How reliable are left ventricular ejection fraction cut offs assessed by echocardiography for clinical decision making in patients with heart failure?

Usefulness of Heart Failure Categories Based on Left Ventricular Ejection Fraction

BACKGROUND

Heart failure guidelines have recently introduced a narrow category with mildly reduced left ventricular ejection fraction (LVEF) (heart failure with mildly reduced ejection fraction; LVEF 41%-49%) between the previous categories of reduced (heart failure with reduced ejection fraction; LVEF ≤40%) and preserved (heart failure with preserved ejection fraction; LVEF ≥50%) ejection fraction. Grouping of continuous measurements into narrow categories can be questioned if their variability is high.

METHODS AND RESULTS

We constructed a cohort of all 9716 new cases of chronic heart failure with an available LVEF in Stockholm, Sweden, from January 1, 2015, until December 31, 2020. All values of LVEF were collected over time, and patients were followed up until death, moving out of Stockholm, or end of study. Mixed models were used to quantify within-person variance in LVEF, and multistate Markov models, with death as an absorbing state, to quantify the stability of LVEF categories. LVEF values followed a normal distribution. The SD of the within-person variance in LVEF over time was 7.4%. The mean time spent in any LVEF category before transition to another category was on average <1 year for heart failure with mildly reduced ejection fraction. Probabilities of transitioning between categories during the first year were substantial; patients with heart failure with mildly reduced ejection fraction had a probability of <25% of remaining in that category 1 year later.

CONCLUSIONS

LVEF follows a normal distribution and has considerable variability over time, which may impose a risk for underuse of efficient treatment. The heart failure with mildly reduced ejection fraction category is especially inconstant. Assumptions of a patient's current LVEF should take this variability and the normal distribution of LVEF into account.

Artificial intelligence-assisted interpretation of systolic function by echocardiogram

OBJECTIVE

Precise and reliable echocardiographic assessment of left ventricular ejection fraction (LVEF) is needed for clinical decision-making. Recently, artificial intelligence (AI) models have been developed to estimate LVEF accurately. The aim of this study was to evaluate whether an AI model could estimate an expert read of LVEF and reduce the interinstitutional variability of level 1 readers with the AI-LVEF displayed on the echocardiographic screen.

METHODS

This prospective, multicentre echocardiographic study was conducted by five cardiologists of level 1 echocardiographic skill (minimum level of competency to interpret images) from different hospitals. Protocol 1: Visual LVEFs for the 48 cases were measured without input from the AI-LVEF. Protocol 2: the 48 cases were again shown to all readers with inclusion of AI-LVEF data. To assess the concordance and accuracy with or without AI-LVEF, each visual LVEF measurement was compared with an average of the estimates by five expert readers as a reference.

RESULTS

A good correlation was found between AI-LVEF and reference LVEF (r=0.90, p<0.001) from the expert readers. For the classification LVEF, the area under the curve was 0.95 on heart failure with preserved EF and 0.96 on heart failure reduced EF. For the precision, the SD was reduced from 6.1±2.3 to 2.5±0.9 (p<0.001) with AI-LVEF. For the accuracy, the root-mean squared error was improved from 7.5±3.1 to 5.6±3.2 (p=0.004) with AI-LVEF.

CONCLUSIONS

AI can assist with the interpretation of systolic function on an echocardiogram for level 1 readers from different institutions.

A graphical analysis of aspects contributing to the spreading of measurements of left ventricular function

