Prediction of incident heart failure by serum amino‐terminal pro‐B‐type natriuretic peptide level in a community‐based cohort

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment

Importance

Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment.

Objective

To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs.

Design

Multicohort study.

Setting

Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Participants

Individuals without HF at baseline.

Exposures

AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD).

Main Outcomes and Measures

Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

Results

There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF.

Conclusions and Relevance

Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

KEY POINTS

Question: Can single-lead electrocardiogram (ECG) tracings predict heart failure (HF) risk?Findings: We evaluated a noise-adapted artificial intelligence (AI) algorithm for single-lead ECGs as the sole input across multinational cohorts, spanning a diverse integrated US health system and large community-based cohorts in the UK and Brazil. A positive AI-ECG screen was associated with a 3- to 7-fold higher HF risk, independent of age, sex, and comorbidities. The AI model achieved incremental discrimination and improved reclassification for HF over the pooled cohort equations to prevent HF (PCP-HF).Meaning: A noise-adapted AI model for single-lead ECG predicted the risk of new-onset HF, representing a scalable HF risk-stratification strategy for portable and wearable devices.

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study

Background

Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk.

Methods

Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk.

Results

Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.

Conclusions

An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.

Interlaboratory variation for NT-proBNP among Swedish laboratories in an external quality program 2011-2021

OBJECTIVES

NT-proBNP is frequently used for ruling out heart failure. Different cut-offs are used depending on the clinical context, e.g. an acute or chronic condition. Medical decision limits have been suggested at 125 and 300 ng/L or 400 ng/L in international guidelines. However, there is limited standardization between NT-proBNP methods and using the same blood sample might cause different treatment of patients.

METHODS

Data from the external quality assessment program for NT-proBNP from Equalis, Sweden, were extracted for the period 2011-2021, and categorized according to manufacturer. Manufacturer median NT-proBNP values were compared to total median values. CV% was calculated for each manufacturer and in comparison to different levels of NT-proBNP.

RESULTS

Roche was the most common method, and its median results were closest to the median consensus results. When looking at the total CV at NT-proBNP levels in the range of 0-500 ng/L, the total CV varied from 4 to 27%. During 2019-2021, Siemens (Immulite, Centaur, Atellica) yielded results 16-20% above the consensus median depending on sample level. Similarly, Abbott was 5-7% above, while Roche and Siemens Stratus were 1% respectively 6-10% below the consensus median.

CONCLUSIONS

The introduction of new manufacturers and methods in 2017 have caused the agreement between manufacturers to decline. This highlights the need for a common calibrator and reference materials, particularly since medical decision limits in guidelines, e.g. European Society of Cardiology 2021, which are mostly based on Roche methods, do not take these method differences into account.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

An inevitable or modifiable trajectory towards heart failure in high-risk individuals: insights from the nurse-led intervention for less chronic heart failure (NIL-CHF) study

AIMS

We extended follow-up of a heart failure (HF) prevention study to determine if initially positive findings of improved cardiac recovery were translated into less de novo HF and/or all-cause mortality (primary endpoint) in the longer term.

METHODS AND RESULTS

The Nurse-led Intervention for Less Chronic HF (NIL-CHF) study was a single-centre randomized trial of nurse-led prevention involving cardiac inpatients without HF. At 3 years, 454 survivors (aged 66 ± 11 years, 71% men and 68% coronary artery disease) had the following: (i) a normal echocardiogram (128 cases/28.2%), (ii) structural heart disease (196/43.2%), or (iii) left ventricular diastolic dysfunction/left ventricular systolic dysfunction (LVDD/LVSD: 130/28.6%). Outcomes were examined during median 8.3 (interquartile range 7.8-8.8) years according to these hierarchal groups and change in cardiac status from baseline to 3 years. Overall, 109 (24.0%) participants had a de novo HF admission or died while accumulating 551 cardiovascular-related admissions/3643 days of hospital stay. Progressively worse cardiac status correlated with increased hospitalizations (P < 0.001). The mean rate (95% confidence interval) of cardiovascular admissions/days of hospital stay being 0.09 (0.05-0.12) admissions/0.33 (0.13-0.54) days vs. 0.27 (0.20-0.34) admissions/2.20 (1.36-3.04) days per annum for those with a normal echocardiogram vs. LVDD/LVSD at 3 years. With progressively higher event rates, the adjusted hazard ratio for a de novo HF admission and/or death associated with a structural abnormality (24.5% of cases) and LVDD/LVSD (36.2%) at 3 years was 1.57 (0.82-3.01; P = 0.173) and 2.07 (1.05-4.05; P = 0.035) compared with a normal echocardiogram (10.9%). Mortality also mirrored the direction/extent of cardiac status/trajectory.

