Risk stratification scores for patients with acute heart failure in the Emergency Department: A systematic review

Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients

AIMS

In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive interpretation of the prediction results. We aimed to develop ML models with high prediction performance, interpretability, and superior risk stratification to predict in-hospital mortality and worsening heart failure (WHF) in patients with acute heart failure (AHF).

METHODS AND RESULTS

Based on the Kyoto Congestive Heart Failure registry, which enrolled 4056 patients with AHF, we developed prediction models for in-hospital mortality and WHF using information obtained on the first day of admission (demographics, physical examination, blood test results, etc.). After excluding 16 patients who died on the first or second day of admission, the original dataset (n = 4040) was split 4:1 into training (n = 3232) and test datasets (n = 808). Based on the training dataset, we developed three types of prediction models: (i) the classification and regression trees (CART) model; (ii) the random forest (RF) model; and (iii) the extreme gradient boosting (XGBoost) model. The performance of each model was evaluated using the test dataset, based on metrics including sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and calibration slope. For the complex structure of the XGBoost model, we performed SHapley Additive exPlanations (SHAP) analysis, classifying patients into interpretable clusters. In the original dataset, the proportion of females was 44.8% (1809/4040), and the average age was 77.9 ± 12.0. The in-hospital mortality rate was 6.3% (255/4040) and the WHF rate was 22.3% (900/4040) in the total study population. In the in-hospital mortality prediction, the AUC for the XGBoost model was 0.816 [95% confidence interval (CI): 0.815-0.818], surpassing the AUC values for the CART model (0.683, 95% CI: 0.680-0.685) and the RF model (0.755, 95% CI: 0.753-0.757). Similarly, in the WHF prediction, the AUC for the XGBoost model was 0.766 (95% CI: 0.765-0.768), outperforming the AUC values for the CART model (0.688, 95% CI: 0.686-0.689) and the RF model (0.713, 95% CI: 0.711-0.714). In the XGBoost model, interpretable clusters were formed, and the rates of in-hospital mortality and WHF were similar among each cluster in both the training and test datasets.

CONCLUSIONS

The XGBoost models with SHAP analysis provide high prediction performance, interpretability, and reproducible risk stratification for in-hospital mortality and WHF for patients with AHF.

Admission prioritization of heart failure patients with multiple comorbidities

The primary objective of this study was to enhance the operational efficiency of the current healthcare system by proposing a quicker and more effective approach for healthcare providers to deliver services to individuals facing acute heart failure (HF) and concurrent medical conditions. The aim was to support healthcare staff in providing urgent services more efficiently by developing an automated decision-support Patient Prioritization (PP) Tool that utilizes a tailored machine learning (ML) model to prioritize HF patients with chronic heart conditions and concurrent comorbidities during Urgent Care Unit admission. The study applies key ML models to the PhysioNet dataset, encompassing hospital admissions and mortality records of heart failure patients at Zigong Fourth People's Hospital in Sichuan, China, between 2016 and 2019. In addition, the model outcomes for the PhysioNet dataset are compared with the Healthcare Cost and Utilization Project (HCUP) Maryland (MD) State Inpatient Data (SID) for 2014, a secondary dataset containing heart failure patients, to assess the generalizability of results across diverse healthcare settings and patient demographics. The ML models in this project demonstrate efficiencies surpassing 97.8% and specificities exceeding 95% in identifying HF patients at a higher risk and ranking them based on their mortality risk level. Utilizing this machine learning for the PP approach underscores risk assessment, supporting healthcare professionals in managing HF patients more effectively and allocating resources to those in immediate need, whether in hospital or telehealth settings.

Acute Heart Failure: From The Emergency Department to the Intensive Care Unit

Acute heart failure (AHF) is a frequent cause of hospitalization around the world and is associated with high in-hospital and post-discharge morbidity and mortality. This review summarizes data on diagnosis and management of AHF from the emergency department to the intensive care unit. While more evidence is needed to guide risk stratification and care of patients with AHF, hospitalization is a key opportunity to optimize evidence-based medical therapy for heart failure. Close linkage to outpatient care is essential to improve post-hospitalization outcomes.

Proteomic prediction of incident heart failure and its main subtypes

AIM

To examine the ability of serum proteins in predicting future heart failure (HF) events, including HF with reduced or preserved ejection fraction (HFrEF or HFpEF), in relation to event time, and with or without considering established HF-associated clinical variables.

