Admission diagnosis and mortality risk prediction in a contemporary cardiac intensive care unit population

The prognostic value of pulmonary hypertension in intensive care unit patients from Beth Israel Deaconess Medical Center (BIDMC)

BACKGROUND

The impact of pulmonary hypertension (PH) on critically ill patients has not been fully understood. Our objective was to explore the possible relationship between PH and the outcomes in Intensive Care Unit (ICU) patients, and to determine risk factors of in-hospital mortality of ICU PH patients.

METHODS

The Medical Information Mart for Intensive Care (MIMIC)-IV database was used. Patient characteristics and clinical outcomes of ICU patients with or without PH were compared. The primary outcome was the in-hospital-mortality, and secondary outcome was 28-day mortality. Multivariate logistic regression analysis was conducted to determine independent risk factors of in-hospital mortality.

RESULTS

A total of 42,255 patients were included in the study, of which 1,210 patients had a diagnosis of PH and 4,262 patients died during the hospital stay. In-hospital mortality in the PH and non-PH groups were 15.1% and 9.9% respectively (P < 0.01). The length of stay in ICU and in hospital among ICU PH patients were longer than those without (P < 0.01), and PH group also showed higher 28-day mortality (P < 0.01). Multivariate logistic regression analysis indicated that PH was an independent risk factor for in-hospital mortality in critical ill patients [OR = 1.22, (95%CI: 1.02-1.46), P = 0.033]. Oxford Acute Severity of Illness (OASIS) [OR = 1.10, (95%CI: 1.08-1.12), P < 0.01] anion gap [OR = 1.07, (95%CI: 1.04-1.11), P < 0.01], and Charlson's score [OR = 1.09, (95%CI: 1.03-1.16), P < 0.01] were independent risk factors for in-hospital mortality among ICU PH patients.

CONCLUSIONS

PH diangsoed in the ICU setting has unfavorable clinical outcomes. The Bigger the value of OASIS score, anion gap, Charlson's score were the predictors for in-hospital mortality in ICU patients with PH.

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure

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.

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network

BACKGROUND

Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and systems. This makes it difficult to predict in-hospital mortality events comprehensively and accurately. Traditional analysis methods based on statistics and machine learning suffer from insufficient model performance, poor accuracy caused by prior dependence, and difficulty in adequately considering the complex relationships between multiple risk factors. Therefore, the application of deep neural network (DNN) techniques to the specific scenario, predicting mortality events of patients with AHF under intensive care, has become a research frontier.

METHODS

This research utilized the MIMIC-IV critical care database as the primary data source and employed the synthetic minority over-sampling technique (SMOTE) to balance the dataset. Deep neural network models-backpropagation neural network (BPNN) and recurrent neural network (RNN), which are based on electronic medical record data mining, were employed to investigate the in-hospital death event judgment task of patients with AHF under intensive care. Additionally, multiple single machine learning models and ensemble learning models were constructed for comparative experiments. Moreover, we achieved various optimal performance combinations by modifying the classification threshold of deep neural network models to address the diverse real-world requirements in the ICU. Finally, we conducted an interpretable deep model using SHapley Additive exPlanations (SHAP) to uncover the most influential medical record features for each patient from the aspects of global and local interpretation.

RESULTS

In terms of model performance in this scenario, deep neural network models outperform both single machine learning models and ensemble learning models, achieving the highest Accuracy, Precision, Recall, F1 value, and Area under the ROC curve, which can reach 0.949, 0.925, 0.983, 0.953, and 0.987 respectively. SHAP value analysis revealed that the ICU scores (APSIII, OASIS, SOFA) are significantly correlated with the occurrence of in-hospital fatal events.

CONCLUSIONS

Our study underscores that DNN-based mortality event classifier offers a novel intelligent approach for forecasting and assessing the prognosis of AHF patients in the ICU. Additionally, the ICU scores stand out as the most predictive features, which implies that in the decision-making process of the models, ICU scores can provide the most crucial information, making the greatest positive or negative contribution to influence the incidence of in-hospital mortality among patients with acute heart failure.

Arterial hyperoxia and mortality in the cardiac intensive care unit

BACKGROUND

Arterial hyperoxia (hyperoxemia), defined as a high arterial partial pressure of oxygen (PaO2), has been associated with adverse outcomes in critically ill populations, but has not been examined in the cardiac intensive care unit (CICU). We evaluated the association between exposure to hyperoxia on admission with in-hospital mortality in a mixed CICU cohort.

METHODS

We included unique Mayo Clinic CICU patients admitted from 2007 to 2018 with admission PaO2 data (defined as the PaO2 value closest to CICU admission) and no hypoxia (PaO2 < 60mmHg). The admission PaO2 was evaluated as a continuous variable and categorized (60-100 mmHg, 101-150 mmHg, 151-200 mmHg, 201-300 mmHg, >300 mmHg). Logistic regression was used to evaluate predictors of in-hospital mortality before and after multivariable adjustment.

RESULTS

We included 3,368 patients with a median age of 70.3 years; 70.3% received positive-pressure ventilation. The median PaO2 was 99 mmHg, with a distribution as follows: 60-100 mmHg, 51.9%; 101-150 mmHg, 28.6%; 151-200 mmHg, 10.6%; 201-300 mmHg, 6.4%; >300 mmHg, 2.5%. A J-shaped association between admission PaO2 and in-hospital mortality was observed, with a nadir around 100 mmHg. A higher PaO2 was associated with increased in-hospital mortality (adjusted OR 1.17 per 100 mmHg higher, 95% CI 1.01-1.34, p = 0.03). Patients with PaO2 >300 mmHg had higher in-hospital mortality versus PaO2 60-100 mmHg (adjusted OR 2.37, 95% CI 1.41-3.94, p < 0.001).

CONCLUSIONS

Hyperoxia at the time of CICU admission is associated with higher in-hospital mortality, primarily in those with severely elevated PaO2 >300 mmHg.

Sex-Based Survival Outcomes in Cardiogenic Shock

BACKGROUND

Sex-based disparities have been demonstrated in care delivery for females with cardiogenic shock (CS), including lower use of coronary angiography (CAG), percutaneous intervention (PCI) and mechanical circulatory support (MCS). We evaluated whether sex-based disparities exist and are associated with worse CS outcomes in females.

METHODS

We studied a retrospective cohort of 1498 consecutive, unique adult cardiovascular intensive care unit (CICU) admissions with CS from 2007-2018.

RESULTS

Compared to males, females (n = 566, 37.1%) were older (71.7 vs 67.8 years; P < 0.001) but had similar burdens of medical comorbidities. Acute myocardial infarction (AMI) was present in 54.1% of females and 59.1% of males (P = 0.06). There were no sex-based differences in the use of CAG and PCI, but females received temporary MCS less commonly. Specifically, females with non-AMI CS received MCS devices less commonly (17.6% vs 24.4%; P = 0.04). There was no difference in in-hospital or 1-year mortality rates between the sexes. Compared to males, females who received PCI had lower risks of 1-year mortality (unadjusted HR 0.72; P = 0.03), whereas females who received CAG without PCI had higher risks of 1-year mortality (unadjusted HR 1.41; P = 0.02).

CONCLUSIONS

No sex-based disparities in mortality due to CS were demonstrated in this large, diverse cohort of patients with CICU admissions. Females who underwent PCI demonstrated lower risks of 1-year mortality, whereas females who underwent CAG without PCI demonstrated higher risks of 1-year mortality compared to males. This may reflect underuse of PCI as a mortality-reducing therapy in females.

