Variation in critical care unit admission rates and outcomes for patients with acute coronary syndromes or heart failure among high- and low-volume cardiac hospitals

The Role of Prognostic Scores in Assessing the Prognosis of Patients Admitted in the Cardiac Intensive Care Unit: Emphasis on Heart Failure Patients

Background/Objectives: Patient care in Cardiac Intensive Care Units (CICU) has evolved but data on patient characteristics and outcomes are sparse. This retrospective observational study aimed to define clinical characteristics and risk factors of CICU patients, their in-hospital and 30-day mortality, and compare it with established risk scores. Methods: Consecutive patients (n = 294, mean age 70 years, 74% males) hospitalized within 15 months were studied; APACHE II, EHMRG, GWTG-HF, and GRACE II were calculated on admission. Results: Most patients were admitted for ACS (48.3%) and acute decompensated heart failure (ADHF) (31.3%). Median duration of hospitalization was 2 days (IQR = 1, 4). In-hospital infection occurred in 20%, 18% needed mechanical ventilation, 10% renal replacement therapy and 4% percutaneous ventricular assist devices (33%, 29%, 20% and 4%, respectively, for ADHF). In-hospital and 30-day mortality was 18% and 11% for all patients (29% and 23%, respectively, for ADHF). Established scores (especially APACHE II) had a good diagnostic accuracy (area under the curve-AUC). In univariate and multivariate analyses in-hospital intubation and infection, history of coronary artery disease, hypotension, uremia and hypoxemia on admission were the most important risk factors. Based on these, a proposed new score showed a diagnostic accuracy of 0.954 (AUC) for in-hospital mortality, outperforming previous scores. Conclusions: Patients are admitted mainly with ACS or ADHF, the latter with worse prognosis. Several patients need advanced support; intubation and infections adversely affect prognosis. Established scores predict mortality satisfactorily, but larger studies are needed to develop CICU-directed scores to identify risk factors, improve prediction, guide treatment and staff training.

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

BACKGROUND

Clinical notes contain contextualized information beyond structured data related to patients' past and current health status.

OBJECTIVE

This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data.

METHODS

Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors.

RESULTS

The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments.

CONCLUSIONS

The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

What are key characteristics of adults with advanced heart failure discharged from ICU?

BACKGROUND

As the number of people with heart failure and treatment complexity increases, many hospitals are implementing Advanced Heart Failure Intensive Care Units (AHFICU). However, little evidence concerning the clinical characteristics of people admitted to AHFICUs exists. Understanding the clinical characteristics of people admitted to the AHFICU will assist nurses with implementing tailored interventions to ensure high-quality care delivery.

AIM

The purpose of this study was to describe the clinical characteristics of people who are admitted to and discharged from an AHFICU.

STUDY DESIGN

Baseline data from a longitudinal descriptive study were collected on adults (N = 43) admitted to an AHFICU. Heart failure severity, self-management ability, cognition, sleep quality, and other clinical characteristics were assessed.

RESULTS

Most study participants were New York Heart Association functional class IV (n = 24) or class III (n = 14), indicating poor functional capacity. Over half had mild cognitive impairment and poor sleep quality was prevalent (92.7%). Participants had adequate levels of heart failure knowledge, but low levels of heart failure self-management decision-making and ability.

CONCLUSIONS

Interventions to address the unique clinical characteristics of AHFICU patients include sleep hygiene, integration of cognitive, sleep, and self-management assessments into the electronic medical record. Addressing the unique clinical needs of people with heart failure will lead to patient-centered, evidence-based, and safe care.

RELEVANCE TO CLINICAL PRACTICE

Understanding characteristics of this population addresses this evidence gap and targeted clinical interventions to address unique discharge needs of this population are proposed. Sleep quality education should be done throughout hospitalization on sleep strategies and self-management coaching to facilitate adoption of new sleep routines. Healthcare providers should ensure each patient has care support upon discharge and take cognitive status into consideration during teaching. Addressing self-management readiness should include providing scenarios as part of discharge preparation. Providers must include addressing comorbidities and how they may affect heart failure self-management, such as teaching about sleep apnea device use and encouraging compliance.

Interhospital Variation in Admissions Managed With Critical Care Therapies or Invasive Hemodynamic Monitoring in Tertiary Cardiac Intensive Care Units: An Analysis From the Critical Care Cardiology Trials Network Registry

BACKGROUND

Wide interhospital variations exist in cardiovascular intensive care unit (CICU) admission practices and the use of critical care restricted therapies (CCRx), but little is known about the differences in patient acuity, CCRx utilization, and the associated outcomes within tertiary centers.

METHODS

The Critical Care Cardiology Trials Network is a multicenter registry of tertiary and academic CICUs in the United States and Canada that captured consecutive admissions in 2-month periods between 2017 and 2022. This analysis included 17 843 admissions across 34 sites and compared interhospital tertiles of CCRx (eg, mechanical ventilation, mechanical circulatory support, continuous renal replacement therapy) utilization and its adjusted association with in-hospital survival using logistic regression. The Pratt index was used to quantify patient-related and institutional factors associated with CCRx variability.

RESULTS

The median age of the study population was 66 (56-77) years and 37% were female. CCRx was provided to 62.2% (interhospital range of 21.3%-87.1%) of CICU patients. Admissions to CICUs with the highest tertile of CCRx utilization had a greater burden of comorbidities, had more diagnoses of ST-elevation myocardial infarction, cardiac arrest, or cardiogenic shock, and had higher Sequential Organ Failure Assessment scores. The unadjusted in-hospital mortality (median, 12.7%) was 9.6%, 11.1%, and 18.7% in low, intermediate, and high CCRx tertiles, respectively. No clinically meaningful differences in adjusted mortality were observed across tertiles when admissions were stratified by the provision of CCRx. Baseline patient-level variables and institutional differences accounted for 80% and 5.3% of the observed CCRx variability, respectively.

CONCLUSIONS

In a large registry of tertiary and academic CICUs, there was a >4-fold interhospital variation in the provision of CCRx that was primarily driven by differences in patient acuity compared with institutional differences. No differences were observed in adjusted mortality between low, intermediate, and high CCRx utilization sites.

Association Between Vaccination Status and Outcomes in Patients Admitted to the ICU With COVID-19

OBJECTIVES

Although COVID-19 vaccines can reduce the need for intensive care unit admission in COVID-19, their effect on outcomes in critical illness remains unclear. We evaluated outcomes in vaccinated patients admitted to the ICU with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the association between vaccination and booster status on clinical outcomes.