METHODS AND RESULTS

In the prospective population-based Age, Gene/Environment Susceptibility Reykjavik Study (AGES-RS), 440 individuals developed HF after their first visit with a median follow-up of 5.45 years. Among them, 167 were diagnosed with HFrEF and 188 with HFpEF. A least absolute shrinkage and selection operator regression model with non-parametric bootstrap were used to select predictors from an analysis of 4782 serum proteins, and several pre-established clinical parameters linked to HF. A subset of 8-10 distinct or overlapping serum proteins predicted different future HF outcomes, and C-statistics were used to assess discrimination, revealing proteins combined with a C-index of 0.80 for all incident HF, 0.78 and 0.80 for incident HFpEF or HFrEF, respectively. In the AGES-RS, protein panels alone encompassed the risk contained in the clinical information and improved the performance characteristics of prediction models based on N-terminal pro-B-type natriuretic peptide and clinical risk factors. Finally, the protein predictors performed particularly well close to the time of an HF event, an outcome that was replicated in the Cardiovascular Health Study.

CONCLUSION

A small number of circulating proteins accurately predicted future HF in the AGES-RS cohort of older adults, and they alone encompass the risk information found in a collection of clinical data. Incident HF events were predicted up to 8 years, with predictor performance significantly improving for events occurring less than 1 year ahead, a finding replicated in an external cohort study.

Impact of the MEESSI-AHF tool to guide disposition decision-making in patients with acute heart failure in the emergency department: a before-and-after study

OBJECTIVES

To determine the impact of risk stratification using the MEESSI-AHF (Multiple Estimation of risk based on the Emergency department Spanish Score In patients with acute heart failure) scale to guide disposition decision-making on the outcomes of ED patients with acute heart failure (AHF), and assess the adherence of emergency physicians to risk stratification recommendations.

METHODS

This was a prospective quasi-experimental study (before/after design) conducted in eight Spanish EDs which consecutively enrolled adult patients with AHF. In the pre-implementation stage, the admit/discharge decision was performed entirely based on emergency physician judgement. During the post-implementation phase, emergency physicians were advised to 'discharge' patients classified by the MEESSI-AHF scale as low risk and 'admit' patients classified as increased risk. Nonetheless, the final decision was left to treating emergency physicians. The primary outcome was 30-day all-cause mortality. Secondary outcomes were days alive and out of hospital, in-hospital mortality and 30-day post-discharge combined adverse event (ED revisit, hospitalisation or death).

RESULTS

The pre-implementation and post-implementation cohorts included 1589 and 1575 patients, respectively (median age 85 years, 56% females) with similar characteristics, and 30-day all-cause mortality was 9.4% and 9.7%, respectively (post-implementation HR=1.03, 95% CI=0.82 to 1.29). There were no differences in secondary outcomes or in the percentage of patients entirely managed in the ED without hospitalisation (direct discharge from the ED, 23.5% vs 24.4%, OR=1.05, 95% CI=0.89 to 1.24). Adjusted models did not change these results. Emergency physicians followed the MEESSI-AHF-based recommendation on patient disposition in 70.9% of cases (recommendation over-ruling: 29.1%). Physicians were more likely to over-rule the recommendation when 'discharge' was recommended (56.4%; main reason: need for hospitalisation for a second diagnosis) than when 'admit' was recommended (12.8%; main reason: no appreciation of severity of AHF decompensation by emergency physician), with an OR for over-ruling the 'discharge' compared with the 'admit' recommendation of 8.78 (95% CI=6.84 to 11.3).

CONCLUSIONS

Implementing the MEESSI-AHF risk stratification tool in the ED to guide disposition decision-making did not improve patient outcomes.

Pre-discharge and early post-discharge management of patients hospitalized for acute heart failure: A scientific statement by the Heart Failure Association of the ESC

Acute heart failure is a major cause of urgent hospitalizations. These are followed by marked increases in death and rehospitalization rates, which then decline exponentially though they remain higher than in patients without a recent hospitalization. Therefore, optimal management of patients with acute heart failure before discharge and in the early post-discharge phase is critical. First, it may prevent rehospitalizations through the early detection and effective treatment of residual or recurrent congestion, the main manifestation of decompensation. Second, initiation at pre-discharge and titration to target doses in the early post-discharge period, of guideline-directed medical therapy may improve both short- and long-term outcomes. Third, in chronic heart failure, medical treatment is often left unchanged, so the acute heart failure hospitalization presents an opportunity for implementation of therapy. The aim of this scientific statement by the Heart Failure Association of the European Society of Cardiology is to summarize recent findings that have implications for clinical management both in the pre-discharge and the early post-discharge phase after a hospitalization for acute heart failure.

