Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance.

BACKGROUND: Machine learning (ML) algorithms can accurately estimate left ventricular ejection fraction (LVEF) from echocardiography, but their performance on cardiac point-of-care ultrasound (POCUS) is not well understood. OBJECTIVES: We evaluate the performance of an ML model for estimation of LVEF on cardiac POCUS compared with Level III echocardiographers' interpretation and formal echo reported LVEF. METHODS: Clinicians at a tertiary care heart failure clinic prospectively scanned 138 participants using hand-carried devices. Video data were analyzed offline by an ML model for LVEF. We compared the ML model's performance with Level III echocardiographers' interpretation and echo reported LVEF. RESULTS: There were 138 participants scanned, yielding 1257 videos. The ML model generated LVEF predictions on 341 videos. We observed a good intraclass correlation (ICC) between the ML model's predictions and the reference standards (ICC = 0.77-0.84). When comparing LVEF estimates for randomized single POCUS videos, the ICC between the ML model and Level III echocardiographers' estimates was 0.772, and it was 0.778 for videos where quantitative LVEF was feasible. When the Level III echocardiographer reviewed all POCUS videos for a participant, the ICC improved to 0.794 and 0.843 when only accounting for studies that could be segmented. The ML model's LVEF estimates also correlated well with LVEF derived from formal echocardiogram reports (ICC = 0.798). CONCLUSION: Our results suggest that clinician-driven cardiac POCUS produces ML model LVEF estimates that correlate well with expert interpretation and echo reported LVEF.

Novel Magnetic Resonance Imaging Tools for Hypertrophic Cardiomyopathy Risk Stratification.

Hypertrophic cardiomyopathy (HCM) is a common genetic disorder with a well described risk of sudden cardiac death; however, risk stratification has remained a challenge. Recently, novel parameters in cardiac magnetic resonance imaging (CMR) have shown promise in helping to improve upon current risk stratification paradigms. In this manuscript, we have reviewed novel CMR risk markers and their utility in HCM. The results of the review showed that T1, extracellular volume, CMR feature tracking, and other miscellaneous novel CMR variables have the potential to improve sudden death risk stratification and may have additional roles in diagnosis and prognosis. The strengths and weaknesses of these imaging techniques, and their potential utility and implementation in HCM risk stratification are discussed.

Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

BACKGROUND: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data. METHODS: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm. The study was first interpreted by level III-trained echocardiography cardiologists as the gold standard for rhythm diagnosis based on ECG rhythm strip and imaging assessment, which was also verified with a 12-lead ECG around the time of the study. AP4 cines with three cardiac cycles were then extracted from these studies with the rhythm strip and Doppler information removed and introduced to the deep learning model ResNet(2+1)D with an 80:10:10 training-validation-test split ratio. RESULTS: 634 patient studies (1205 cines) were included. After training, the AI model achieved high accuracy on validation for detection of both AF and sinus rhythm (mean F1-score = 0.92; AUROC = 0.95). Performance was consistent on the test dataset (mean F1-score = 0.94, AUROC = 0.98) when using the cardiologist's assessment of the ECG rhythm strip as the gold standard, who had access to the full study and external ECG data, while the AI model did not. CONCLUSIONS: AF detection by AI on echocardiography without ECG appears accurate when compared to an echocardiography cardiologist's assessment of the ECG rhythm strip as the gold standard. This has potential clinical implications in point-of-care ultrasound and stroke risk stratification.

Cardiac Phenotyping of SARS-CoV-2 in British Columbia: A Prospective Echo Study With Strain Imaging.

BACKGROUND: There is limited data on the residual echocardiographic findings including strain analysis among post-coronavirus disease (COVID) patients. The aim of our study is to prospectively phenotype post-COVID patients. METHODS: All patients discharged following acute COVID infection were systematically followed in the post-COVID-19 Recovery Clinic at Vancouver General Hospital and St. Paul's Hospital. At 4-18 weeks post diagnosis, patients underwent comprehensive echocardiographic assessment. Left ventricular ejection fraction (LVEF) was assessed by 3D, 2D Biplane Simpson's, or visual estimate. LV global longitudinal strain (GLS) was measured using a vendor-independent 2D speckle-tracking software (TomTec). RESULTS: A total of 127 patients (53% female, mean age 58 years) were included in our analyses. At baseline, cardiac conditions were present in 58% of the patients (15% coronary artery disease, 4% heart failure, 44% hypertension, 10% atrial fibrillation) while the remainder were free of cardiac conditions. COVID-19 serious complications were present in 79% of the patients (76% pneumonia, 37% intensive care unit admission, 21% intubation, 1% myocarditis). Normal LVEF was seen in 96% of the cohort and 97% had normal right ventricular systolic function. A high proportion (53%) had abnormal LV GLS defined as < 18%. Average LV GLS of septal and inferior segments were lower compared to that of other segments. Among patients without pre-existing cardiac conditions, LVEF was abnormal in only 1.9%, but LV GLS was abnormal in 46% of the patients. CONCLUSIONS: Most post-COVID patients had normal LVEF at 4-18 weeks post diagnosis, but over half had abnormal LV GLS.

