Ischemic Electrocardiographic Abnormalities and Prognosis in Decompensated Heart Failure

Comparison of machine learning and conventional statistical modeling for predicting readmission following acute heart failure hospitalization

INTRODUCTION

Developing accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical models (CSM), but the calibration of such models is unclear.

OBJECTIVES

To compare models developed using ML with those developed using CSM to predict 30-day readmission for cardiovascular and noncardiovascular causes in HF patients.

METHODS

We retrospectively enrolled 10,919 patients with HF (> 18 years) discharged alive from a hospital or emergency department (2004-2007) in Ontario, Canada. The study sample was randomly divided into training and validation sets in a 2:1 ratio. CSMs to predict 30-day readmission were developed using Fine-Gray subdistribution hazards regression (treating death as a competing risk), and the ML algorithm employed random survival forests for competing risks (RSF-CR). Models were evaluated in the validation set using both discrimination and calibration metrics.

RESULTS

In the validation sample of 3602 patients, RSF-CR (c-statistic=0.620) showed similar discrimination to the Fine-Gray competing risk model (c-statistic=0.621) for 30-day cardiovascular readmission. In contrast, for 30-day noncardiovascular readmission, the Fine-Gray model (c-statistic=0.641) slightly outperformed the RSF-CR model (c-statistic=0.632). For both outcomes, The Fine-Gray model displayed better calibration than RSF-CR using calibration plots of observed vs predicted risks across the deciles of predicted risk.

CONCLUSIONS

Fine-Gray models had similar discrimination but superior calibration to the RSF-CR model, highlighting the importance of reporting calibration metrics for ML-based prediction models. The discrimination was modest in all readmission prediction models regardless of the methods used.

The Cardioprotective Potential of Herbal Formulas in Myocardial Infarction-Induced Heart Failure through Inhibition of JAK/STAT3 Signaling and Improvement of Cardiac Function

Myocardial infarction (MI) is a leading cause of heart failure, characterized by adverse cardiac remodeling. This study evaluated the cardioprotective potential of Dohongsamul-tang (DHT), a traditional Korean herbal formula, in a rat model of MI-induced heart failure. Rats underwent left anterior descending (LAD) artery ligation and were treated with either 100 mg/kg or 200 mg/kg of DHT daily for 8 weeks. DHT treatment significantly improved cardiac function, as evidenced by increased ejection fraction (EF) from 62.1% to 70.1% (100 mg/kg) and fractional shortening (FS) from 32.3% to 39.4% (200 mg/kg) compared to the MI control group. Additionally, DHT reduced infarct size by approximately 63.3% (from 60.0% to 22.0%) and heart weight by approximately 16.7% (from 3.6 mg/g to 3.0 mg/g), and significantly decreased levels of heart failure biomarkers: LDH was reduced by 37.6% (from 1409.1 U/L to 879.1 U/L) and CK-MB by 47.6% (from 367.3 U/L to 192.5 U/L). Histological analysis revealed a reduction in left ventricle (LV) fibrosis by approximately 50% (from 24.0% to 12.0%). At the molecular level, DHT inhibited the expression of phospho-JAK by 75% (from 2-fold to 0.5-fold), phospho-STAT3 by 30.8% (from 1.3-fold to 0.9-fold), Bax/Bcl-2 by 56.3% (from 3.2-fold to 1.4-fold), and caspase-3 by 46.3% (from 1.23-fold to 0.66-fold). These results suggest that DHT exerts cardioprotective effects by modulating the JAK/STAT3 signaling pathway, highlighting its potential as a therapeutic option for heart failure.

The HEARTRISK6 Scale: Predicting Short-Term Serious Outcomes in Emergency Department Acute Heart Failure Patients

Background

Acute heart failure (AHF) is a common emergency department (ED) presentation that may have poor outcomes but often does not require hospital admission. There is little evidence to guide dispositional decisions.

Objectives

The authors sought to create a risk score for predicting short-term serious outcomes (SSO) in patients with AHF.

Methods

We pooled data from 3 prospective cohorts: 2 published studies and 1 new cohort. The 3 cohorts prospectively enrolled patients who required treatment for AHF at 10 tertiary care hospital EDs. The primary outcome was SSO, defined as death <30 days, intubation or noninvasive ventilation (NIV), myocardial infarction, or relapse to ED <14 days. The logistic regression model evaluated 13 predictors, used an AIC-based step-down procedure, and bootstrapped internal validation.

