Incidence of heart failure and mortality after acute coronary syndromes

doi:10.1016/j.ahj.2012.12.005

American Heart Journal

Volume 165, Issue 3, March 2013, Pages 379-385.e2

https://doi.org/10.1016/j.ahj.2012.12.005 Get rights and content

Background

The long-term incidence of heart failure (HF) in ST-elevation myocardial infarction (STEMI), non–ST-elevation myocardial infarction (NSTEMI), or unstable angina (UA) patients is uncertain. We examined the 1-year incidence of HF and its association with mortality among patients surviving their first acute coronary syndrome (ACS) hospitalization.

Methods and results

A retrospective cohort study of patients, aged ≥20 years, with no prior HF, hospitalized for the first time with ACS between April 1, 2002, and December 31, 2008, in Alberta, Canada, and followed up for 1 year. Index HF was defined as HF that developed as a complication during the index ACS hospitalization, and post-discharge HF, as HF developing after discharge from the index ACS hospitalization.

Among 9,406 STEMI, 11,008 NSTEMI, and 4,910 UA patients, 13.6%, 14.8%, and 5.2% had index HF, respectively (P < .01). At 1-year, cumulative HF rates were 23.4% in STEMI, 25.4% in NSTEMI, and 16% in UA patients. Among hospital survivors, 1-year mortality rate was 13.9% in patients with index HF, 10.6% in patients with postdischarge HF, and 2.4% in patients with no HF. In multivariable analysis, both index HF (adjusted hazard ratio 3.2, 95% CI 2.7-3.7) and postdischarge HF (adjusted hazard ratio 4.6, 95% CI 3.9-5.4) were associated with 1-year mortality.

Conclusions

There are significant differences in the incidence of HF among STEMI, NSTEMI, and UA patients. The increased mortality risk associated with index HF and postdischarge HF suggests a need for vigilant follow-up of all ACS patients for prompt detection and treatment of HF.

Section snippets

Study population

Our study cohort consists of all residents of Alberta aged 20 years or older hospitalized at an acute care facility with a primary diagnosis of ACS between April 1, 2002, and December 31, 2008 (subsequently referred to as the index hospitalization). For transfer patients, the index hospitalization refers to the index episode and includes concurrent hospitalizations occurring with 24 hours of each other. The diagnosis of ACS is based on International Statistical Classification of Diseases, 10th

Results

A total of 31,469 patients had an acute care hospitalization for ACS between April 1, 2002, and December 31, 2008 (Figure 1). Patients with prior ACS (n = 4,988, 15.9%) or with prior HF (n = 2,492, 7.9%) were excluded from the cohort. The final study population consisted of 9,406 (37.1%) STEMI, 11,008 (43.5%) NSTEMI, and 4,910 (19.4%) UA patients. ST-elevation myocardial infarction patients were younger, less likely to be female, and generally had lower rates of comorbid disease relative to

Discussion

In this population-level analysis of 25,324 patients hospitalized for the first time with ACS between 2002 and 2008, there were significant differences in HF developing both during (index HF) and after discharge from the index ACS hospitalization (postdischarge HF) across the 3 ACS subtypes (STEMI, NSTEMI, and UA). Among patients who survived the index ACS hospitalization, both index HF and postdischarge HF were associated with a higher 1-year mortality risk, although the magnitude of the

Conclusion

In a population-level cohort of patients hospitalized for the first time with an ACS event, there were significant differences in the long-term incidence of HF among STEMI, NSTEMI, and UA patients. Among hospital survivors, HF developed either during the ACS hospitalization or after discharge confers a substantial risk of death during the first year. The downstream morbidity and mortality associated with HF suggest a need for increased surveillance and timely diagnosis and treatment among all