The Simpson's method is the standard technique to determine left ventricular (LV) ejection fraction (EF) on echocardiography. The large inter-observer variability of measuring LVEF is well documented but not fully understood. A graphical analysis was used to elaborate what contributes to the inter-observer difference. Forty-two cardiologists (32 male, 39 ± 7 years) evaluated the LVEF using the Simpson's method on 15 different echocardiograms (2 and 4 chamber view (2CH/4CH)); the program did not show the result of EF to prevent a bias. End-diastolic (ED) and end-systolic (ES) frames were predefined ensuring measurement at the same time point of the cardiac cycles. After standardization of the LV contour, the differences of the individual contours compared to a reference contour were measured. Also, the spreading of lateral/medial mitral annulus contours and the apex were depicted. A significant spreading of LV-contours was seen with larger contours leading to higher EFs (p < 0.001). Experience did not influence the determination of LVEF. ED-volumes showed more spreading than ES-volumes ((3.6 mm (IQR: 2.6-4.0) vs. 3.4 mm (IQR: 2.8-3.8), p < 0.001). Also, the differences were larger for the 2CH compared to the 4CH (p < 0.001). Variability was significantly larger for lateral than septal wall (p < 0.001) as well as the anterior compared to the inferior wall (p < 0.001). There was a relevant scattering of the apex and medial/ lateral mitral annulus ring. There was a large variability of LV-volumes and LVEF as well as position of mitral valve ring and apex. There were global differences (apical 2CH or 4CH), regional aspects (LV walls) and temporal factors (ED vs. ES). Thus, multiple factors contributed to the large variability.Trial registration: The study was registered at "Netherlands Trial Register" ( www.trialregister.nl ; study number: NL5131).

Machine Learning-Enabled Fully Automated Assessment of Left Ventricular Volume, Ejection Fraction and Strain: Experience in Pediatric and Young Adult Echocardiography

BACKGROUND

Left ventricular (LV) volumes, ejection fraction (EF), and myocardial strain have been shown to be predictive of clinical and subclinical heart disease. Automation of LV functional assessment overcomes difficult technical challenges and complexities. We sought to assess whether a fully automated assessment of LV function could be reliably used in children and young adults.

METHODS

Fifty normal volunteers (22/28, female/male) were prospectively recruited for research echocardiography. LV volumes, EF, and strain were measured both manually and automatically. An experienced sonographer performed all the manual analysis and recorded the analysis timing. The fully automated analyses were accomplished by 5 groups of observers with different knowledge and medical background. AutoLV and AutoSTRAIN (TomTec) were employed for the fully automated LV analysis. The LV volumes, EF, strain, and analysis time were compared between manual and automated methods, and among the 5 groups of observers.

RESULTS

Software-determined endocardial border detection was achievable in all subjects. The analysis times of the experienced sonographer were significantly shorter for AutoLV and AutoSTRAIN than manual analyses (both p < 0.001). Strong correlations were seen between conventional EF and AutoLV (r = 0.8373), and between conventional three view global longitudinal strain (GLS) and AutoSTRAIN (r = 0.9766). The volumes from AutoLV and three view GLS from AutoSTRAIN had strong correlations among different observers regardless of level of expertise. EF from AutoLV analysis had moderately strong correlations among different observers.

CONCLUSION

Automated pediatric LV analysis is feasible in normal hearts. Machine learning-enabled image analysis saves time and produces results that are comparable to traditional methods.

Application of AI in cardiovascular multimodality imaging

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

Automated three-dimensional echocardiographic quantification for left ventricular volume and function in patients with hypertrophic cardiomyopathy

BACKGROUND

Accurate, reproducible, noninvasive determination of left ventricular (LV) volumes and ejection fraction (EF) is important for clinical assessment, selection of therapy, and serial monitoring of patients with hypertrophic cardiomyopathy (HCM). Current clinical Two-dimensional echocardiography (2DE) may cause inaccurate measurements in patients with HCM because of their asymmetric ventricles and limitations of 2DE technology. Three-dimensional echocardiography (3DE) have demonstrated significantly greater accuracy. However, the time-consuming workflow limits the clinical utility of 3DE.

AIM

We aim to compare the performance of a novel automated 3DE system (HeartModel, Philips Healthcare) with 2DE in a group of patients with HCM. Cardiac magnetic resonance (CMR) was reference standard.

METHODS

Fifty-three patients with HCM were examined by automated 3DE (3DEA), two-dimensional biplane Simpson's method (2DBP), manual 3DE method, and CMR, respectively. For patients with poor automated quantification, manual correction was performed. The Pearson correlation coefficient and Bland-Altman analysis and paired Student t tests were used to assess inter-technique agreement.

RESULTS

3DEA measurements with contour editing correlate well with CMR and manual 2DE and 3DE measurements (r = .80-.96). The analysis time of 3DEA was shorter than that of 2DBP (3DEA, 141 ± 15s; 2DBP, 174 ± 17 s). Inter-observer variability was reduced significantly with use of 3DEA.