Results

Of the 2,246 patients in the 3 cohorts (N = 559; 1,100; 587), the mean age was 77.4 years, 54.5% were male, 3.1% received intravenous nitroglycerin, 5.2% received ED NIV, and 48.6% were admitted to the hospital. There were 281 (12.5%) SSOs including 70 deaths (3.1%) with many in discharged patients. The final HEARTRISK6 Scale included 6 variables: valvular heart disease, tachycardia, need for NIV, creatinine, troponin, and failed reassessment (walk test). Choosing HEARTRISK6 total-point admission thresholds of ≥1 or ≥2 would yield, respectively, sensitivities of 88.3% (95% CI: 83.9%-91.8%) and 71.5% (95% CI: 65.9%-76.7%) and specificities of 24.7% (95% CI: 22.8%-26.7%) and 50.1% (95% CI: 47.9%-52.4%) for SSO.

Conclusions

Using 3 large prospectively collected datasets, we created a concise and sensitive risk scale for patients with AHF in the ED. Implementation of the HEARTRISK6 scale could lead to safer and more efficient disposition decisions.

Association of abnormalities in electrocardiography and ultrasonic echocardiography with the occurrence of cardiovascular disease in patients with advanced chronic kidney disease

BACKGROUND

In patients with chronic kidney disease (CKD), the incidence of cardiovascular disease (CVD) increases with disease progression. CVD screening tests in those with CKD were researched to determine whether abnormalities observed in electrocardiography (ECG) and ultrasonic echocardiography (UCG) were risk factors associated with the development of CVD.

METHODS

This study included 604 patients with CKD G4 and G5, for whom both ECG and UCG were performed. They were divided into four groups: those without ECG- and UCG-indicated abnormalities (group A, n = 333), with only ECG abnormalities (group B, n = 106), with only UCG abnormalities (group C, n = 75), and with both ECG and UCG abnormalities (group D, n = 90). Multivariate analysis using Cox regression analysis of the occurrence of CVD was performed during a follow-up period.

RESULTS

During the observation period, 124 patients had clinical events. Among them, 45 patients (13.5%) were in Group A, 25 patients (23.6%) in Group B, 19 patients (25.3%) in Group C, and 35 patients (38.9%) in Group D, respectively. CVD event occurrence was highest in Group D. The results of the multivariate analysis also showed that the CVD event rates were significantly higher in Group C (HR: 2.96, P = < .001) and D (HR: 4.22, P < .001) than in Group A.

CONCLUSION

In patients with advanced CKD, there was a significant correlation of ECG and UCG abnormalities with CVD events. Additionally, those having both types of abnormalities may have a higher risk of coronary artery disease than other groups.

Home Hospital Outcomes for Acute Decompensated Heart Failure and Factors Associated With Escalation of Care

BACKGROUND

Overall outcomes and the escalation rate for home hospital admissions for heart failure (HF) are not known. We report overall outcomes, predict escalation, and describe care provided after escalation among patients admitted to home hospital for HF.

METHODS

Our retrospective analysis included all patients admitted for HF to 2 home hospital programs in Massachusetts between February 2020 and October 2022. Escalation of care was defined as transfer to an inpatient hospital setting (emergency department, inpatient medical unit) for at least 1 overnight stay. Unexpected mortality was defined as mortality excluding those who desired to pass away at home on admission or transitioned to hospice. We performed the least absolute shrinkage and selection operator logistic regression to predict escalation.

RESULTS

We included 437 hospitalizations; patients had a median age of 80 (interquartile range, 69-89) years, 58.1% were women, and 64.8% were White. Of the cohort, 29.2% had reduced ejection fraction, 50.9% had chronic kidney disease, and 60.6% had atrial fibrillation. Median admission Get With The Guidelines HF score was 39 (interquartile range, 35-45; 1%-5% predicted inpatient mortality). Escalation occurred in 10.3% of hospitalizations. Thirty-day readmission occurred in 15.1%, 90-day readmission occurred in 33.8%, and 6-month mortality occurred in 11.5%. There was no unexpected mortality during home hospitalization. Patients who experienced escalation had significantly longer median length of stays (19 versus 7.5 days, P<0.001). The most common reason for escalation was progressive renal dysfunction (36.2%). A low mean arterial pressure at the time of admission to home hospital was the most significant predictor of escalation in the least absolute shrinkage and selection operator regression.