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Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.
Gender differences in the development of heart failure after acute coronary syndrome: Insight from the CORALYS registry
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Impact of gender on heart remodeling after acute coronary syndrome (ACS) and consequently on development of heart failure (HF) remains to be elucidated.
CORALYS is a multicenter, retrospective, observational registry enrolling consecutive patients admitted for ACS and treated with percutaneous coronary intervention. HF hospitalization was the primary endpoint while all-cause mortality and the composite endpoint of incidence of first HF hospitalization and cardiovascular mortality were the secondary ones.
Among 14,699 patients enrolled in CORALYS registry, 4578 (31%) were women and 10,121 (69%) males. Women were older, had more frequently hypertension and diabetes and less frequently smoking habit. History of myocardial infarction (MI), STEMI at admission and multivessel disease were less common in women. After median follow up of 2.9 ± 1.8 years, women had higher incidence of primary and secondary endpoints and female sex was an independent predictor of HF hospitalization (HR 1.26;1.05–1.50; p = 0.011) and cardiovascular death/HF hospitalization (HR 1.18;1.02–1.37; p = 0.022). At multivariable analysis women and men share as predictors of HF diabetes, history of cancer, chronic kidney disease, atrial fibrillation, complete revascularization and left ventricular ejection fraction. Chronic obstructive pulmonary disease (HR 2.34;1.70–3.22, p < 0.001) and diuretics treatment (HR 1.61;1.27–2.04, p < 0.001) were predictor of HF in men, while history of previous MI (HR 1.46;1.08–1.97, p = 0.015) and treatment with inhibitors of renin-angiotensin system (HR 0.69;0,49–0.96 all 95% CI, p = 0.030) in women.
Women are at increased risk of HF after ACS and gender seems to be an outcome-modifier of the relationship between a variable and primary outcome.
Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach
2023, Current Problems in Cardiology
Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS. A total of 1,028 patients with ACS admitted to Guangdong Second Provincial General Hospital between October 2019 and May 2022 were included in this study. 128 clinical features were ranked using Shapley additive exPlanations (SHAP) values and the top 20% of features were selected for building a balanced random forest (BRF) model. We compared the discriminatory capability of BRF with linear logistic regression (LLR). In the hold-out test set, the BRF model predicted subsequent HF with areas under the curve (AUC) of 0.76 (95% CI: 0.75-0.77), sensitivity of 0.97 (95% CI: 0.96-0.97), positive predictive value (PPV) of 0.73 (95% CI: 0.72-0.74), negative predictive value (NPV) of 0.63 (95% CI: 0.60-0.66), and accuracy of 0.73 (95% CI: 0.72-0.73), respectively. BRF outperforms linear logistic regression by 15.6% in AUC, 3.0% in sensitivity, and 60.8% in NPV. End-to-end machine learning approaches can predict the acute onset of HF following ACS with high prediction accuracy. This proof-of-concept study has the potential to substantially advance the management of ACS patients by utilizing the machine learning model as a triage tool to automatically identify clinically significant patients allowing for prioritization of interventions.
Primary left ventricular unloading with delayed reperfusion in patients with anterior ST-elevation myocardial infarction: Rationale and design of the STEMI-DTU randomized pivotal trial
2022, American Heart Journal
Despite successful primary percutaneous coronary intervention (PCI) in ST-elevation myocardial infarction (STEMI), myocardial salvage is often suboptimal, resulting in large infarct size and increased rates of heart failure and mortality. Unloading of the left ventricle (LV) before primary PCI may reduce infarct size and improve prognosis.
STEMI-DTU (NCT03947619) is a prospective, randomized, multicenter trial designed to compare mechanical LV unloading with the Impella CP device for 30 minutes prior to primary PCI to primary PCI alone without LV unloading. The trial aims to enroll approximately 668 subjects, with a potential sample size adaptation, with anterior STEMI with a primary end point of infarct size as a percent of LV mass evaluated by cardiac magnetic resonance at 3-5 days after PCI. The key secondary efficacy end point is a hierarchical composite of the 1-year rates of cardiovascular mortality, cardiogenic shock ≥24 hours after PCI, use of a surgical left ventricular assist device or heart transplant, heart failure, intra-cardiac defibrillator or chronic resynchronization therapy placement, and infarct size at 3 to 5 days post-PCI. The key secondary safety end point is Impella CP-related major bleeding or major vascular complications within 30 days. Clinical follow-up is planned for 5 years.