CONCLUSION

Compared with current clinical 2DBP method, the analysis time of automated 3DE was much shorter with the added benefit of enhanced accuracy and reproducibility. Patients with asymmetric chamber may rely more on the timesaving automated 3DE quantification in the future.

Clinical validation of an artificial intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in real time by a novel handheld ultrasound device

Aims

We sought to evaluate the reliability and diagnostic accuracy of a novel handheld ultrasound device (HUD) with artificial intelligence (AI) assisted algorithm to automatically calculate ejection fraction (autoEF) in a real-world patient population.

Methods and results

We studied 100 consecutive patients (57 ± 15 years old, 61% male), including 38 with abnormal left ventricular (LV) function [LV ejection fraction (LVEF) < 50%]. The autoEF results acquired using the HUD were independently compared with manually traced biplane Simpson's rule measurements on cart-based systems to assess method agreement using intra-class correlation coefficient (ICC), linear regression analysis, and Bland-Altman analysis. The diagnostic accuracy for the detection of LVEF <50% was also calculated. Test-retest reliability of measured EF by the HUD was assessed by calculating the ICC and the minimal detectable change (MDC). The ICC, linear regression analysis, and Bland-Altman analysis revealed good agreement between autoEF and reference manual EF (ICC = 0.85; r = 0.87, P < 0.001; mean bias -1.42% with limits of agreement 14.5%, respectively). Detection of abnormal LV function (EF < 50%) by autoEF algorithm was feasible with sensitivity 90% (95% CI 75-97%), specificity 87% (95% CI 76-94%), PPV 81% (95% CI 66-91%), NPV 93% (95% CI 83-98%), and a total diagnostic accuracy of 88%. Test-retest reliability was excellent (ICC = 0.91, P < 0.001; r = 0.91, P < 0.001; mean difference ± SD: 0.54% ± 5.27%, P = 0.308) and MDC for LVEF measurement by autoEF was calculated at 4.38%.

Conclusion

Use of a novel HUD with AI-enabled capabilities provided similar LVEF results with those derived by manual biplane Simpson's method on cart-based systems and shows clinical potential.

Analysis of Differences in Assessment of Left Ventricular Function on Echocardiography and Nuclear Perfusion Imaging

Two widely used methods for left ventricular (LV) ejection fraction (EF) determination, echocardiography (echo) and gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), often have wide limits of agreement. Factors influencing discrepancies between core laboratory echo and MPI LVEF determinations were examined in a large series of heart failure (HF) subjects and normal controls. 879 HF and 101 control subjects had core lab analyses of echo and MPI (mean time between procedures 7-8 days). LVEF differences were analyzed using one-way analysis of variance and Bland-Altman plots. Relationships between LVEF differences and patient characteristics and outcome endpoints (mortality and arrhythmias) were explored with logistic regression, Cox proportional hazards models, and Kaplan-Meier survival analyses. There was a systematic difference between the 2 modalities; echo LVEF was higher with more severe LV dysfunction, MPI LVEF higher when systolic function was normal. LVEF results were within ±5% in only 37% of HF and 23% of control subjects. Considering discordance around the LVEF threshold 35%, there was disagreement between the 2 methods in 305 HF subjects (35%). Male gender (odds ratio (OR) = 0.200), atrial fibrillation (OR = 2.314), higher body mass index (OR = 1.051) and lower LV end-diastolic volume (OR = 0.985) were the strongest predictors of methodologic discordance. Cardiac event rates were highest if both LVEF values were ≤35% and lowest when both LVEF values were >35%. In conclusion, substantial disagreements between LVEF results by echo and MPI are common. HF patients with LVEF ≤35% by both techniques have the highest 2-year event risk.

Improvement in left ventricular ejection fraction after pharmacological up-titration in new-onset heart failure with reduced ejection fraction

Objective

Recent studies have reported suboptimal up-titration of heart failure (HF) therapies in patients with heart failure and a reduced ejection fraction (HFrEF). Here, we report on the achieved doses after nurse-led up-titration, reasons for not achieving the target dose, subsequent changes in left ventricular ejection fraction (LVEF), and mortality.