Shared Decision-making in Palliative and End-of-life Care in the Cardiac Intensive Care Unit

Patients and clinicians in the cardiac intensive care unit (CICU) are often tasked with making high-stakes decisions about aggressive or life- sustaining therapies. Shared decision-making (SDM), a collaborative process where patients and clinicians work together to make medical decisions that are aligned with a patient's goals and values, is therefore highly relevant in the CICU, especially in the context of palliative or end-of-life decisions. Despite its importance, there are barriers to optimal integration and implementation of SDM. This review describes the fundamentals and models of SDM, the role of SDM in the CICU, and evidence-based strategies to promote SDM in the CICU.

Mortality Risk Stratification Utilizing Artificial Intelligence Electrocardiogram for Hyperkalemia in Cardiac Intensive Care Unit Patients

Background

Hyperkalemia has been associated with increased mortality in cardiac intensive care unit (CICU) patients. An artificial intelligence (AI) enhanced electrocardiogram (ECG) can predict hyperkalemia, and other AI-ECG algorithms have demonstrated mortality risk-stratification in CICU patients.

Objectives

The authors hypothesized that the AI-ECG hyperkalemia algorithm could stratify mortality risk beyond laboratory serum potassium measurement alone.

Methods

We included 11,234 unique Mayo Clinic CICU patients admitted from 2007 to 2018 with a 12-lead ECG and blood potassium (K) level obtained at admission with K ≥5 mEq/L defining hyperkalemia. ECGs underwent AI evaluation for the probability of hyperkalemia (probability >0.5 defined as positive). Hospital mortality was analyzed using logistic regression, and survival to 1 year was estimated using Kaplan-Meier and Cox analysis.

Results

In the final cohort (n = 11,234), the mean age was 69.6 ± 10.5 years, 37.8% were females, and 92.4% were White. Chronic kidney disease was present in 20.2%. The mean laboratory potassium value for the cohort was 4.2 ± 0.3 mEq/L. The AI-ECG predicted hyperkalemia in 33.9% (n = 3,810) of CICU patients and 12.9% (n = 1,451) of patients had laboratory-confirmed hyperkalemia (K ≥5 mEq/L). In-hospital mortality increased in false-positive, false-negative, and true-positive patients, respectively (P < 0.001), and each of these patient groups had successively lower survival out to 1 year.

Conclusions

AI-ECG-based prediction of hyperkalemia, even with a normal laboratory potassium value, was associated with higher in-hospital mortality and lower 1-year survival in CICU patients. This study demonstrated that AI-ECG probability of hyperkalemia may enable rapid individualized risk stratification in critically ill patients beyond laboratory value alone.

Clinical complexity of an Italian cardiovascular intensive care unit: the role of mortality and severity risk scores

AIMS

The identification of patients at greater mortality risk of death at admission into an intensive cardiovascular care unit (ICCU) has relevant consequences for clinical decision-making. We described patient characteristics at admission into an ICCU by predicted mortality risk assessed with noncardiac intensive care unit (ICU) and evaluated their performance in predicting patient outcomes.

METHODS

A total of 202 consecutive patients (130 men, 75 ± 12 years) were admitted into our tertiary-care ICCU in a 20-week period. We evaluated, on the first 24 h data, in-hospital mortality risk according to Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score 3 (SAPS 3); Sepsis related Organ Failure Assessment (SOFA) Score and the Mayo Cardiac intensive care unit Admission Risk Score (M-CARS) were also calculated.

RESULTS

Predicted mortality was significantly lower than observed (5% during ICCU and 7% at discharge) for APACHE II and SAPS 3 (17% for both scores). Mortality risk was associated with older age, more frequent comorbidities, severe clinical presentation and complications. The APACHE II, SAPS 3, SOFA and M-CARS had good discriminative ability in distinguishing deaths and survivors with poor calibration of risk scores predicting mortality.

CONCLUSION

In a recent contemporary cohort of patients admitted into the ICCU for a variety of acute and critical cardiovascular conditions, scoring systems used in general ICU had good discrimination for patients' clinical severity and mortality. Available scores preserve powerful discrimination but the overestimation of mortality suggests the importance of specific tailored scores to improve risk assessment of patients admitted into ICCUs.

Association between the shock index on admission and in-hospital mortality in the cardiac intensive care unit

BACKGROUND

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 cardiac intensive care unit patients.

METHODS

We included admissions to the Mayo Clinic from 2007 to 2015 and stratified them based on admission SI. 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 cardiac intensive care unit patients with available data for SI. 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 heart rate (adjusted OR 1.06 per 10 beats per minute higher; CI 1.02-1.10; p-value 0.005) and systolic blood pressure (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 beats per minute/mmHg higher, CI 1.04-1.10, p-Value < 0.001). A higher SI was associated with increased mortality across all examined admission diagnoses.

CONCLUSION

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 cardiac intensive care unit patients.

Prognostic performance of the IABP-SHOCK II Risk Score among cardiogenic shock subtypes in the critical care cardiology trials network registry

BACKGROUND

Risk stratification has potential to guide triage and decision-making in cardiogenic shock (CS). We assessed the prognostic performance of the IABP-SHOCK II score, derived in Europe for acute myocardial infarct-related CS (AMI-CS), in a contemporary North American cohort, including different CS phenotypes.

METHODS

The critical care cardiology trials network (CCCTN) coordinated by the TIMI study group is a multicenter network of cardiac intensive care units (CICU). Participating centers annually contribute ≥2 months of consecutive medical CICU admissions. The IABP-SHOCK II risk score includes age > 73 years, prior stroke, admission glucose > 191 mg/dl, creatinine > 1.5 mg/dl, lactate > 5 mmol/l, and post-PCI TIMI flow grade < 3. We assessed the risk score across various CS etiologies.

RESULTS

Of 17,852 medical CICU admissions 5,340 patients across 35 sites were admitted with CS. In patients with AMI-CS (n = 912), the IABP-SHOCK II score predicted a >3-fold gradient in in-hospital mortality (low risk = 26.5%, intermediate risk = 52.2%, high risk = 77.5%, P < .0001; c-statistic = 0.67; Hosmer-Lemeshow P = .79). The score showed a similar gradient of in-hospital mortality in patients with non-AMI-related CS (n = 2,517, P < .0001) and mixed shock (n = 923, P < .001), as well as in left ventricular (<0.0001), right ventricular (P = .0163) or biventricular (<0.0001) CS. The correlation between the IABP-SHOCK II score and SOFA was moderate (r2 = 0.17) and the IABP-SHOCK II score revealed a significant risk gradient within each SCAI stage.

CONCLUSIONS

In an unselected international multicenter registry of patients admitted with CS, the IABP- SHOCK II score only moderately predicted in-hospital mortality in a broad population of CS regardless of etiology or irrespective of right, left, or bi-ventricular involvement.

Development and validation of a nomogram for predicting in-hospital mortality in ICU patients with infective endocarditis

BACKGROUND

Infective endocarditis (IE) is a disease with high in-hospital mortality. The objective of the present investigation was to develop and validate a nomogram that precisely anticipates in-hospital mortality in ICU individuals diagnosed with infective endocarditis.