Machine learning derived echocardiographic image quality in patients with left ventricular systolic dysfunction: insights on the echo views of greatest image quality.

We sought to determine the cardiac ultrasound view of greatest quality using a machine learning (ML) approach on a cohort of transthoracic echocardiograms (TTE) with abnormal left ventricular (LV) systolic function. We utilize an ML model to determine the TTE view of highest quality when scanned by sonographers. A random sample of TTEs with reported LV dysfunction from 09/25/2017-01/15/2019 were downloaded from the regional database. Component video files were analyzed using ML models that jointly classified view and image quality. The model consisted of convolutional layers for extracting spatial features and Long Short-term Memory units to temporally aggregate the frame-wise spatial embeddings. We report the view-specific quality scores for each TTE. Pair-wise comparisons amongst views were performed with Wilcoxon signed-rank test. Of 1,145 TTEs analyzed by the ML model, 74.5% were from males and mean LV ejection fraction was 43.1 ± 9.9%. Maximum quality score was best for the apical 4 chamber (AP4) view (70.6 ± 13.9%, p<0.001 compared to all other views) and worst for the apical 2 chamber (AP2) view (60.4 ± 15.4%, p<0.001 for all views except parasternal short-axis view at mitral/papillary muscle level, PSAX M/PM). In TTEs scanned by professional sonographers, the view with greatest ML-derived quality was the AP4 view.

Quantification of pleural effusions by two-dimensional transthoracic echocardiography.

PURPOSE: There is lack of validated methods for quantifying the size of pleural effusion from standard transthoracic (TTE) windows. The purpose of this study is to determine whether pleural effusion (Peff) measured from routine two-dimensional (2D) TTE views correlate with chest radiograph (CXR). MATERIALS AND METHODS: We retrospectively identified all inpatients who underwent a TTE and CXR within 2 days in a large tertiary care center. Peff was measured on TTE from parasternal long axis (PLAX), apical four-chamber (A4C), and subcostal views and on CXR. Logistic regression models were used determine optimal cut points to predict moderate or greater Peff. RESULTS: In 200 patients (mean age 69.3 ± 14.3 years, 49.5% female), we found statistically significant associations between Peff size assessed by all TTE views and CXR, with weak to moderate correlation (PLAX length: 0.21 (95% CI [0.05, 0.35]); PLAX depth: 0.21 (95% CI [0.05, 0.35]); A4C left: 0.31 (95% CI [0.13, 0.46]); A4C right: 0.39 (95% CI [0.17, 0.57]); subcostal: 0.38 (95% CI [0.07, 0.61]). The best TTE thresholds for predicting moderate or greater left-sided Peff on CXR was PLAX length left > = 8.6 cm (sensitivity 78%, specificity 54%, PPV 26%, and NPV 92%). The best TTE thresholds for predicting moderate or greater right-sided Peff on CXR was A4C right > = 2.6 cm (sensitivity 87%, specificity 60%, PPV 37%, and NPV 94%). CONCLUSIONS: We identified statistically significant associations with Peff size measured on TTE and CXR. The predictive ability of TTE to identify moderate or large pleural effusion is limited.

Characterization of myocardial mechanics and its prognostic significance in patients with severe aortic stenosis undergoing aortic valve replacement.

Aims: Aortic stenosis (AS) induces characteristic changes in left ventricular (LV) mechanics that can be reversed after aortic valve replacement (AVR). We aimed to comprehensively characterize LV mechanics before and after AVR in patients with severe AS and identify predictors of short-term functional recovery and long-term survival. Methods and results: We prospectively performed comprehensive strain analysis by 2D speckle-tracking echocardiography in 88 patients with severe AS and LV ejection fraction ≥50% (mean age 71 ± 12 years, 42% female) prior to and within 7 days after AVR. Patients were followed for up to 5.2 years until death from any cause or last encounter. Within days after AVR, we observed an absolute increase in global longitudinal strain (GLS) (-16.0 ± 2.0% vs. -18.5 ± 2.1%, P<0.0001) and a decrease in apical rotation (10.5 ± 4.0° vs. 8.3 ± 2.8°, P = 0.0002) and peak systolic twist (18.2 ± 5.0° vs. 15.5 ± 3.8°, P = 0.0008). A baseline GLS is less negative than -16.2% was 90% sensitive and 67% specific in predicting a ≥ 20% relative increase in GLS. During a median follow-up of 3.8 years, a global circumferential systolic strain rate (GCSRs) less negative than -1.9% independently predicted lower survival. Conclusion: In patients with severe AS, a reversal in GLS, apical rotation, and peak systolic twist abnormalities towards normal occurs within days of AVR. Baseline GLS is the strongest predictor of GLS recovery but neither was associated with long-term survival. In contrast, abnormal baseline GCSRs are associated with worse outcomes.