Methods

From 2012 to 2018, 378 HFrEF patients with a recent (< 3 months) diagnosis of HF were referred to a specialised HF-nurse led clinic for protocolised up-titration of guideline-directed medical therapy (GDMT). The achieved doses of GDMT at 9 months were recorded, as well as reasons for not achieving the optimal dose in all patients. Echocardiography was performed at baseline and after up-titration in 278 patients.

Results

Of 345 HFrEF patients with a follow-up visit after 9 months, 69% reached ≥ 50% of the recommended dose of renin-angiotensin-system (RAS) inhibitors, 73% reached ≥ 50% of the recommended dose of beta-blockers and 77% reached ≥ 50% of the recommended dose of mineralocorticoid receptor antagonists. The main reasons for not reaching the target dose were hypotension (RAS inhibitors and beta-blockers), bradycardia (beta-blockers) and renal dysfunction (RAS inhibitors). During a median follow-up of 9 months, mean LVEF increased from 27.6% at baseline to 38.8% at follow-up. Each 5% increase in LVEF was associated with an adjusted hazard ratio of 0.84 (0.75–0.94, p = 0.002) for mortality and 0.85 (0.78–0.94, p = 0.001) for the combined endpoint of mortality and/or HF hospitalisation after a mean follow-up of 3.3 years.

Conclusions

This study shows that protocolised up-titration in a nurse-led HF clinic leads to high doses of GDMT and improvement of LVEF in patients with new-onset HFrEF.

Supplementary Information

The online version of this article (10.1007/s12471-021-01591-6) contains supplementary material, which is available to authorized users.

Preoperative heart failure worsens outcome after aortic valve replacement irrespective of left ventricular ejection fraction

BACKGROUND

Left ventricular ejection fraction (LVEF) affects outcome of valve replacement (AVR) in aortic stenosis (AS). The study aim was to investigate the prognostic importance of concomitant cardiovascular disease in relation to preoperative LVEF.

METHODS AND RESULTS

All adult patients undergoing AVR due to AS 2008-2014 in a national register for heart diseases were included. All-cause mortality and hospitalization for heart failure during follow-up after AVR, stratified by preserved or reduced LVEF (=50%), was derived from national patient registers and analyzed by Cox regression.During the study period 10,406 patients, median age 73 years, a median follow-up of 35 months were identified. Preserved LVEF was present in 7,512 (72.2%). Among them 647 (8.6%) had a history of heart failure (HF) and 1,099 (14.6%) atrial fibrillation (AF) before intervention. Preoperative HF was associated with higher mortality irrespective of preserved or reduced LVEF: Hazard Ratio (HR) 1.64 (95% C.I. 1.35 -1.99) and 1.58 (95% C.I. 1.30 -1.92). Prior AF was associated with a higher risk of mortality in patients with preserved but not in those with reduced LVEF: HR 1.62 (95% C.I. 1.36 -1.92) and 1.05 (95% C.I. 0.86 -1.28). Irrespective of LVEF preoperative HF and AF were associated with an increased risk of postoperative heart failure hospitalization.

CONCLUSION

In patients planned for AVR, a history of HF or AF, irrespective of LVEF, worsens the postoperative prognosis. HF and AF can be seen as markers of myocardial fibrosis not necessarily discovered by LVEF and the merely use of it, besides symptoms, for timing of AVR seems suboptimal.

Cardiovascular Outcomes with Sacubitril-Valsartan in Heart Failure: Emerging Clinical Data