CONCLUSIONS

These data suggest the positive initial effects of NIL-CHF intervention on cardiac recovery contributed to better long-term outcomes among patients at high risk of HF. However, prevention of HF remains challenging.

Development and Validation of a Risk Score in Chinese Patients With Chronic Heart Failure

Background

Acute exacerbation of chronic heart failure contributes to substantial increases in major adverse cardiovascular events (MACE). The study developed a risk score to evaluate the severity of heart failure which was related to the risk of MACE.

Methods

This single-center retrospective observational study included 5,777 patients with heart failure. A credible random split-sample method was used to divide data into training and validation dataset (split ratio = 0.7:0.3). Least absolute shrinkage and selection operator (Lasso) logistic regression was applied to select predictors and develop the risk score to predict the severity category of heart failure. Receiver operating characteristic (ROC) curves, and calibration curves were used to assess the model’s discrimination and accuracy.

Results

Body-mass index (BMI), ejection fraction (EF), serum creatinine, hemoglobin, C-reactive protein (CRP), and neutrophil lymphocyte ratio (NLR) were identified as predictors and assembled into the risk score (P &lt; 0.05), which showed good discrimination with AUC in the training dataset (0.770, 95% CI:0.746–0.794) and validation dataset (0.756, 95% CI:0.717–0.795) and was well calibrated in both datasets (all P &gt; 0.05). As the severity of heart failure worsened according to risk score, the incidence of MACE, length of hospital stay, and treatment cost increased (P &lt; 0.001).

Conclusion

A risk score incorporating BMI, EF, serum creatinine, hemoglobin, CRP, and NLR, was developed and validated. It effectively evaluated individuals’ severity classification of heart failure, closely related to MACE.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Risk factors for asymptomatic echocardiographic abnormalities that predict symptomatic heart failure

Aims

Risk factors for asymptomatic echocardiographic abnormalities that predict symptomatic heart failure (HF) may provide insight into early mechanisms of HF pathogenesis. We examined risk factors associated with asymptomatic echocardiographic structural, systolic, and diastolic abnormalities, separately and in combination, and interactions between risk factors, in the prospective community‐based SCReening Evaluation of the Evolution of New HF (SCREEN‐HF) Study cohort of 3190 participants at increased risk of cardiovascular disease.

Methods and results

Inclusion criteria were age ≥ 60 years with one or more of hypertension, diabetes, ischaemic heart disease, valvular heart disease, abnormal heart rhythm, cerebrovascular disease, or renal impairment. Exclusion criteria were known HF, ejection fraction < 50%, or >mild valve abnormality. Structural, systolic, and diastolic echocardiographic abnormalities were defined according to the Atherosclerosis Risk in Communities study criteria, and risk factors for asymptomatic structural, systolic, and diastolic abnormalities were identified using logistic regression analysis. In multivariable analysis, increased body mass index (BMI), non‐steroidal anti‐inflammatory drug therapy, and alcohol intake were risk factors for isolated structural abnormality, whereas male gender, increased heart rate, atrial fibrillation (AF), angiotensin‐converting enzyme inhibitor therapy, and obstructive sleep apnoea were associated with a lower risk. Moreover, male gender, smoking, increased systolic blood pressure, and physical inactivity were risk factors for isolated systolic abnormality, whereas increased pulse pressure and antihypertensive therapy were associated with a lower risk. Furthermore, increased age, blood pressure, amino‐terminal pro‐B‐type natriuretic peptide level, and warfarin therapy (associated with AF) were risk factors for isolated diastolic abnormality, whereas increased heart rate and triglyceride level (associated with BMI) were associated with a lower risk. The association of increased heart rate with lower risk of structural and diastolic abnormalities was independent of β‐blocker therapy. Interactions between risk factors differed for structural, systolic, and diastolic abnormalities.