CONCLUSIONS

About 1 in 10 home hospital patients with HF required escalation; none had unexpected mortality. Patients requiring escalation had longer length of stays. A low mean arterial pressure at the time of admission to home hospital was the most important predictor of escalation of care in the least absolute shrinkage and selection operator logistic regression model.

What are the Data for Current Prognostic Tools Used to Determine the Risk of Short-Term Adverse Events in Patients with Acute Heart Failure?

BACKGROUND

Acute heart failure (AHF) is a common condition evaluated in the emergency department (ED). Patients may present with a wide range of signs and symptoms, comorbidities, exacerbating factors, and ability to follow-up. Having a decision tool to objectively assess the risk of near-term events would help guide disposition decisions in these patients.

CLINICAL QUESTION

What are the data for current tools used to determine the short-term risk of adverse events of patients with AHF in the ED setting?

EVIDENCE REVIEW

Studies retrieved included six prospective studies and three retrospective cohort studies that evaluated the following five different risk scores that may predict the risk of serious adverse events in those with AHF: Ottawa Heart Failure Risk Score (OHFRS), Emergency Heart Failure Mortality Risk Grade (EHMRG), EHMRG at 30 days with addition of an ST depression variable (EHMRG30-ST), Multiple Estimation of Risk Based on the Emergency Department Spanish 40 Score in Patients with AHF Score (MEESSI-AHF), and the Improving Heart Failure Risk Stratification in the ED (STRATIFY) tool.

CONCLUSIONS

Based on the available literature, risk scores, including the OHFRS; EHMRG; EHMRG30-ST; MEESSI-AHF; and STRATIFY, can help identify short-term risk of adverse events, but are insufficient in isolation. Clinicians should use these tools in conjunction with other factors, such as the patient's symptom trajectory, hemodynamics, and access to follow-up care.

Abnormal electrocardiogram and poor prognosis in heart failure with preserved ejection fraction

PURPOSE

Electrocardiogram (ECG) is generally performed in patients with heart failure with preserved ejection fraction (HFpEF), but the prognostic value of abnormal ECG is not fully understood. We aim to explore the prognostic value of abnormal ECG at baseline in HFpEF using data from the TOPCAT trial.

METHODS

A total of 1736 patients from TOPCAT-Americas were included and divided into normal versus abnormal ECG groups. Survival analyses were performed for the following outcomes: the primary endpoint [a composite of cardiovascular death, heart failure (HF) hospitalization, and aborted cardiac arrest], all-cause death, cardiovascular death, and HF hospitalization.

RESULTS

Abnormal ECG was significantly associated with higher risks of the primary endpoint [hazard ratio (HR): 1.480, P = 0.001] and HF hospitalization (HR: 1.400, P = 0.015), and borderline significantly with cardiovascular death (HR: 1.453, P = 0.052) in patients with HFpEF after multivariate adjustment. As for specific ECG abnormalities, bundle branch block was associated with the primary endpoint (HR: 1.278, P = 0.020) and HF hospitalization (HR: 1.333, P = 0.016), whereas atrial fibrillation/flutter was associated with all-cause death (HR: 1.345, P = 0.051) and cardiovascular death (HR: 1.570, P = 0.023), but ventricular paced rhythm, pathological Q waves, and left ventricular hypertrophy were not of prognostic significance. Besides, other unspecific abnormalities together were associated with the primary endpoint (HR: 1.213, P = 0.032).

