A prediction rule to identify low-risk patients with heart failure

Heart failure management at home: a non-randomised prospective case-controlled trial (HeMan at Home)

BACKGROUND/OBJECTIVES

Heart failure (HF) is a growing clinical and economic burden for patients and health systems. The COVID-19 pandemic has led to avoidance and delay in care, resulting in increased morbidity and mortality among many patients with HF. The increasing burden of HF during the COVID-19 pandemic led us to evaluate the quality and safety of the Hospital at Home (HAH) for patients presenting to their community providers or emergency department (ED) with symptoms of acute on chronic HF (CHF) requiring admission.

DESIGN/OUTCOMES

A non-randomised prospective case-controlled of patients enrolled in the HAH versus admission to the hospital (usual care, UC). Primary outcomes included length of stay (LOS), adverse events, discharge disposition and patient satisfaction. Secondary outcomes included 30-day readmission rates, 30-day ED usage and ED dwell time.

RESULTS

Sixty patients met inclusion/exclusion criteria and were included in the study. Of the 60 patients, 40 were in the HAH and 20 were in the UC group. Primary outcomes demonstrated that HAH patients had slightly longer LOS (6.3 days vs 4.7 days); however, fewer adverse events (12.5% vs 35%) compared with the UC group. Those enrolled in the HAH programme were less likely to be discharged with postacute services (skilled nursing facility or home health). HAH was associated with increased patient satisfaction compared with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) score in North Carolina. Secondary outcomes of 30-day readmission and ED usage were similar between HAH and UC.

CONCLUSIONS

The HAH pilot programme was shown to be a safe and effective alternative to hospitalisation for the appropriately selected patient presenting with acute on CHF.

Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure

Importance

Traditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH).

Objective

To develop and validate novel machine learning (ML) models for HF mortality that incorporate SDOH.

Design, Setting, and Participants

This retrospective study used the data from the Get With The Guidelines-Heart Failure (GWTG-HF) registry to identify HF hospitalizations between January 1, 2010, and December 31, 2020. The study included patients with acute decompensated HF who were hospitalized at the GWTG-HF participating centers during the study period. Data analysis was performed January 6, 2021, to April 26, 2022. External validation was performed in the hospitalization cohort from the Atherosclerosis Risk in Communities (ARIC) study between 2005 and 2014.

Main Outcomes and Measures

Random forest-based ML approaches were used to develop race-specific and race-agnostic models for predicting in-hospital mortality. Performance was assessed using C index (discrimination), regression slopes for observed vs predicted mortality rates (calibration), and decision curves for prognostic utility.

Results

The training data set included 123 634 hospitalized patients with HF who were enrolled in the GWTG-HF registry (mean [SD] age, 71 [13] years; 58 356 [47.2%] female individuals; 65 278 [52.8%] male individuals. Patients were analyzed in 2 categories: Black (23 453 [19.0%]) and non-Black (2121 [2.1%] Asian; 91 154 [91.0%] White, and 6906 [6.9%] other race and ethnicity). The ML models demonstrated excellent performance in the internal testing subset (n = 82 420) (C statistic, 0.81 for Black patients and 0.82 for non-Black patients) and in the real-world-like cohort with less than 50% missingness on covariates (n = 553 506; C statistic, 0.74 for Black patients and 0.75 for non-Black patients). In the external validation cohort (ARIC registry; n = 1205 Black patients and 2264 non-Black patients), ML models demonstrated high discrimination and adequate calibration (C statistic, 0.79 and 0.80, respectively). Furthermore, the performance of the ML models was superior to the traditional GWTG-HF risk score model (C index, 0.69 for both race groups) and other rederived logistic regression models using race as a covariate. The performance of the ML models was identical using the race-specific and race-agnostic approaches in the GWTG-HF and external validation cohorts. In the GWTG-HF cohort, the addition of zip code-level SDOH parameters to the ML model with clinical covariates only was associated with better discrimination, prognostic utility (assessed using decision curves), and model reclassification metrics in Black patients (net reclassification improvement, 0.22 [95% CI, 0.14-0.30]; P < .001) but not in non-Black patients.

Conclusions and Relevance

ML models for HF mortality demonstrated superior performance to the traditional and rederived logistic regressions models using race as a covariate. The addition of SDOH parameters improved the prognostic utility of prediction models in Black patients but not non-Black patients in the GWTG-HF registry.

Implementation of EHMRG Risk Model in an Italian Population of Elderly Patients with Acute Heart Failure

Acute heart failure (AHF) is a cardiac emergency with an increasing incidence, especially among elderly patients. The Emergency Heart failure Mortality Risk Grade (EHMRG) has been validated to assess the 7-days AHF mortality risk, suggesting the management of patients admitted to an emergency department (ED). EHMRG has never been implemented in Italian ED nor among elderly patients. We aimed to assess EHMRG score accuracy in predicting in-hospital death in a retrospective cohort of elderly subjects admitted for AHF from the ED to an Internal Medicine Department. We enrolled, in a 24-months timeframe, all the patients admitted to an Internal Medicine Department from ED for AHF. We calculated the EHMRG score, subdividing patients into six categories, and assessing in-hospital mortality and length of stay. We evaluated EHMRG accuracy with ROC curve analysis and survival with Kaplan−Meier and Cox models. We collected 439 subjects, with 45 in-hospital deaths (10.3%), observing a significant increase of in-hospital death along with EHMRG class, from 0% (class 1) to 7.7% (class 5b; p < 0.0001). EHMRG was fairly accurate in the whole cohort (AUC: 0.75; 95%CI: 0.68−0.83; p < 0.0001), with the best cutoff observed at >103 (Se: 71.1%; Sp: 72.8%; LR+: 2.62; LR-: 0.40; PPV: 23.0%; NPV: 95.7%), but performed better considering the events in the first seven days of admission (AUC: 0.83; 95%; CI: 0.75−0.91; p < 0.0001). In light of our observations, EHMRG can be useful also for the Italian emergency system to predict the risk of short-term mortality for AHF among elderly patients. EHMRG performance was better in the first seven days but remained acceptable when considering the whole period of hospitalization.

Role of confirmed and potential predictors of an unfavorable outcome in heart failure in everyday clinical practice

Heart failure (HF) is the only cardiovascular disease with an ever increasing incidence. HF, through reduced functional capacity, frequent exacerbations of disease, and repeated hospitalizations, results in poorer quality of life, decreased work productivity, and significantly increased costs of the public health system. The main challenge in the treatment of HF is the availability of reliable prognostic models that would allow patients and doctors to develop realistic expectations about the prognosis and to choose the appropriate therapy and monitoring method. At this moment, there is a lack of universal parameters or scales on the basis of which we could easily capture the moment of deterioration of HF patients' condition. Hence, it is crucial to identify such factors which at the same time will be widely available, cheap, and easy to use. We can find many studies showing different predictors of unfavorable outcome in HF patients: thorough assessment with echocardiography imaging, exercise testing (e.g., 6-min walk test, cardiopulmonary exercise testing), and biomarkers (e.g., N-terminal pro-brain type natriuretic peptide, high-sensitivity troponin T, galectin-3, high-sensitivity C-reactive protein). Some of them are very promising, but more research is needed to create a specific panel on the basis of which we will be able to assess HF patients. At this moment despite identification of many markers of adverse outcomes, clinical decision-making in HF is still predominantly based on a few basic parameters, such as the presence of HF symptoms (NYHA class), left ventricular ejection fraction, and QRS complex duration and morphology.