Conclusions

The different risk factors for asymptomatic structural, systolic, and diastolic abnormalities that predict symptomatic HF, and the interactions between risk factors, illustrate how these structural, systolic, and diastolic abnormalities represent unique trajectories that lead to symptomatic HF. Improved understanding of these trajectories may assist in the design of HF prevention strategies.

Kidney age - chronological age difference (KCD) score provides an age-adapted measure of kidney function

BACKGROUND

Given the age-related decline in glomerular filtration rate (GFR) in healthy individuals, we examined the association of all-cause death or cardiovascular event with the Kidney age - Chronological age Difference (KCD) score, whereby an individual's kidney age is estimated from their estimated GFR (eGFR) and the age-dependent eGFR decline reported for healthy living potential kidney donors.

METHODS

We examined the association between death or cardiovascular event and KCD score, age-dependent stepped eGFR criteria (eGFRstep), and eGFR < 60 ml/min/1.73 m2 (eGFR60) in a community-based high cardiovascular risk cohort of 3837 individuals aged ≥60 (median 70, interquartile range 65, 75) years, followed for a median of 5.6 years.

RESULTS

In proportional hazards analysis, KCD score ≥ 20 years (KCD20) was associated with increased risk of death or cardiovascular event in unadjusted analysis and after adjustment for age, sex and cardiovascular risk factors. Addition of KCD20, eGFRstep or eGFR60 to a cardiovascular risk factor model did not improve area under the curve for identification of individuals who experienced death or cardiovascular event in receiver operating characteristic curve analysis. However, addition of KCD20 or eGFR60, but not eGFRstep, to a cardiovascular risk factor model improved net reclassification and integrated discrimination. KCD20 identified individuals who experienced death or cardiovascular event with greater sensitivity than eGFRstep for all participants, and with greater sensitivity than eGFR60 for participants aged 60-69 years, with similar sensitivities for men and women.

CONCLUSIONS

In this high cardiovascular risk cohort aged ≥60 years, the KCD score provided an age-adapted measure of kidney function that may assist patient education, and KCD20 provided an age-adapted criterion of eGFR-related increased risk of death or cardiovascular event. Further studies that include the full age spectrum are required to examine the optimal KCD score cut point that identifies increased risk of death or cardiovascular event, and kidney events, associated with impaired kidney function, and whether the optimal KCD score cut point is similar for men and women.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00400257 , NCT00604006 , and NCT01581827 .

Heart failure in the last year: progress and perspective

Research about heart failure (HF) has made major progress in the last years. We give here an update on the most recent findings. Landmark trials have established new treatments for HF with reduced ejection fraction. Sacubitril/valsartan was superior to enalapril in PARADIGM‐HF trial, and its initiation during hospitalization for acute HF or early after discharge can now be considered. More recently, new therapeutic pathways have been developed. In the DAPA‐HF and EMPEROR‐Reduced trials, dapagliflozin and empagliflozin reduced the risk of the primary composite endpoint, compared with placebo [hazard ratio (HR) 0.74; 95% confidence interval (CI) 0.65–0.85; P < 0.001 and HR 0.75; 95% CI 0.65–0.86; P < 0.001, respectively]. Second, vericiguat, an oral soluble guanylate cyclase stimulator, reduced the composite endpoint of cardiovascular death or HF hospitalization vs. placebo (HR 0.90; 95% CI 0.82–0.98; P = 0.02). On the other hand, both the diagnosis and treatment of HF with preserved ejection fraction, as well as management of advanced HF and acute HF, remain challenging. A better phenotyping of patients with HF would be helpful for prognostic stratification and treatment selection. Further aspects, such as the use of devices, treatment of arrhythmias, and percutaneous treatment of valvular heart disease in patients with HF, are also discussed and reviewed in this article.