METHODS

Retrospectively collected clinical data of patients with IE admitted to the ICU in the MIMIC IV database were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential hazards. A logistic regression model incorporating multiple factors was established, and a dynamic nomogram was generated to facilitate predictions. To assess the classification performance of the model, an ROC curve was generated, and the AUC value was computed as an indicator of its diagnostic accuracy. The model was subjected to calibration curve analysis and the Hosmer-Lemeshow (HL) test to assess its goodness of fit. To evaluate the clinical relevance of the model, decision-curve analysis (DCA) was conducted.

RESULTS

The research involved a total of 676 patients, who were divided into two cohorts: a training cohort comprising 473 patients and a validation cohort comprising 203 patients. The allocation ratio between the two cohorts was 7:3. Based on the independent predictors identified through LASSO regression, the final selection for constructing the prediction model included five variables: lactate, bicarbonate, white blood cell count (WBC), platelet count, and prothrombin time (PT). The nomogram model demonstrated a robust diagnostic ability in both the cohorts used for training and validation. This is supported by the respective area under the curve (AUC) values of 0.843 and 0.891. The results of the calibration curves and HL tests exhibited acceptable conformity between observed and predicted outcomes. According to the DCA analysis, the nomogram model demonstrated a notable overall clinical advantage compared to the APSIII and SAPSII scoring systems.

CONCLUSIONS

The nomogram developed during the study proved to be highly accurate in forecasting the mortality of patients with IE during hospitalization in the ICU. As a result, it may be useful for clinicians in decision-making and treatment.

Patients Aged 90 Years and Above With Acute Coronary Syndrome in the Cardiac Intensive Care Unit: Management and Outcomes

Limited data exist regarding outcomes after coronary angiography (CAG) and percutaneous coronary intervention (PCI) in patients aged ≥90 years admitted to the cardiac intensive care unit (CICU) with acute coronary syndrome (ACS). We studied sequential CICU patients ≥90 years admitted with ACS from 2007 to 2018. Three therapeutic approaches were defined: (1) No CAG; (2) CAG without PCI (CAG/No PCI); and (3) CAG with PCI (CAG/PCI). In-hospital mortality was evaluated using multivariable logistic regression. All-cause 1-year mortality was evaluated using Kaplan-Meier and multivariable Cox proportional hazards analysis. The study included 239 patients with a median age of 92 (range 90 to 100) years (57% females; 45% ST-elevation myocardial infarction; 8% cardiac arrest; 16% shock). The No CAG group had higher Day 1 Sequential Organ Failure Assessment scores, more co-morbidities, worse kidney function, and fewer ST-elevation myocardial infarctions. In-hospital mortality was 20.8% overall and did not differ between the No CAG (n = 103; 21.4%), CAG/No PCI (n = 47; 21.3%), and CAG/PCI (n = 90; 20.0%) groups, before or after adjustment. Overall 1-year mortality was 52.5% and did not differ between groups before or after adjustment. Median survival was 6.9 months overall and 41.2% of hospital survivors died within 1 year of CICU admission. CICU patients aged ≥90 years with ACS have a substantial burden of illness with high in-hospital and 1-year mortality that was not lower in those who underwent CAG or PCI. These results suggest that careful patient selection for invasive coronary procedures is essential in this vulnerable population.

The value of five scoring systems in predicting the prognosis of patients with sepsis-associated acute respiratory failure

Our study aimed to identify the optimal scoring system for predicting the prognosis of patients with sepsis-associated acute respiratory failure (SA-ARF). All data were taken from the fourth version of the Markets in Intensive Care Medicine (MIMIC-IV) database. Independent risk factors for death in hospitals were confirmed by regression analysis. The predictive value of the five scoring systems was evaluated by receiving operating characteristic (ROC) curves. Kaplan‒Meier curves showed the impact of acute physiology score III (APSIII) on survival and prognosis in patients with SA-ARF. Decision curve analysis (DCA) identified a scoring system with the highest net clinical benefit. ROC curve analysis showed that APS III (AUC: 0.755, 95% Cl 0.714-0.768) and Logical Organ Dysfunction System (LODS) (AUC: 0.731, 95% Cl 0.717-0.7745) were better than Simplified Acute Physiology Score II (SAPS II) (AUC: 0.727, 95% CI 0.713-0.741), Oxford Acute Severity of Illness Score (OASIS) (AUC: 0.706, 95% CI 0.691-0.720) and Sequential Organ Failure Assessment (SOFA) (AUC: 0.606, 95% CI 0.590-0.621) in assessing in-hospital mortality. Kaplan‒Meier survival analysis patients in the high-APS III score group had a considerably poorer median survival time. The DCA curve showed that APS III may provide better clinical benefits for patients. We demonstrated that the APS III score is an excellent predictor of in-hospital mortality.

Association of Shock Index with Echocardiographic Parameters in Cardiac Intensive Care Unit

BACKGROUND

A high shock index (SI), the ratio of heart rate (HR) to systolic blood pressure (SBP), has been associated with unfavorable outcomes. We sought to determine the hemodynamic underpinnings of an elevated SI using 2-D and doppler Transthoracic Echocardiography (TTE) in unselected cardiac intensive care unit (CICU) patients.

METHODS

We included Mayo Clinic CICU admissions from 2007 to 2018 who were in sinus rhythm at the time of TTE. The SI was calculated using HR and SBP at the time of TTE. Patients were grouped according to SI: <0.7, 4012 (64%); 0.7-0.99, 1764 (28%); and ≥ 1.0, 513 (8%). Pearson's correlation coefficient was used to assess associations between continuous variables.

RESULTS

We included 6289 unique CICU patients, 58% of whom had acute coronary syndrome. The median age was 67.9 years old and 37.8% were females. The mean SI was 0.67 BPM/mmHg. As the SI increased, markers of left ventricular (LV) systolic function and forward flow decreased, including left ventricular ejection fraction (LVEF), fractional shortening, left ventricular outflow tract (LVOT) velocity time integral (VTI), stroke volume, LV stroke work index, and cardiac power output. Biventricular filling pressures increased, and markers of right ventricular function worsened with rising SI. Most TTE measurements reflecting LV function and forward flow were inversely correlated with SI, including LV stroke work index (r = -0.59) and LVOT VTI (r = -0.41), as were both systemic vascular resistance index (r = -0.43) and LVEF (r = -0.23).

CONCLUSION

CICU patients with elevated SI have worse biventricular function and systemic hemodynamics, particularly decreased stroke volume and related calculated TTE parameters. The SI is an easily available marker that can be used to identify CICU patients with unfavorable hemodynamics who may require further assessment.

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence

BACKGROUND

In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2.

METHODS

We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm's performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability.

MEASUREMENTS AND MAIN RESULTS

The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2.

CONCLUSIONS

The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.

PROGNOSTIC PERFORMANCE OF SERIAL DETERMINATION OF THE SOCIETY FOR CARDIOVASCULAR ANGIOGRAPHY AND INTERVENTIONS SHOCK CLASSIFICATION IN ADULTS WITH CRITICAL ILLNESS

ABSTRACT

Purpose: The aim of the study is to evaluate whether serial assessment of shock severity can improve prognostication in intensive care unit (ICU) patients. Materials and Methods: This is a retrospective cohort of 21,461 ICU patient admissions from 2014 to 2018. We assigned the Society for Cardiovascular Angiography and Interventions (SCAI) Shock Stage in each 4-h block during the first 24 h of ICU admission; shock was defined as SCAI Shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: The admission SCAI Shock stages were as follows: A, 39.0%; B, 27.0%; C, 28.9%; D, 2.6%; and E, 2.5%. The SCAI Shock stage subsequently increased in 30.6%, and late-onset shock developed in 30.4%. In-hospital mortality was higher in patients who had shock on admission (11.9%) or late-onset shock (7.3%) versus no shock (4.3%). Persistence of shock predicted higher mortality (adjusted OR = 1.09; 95% CI = 1.06-1.13, for each ICU block with shock). The mean SCAI Shock stage had higher discrimination for in-hospital mortality than the admission or maximum SCAI Shock stage. Dynamic modeling of the SCAI Shock classification improved discrimination for in-hospital mortality (C-statistic = 0.64-0.71). Conclusions: Serial application of the SCAI Shock classification provides improved mortality risk stratification compared with a single assessment on admission, facilitating dynamic prognostication.