Disposition of acute decompensated heart failure from the emergency department: An evidence-based review

INTRODUCTION

Acute heart failure (HF) exacerbation is a serious and common condition seen in the Emergency Department (ED) that has significant morbidity and mortality. There are multiple clinical decision tools that Emergency Physicians (EPs) can use to reach an appropriate evidence-based disposition for these patients.

OBJECTIVE

This narrative review is an evidence-based discussion of clinical decision-making tools aimed to assist EPs risk stratify patients with AHF and determine disposition.

DISCUSSION

Risk stratification in patients with AHF exacerbation presenting to the ED is paramount in reaching an appropriate disposition decision. High risk features include hypotension, hypoxemia, elevated brain natriuretic peptide (BNP) and/or troponin, elevated creatinine, and hyponatremia. Patients who require continuous vasoactive infusions, respiratory support, or are initially treatment-resistant generally require intensive care unit admission. In most instances, new-onset AHF patients should be admitted for further evaluation. Other AHF patients in the ED can be risk stratified with the Ottawa HF Risk Score (OHFRS), the Multiple Estimation of Risk Based on Spanish Emergency Department Score (MEESSI), or the Emergency HF Mortality Risk Grade (EHFMRG). These tools take various factors into account such as mode of arrival to the ED, vital signs, laboratory values like troponin and pro-BNP, and clinical course. If used appropriately, these scores can predict patients at low risk for adverse outcomes.

CONCLUSION

This article discusses evidence-based disposition of patients in acute decompensated HF presenting to the ED. Knowledge of these factors and risk tools can assist emergency clinicians in determining appropriate disposition of patients with HF.

The role of acute heart failure index (AHFI) combined with emergency heart failure mortality risk grade (EHMRG) in the evaluation of clinical outcomes and prognosis in patients with acute heart failure

OBJECTIVE

To investigate clinical value for the risk model of acute heart failure index (AHFI) combined with emergency heart failure mortality risk grade (EHMRG) in evaluating clinical outcomes and prognosis of patients with acute heart failure (AHF).

METHODS

The present prospective observational cohort study enrolled a total of 228 patients with AHF who were admitted to our hospital from January 2019 to January 2020. The AHF patients were divided into four groups: (1) the high AHFI and high EHMRG group, n = 61; (2) the low AHFI and low EHMRG group, n = 92; (3) the high AHFI and low EHMRG group, n = 34; (4) the low AHFI and high EHMRG group, n = 41. AHFI and EHMRG were used to identify the risk of death for AHF patients. Serum levels of Troponin I, B-type natriuretic peptide (BNP), and NT-pro-B-type natriuretic peptide (NT-proBNP) were detected by the ELISA method. Kaplan-Meier curve was performed for analysis of survival time and a logistic regression model was used to analyse 1-year mortality of patients. Pearson's analysis was used to determine the correlation between biomarkers and EHMRG.

RESULTS

AHFI combined with the EHMRG model was associated with cardiac function status and EHMRG score was positively related to the level of Troponin I, BNP, and NT-proBNP. AHF high-risk AHFI and high-risk EHMRG indicated that patients might have a higher incidence of MACEs during hospitalisation. In addition, AHFI and high-risk EHMRG groups had shorter survival times, and AHFI was associated with 1-year mortality and was the risk factor for 1-year mortality.

CONCLUSION

AHFI combined with a high EHMRG risk model was associated with clinical outcomes and prognosis.

A new scoring system for predicting short-term outcomes in Chinese patients with critically-ill acute decompensated heart failure

Background

Acute decompensated heart failure (ADHF) contributes millions of emergency department (ED) visits and it is associated with high in-hospital mortality. The aim of this study was to develop and validate a multiparametric score for critically-ill ADHF patients.

Methods

In this single-center, retrospective study, a total of 1268 ADHF patients in China were enrolled and divided into derivation (n = 1014) and validation (n = 254) cohorts. The primary endpoint was any in-hospital death, cardiac arrest or utilization of mechanical support devices. Logistic regression model was preformed to identify risk factors and build the new scoring system. The assigning point of each parameter was determined according to its β coefficient. The discrimination was validated internally using C statistic and calibration was evaluated by the Hosmer-Lemeshow goodness-of-fit test.

Results

We constructed a predictive score based on six significant risk factors [systolic blood pressure (SBP), white blood cell (WBC) count, hematocrit (HCT), total bilirubin (TBIL), estimated glomerular filtration rate (eGFR) and NT-proBNP]. This new model was computed as (1 × SBP < 90 mmHg) + (2 × WBC > 9.2 × 10⁹/L) + (1 × HCT ≤ 0.407) + (2 × TBIL > 34.2 μmol/L) + (2 × eGFR < 15 ml/min/1.73 m²) + (1 × NTproBNP ≥ 10728.9 ng/ml). The C statistic for the new score was 0.758 (95% CI 0.667–0.838) higher than APACHE II, AHEAD and ADHERE score. It also demonstrated good calibration for detecting high-risk patients in the validation cohort (χ² = 6.681, p = 0.463).

Conclusions

The new score including SBP, WBC, HCT, TBIL, eGFR and NT-proBNP might be used to predict short-term prognosis of Chinese critically-ill ADHF patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02041-2.

Risk assessment of post-discharge mortality among recently hospitalized Medicare heart failure patients with reduced or preserved ejection fraction

Objective: Targeted care management for hospitalized patients with acute decompensated heart failure (ADHF) with reduced or preserved ejection fraction (HFrEF/HFpEF) who are at higher risk for post-discharge mortality may mitigate this outcome. However, identification of the most appropriate population for intervention has been challenging. This study developed predictive models to assess risk of 30 day and 1 year post-discharge all-cause mortality among Medicare patients with HFrEF or HFpEF recently hospitalized with ADHF.Methods: A retrospective study was conducted using the 100% Centers for Medicare Services fee-for-service sample with complementary Part D files. Eligible patients had an ADHF-related hospitalization and ICD-9-CM diagnosis code for systolic or diastolic heart failure between 1 January 2010 and 31 December 2014. Data partitioned into training (60%), validation (20%) and test sets (20%) were used to evaluate the three model approaches: classification and regression tree, full logistic regression, and stepwise logistic regression. Performance across models was assessed by comparing the receiver operating characteristic (ROC), cumulative lift, misclassification rate, the number of input variables and the order of selection/variable importance.Results: In the HFrEF (N = 83,000) and HFpEF (N = 123,644) cohorts, 30 day all-cause mortality rates were 6.6% and 5.5%, respectively, and 1 year all-cause mortality rates were 33.6% and 29.5%. The stepwise logistic regression models performed best across both cohorts, having good discrimination (test set ROC of 0.75 for both 30 day mortality models and 0.74 for both 1 year mortality models) and the lowest number of input variables (18-34 variables).Conclusions: Post-discharge mortality risk models for recently hospitalized Medicare patients with HFrEF or HFpEF were developed and found to have good predictive ability with ROCs of greater than or equal to 0.74 and a reasonable number of input variables. Applying this risk model may help providers and health systems identify hospitalized Medicare patients with HFrEF or HFpEF who may benefit from more targeted care management.

Prediction of Survival in Asian Patients Hospitalized With Heart Failure: Validation of the OPTIMIZE-HF Risk Score

BACKGROUND

Risk scores predicting in-patient mortality in heart failure patients have not been designed specifically for Asian patients. We aimed to validate and recalibrate the OPTIMIZE-HF risk model for in-hospital mortality in a multiethnic Asian population hospitalized for heart failure.