CONCLUSION

Abnormal ECG at baseline could be associated with poor prognosis in patients with HFpEF. Physicians are encouraged to pay more attention to HFpEF patients who present an abnormal ECG instead of ignoring those obscure abnormalities. Key messages What is already known on this topic Electrocardiogram (ECG) is a basic and easily accessible examination for patients with heart failure with preserved ejection fraction (HFpEF). Some findings from ECG such as frontal QRS-T angle, QTc interval, and the Cornell product have been shown to be associated with the prognosis of HFpEF but these results are from studies with relatively small sample sizes. What this study adds Using data from TOPCAT-Americas, this study found that an overall estimation of abnormal ECG significantly predicted poor prognosis in patients with HFpEF. As for specific abnormalities in ECG, bundle branch block mainly predicted heart failure hospitalization and atrial fibrillation mainly predicted death. How this study might affect research, practice, or policy This study reminds physicians to pay more attention to HFpEF patients who present an abnormal ECG.

Outcomes of acute heart failure patients managed in the emergency department

BACKGROUND

Acute heart failure is a serious condition commonly seen in the emergency department (ED). The HEARTRISK6 Scale has been recently developed to identify the risk of poor outcomes but has not been tested. We sought to describe the management and outcomes of ED patients with acute heart failure and to evaluate the potential impact of the HEARTRISK6 Scale.

METHODS

We conducted a health records review of 300 consecutive acute heart failure patients presenting to two tertiary care EDs. Two evaluators abstracted clinical variables, ED management and treatment details, and patient outcomes using the electronic health records platform (EPIC) and attending physicians verified the data. The primary outcome measure was a short-term serious outcome, as shown in Results. In addition, the HEARTRISK6 score was calculated retrospectively.

RESULTS

We included 300 patients with mean age of 78.5 years, 51.0% male, 56.3% arrival by ambulance, and 67.0% admitted to hospital. 25.3% experienced a short-term serious outcome 1) after admission (N = 201): non-invasive ventilation 14.9%, intubation 1.5%, major cardiac procedure 5.0%, myocardial infarction 2.0%, death 8.5%; 2) after ED discharge (N = 99): return to ED 21.2%, death 4.0%. Those initially admitted experienced a much higher proportion of serious outcomes compared to those discharged (29.9% vs. 16.2%). A HEARTRISK6 Scale cut-point score of ≥ 1 would have had a sensitivity of 91.0%, specificity 24.5%, and negative likelihood ratio 0.37 for short-term serious outcomes and suggested hospital admission for 80.7% of cases.

CONCLUSION

There was a large range of severity of illness of acute heart failure patients and a wide variety of treatments were administered in the ED. Both admitted and discharged patients experienced a high proportion of poor outcomes. The HEARTRISK6 Scale showed a high sensitivity for short-term serious outcomes but with the potential to increase hospital admissions. Further validation of the HEARTRISK6 Scale is required before routine clinical use.

Novel Oxidative Stress Biomarkers with Risk Prognosis Values in Heart Failure

Oxidative stress (OS) is mediated by reactive oxygen species (ROS), which in cardiovascular and other disease states, damage DNA, lipids, proteins, other cellular and extra-cellular components. OS is both initiated by, and triggers inflammation, cardiomyocyte apoptosis, matrix remodeling, myocardial fibrosis, and neurohumoral activation. These have been linked to the development of heart failure (HF). Circulating biomarkers generated by OS offer potential utility in patient management and therapeutic targeting. Novel OS-related biomarkers such as NADPH oxidases (sNox2-dp, Nrf2), advanced glycation end-products (AGE), and myeloperoxidase (MPO), are signaling molecules reflecting pathobiological changes in HF. This review aims to evaluate current OS-related biomarkers and their associations with clinical outcomes and to highlight those with greatest promise in diagnosis, risk stratification and therapeutic targeting in HF.

Trial of an Intervention to Improve Acute Heart Failure Outcomes

BACKGROUND

Patients with acute heart failure are frequently or systematically hospitalized, often because the risk of adverse events is uncertain and the options for rapid follow-up are inadequate. Whether the use of a strategy to support clinicians in making decisions about discharging or admitting patients, coupled with rapid follow-up in an outpatient clinic, would affect outcomes remains uncertain.

METHODS

In a stepped-wedge, cluster-randomized trial conducted in Ontario, Canada, we randomly assigned 10 hospitals to staggered start dates for one-way crossover from the control phase (usual care) to the intervention phase, which involved the use of a point-of-care algorithm to stratify patients with acute heart failure according to the risk of death. During the intervention phase, low-risk patients were discharged early (in ≤3 days) and received standardized outpatient care, and high-risk patients were admitted to the hospital. The coprimary outcomes were a composite of death from any cause or hospitalization for cardiovascular causes within 30 days after presentation and the composite outcome within 20 months.