Larissa Heart Failure Risk Score and Mode of Death in Acute Heart Failure: Insights from REALITY-AHF

Patients with heart failure (HF) patients may die either suddenly (sudden cardiac death/SCD) or progressively from pump failure. The heightened risk of SCD in patients with HF may expedite important decisions about medications or devices. We used the Larissa Heart Failure Risk Score (LHFRS), a validated risk model for all-cause mortality and HF rehospitalization, to investigate the mode of death in 1363 patients enrolled in the Registry Focused on Very Early Presentation and Treatment in Emergency Department of Acute Heart Failure (REALITY-AHF). Cumulative incidence curves were generated using a Fine-Gray competing risk regression, with deaths that were not due to the cause of death of interest as a competing risk. Likewise, the Fine-Gray competing risk regression analysis was used to evaluate the association between each variable and the incidence of each cause of death. The AHEAD score, a well-validated HF risk score ranging from 0 to 5 (atrial fibrillation, anemia, age, renal dysfunction, and diabetes mellitus), was used for the risk adjustment. Patients with LHFRS 2-4 exhibited a significantly higher risk of SCD (HR hazard ratio adjusted for AHEAD score 3.15, 95% confidence interval (CI) (1.30-7.65), p = 0.011) and HF death (adjusted HR for AHEAD score 1.48, 95% CI (1.04-2.09), p = 0.03), compared to those with LHFRS 0,1. Regarding cardiovascular death, patients with higher LHFRS had significantly increased risk compared to those with lower LHFRS (HR 1.44 adjusted for AHEAD score, 95% CI (1.09-1.91), p = 0.01). Lastly, patients with higher LHFRS exhibited a similar risk of non-cardiovascular death compared to those with lower LHFRS (HR 1.44 adjusted for AHEAD score, 95% CI (0.95-2.19), p = 0.087). In conclusion, LHFRS was associated independently with the mode of death in a prospective cohort of hospitalized HF patients.

Time-Dependent Changes in N-Terminal Pro-Brain Natriuretic Peptide and B-Type Natriuretic Peptide Ratio During Hospitalization for Acute Heart Failure

The time-dependent changes in the simultaneous evaluation of B-type natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP) levels during hospitalization for acute heart failure (AHF) remain obscure.A total of 356 AHF patients were analyzed. Blood samples were collected within 15 minutes of admission (Day 1), 48-120 hours (Day 2-5) and between days 7 and 21 (Before-discharge). Plasma BNP and serum NT-proBNP were significantly decreased on Days 2-5 and Before-discharge in comparison to Day 1, but the NT-proBNP/BNP ratio was not changed. Patients were divided into 2 groups according to the median NT-proBNP/BNP (N/B) ratio on Day 2-5 (Low-N/B versus High-N/B). A multivariate logistic regression model showed that age (per 1-year increase), serum creatinine (per 1.0-mg/dL increase), and serum albumin (per 1.0-mg/dL decrease) were independently associated with High-N/B (odds ratio [OR]: 1.071, 95%confidence interval [CI]: 1.036-1.108, OR: 1.190, 95%CI: 1.121-1.264 and OR: 2.410, 95%CI: 1.121-5.155, respectively). Kaplan-Meier curve analysis showed that the High-N/B group had a significantly poorer prognosis than the Low-N/B group, and a multivariate Cox regression model revealed that High-N/B was an independent predictor of 365-day mortality (hazard ratio [HR]: 1.796, 95%CI: 1.041-3.100) and HF events (HR: 1.509, 95%CI: 1.007-2.263). The same trend in prognostic impact was significantly observed in both low and high delta-BNP cohorts (< 55% and ≥ 55% BNP value on the start date/BNP value at 2-5-days).A high NT-proBNP/BNP ratio on Day 2-5 was associated with non-cardiac conditions and was associated with adverse outcomes even if BNP was adequately decreased by the treatment of AHF.