Admission Total Leukocyte Count as a Predictor of Mortality in Cardiac Intensive Care Unit Patients

Background

Inflammation is a sequela of cardiovascular critical illness and a risk factor for mortality.

Objectives

This study aimed to evaluate the association between white blood cell count (WBC) and mortality in a broad population of patients admitted to the cardiac intensive care unit (CICU).

Methods

This retrospective cohort study included patients admitted to the Mayo Clinic CICU between 2007 and 2018. We analyzed WBC as a continuous variable and then categorized WBC as low (<4.0 × 103/mL), normal (≥4.0 to <11.0 × 103/mL), high (≥11.0 to <22.0 × 103/mL), or very high (≥22.0 × 103/mL). The association between WBC and in-hospital mortality was evaluated using multivariable logistic regression and random forest models.

Results

We included 11,699 patients with a median age of 69.3 years (37.6% females). Median WBC was 9.6 (IQR: 7.4-12.7). Mortality was higher in the low (10.5%), high (12.0%), and very high (33.3%) WBC groups relative to the normal WBC group (5.3%). A rising WBC was incrementally associated with higher in-hospital mortality after adjustment (AICc adjusted OR: 1.03 [95% CI: 1.02-1.04] per 1 × 103 increase in WBC). After adjustment, only the high (AICc adjusted OR: 1.37 [95% CI: 1.15-1.64]) and very high (AICc adjusted OR: 1.99 [1.47-2.71]) WBC groups remained associated with increased risk of in-hospital mortality.

Conclusions

Leukocytosis is associated with an increased mortality risk in a diverse cohort of CICU patients. This readily available marker of systemic inflammation may be useful for risk stratification within the increasingly complex CICU patient population.

Serial Assessment of Shock Severity in Cardiac Intensive Care Unit Patients

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.

Noninvasive Hemodynamic Characterization of Shock and Preshock Using Echocardiography in Cardiac Intensive Care Unit Patients

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.

Prognosis of Venoarterial Extracorporeal Membrane Oxygenation in Mixed, Cardiogenic and Septic Shock

Mixed cardiogenic-septic shock (MS), defined as the combination of cardiogenic (CS) and septic (SS) shock, is often encountered in cardiac intensive care units. Herein, the authors compared the impact of venoarterial extracorporeal membrane oxygenation (VA-ECMO) in MS, CS, and SS. Of 1,023 patients who received VA-ECMO from January 2012 to February 2020 at a single center, 211 with pulmonary embolism, hypovolemic shock, aortic dissection, and unknown causes of shock were excluded. The remaining 812 patients were grouped based on the cause of shock at VA-ECMO application: i) MS (n = 246, 30.3%), ii) CS (n = 466, 57.4%), iii) SS (n = 100, 12.3%). The MS group was younger and had lower left ventricular ejection fraction than the CS or SS group did. The 30 day and 1 year mortalities were the highest in SS (30 day mortality: 50.4% vs. 43.3% vs. 69.0%, p < 0.001 for MS versus CS versus SS, respectively; 1 year mortality: 67.5% vs. 53.2% vs. 81.0%, p < 0.001 for MS versus CS versus SS, respectively). Posthoc analysis showed that the 30 day mortality of MS was not different from CS, while the 1 year mortality of MS was worse than CS but better than SS. Venoarterial extracorporeal membrane oxygenation application for MS may help improve survival and should therefore be considered if indicated.

CHANGES IN VASOACTIVE DRUG REQUIREMENTS AND MORTALITY IN CARDIAC INTENSIVE CARE UNIT PATIENTS

ABSTRACT

Background: The Society for Cardiovascular Angiography and Intervention (SCAI) Shock Classification can define shock severity. We evaluated the vasoactive-inotropic score (VIS) combined with the SCAI Shock Classification for mortality risk stratification. Methods: This was a single-center retrospective cohort analysis including Mayo Clinic cardiac intensive care unit patients from 2007 to 2015. The peak VIS was calculated at 1 and 24 h after cardiac intensive care unit admission. In-hospital mortality was evaluated using multivariable logistic regression. Results: Of 9,916 included patients, vasoactive drugs were used in 875 (8.8%) within 1 h and 2,196 (22.1%) within 24 h. A total of 888 patients (9.0%) died during hospitalization. Patients who required vasoactive drugs within 1 h had higher in-hospital mortality (adjusted odds ratio [OR], 1.30; 95% confidence interval [CI], 1.03-1.65; P = 0.03) and in-hospital mortality rose with the VIS during the first 1 h (adjusted OR per 10 units, 1.22; 95% CI, 1.12-1.33; P < 0.001). The increase in VIS from 1 to 24 h was associated with higher in-hospital mortality (adjusted OR per 10 units, 1.16; 95% CI, 1.10-1.21; P < 0.001). These results were consistent in the 1,067 patients (10.9%) with cardiogenic shock. A gradient of in-hospital mortality was observed according to the VIS at 1 h and the increase in VIS from 1 to 24 h. Conclusions: Higher vasoactive drug requirements portend a higher risk of mortality, particularly a high VIS early after admission. The VIS provides incremental prognostic information beyond the SCAI Shock Classification, emphasizing the continuum of risk that exists across the spectrum of shock severity.

Echocardiographic findings in cardiogenic shock due to acute myocardial infarction versus heart failure

BACKGROUND

Acute myocardial infarction (AMI) is the prototypical cause of cardiogenic shock (CS), yet CS due to heart failure (HF-CS) is increasingly common. Little is known regarding cardiac function in AMI-CS versus HF-CS. We compared transthoracic echocardiography (TTE) findings in AMI-CS versus HF-CS and identified predictors of mortality in AMI-CS patients.

METHODS

We performed a single-center, retrospective analysis of CS admissions between 2007 and 2018. We compared baseline demographic and TTE parameters in patients with AMI-CS and HF-CS as well as ST elevation myocardial infarction (STEMI)-CS versus non-ST elevation myocardial infarction (NSTEMI)-CS.

RESULTS

We included 893 unique patients, including 581 (65%) with AMI-CS. AMI-CS patients were older but had lower illness severity and non-cardiac comorbidity burden. AMI-CS patients had better left ventricular function (LVEF 35% versus 28%), lower biventricular filling pressures, and higher stroke volume versus those with HF-CS. Among TTE measurements, myocardial contraction fraction had the highest discrimination for mortality in AMI-CS (AUC: 0.64); AUC values for LVEF and SOFA score were 0.61 and 0.65, respectively. Differences in TTE findings between STEMI-CS versus NSTEMI-CS were modest. There were no significant differences in unadjusted or adjusted in-hospital mortality between AMI-CS and HF-CS (31% versus 35%) or STEMI-CS and NSTEMI-CS (31% versus 30%) groups (all p > 0.05).