RESULTS

A total of 5452 patients were enrolled in the trial (2972 during the control phase and 2480 during the intervention phase). Within 30 days, death from any cause or hospitalization for cardiovascular causes occurred in 301 patients (12.1%) who were enrolled during the intervention phase and in 430 patients (14.5%) who were enrolled during the control phase (adjusted hazard ratio, 0.88; 95% confidence interval [CI], 0.78 to 0.99; P = 0.04). Within 20 months, the cumulative incidence of primary-outcome events was 54.4% (95% CI, 48.6 to 59.9) among patients who were enrolled during the intervention phase and 56.2% (95% CI, 54.2 to 58.1) among patients who were enrolled during the control phase (adjusted hazard ratio, 0.95; 95% CI, 0.92 to 0.99). Fewer than six deaths or hospitalizations for any cause occurred in low- or intermediate-risk patients before the first outpatient visit within 30 days after discharge.

CONCLUSIONS

Among patients with acute heart failure who were seeking emergency care, the use of a hospital-based strategy to support clinical decision making and rapid follow-up led to a lower risk of the composite of death from any cause or hospitalization for cardiovascular causes within 30 days than usual care. (Funded by the Ontario SPOR Support Unit and others; COACH ClinicalTrials.gov number, NCT02674438.).

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.

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure

BACKGROUND

Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints.

METHODS

Patients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration.

RESULTS

Among 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, -0.657, 0.692, and calibration slope 0.954; 95% CI,0.769, 1.139) and at 30 days (-0.026; 95% CI, -0.374, 0.322 and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches.

CONCLUSIONS

Logistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.

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.

Rationale and design of the comparison of outcomes and access to care for heart failure (COACH) trial: A stepped wedge cluster randomized trial

BACKGROUND

Heart failure (HF) is an ambulatory care sensitive condition and a leading reason for emergency department (ED) visits and hospitalizations. Improved decision-making and care may enhance safety and efficiency for patients presenting to the ED with acute HF.

OBJECTIVES

We will evaluate an intervention comprised of 2 complementary components: (1) the Emergency Heart Failure Mortality Risk Grade simultaneous 7- and 30-day (EHMRG30-ST) risk scores, which will inform admission-discharge decisions, and (2) a rapid outpatient follow-up (RAPID-HF) clinic for low-to-intermediate risk patients on cardiovascular readmissions or death.

STUDY DESIGN

Stepped wedge cluster randomized trial with cross-sectional measurement at 10 acute care hospitals in Ontario, Canada. Patients presenting during control and intervention periods are eligible if they have a primary ED diagnosis of HF. In the intervention periods, access to the EHMRG30-ST web calculator will become available to hospitals' internet protocol (IP) addresses, and referral to the RAPID-HF clinic will be facilitated by a study nurse navigator. Patients with a high risk EHMRG30-ST score will be admitted to hospital. The RAPID-HF clinic will accept referrals for patients: (1) with low risk 7- and 30-day EHMRG30-ST scores who are discharged directly from the ED, or (2) intermediate risk patients with hospital length of stay < 72 hours. The RAPID-HF clinic, staffed by a nurse-clinician and cardiologist, will provide care during the 30-day transition after hospital separation.

CONCLUSION

This trial will determine whether novel risk stratification coupled with rapid ambulatory care achieves better outcomes than conventional decision-making and care for patients with HF.

Early invasive coronary angiography and acute ischaemic heart failure outcomes

Aims 

While myocardial ischaemia plays a major role in the pathogenesis of heart failure (HF), the indications for coronary angiography during acute HF are not established. We determined the association of early coronary angiography during acute HF hospitalization with 2-year mortality, cardiovascular death, HF readmissions, and coronary revascularization.