Usefulness of High-Sensitivity Troponin I in Risk Stratification and Final Disposition of Patients with Acute Heart Failure in the Emergency Department: Comparison between HFpEF vs. HFrEF

Background and Objectives: In patients with acute heart failure (AHF), there is no definite evidence on the relationship between high-sensitivity cardiac troponin (hs-cTnI) and the left ventricular ejection fraction (LVEF) comparing the reduced and preserved EF conditions. Materials and Methods: Between January and April 2022, we retrospectively analyzed the data from 386 patients admitted to the emergency departments (ED) of five hospitals in Rome, Italy, for AHF. The criteria for inclusion were a final diagnosis of AHF; a cardiac ultrasound and hs-cTnI evaluations in the ED; and age > 18 yrs. We excluded patients with acute coronary syndrome (ACS). Based on echocardiography and hs-cTnI evaluations, the patients were grouped for (1) preserved (HFpEF) or (2) reduced LVEF (HFrEF) and a a) negative (within the normal range value) or b) positive (above the normal range value) of hs-cTnI, respectively. Results: There was a significant negative relationship between a positive test for hs-cTnI and LVEF. When compared to the group with a negative hs-cTnI test, the patients with a positive test, both from the HFpEF and HFrEF subgroups, were significantly more likely to have an adverse outcome, such as being admitted to the intensive care unit (ICU) or dying in the ED. Moreover, a reduced ejection fraction was linked with a final disposition to a higher level of care. Conclusions: In patients admitted to the ED for AHF without ACS, there is a negative relationship between hs-cTnI and a reduced LVEF, although a significant percentage of patients with a preserved LVEF also resulted to have high levels of hs-cTnI. In the absence of ACS, hs-cTnI seems to be a reliable biomarker of myocardial injury in AHF in the ED and should be considered as a risk stratification parameter for these subjects regardless of the left ventricular function. Further larger prospective studies are needed to confirm these preliminary data.

Clinical profile and 90 day outcomes of 10 851 heart failure patients across India: National Heart Failure Registry

AIMS

Limited data on the uptake of guideline-directed medical therapies (GDMTs) and the mortality of acute decompensated HF (ADHF) patients are available from India. The National Heart Failure Registry (NHFR) aimed to assess clinical presentation, practice patterns, and the mortality of ADHF patients in India.

METHODS AND RESULTS

The NHFR is a facility-based, multi-centre clinical registry of consecutive ADHF patients with prospective follow-up. Fifty three tertiary care hospitals in 21 states in India participated in the NHFR. All consecutive ADHF patients who satisfied the European Society of Cardiology criteria were enrolled in the registry. All-cause mortality at 90 days was the main outcome measure. In total, 10 851 consecutive patients were recruited (mean age: 59.9 years, 31% women). Ischaemic heart disease was the predominant aetiology for HF (72%), followed by dilated cardiomyopathy (18%). Isolated right HF was noted in 62 (0.6%) participants. In eligible HF patients, 47.5% received GDMT. The 90 day mortality was 14.2% (14.9% and 13.9% in women and men, respectively) with a re-admission rate of 8.4%. An inverse relationship between educational class based on years of education and 90 day mortality (high mortality in the lowest educational class) was observed in the study population. Patients with HF with reduced ejection fraction and HF with mildly reduced ejection fraction who did not receive GDMT experienced higher mortality (log-rank P < 0.001) than those who received GDMT. Baseline educational class, body mass index, New York Heart Association functional class, ejection fraction, dependent oedema, serum creatinine, QRS > 120 ms, atrial fibrillation, mitral regurgitation, haemoglobin levels, serum sodium, and GDMT independently predicted 90 day mortality.

CONCLUSION

One of seven ADHF patients in the NHFR died during the first 90 days of follow-up. One of two patients received GDMT. Adherence to GDMT improved survival in HF patients with reduced and mildly reduced ejection fractions. Our findings call for innovative quality improvement initiatives to improve the uptake of GDMT among HF patients in India.