Depressive Symptoms Are Associated with Reduced Cardiac Function Among Hispanics: Results from the PREVENCION Study

BACKGROUND

Depressive symptoms are common among patients with heart failure and are often associated with adverse outcomes, including re-hospitalization and mortality. However, little is known about the association between depressive symptoms and subclinical markers of heart failure and cardiac function in community-based samples and little research has focused on South American Hispanics. The current study examined the cross-sectional association between depressive symptoms and cardiac function in South American Hispanic community-based adults.

METHODS

Participants included 527 adults enrolled in the Peruvian Study of Cardiovascular Disease (PREVENCION). Depressive symptoms were assessed with the Hospital Anxiety and Depression Scale (HADS). Markers of cardiac function were assessed by impedance cardiography and included cardiac output, cardiac index, stroke volume, and stroke volume index. Several multiple regression analyses were used to examine the association between depressive symptoms and markers of cardiac function.

RESULTS

In adjusted analyses, depressive symptoms were associated with reduced cardiac output, cardiac index, stroke volume, and stroke volume index. These associations remained significant between depressive symptoms and cardiac output (β = - 0.106, p = 0.014), cardiac index (β = - 0.099, p = 0.029), and stroke volume (β = - 0.095, p = 0.022), and a trend was still observed between depressive symptoms and stroke index (β = - 0.083, p = 0.061), even after having controlled for demographic factors (age, gender, education), cardiovascular risk factors (smoking status, body mass index, low- and high-density lipoprotein cholesterol, triglycerides, fasting glucose, serum creatinine), and comorbidities (diabetes mellitus, hypertension, hypercholesterolemia).

CONCLUSIONS

In the PREVENCION sample tested, depressive symptoms were independently associated with cardiac function among Hispanic adults, even above and beyond pertinent factors such as demographic factors, cardiovascular risk factors, and comorbidities. Future studies should determine whether depressive symptoms are prospectively associated with systolic dysfunction, and examine the bio-behavioral pathways of this association.

Elevated N-terminal prohormone of brain natriuretic peptide among persons living with HIV in a South African peri-urban township

AIMS

Efforts to improve access to antiretroviral therapy (ART) have shifted morbidity and mortality among persons living with HIV (PLWH) from AIDS to non-communicable diseases, such as cardiovascular disease (CVD). However, contemporary data on CVD among PLWH in sub-Saharan Africa in the current ART era are lacking. The aim of this study was to assess the burden of cardiac stress among PLWH in South Africa via measurement of N-terminal prohormone of brain natriuretic peptide (NT-proBNP).

METHODS AND RESULTS

NT-proBNP was measured at baseline in 224 PLWH enrolled in a sub-study of a tuberculosis vaccine trial in Khayelitsha township near Cape Town, South Africa. Thresholds were applied at the assay's limit of detection (≥137 pg/mL) and a level indicative of symptomatic heart failure in the acute setting (>300 pg/mL). Mean (SD) age of participants was 39 (6) years, 86% were female, and 19% were hypertensive. Mean (SD) duration of HIV diagnosis was 8.3 (3.9) years and CD4 + count was 673 (267) with 79% prescribed ART for a duration of 5.6 (2.7) years. Thirty-one percent of participants had NT-proBNP > 300 pg/mL. Elevated vs. undetectable NT-proBNP level was associated with older age (P = 0.04), no ART (P = 0.03), and higher plasma tumour necrosis factor-α (P = 0.01).

CONCLUSIONS

Among South African PLWH largely free of known CVD and on ART with high CD4 + counts and few comorbidities, we observed a high proportion with elevated NT-proBNP levels, suggesting the burden of cardiac stress in this population may be high. This observation underscores the need for more in-depth research, including the current effect of HIV on heart failure risk among a growing ART-treated population in sub-Saharan Africa.

