Patterns of intensive care unit admissions in patients hospitalized for heart failure: insights from the RO-AHFS registry

Association between serum albumin creatinine ratio and all-cause mortality in intensive care unit patients with heart failure

Background

The serum albumin creatinine ratio (sACR) has been established as a potential indicator for heart disease, however, its relationship with prognosis in intensive care unit (ICU) patients with heart failure remains uncertain. This study aimed to investigate the association between sACR levels and all-cause mortality ICU patients with heart failure.

Methods

Clinical data from MIMIC-Ⅳ database was utilized for the analysis of ICU patients with heart failure. Patients were categorized into quartiles (Q1-Q4) based on sACR levels. Kaplan-Meier survival analysis and multivariate adjusted Cox regression models were employed to assess the association between sACR levels and mortality outcomes within 365 days. Subgroup analysis was used to evaluate the prognostic impact of sACR across diverse populations. Restricted cubic spline curves and threshold effect analysis were utilized to quantify the dose-response relationship between sACR levels and risk of all-cause mortality. Mediating effects analysis was conducted to present the involvement of albumin and creatinine in the association between sACR and outcomes.

Results

The analysis encompassed a cohort of 4,506 patients, with Kaplan-Meier curves indicating that individuals with lower sACR levels exhibited an elevated risk of all-cause mortality (log-rank p < 0.001). Multivariate adjusted Cox regression and subgroup analysis demonstrated that individuals in Q2 [hazard ratio (HR) 0.82, 95%CI 0.71∼0.96], Q3 (HR 0.76, 95%CI 0.64∼0.91) and Q4 (HR 0.62, 95%CI 0.50∼0.76) had a decreased risk of mortality compared to individuals in Q1 (lower levels of sACR) (p for trend < 0.001), and this inverse relationship was consistently observed across various subgroups. Subsequent restricted cubic spline analysis revealed a negative yet nonlinear relationship between sACR and all-cause mortality (p for nonlinear < 0.001), and threshold effect analysis indicated an effect threshold of 3.75. Additionally, mediating effects analysis emphasized that sACR influenced the outcome not only through serum albumin and creatinine pathways, but also through direct mechanisms.

Conclusion

The study found that low levels of sACR were independently associated with an increased risk of one-year all-cause mortality in ICU patients with heart failure, with a threshold effect, which could potentially serve as an early warning indicator for high-risk populations.

Development and validation of a novel combinatorial nomogram model to predict in-hospital deaths in heart failure patients

BACKGROUND

The purpose of this study was to develop a Nomogram model to identify the risk of all-cause mortality during hospitalization in patients with heart failure (HF).

METHODS

HF patients who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV databases were included. The primary outcome was the occurrence of all-cause mortality during hospitalization. Two Logistic Regression models (LR1 and LR2) were developed to predict in-hospital death for HF patients from the MIMIC-IV database. The MIMIC-III database were used for model validation. The area under the receiver operating characteristic curve (AUC) was used to compare the discrimination of each model. Calibration curve was used to assess the fit of each developed models. Decision curve analysis (DCA) was used to estimate the net benefit of the predictive model.

RESULTS

A total of 16,908 HF patients were finally enrolled through screening, of whom 2,283 (13.5%) presented with in-hospital death. Totally, 48 variables were included and analyzed in the univariate and multifactorial regression analysis. The AUCs for the LR1 and LR2 models in the test cohort were 0.751 (95% CI: 0.735∼0.767) and 0.766 (95% CI: 0.751-0.781), respectively. Both LR models performed well in the calibration curve and DCA process. Nomogram and online risk assessment system were used as visualization of predictive models.

CONCLUSION

A new risk prediction tool and an online risk assessment system were developed to predict mortality in HF patients, which performed well and might be used to guide clinical practice.

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.

Development and validation of nomogram for unplanned ICU admission in patients with dilated cardiomyopathy

Objective

Unplanned admission to the intensive care unit (ICU) is the major in-hospital adverse event for patients with dilated cardiomyopathy (DCM). We aimed to establish a nomogram of individualized risk prediction for unplanned ICU admission in DCM patients.

Methods

A total of 2,214 patients diagnosed with DCM from the First Affiliated Hospital of Xinjiang Medical University from January 01, 2010, to December 31, 2020, were retrospectively analyzed. Patients were randomly divided into training and validation groups at a 7:3 ratio. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used for nomogram model development. The area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. The primary outcome was defined as unplanned ICU admission.