Evaluation of the HEFESTOS scale to predict outcomes in emergency department acute heart failure patients

The HEFESTOS scale was developed in 14 Spanish primary care centres and validated in 9 primary care centres of other European countries. It showed good performance to predict death/hospitalisation during the first 30 days after an episode of acute heart failure (AHF), with c-statistics of 0.807/0.730 in the derivation/validation cohorts. We evaluated this scale in the emergency department (ED) setting, comparing it to the EHMRG and MEESSI scales in the ED and the EFFECT and GWTG scales in hospitalised patients, to predict 30-day outcomes, including death and hospitalisation. Consecutive AHF patients were enrolled in 34 Spanish EDs in January-February 2016, 2018, and 2019 with variables needed to calculate outcome scores. Thirty-day hospitalisation/death (together and separately) and post-discharge combined adverse event (ED revisit or hospitalisation for AHF or all-cause death) were determined for patients discharged home after ED care. Predictive capacity was assessed by c-statistic with 95% confidence intervals. Of 10,869 patients, 4,044 were included (median age: 83 years, 54% women). The performance of HEFESTOS was modest for 30-day hospitalisation/death, c-statistic=0.656 (0.637-0.675), hospitalisation, 0.650 (0.631-0.669), and death, 0.610 (0.576-0.644). Of 1,034 patients with scores for the 5 scales, HEFESTOS had the numerically highest c-statistic for hospitalisation/death at 30 days, 0.666 (0.627-0.704), vs. MEESSI= 0.650 (0.612-0.687, p=0.51), EFFECT=0.633 (0.595-0.672, p=0.21), GWTG=0.618 (0.578-0.657, p=0.06) and EHMRG=0.617 (0.577-0.704, p=0.07). Similar modest performances were observed for predicting hospitalisation [ranging from HEFESTOS=0.656 (0.618-0.695) to GWTG=0.603 (0.564-0.643)]. Conversely, prediction of 30-day death was good with the MEESSI=0.787 (0.728-845), EFFECT=0.754 (0.691-0.818) and GWTG=0.749 (0.689-0.809) scales, and modest with EHMRG=0.649 (0.581-0.717) and HEFESTOS=0.610 (0.538-0.683). Although the HEFESTOS scale was numerically better for predicting 30-day hospitalisation/death in ED AHF patients, its modest performance precludes routine use. Only 30-day mortality was adequately predicted by some scales, with the MEESSI achieving the best results.

Incidence of Timely Outpatient Follow-Up Care After Emergency Department Encounters for Acute Heart Failure

BACKGROUND

Patients who are discharged from the emergency department (ED) after an encounter for acute heart failure are at high risk for return hospitalization. These patients may benefit from timely outpatient follow-up care to reassess volume status, adjust medications, and reinforce self-care strategies. This study examines the incidence of outpatient follow-up care after ED encounters for acute heart failure and describes patient characteristics associated with obtaining timely follow-up care.

METHODS

We conducted a retrospective cohort study using an administrative claims database for a large US commercial insurer, from January 1, 2012 to June 30, 2019. Participants included adult patients discharged from the ED with principal diagnosis of acute heart failure. The primary outcome was obtaining an in-person outpatient clinic visit for heart failure within 30 days. We also examined the competing risk of all-cause hospitalization within 30 days and without an intervening outpatient clinic visit. We estimated competing risk regression models to identify patient characteristics associated with obtaining outpatient follow-up and report cause-specific hazard ratios.

RESULTS

The cohort included 52 732 patients, with mean age of 73.9 years (95% CI, 73.8-74.0) and 27 395 (52.0% [95% CI, 51.5-52.4]) female patients. Within 30 days of the ED encounter, 12 279 (23.2%) patients attended an outpatient clinic visit for heart failure, with 8382 (15.9%) patients hospitalized before they could obtain an outpatient clinic visit. In the adjusted analysis, patients that were younger, women, reporting non-Hispanic Black race, and had fewer previous clinic visits were less likely to obtain outpatient follow-up care.

CONCLUSIONS

Few patients obtain timely outpatient follow-up after ED visits for heart failure, although nearly 20% require hospitalization within 30 days. Improved transitions following discharge from the ED may represent an opportunity to improve outcomes for patients with acute heart failure.

Acute Heart Failure in the 2021 ESC Heart Failure Guidelines: a scientific statement from the Association for Acute CardioVascular Care (ACVC) of the European Society of Cardiology

The current European Society of Cardiology (ESC) Heart Failure Guidelines are the most comprehensive ESC document covering heart failure to date; however, the section focused on acute heart failure remains relatively too concise. Although several topics are more extensively covered than in previous versions, including some specific therapies, monitoring and disposition in the hospital, and the management of cardiogenic shock, the lack of high-quality evidence in acute, emergency, and critical care scenarios, poses a challenge for providing evidence-based recommendations, in particular when by comparison the data for chronic heart failure is so extensive. The paucity of evidence and specific recommendations for the general approach and management of acute heart failure in the emergency department is particularly relevant, because this is the setting where most acute heart failure patients are initially diagnosed and stabilized. The clinical phenotypes proposed are comprehensive, clinically relevant and with minimal overlap, whilst providing additional opportunity for discussion around respiratory failure and hypoperfusion.