Moderate Levels of N-Terminal Pro-B-Type Natriuretic Peptide is Associated with Increased Risks of Total and Ischemic Strokes among Japanese: The Circulatory Risk in Communities Study

AIM

N-terminal pro-B-type natriuretic peptide (NT-proBNP), frequently used as a biochemical marker for detecting and monitoring heart failure, is also a risk marker for development of coronary heart disease and total stroke. However, studies that explore subtypes of ischemic stroke with regard to NT-proBNP are scarce. Here, we examined NT-proBNP and its impact upon subtypes of ischemic stroke (lacunar stroke, large-artery occlusive stroke and embolic stroke) among Japanese.

METHODS

We measured NT-proBNP and categorized 4,393 participants of the Circulatory Risk in Communities Study into four groups (＜55, 55-124, 125-399, and ≥ 400 pg/ml). We used a multivariable Cox proportional hazards model to examine association with risks of stroke and subtypes.

RESULTS

During 4.7 years of follow-up, we identified 50 strokes, including 35 ischemic (15 lacunar, 6 largeartery occlusive, 10 embolic strokes) and 14 hemorrhagic strokes. NT-proBNP was associated with stroke risk: the multivariable hazard ratio of total strokes was 7.29 (2.82-18.9) for the highest and 2.78 (1.25-6.16) for the second highest NT-proBNP groups compared with the lowest group. The respective hazard ratios for the highest NT-proBNP group were 9.37 (3.14-28.0) for ischemic stroke and 6.81 (1.11-41.7) for lacunar stroke. Further adjustment for atrial fibrillation did not attenuate these associations. The associations were similarly observed for large-artery occlusive and embolic strokes.

CONCLUSION

We found that even moderate serum levels of NT-proBNP were associated with the risk of total and ischemic strokes among Japanese whose NT-proBNP levels were relatively low compared with Westerners.

Age-related longitudinal change in cardiac structure and function in adults at increased cardiovascular risk

AIM

Heart failure (HF) incidence increases markedly with age. We examined age-associated longitudinal change in cardiac structure and function, and their prediction by age and cardiovascular disease (CVD) risk factors, in a community-based cohort aged ≥60 years at increased CVD risk but without HF.

METHODS AND RESULTS

CVD risk factors were recorded in 3065 participants who underwent a baseline echocardiographic examination, of whom 2358 attended a follow-up examination 3.8 [median, inter-quartile range (IQR) 3.5, 4.2] years later. Median age was 71 (IQR 67, 76) years and 55% of participants were male. Age was associated with longitudinal increase in left ventricular (LV) mass index (LVMI); decrease in LV volumes; increase in LV ejection fraction; decrease in mitral annular systolic velocity; decrease in diastolic function (decreased mitral early diastolic annular velocity (e'); and increase in left atrial volume index, mitral peak early diastolic flow velocity (E)/e' ratio, and tricuspid regurgitant velocity (TR_Vmax ) in men and women, except for TR_Vmax in men). In multivariable analysis, longitudinal increase in LVMI was explained by CVD risk factors alone, whereas age, together with CVD risk factors, independently predicted longitudinal change in all other echocardiographic parameters. CVD risk factors were differentially associated with longitudinal change in different echocardiographic parameters.

CONCLUSIONS

Whereas the increase in LVMI with age was explained by CVD risk factors alone, age, together with risk factors, independently predicted longitudinal change in all other echocardiographic parameters, providing evidence for age-specific mechanisms of change in cardiac structure and function as people age. Age-associated change in LVMI, LV volumes, and diastolic function resembled what might be expected for the evolution of HF with preserved ejection fraction. Given the differential association of different CVD risk factors with longitudinal change in different echocardiographic parameters, therapies aimed at attenuation of age-associated change in cardiac structure and function, and HF evolution, will likely need to address multiple CVD risk factors.

Threshold body mass index and sex-specific waist circumference for increased risk of heart failure with preserved ejection fraction

Background

Body mass index †Deceased. (BMI) is a risk factor for heart failure with preserved ejection fraction (HFpEF).

Design

We investigated the threshold BMI and sex-specific waist circumference associated with increased HFpEF incidence in the SCReening Evaluation of the Evolution of New Heart Failure (SCREEN-HF) study, a cohort study of a community-based population at increased cardiovascular disease risk.