Methods and results 

In a two-stage sampling process, we identified acute HF patients who presented to 70 emergency departments in Ontario (April 2010 to March 2013) and determined whether they underwent early coronary angiography within 14 days after presentation using administrative databases. After clinical record review, we defined a cohort with acute ischaemic HF as patients with at least one factor suggesting underlying ischaemic heart disease, including previous myocardial infarction, troponin elevation, or angina on presentation. We oversampled patients undergoing angiography. We used inverse-probability-of-treatment weighting (IPTW) to adjust for baseline differences. Of 7239 patients with acute HF, 2994 met inclusion criteria [median age 75 (interquartile range 65–83) years; 40.9% women]. Early angiography was performed in 1567 patients (52.3%) and was associated with lower all-cause mortality [hazard ratio (HR) 0.74, 95% confidence interval (CI) 0.61–0.90, P = 0.002], cardiovascular death (HR 0.72, 95% CI 0.56–0.93, P = 0.012), and HF readmissions (HR 0.84, 95% CI 0.71–0.99, P = 0.042) after IPTW. Those undergoing early angiography experienced higher rates of percutaneous coronary intervention (HR 2.58, 95% CI 1.73–3.86, P < 0.001) and coronary artery bypass grafting (HR 2.94, 95% CI 1.75–4.93, P < 0.001) within 2 years.

Conclusions 

Early coronary angiography was associated with lower all-cause mortality, cardiovascular death, HF readmissions, and higher rates of coronary revascularization in acute HF patients with possible ischaemia.

Assessment of acute heart failure prognosis: the promising role of prognostic models and biomarkers

Numerous models and biomarkers have been proposed to estimate prognosis and improve decision-making in patients with acute heart failure (AHF). The present literature review provides a critical appraisal of externally validated prognostic models in AHF, combining clinical data and biomarkers. We perform a literature review of clinical studies, using the following terms: "acute heart failure," "acute decompensated heart failure," "prognostic models," "risk scores," "mortality," "death," "hospitalization," "admission," and "biomarkers." We searched MEDLINE and EMBASE databases from 1990 to 2020 for studies documenting prognostic models in AHF. External validation of each prognostic model to another AHF cohort, containing at least one biomarker, was prerequisites for study selection. Among 358 initially screened studies, 9 of them fulfilled all searching criteria. The majority of prognostic models were simplified, including a narrow number of variables (up to 10), with good performance regarding calibration and discrimination (c-statistics > 0.65). Unfortunately, the derived and validated cohorts showed a wide variety in patients' characteristics (e.g., cause of AHF and therapy). Moreover, the prognostic models used various time-points and a plethora of combinations of variables determining different cut-off values. Although the application of valid prognostic models in AHF population is quite promising, a precise methodological approach should be set for the derivation and validation of prognostic models in AHF with unified characteristics to establish an effective performance in clinical practice.

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

AIMS

This study aimed to systematically identify and summarise all risk scores evaluated in the emergency department setting to stratify acute heart failure patients.

METHODS AND RESULTS

A systematic review of PubMed and Web of Science was conducted including all multicentre studies reporting the use of risk predictive models in emergency department acute heart failure patients. Exclusion criteria were: (a) non-original articles; (b) prognostic models without predictive purposes; and (c) risk models without consecutive patient inclusion or exclusively tested in patients admitted to a hospital ward. We identified 28 studies reporting findings on 19 scores: 13 were originally derived in the emergency department (eight exclusively using acute heart failure patients), and six in emergency department and hospitalised patients. The outcome most frequently predicted was 30-day mortality. The performance of the scores tended to be higher for outcomes occurring closer to the index acute heart failure event. The eight scores developed using acute heart failure patients only in the emergency department contained between 4-13 predictors (age, oxygen saturation and creatinine/urea included in six scores). Five scores (Emergency Heart Failure Mortality Risk Grade, Emergency Heart Failure Mortality Risk Grade 30 Day mortality ST depression, Epidemiology of Acute Heart Failure in Emergency department 3 Day, Acute Heart Failure Risk Score, and Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure) have been externally validated in the same country, and two (Emergency Heart Failure Mortality Risk Grade and Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure) further internationally validated. The c-statistic for Emergency Heart Failure Mortality Risk Grade to predict seven-day mortality was between 0.74-0.81 and for Multiple Estimation of risk based on Emergency department Spanish Score In patients with Acute Heart Failure to predict 30-day mortality was 0.80-0.84.