CONCLUSIONS

Patients with HF-CS and AMI-CS differ in terms of clinical and TTE variables yet have similar prognoses. TTE is useful in determining prognosis of patients admitted with AMI-CS and may allow for early triage and directed therapy.

Urine Output and Mortality in Patients Resuscitated from out of Hospital Cardiac Arrest

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.

Clinical phenotypes of delirium in patients admitted to the cardiac intensive care unit

Background

Limited data are available on clinical phenotype for delirium that occurs frequently among patients admitted to the cardiac intensive care unit (CICU). The objective of this study was to investigate the clinical pictures of delirium, and their association with clinical outcomes in CICU patients.

Methods

A total of 4,261 patients who were admitted to the CICU between September 1 2012 to December 31 2018 were retrospectively registered. Patients were excluded if they were admitted to the CICU for less than 24 hours or had missed data. Ultimately, 2,783 patients were included in the analysis. A day of delirium was defined as any day during which at least one CAM-ICU assessment was positive. The clinical risk factors of delirium were classified by the delirium phenotype, as follows; hypoxic, septic, sedative-associated, and metabolic delirium.

Results

The incidence of delirium was 24.4% at the index hospitalization in all CICU patients, and 22.6% within 7 days after CICU admission. The most common delirium phenotype was septic delirium (17.2%), followed by hypoxic delirium (16.8%). Multiple phenotypes were observed during most delirium days. Delirium most frequently occurred in patients with heart failure. Of all patients affected by delirium within 7 days, both ICU and hospital mortality significantly increased according to the combined number of delirium phenotypes.

Conclusions

Delirium occurred in a quarter of patients admitted to the modern CICU and was associated with increased in-hospital mortality. Therefore, more efforts are needed to reduce the clinical risk factors of delirium, and to prevent it in order to improve clinical outcomes in the CICU.

Doppler-derived haemodynamics performed during admission echocardiography predict in-hospital mortality in cardiac intensive care unit patients

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 < 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 < 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 <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 <16 cm or mitral E/e' ratio >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.

Predictive ability of the sequential organ failure assessment score for in-hospital mortality in patients with cardiac critical illnesses: a nationwide observational study

AIMS

Several studies have reported a high predictive ability of the Sequential Organ Failure Assessment (SOFA) score for in-hospital mortality specifically for patients with cardiac critical illnesses, however, differences according to the admission classification (surgical or non-surgical) are unknown. The present study aimed to evaluate the predictive ability of the SOFA score in surgical and non-surgical patients with cardiac critical illnesses.

METHODS AND RESULTS

Using the Japanese nationwide Diagnosis Procedure Combination database, we identified patients with cardiac critical illnesses, defined as patients admitted to the intensive care unit (ICU) and treated by cardiologists or cardiovascular surgeons as their physicians in charge from April 2018 to March 2020. The discriminatory ability of the SOFA score for in-hospital mortality was assessed by calculating the area under the receiver operating characteristic curve (AUROC). Among 52 819 eligible patients with available data on their SOFA scores, 33 526 (64%) were postoperative cardiac surgeries. The median SOFA score on ICU admission was 5.0 (interquartile range, 2.0-8.0) and overall in-hospital mortality 6.8%. The AUROC of the SOFA score was 0.75 [95% confidence interval (CI), 0.75-0.76]. In the subgroup analyses, the AUROCs were 0.76 (95% CI, 0.74-0.77) in the surgical patients, 0.83 (95% CI, 0.83-0.84) in the non-surgical patients, and 0.88 (95% CI, 0.87-0.89) in the non-surgical acute coronary syndrome patients.

CONCLUSIONS

The predictive ability of the SOFA score on the day of ICU admission for in-hospital mortality was confirmed to be acceptable in the patients with cardiac critical illnesses and varied according to the admission classification and primary diagnoses.

Echocardiographic Characteristics of Cardiogenic Shock Patients with and Without Cardiac Arrest

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.

Braden Skin Score Subdomains Predict Mortality Among Cardiac Intensive Care Patients

BACKGROUND

The Braden Skin Score (BSS) is a bedside nursing assessment that may be a measure of frailty and predicts mortality among patients in the cardiac intensive care unit (CICU). We examined the association between each of the 6 individual BSS subscores with hospital mortality in patients in the CICU. We hypothesized that BSS subscores reflecting patient frailty would have a stronger association with outcomes.

METHODS

Retrospective cohort study of unique adult patients admitted to the Mayo Clinic CICU from 2007 to 2018 with BSS documented on admission. Primary outcome was all-cause hospital mortality. Odds ratios (ORs) were determined using multivariable logistic regression.

RESULTS

The 11,954 included patients had a mean age of 67.4 ± 15.2 years (37.8% women). Each individual BSS subscore was lower among patients who died in the hospital (all P < .001). The total BSS was inversely associated with in-hospital mortality across admission diagnoses and among patients with coma or mechanical ventilation; each individual subscore was inversely associated with in-hospital mortality. On multivariable regression, all subscores were inversely associated with hospital mortality after full adjustment. Shear had the strongest association (adjusted OR 0.59), followed by nutrition (adjusted OR 0.67), skin moisture (adjusted OR 0.76), mobility (adjusted OR 0.76), sensory perception (adjusted OR 0.82), and activity level (adjusted OR 0.85).

CONCLUSION

BSS can serve as a rapid noninvasive screening tool for identifying poor outcomes in patients in the CICU. BSS subdomains that are more strongly associated with mortality appear to reflect physical frailty. Insofar as the BSS and its subscores measure frailty, a low BSS may identify frail patients.

Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches

The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.

Epidemiology and Outcomes of Patients Readmitted to the Intensive Care Unit After Cardiac Intensive Care Unit Admission

Readmission to the intensive care unit (ICU) during the index hospitalization is associated with poor outcomes in medical or surgical ICU survivors. Little is known about critically ill patients with acute cardiovascular conditions cared for in a cardiac intensive care unit (CICU). We sought to describe the incidence, risk factors, and outcomes of all ICU readmissions in patients who survived to CICU discharge. We retrospectively reviewed Mayo Clinic patients from 2007 to 2015 who survived the index CICU admission and identified patients with a second ICU stay during their index hospitalization; these patients were categorized as ICU transfers (patients who went directly from the CICU to another ICU) or ICU readmissions (patients initially transferred from the CICU to the ward, and then back to an ICU). Among 9,434 CICU survivors (mean age 67 years), 138 patients (1.5%) had a second ICU stay during the index hospitalization: 60 ICU transfers (0.6%) and 78 ICU readmissions (0.8%). The most common indications for ICU readmission were respiratory failure and procedure/surgery. On multivariable modeling, respiratory failure, severe acute kidney injury, and Charlson Comorbidity Index at the time of discharge from the index ICU stay were associated with ICU readmission. Death during the first ICU readmission (n = 78) occurred in 7.7% of patients. In-hospital mortality was higher for patients with a second ICU stay. In conclusion, few CICU survivors have a second ICU stay during their index hospitalization; these patients are at a higher risk of in-hospital and 1-year mortality. Respiratory failure, severe acute kidney injury, and higher co-morbidity burden identify CICU survivors at elevated risk of ICU readmission.