Results

A total of 209 (9.44%) patients experienced unplanned ICU admission. The variables in our final nomogram included emergency admission, previous stroke, New York Heart Association Class, heart rate, neutrophil count, and levels of N-terminal pro b-type natriuretic peptide. In the training group, the nomogram showed good calibration (Hosmer-Lemeshow χ 2 = 14.40, P = 0.07) and good discrimination, with an optimal-corrected C-index of 0.76 (95% confidence interval: 0.72-0.80). DCA confirmed the clinical net benefit of the nomogram model, and the nomogram maintained excellent performances in the validation group.

Conclusion

This is the first risk prediction model for predicting unplanned ICU admission in patients with DCM by simply collecting clinical information. This model may assist physicians in identifying individuals at a high risk of unplanned ICU admission for DCM inpatients.

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.

Clinical presentation and outcomes of acute heart failure in the critically ill patient: A prospective, observational, multicentre study

AIMS

To assess the clinical profile and factors associated with 30-day mortality in patients with acute heart failure (AHF) admitted to the intensive care unit (ICU).

DESIGN

Prospective, multicentre cohort study.

SCOPE

Thirty-two Spanish ICUs.

PATIENTS

Adult patients admitted to the ICU between April and June 2017.

INTERVENTION

Patients were classified into three groups according to AHF status: without AHF (no AHF); AHF as the primary reason for ICU admission (primary AHF); and AHF developed during the ICU stay (secondary AHF).

MAIN VARIABLES OF INTEREST

Incidence of AHF and 30-day mortality.

RESULTS

A total of 4330 patients were included. Of these, 627 patients (14.5%) had primary (n=319; 7.4%) or secondary (n=308; 7.1%) AHF. Among the main precipitating factors, fluid overload was more common in the secondary AHF group than in the primary group (12.9% vs 23.4%, p<0.001). Patients with AHF had a higher risk of 30-day mortality than those without AHF (OR 2.45; 95% CI: 1.93-3.11). APACHE II, cardiogenic shock, left ventricular ejection fraction, early inotropic therapy, and diagnostic delay were independently associated with 30-day mortality in AHF patients. Diagnostic delay was associated with a significant increase in 30-day mortality in the secondary group (OR 6.82; 95% CI 3.31-14.04).

CONCLUSIONS

The incidence of primary and secondary AHF was similar in this cohort of ICU patients. The risk of developing AHF in ICU patients can be reduced by avoiding modifiable precipitating factors, particularly fluid overload. Diagnostic delay was associated with significantly higher mortality rates in patients with secondary AHF.

Causes and outcomes of ICU hospitalisations in patients with pulmonary arterial hypertension

Rationale

Pulmonary arterial hypertension (PAH) is a rare disease characterised by limited survival despite remarkable improvements in therapy. The causes, clinical burden and outcomes of patients admitted to the intensive care unit (ICU) remain poorly characterised. The aim of this study was to describe patient characteristics, causes of ICU hospitalisation, and risk factors for ICU and 1-year mortality.

Methods

Data from patients enrolled in the Johns Hopkins Pulmonary Hypertension Registry were analysed for the period between January 2010 and December 2020. Clinical, functional, haemodynamic and laboratory data were collected.

Measurements and main results

102 adult patients with 155 consecutive ICU hospitalisations were included. The leading causes for admission were right heart failure (RHF, 53.3%), infection (17.4%) and arrhythmia (11.0%). ICU mortality was 27.1%. Mortality risk factors included Na <136 mEq·mL-1 (OR: 3.10, 95% CI: 1.41-6.82), elevated pro-B-type natriuretic peptide (proBNP) (OR: 1.75, 95% CI: 1.03-2.98), hyperbilirubinaemia (OR: 1.40, 95% CI: 1.09-1.80), hyperlactaemia (OR: 1.42, 95% CI: 1.05-1.93), and need for vasopressors/inotropes (OR: 5.29, 95% CI: 2.28-12.28), mechanical ventilation (OR: 3.76, 95% CI: 1.63-8.76) and renal replacement therapy (OR: 5.57, 95% CI: 1.25-24.76). Mortality rates at 3, 6 and 12 months were 17.5%, 27.6% and 39.0%, respectively. Connective tissue disease-associated PAH has lower 1-year survival compared to idiopathic PAH (51.4% versus 79.8%, log-rank test p=0.019).

Conclusions

RHF is the most common cause for ICU admission. In-hospital and 1-year mortality remain exceedingly high despite improved ICU care. Recognising specific risk factors on admission can help identifying patients at risk for poor outcomes.

Impact of Hypertension History and Blood Pressure at Presentation on Cardiac Remodeling and Mortality in Aortic Dissection

Objective

This study compared clinical, echocardiographic, and prognostic characteristics among patients with aortic dissection (AD) with (HypHist) and without (No-HypHist) hypertension history and evaluated the association of blood pressure (BP) at presentation with 1-year mortality, left ventricular (LV) remodeling and renal dysfunction.