METHODS AND RESULTS

Data from the Singapore Cardiac Databank Heart Failure on patients admitted for heart failure from January 1, 2008, to December 31, 2013, were included. The primary outcome studied was in-hospital mortality. Two models were compared: the original OPTIMIZE-HF risk model and a modified OPTIMIZE-HF risk model (similar variables but with coefficients derived from our cohort). A total of 15,219 patients were included. The overall in-hospital mortality was 1.88% (n = 286). The original model had a C-statistic of 0.739 (95% CI 0.708-0.770) with a good match between predicted and observed mortality rates (Hosmer-Lemeshow statistic 13.8; P = .086). The modified model had a C-statistic of 0.741 (95% CI 0.709-0.773) but a significant difference between predicted and observed mortality rates (Hosmer-Lemeshow statistic 17.2; P = .029). The modified model tended to underestimate risk at the extremes (lowest and highest ends) of risk.

CONCLUSIONS

We provide the first independent validation of the OPTIMIZE-HF risk score in an Asian population. This risk model has been shown to perform reliably in our Asian cohort and will potentially provide clinicians with a useful tool to identify high-risk heart failure patients for more intensive management.

Critical Appraisal of Multivariable Prognostic Scores in Heart Failure: Development, Validation and Clinical Utility

Optimal management of heart failure requires accurate risk assessment. Many prognostic risk models have been proposed for patient with chronic and acute heart failure. Methodological critical issues are the data source, the outcome of interest, the choice of variables entering the model, the validation of the model in external population. Up to now, the proposed risk models can be a useful tool to help physician in the clinical decision-making. The availability of big data and of new methods of analysis may lead to developing new models in the future.

Design and rationale of a randomized trial: Using short stay units instead of routine admission to improve patient centered health outcomes for acute heart failure patients (SSU-AHF)

Nearly 85% of acute heart failure (AHF) patients who present to the emergency department (ED) with acute heart failure are hospitalized. Once hospitalized, within 30 days post-discharge, 27% of patients are re-hospitalized or die. Attempts to improve outcomes with novel therapies have all failed. The evidence for existing AHF therapies are poor: No currently used AHF treatment is known to improve long-term outcomes. ED treatment is largely the same today as 40 years ago. Admitting patients who could have avoided hospitalization may contribute to adverse outcomes. Hospitalization is not benign; patients enter a vulnerable phase post-discharge, at increased risk for morbidity and mortality. When hospitalization is able to be shortened or avoid completely, certain risks can be mitigated, including risk of medication errors, in-hospital falls, delirium, nosocomial infections, and other iatrogenic complications. Additionally, patients would prefer to be home, not hospitalized. Furthermore, hospitalization and re-hospitalization for AHF predominantly affects patients of lower socioeconomic status (SES). Avoiding hospitalization in patients who do not require admission may improve outcomes and quality of life, while reducing costs. Short stay unit (SSU: <24 h, also referred to as an 'observation unit') management of AHF may be effective for lower risk patients. However, to date there have only been small studies or retrospective analyses on the SSU management for AHF patients. In addition, SSU management has been considered 'cheating' for hospitals trying to avoid 30-day readmission penalties, as SSUs or observation units do not count as an admission. However, more recent analyses demonstrate differential use of observation status has not led to decreases in re-admission, suggesting this concern may be misplaced. Thus, we propose a robust clinical effectiveness trial to demonstrate the effectiveness of this patient-centered strategy.

Misconceptions in acute heart failure diagnosis and Management in the Emergency Department

INTRODUCTION

Acute heart failure (AHF) accounts for a significant number of emergency department (ED) visits, and the disease may present along a spectrum with a variety of syndromes.

OBJECTIVE

This review evaluates several misconceptions concerning heart failure evaluation and management in the ED, followed by several pearls.

DISCUSSION

AHF is a heterogeneous syndrome with a variety of presentations. Physicians often rely on natriuretic peptides, but the evidence behind their use is controversial, and these should not be used in isolation. Chest radiograph is often considered the most reliable imaging test, but bedside ultrasound (US) provides a more sensitive and specific evaluation for AHF. Diuretics are a foundation of AHF management, but in pulmonary edema, these medications should only be provided after vasodilator administration, such as nitroglycerin. Nitroglycerin administered in high doses for pulmonary edema is safe and effective in reducing the need for intensive care unit admission. Though classically dopamine is the first vasopressor utilized in patients with hypotensive cardiogenic shock, norepinephrine is associated with improved outcomes and lower mortality. Disposition is complex in patients with AHF, and risk stratification tools in conjunction with other assessments allow physicians to discharge patients safely with follow up.

CONCLUSION

A variety of misconceptions surround the evaluation and management of heart failure including clinical assessment, natriuretic peptide use, chest radiograph and US use, nitroglycerin and diuretics, vasopressor choice, and disposition. This review evaluates these misconceptions while providing physicians with updates in evaluation and management of AHF.

Prediction of Early Adverse Events in Emergency Department Patients With Acute Heart Failure: A Systematic Review

BACKGROUND

Acute heart failure (AHF) accounts for a substantial proportion of Emergency Department (ED) visits and hospitalizations. Previous studies have shown that emergency physicians' clinical gestalt is not sufficient to stratify patients with AHF into severe and requiring hospitalization vs nonsevere and safe to be discharged. Various prognostic algorithms have been developed to risk-stratify patients with AHF, however there is no consensus as to the best-performing risk assessment tool in the ED.

METHODS

A systematic review of Medline, PubMed, and Embase up to May 2016 was conducted using established methods. Major cardiology and emergency medicine conference proceedings from 2010 to 2016 were also screened. Two independent reviewers identified studies that evaluated clinical risk scores in adult (ED) patients with AHF, with risk prognostication for mortality or significant morbidity within 7-30 days. Studies included patients who were discharged or admitted.

RESULTS

The systematic review search generated 2950 titles that were screened according to title and abstract. Nine articles, describing 6 risk prediction tools met full inclusion criteria, however, prognostic performance and ease of bedside application is limited for most. Because of clinical heterogeneity in the prognostic tools and study outcomes, a meta-analysis was not performed.

CONCLUSIONS

Several risk scores exist for predicting short-term mortality or morbidity in ED patients with AHF. No single risk tool is clearly superior, however, the Emergency Heart Failure Mortality Risk Grade might aid in prognostication of mortality and the Ottawa Heart Failure Risk Score might provide useful prognostic information in patients suitable for ED discharge.

Creation and validation of the acute heart failure risk score: AHFRS

Our aims were to create and validate a clinical decision rule to assess severity in acute heart failure. We conducted a prospective cohort study of patients with symptoms of acute heart failure who attended the emergency departments (EDs) of three hospitals between April 2011 and April 2013. The following data were collected on arrival to or during the stay in the ED: baseline severity of symptoms; presence of decompensated comorbidities; number of hospital admissions/visits to EDs for acute heart failure during the previous 24 months; triggers of the exacerbation; clinical signs and symptoms; results of ancillary tests requested in the ED; treatments prescribed; and response to the initial treatment in the ED. The main outcome was poor course during the acute phase, in-hospital for admitted patients and during the first week following the ED visit for discharged patients, this being a composite endpoint that included death, admission to an intensive care unit, need for invasive mechanical ventilation, cardiac arrest and use of non-invasive mechanical ventilation. Multivariate logistic regression models were developed. Predictors of poor course in acute heart failure were oedema on chest radiography, visits to the ED and/or admissions in the previous two years, and levels of glycemia and blood urea nitrogen (areas under the curve of 0.83 in the derivation sample, and 0.82 in the validation sample). Four clinical predictors available in the ED can be used to create a simple score to predict poor course in acute heart failure.Clinical Trials.gov ID: NCT02437058.

