypernatremia (adjusted hazard ratio, 1.36; 95% CI, 1.12–1.64; P =0.002). Conclusions Hyponatremia and hypernatremia on admission to the cardiac intensive care unit are associated with increased unadjusted short‐ and long‐term mortality. Further studies are needed to determine whether correcting abnormal sodium levels can improve outcomes in cardiac intensive care unit patients.

Background There are no risk scores designed specifically for mortality risk prediction in unselected cardiac intensive care unit ( CICU ) patients. We sought to develop a novel CICU ‐specific risk score for prediction of hospital mortality using variables available at the time of CICU admission. Methods and Results A database of CICU patients admitted from January 1, 2007 to April 30, 2018 was divided into derivation and validation cohorts. The top 7 predictors of hospital mortality were identified using stepwise backward regression, then used to develop the Mayo CICU Admission Risk Score (M‐ CARS ), with integer scores ranging from 0 to 10. Discrimination was assessed using area under the receiver‐operator curve analysis. Calibration was assessed using the Hosmer–Lemeshow statistic. The derivation cohort included 10 004 patients and the validation cohort included 2634 patients (mean age 67.6 years, 37.7% females). Hospital mortality was 9.2%. Predictor variables included in the M‐ CARS were cardiac arrest, shock, respiratory failure, Braden skin score, blood urea nitrogen, anion gap and red blood cell distribution width at the time of CICU admission. The M‐ CARS showed a graded relationship with hospital mortality (odds ratio 1.84 for each 1‐point increase in M‐ CARS , 95% CI 1.78–1.89). In the validation cohort, the M‐ CARS had an area under the receiver‐operator curve of 0.86 for hospital mortality, with good calibration ( P =0.21). The 47.1% of patients with M‐ CARS <2 had hospital mortality of 0.8%, and the 5.2% of patients with M‐ CARS >6 had hospital mortality of 51.6%. Conclusions Using 7 variables available at the time of CICU admission, the M‐ CARS can predict hospital mortality in unselected CICU patients with excellent discrimination.

Background This study sought to evaluate the 15‐year national utilization, trends, predictors, disparities, and outcomes of palliative care services ( PCS ) use in cardiogenic shock complicating acute myocardial infarction. Methods and Results A retrospective cohort from January 1, 2000 through December 31, 2014 was analyzed using the National Inpatient Sample database. Administrative codes for acute myocardial infarction–cardiogenic shock and PCS were used to identify eligible admissions. The primary outcomes were the frequency, utilization trends, and predictors of PCS . Secondary outcomes included in‐hospital mortality and resources utilization. Multivariable regression and propensity‐matching analyses were used to control for confounding. In this 15‐year period, there were 444 253 acute myocardial infarction–cardiogenic shock admissions, of which 4.5% received PCS . The cohort receiving PCS was older, of white race, female sex, and with higher comorbidity and acute organ failure. The PCS cohort received fewer cardiac procedures, but more noncardiac organ support therapies. Older age, female sex, white race, higher comorbidity, higher socioeconomic status, admission to a larger hospital, and admission after 2008 were independent predictors of PCS use. Use of PCS was independently associated with higher in‐hospital mortality (odds ratio 6.59 [95% CI 6.37–6.83]; P <0.001). The cohort with PCS use had >2‐fold higher in‐hospital mortality, 12‐fold higher use of do‐not‐resuscitate status, lesser in‐hospital resource utilization, and fewer discharges to home. Similar findings were observed in the propensity‐matched cohort. Conclusions PCS use in patients with acute myocardial infarction–cardiogenic shock is low, though there is a trend towards increased adoption. There are significant patient and hospital‐specific disparities in the utilization of PCS .