Outcomes Associated With Cardiac Arrest in Patients in the Cardiac Intensive Care Unit With Cardiogenic Shock

Cardiac arrest (CA) is common and has been associated with adverse outcomes in patients with cardiogenic shock (CS). We sought to determine the prevalence, patient characteristics, and outcomes of CA in cardiovascular intensive care unit patients with CS. We queried cardiovascular intensive care unit admissions from 2007 to 2018 with an admission diagnosis of CS and compared patients with and without CA. Temporal trends were assessed using linear regression. The primary and secondary outcomes of in-hospital and 1-year mortality were analyzed using logistic regression and Cox proportional-hazards analysis, respectively. We included 1,498 patients, and CA was present in 510 patients (34%), with 258 (50.6% of patients with CA) having ventricular fibrillation (VF). Mean age was 68 ± 14 years, and 37% were females. The prevalence of CA decreased over time (from 43% in 2007 to 24% in 2018, p <0.001). Hospital mortality was 33.3% and decreased over time in patients without CA (from 30% in 2007 to 22% in 2018, p = 0.05), but not in patients with CA (p = 0.71). CA was associated with a higher risk of hospital mortality (51.0% vs 24.2%, adjusted odds ratio 2.15, 95% confidence interval [CI] 1.52 to 3.05, p <0.001), with no difference between VF CA and non-VF CA (p = 0.64). CA was associated with higher 1-year mortality (adjusted hazard ratio 1.53, 95% CI 1.24 to 1.89, p <0.001). In conclusion, CA is present in 1 of 3 of CS hospitalizations and confers a substantially higher risk of hospital and 1-year mortality with no improvement during our 12-year study period contrary to prevailing trends.

Peripheral blood neutrophil-to-lymphocyte ratio is associated with mortality across the spectrum of cardiogenic shock severity

PURPOSE

To evaluate the association between the neutrophil-to-lymphocyte ratio (NLR) and mortality across the cardiogenic shock (CS) severity spectrum, defined using the Society of Cardiovascular Interventions and Angiography (SCAI) shock stages.

MATERIALS AND METHODS

We retrospectively analyzed cardiac intensive care unit (CICU) patients between 2007 and 2015. Predictors of in-hospital mortality were analyzed using logistic regression.

RESULTS

We included 8280 patients aged 67.3 ± 15.2 years (37.2% females). Elevated NLR (≥7) was present in 45% of patients. NLR increased with worsening SCAI stage and was associated with higher in-hospital mortality in shock stages A to C (all p < 0.001). After multivariable adjustment, NLR remained associated with higher in-hospital mortality (adjusted odds ratio 1.05 per 3.5 NLR units, 95% CI 1.03-1.08, p < 0.001), with an optimal cut-off of ≥7 (in-hospital mortality 13.1% vs. 4.1%, adjusted odds ratio 1.44, 95% CI 1.14-1.81, p = 0.002). Patients in SCAI stage A or B with NLR ≥7 had higher in-hospital mortality than patients in SCAI stage B or C with NLR <7, respectively.

CONCLUSIONS

Elevated NLR is associated with higher in-hospital mortality in CICU patients with or at risk for CS, emphasizing the importance of systemic inflammation as a determinant of outcomes in CS patients.

A pragmatic lab-based tool for risk assessment in cardiac critical care: data from the Critical Care Cardiology Trials Network (CCCTN) Registry

AIMS

Contemporary cardiac intensive care unit (CICU) outcomes remain highly heterogeneous. As such, a risk-stratification tool using readily available lab data at time of CICU admission may help inform clinical decision-making.

METHODS AND RESULTS

The primary derivation cohort included 4352 consecutive CICU admissions across 25 tertiary care CICUs included in the Critical Care Cardiology Trials Network (CCCTN) Registry. Candidate lab indicators were assessed using multivariable logistic regression. An integer risk score incorporating the top independent lab indicators associated with in-hospital mortality was developed. External validation was performed in a separate CICU cohort of 9716 patients from the Mayo Clinic (Rochester, MN, USA). On multivariable analysis, lower pH [odds ratio (OR) 1.96, 95% confidence interval (CI) 1.72-2.24], higher lactate (OR 1.40, 95% CI 1.22-1.62), lower estimated glomerular filtration rate (OR 1.26, 95% CI 1.10-1.45), and lower platelets (OR 1.18, 95% CI 1.05-1.32) were the top four independent lab indicators associated with higher in-hospital mortality. Incorporated into the CCCTN Lab-Based Risk Score, these four lab indicators identified a 20-fold gradient in mortality risk with very good discrimination (C-index 0.82, 95% CI 0.80-0.84) in the derivation cohort. Validation of the risk score in a separate cohort of 3888 patients from the Registry demonstrated good performance (C-index of 0.82; 95% CI 0.80-0.84). Performance remained consistent in the external validation cohort (C-index 0.79, 95% CI 0.77-0.80). Calibration was very good in both validation cohorts (r = 0.99).

CONCLUSION

A simple integer risk score utilizing readily available lab indicators at time of CICU admission may accurately stratify in-hospital mortality risk.

Echocardiographic left ventricular stroke work index: An integrated noninvasive measure of shock severity

Background

Echocardiographic findings vary with shock severity, as defined by the Society for Cardiovascular Angiography and Intervention (SCAI) shock stage. Left ventricular stroke work index (LVSWI) measured by transthoracic echocardiography (TTE) can predict mortality in the cardiac intensive care unit (CICU). We sought to determine whether LVSWI could refine mortality risk stratification by the SCAI shock classification in the CICU.

Methods

We included consecutive CICU patients from 2007 to 2015 with TTE data available to calculate the LVSWI, specifically the mean arterial pressure, stroke volume index and medial mitral E/e’ ratio. In-hospital mortality as a function of LVSWI was evaluated across the SCAI shock stages using logistic regression, before and after multivariable adjustment.

Results

We included 3635 unique CICU patients, with a mean age of 68.1 ± 14.5 years (36.5% females); 61.1% of patients had an acute coronary syndrome. The LVSWI progressively decreased with increasing shock severity, as defined by increasing SCAI shock stage. A total of 203 (5.6%) patients died during hospitalization, with higher in-hospital mortality among patients with lower LVSWI (adjusted OR 0.66 per 10 J/m2 higher) or higher SCAI shock stage (adjusted OR 1.24 per each higher stage). A LVSWI <33 J/m2 was associated with higher adjusted in-hospital mortality, particularly among patients with shock (SCAI stages C, D and E).

Conclusions

The LVSWI by TTE noninvasively characterizes the severity of shock, including both systolic and diastolic parameters, and can identify low-risk and high-risk patients at each level of clinical shock severity.

Noninvasive echocardiographic cardiac power output predicts mortality in cardiac intensive care unit patients

BACKGROUND

Low cardiac power output (CPO), measured invasively, can identify critically ill patients at increased risk of adverse outcomes, including mortality. We sought to determine whether non-invasive, echocardiographic CPO measurement was associated with mortality in cardiac intensive care unit (CICU) patients.

METHODS

Patients admitted to CICU between 2007 and 2018 with echocardiography performed within one day (before or after) admission and who had available data necessary for calculation of CPO were evaluated. Multivariable logistic regression determined the relationship between CPO and adjusted hospital mortality.

RESULTS

A total of 5,585 patients (age of 68.3 ± 14.8 years, 36.7% female) were evaluated with admission diagnoses including acute coronary syndrome (ACS) in 56.7%, heart failure (HF) in 50.1%, cardiac arrest (CA) in 12.2%, shock in 15.5%, and cardiogenic shock (CS) in 12.8%. The mean left ventricular ejection fraction (LVEF) was 47.3 ± 16.2%, and the mean CPO was 1.04 ± 0.37 W. There were 419 in-hospital deaths (7.5%). CPO was inversely associated with the risk of hospital mortality, an association that was consistent among patients with ACS, HF, and CS. On multivariable analysis, higher CPO was associated with reduced hospital mortality (OR 0.960 per 0.1 W, 95CI 0.0.926-0.996, P = .03). Hospital mortality was particularly high in patients with low CPO coupled with reduced LVEF, increased vasopressor requirements, or higher admission lactate.