Derivation and Validation of a Novel Prediction Model to Identify Low-Risk Patients With Acute Pulmonary Embolism

Accurate identification of low-risk patients with acute pulmonary embolism (PE) who may be eligible for outpatient treatment or early discharge can have substantial cost-saving benefit. The purpose of this study was to derive and validate a prediction model to effectively identify patients with PE at low risk of short-term mortality, right ventricular dysfunction, and other nonfatal outcomes. This study analyzed data from 400 consecutive patients with acute PE. We derived and internally validated our prediction rule based on clinically significant variables that are routinely available at initial examination and that were categorized and weighted using coefficients in the multivariate logistic regression. The model was externally validated in an independent cohort of 82 patients. The final model (HOPPE score) consisted of 5 categorized patient variables (1, 2, or 3 points, respectively): systolic blood pressure (>120, 100 to 119, <99 mm Hg), diastolic blood pressure (>80, 65 to 79, <64 mm Hg), heart rate (<80, 81 to 100, >101 beats/min), arterial partial pressure of oxygen (>80, 60 to 79, <59 mm Hg), and modified electrocardiographic score (<2, 2 to 4, >4). The 30-day mortality rates were 0% in low risk (0 to 6 points), 7.5% to 8.5% in intermediate risk (7 to 10), and 18.2% to 18.8% in high-risk patients (≥11) across the derivation and validation cohorts. In comparison with the previously validated PESI score, the HOPPE score had a higher discriminatory power (area under the curve 0.74 vs 0.85, p = 0.033) and significantly improved both the discrimination (integrated discrimination improvement, p = 0.002) and reclassification (net reclassification improvement, p = 0.003) of the model for short-term mortality. In conclusion, the HOPPE score accurately identifies acute patients with PE at low risk of short-term mortality, right ventricular dysfunction, and other nonfatal outcomes. Prospective validation of the prediction model is necessary before implementation in clinical practice.

Why do authors derive new cardiovascular clinical prediction rules in the presence of existing rules? A mixed methods study

Background

Researchers should examine existing evidence to determine the need for a new study. It is unknown whether developers evaluate existing evidence to justify new cardiovascular clinical prediction rules (CPRs).

Objective

We aimed to assess whether authors of cardiovascular CPRs cited existing CPRs, why some authors did not cite existing CPRs, and why they thought existing CPRs were insufficient.

Method

Derivation studies of cardiovascular CPRs from the International Register of Clinical Prediction Rules for Primary Care were evaluated. We reviewed the introduction sections to determine whether existing CPRs were cited. Using thematic content analysis, the stated reasons for determining existing cardiovascular CPRs insufficient were explored. Study authors were surveyed via e-mail and post. We asked whether they were aware of any existing cardiovascular CPRs at the time of derivation, how they searched for existing CPRs, and whether they thought it was important to cite existing CPRs.

Results

Of 85 derivation studies included, 48 (56.5%) cited existing CPRs, 33 (38.8%) did not cite any CPR, and four (4.7%) declared there was none to cite. Content analysis identified five categories of existing CPRs insufficiency related to: (1) derivation (5 studies; 11.4% of 44), (2) construct (31 studies; 70.5%), (3) performance (10 studies; 22.7%), (4) transferability (13 studies; 29.5%), and (5) evidence (8 studies; 18.2%). Authors of 54 derivation studies (71.1% of 76 authors contacted) responded to the survey. Twenty-five authors (46.3%) reported they were aware of existing CPR at the time of derivation. Twenty-nine authors (53.7%) declared they conducted a systematic search to identify existing CPRs. Most authors (90.7%) indicated citing existing CPRs was important.

Conclusion

Cardiovascular CPRs are often developed without citing existing CPRs although most authors agree it is important. Common justifications for new CPRs concerned construct, including choice of predictor variables or relevance of outcomes. Developers should clearly justify why new CPRs are needed with reference to existing CPRs to avoid unnecessary duplication.

European Society of Cardiology - Acute Cardiovascular Care Association position paper on safe discharge of acute heart failure patients from the emergency department

Heart failure is a global public health challenge frequently presenting to the emergency department. After initial stabilization and management, one of the most important decisions is to determine which patients can be safely discharged and which require hospitalization. This is a complex decision that depends on numerous subjective factors, including both the severity of the patient's underlying condition and an estimate of the acuity of the presentation. An emergency department observation period may help select the correct option. Ideally, during an observation period, risk stratification should be carried out using parameters specifically designed for use in the emergency department. Unfortunately, there is little objective literature to guide this disposition decision. An objective and reliable definition of low-risk characteristics to identify early discharge candidates is needed. Benchmarking outcomes in patients discharged from the emergency department without hospitalization could aid this process. Biomarker determinations, although undoubtedly useful in establishing diagnosis and predicting longer-term prognosis, require prospective validation for emergency department disposition guidance. The challenge of identifying emergency department acute heart failure discharge candidates will only be overcome by future multidisciplinary research defining the current knowledge gaps and identifying potential solutions.

Acute Heart Failure Registry: Risk Assessment Model in Decompensated Heart Failure

Background

Heart failure (HF) is a highly prevalent syndrome. Although the long-term prognostic factors have been identified in chronic HF, this information is scarcer with respect to patients with acute HF. despite available data in the literature on long-term prognostic factors in chronic HF, data on acute HF patients are more scarce.

Objectives

To develop a predictor of unfavorable prognostic events in patients hospitalized for acute HF syndromes, and to characterize a group at higher risk regarding their clinical characteristics, treatment and outcomes.

Methods

cohort study of 600 patients admitted for acute HF, defined according to the European Society of Cardiology criteria. Primary endpoint for score derivation was defined as all-cause mortality and / or rehospitalization for HF at 12 months. For score validation, the following endpoints were used: all-cause mortality and / or readmission for HF at 6, 12 and 24 months. The exclusion criteria were: high output HF; patients with acute myocardial infraction, acute myocarditis, infectious endocarditis, pulmonary infection, pulmonary artery hypertension and severe mitral stenosis.

Results

505 patients were included, and prognostic predicting factors at 12 months were identified. One or two points were assigned according to the odds ratio (OR) obtained (p < 0.05). After the total score value was determined, a 4-point cut-off was determined for each ROC curve at 12 months. Two groups were formed according to the number of points, group A < 4 points, and group B = 4 points. Group B was composed of older patients, with higher number of comorbidities and predictors of the combined endpoint at 6, 12 and 24 months, as linearly represented in the survival curves (Log rank).

Conclusions

This risk score enabled the identification of a group with worse prognosis at 12 months.

The Effect of Frailty on 30-day Mortality Risk in Older Patients With Acute Heart Failure Attended in the Emergency Department

OBJECTIVE

The objective was to determine the effect of frailty on risk of 30-day mortality in nonseverely disabled older patients with acute heart failure (AHF) attended in emergency departments (EDs).

METHODOLOGY

The Frailty-AHF Study is a retrospective analysis of a multicenter, observational, prospective, cohort study (Older-AHF Register). This study included consecutive patients ≥ 65 years of age without severe functional dependence or dementia attended for AHF in three Spanish EDs for 4 months. Frailty was defined by frailty phenotype as the presence of three or more domains. Baseline and episode characteristics and 30-day mortality were collected in all the patients.