Methods

Inclusion criteria were age ≥60 years with one or more of self-reported hypertension, diabetes, heart disease, abnormal heart rhythm, cerebrovascular disease or renal impairment. Exclusion criteria were known heart failure, ejection fraction <50% or more than mild valve abnormality. Among 3847 SCREEN-HF participants, 73 were diagnosed with HFpEF at a median of 4.5 (interquartile range: 2.9–5.5) years after enrolment.

Results

HFpEF incidence rates were higher for BMI ≥27.5 kg/m2 than for BMI < 25 kg/m2, and for waist circumference >100 cm (men) or > 90 cm (women) than for waist circumference ≤94 cm (men) or ≤ 83 cm (women) in Poisson regression analysis. Semiparametric proportional hazards analyses confirmed these BMI and waist circumference thresholds, and exceeding these thresholds was associated with an attributable risk of HFpEF of 44–49%.

Conclusions

Both central obesity and overweight were associated with increased HFpEF incidence. Although a randomised trial of weight control would be necessary to establish a causal relationship between obesity/overweight and HFpEF incidence, these data suggest that maintenance of BMI and waist circumference below these thresholds in a community similar to that of the SCREEN-HF cohort may reduce the HFpEF incidence rate by as much as 50%.

Age-specific diastolic dysfunction improves prediction of symptomatic heart failure by Stage B heart failure

Aims

We investigated whether addition of diastolic dysfunction (DD) and longitudinal strain (LS) to Stage B heart failure (SBHF) criteria (structural or systolic abnormality) improves prediction of symptomatic HF in participants of the SCReening Evaluation of the Evolution of New Heart Failure study, a self‐selected population at increased cardiovascular disease risk recruited from members of a health insurance fund in Melbourne and Shepparton, Australia. Both American Society of Echocardiography and European Association of Cardiovascular Imaging (ASE/EACVI) criteria and age‐specific Atherosclerosis Risk in Communities (ARIC) study criteria, for SBHF and DD, and ARIC criteria for abnormal LS, were examined.

Methods and results

Inclusion criteria were age ≥60 years with one or more of self‐reported ischaemic or other heart disease, irregular or rapid heart rhythm, cerebrovascular disease, renal impairment, or treatment for hypertension or diabetes for ≥2 years. Exclusion criteria were known HF, or ejection fraction <50% or >mild valve abnormality detected on previous echocardiography or other imaging. Echocardiography was performed in 3190 participants who were followed for a median of 3.9 (interquartile range: 3.4, 4.5) years after echocardiography. Symptomatic HF was diagnosed in 139 participants at a median of 3.1 (interquartile range: 2.1, 3.9) years after echocardiography. ARIC structural, systolic, and diastolic abnormalities predicted HF in univariate and multivariable proportional hazards analyses, whereas ASE/EACVI structural and systolic, but not diastolic, abnormalities predicted HF. ARIC and ASE/EACVI SBHF criteria predicted HF with sensitivities of 81% and 55%, specificities of 39% and 76%, and C statistics of 0.60 (95% confidence interval: 0.57, 0.64) and 0.66 (0.61, 0.71), respectively. Adding ARIC DD to SBHF increased sensitivity to 94% with specificity of 24% and C statistic of 0.59 (0.57, 0.61), whereas addition of ASE/EACVI DD to SBHF increased sensitivity to 97% but reduced specificity to 9% and the C statistic to 0.52 (0.50, 0.54, P < 0.0001). Addition of LS to ARIC or ASE/EACVI SBHF criteria had minimal impact on prediction of HF.

Conclusions

Age‐specific ARIC DD criteria, but not ASE/EACVI DD criteria, predicted symptomatic HF, and addition of age‐specific ARIC DD criteria to ARIC SBHF criteria improved prediction of symptomatic HF in asymptomatic individuals with cardiovascular disease risk factors. Addition of LS to ASE/EACVI or ARIC SBHF criteria did not improve prediction of symptomatic HF.