One of the defining features of heart failure (HF) is neurohormonal activation. The renin-angiotensin-aldosterone-system (RAAS) and sympathetic nervous system (SNS) cause vasoconstriction and fluid retention and, in response, the secretion of natriuretic peptides (NPs) from volume and pressure-overloaded myocardium promotes vasodilation and diuresis. Inhibition of the RAAS with either angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) has been the cornerstone of medical treatment for HF with a reduced ejection fraction (HFrEF) but, until recently, it was unclear how the beneficial effects of NPs may be augmented in patients with HF. Neprilysin, a metalloproteinase widely distributed throughout the body, plays a role in degrading the gross excess of circulating NPs in patients with HF. Early studies of neprilysin inhibition suggested possible physiological benefits. In 2014, the PARADIGM-HF trial found that sacubitril-valsartan, a combination of the ARB valsartan, and the neprilysin inhibitor sacubitril, was superior to enalapril in patients with HFrEF, reducing the relative risk of cardiovascular (CV) death or first hospitalisation with HF by 20%. Almost half of the patients with HF symptoms have a “preserved” ejection fraction (HFpEF); however, the PARAGON-HF study found that sacubitril-valsartan in patients with LVEF ≥45% had no effect on CV death or first and recurrent hospitalisations with HF compared to valsartan. Guidelines across the world have changed to include sacubitril-valsartan for patients with HFrEF yet, nearly 6 years after PARADIGM-HF, there is still uncertainty as to when and in whom sacubitril-valsartan should be started. Furthermore, there may yet be subsets of patients with HFpEF who might benefit from treatment with sacubitril-valsartan. This review will describe the mechanisms behind the outcome benefit of sacubitril-valsartan in patients with HFrEF and to consider its future role in the management of patients with HF.

Decline in Left Ventricular Ejection Fraction During Follow-Up in Patients With Severe Aortic Stenosis

OBJECTIVES

The aim of this study was to investigate the prognostic impact of the decline in left ventricular ejection fraction (LVEF) at 1-year follow-up in patients with severe aortic stenosis (AS) managed conservatively.

BACKGROUND

No previous study has explored the association between LVEF decline during follow-up and clinical outcomes in patients with severe AS.

METHODS

Among 3,815 patients with severe AS enrolled in the multicenter CURRENT AS (Contemporary Outcomes After Surgery and Medical Treatment in Patients With Severe Aortic Stenosis) registry in Japan, 839 conservatively managed patients who underwent echocardiography at 1-year follow-up were analyzed. The primary outcome measure was a composite of AS-related deaths and hospitalization for heart failure.

RESULTS

There were 91 patients (10.8%) with >10% declines in LVEF and 748 patients (89.2%) without declines. Left ventricular dimensions and the prevalence of valve regurgitation and atrial fibrillation or flutter significantly increased in the group with declines in LVEF. The cumulative 3-year incidence of the primary outcome measure was significantly higher in the group with declines in LVEF than in the group with no decline (39.5% vs. 26.5%; p < 0.001). After adjusting for confounders, the excess risk of decline in LVEF over no decline for the primary outcome measure remained significant (hazard ratio: 1.98; 95% confidence interval: 1.29 to 3.06). When stratified by LVEF at index echocardiography (≥70%, 60% to 69%, and <60%), the risk of decline in LVEF on the primary outcome was consistently seen in all the subgroups, without any interaction (p = 0.77).

CONCLUSIONS

Patients with severe AS with >10% declines in LVEF at 1 year after diagnosis had worse AS-related clinical outcomes than those without declines in LVEF under conservative management. (Contemporary Outcomes After Surgery and Medical Treatment in Patients With Severe Aortic Stenosis Registry; UMIN000012140).

Radiomics in Echocardiography: Deep Learning and Echocardiographic Analysis

PURPOSE OF REVIEW

Recent development in artificial intelligence (AI) for cardiovascular imaging analysis, involving deep learning, is the start of a new phase in the research field. We review the current state of AI in cardiovascular field and discuss about its potential to improve clinical workflows and accuracy of diagnosis.

RECENT FINDINGS

In the AI cardiovascular imaging field, there are many applications involving efficient image reconstruction, patient triage, and support for clinical decisions. These tools have a role to support repetitive clinical tasks. Although they will be powerful in some situations, these applications may have new potential in the hands of echo cardiologists, assisting but not replacing the human observer. We believe AI has the potential to improve the quality of echocardiography. Someday AI may be incorporated into the daily clinical setting, being an instrumental tool for cardiologists dealing with cardiovascular diseases.