RESULTS

A total of 465 patients with a mean (±SD) age of 82 (±7) years were included, 283 (61.0%) being female and 225 (51.3%) with severe comorbidity (Charlson index ≥ 3). Frailty was present in 169 (36.3%). The rate of 30-day mortality was 7.3%. Frailty adjusted for potential confounding factors was an independent factor associated with 30-day mortality (adjusted hazard ratio = 2.5; 95% confidence interval = 1.0 to 6.0; p = 0.047).

CONCLUSION

The presence of frailty is an independent risk factor of 30-day mortality in nonsevere dependent older patients attended with AHF in EDs.

Prospective and Explicit Clinical Validation of the Ottawa Heart Failure Risk Scale, With and Without Use of Quantitative NT-proBNP

OBJECTIVES

We previously developed the Ottawa Heart Failure Risk Scale (OHFRS) to assist with disposition decisions for acute heart failure patients in the emergency department (ED). We sought to prospectively evaluate the accuracy, acceptability, and potential impact of OHFRS.

METHODS

This prospective observational cohort study was conducted at six tertiary hospital EDs. Patients with acute heart failure were evaluated by ED physicians for the 10 OHFRS criteria and then followed for 30 days. Quantitative NT-proBNP was measured where feasible. Serious adverse event (SAE) was defined as death within 30 days, admission to monitored unit, intubation, noninvasive ventilation, myocardial infarction, or relapse resulting in hospital admission within 14 days.

RESULTS

We enrolled 1,100 patients with mean (±SD) age 77.7 (±10.7) years. SAEs occurred in 170 (15.5%) cases (19.4% if admitted and 10.2% if discharged). Compared to actual practice, using an admission threshold of OHFRS score > 1 would have increased sensitivity (71.8% vs. 91.8%) but increased admissions (57.2% vs. 77.6%). For 684 cases with NT-proBNP values, using a threshold score > 1 would have significantly increased sensitivity (69.8% vs. 95.8%) while increasing admissions (60.8% vs. 88.0%). In only 11.9% of cases did physicians indicate discomfort with use of OHFRS.

CONCLUSION

Prospective clinical validation found the OHFRS tool to be highly sensitive for SAEs in acute heart failure patients, albeit with an increase in admission rates. When available, NT-proBNP values further improve sensitivity. With adequate physician training, OHFRS should help improve and standardize admission practices, diminishing both unnecessary admissions for low-risk patients and unsafe discharge decisions for high-risk patients.

Use of High-Sensitivity Troponin T to Identify Patients With Acute Heart Failure at Lower Risk for Adverse Outcomes: An Exploratory Analysis From the RELAX-AHF Trial

OBJECTIVES

The aim of this study was to determine if a baseline high-sensitivity troponin T (hsTnT) value ≤99th percentile upper reference limit (0.014 μg/l ["low hsTnT"]) identifies patients at low risk for adverse outcomes.

BACKGROUND

Approximately 85% of patients who present to emergency departments with acute heart failure are admitted. Identification of patients at low risk might decrease unnecessary admissions.

METHODS

A post-hoc analysis was conducted from the RELAX-AHF (Serelaxin, Recombinant Human Relaxin-2, for Treatment of Acute Heart Failure) trial, which randomized patients within 16 h of presentation who had systolic blood pressure >125 mm Hg, mild to moderate renal impairment, and N-terminal pro-brain natriuretic peptide ≥1,600 ng/l to serelaxin versus placebo. Linear regression models for continuous endpoints and Cox models for time-to-event endpoints were used.

RESULTS

Of the 1,076 patients with available baseline hsTnT values, 107 (9.9%) had low hsTnT. No cardiovascular (CV) deaths through day 180 were observed in the low-hsTnT group compared with 79 CV deaths (7.3%) in patients with higher hsTnT. By univariate analyses, low hsTnT was associated with lower risk for all 5 primary outcomes: 1) days alive and out of the hospital by day 60; 2) CV death or rehospitalization for heart failure or renal failure by day 60; 3) length of stay; 4) worsening heart failure through day 5; and 5) CV death through day 180. After multivariate adjustment, only 180-day CV mortality remained significant (hazard ratio: 0.0; 95% confidence interval: 0.0 to 0.736; p = 0.0234; C-index = 0.838 [95% confidence interval: 0.798 to 0.878]).

CONCLUSIONS

No CV deaths through day 180 were observed in patients with hsTnT levels ≤0.014 μg/l despite high N-terminal pro-brain natriuretic peptide levels. Low baseline hsTnT may identify patients with acute heart failure at very low risk for CV mortality.

Considerations for initial therapy in the treatment of acute heart failure

The diagnosis of patients presenting to the emergency department with acute heart failure (AHF) is challenging due to the similarity of AHF symptoms to other conditions such as chronic obstructive pulmonary disease and pneumonia. Additionally, because AHF is most common in an older population, the presentation of coexistent pathologies further increases the challenge of making an accurate diagnosis and selecting the most appropriate treatment. Delays in the diagnosis and treatment of AHF can result in worse outcomes and higher healthcare costs. Rapid initiation of treatment is thus necessary for optimal disease management. Early treatment decisions for patients with AHF can be guided by risk-stratification models based on initial clinical data, including blood pressure, levels of troponin, blood urea nitrogen, serum creatinine, B-type natriuretic peptide, and ultrasound. In this review, we discuss methods for differentiating high-risk and low-risk patients and provide guidance on how treatment decisions can be informed by risk-level assessment. Through the use of these approaches, emergency physicians can play an important role in improving patient management, preventing unnecessary hospitalizations, and lowering healthcare costs. This review differs from others published recently on the topic of treating AHF by providing a detailed examination of the clinical utility of diagnostic tools for the differentiation of dyspneic patients such as bedside ultrasound, hemodynamic changes, and interrogation of implantable cardiac devices. In addition, our clinical guidance on considerations for initial pharmacologic therapy in the undifferentiated patient is provided. It is crucial for emergency physicians to achieve an early diagnosis of AHF and initiate therapy in order to reduce morbidity, mortality, and healthcare costs.

Identification of Emergency Department Patients With Acute Heart Failure at Low Risk for 30-Day Adverse Events: The STRATIFY Decision Tool

OBJECTIVES

No prospectively derived or validated decision tools identify emergency department (ED) patients with acute heart failure (AHF) at low risk for 30-day adverse events who are thus potential candidates for safe ED discharge. This study sought to accomplish that goal.

BACKGROUND

The nearly 1 million annual ED visits for AHF are associated with high proportions of admissions and consume significant resources.

METHODS

We prospectively enrolled 1,033 patients diagnosed with AHF in the ED from 4 hospitals between July 20, 2007, and February 4, 2011. We used an ordinal outcome hierarchy, defined as the incidence of the most severe adverse event within 30 days of ED evaluation (acute coronary syndrome, coronary revascularization, emergent dialysis, intubation, mechanical cardiac support, cardiopulmonary resuscitation, and death).

RESULTS

Of 1,033 patients enrolled, 126 (12%) experienced at least one 30-day adverse event. The decision tool had a C statistic of 0.68 (95% confidence interval: 0.63 to 0.74). Elevated troponin (p < 0.001) and renal function (p = 0.01) were significant predictors of adverse events in our multivariable model, whereas B-type natriuretic peptide (p = 0.09), tachypnea (p = 0.09), and patients undergoing dialysis (p = 0.07) trended toward significance. At risk thresholds of 1%, 3%, and 5%, we found 0%, 1.4%, and 13.0% patients were at low risk, with negative predictive values of 100%, 96%, and 93%, respectively.