ABSTRACT Background: The use of norepinephrine may be associated with better outcomes in some patients with shock. We sought to determine whether norepinephrine was associated with lower mortality in unselected cardiac intensive care unit (CICU) patients compared with other vasopressors, and whether patterns of vasopressor and inotrope usage in the CICU have changed over time. Methods: We retrospectively evaluated consecutive adult patients admitted to a tertiary care hospital CICU from January 1, 2007 to December 31, 2015. Vasoactive drug doses were quantified using the peak Vasoactive-Inotropic Score (VIS). Temporal trends were assessed using the Cochran–Armitage trends test and multivariable logistic regression was used to determine predictors of hospital mortality. Results: We included 10,004 patients with a mean age of 67 ± 15 years; vasoactive drugs were used in 2,468 (24.7%) patients. Use of norepinephrine increased over time, whereas dopamine utilization decreased ( P < 0.001 for trends). After adjustment for illness severity and other variables, the peak VIS was a predictor of hospital mortality across the entire population (unit odds ratio [OR] 1.013, 95% confidence interval [CI], 1.009–1.017, P < 0.001) and among patients receiving vasoactive drugs (OR 1.018, 95% CI, 1.013–1.022, P < 0.001). Among patients receiving vasoactive drugs, norepinephrine was associated with a lower risk of hospital mortality (OR 0.66, 95% CI, 0.49–0.90, P = 0.008) after adjustment for illness severity and peak VIS. Conclusions: Vasoactive drug use in CICU patients has a dose-dependent association with short-term mortality. Use of norepinephrine in CICU patients is associated with decreased odds of death when compared with other vasoactive drugs.

Introduction: The composition of acute ischemic stroke (AIS) blood clots can impact their successful removal via endovascular therapy. Identification of the clot composition prior to endovascular treatment may influence the optimal treatment strategy. In this study, we aimed to investigate the relationship between the Hounsfield Unit intensity on CT and machine-learned characterization of clot composition. Methods: Prior to endovascular treatment, each patient had a non-contrast CT performed. Histopathological analysis of the clots was performed and clot composition was quantified via standard thresholding techniques using Adobe Photoshop. In addition, following representative cell labeling by an expert pathologist, image segmentation algorithms (Orbital Image Analysis, Actelion Ltd.) were trained on each case to quantify composition by cell type. The clots were categorized into 3 groups: red blood cell (RBC) dominant (>50%), RBC proportion equal to fibrin and fibrin dominant (>50%). Correlations between clot composition and Hounsfield Units (HU) density on CT were assessed. A positive Hyperdense Artery Sign was defined as ≥50 HU. Results: Twenty patients were included in the study. There was a significant correlation between clots quantified using Adobe Photoshop Image-Pro and Orbit image analysis ( ρ = 0.837, p < 0.001). A significant relationship was found between clot composition and the presence of a HAS sign, ( X 2 (2) = 6.496, p = 0.039). There is a significant positive correlation between percentage of RBC in the clot and the presence of a HAS sign ( ρ = 0.632, p = 0.002**). There is also a significant positive correlation between the presence of fibrin-rich and the absence of a HAS sign ( ρ = 0.600, p = 0.004**). Conclusion: Machine learning software can accurately quantify AIS clot composition. The Hounsfield unit density of an AIS clot on a non-contrast CT correlates with the composition of the clot. Measuring the HU of the clot on CT prior to endovascular intervention may inform the clinician of the clot type and help to determine the most appropriate treatment strategy for that clot.

Background Optimal methods of mortality risk stratification in patients in the cardiac intensive care unit ( CICU ) remain uncertain. We evaluated the ability of the Sequential Organ Failure Assessment ( SOFA ) score to predict mortality in a large cohort of unselected patients in the CICU. Methods and Results Adult patients admitted to the CICU from January 1, 2007, to December 31, 2015, at a single tertiary care hospital were retrospectively reviewed. SOFA scores were calculated daily, and Acute Physiology and Chronic Health Evaluation ( APACHE )‐ III and APACHE ‐ IV scores were calculated on CICU day 1. Discrimination of hospital mortality was assessed using area under the receiver‐operator characteristic curve values. We included 9961 patients, with a mean age of 67.5±15.2 years; all‐cause hospital mortality was 9.0%. Day 1 SOFA score predicted hospital mortality, with an area under the receiver‐operator characteristic curve value of 0.83; area under the receiver‐operator characteristic curve values were similar for the APACHE ‐ III score, and APACHE ‐ IV predicted mortality ( P >0.05). Mean and maximum SOFA scores over multiple CICU days had greater discrimination for hospital mortality ( P <0.01). Patients with an increasing SOFA score from day 1 and day 2 had higher mortality. Patients with day 1 SOFA score <2 were at low risk of mortality. Increasing tertiles of day 1 SOFA score predicted higher long‐term mortality ( P <0.001 by log‐rank test). Conclusions The day 1 SOFA score has good discrimination for short‐term mortality in unselected patients in the CICU, which is comparable to APACHE ‐ III and APACHE ‐ IV . Advantages of the SOFA score over APACHE include simplicity, improved discrimination using serial scores, and prediction of long‐term mortality.