A Combination of Chest Radiography and Estimated Plasma Volume May Predict In-Hospital Mortality in Acute Heart Failure

Background: Patients with heart failure (HF) often display dyspnea associated with pulmonary congestion, along with intravascular congestion, both may result in urgent hospitalization and subsequent death. A combination of radiographic pulmonary congestion and plasma volume might screen patients with a high risk of in-hospital mortality in the emergency department (ED).

Methods: In the pathway of dyspneic patients in emergency (PARADISE) cohort, patients admitted for acute HF were stratified into 4 groups based on high or low congestion score index (CSI, ranging from 0 to 3, high value indicating severe congestion) and estimated plasma volume status (ePVS) calculated from hemoglobin/hematocrit.

Results: In a total of 252 patients (mean age, 81.9 years; male, 46.8%), CSI and ePVS were not correlated (Spearman rho <0 .10, p > 0.10). High CSI/high ePVS was associated with poorer renal function, but clinical congestion markers (i.e., natriuretic peptide) were comparable across CSI/ePVS categories. High CSI/high ePVS was associated with a four-fold higher risk of in-hospital mortality (adjusted-OR, 95%CI = 4.20, 1.10-19.67) compared with low CSI/low ePVS, whereas neither high CSI nor ePVS alone was associated with poor prognosis (all-p-value > 0.10; P_interaction = 0.03). High CSI/high ePVS improved a routine risk model (i.e., natriuretic peptide and lactate)(NRI = 46.9%, p = 0.02), resulting in high prediction of risk of in-hospital mortality (AUC = 0.85, 0.82-0.89).

Conclusion: In patients hospitalized for acute HF with relatively old age and comorbidity burdens, a combination of CSI and ePVS was associated with a risk of in-hospital death, and improved prognostic performance on top of a conventional risk model.

Usefulness of the 2-year iodine-123 metaiodobenzylguanidine-based risk model for post-discharge risk stratification of patients with acute decompensated heart failure

PURPOSE

A four-parameter risk model that included cardiac iodine-123 metaiodobenzylguanidine (MIBG) imaging and readily available clinical parameters was recently developed for prediction of 2-year cardiac mortality risk in patients with chronic heart failure. We sought to validate the ability of this risk model to predict post-discharge clinical outcomes in patients with acute decompensated heart failure (ADHF) and to compare its prognostic value with that of the Acute Decompensated Heart Failure National Registry (ADHERE) and Get With The Guidelines-Heart Failure (GWTG-HF) risk scores.

METHODS

We studied 407 consecutive patients who were admitted for ADHF and survived to discharge, with definitive 2-year outcomes (death or survival). Cardiac MIBG imaging was performed just before discharge. The 2-year cardiac mortality risk was calculated using four parameters, namely age, left ventricular ejection fraction, New York Heart Association functional class, and cardiac MIBG heart-to-mediastinum ratio on delayed images. Patients were stratified into three groups based on the 2-year cardiac mortality risk: low- (< 4%), intermediate- (4-12%), and high-risk (> 12%) groups. The ADHERE and GWTG-HF risk scores were also calculated.

RESULTS

There was a significant difference in the incidence of cardiac death among the three groups stratified using the 2-year cardiac mortality risk model (p < 0.0001). The 2-year cardiac mortality risk model had a higher C-statistic (0.732) for the prediction of cardiac mortality than the ADHERE and GWTG-HF risk scores.

CONCLUSION

The 2-year MIBG-based cardiac mortality risk model is useful for predicting post-discharge clinical outcomes in patients with ADHF.

TRIAL REGISTRATION NUMBER

UMIN000015246, 25 September 2014.

Prevalence, Related Factors and Association of Left Bundle Branch Block With Prognosis in Patients With Acute Heart Failure: a Simultaneous Analysis in 3 Independent Cohorts

OBJECTIVES

To determine the prevalence, characteristics and association with prognosis of left bundle branch block (LBBB) in 3 different cohorts of patients with acute heart failure (AHF).