Methods

We investigated clinical and echocardiographic characteristics and 1-year mortality among 367 patients with AD (81% HypHist, 66% Type-A) from three Brazilian centers.

Results

Patients with No-HypHist were more likely to have Marfan syndrome, bicuspid aortic valve, to undergo surgical therapy, were less likely to have LV hypertrophy and concentricity, and had similar mortality compared with HypHist patients. Adjusted restricted cubic spline analysis showed that systolic BP (SBP) and diastolic BP (DBP) at presentation had a J-curve association with mortality among patients with No-HypHist, but did not associate with death among patients with HypHist (p for interaction = 0.001 for SBP and = 0.022 for DBP). Conversely, the association between SBP at presentation and mortality was influenced by previous use of antihypertensive medications in the HypHist group (p for interaction = 0.002). Results of multivariable logistic regression analysis comprising the whole sample showed direct associations of SBP and DBP at presentation with LV hypertrophy (p = 0.009) and LV concentricity (p = 0.015), respectively, and an inverse association between pulse pressure at presentation and estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m² (p = 0.008).

Conclusion

Combined information on BP at presentation, previous diagnosis of hypertension, and use of antihypertensive medications might be useful to predict mortality risk and to estimate extra-aortic end-organ damage among patients with AD.

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.

A review of the management of patients with advanced heart failure in the intensive care unit

Despite progress in the medical and device therapy for heart failure (HF), the prognosis for those with advanced HF remains poor. Acute heart failure (AcHF) is the rapid development of, or worsening of symptoms and signs of HF typically leading to hospitalization. Whilst many HF decompensations are managed at a ward-based level, a proportion of patients require higher acuity care in the intensive care unit (ICU). Admission to ICU is associated with a higher risk of in-hospital mortality, and in those who fail to respond to standard supportive and medical therapy, a proportion maybe suitable for mechanical circulatory support (MCS). The optimal pre-operative management of advanced HF patients awaiting durable MCS or cardiac transplantation (CTx) is vital in improving both short and longer-term outcomes. This review will summarize the clinical assessment, hemodynamic profiling and management of the patient with AcHF in the ICU. The general principles of pre-surgical optimization encompassing individual systems (the kidneys, the liver, blood and glycemic control) will be discussed. Other factors impacting upon post-operative outcomes including nutrition and sarcopenia and pre-surgical skin decolonization have been included. Issues specific to durable MCS including the assessment of the right ventricle and strategies for optimization will also be discussed.

Association Between Intensive Care Unit Usage and Long-Term Medication Adherence, Mortality, and Readmission Among Initially Stable Patients With Non-ST-Segment-Elevation Myocardial Infarction

Background

Hospitals in the United States vary in their use of intensive care units (ICUs) for hemodynamically stable patients with non–ST‐segment–elevation myocardial infarction (NSTEMI). The association between ICU use and long‐term outcomes after NSTEMI is unknown.

Methods and Results

Using data from the National Cardiovascular Data Registry linked to Medicare claims, we identified 65 256 NSTEMI patients aged ≥ 65 years without cardiogenic shock or cardiac arrest on presentation between 2011 and 2014. We compared 1‐year medication non‐adherence, cardiovascular readmission, and mortality across hospitals by ICU use using multivariable regression models. Among 520 hospitals, 154 (29.6%) were high ICU users (>70% of stable NSTEMI patients admitted to ICU), 270 (51.9%) were intermediate (30%–70%), and 196 (37.7%) were low (<30%). Compared with low ICU usage hospitals, no differences were observed in the risks of 1‐year medication non‐adherence (adjusted odds ratio 1.08, 95% CI, 0.97–1.21), mortality (adjusted hazard ratio 1.06, 95% CI, 0.98–1.15), and cardiovascular readmission (adjusted hazard ratio 0.99, 95% CI, 0.95–1.04) at high usage hospitals. Patients hospitalized at intermediate ICU usage hospitals had lower rates of evidence‐based therapy and diagnostic catheterization within 24 hours of hospital arrival, and higher risks of 1‐year mortality (adjusted hazard ratio 1.07, 95% CI, 1.02–1.12) and medication non‐adherence (adjusted odds ratio 1.09, 95% CI, 1.02–1.15) compared with low ICU usage hospitals.

Conclusions

Routine ICU use is unlikely to be beneficial for hemodynamically stable NSTEMI patients; medication adherence, long‐term mortality, and cardiovascular readmission did not differ for high ICU usage hospitals compared with hospitals with low ICU usage rates.