DESIGN

Retrospective cohort.

SETTING AND PATIENTS

All patients were admitted to an ICU between January 2021 (after vaccination was available) and July 2022 with a diagnosis of COVID-19 based on a SARS-CoV-2 polymerase chain reaction test in Alberta, Canada.

INTERVENTIONS

None.

MEASUREMENT

The propensity-matched primary outcome of all-cause in-hospital mortality was compared between vaccinated and unvaccinated patients, and vaccinated patients were stratified by booster dosing. Secondary outcomes were mechanical ventilation (MV) duration ICU length of stay (LOS).

MAIN RESULTS

The study included 3,293 patients: 743 (22.6%) were fully vaccinated (54.6% with booster), 166 (5.0%) were partially vaccinated, and 2,384 (72.4%) were unvaccinated. Unvaccinated patients were more likely to require invasive MV (78.4% vs 68.2%), vasopressor use (71.1% vs 66.6%), and extracorporeal membrane oxygenation (2.1% vs 0.5%). In a propensity-matched analysis, in-hospital mortality was similar (31.8% vs 34.0%, adjusted odds ratio [OR], 1.25; 95% CI, 0.97-1.61), but median duration MV (7.6 vs 4.7 d; p < 0.001) and ICU LOS (6.6 vs 5.2 d; p < 0.001) were longer in unvaccinated compared to fully vaccinated patients. Among vaccinated patients, greater than or equal to 1 booster had lower in-hospital mortality (25.5% vs 40.9%; adjusted OR, 0.50; 95% CI, 0.0.36-0.68) and duration of MV (3.8 vs 5.6 d; p = 0.025).

CONCLUSIONS

Nearly one in four patients admitted to the ICU with COVID-19 after widespread COVID-19 vaccine availability represented a vaccine-breakthrough case. Mortality risk remains substantial in vaccinated patients and similar between vaccinated and unvaccinated patients after the onset of critical illness. However, COVID-19 vaccination is associated with reduced ICU resource utilization and booster dosing may increase survivability from COVID-19-related critical illness.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

BACKGROUND

There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality.

OBJECTIVE

The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency.

METHODS

Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map.

RESULTS

Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics.

CONCLUSION

Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure

Objective

Risk stratification of patients with congestive heart failure (HF) is vital in clinical practice. The aim of this study was to construct a machine learning model to predict the in-hospital all-cause mortality for intensive care unit (ICU) patients with HF.

Methods

eXtreme Gradient Boosting algorithm (XGBoost) was used to construct a new prediction model (XGBoost model) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) (training set). The eICU Collaborative Research Database dataset (eICU-CRD) was used for the external validation (test set). The XGBoost model performance was compared with a logistic regression model and an existing model (Get with the guideline-Heart Failure model) for mortality in the test set. Area under the receiver operating characteristic cure and Brier score were employed to evaluate the discrimination and the calibration of the three models. The SHapley Additive exPlanations (SHAP) value was applied to explain XGBoost model and calculate the importance of its features.

Results

The total of 11,156 and 9,837 patients with congestive HF from the training set and test set, respectively, were included in the study. In-hospital all-cause mortality occurred in 13.3% (1,484/11,156) and 13.4% (1,319/9,837) of patients, respectively. In the training set, of 17 features with the highest predictive value were selected into the models with LASSO regression. Acute Physiology Score III (APS III), age and Sequential Organ Failure Assessment (SOFA) were strongest predictors in SHAP. In the external validation, the XGBoost model performance was superior to that of conventional risk predictive methods, with an area under the curve of 0.771 (95% confidence interval, 0.757–0.784) and a Brier score of 0.100. In the evaluation of clinical effectiveness, the machine learning model brought a positive net benefit in the threshold probability of 0%–90%, prompting evident competitiveness compare to the other two models. This model has been translated into an online calculator which is accessible freely to the public (https://nkuwangkai-app-for-mortality-prediction-app-a8mhkf.streamlit.app).

Conclusion

This study developed a valuable machine learning risk stratification tool to accurately assess and stratify the risk of in-hospital all-cause mortality in ICU patients with congestive HF. This model was translated into a web-based calculator which access freely.

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases

Background

Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension.

Methods

We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual’s Shapley values.

Results

A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO₂), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death.

Conclusion

We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.

Serum albumin and the short-term mortality in individuals with congestive heart failure in intensive care unit: an analysis of MIMIC

Decreased albumin levels are common in congestive heart failure (CHF), but little is known about its role in mortality risk in CHF. This study developed a cohort prediction model based on 7121 individuals with heart failure to evaluate the short-term mortality and prognostic role of albumin in patients with CHF. The cohort was from intensive care unit between 2001 and 2012 in a publicly available clinical database in intensive care called MIMIC III. We used a generalized additive model to determine the nonlinear correlation between serum albumin and 14th day, 28th day and 90th day all-cause mortality in patients with heart failure. The results showed that serum albumin is an independent risk factor for 14th, 28th and 90th day all-cause mortality, and has a linear relationship with all-cause mortality in congestive heart failure. Cox regression analysis using restricted cubic spline with albumin as continuous parameter showed that the decrease of albumin level is directly related to the increase of mortality (14th day mortality: hazard ratio [HR], 0.65 [95% CI, 0.58 to 0.73]); 28th day mortality: HR, 0.56 [95% CI, 0.51 to 0.63]; 90th day mortality: HR, 0.52 [95% CI, 0.47 to 0.57]; P for trend < 0.001). The multivariate adjusted association between albumin (as a continuous variable) and all-cause mortality on the 90th days is mixed by ARDS [HR, 0.64, 95% CI (0.47–0.87), P = 0.005]. The all-cause mortality on the 90th day predicted better clinical results with the all-cause mortality on the 14th day.

Prospective Interventions to Reduce Stroke Care Variation in a Hub-and-Spokes System

BACKGROUND

Care variation reduction (CVR) is a central objective of quality management to decrease wasted spending.

OBJECTIVE

To analyze stroke care variation at a hub-and-spokes system and determine interventions to prospectively reduce unwarranted variation.

METHODS

In this prospective cohort single arm intervention study providers were blinded to pre-specified endpoints. Care variation was measured for DRGs 61-66 and 69 in USD, and severity level by Case Mix Index (CMI) by provider. A multi-disciplinary task force chaired by Vascular Neurologist analyzed data extracted from Crimson, a patient centric data analysis tool, and determined interventions. The primary measure outcome was change in CMI post intervention.