CONCLUSIONS

The STRATIFY decision tool identifies ED patients with AHF who are at low risk for 30-day adverse events and may be candidates for safe ED discharge. After external testing, and perhaps when used as part of a shared decision-making strategy, it may significantly affect disposition strategies. (Improving Heart Failure Risk Stratification in the ED [STRATIFY]; NCT00508638).

Personalized Modeling for Prediction with Decision-Path Models

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach.

Early management of patients with acute heart failure: state of the art and future directions. A consensus document from the society for academic emergency medicine/heart failure society of America acute heart failure working group

Heart failure (HF) afflicts nearly 6 million Americans, resulting in one million emergency department (ED) visits and over one million annual hospital discharges. An aging population and improved survival from cardiovascular diseases is expected to further increase HF prevalence. Emergency providers play a significant role in the management of patients with acute heart failure (AHF). It is crucial that emergency physicians and other providers involved in early management understand the latest developments in diagnostic testing, therapeutics and alternatives to hospitalization. Further, clinical trials must be conducted in the ED in order to improve the evidence base and drive optimal initial therapy for AHF. Should ongoing and future studies suggest early phenotype-driven therapy improves in-hospital and post-discharge outcomes, ED treatment decisions will need to evolve accordingly. The potential impact of future studies which incorporate risk-stratification into ED disposition decisions cannot be underestimated. Predictive instruments that identify a cohort of patients safe for ED discharge, while simultaneously addressing barriers to successful outpatient management, have the potential to significantly impact quality of life and resource expenditures.

Early management of patients with acute heart failure: state of the art and future directions--a consensus document from the SAEM/HFSA acute heart failure working group

Heart failure (HF) afflicts nearly 6 million Americans, resulting in 1 million emergency department (ED) visits and over 1 million annual hospital discharges. The majority of inpatient admissions originate in the ED; thus, it is crucial that emergency physicians and other providers involved in early management understand the latest developments in diagnostic testing, therapeutics, and alternatives to hospitalization. This article discusses contemporary ED management as well as the necessary next steps for ED-based acute HF research.

Moving toward comprehensive acute heart failure risk assessment in the emergency department: the importance of self-care and shared decision making

Nearly 700,000 emergency department (ED) visits were due to acute heart failure (AHF) in 2009. Most visits result in a hospital admission and account for the largest proportion of a projected $70 billion to be spent on heart failure care by 2030. ED-based risk prediction tools in AHF rarely impact disposition decision making. This is a major factor contributing to the 80% admission rate for ED patients with AHF, which has remained unchanged over the last several years. Self-care behaviors such as symptom monitoring, medication taking, dietary adherence, and exercise have been associated with decreased hospital readmissions, yet self-care remains largely unaddressed in ED patients with AHF and thus represents a significant lost opportunity to improve patient care and decrease ED visits and hospitalizations. Furthermore, shared decision making encourages collaborative interaction between patients, caregivers, and providers to drive a care path based on mutual agreement. The observation that “difficult decisions now will simplify difficult decisions later” has particular relevance to the ED, given this is the venue for many such issues. We hypothesize patients as complex and heterogeneous as ED patients with AHF may need both an objective evaluation of physiologic risk as well as an evaluation of barriers to ideal self-care, along with strategies to overcome these barriers. Combining physician gestalt, physiologic risk prediction instruments, an evaluation of self-care, and an information exchange between patient and provider using shared decision making may provide the critical inertia necessary to discharge patients home after a brief ED evaluation.

The role of the emergency department in the patient with acute heart failure

The diagnosis and management of the patient with acute decompensated heart failure (ADHF) presents a unique challenge to the emergency medicine (EM) physician. ADHF is one of the most common cardiac emergencies managed in the emergency department (ED). ED presentations for ADHF will grow as survival rates after myocardial infarction continue to increase and thus, the incidence and prevalence of heart failure (HF) increases. There are very little data to aid EM physicians when trying to identify low-risk patients who are safe for ED discharge and observation units are not yet universally utilized. This results in 80% of patients with ADHF getting admitted to the hospital. The aim of this review is to evaluate current strategies for diagnosis, treatment, and disposition of the ADHF patient in the ED while highlighting new approaches for treatment and disposition, and areas in need of additional research.

Initial management of patients with acute heart failure

Pressure exists to manage patients with acute decompensated heart failure (ADHF) efficiently in the acute-care environment. Although most patients present with worsening of chronic heart failure, some may present with undifferentiated dyspnea and new-onset heart failure. Others have significant comorbidities that complicate both the diagnosis and treatment. The treatment of patients with ADHF is prioritized based on vital signs and presenting phenotype. The risk stratification of patients is the subject of ongoing evaluation. The disposition of patients to areas other than a monitored inpatient bed, such as an emergency department-based observation unit, may prove effective.

The challenge of heart failure discharge from the emergency department

Acute decompensated heart failure is a common reason for presentation to the emergency department and is associated with high rates of admission to hospital. Distinguishing between higher-risk patients needing hospitalization and lower-risk patients suitable for discharge home is important to optimize both cost-effectiveness and clinical outcomes. However, this can be challenging and few validated risk stratification tools currently exist to help clinicians. Some prognostic variables predict risks broadly in those who are admitted or discharged from the emergency department. Risk stratification methods such as the Emergency Heart Failure Mortality Risk Grade and Acute Heart Failure Index clinical decision support tools, which utilize many of these predictors, have been found to be accurate in identifying low-risk patients. The use of observation units may also be a cost-effective adjunctive strategy that can assist in determining disposition from the emergency department.

Validating the acute heart failure index for patients presenting to the emergency department with decompensated heart failure

BACKGROUND

The acute heart failure index (AHFI) is a previously derived prediction rule to identify patients presenting to emergency departments (ED) with decompensated heart failure (DHF) at low risk of early life-threatening events.

STUDY OBJECTIVES

To validate the AHFI prospectively.

METHODS

Using a prospective cohort study, adult patients presenting to an urban university hospital ED with DHF were included. Data on 21 variables were gathered to calculate the AHFI. Primary endpoints included inpatient death and non-fatal serious outcomes (myocardial infarction, ventricular fibrillation, cardiogenic shock, cardiac arrest, intubation, or cardiac reperfusion). Secondary endpoints included death from any cause or readmission for heart failure within 30 days. Primary and secondary endpoint rates were calculated with 95% CI for the low and higher-risk subgroups.

RESULTS

259 patients were enrolled. 245/259 (95%) were admitted. 60/259 (23%) met low-risk criteria, of whom 1/60 (1.7%, CI 0.04 to 8.9) was discharged after sustaining pulseless electrical activity arrest. The comparable primary outcome rate in the derivation study was 1.4% (CI 1.1 to 1.7). 17/199 (8.5%, CI 5.1 to 13.3) higher-risk patients experienced an endpoint, compared with 13.3% (CI 12.9 to 13.7) in the derivation cohort. One low-risk patient (1.7%, CI 0.04 to 8.9) died within 30 days, and five (8.3%, CI 2.8 to 18.4) were readmitted. Corresponding rates in the derivation study were 2% and 5%, respectively.

CONCLUSION

The results are consistent with those previously reported for the low-risk subgroup of the AHFI. Further research is needed to determine the impact, safety and full range of generalisability of the AHFI as an adjunct to decision making.