Therapeutic Advances in the Management of Acute Decompensated Heart Failure

BACKGROUND

Acute decompensated heart failure (ADHF) is the most common presenting phenotype of acute heart failure (AHF). The main goal of this article was to review the contemporary management strategies in these patients and to describe how future clinical trials may address unmet clinical needs.

AREAS OF UNCERTAINTY

The current pathophysiologic understanding of AHF is incomplete. The guideline recommendations for the management of ADHF are based only on algorithms provided by expert consensus guided by blood pressure and/or clinical signs of congestion or hypoperfusion. The lack of adequately conducted trials to address the unmet need for evidence therapy in AHF has not yet been surpassed, and at this time, there is no evidence-based strategy for targeted decongestive therapy to improve outcomes. The precise time point for initiation of guideline-directed medical therapies (GDMTs), as respect to moment of decompensation, is also unknown.

DATA SOURCES

The available data informing current management of patients with ADHF are based on randomized controlled trials, observational studies, and administrative databases.

THERAPEUTIC ADVANCES

A major step-forward in the management of ADHF patients is recognizing congestion, either clinical or hemodynamic, as a major trigger for heart failure (HF) hospitalization and most important target for therapy. However, a strategy based exclusively on congestion is not sufficient, and at present, comprehensive assessment during hospitalization of cardiac and noncardiovascular substrate with identification of potential therapeutic targets represents "the corner-stone" of ADHF management. In the last years, substantial data have emerged to support the continuation of GDMTs during hospitalization for HF decompensation. Recently, several clinical trials raised hypothesis of "moving to the left" concept that argues for very early implementation of GDMTs as potential strategy to improve outcomes.

CONCLUSIONS

The management of ADHF is still based on expert consensus documents. Further research is required to identify novel therapeutic targets, to establish the precise time point to initiate GDMTs, and to identify patients at risk of recurrent hospitalization.

Risk Score to Predict Need for Intensive Care in Initially Hemodynamically Stable Adults With Non-ST-Segment-Elevation Myocardial Infarction

Background

Intensive care unit (ICU) use for initially stable patients presenting with non–ST‐segment–elevation myocardial infarction (NSTEMI) varies widely across hospitals and minimally correlates with severity of illness. We aimed to develop a bedside risk score to assist in identifying high‐risk patients with NSTEMI for ICU admission.

Methods and Results

Using the Acute Coronary Treatment and Intervention Outcomes Network (ACTION) Registry linked to Medicare data, we identified patients with NSTEMI aged ≥65 years without cardiogenic shock or cardiac arrest on presentation. Complications requiring ICU care were defined as subsequent development of cardiac arrest, shock, high‐grade atrioventricular block, respiratory failure, stroke, or death during the index hospitalization. We developed and validated a model and integer risk score (Acute Coronary Treatment and Intervention Outcomes Network (ACTION) ICU risk score) that uses variables present at hospital admission to predict requirement for ICU care. Of 29 973 patients with NSTEMI, 4282 (14%) developed a complication requiring ICU‐level care, yet 12 879 (43%) received care in an ICU. Signs or symptoms of heart failure, initial heart rate, initial systolic blood pressure, initial troponin, initial serum creatinine, prior revascularization, chronic lung disease, ST‐segment depression, and age had statistically significant associations with requirement for ICU care after adjusting for other risk factors. The ACTION ICU risk score had a C‐statistic of 0.72. It identified 11% of patients as having very high risk (>30%) of developing complications requiring ICU care and 49% as having low likelihood (<10%) of requiring an ICU.

Conclusions

The ACTION ICU risk score quantifies the risk of initially stable patients with NSTEMI developing a complication requiring ICU care, and could be used to more effectively allocate limited ICU resources.

Intensive Care Unit Admission and Survival among Older Patients with Chronic Obstructive Pulmonary Disease, Heart Failure, or Myocardial Infarction

RATIONALE

Admission to an intensive care unit (ICU) may be beneficial to patients with pneumonia with uncertain ICU needs; however, evidence regarding the association between ICU admission and mortality for other common conditions is largely unknown.

OBJECTIVES

To estimate the relationship between ICU admission and outcomes for hospitalized patients with exacerbation of chronic obstructive pulmonary disease (COPD), exacerbation of heart failure (HF), or acute myocardial infarction (AMI).