RESULTS

Annualized baseline care variation was $ 0.7-1.2M (2017) in a drip-and-ship thrombolytic treatment model within the hub-and-spokes system. Pharmacy expenses contributed to 42% of variation followed by laboratory 12%, physical therapy 11%, supplies 11% and imaging 9%. Interventions to achieve CVR were prospectively implemented in 2018 and CVR was measured in January 2019. Based on 2017 CMI of 1.28, the goal of intervention was set to achieve 7% increase to 1.37 with projected increased revenue of $774,144. After implementation of interventions the actual achieved average CMI in 2018 was 1.40 paralleled by improvement in secondary outcomes of length of stay, observed over expected mortality and re-admission.

CONCLUSIONS

A drip-and-ship stroke model within a single hub-and-spokes healthcare system can achieve substantial reduction in care variation and associated cost along with improvement in patient care indicators.

The Mayo Cardiac Intensive Care Unit Admission Risk Score is Associated with Medical Resource Utilization During Hospitalization

Objective

To determine whether the Mayo Cardiac Intensive Care Unit (CICU) Admission Risk Score (M-CARS) is associated with CICU resource utilization.

Patients and Methods

Adult patients admitted to our CICU from 2007 to 2018 were retrospectively reviewed, and M-CARS was calculated from admission data. Groups were compared using Wilcoxon test for continuous variables and χ² test for categorical variables.

Results

We included 12,428 patients with a mean age of 67±15 years (37% female patients). The mean M-CARS was 2.1±2.1, including 5890 (47.4%) patients with M-CARS less than 2 and 644 (5.2%) patients with M-CARS greater than 6. Critical care restricted therapies were frequently used, including mechanical ventilation in 28.0%, vasoactive medications in 25.5%, and dialysis in 4.8%. A higher M-CARS was associated with greater use of critical-care therapies and longer CICU and hospital length of stay. The low-risk cohort with M-CARS less than 2 was less likely to require critical-care–restricted therapies, including invasive or noninvasive mechanical ventilation (8.0% vs 46.1%), vasoactive medications (10.1% vs 38.8%), or dialysis (1.0% vs 8.2%), compared with patients with M-CARS greater than or equal to 2 (all P<.001).

Conclusion

Patients with M-CARS less than 2 infrequently require critical-care resources and have extremely low mortality, suggesting that the M-CARS could be used to facilitate the triage of critically ill cardiac patients.

Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database

OBJECTIVE

The predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.

DESIGN

A retrospective cohort study.

SETTING AND PARTICIPANTS

Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Data on 1177 heart failure patients were analysed.

METHODS

Patients meeting the inclusion criteria were identified from the MIMIC-III database and randomly divided into derivation (n=825, 70%) and a validation (n=352, 30%) group. Independent risk factors for in-hospital mortality were screened using the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression models in the derivation sample. Multivariate logistic regression analysis was used to build prediction models in derivation group, and then validated in validation cohort. Discrimination, calibration and clinical usefulness of the predicting model were assessed using the C-index, calibration plot and decision curve analysis. After pairwise comparison, the best performing model was chosen to build a nomogram according to the regression coefficients.

RESULTS

Among the 1177 admissions, in-hospital mortality was 13.52%. In both groups, the XGBoost, LASSO regression and Get With the Guidelines-Heart Failure (GWTG-HF) risk score models showed acceptable discrimination. The XGBoost and LASSO regression models also showed good calibration. In pairwise comparison, the prediction effectiveness was higher with the XGBoost and LASSO regression models than with the GWTG-HF risk score model (p<0.05). The XGBoost model was chosen as our final model for its more concise and wider net benefit threshold probability range and was presented as the nomogram.

CONCLUSIONS

Our nomogram enabled good prediction of in-hospital mortality in ICU-admitted HF patients, which may help clinical decision-making for such patients.

Transitioning from a coronary to a critical cardiovascular care unit: trends over the past three decades

BACKGROUND

Coronary care units were established in the 1960s to reduce acute-phase mortality in acute coronary syndrome. In the 21st century, the original coronary care unit concept has evolved into an intensive cardiovascular care unit. The aim of this study was to analyse trend changes in characteristics and mortality of patients admitted to a coronary care unit over the past three decades.

METHOD

Between February 1989 and December 2017, a total of 18,334 patients was consecutively admitted to the coronary care unit of a university hospital in Barcelona. Data were analysed in five time frames: 1989-1994, 1995-1999, 2000-2004, 2005-2009 and 2010-2017. We analysed demographic profile, diagnoses at admission and trend changes in mortality across periods.

RESULTS

During the periods, the patients' ages and comorbidities increased. Diagnoses at admission have evolved. Acute coronary syndrome cases declined from the first to the last period (72.6% vs. 62.8%) while heart failure (6.0% vs. 8.6%) and malignant arrhythmias (0.8% vs. 4.0%) increased significantly. Overall, coronary care unit mortality decreased 34% from the first to the last period (6.8% vs. 4.5%, P<0.001). Furthermore, the cause of death has changed, those due to acute coronary syndrome declining (66.7% vs. 45.5%), and death from malignant arrhythmias increasing (1.9% vs. 16.2%) from the first to the last period.

CONCLUSIONS

Although acute coronary syndrome remained the main diagnosis, heart failure and arrhythmias have increased. Despite the aging and comorbidities, overall mortality in the coronary care unit decreased by 34% in the past three decades. Deaths due to acute coronary syndrome have declined, whereas those due to malignant arrhythmias have increased.

The associations between direct and delayed critical care unit admission with mortality and readmissions among patients with heart failure

BACKGROUND

Although greater than 20% of patients hospitalized with heart failure (HF) are admitted to a critical care unit, associated outcomes, and costs have not been delineated. We determined 30-day mortality, 30-day readmissions, and hospital costs associated with direct or delayed critical care unit admission.

METHODS

In a population-based analysis, we compared HF patients who were admitted to critical care directly from the emergency department (direct), after initial ward admission (delayed), or never admitted to critical care during their hospital stay (ward-only).

RESULTS

Among 178,997 HF patients (median age 80 [IQR 71-86] years, 49.6% men) 36,175 (20.2%) were admitted to critical care during their hospitalization (April 2003 to March 2018). Critical care patients were admitted directly from the emergency department (direct, 81.9%) or after initial ward admission (delayed, 18.1%). Multivariable-adjusted hazard ratios (HR) for all-cause 30-day mortality were: 1.69 for direct (95% confidence interval [CI]; 1.55, 1.84) and 4.92 for delayed (95% CI; 4.26, 5.68) critical care-admitted compared to ward-only patients. Multivariable-adjusted repeated events analysis demonstrated increased risk for all-cause 30-day readmission with both direct (HR 1.04, 95% CI; 1.01, 1.08, P = .013) and delayed critical care unit admissions (HR 1.20, 95% CI; 1.13, 1.28, P < .001). Median 30-day costs were $12,163 for direct admissions, $20,173 for delayed admissions, and $9,575 for ward-only patients (P < .001).