METHODS AND RESULTS

We retrospectively analyzed 12,950 patients with AHF who were included in the EAHFE (Epidemiology Acute Heart Failure Emergency), RICA (National Heart Failure Registry of the Spanish Internal Medicine Society), and BASEL-V (Basics in Acute Shortness of Breath Evaluation of Switzerland) registries. We independently analyzed the relationship between baseline and clinical characteristics and the presence of LBBB and the potential association of LBBB with 1-year all-cause mortality and a 90-day postdischarge combined endpoint (Emergency Department reconsultation, hospitalization or death). The prevalence of LBBB was 13.5% (95% confidence interval: 12.9%-14.0%). In all registries, patients with LBBB more commonly had coronary artery disease and previous episodes of AHF, were taking chronic spironolactone treatment, had lower left ventricular ejection fraction and systolic blood pressure values and higher NT-proBNP levels. There were no differences in risk for patients with LBBB in any cohort, with adjusted hazard ratios (95% confidence interval) for 1-year mortality in EAHFE/RICA/BASEL-V cohorts of 1.02 (0.89-1.17), 1.15 (0.95-1.38) and 1.32 (0.94-1.86), respectively, and for 90-day postdischarge combined endpoint of 1.00 (0.88-1.14), 1.14 (0.92-1.40) and 1.26 (0.84-1.89). These results were consistent in sensitivity analyses.

CONCLUSIONS

Less than 20% of patients with AHF present LBBB, which is consistently associated with cardiovascular comorbidities, reduced left ventricular ejection fraction and more severe decompensations. Nonetheless, after taking these factors into account, LBBB in patients with AHF is not associated with worse outcomes.

Risk stratification in heart failure decompensation in the community: HEFESTOS score

Aims

Because evidence regarding risk stratification predicting prognosis of patients with heart failure (HF) decompensation attended in primary care is lacking, we developed and externally validated a model to forecast death/hospitalization during the first 30 days after an episode of decompensation. The predictive model is based on variables easily obtained in primary care settings.

Methods and results

HEFESTOS is a multinational study consisting of a derivation cohort of HF patients recruited in 14 primary healthcare centres in Barcelona and a validation cohort from primary healthcare in 9 other European countries. The derivation and validation cohorts included 561 and 250 patients, respectively. Percentages of women in the derivation and validation cohorts were 56.3% and 47.6% (P = 0.026), respectively. Mean age was 82.2 years (SD 8.03) in the derivation cohort, and 79.3 years (SD 10.3) in the validation one (P = 0.001). HF with preserved ejection fraction represented 72.1% in the derivation cohort and 58.8% in the validation one (P = 0.004). Mortality/hospitalization during the first 30 days after a decompensation episode was 30.5% and 26% (P = 0.225) for the derivation and validation cohorts, respectively. Multivariable logistic regression models were performed to develop a score of risk. The identified predictors were worsening of dyspnoea [odds ratio (OR): 2.5; P = 0.001], orthopnoea (OR: 2.16; P = 0.01), paroxysmal nocturnal dyspnoea (OR: 2.25; P = 0.01), crackles (OR: 2.35; P = 0.01), New York Heart Association functional class III/IV (OR: 2.11; P = 0.001), oxygen saturation ≤ 90% (OR: 4.98; P < 0.001), heart rate > 100 b.p.m. (OR: 2.72; P = 0.002), and previous hospitalization due to HF (OR: 2.45; P < 0.001). The model showed an area under the curve (AUC) of 0.807, 95% confidence interval (CI): [0.770; 0.845] in the derivation cohort and AUC 0.73, 95% CI: [0.660; 0.808] in the validation one. No significant differences between both cohorts were observed (P = 0.08). Regarding probability of hospitalization/death, three risk groups were defined: low <5%, medium 5–20%, and high >20%. Outcome incidence was 2.7% for the low‐risk group, 12.8% for medium risk, and 46.2% for high risk in the derivation cohort, and 9.1%, 12.9%, and 39.6% in the validation one.

Conclusions

The HEFESTOS score, based on variables easily accessible in a community setting and validated in an external European cohort, properly predicted the risk of death/hospitalization during the first 30 days after an HF decompensation episode.