Heart failure with mid-range ejection fraction: a distinct clinical entity? Insights from the Trial of Intensified versus standard Medical therapy in Elderly patients with Congestive Heart Failure (TIME-CHF)

AIMS

While the conditions of heart failure (HF) with reduced (HFrEF, LVEF < 40%) and preserved (HFpEF, LVEF ≥ 50%) left ventricular ejection fraction (LVEF) are well characterized, it is unknown whether patients with HF and mid-range LVEF (HFmrEF, LVEF 40-49%) have to be regarded as a separate clinical entity. The aim of this study was to characterize these three populations and to compare outcome and response to therapy.

METHODS AND RESULTS

The analysis was based on the Trial of Intensified versus standard Medical therapy in Elderly patients with Congestive Heart Failure (TIME-CHF) comprising a population with established HF including the whole spectrum of LVEF. Of the 622 patients, 108 (17%) were classified as having HFmrEF. This group was in general found to be 'intermediate' regarding clinical characteristics with a comparable and high burden of comorbidities and equally impaired quality of life but was more likely to have coronary artery disease as compared with the HFpEF group. During a median follow-up of 794 days, mortality was 39.7% without significant differences between groups. N-terminal pro-B-type natriuretic peptide (NT-proBNP)-guided as compared with standard therapy resulted in improved survival free of HF hospitalizations in HFrEF and HFmrEF, but not in HFpEF.

CONCLUSION

Although the 'intermediate' clinical profile of HFmrEF between HFrEF and HFpEF would support the conclusion that HFmrEF is a distinct clinical entity, we hypothesize that HFmrEF has to be categorized as HFrEF because of the high prevalence of coronary artery disease and the similar benefit of NT-proBNP-guided therapy in HFrEF and HFmrEF, in contrast to HFpEF.

B-type natriuretic peptide-guided treatment for heart failure

BACKGROUND

Heart failure is a condition in which the heart does not pump enough blood to meet all the needs of the body. Symptoms of heart failure include breathlessness, fatigue and fluid retention. Outcomes for patients with heart failure are highly variable; however on average, these patients have a poor prognosis. Prognosis can be improved with early diagnosis and appropriate use of medical treatment, use of devices and transplantation. Patients with heart failure are high users of healthcare resources, not only due to drug and device treatments, but due to high costs of hospitalisation care. B-type natriuretic peptide levels are already used as biomarkers for diagnosis and prognosis of heart failure, but could offer to clinicians a possible tool to guide drug treatment. This could optimise drug management in heart failure patients whilst allaying concerns over potential side effects due to drug intolerance.

OBJECTIVES

To assess whether treatment guided by serial BNP or NT-proBNP (collectively referred to as NP) monitoring improves outcomes compared with treatment guided by clinical assessment alone.

SEARCH METHODS

Searches were conducted up to 15 March 2016 in the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library; MEDLINE (OVID), Embase (OVID), the Database of Abstracts of Reviews of Effects (DARE) and the NHS Economic Evaluation Database in the Cochrane Library. Searches were also conducted in the Science Citation Index Expanded, the Conference Proceedings Citation Index on Web of Science (Thomson Reuters), World Health Organization International Clinical Trials Registry and ClinicalTrials.gov. We applied no date or language restrictions.

SELECTION CRITERIA

We included randomised controlled trials of NP-guided treatment of heart failure versus treatment guided by clinical assessment alone with no restriction on follow-up. Adults treated for heart failure, in both in-hospital and out-of-hospital settings, and trials reporting a clinical outcome were included.

DATA COLLECTION AND ANALYSIS

Two review authors independently selected studies for inclusion, extracted data and evaluated risk of bias. Risk ratios (RR) were calculated for dichotomous data, and pooled mean differences (MD) (with 95% confidence intervals (CI)) were calculated for continuous data. We contacted trial authors to obtain missing data. Using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, we assessed the quality of the evidence and GRADE profiler (GRADEPRO) was used to import data from Review Manager to create a 'Summary of findings' table.