Prediction of emergent heart failure death by semi-quantitative triage risk stratification

OBJECTIVES

Generic triage risk assessments are widely used in the emergency department (ED), but have not been validated for prediction of short-term risk among patients with acute heart failure (HF). Our objective was to evaluate the Canadian Triage Acuity Scale (CTAS) for prediction of early death among HF patients.

METHODS

We included patients presenting with HF to an ED in Ontario from Apr 2003 to Mar 2007. We used the National Ambulatory Care Reporting System and vital statistics databases to examine care and outcomes.

RESULTS

Among 68,380 patients (76±12 years, 49.4% men), early mortality was stratified with death rates of 9.9%, 1.9%, 0.9%, and 0.5% at 1-day, and 17.2%, 5.9%, 3.8%, and 2.5% at 7-days, for CTAS 1, 2, 3, and 4-5, respectively. Compared to lower acuity (CTAS 4-5) patients, adjusted odds ratios (aOR) for 1-day death were 1.32 (95%CI; 0.93-1.88; p = 0.12) for CTAS 3, 2.41 (95%CI; 1.71-3.40; p<0.001) for CTAS 2, and highest for CTAS 1: 9.06 (95%CI; 6.28-13.06; p<0.001). Predictors of triage-critical (CTAS 1) status included oxygen saturation <90% (aOR 5.92, 95%CI; 3.09-11.81; p<0.001), respiratory rate >24 breaths/minute (aOR 1.96, 95%CI; 1.05-3.67; p = 0.034), and arrival by paramedic (aOR 3.52, 95%CI; 1.70-8.02; p = 0.001). While age/sex-adjusted CTAS score provided good discrimination for ED (c-statistic = 0.817) and 1-day (c-statistic = 0.724) death, mortality prediction was improved further after accounting for cardiac and non-cardiac co-morbidities (c-statistics 0.882 and 0.810, respectively; both p<0.001).

CONCLUSIONS

A semi-quantitative triage acuity scale assigned at ED presentation and based largely on respiratory factors predicted emergent death among HF patients.

Critical pathways for post-emergency outpatient diagnosis and treatment: tools to improve the value of emergency care

The decision to admit a patient to the hospital after an emergency department (ED) visit is expensive, frequently not evidence-based, and variable. Outpatient critical pathways are a promising approach to reduce hospital admission after emergency care. Critical pathways exist to risk stratify patients for potentially serious diagnoses (e.g., acute myocardial infarction [AMI]) or evaluate response to therapy (e.g., community-acquired pneumonia) within a short time period (i.e., less than 36 hours), to determine if further hospital-based acute care is needed. Yet, such pathways are variably used while many patients are admitted for conditions for which they could be treated as outpatients. In this article, the authors propose a model of post-ED critical pathways, describe their role in emergency care, list common diagnoses that are amenable to critical pathways in the outpatient setting, and propose a research agenda to address barriers and solutions to increase the use of outpatient critical pathways. If emergency providers are to routinely conduct rapid evaluations in outpatient or observation settings, they must have several conditions at their disposal: 1) evidence-based tools to accurately risk stratify patients for protocolized care, 2) systems of care that reliably facilitate workup in the outpatient setting, and 3) a medical environment conducive to noninpatient pathways, with aligned risks and incentives among patients, providers, and payers. Increased use of critical pathways after emergency care is a potential way to improve the value of emergency care.

Learning patient-specific predictive models from clinical data

We introduce an algorithm for learning patient-specific models from clinical data to predict outcomes. Patient-specific models are influenced by the particular history, symptoms, laboratory results, and other features of the patient case at hand, in contrast to the commonly used population-wide models that are constructed to perform well on average on all future cases. The patient-specific algorithm uses Markov blanket (MB) models, carries out Bayesian model averaging over a set of models to predict the outcome for the patient case at hand, and employs a patient-specific heuristic to locate a set of suitable models to average over. We evaluate the utility of using a local structure representation for the conditional probability distributions in the MB models that captures additional independence relations among the variables compared to the typically used representation that captures only the global structure among the variables. In addition, we compare the performance of Bayesian model averaging to that of model selection. The patient-specific algorithm and its variants were evaluated on two clinical datasets for two outcomes. Our results provide support that the performance of an algorithm for learning patient-specific models can be improved by using a local structure representation for MB models and by performing Bayesian model averaging.

Early deaths in patients with heart failure discharged from the emergency department: a population-based analysis

BACKGROUND

Although approximately one third of patients with heart failure (HF) visiting the emergency department (ED) are discharged home, little is known about their care and outcomes.

METHODS AND RESULTS

We examined the acute care and early outcomes of patients with HF who visited an ED and were discharged without hospital admission in Ontario, Canada, from April 2004 to March 2007. Among 50 816 patients (age, 76.4+/-11.6 years; 49.4% men) visiting an ED for HF, 16 094 (31.7%) were discharged without hospital admission. A total of 4.0% died within 30 days from admission, and 1.3% died within 7 days of discharge from the ED. Although multiple (>or=2) previous HF admissions (odds ratio [OR], 1.64; 95% CI, 1.14 to 2.31), valvular heart disease (OR, 1.37; 95% CI, 1.00 to 1.84), peripheral vascular disease (OR, 1.41; 95% CI, 1.00 to 1.93), and respiratory disease (OR, 1.33; 95% CI, 1.08 to 1.63) increased the risk of 30-day death among those discharged from the ED, presence of these conditions did not increase the likelihood of admission. Patients were more likely to be admitted if they were older (OR, 1.08; 95% CI, 1.06 to 1.10 per decade), arrived by ambulance (OR, 2.02; 95% CI, 1.93 to 2.12), had a higher triage acuity score (OR, 4.12; 95% CI, 3.84 to 4.42), or received resuscitation in the ED (OR, 2.85; 95% CI, 2.68 to 3.04). In those with comparable predicted risks of death, subsequent 90-day mortality rates were higher among discharged than admitted patients (11.9% versus 9.5%; log-rank P=0.016).

CONCLUSIONS

Patients with HF who are discharged from the ED have substantial risks of early death, which, in some cases, may exceed that of hospitalized patients.

Emergency physicians' risk attitudes in acute decompensated heart failure patients

OBJECTIVES

Despite the existence of various clinical prediction rules, no data exist defining what frequency of death or serious nonfatal outcomes comprises a realistic "low-risk" group for clinicians. This exploratory study sought to identify emergency physicians' (EPs) definition of low-risk acute decompensated heart failure (ADHF) emergency department (ED) patients.

METHODS

Surveys were mailed to full-time physicians (n = 88) in a multihospital EP group in southwestern Pennsylvania between December 2004 and February 2005. Participation was voluntary, and each EP was asked to define low risk (low risk of all-cause 30-day death and low risk of either hospital death or other serious medical complications) and choose a risk threshold at which they might consider outpatient management for those with ADHF. A range of choices was offered (<0.5, <1, <2, <3, <4, and <5%), and demographic data were collected.

RESULTS

The response rate was 80%. Physicians defined low risk both for all-cause 30-day death and for hospital death or other serious complications, at <1% (38.8 and 40.3%, respectively). The decision threshold to consider outpatient therapy was <0.5% risk both for all-cause 30-day death (44.6%) and for hospital death or serious medical complications (44.4%).

CONCLUSIONS

Emergency physicians in this exploratory study define low-risk ADHF patients as having less than a 1% risk of 30-day death or inpatient death or complications. They state a desire to have and use an ADHF clinical prediction rule that can identify low-risk ADHF patients who have less than a 0.5% risk of 30-day death or inpatient death or complications.