CONCLUSIONS

Echocardiographic CPO was inversely associated with hospital mortality in unselected CICU patients, particularly among patients with increased lactate and vasopressor requirements. Routine calculation and reporting of CPO should be considered for echocardiograms performed in CICU patients.

Echocardiographic Correlates of Mortality Among Cardiac Intensive Care Unit Patients With Cardiogenic Shock

BACKGROUND

Prior studies have shown worse outcomes in patients with cardiogenic shock (CS) who have reduced left ventricular ejection fraction (LVEF), but the association between other transthoracic echocardiogram (TTE) findings and mortality in CS patients remains uncertain. We hypothesized that Doppler TTE measurements would outperform LVEF for risk stratification.

METHODS

Retrospective analysis of cardiac intensive care unit patients with an admission diagnosis of CS and a TTE within 1 day of admission. Hospital survivors and inpatient deaths were compared, and multivariable logistic regression was used to analyze the associations between TTE variables and hospital mortality.

RESULTS

We included 1,085 patients, with a median age of 69.5 (59.6, 77.5) years; 37% were females and 62% had an acute coronary syndrome. Most patients (66%) had moderate or severe left ventricular (LV) systolic dysfunction, and 48% had moderate or severe right ventricular (RV) systolic dysfunction. Hospital mortality occurred in 31%, and inpatient deaths had a lower median LVEF (29% vs. 35%, P < 0.001). Patients with mild or no LV or RV dysfunction were at lower risk of adjusted hospital mortality (P < 0.01). The LV outflow tract (LVOT) velocity-time integral (VTI) was the single best predictor of hospital mortality. After multivariable adjustment, both the LVEF and LVOT VTI remained strongly associated with hospital mortality (P < 0.001).

CONCLUSIONS

Early comprehensive Doppler TTE can provide important prognostic insights in CS patients, highlighting its potential utility in clinical practice. The LVOT VTI, reflecting forward flow, is an important measurement to obtain on bedside TTE.

The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury

Objective

To compare the performance of the Oxford Acute Severity of Illness Score (OASIS), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Simplified Acute Physiology Score II (SAPS II), and the Sequential Organ Failure Assessment (SOFA) score in predicting 28-day mortality in acute kidney injury (AKI) patients.

Methods

Data were extracted from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 2954 patients with complete clinical data were included in this study. Receiver operating characteristic (ROC) curves were used to analyze and evaluate the predictive effects of the four scoring systems on the 28-day mortality risk of AKI patients and each subgroup. The best cutoff value was identified by the highest combined sensitivity and specificity using Youden’s index.

Results

Among the four scoring systems, the area under the curve (AUC) of OASIS was the highest. The comparison of AUC values of different scoring systems showed that there were no significant differences among OASIS, APACHE II, and SAPS II, which were better than SOFA. Moreover, logistic analysis revealed that OASIS was an independent risk factor for 28-day mortality in AKI patients. OASIS also had good predictive ability for the 28-day mortality of each subgroup of AKI patients.

Conclusion

OASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of AKI patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute.

Trial Registration

This study was registered at www.chictr.org.cn (registration number Chi CTR-ONC-11001875). Registered on 14 December 2011.

Early risk stratification in patients with cardiogenic shock irrespective of the underlying cause - The Cardiogenic Shock Score (CSS)

AIMS

Early risk stratification is essential to guide treatment in cardiogenic shock (CS). Existing CS risk scores were derived in selected cohorts, without accounting for the heterogeneity of CS. The aim of this study was to develop a universal risk score (CSS) for all CS patients, irrespective of underlying cause.

METHODS AND RESULTS

Within a registry of 1,308 CS unselected patients admitted to a tertiary-care hospital between 2009 and 2019, a Cox regression model was fitted to derive the CSS, with 30-day mortality as main outcome. CSS's predictive ability was compared to the IABP-Shock-II score, the CardShock score and SCAI classification by C-indices and validated in an external cohort of 934 CS patients. Based on the Cox regression, 9 predictors were included in the CSS: age, sex, acute myocardial infarction (AMI-CS), systolic blood pressure, heart rate, pH, lactate, glucose and cardiac arrest. CSS had the highest C-index in the overall cohort (0.740 vs. 0.677/0.683 for IABP-Shock-II score/CardShock score), in patients with AMI-CS (0.738 vs. 0.675/0.689 for IABP-Shock-II score/CardShock score) and in patients with non-AMI-CS (0.734 vs. 0.677/0.669 for IABP-Shock-II score/CardShock score). In the external validation cohort, the CSS had a C-index of 0.73, which was higher than all other tested scores.

CONCLUSION

The CSS provides improved information on the risk of death in unselected patients with CS compared to existing scores, irrespective of its cause. Because it is based on point-of-care variables which can be obtained even in critical situations, the CSS has the potential to guide treatment decisions in CS. This article is protected by copyright. All rights reserved.

Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population

BACKGROUND

Proposed 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.

METHODS

We 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.

RESULTS

We 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).

CONCLUSIONS

We 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.

Epidemiology and outcomes of pulmonary hypertension in the cardiac intensive care unit

AIMS

Pulmonary hypertension (PH) has been consistently associated with adverse outcomes in hospitalized patients. Limited epidemiologic data exist regarding PH in the cardiac intensive care unit (CICU) population. Here, we describe the prevalence, aetiology, and outcomes of PH in the CICU.

METHODS AND RESULTS

Cardiac intensive care unit patients admitted from 2007 to 2018 who had right ventricular systolic pressure (RVSP) measured via transthoracic echocardiography near CICU admission were included. PH was defined as RVSP >35 mmHg, and moderate-to-severe PH as RVSP ≥50 mmHg. Predictors of in-hospital mortality were determined using multivariable logistic regression. Among 5042 patients (mean age 69.4 ± 14.8 years; 41% females), PH was present in 3085 (61%). The majority (68%) of patients with PH had left heart failure, and 29% had lung disease. In-hospital mortality occurred in 8.3% and was more frequent in patients with PH [10.9% vs. 4.2%, adjusted odds ratio (OR) 1.40, 95% confidence interval (CI) 1.03-1.92, P = 0.03], particularly patients with moderate-to-severe PH (14.4% vs. 6.2%, adjusted OR 1.65, 95% CI 1.27-2.14, P < 0.001). In-hospital mortality increased incrementally as a function of higher RVSP (adjusted 1.18 per 10 mmHg increase, 95% CI 1.09-1.28, P < 0.001). Patients with higher RVSP or moderate-to-severe PH had increased in-hospital mortality across admission diagnoses (all P < 0.05).

CONCLUSIONS

Pulmonary hypertension is very common in the CICU population and appears to be independently associated with a higher risk of death during hospitalization, although the strength of this association varies according to the underlying admission diagnosis. These data highlight the importance of PH in patients with cardiac critical illness.