CONCLUSIONS

While critical care unit admission indicates increased risk of mortality and readmission at 30 days, those who experienced delayed critical care unit admission exhibited the highest risk of death and highest costs of care.

Causes of mortality in a department of cardiology over a 15-year period

Aims

Over the last four decades, in-hospital mortality from acute coronary syndromes (ACS) has declined. We characterized the patients who died in our cardiovascular intensive care unit (CICU) over a 15-year period. Based on these data, we described the changing patient population in the CICU.

Methods

This retrospective study compared characteristics of patients who died in our CICU in 2005–6, 2013–4 and 2019. During these 5 years, 13,931 patients were hospitalized; 251 (1.8%) died. The mean age of the patients who died was 76 years, 144 (57%) were men. ACS was the leading cause of admission (93 patients, 37%), and 145 (58%) patients had a history of heart failure prior to hospitalization. The leading cause of death was cardiogenic shock in 104 (41%) patients, septic shock in 48 (19%) patients, and combined cardiogenic and septic shock in 31 (12%). Patients hospitalized in the later years of the study were significantly older (67.7, 69.0 and 70.5 years, 2005–6, 2013–4 and 2019, respectively, p < 0.02) but their medical characteristics did not differ significantly between the years examined.

Conclusions

The profile of the patients who died did not change significantly over the 15-year study period. Age of admitted patients was higher in later years of the study. The leading cause of admission was ACS and the leading causes of death were cardiogenic and septic shock. Based on our observations, additional skills should be added to the curriculum of cardiology, including the management of patients with multiorgan failure.

Organization of intensive cardiac care units in Europe: Results of a multinational survey

Background:

The present survey aims to describe the intensive cardiac care unit organization and admission policies in Europe.

Methods:

A total of 228 hospitals (61% academic) from 27 countries participated in this survey. In addition to the organizational aspects of the intensive cardiac care units, including classification of the intensive cardiac care unit levels, data on the admission diagnoses were gathered from consecutive patients who were admitted during a two-day period. Admission policies were evaluated by comparing illness severity with the intensive cardiac care unit level. Gross national income was used to differentiate high-income countries (n=13) from middle-income countries (n=14).

Results:

A total of 98% of the hospitals had an intensive cardiac care unit: 70% had a level 1 intensive cardiac care unit, 76% had a level 2 intensive cardiac care unit, 51% had a level 3 intensive cardiac care unit, and 60% of the hospitals had more than one intensive cardiac care unit level. High-income countries tended to have more level 3 intensive cardiac care units than middle-income countries (55% versus 41%, p=0.07). A total of 5159 admissions were scored on illness severity: 63% were low severity, 24% were intermediate severity, and 12% were high severity. Patients with low illness severity were predominantly admitted to level 1 intensive cardiac care units, whereas patients with high illness severity were predominantly admitted to level 2 and 3 intensive cardiac care units. A policy mismatch was observed in 12% of the patients; some patients with high illness severity were admitted to level 1 intensive cardiac care units, which occurred more often in middle-income countries, whereas some patients with low illness severity were admitted to level 3 intensive cardiac care units, which occurred more frequently in high-income countries.

Conclusion:

More than one-third of the admitted patients were considered intermediate or high risk. Although patients with higher illness severity were mostly admitted to high-level intensive cardiac care units, an admission policy mismatch was observed in 12% of the patients; this mismatch was partly related to insufficient logistic intensive cardiac care unit capacity.

National trends in coronary intensive care unit admissions, resource utilization, and outcomes

Background:

Emerging evidence suggests that coronary intensive care units are evolving into intensive care environments with an increasing burden of non-cardiovascular illness, but previous studies have been limited to older populations or single center experiences.

Methods:

Canadian national health-care data was used to identify all patients ≥18 years admitted to dedicated coronary intensive care units (2005–2015) and admissions were categorized as primary cardiac or non-cardiac. The outcomes of interest included longitudinal trends in admission diagnoses, critical care therapies, and all-cause in-hospital mortality.

Results:

Among the 373,992 patients admitted to a coronary intensive care unit, minimal changes in the proportion of patients admitted with a primary cardiac (88.2% to 86.9%; p&lt;0.001) and non-cardiac diagnoses (11.8% to 13.1%; p&lt;0.001) were observed. Among cardiac admissions, a temporal increase in the proportion of ST-segment elevation myocardial infarction (19.4% to 24.1%, p&lt;0.001), non-ST-segment elevation myocardial infarction (14.6% to 16.2%, p&lt;0.001), heart failure (7.3% to 8.4%, p&lt;0.001), shock (4.9% to 5.7%, p&lt;0.001), and decline in unstable angina (4.9% to 4.0%, p&lt;0.001) and stable coronary diseases (21.3% to 12.4%, p&lt;0.001) was observed. The proportion of patients requiring critical care therapies (57.8% to 63.5%, p&lt;0.001) including mechanical ventilation (9.6% to 13.1%, p&lt;0.001) increased. In-hospital mortality rates for patients with primary cardiac (4.9% to 4.4%; adjusted odds ratio 0.71, 95% confidence interval 0.63–0.79) and non-cardiac (17.8% to 16.1%; adjusted odds ratio 0.84, 0.73–0.97) declined; results were consistent when stratified by academic vs community hospital, and by the presence of on-site percutaneous coronary intervention.

Conclusion:

In a national dataset we observed a changing case-mix among patients admitted to a coronary intensive care unit, though the proportion of patients with a primary cardiac diagnosis remained stable. There was an increase in clinical acuity highlighted by critical care therapies, but in-hospital mortality rates for both primary cardiac and non-cardiac conditions declined across all hospitals. Our findings confirm the changing coronary intensive care unit case-mix and have implications for future coronary intensive care unit training and staffing.

Organization of intensive cardiac care units in Europe: Results of a multinational survey

Background:

The present survey aims to describe the intensive cardiac care unit organization and admission policies in Europe.