CONCLUSIONS

There are several scales for risk stratification of emergency department acute heart failure patients. Two of them are accurate, have been adequately validated and may be useful in clinical decision-making in the emergency department i.e. about whether to admit or discharge.

External Validation and Refinement of Emergency Heart Failure Mortality Risk Grade Risk Model in Patients With Heart Failure in the Emergency Department

Background

Emergency Heart Failure Mortality Risk Grade (EHMRG) assesses the risk of death within 7 days of emergency department (ED) presentation for patients with acute heart failure (AHF). We aimed to externally validate and refine the EHMRG model in patients who presented to the ED with AHF.

Methods

We performed a cohort study using administrative data for all ambulance-transported patients from Alberta (2012-2016) presenting to the ED with a primary diagnosis of AHF.

Results

Among 6708 patients with AHF, the 7-day mortality was 0.0%, 0.8%, 1.6%, 4.0%, 4.2%, and 12.0% across EHMRG risk categories (1-4, 5A and 5B). The EHMRG score had a c-index of 0.73 (95% confidence interval [CI], 0.71-0.76) for 7-day mortality and 0.71 (95% CI, 0.70-0.73) for 30-day mortality, but lower c-statistics for other outcomes (0.61-0.67). The inclusion of natriuretic peptides to the EHMRG model improved prediction (Net Reclassification Improvement, 0.268; 95% CI, 0.173-0.363; P < 0.01) for 7-day mortality, as did the addition of the Canadian Triage and Acuity Scale (Net Reclassification Improvement, 0.111; 95% CI, 0.005-0.218; P = 0.04).

Conclusion

The EHMRG model exhibited moderate discriminative ability in a large population-based cohort of patients with AHF in the ED. Revision of the EHMRG score through factor inclusion and exclusion could improve the model’s performance.

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.

Design and rationale for the Acute Congestive Heart Failure Urgent Care Evaluation: The ACUTE Study

BACKGROUND

Heart failure (HF) is one of the leading reasons for emergency department (ED) visits and hospitalization. However, externally validated risk algorithms for acute prognostication of heart failure patients are not available. Thus, many low-risk patients are hospitalized and some high-risk patients are discharged home, which, in some cases, may lead to death.

OBJECTIVES

The first objective of the ACUTE study is to perform a prospective validation of the Emergency Heart failure Mortality Risk Grade (EHMRG), which is a risk score derived to predict 7-day mortality in the ED setting. The second objective is to independently validate the 30-day model extension of the risk score (EHMRG30-ST) in the same cohort.

STUDY DESIGN

Patients with HF presenting to the ED will be recruited with a waiver of informed consent as a minimal risk study. The ED physician will calculate the EHMRG 7-day risk score, but treatment decisions will not be influenced by the predictive models. Follow-up will be obtained using probabilistic linkage with the Registered Persons Database of vital statistics, whereby deaths will be ascertained. We will examine mortality rates according to EHMRG and EHMRG30-ST algorithms. We will also compare physician-judged risk estimates, based on clinical judgment alone, with the EHMRG score.

CONCLUSION

The ACUTE study will determine if a retrospectively derived algorithm for simultaneous estimation of 7-day and 30-day mortality risk can accurately identify low- and high-risk patients with acute HF and improve upon physician-judged risk estimation.

Prognostic impact of comorbidities in hospitalized patients with acute exacerbation of chronic heart failure

BACKGROUND

To assess the impact of comorbidities on long-term all-cause mortality in patients hospitalized with exacerbated signs/symptoms of previously chronic stable HF (AE-CHF).

METHODS

1119 patients admitted for AE-CHF and with NT-proBNP levels >900pg/mL were enrolled. Univariable and multivariable Cox analyses were performed to assess the association of age, gender, hypertension, diabetes, obesity, atrial fibrillation, coronary heart disease (CHD), chronic obstructive pulmonary disease, previous cerebrovascular accidents, chronic liver disease (CLD), thyroid disease, renal impairment (RI), and anemia with 3-year all-cause mortality.