Shock Severity Assessment in Cardiac Intensive Care Unit Patients With Sepsis and Mixed Septic-Cardiogenic Shock

We sought to validate the Society for Cardiovascular Angiography and Interventions (SCAI) cardiogenic shock classification for mortality risk stratification in patients with sepsis and concomitant cardiovascular disease or mixed septic-cardiogenic shock. We conducted a single-center retropective cohort study of cardiac intensive care unit patients with an admission diagnosis of sepsis. We used clinical, vital sign, and laboratory data during the first 24 hours after admission to assign SCAI shock stage. We included 605 patients with a median age of 69.4 years (interquartile range, 57.9 to 79.8 years), 222 of whom (36.7%) were female. Acute coronary syndrome or heart failure was present in 480 patients (79.3%), and cardiogenic shock or cardiac arrest was present in 271 patients (44.8%). The median day 1 Sequential Organ Failure Assessment (SOFA) cardiovascular subscore was 1.5 (interquartile range, 1 to 4), and the admission SCAI shock stage distribution was stage B, 40.7% (246); stage C, 19.3% (117); stage D, 32.9% (199); and stage E, 7.1% (43). In-hospital mortality occurred in 177 of the 605 patients (29.3%) and increased incrementally with higher SCAI shock stage. After multivariable adjustment, admission SCAI shock stage was associated with in-hospital mortality (adjusted odds ratio per stage, 1.46; 95% CI, 1.14 to 1.88; P=.003). Admission SCAI shock stage had higher discrimination for in-hospital mortality than the day 1 SOFA cardiovascular subscore (area under the receiver operating characteristic curve, 0.68 vs 0.64; P=.04 by the DeLong test). Admission SCAI shock stage was associated with 1-year mortality (adjusted hazard ratio per stage, 1.19; 95% CI, 1.03 to 1.37; P=.02). The SCAI shock classification provides improved mortality risk stratification over the day 1 SOFA cardiovascular subscore in cardiac intensive care unit patients with sepsis and concomitant cardiovascular disease or mixed septic-cardiogenic shock.

Laboratory Markers of Acidosis and Mortality in Cardiogenic Shock: Developing a Definition of Hemometabolic Shock

BACKGROUND

Acidosis and higher lactate predict worse outcomes in cardiogenic shock (CS) patients. We sought to determine whether overall acidosis severity on admission predicted in-hospital mortality in CS patients.

METHODS

This retrospective descriptive analysis included CS patients admitted to a single academic tertiary cardiac intensive care unit from 2007 to 2015. Admission arterial pH, base excess, and anion gap values were used to generate a Composite Acidosis Score (range 0-5, with a score ≥2 defining Severe Acidosis). Adjusted in-hospital mortality was analyzed using multivariable logistic regression.

RESULTS

We included 1,065 patients with median age of 68.9 (59.0, 77.2) years (36.4% females). Concomitant diagnoses included cardiac arrest in 38.1% and acute coronary syndrome in 59.1%. Severe Acidosis was present in 35.2%, and these patients had worse shock and more organ failure. In-hospital mortality occurred in 34.1% and was higher among patients with Severe Acidosis (54.9% vs. 22.4%, adjusted odds ratio [OR] 2.01, 95% CI 1.43-2.83, P < 0.001). Increasing Composite Acidosis Score was associated with higher in-hospital mortality (adjusted OR 1.25 per point, 95% CI 1.11-1.40, P < 0.001). Severe Acidosis was associated with higher hospital mortality at every level of shock severity and organ failure (all P < 0.05). Admission lactate level had equivalent discrimination for in-hospital mortality as the Composite Acidosis Score (0.69 vs. 0.66; P = 0.32 by De Long test).

CONCLUSION

Given its incremental association with higher in-hospital mortality among CS patients beyond shock severity and organ failure, we propose Severe Acidosis as a marker of hemometabolic shock. Lactate levels performed as well as a composite measure of acidosis for predicting mortality.

Diamond-Forrester classification using echocardiography haemodynamic assessment in cardiac intensive care unit patients

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.

Myocardial contraction fraction by echocardiography and mortality in cardiac intensive care unit patients

BACKGROUND

The myocardial contraction fraction (MCF) is proposed as an improved measure of left ventricular (LV) systolic function that overcomes important limitations of the left ventricular ejection fraction (LVEF). We sought to determine whether a low MCF was associated with higher mortality in cardiac intensive care unit (CICU) patients.

METHODS

We retrospectively analyzed unique Mayo Clinic CICU patients from 2007 to 2018 with MCF calculated as the ratio of the stroke volume to the left ventricular myocardial volume from a transthoracic echocardiogram within 1 day of CICU admission. Multivariable logistic regression analyzed the association between MCF and hospital mortality, after adjustment for LVEF and clinical variables.

RESULTS

We included 4794 patients with a mean age of 68.0 ± 14.8 years (37.1% females). The mean MCF was 0.41 ± 0.16, and was lower in the 6.6% of patients who died in the hospital (0.32 ± 0.14 versus 0.42 ± 0.16, p < 0.001). On multivariable analysis, higher MCF remained associated with lower hospital mortality (adjusted OR 0.78 per 0.1 higher, 95% CI 0.69-0.89, p < 0.001), whereas LVEF was not significantly associated with hospital mortality (unadjusted OR 0.91 per 10% higher, OR 95% CI 0.82-1.02, p = 0.09). Patients with MCF <0.2 had the highest in-hospital mortality, and those with MCF ≥0.5 had the lowest in-hospital mortality, irrespective of admission diagnosis or LVEF.

CONCLUSIONS

MCF demonstrated a strong, inverse relationship with hospital mortality in CICU patients, even after adjusting for LVEF and clinical variables. MCF can be used to identify prognostically-relevant myocardial dysfunction at the bedside, even among patients with preserved LVEF.

Past, present, and future of mortality risk scores in the contemporary cardiac intensive care unit

Risk 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.

The association between cardiac intensive care unit mechanical ventilation volumes and in-hospital mortality

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 (>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 (>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.

Electronic health record risk score provides earlier prognostication of clinical outcomes in patients admitted to the cardiac intensive care unit

In this observational study, we compared the prognostic ability of an electronic health record (EHR)-derived risk score, the Rothman Index (RI), automatically derived on admission, to the first 24-hour Sequential Organ Failure Assessment (SOFA) score for outcome prediction in the modern cardiac intensive care unit (CICU). We found that while the 24-hour SOFA score provided modestly superior discrimination for both in-hospital and CICU mortality, the RI upon CICU admission had better calibration for both outcomes. Given the ubiquitous nature of EHR utilization in the United States, the RI may become an important tool to rapidly risk stratify CICU patients within the ICU and improve resource allocation.

Presentation and outcomes of sepsis in the cardiac intensive care unit

The prevalence of sepsis is increasing in subspecialty intensive care units, including the cardiac intensive care unit (CICU). The clinical characteristics and outcomes of CICU patients with sepsis are not well understood. We conducted a retrospective cohort study of sepsis patients in the CICU compared to other ICUs using the PROGRESS registry. CICU-sepsis patients were older with fewer acute organ failures (median 2 v. 3, p < 0.001), lower SOFA scores (median 7 v. 9, p < 0.001), and more comorbidities. The use of fluid resuscitation, mechanical ventilation, and renal replacement were similar. Mortality was 47.3% for CICU-sepsis patients compared to 43.6% for sepsis patients in other ICU (P = 0.37). We conclude that, in a prior cohort of septic patients, sepsis in CICU patients had outcomes that are comparably poor to sepsis in other ICUs. Septic CICU patients presented with fewer acute organ failures, but more chronic comorbidities. Contemporary data as well as novel interventions and investigations targeted specifically to cardiac patients with sepsis should be prioritized.