METHODS

We performed a retrospective cohort study of all acute care hospitalizations from 2010 to 2012 for U.S. fee-for-service Medicare beneficiaries aged 65 years and older admitted with COPD exacerbation, HF exacerbation, or AMI. We used multivariable adjustment and instrumental variable analysis to assess each condition separately. The instrumental variable analysis used differential distance to a high ICU use hospital (defined separately for each condition) as an instrument for ICU admission to examine marginal patients whose likelihood of ICU admission depended on the hospital to which they were admitted. The primary outcome was 30-day mortality. Secondary outcomes included hospital costs.

RESULTS

Among 1,555,798 Medicare beneficiaries with COPD exacerbation, HF exacerbation, or AMI, 486,272 (31%) were admitted to an ICU. The instrumental variable analysis found that ICU admission was not associated with significant differences in 30-day mortality for any condition. ICU admission was associated with significantly greater hospital costs for HF ($11,793 vs. $9,185, P < 0.001; absolute increase, $2,608 [95% confidence interval, $1,377-$3,840]) and AMI ($19,513 vs. $14,590, P < 0.001; absolute increase, $4,922 [95% confidence interval, $2,665-$7,180]), but not for COPD.

CONCLUSIONS

ICU admission did not confer a survival benefit for patients with uncertain ICU needs hospitalized with COPD exacerbation, HF exacerbation, or AMI. These findings suggest that the ICU may be overused for some patients with these conditions. Identifying patients most likely to benefit from ICU admission may improve health care efficiency while reducing costs.

Emergency department triage of acute heart failure triggered by pneumonia; when an intensive care unit is needed?

Community acquired pneumonia (CAP) is a frequent triggering factor for decompensation of a chronic cardiac dysfunction, leading to acute heart failure (AHF). Patients with AHF exacerbated by CAP, are often admitted through the emergency department for ICU hospitalization, even though more than half the cases do not warrant any intensive care treatment. Emergency department physicians are forced to make disposition decisions based on subjective criteria, due to lack of evidence-based risk scores for AHF combined with CAP. Currently, the available risk models refer distinctly to either AHF or CAP patients. Extrapolation of data by arbitrarily combining these models, is not validated and can be treacherous. Examples of attempts to apply acuity scales provenient from different disciplines and the resulting discrepancies, are given in this review. There is a need for severity classification tools especially elaborated for use in the emergency department, applicable to patients with mixed AHF and CAP, in order to rationalize the ICU dispositions. This is bound to facilitate the efforts to save both lives and resources.

Characteristics of Intensive Care in Patients Hospitalized for Heart Failure in Europe

Patients hospitalized for acute heart failure (AHF) may clinically decompensate and experience life-threatening complications. Regional differences in intensive care unit (ICU) admission rates have been reported by European registries. Variations regarding ICU bed and facilities availability may contribute to these geographic differences. ICU triage decision requires cautious clinical judgment to balance between clinical benefit of ICU care and associated risk and cost. In Europe, despite large variations in treatment practices, in-hospital mortality of AHF patients managed in ICUs is similar, suggesting that high-risk characteristics of AHF patients admitted to ICUs, rather than geographic variation in intensity of therapies, may be the principal determinant of prognosis.

Acute heart failure in the emergency department: a follow-up study

Acute heart failure (AHF) is a major public health issue due to high incidence and poor prognosis. Only a few studies are available on the long-term prognosis and on outcome predictors in the unselected population attending the emergency department (ED) for AHF. We carried out a 1-year follow-up analysis of 1234 consecutive patients from selected Italian EDs from January 2011 to June 2012 for an episode of AHF. Their prognosis and outcome-associated factors were tested by Cox proportional hazard model. Patients' mean age was 84, with 66.0% over 80 years and 56.2% females. Comorbidities were present in over 50% of cases, principally a history of acute coronary syndrome, chronic obstructive pulmonary disease, diabetes, chronic kidney disease, valvular heart disease. Death occurred within 6 h in 24 cases (1.9%). At 30-day follow-up, death was registered in 123 cases (10.0%): 110 cases (89.4%) died of cardiovascular events and 13 (10.6%) of non-cardiovascular causes (cancer, gastrointestinal hemorrhages, sepsis, trauma). At 1-year follow-up, all-cause death was recorded in 50.1% (over 3 out of 4 cases for cardiovascular origin). Six variables (older age, diabetes, systolic arterial pressure <110 mm/Hg, high NT pro-BNP, high troponin levels and impaired cognitive status) were selected as outcome predictors, but with limited discriminant capacity (AUC = 0.649; SE 0.015). Recurrence of AHF was registered in 31.0%. The study identifies a cluster of variables associated with 1-year mortality in AHF, but their predictive capacity is low. Old age and the presence of comorbidities, in particular diabetes are likely to play a major role in dictating the prognosis.