Methods:

A total of 228 hospitals (61% academic) from 27 countries participated in this survey. In addition to the organizational aspects of the intensive cardiac care units, including classification of the intensive cardiac care unit levels, data on the admission diagnoses were gathered from consecutive patients who were admitted during a two-day period. Admission policies were evaluated by comparing illness severity with the intensive cardiac care unit level. Gross national income was used to differentiate high-income countries (n=13) from middle-income countries (n=14).

Results:

A total of 98% of the hospitals had an intensive cardiac care unit: 70% had a level 1 intensive cardiac care unit, 76% had a level 2 intensive cardiac care unit, 51% had a level 3 intensive cardiac care unit, and 60% of the hospitals had more than one intensive cardiac care unit level. High-income countries tended to have more level 3 intensive cardiac care units than middle-income countries (55% versus 41%, p=0.07). A total of 5159 admissions were scored on illness severity: 63% were low severity, 24% were intermediate severity, and 12% were high severity. Patients with low illness severity were predominantly admitted to level 1 intensive cardiac care units, whereas patients with high illness severity were predominantly admitted to level 2 and 3 intensive cardiac care units. A policy mismatch was observed in 12% of the patients; some patients with high illness severity were admitted to level 1 intensive cardiac care units, which occurred more often in middle-income countries, whereas some patients with low illness severity were admitted to level 3 intensive cardiac care units, which occurred more frequently in high-income countries.

Conclusion:

More than one-third of the admitted patients were considered intermediate or high risk. Although patients with higher illness severity were mostly admitted to high-level intensive cardiac care units, an admission policy mismatch was observed in 12% of the patients; this mismatch was partly related to insufficient logistic intensive cardiac care unit capacity.

National Interhospital Transfer for Patients With Acute Cardiovascular Conditions

Background

Treatment of ST-elevation myocardial infarction (STEMI) in Canada is protocolized, and timely patient transfer can improve outcomes. Population-based processes of care in Canada for other cardiovascular conditions remain less clear. We aimed to describe the interhospital transfer of Canadian patients with acute cardiovascular disease.

Methods

We reviewed the Canadian Institute for Health Information Discharge Abstract Database for adult patients hospitalized with acute cardiovascular disease between 2013 and 2018. We compared patient characteristics and clinical outcomes based on transfer status (transferred, nontransferred) and presenting hospital (teaching, large community, medium community, and small community hospitals). The primary outcome of interest was in-hospital mortality.

Results

There were 476,753 patients with primary acute cardiovascular diagnoses, 48,579 (10.2%) of whom were transferred. Transferred patients were more frequently younger, male, and had fewer comorbidities. The most common diagnoses among transferred patients were non-STEMI (44.2%), STEMI (29.0%), and congestive heart failure (9.4%). Using teaching hospitals as a reference, transfer to large and medium community hospitals was associated with lower hospital mortality (adjusted odds ratio: 0.83, 95% confidence interval: 0.75-0.91 and 0.45, 95% confidence interval: 0.39-0.52, respectively).

Conclusions

Approximately 10% of patients with acute cardiovascular conditions are transferred to another hospital. Patient transfer may be associated with lower in-hospital mortality, with possible variability based on diagnosis, comorbidities, hospital of origin, and destination hospital. Further investigation into the optimization of care for patients with acute cardiovascular disease, including transfer practices, is warranted as regionalized care models continue to develop.

Sex disparities in the assessment and outcomes of chest pain presentations in emergency departments

OBJECTIVE

To determine whether sex differences exist in the triage, management and outcomes associated with non-traumatic chest pain presentations in the emergency department (ED).

METHODS

All adults (≥18 years) with non-traumatic chest pain presentations to three EDs in Melbourne, Australia between 2009 and 2013 were retrospectively analysed. Data sources included routinely collected hospital databases. Triage scoring of the urgency of presentation, time to medical examination, cardiac troponin testing, admission to specialised care units, and in-ED and in-hospital mortality were each modelled using the generalised estimating equations approach.

RESULTS

Overall 54 138 patients (48.7% women) presented with chest pain, contributing to 76 216 presentations, of which 26 282 (34.5%) were cardiac. In multivariable analyses, compared with men, women were 18% less likely to be allocated an urgency of 'immediate review' or 'within 10 min review' (OR=0.82, 95% CI 0.79 to 0.85), 16% less likely to be examined within the first hour of arrival to the ED by an emergency physician (0.84, 0.81 to 0.87), 20% less likely to have a troponin test performed (0.80, 0.77 to 0.83), 36% less likely to be admitted to a specialised care unit (0.64, 0.61 to 0.68), and 35% (p=0.039) and 36% (p=0.002) more likely to die in the ED and in the hospital, respectively.

CONCLUSIONS

In the ED, systemic sex bias, to the detriment of women, exists in the early management and treatment of non-traumatic chest pain. Future studies that identify the drivers explaining why women presenting with chest pain are disadvantaged in terms of care, relative to men, are warranted.

The Impact of Surgical Volume on Outcomes and Cost in Cleft Repair: A Kids' Inpatient Database Analysis

BACKGROUND

Centralization of specialist services, including cleft service delivery, is occurring worldwide with the aim of improving the outcomes. This study examines the relationship between hospital surgical volume in cleft palate repair and outcomes.

METHODS

A retrospective analysis of the Kids' Inpatient Database was undertaken. Children 3 years or younger undergoing cleft palate repair in 2012 were identified. Hospital volume was categorized by cases per year as low volume (LV; 0-14), intermediate volume (IV; 15-46), or high volume (HV; 47-99); differences in complications, hospital costs, and length of stay (LOS) were determined by hospital volume.

RESULTS

Data for 2389 children were retrieved: 24.9% (n = 595) were LV, 50.1% (n = 1196) were IV, and 25.0% (n = 596) were HV. High-volume centers were more frequently located in the West (71.9%) compared with LV (19.9%) or IV (24.5%) centers (P < 0.001 for hospital region). Median household income was more commonly highest quartile in HV centers compared with IV or LV centers (32.3% vs 21.7% vs 18.1%, P < 0.001). There was no difference in complications between different volume centers (P = 0.74). Compared with HV centers, there was a significant decrease in mean costs for LV centers ($9682 vs $,378, P < 0.001) but no significant difference in cost for IV centers ($9260 vs $9682, P = 0.103). Both IV and LV centers had a significantly greater LOS when compared with HV centers (1.97 vs 2.10 vs 1.74, P < 0.001).

CONCLUSIONS

Despite improvement in LOS in HV centers, we did not find a reduction in cost in HV centers. Further research is needed with analysis of outpatient, long-term outcomes to ensure widespread cost-efficiency.