MAIN RESULTS

We included 18 randomised controlled trials with 3660 participants (range of mean age: 57 to 80 years) comparing NP-guided treatment with clinical assessment alone. The evidence for all-cause mortality using NP-guided treatment showed uncertainty (RR 0.87, 95% CI 0.76 to 1.01; patients = 3169; studies = 15; low quality of the evidence), and for heart failure mortality (RR 0.84, 95% CI 0.54 to 1.30; patients = 853; studies = 6; low quality of evidence).The evidence suggested heart failure admission was reduced by NP-guided treatment (38% versus 26%, RR 0.70, 95% CI 0.61 to 0.80; patients = 1928; studies = 10; low quality of evidence), but the evidence showed uncertainty for all-cause admission (57% versus 53%, RR 0.93, 95% CI 0.84 to 1.03; patients = 1142; studies = 6; low quality of evidence).Six studies reported on adverse events, however the results could not be pooled (patients = 1144; low quality of evidence). Only four studies provided cost of treatment results, three of these studies reported a lower cost for NP-guided treatment, whilst one reported a higher cost (results were not pooled; patients = 931, low quality of evidence). The evidence showed uncertainty for quality of life data (MD -0.03, 95% CI -1.18 to 1.13; patients = 1812; studies = 8; very low quality of evidence).We completed a 'Risk of bias' assessment for all studies. The impact of risk of bias from lack of blinding of outcome assessment and high attrition levels was examined by restricting analyses to only low 'Risk of bias' studies.

AUTHORS' CONCLUSIONS

In patients with heart failure low-quality evidence showed a reduction in heart failure admission with NP-guided treatment while low-quality evidence showed uncertainty in the effect of NP-guided treatment for all-cause mortality, heart failure mortality, and all-cause admission. Uncertainty in the effect was further shown by very low-quality evidence for patient's quality of life. The evidence for adverse events and cost of treatment was low quality and we were unable to pool results.

Echocardiography and cardiac resynchronisation therapy, friends or foes?

Echocardiography is used in cardiac resynchronisation therapy (CRT) to assess cardiac function, and in particular left ventricular (LV) volumetric status, and prediction of response. Despite its widespread applicability, LV volumes determined by echocardiography have inherent measurement errors, interobserver and intraobserver variability, and discrepancies with the gold standard magnetic resonance imaging. Echocardiographic predictors of CRT response are based on mechanical dyssynchrony. However, parameters are mainly tested in single-centre studies or lack feasibility. Speckle tracking echocardiography can guide LV lead placement, improving volumetric response and clinical outcome by guiding lead positioning towards the latest contracting segment. Results on optimisation of CRT device settings using echocardiographic indices have so far been rather disappointing, as results suffer from noise. Defining response by echocardiography seems valid, although re-assessment after 6 months is advisable, as patients can show both continuous improvement as well as deterioration after the initial response. Three-dimensional echocardiography is interesting for future implications, as it can determine volume, dyssynchrony and viability in a single recording, although image quality needs to be adequate. Deformation patterns from the septum and the derived parameters are promising, although validation in a multicentre trial is required. We conclude that echocardiography has a pivotal role in CRT, although clinicians should know its shortcomings.

Heart failure in nursing home residents; a cross-sectional study to determine the prevalence and clinical characteristics

Background

Heart failure (HF) is expected to be highly prevalent in nursing home residents, but precise figures are scarce. The aim of this study was to determine the prevalence of HF in nursing home residents and to get insight in the clinical characteristics of residents with HF.

Methods

The study followed a multi-centre cross-sectional design.

Nursing home residents (n = 501) in the southern part of the Netherlands aged over 65 years and receiving long-term somatic or psychogeriatric care were included in the study. The diagnosis of HF and related characteristics were based on data collected from actual clinical examinations (including history, physical examination, ECG, cardiac markers and echocardiography), patient records and questionnaires. A panel of two cardiologists and a geriatrician ultimately judged the data to diagnose HF.

Results

The overall prevalence of HF in nursing home residents was 33 %, of which 52 % had HF with preserved ejection fraction. The symptoms dyspnoea and oedema and a cardiac history were more common in residents with HF. Diabetes mellitus, chronic obstructive pulmonary disease (COPD) were also more prevalent in those with HF. Residents with HF had a higher score on the Mini Mental State Examination. 54 % of those with HF where not known before, and in 31 % with a history of HF, this diagnosis was not confirmed by the expert panel.