The Development of a Simple Risk Score to Predict Early Outcome in Severe Acute Acidotic Cardiogenic Pulmonary Edema

Background— Acute cardiogenic pulmonary edema is a common medical emergency with high early mortality. Initial clinical assessment would benefit from accurate mortality prediction. We aimed to develop a simple clinical score based on presenting characteristics that would predict 7-day mortality in patients with acute cardiogenic pulmonary edema.

Methods and Results— We used data from patients recruited to the 3CPO trial (a pragmatic multicenter trial comparing continuous positive airway pressure, noninvasive positive pressure ventilation, and standard oxygen therapy in emergency department patients with acute cardiogenic pulmonary edema) to investigate the association between baseline characteristics and 7-day mortality. Factors associated with mortality ( P <0.1) were entered into a multivariable model. Independent predictors of mortality from the multivariable model ( P <0.05) were assigned integer weights based on their coefficients and incorporated into a risk score. The discriminant ability of the score was tested by receiver operator characteristic analysis. Data from 1069 patients (78±10 years; 43% men; 7-day mortality, 9.6%) were analyzed. Multivariable analysis identified age ( P =0.003), systolic blood pressure ( P <0.001), and Glasgow Coma Scale motor component dichotomized and simplified to the ability to obey commands or not ( P =0.02) as the only independent predictors of 7-day mortality. These were weighted and used to develop a risk score ranging from 0 (7-day mortality, 1.9%; 95% CI, 0.8 to 4.5) to 7 (7-day mortality, 100%; 95% CI, 34.2 to 100). Receiver operator characteristic analysis demonstrated good risk prediction with a c-statistic of 0.794 (95% CI, 0.745 to 0.843). A simplified 3-point score with no weighting had a c-statistic of 0.754 (95% CI, 0.701 to 0.807).

Conclusions— A simple clinical score based on age, systolic blood pressure, and the ability to obey commands predicts early mortality in patients with acute cardiogenic pulmonary edema.

Clinical Trial Registration— clinicaltrials.gov Identifier: ISRCTN077448447.

Low-risk acute heart failure patients: external validation of the Society of Chest Pain Center's recommendations

INTRODUCTION

Risk-stratification in acute heart failure syndromes (AHFS) is problematic. A recent set of recommendations describes emergency department (ED) patients with AHFS who do not fulfill high-risk criteria and may be good candidates for observation unit (OU) management. The goal of this analysis was to report on the outcomes experienced by ED patients with AHFS who do not have any of these high-risk criteria.

METHODS

We performed a secondary analysis of the HEart failure and Audicor technology for Rapid Diagnosis and Initial Treatment (HEARD-IT) multinational study. HEARD-IT was a multicenter study designed to test the impact of acoustic cardiography on ED decision making in patients with possible AHFS. For the purposes of the current analysis we identified a subset of HEARD-IT patients who did not fulfill any high-risk criteria based on published data. The proportion of these patients who experienced an adverse outcome was determined.

RESULTS

The 201 subjects who fulfilled the inclusion criteria had a mean age of 64 years (SD: 13), 61% were male, 34% were Caucasian, and 55% were black. There were a total of 25 (12.4%) cardiac events, including 1 death due to AHFS. The majority of the cardiac events were 30-day readmissions related to AHFS (16/25, 64.0%).

CONCLUSION

AHFS patients at low-risk for subsequent morbidity and mortality based on recent consensus guidelines may be good candidates for early discharge after a brief period of observation in the OU or ED. Additional prospective research is needed to determine the impact of implementation of these criteria in ED patients with AHFS.

Validation of a clinical prognostic model to identify low-risk patients with pulmonary embolism

OBJECTIVE

To validate the Pulmonary Embolism Severity Index (PESI), a clinical prognostic model which identifies low-risk patients with pulmonary embolism (PE).

DESIGN

Validation study using prospectively collected data.

SETTING

A total of 119 European hospitals.

SUBJECTS

A total of 899 patients diagnosed with PE.

INTERVENTION

The PESI uses 11 clinical factors to stratify patients with PE into five classes (I-V) of increasing risk of mortality. We calculated the PESI risk class for each patient and the proportion of patients classified as low-risk (classes I and II). The outcomes were overall and PE-specific mortality for low-risk patients at 3 months after presentation. We calculated the sensitivity, specificity and predictive values to predict overall and PE-specific mortality and the discriminatory power using the area under the receiver operating characteristic curve.

RESULTS

Overall and PE-specific mortality was 6.5% (58/899) and 2.3% (21/899) respectively. Forty-seven per cent of patients (426/899) were classified as low-risk. Low-risk patients had an overall mortality of only 1.2% (5/426) and a PE-specific mortality of 0.7% (3/426). The sensitivity was 91 [95% confidence interval (CI): 81-97%] and the negative predictive value was 99% (95% CI: 97-100%) for overall mortality. The sensitivity was 86% (95% CI: 64-97%) and the negative predictive value was 99% (95% CI: 98-100%) for PE-specific mortality. The areas under the receiver operating characteristic curve for overall and PE-specific mortality were 0.80 (95% CI: 0.75-0.86) and 0.77 (95% CI: 0.68-0.86) respectively.

CONCLUSIONS

This validation study confirms that the PESI reliably identifies low-risk patients with PE who are potential candidates for less costly outpatient treatment.

Racial differences in 30-day mortality for pulmonary embolism

OBJECTIVES

Previous studies reported a higher incidence of in-hospital mortality for Black patients who had pulmonary embolism than for White patients. We used a large statewide database to compare 30-day mortality (defined as death within 30 days from the date of latest hospital admission) for Black and White patients who were hospitalized because of pulmonary embolism.

METHODS

The study cohort consisted of 15531 discharged patients who had been treated for pulmonary embolism at 186 Pennsylvania hospitals between January 2000 and November 2002. We used random-effects logistic regression to model 30-day mortality for Black and White patients, and adjusted for patient demographic and clinical characteristics.

RESULTS

The unadjusted 30-day mortality rates were 9.0% for White patients, 10.3% for Blacks, and 10.9% for patients of other or unknown race. When adjusted for severity of disease using a validated clinical prognostic model for pulmonary embolism, Black patients had 30% higher odds of 30-day mortality compared with White patients at the same site (adjusted odds ratio = 1.3; 95% confidence interval, 1.1,1.6). Neither insurance status nor hospital volume was a significant predictor of 30-day mortality.

CONCLUSION

Black patients who had pulmonary embolism had significantly higher odds of 30-day mortality compared with White patients.

Derivation and validation of a prognostic model for pulmonary embolism

RATIONALE

An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment.

OBJECTIVES

To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes.

METHODS

We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France.

MEASUREMENTS

We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples.

MAIN RESULTS

The prediction rule is based on 11 simple patient characteristics that were independently associated with mortality and stratifies patients with pulmonary embolism into five severity classes, with 30-day mortality rates of 0-1.6% in class I, 1.7-3.5% in class II, 3.2-7.1% in class III, 4.0-11.4% in class IV, and 10.0-24.5% in class V across the derivation and validation samples. Inpatient death and nonfatal complications were <or= 1.1% among patients in class I and <or= 1.9% among patients in class II.

CONCLUSIONS

Our rule accurately classifies patients with pulmonary embolism into classes of increasing risk of mortality and other adverse medical outcomes. Further validation of the rule is important before its implementation as a decision aid to guide the initial management of patients with pulmonary embolism.