Changes in comorbidities, diagnoses, therapies and outcomes in a contemporary cardiac intensive care unit population

Prior studies have demonstrated that the cardiac intensive care unit (CICU) patient population has evolved over time. We sought to describe the temporal changes in comorbidities, illness severity, diagnoses, procedures and adjusted mortality within our CICU practice in recent years.

METHODS

We retrospectively reviewed unique CICU admissions at the Mayo Clinic from January 2007 to April 2018. Comorbidities, severity of illness scores, discharge diagnosis codes and CICU procedures and therapies were recorded, and temporal trends were assessed using linear regression and Cochran-Armitage trend tests. Trends in adjusted hospital mortality over time were assessed using multivariable logistic regression.

RESULTS

We included 12,418 patients with a mean age of 67.6 years (including 37.7% females). Temporal trends in the prevalence of several comorbidities and discharge diagnoses were observed, reflecting an increase in the prevalence of non-coronary cardiovascular diseases, critical care diagnoses, and organ failure (all P ≪ .05). The use of several CICU therapies and procedures increased over time, including mechanical ventilation, invasive lines and vasoactive drugs (all P ≪ .05). A temporal decrease in adjusted hospital mortality was observed among the subgroup of patients with (adjusted OR per year 0.97, 95% CI 0.94-0.99, P = .023) and without (adjusted OR per year 0.91, 95% CI 0.85-0.96, P = .002) a critical care discharge diagnosis.

CONCLUSIONS

We observed an increasing prevalence of critical care and organ failure diagnoses as well as increased utilization of critical care therapies in this CICU cohort, associated with a decrease in risk-adjusted hospital mortality over time.

Primary care use of laboratory tests in Northern Ireland's Western Health and Social Care Trust: a cross-sectional study

OBJECTIVES

To describe the laboratory test ordering patterns by general practitioners (GPs) in Northern Ireland Western Health and Social Care Trust (WHSCT) and explore demographic and socioeconomic associations with test requesting.

DESIGN

Cross-sectional study.

SETTING

WHSCT, Northern Ireland. : Particip ANTS: 55 WHSCT primary care medical practices that remained open throughout the study period 1 April 2011-31 March 2016.

OUTCOMES

To identify the temporal patterns of laboratory test ordering behaviour for eight commonly requested clinical biochemistry tests/test groups in WHSCT. To analyse the extent of variations in laboratory test requests by GPs and to explore whether these variations can be accounted for by clinical outcomes or geographical, demographic and socioeconomic characteristics.

RESULTS

The median number of adjusted test request rates over 5 consecutive years of the study period decreased by 45.7% for urine albumin/creatinine ratio (p<0.000001) and 19.4% for lipid profiles (p<0.000001) while a 60.6%, 36.6% and 29.5% increase was observed for HbA_1c (p<0.000001), immunoglobulins (p=0.000007) and prostate-specific antigen (PSA) (p=0.0003), respectively. The between-practice variation in test ordering rates increased by 272% for immunoglobulins (p=0.008) and 500% for HbA_1c (p=0.0001). No statistically significant relationship between ordering activity and either demographic (age and gender) and socioeconomic factors (deprivation) or Quality and Outcome Framework scores was observed. We found the rural-urban differences in between-practice variability in ordering rates for lipid profiles, thyroid profiles, PSA and immunoglobulins to be statistically significant at the Bonferroni-adjusted significance level p<0.01.

CONCLUSIONS

We explored potential factors of the interpractice variability in the use of laboratory tests and found that differences in requesting activity appear unrelated to either demographic and socioeconomic characteristics of GP practices or clinical outcome indicators.

Associations Among Practice Variation, Clinician Characteristics, and Care Algorithm Usage: A Multispecialty Vignette Study

The objective was to quantitatively evaluate clinician characteristics associated with unwarranted practice variation, and how clinical care algorithms influence this variation. Participants (142 physicians, 53 nurse practitioners, and 9 physician assistants in family medicine, internal medicine, and cardiology) described their management of 4 clinical vignettes, first based on their own practice (unguided), then using care algorithms (guided). The authors quantitatively estimated variation in management. Cardiologists demonstrated 17% lower variation in unguided responses than generalists (fold-change 0.83 [95% confidence interval (CI) 0.68, 0.97]), and those who agreed that practice variation can realistically be reduced had 16% lower variation than those who did not (fold-change 0.84 [CI, 0.71, 0.99]). A 17% reduction in variation was observed for guided responses compared with baseline (unguided) responses (fold-change 0.83 [CI, 0.76, 0.90]). Differences were otherwise similar across clinician subgroups and attitudes. Unwarranted practice variation was similar across most clinician subgroups. The authors conclude that care algorithms can reduce variation in management.

Ambulance use, distance and outcomes in patients with suspected cardiovascular disease: a registry-based geographic information system study

BACKGROUND

Despite guideline recommendations, the majority of patients with symptoms suggestive of acute coronary syndrome do not use emergency medical services to reach the emergency department (ED). The aim of this study was to investigate the factors associated with EMS utilisation and subsequent patient outcomes.

METHODS

Using administrative data, all patients who presented to an ED in the metropolitan areas of Edmonton and Calgary in the years of 2007-2013 with main ED diagnosis of acute coronary syndrome, stable angina or chest pain were included. The travel distance was estimated using the geographic information system method to approximate the distance between the ED and patient home. The clinical endpoints were the 7-day and 30-day all-cause events (death, re-hospitalisation and repeat ED visit).

RESULTS

Of 50,881 patients, 30.5% presented by emergency medical services. Patients with older age, female sex, ED diagnosis of acute coronary syndrome, more comorbidities and lower household income were more likely to use emergency medical services to reach the hospital. Longer travel distance was associated with higher emergency medical services use (odds ratio 1.09, 95% confidence interval 1.09-1.10), but it was not a predictor of clinical events. After adjustment for covariates and inverse propensity score weighting, emergency medical services use was associated with a higher risk of 7-day and 30-day clinical events.

CONCLUSION

Several demographic and clinical features were associated with higher emergency medical services use including geographical variation. Although longer travel distance was shown to be linked to higher emergency medical services use, it was not an independent predictor of patient outcome. This has implications for the design of emergency medical services systems, triage and early diagnosis and treatment options.

Practice variation and practice guidelines: Attitudes of generalist and specialist physicians, nurse practitioners, and physician assistants

Objective

To understand clinicians' beliefs about practice variation and how variation might be reduced.