Conclusion

This study shows that HF is highly prevalent in nursing home residents with many unknown or falsely diagnosed with HF. Equal number of HF patients had reduced and preserved left-ventricular ejection fraction.

Trial registration

The Netherlands National Trial Register NTR2663 (27-12-2010)

Global longitudinal strain as a potential prognostic marker in patients with chronic heart failure and systolic dysfunction

INTRODUCTION AND OBJECTIVE

The prognostic value of myocardium deformation measurements in chronic heart failure (CHF) is still poorly addressed. The purpose of this study was to evaluate the correlation of left ventricular (LV) global longitudinal strain (GLS) with clinical and prognostic indicators in patients with CHF and systolic dysfunction.

METHODS

Ambulatory patients with CHF and LV ejection fraction (LVEF) <45% were studied by two-dimensional and Doppler transthoracic echocardiogram with assessment of GLS. An indication of prognostic status was obtained by the Seattle Heart Failure Model (SHFM) prognostic estimates for life expectancy.

RESULTS

We included 54 CHF patients (mean age 55±12 years; 80% male). GLS was significantly correlated with NYHA functional class (r=0.41, p=0.002), BNP levels (r=0.47, p=0.001), LVEF (r=-0.69, p<0.001) and LV end-diastolic pressure, assessed by E/e' ratio (r=0.35, p<0.014) and left atrial maximal volume index (r=0.57, p<0.001). A significant correlation was found between GLS and SHFM prognostic estimates for life expectancy (r=-0.41, p=0.002). The multivariate logistic regression analysis showed that GLS independently predicted an estimated life expectancy <10 years (OR 2.614 [95% CI 1.010-6.763]). The corresponding area under the ROC curve was 0.802 (0.653-0.951) and the best obtained threshold was -9.5 (80% sensitivity, 65% specificity, p=0.003).

CONCLUSIONS

GLS was strongly associated with a higher disease severity status and predicted a lower prognostic estimate for life expectancy.

Changes in BNP and QTc for prediction of sudden death in heart failure

Evaluation of: Vrtovec B, Knezevic I, Poglajen G, Sebesjen M, Okrajsek R, Haddad F. Relation of B-type natriuretic peptide level in heart failure to sudden cardiac death in patients with and without QT interval prolongation. Am. J. Cardiol. 111(6), 886–890 (2013). Guidelines recommend an implantable cardioverter defibrillator (ICD) for patients with chronic heart failure (HF) and left ventricular ejection fraction (LVEF) <35%, and New York Heart Association (NYHA) class II/III, despite optimal medical treatment. However, by this mode of patient selection, many patients receive an ICD but never use it. Therefore, additional clinical and laboratory parameters, including estimated glomerular filtration rate and B-type natriuretic petide (BNP), and ECG parameters such as the corrected QT-interval (QTc), have been suggested for a more refined assessment of the risk of sudden cardiac death (SCD). However, changes in these parameters over time may be even more informative for SCD prediction than single measures, but this had not been investigated so far. In the present paper, the authors assessed the association between changes in BNP and QTc during a 3-month period in 398 patients with advanced chronic HF (NYHA III/IV) and LVEF <40%. After a follow-up of 1 year, 20 patients had suffered SCD. Patients with a significant (≥10%) increase in BNP were more likely to have a significant (≥10%) increase in QTc and had a longer QTc at 3 months than those without. The risk of SCD did not differ between patients with and without a significant increase in BNP, but was higher in patients with a significant increase in QTc compared with those without. Among patients with an increase in BNP of ≥10%, those with an increase in QTc of ≥10% were several-fold more likely to experience SCD compared with those without, whereas there was no such association between the change in QTc and SCD among patients without an increase in BNP of ≥10%. Thus, this study showed that changes in QTc better predicted SCD than changes in BNP, and that a strategy using both a marker of heart failure severity and a marker of the propensity of the left ventricle for arrhythmia better predicted SCD than a single-marker strategy. Further studies are required to evaluate whether novel markers besides LVEF and NYHA class alone (e.g., biomarkers and cardiac MRI) will allow a more accurate selection of patients with chronic HF who need an ICD.