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

Risk stratification dates to the dawn of the cardiac intensive care unit (CICU). As the CICU has evolved from a dedicated unit caring for patients with acute myocardial infarction to a complex healthcare environment encompassing a broad array of acute and chronic cardiovascular pathology, an expanding array of risk scores are available that can be applied to CICU patients. Most of these scores were designed for use either in patients with a specific acute cardiovascular diagnosis or unselected critically ill patients, and risk scores developed in other populations often underperform in the CICU. More recently, risk scores have been developed specific to the CICU population, demonstrating improved performance. All existing risk scores have relevant limitations, both in terms of performance and applicability to patient care. Risk scores have been predominantly developed to predict short-term mortality, either by quantifying severity of illness or by incorporating other risk factors for mortality. It is essential to distinguish mortality risk attributable to severity of illness, which may be modifiable through intervention, from mortality risk attributable to non-modifiable risk factors. This review discusses established risk scores applicable to the CICU population, details how risk score performance is characterized, describes how new risk scores can be developed, explains how the information provided by risk scores can be used in clinical practice, and highlights how novel risk stratification approaches can be developed.

Improvement in Kansas City Cardiomyopathy Questionnaire Scores After a Self-Care Intervention in Patients With Acute Heart Failure Discharged From the Emergency Department

BACKGROUND

We conducted a secondary analysis of changes in the Kansas City Cardiomyopathy Questionnaire (KCCQ)-12 over 30 days in a randomized trial of self-care coaching versus structured usual care in patients with acute heart failure who were discharged from the emergency department.

METHODS

Patients in 15 emergency departments completed the KCCQ-12 at emergency department discharge and at 30 days. We compared change in KCCQ-12 scores between the intervention and usual care arms, adjusted for enrollment KCCQ-12 and demographic characteristics. We used linear regression to describe changes in KCCQ-12 summary scores and logistic regression to characterize clinically meaningful KCCQ-12 subdomain changes at 30 days.

RESULTS

There were 350 patients with both enrollment and 30-day KCCQ summary scores available; 166 allocated to usual care and 184 to the intervention arm. Median age was 64 years (interquartile range, 55-70), 37% were female participants, 63% were Black, median KCCQ-12 summary score at enrollment was 47 (interquartile range, 33-64). Self-care coaching resulted in significantly greater improvement in health status compared with structured usual care (5.4-point greater improvement, 95% CI, 1.12-9.68; P=0.01). Improvements in health status in the intervention arm were driven by improvements within the symptom frequency (adjusted odds ratio, 1.62 [95% CI, 1.01-2.59]) and quality of life (adjusted odds ratio, 2.39 [95% CI, 1.46-3.90]) subdomains.

CONCLUSIONS

In this secondary analysis, patients with acute heart failure who received a tailored, self-care intervention after emergency department discharge had clinically significant improvements in health status at 30 days compared with structured usual care largely due to improvements within the symptom frequency and quality of life subdomains of the KCCQ-12. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02519283.

MEESSI-AHF risk score performance to predict multiple post-index event and post-discharge short-term outcomes

BACKGROUND

The multiple estimation of risk based on the emergency department Spanish score in patients with acute heart failure (MEESSI-AHF) is a risk score designed to predict 30-day mortality in acute heart failure patients admitted to the emergency department. Using a derivation cohort, we evaluated the performance of the MEESSI-AHF risk score to predict 11 different short-term outcomes.

METHODS

Patients with acute heart failure from 41 Spanish emergency departments (n=7755) were recruited consecutively in two time periods (2014 and 2016). Logistic regression models based on the MEESSI-AHF risk score were used to obtain c-statistics for 11 outcomes: three with follow-up from emergency department admission (inhospital, 7-day and 30-day mortality) and eight with follow-up from discharge (7-day mortality, emergency department revisit and their combination; and 30-day mortality, hospital admission, emergency department revisit and their two combinations with mortality).

RESULTS

The MEESSI-AHF risk score strongly predicted mortality outcomes with follow-up starting at emergency department admission (c-statistic 0.83 for 30-day mortality; 0.82 for inhospital death, P=0.121; and 0.85 for 7-day mortality, P=0.001). Overall, mortality outcomes with follow-up starting at hospital discharge predicted slightly less well (c-statistic 0.80 for 7-day mortality, P=0.011; and 0.75 for 30-day mortality, P<0.001). In contrast, the MEESSI-AHF score predicted poorly outcomes involving emergency department revisit or hospital admission alone or combined with mortality (c-statistics 0.54 to 0.62).

CONCLUSIONS

The MEESSI-AHF risk score strongly predicts mortality outcomes in acute heart failure patients admitted to the emergency department, but the model performs poorly for outcomes involving hospital admission or emergency department revisit. There is a need to optimise this risk score to predict non-fatal events more effectively.