Methods

We surveyed board-certified physicians (N = 178), nurse practitioners (N = 60), and physician assistants (N = 12) at an academic medical center and two community clinics, representing family medicine, general internal medicine, and cardiology, from February—April 2016. The Internet-based questionnaire ascertained clinicians' beliefs regarding practice variation, clinical practice guidelines, and costs.

Results

Respondents agreed that practice variation should be reduced (mean [SD] 4.5 [1.1]; 1 = strongly disagree, 6 = strongly agree), but agreed less strongly (4.1 [1.0]) that it can realistically be reduced. They moderately agreed that variation is justified by situational differences (3.9 [1.2]). They strongly agreed (5.2 [0.8]) that clinicians should help reduce healthcare costs, but agreed less strongly (4.4 [1.1]) that reducing practice variation would reduce costs. Nearly all respondents (234/249 [94%]) currently depend on practice guidelines. Clinicians rated differences in clinician style and experience as most influencing practice variation, and inaccessibility of guidelines as least influential. Time to apply standards, and patient decision aids, were rated most likely to help standardize practice. Nurse practitioners and physicians assistants (vs physicians) and less experienced (vs senior) clinicians rated more favorably several factors that might help to standardize practice. Differences by specialty and academic vs community practice were small.

Conclusions

Clinicians believe that practice variation should be reduced, but are less certain that this can be achieved. Accessibility of guidelines is not a significant barrier to practice standardization, whereas more time to apply standards is viewed as potentially helpful.

The Difficult Evolution of Intensive Cardiac Care Units: An Overview of the BLITZ-3 Registry and Other Italian Surveys

Coronary care units, initially developed to treat acute myocardial infarction, have moved to the care of a broader population of acute cardiac patients and are currently defined as Intensive Cardiac Care Units (ICCUs). However, very limited data are available on such evolution. Since 2008, in Italy, several surveys have been designed to assess ICCUs' activities. The largest and most comprehensive of these, the BLITZ-3 Registry, observed that patients admitted are mainly elderly males and suffer from several comorbidities. Direct admission to ICCUs through the Emergency Medical System was rather rare. Acute coronary syndromes (ACS) account for more than half of the discharge diagnoses. However, numbers of acute heart failure (AHF) admissions are substantial. Interestingly, age, resources availability, and networking have a strong influence on ICCUs' epidemiology and activities. In fact, while patients with ACS concentrate in ICCUs with interventional capabilities, older patients with AHF or non-ACS, non-AHF cardiac diseases prevail in peripheral ICCUs. In conclusion, although ACS is still the core business of ICCUs, aging, comorbidities, increasing numbers of non-ACS, technological improvements, and resources availability have had substantial effects on epidemiology and activities of ICCUs. The Italian surveys confirm these changes and call for a substantial update of ICCUs' organization and competences.

2017 Comprehensive Update of the Canadian Cardiovascular Society Guidelines for the Management of Heart Failure

Since the inception of the Canadian Cardiovascular Society heart failure (HF) guidelines in 2006, much has changed in the care for patients with HF. Over the past decade, the HF Guidelines Committee has published regular updates. However, because of the major changes that have occurred, the Guidelines Committee believes that a comprehensive reassessment of the HF management recommendations is presently needed, with a view to producing a full and complete set of updated guidelines. The primary and secondary Canadian Cardiovascular Society HF panel members as well as external experts have reviewed clinically relevant literature to provide guidance for the practicing clinician. The 2017 HF guidelines provide updated guidance on the diagnosis and management (self-care, pharmacologic, nonpharmacologic, device, and referral) that should aid in day-to-day decisions for caring for patients with HF. Among specific issues covered are risk scores, the differences in management for HF with preserved vs reduced ejection fraction, exercise and rehabilitation, implantable devices, revascularization, right ventricular dysfunction, anemia, and iron deficiency, cardiorenal syndrome, sleep apnea, cardiomyopathies, HF in pregnancy, cardio-oncology, and myocarditis. We devoted attention to strategies and treatments to prevent HF, to the organization of HF care, comorbidity management, as well as practical issues around the timing of referral and follow-up care. Recognition and treatment of advanced HF is another important aspect of this update, including how to select advanced therapies as well as end of life considerations. Finally, we acknowledge the remaining gaps in evidence that need to be filled by future research.

Characterization of hemodynamically stable acute heart failure patients requiring a critical care unit admission: Derivation, validation, and refinement of a risk score

BACKGROUND

Most patients with acute heart failure (AHF) admitted to critical care units (CCUs) are low acuity and do not require CCU-specific therapies, suggesting that they could be managed in a lower-cost ward environment. This study identified the predictors of clinical events and the need for CCU-specific therapies in patients with AHF.

METHODS

Model derivation was performed using data from patients in the ASCEND-HF trial cohort (n=7,141), and the Acute Heart Failure Emergency Management community-based registry (n=666) was used to externally validate the model and to test the incremental prognostic utility of 4 variables (heart failure etiology, troponin, B-type natriuretic peptide [BNP], ejection fraction) using net reclassification index and integrated discrimination improvement. The primary outcome was an in-hospital composite of the requirement for CCU-specific therapies or clinical events.

RESULTS

The primary composite outcome occurred in 545 (11.4%) derivation cohort participants (n=4,767) and 7 variables were predictors of the primary composite outcome: body mass index, chronic respiratory disease, respiratory rate, resting dyspnea, hemoglobin, sodium, and blood urea nitrogen (c index=0.633, Hosmer-Lemeshow P=.823). In the validation cohort (n=666), 87 (13.1%) events occurred (c index=0.629, Hosmer-Lemeshow P=.386) and adding ischemic heart failure, troponin, and B-type natriuretic peptide improved model performance (net reclassification index 0.79, 95% CI 0.046-0.512; integrated discrimination improvement 0.014, 95% CI 0.005-0.0238). The final 10-variable clinical prediction model demonstrated modest discrimination (c index=0.702) and good calibration (Hosmer-Lemeshow P=.547).

CONCLUSIONS

We derived, validated, and improved upon a clinical prediction model in an international trial and a community-based cohort of AHF. The model has modest discrimination; however, these findings deserve further exploration because they may provide a more accurate means of triaging level of care for patients with AHF who need admission.

Volume Matters: Improved Outcomes for Patients Presenting to High-Volume Emergency Departments with Atrial Flutter and Fibrillation

Objectives

Clinical familiarity plays a role in health outcomes; the relationship between emergency department (ED) volume and outcomes for atrial fibrillation and flutter (AFF) are not clear. We compared ED presentation outcomes for AFF between high (HV) and low volume (LV) EDs in Alberta, Canada.