RESULTS

During the follow-up, 441 patients died and 126 underwent heart transplantation (HT) or ventricular assist device (VAD) implantation. 45.8% of the fatal events and 52.4% of HT/VAD implantations occurred within 180days after admission. Increasing age (p=.012), obesity (p=.037), atrial fibrillation (p=.030), CHD (p=.015), CLD (p=.001), RI (p<.001), and anemia (p<.001) were independently associated with 3-year all-cause mortality. Most of the prognostic impact of CHD, took place within the first 180days after admission. Male gender was associated with mortality beyond 180days. Compared with normal weight, obesity was associated with better overall survival. Obese patients, however, had significantly lower NT-proBNP concentrations and less frequently presented with hypotension, hyponatremia, and severe left ventricular systolic dysfunction, despite a similar prevalence of severe dyspnea at admission.

CONCLUSIONS

Several comorbidities are associated with long-term risk of death in hospitalized patients with worsening HF, although the nature of this association does appear to be complex. Our data may help to raise awareness about the clinical relevance of comorbid conditions.

Care Setting Intensity and Outcomes After Emergency Department Presentation Among Patients With Acute Heart Failure

Background

Patients with heart failure (HF) presenting to the emergency department (ED) can be admitted to care settings of different intensity, where the intensive care unit (ICU) is the highest intensity, ward admission is intermediate intensity, and those discharged home are of lowest intensity. Despite the costs associated with higher‐intensity care, little is known about disposition decisions and outcomes of HF patients treated in different care settings.

Methods and Results

We identified predictors of ICU or ward admission and determined whether survival differs in patients admitted to higher‐intensity versus lower‐intensity care settings (ie, ICU vs ward, or ward vs ED‐discharged). Among 9054 patients (median, 78 years; 51% men) presenting to an ED in Ontario, Canada, 1163 were ICU‐admitted, 5240 ward‐admitted, and 2651 were ED‐discharged. Predictors of ICU (vs ward) admission included: use of noninvasive positive pressure ventilation (adjusted odds ratio [OR], 2.01; 95% CI, 1.36–2.98), higher respiratory rate (OR, 1.10 per 5 breaths/min; 95% CI, 1.05–1.15), and lower oxygen saturation (OR, 0.90 per 5%; 95% CI, 0.86–0.94; all P<0.001). Predictors of ward‐admitted versus ED‐discharged were similar. Propensity‐matched analysis comparing lower‐risk ICU to ward‐admitted patients demonstrated a nonsignificant trend at 100 days (relative risk [RR], 0.69; 95% CI, 0.43–1.10; P=0.148). At 1 year, however, survival was higher among those initially admitted to ICU (RR, 0.68; 95% CI, 0.49–0.94; P=0.022). There was no survival difference among low‐risk ward‐admitted versus ED‐discharged patients.

Conclusions

Respiratory factors were associated with admission to higher‐intensity settings. There was no difference in early survival between some lower‐risk patients admitted to higher‐intensity units compared to those treated in lower‐intensity settings.

Assessing Risk and Preventing 30-Day Readmissions in Decompensated Heart Failure: Opportunity to Intervene?

Heart failure (HF) patients are at high risk of hospital readmission, which contributes to substantial health care costs. There is great interest in strategies to reduce rehospitalization for HF. However, many readmissions occur within 30 days of initial hospital discharge, presenting a challenge for interventions to be instituted in a short time frame. Potential strategies to reduce readmissions for HF can be classified into three different forms. First, patients who are at high risk of readmission can be identified even before their initial index hospital discharge. Second, ambulatory remote monitoring strategies may be instituted to identify early warning signs before acute decompensation of HF occurs. Finally, strategies may be employed in the emergency department to identify low-risk patients who may not need hospital readmission. If symptoms improve with initial therapy, low-risk patients could be referred to specialized, rapid outpatient follow-up care where investigations and therapy can occur in an outpatient setting.

Inequalities in the Access to Advanced Therapy in Heart Failure

At present, heart failure (HF) is a worldwide problem, characterized by a high morbidity and mortality. In industrialized countries or regions, such as the United States, Canada, and western European countries, HF has a prevalence of 1.5% to 2.7%. Chile represents a growing economy in Latin America; however, the relatively high cost of more advanced therapies, in addition to other variables (ie, adequate and timely evaluation by HF specialists), makes access difficult for patients with HF. In this article, the authors review the principal difficulties in accessing advanced HF therapies in Chile, as a model of developing country.