STEMI-DTU is a large-scale, prospective, randomized trial evaluating whether mechanical unloading of the LV by the Impella CP prior to primary PCI reduces infarct size and improves prognosis in patients with STEMI compared to primary PCI alone without LV unloading.
Acute Decompensated Heart Failure in the Setting of Acute Coronary Syndrome
2022, JACC: Heart Failure
Citation Excerpt :
Risk of ACS-HF increases in those with a history of prior cardiovascular disease, including coronary artery disease or prior MI.3-7,27 The relative risk for ACS-HF after ST elevation MI and NSTEMI appears to be similar,2 with lower risk for ACS-HF associated with UA.5,7,32 Limited data are available on burden of coronary disease and the risk of ACS-HF, but multivessel and left main disease are associated with higher risk of ACS-HF,4,5 as are anterior MIs.2,6,8,10,33,34
Acute coronary syndrome (ACS) is frequently complicated by evidence of heart failure (HF). Those at highest risk for acute decompensated HF in the setting of ACS (ACS-HF) are older, female, and have preexisting heart disease, type 2 diabetes mellitus, hypertension, and/or kidney disease. The presence of ACS-HF is strongly associated with higher mortality and more frequent readmissions, especially for HF. Low implementation of guideline-directed medical therapy has further complicated the clinical care of this high-risk population. Improved utilization of current therapies, coupled with further investigation of strategies to manage ACS-HF, is desperately needed to improve outcomes in this vulnerable population, and the results of currently ongoing or recently concluded ACS-HF studies in this population are of great interest. In this review, we explore the pathophysiology, epidemiology, risk factors, and outcomes for patients with ACS-HF, and describe both existing evidence for management of this challenging condition and areas requiring further research.
Heart Failure Incidence Following ST-Elevation Myocardial Infarction
2022, American Journal of Cardiology
Citation Excerpt :
Desta et al9 reported a decrease of the incidence from 50% to 28% between 1996 and 2008 but considered development of HF during index STEMI hospitalization. Kaul et al10 estimated an HF incidence of 23.4% (HF during STEMI hospitalization and after discharge). However, primary PCI was only performed in half of STEMI patients.
ST-elevation myocardial infarction (STEMI) survivors have a heightened risk of developing heart failure (HF). The magnitude of this risk with the advent of primary percutaneous coronary intervention is less characterized. We aimed to examine the incidence and predictors of incident HF and all-cause mortality in a contemporary STEMI cohort. We performed a retrospective analysis of 700 consecutive patients with STEMI treated with primary percutaneous coronary intervention at a tertiary hospital. The primary outcome was the occurrence of HF during follow-up. HF was defined by HF hospitalization or the presence of congestion that led to de novo prescription or up-titration of diuretics in the outpatient clinic. The secondary outcome was defined by the occurrence of HF or all-cause mortality. During a median follow-up period of 43.6 months, HF events occurred in 110 patients (15.7%), 34 (4.8%) managed as outpatient and 76 (10.9%) requiring hospitalization. Left ventricular ejection fraction (LVEF) <50% was present in 76% of those who developed HF. Age (hazard ratio [HR] 1.03, 95% confidence interval [CI] 1.01 to 1.06), diabetes (HR 1.85, 95% CI 1.12 to 3.05), door-to-balloon time (HR 1.002, 95% CI 1.000 to 1.003), Killip-Kimball class ≥II (HR 2.24, 95% CI 1.32 to 3.80) and LVEF <50% (HR 1.71, 95% CI 1.01 to 2.92) were independent predictors. All-cause mortality incidence was 8.7%. HF was independently associated with a threefold increased risk of dying (HR 3.52, 95% CI 1.85 to 6.69, p <0.001). In conclusion, a substantial proportion of contemporary patients with STEMI develop HF, which triplicates the risk of dying. Older age, diabetes and LVEF <50% independently predicted the development of HF and all-cause death.

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Clinical InvestigationCongestive Heart FailureIncidence of heart failure and mortality after acute coronary syndromes

Background

Methods and results

Conclusions

Section snippets

Study population

Results

Discussion

Conclusion

Am Heart J

Am J Cardiol

J Am Coll Cardiol

Am J Cardiol

J Am Coll Cardiol

J Am Coll Cardiol

Heart disease and stroke statistics—2010 update: a report from the American Heart Association

Circulation

Heterogeneity in the management and outcomes of patients with acute myocardial infarction complicated by heart failure: The National Registry of Myocardial Infarction

Circulation

Decline in rates of death and heart failure in acute coronary syndromes, 1999-2006

JAMA

Clinical Investigation
Congestive Heart Failure
Incidence of heart failure and mortality after acute coronary syndromes