Methods

45,372 AFF presentations for patients aged ≥ 35 years from all 104 EDs in Alberta during 1999 to 2011 using administrative health databases formed a retrospective cohort. EDs were grouped by annual AFF volume: 11 high (>100 presentations) or 93 low (≤100 presentations). Outcomes included hospital admission rate, return to ED for AFF within 30 and 90 days, and death within 30 and 90 days. Analyses included statistical tests and mixed effects modeling.

Results

Mean age at ED presentation was 69.8 years (52% male). HV ED presentations were associated with lower admissions (adjusted odds ratio [aOR] = 0.68, 95% confidence interval [CI] 0.64, 0.72; p-value [p]<0.001), ED returns at 90 (aOR = 0.81, 95% CI 0.73, 0.90; p<0.001) days, and a higher likelihood of specialist visits at 30 (aOR = 1.81, 95% CI 1.68, 1.94; p<0.001) and 90 (aOR = 1.82, 95% CI 1.76, 2.03; p<0.001) days. For admitted patients, there were fewer returns to HV EDs at 30 (aOR = 0.37, 95% CI 0.15, 0.87; p = 0.02) and 90 (aOR = 0.48, 95% CI 0.26, 0.89; p = 0.02) days after hospital discharge. There was no difference in death between the two groups.

Conclusions

AFF patients presenting to HV EDs experienced fewer admissions and AFF ED revisit and higher specialist referrals compared to LV EDs.

Interprovincial Differences in Canadian Coronary Care Unit Resource Use and Outcomes

International registries have reported a wide variation in coronary care unit (CCU) admission rates for patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF). Little is known about variation in Canadian interprovincial use and outcomes. Canadian Institute of Health Information data were used to identify hospitalized patients admitted to a CCU with a primary diagnosis of ACS or HF between April 1, 2007 and March 31, 2013. We examined interprovincial differences in CCU admission rates, use of CCU restricted therapies in the first 2 days of admission, and the association between CCU admission rate and risk-adjusted in-hospital mortality at the provincial level. The CCU admission rate among 220,759 patients hospitalized with ACS and HF was 33%, and this varied significantly across provinces (interprovincial range [IPR] 17%-50%; P < 0.001). A majority (59%; IPR, 48%-84%; P < 0.001) of patients admitted to the CCU did not receive critical care restricted therapies within 2 days. In-hospital mortality also varied across provinces (10%; IPR, 5%-13%; P < 0.001). Although statistically significant (P < 0.001), the correlation between CCU admission rates and provincial risk-adjusted in-hospital mortality was low (r = -0.30). These findings highlight the need for national CCU admission criteria designed to reduce variability and improve health care resource use and outcomes.

Do stable non-ST-segment elevation acute coronary syndromes require admission to coronary care units?

BACKGROUND

Clinical practice guidelines recommend admitting patients with stable non-ST-segment elevation acute coronary syndrome (NSTE ACS) to telemetry units, yet up to two-thirds of patients are admitted to higher-acuity critical care units (CCUs). The outcomes of patients with stable NSTE ACS initially admitted to a CCU vs a cardiology ward with telemetry have not been described.

METHODS

We used population-based data of 7,869 patients hospitalized with NSTE ACS admitted to hospitals in Alberta, Canada, between April 1, 2007, and March 31, 2013. We compared outcomes among patients initially admitted to a CCU (n=5,141) with those admitted to cardiology telemetry wards (n=2,728).

RESULTS

Patients admitted to cardiology telemetry wards were older (median 69 vs 65years, P<.001) and more likely to be female (37.2% vs 32.1%, P<.001) and have a prior myocardial infarction (14.3% vs 11.5%, P<.001) compared with patients admitted to a CCU. Patients admitted directly to cardiology telemetry wards had similar hospital stays (6.2 vs 5.7days, P=.29) and fewer cardiac procedures (40.3% vs 48.5%, P<.001) compared with patients initially admitted to CCUs. There were no differences in the frequency of in-hospital mortality (1.3% vs 1.2%, adjusted odds ratio [aOR] 1.57, 95% CI 0.98-2.52), cardiac arrest (0.7% vs 0.9%, aOR 1.37, 95% CI 0.94-2.00), 30-day all-cause mortality (1.6% vs 1.5%, aOR 1.50, 95% CI 0.82-2.75), or 30-day all-cause postdischarge readmission (10.6% vs 10.8%, aOR 1.07, 95% CI 0.90-1.28) between cardiology telemetry ward and CCU patients. Results were similar across low-, intermediate-, and high-risk Duke Jeopardy Scores, and in patients with non-ST-segment myocardial infarction or unstable angina.

CONCLUSIONS

There were no differences in clinical outcomes observed between patients with NSTE ACS initially admitted to a ward or a CCU. These findings suggest that stable NSTE ACS may be managed appropriately on telemetry wards and presents an opportunity to reduce hospital costs and critical care capacity strain.

Analysis of Unplanned Postoperative Admissions to the Intensive Care Unit

PURPOSE

To investigate factors associated with unplanned postoperative admissions to the intensive care unit (ICU).

METHODS

Data from the National Anesthesia Clinical Outcomes Registry (NACOR) were analyzed. We performed univariate and multivariate logistic regression to identify patient- and surgery-specific characteristics associated with unplanned postoperative ICU admission. We also recorded the prevalence of Current Procedural Terminology (CPT) and International Classification of Diseases, ninth revision ( ICD-9) billing codes and outcomes for unplanned postoperative ICU admissions.

RESULTS

Of 23 341 130 records in the database, 2 910 738 records met our inclusion criteria. A higher American Society of Anesthesiologists physical status (ASA PS) class, case duration greater than 4 hours, and advanced age were associated with a greater likelihood of unplanned ICU admission. Vascular and thoracic surgery patients were more likely to have an unplanned ICU admission. The most common CPT and ICD-9 codes involved repair of femur/hip fracture, bowel resection, and acute postoperative pain. Large community hospitals were more likely than university hospitals to have unplanned postoperative ICU admissions. Patients in the unplanned postoperative ICU admission group were more likely to have experienced intraoperative cardiac arrest, hemodynamic instability, or respiratory failure and were more likely to die in the immediate perioperative period.

CONCLUSION

Our study is the first diverse analysis of unplanned postoperative ICU admissions in the literature across multiple specialties and practice models. We found an association of advanced age, higher ASA PS class, and duration of procedure with unplanned ICU admission after surgery. Surgical specialties and procedures with the most unplanned ICU admissions could be areas for quality improvement and clinical pathways in the future.