A Novel Continuous Left Ventricular Diastolic Function Score Using Machine Learning.

BACKGROUND: Unlike left ventricular (LV) ejection fraction, which provides a precise, reliable, and prognostically valuable measure of systolic function, there is no single analogous measure of LV diastolic function. OBJECTIVES: We aimed to develop a continuous score to grade LV diastolic function using machine learning modeling of echocardiographic data. METHODS: Consecutive echo studies performed at a tertiary-care center between February 1, 2010, and March 31, 2016, were assessed, excluding studies containing features that would interfere with diastolic function assessment as well as studies in which 1 or more parameters within the contemporary diastolic function assessment algorithm were not reported. Diastolic function was graded based on 2016 American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) guidelines, excluding indeterminate studies. Machine learning models were trained (support vector machine [SVM], decision tree [DT], XGBoost [XGB], and dense neural network [DNN]) to classify studies within the training set by diastolic dysfunction severity, blinded to the ASE/EACVI classification. The DNN model was retrained to generate a regression model (R-DNN) to predict a continuous LV diastolic function score. RESULTS: A total of 28,986 studies were included; 23,188 studies were used to train the models, and 5,798 studies were used for validation. The models were able to reclassify studies with high agreement to the ASE/EACVI algorithm (SVM, 83%; DT, 100%; XGB, 100%; DNN, 98%). The continuous diastolic function score corresponded well with ASE/EACVI guidelines, with scores of 1.00 ± 0.01 for studies with normal function and 0.74 ± 0.05, 0.51 ± 0.06, and 0.27 ± 0.11 for mild, moderate, and severe diastolic dysfunction, respectively (mean ± 1 SD). A score of <0.91 predicted abnormal diastolic function (area under the receiver operator curve = 0.99), while a score of <0.65 predicted elevated filling pressure (area under the receiver operator curve = 0.99). CONCLUSIONS: Machine learning can assimilate echocardiographic data and generate an automated continuous diastolic function score that corresponds well with current diastolic function grading recommendations.

Incidentally identified left ventricular apical aneurysm in a patient with Fabry disease.

Fabry disease is a rare X-linked lysosomal storage disorder caused by a deficiency in the lysosomal enzyme, galactosidase A, that can result in a progressive increase in the left ventricle (LV) wall thickness from glycosphingolipid deposition leading to myocardial fibrosis, conduction abnormalities, arrhythmias, and heart failure. We present a case of a patient with advanced Fabry cardiomyopathy, in whom a small LV apical aneurysm was incidentally discovered on abdominal imaging, which could have easily evaded detection on standard transthoracic echocardiography. The LV apex should be thoroughly interrogated in patients with Fabry cardiomyopathy, as the finding of LV aneurysm could have important management implications with respect to the prevention of stroke and sudden cardiac death.

Relationship between enlarged cardiac silhouette on chest X-ray and left ventricular size on transthoracic echocardiography.

The diagnostic accuracy of the cardiothoracic ratio on chest X-ray to detect left ventricular (LV) enlargement has not been well defined despite its traditional association with cardiomegaly. We aimed to determine whether the cardiothoracic ratio can accurately predict LV enlargement based on indexed linear measurements of the LV on transthoracic echocardiography (TTE). We included consecutive patients who had a TTE and a posteroanterior chest X-ray performed within 90 days of each other at a tertiary care center. LV size was determined by measuring the LV end-diastolic dimension (LVEDD) and LV end-diastolic dimension indexed (LVEDDI) to body surface area. The cardiothoracic ratio was calculated by dividing the maximum transverse diameter of the cardiac silhouette by the maximum transverse diameter of the right and left lung boundaries. 173 patients were included in the study (mean age 68 ± 15 years, 49.1% female). Mean cardiothoracic ratio was 0.56 ± 0.09, and the mean LVEDD and indexed LVEDDI were of 47 ± 8.6 mm and dimension of 27 ± 4.5 mm/m2 respectively. There was no significant correlation between the cardiothoracic ratio measured on chest X-ray and either the LVEDD or LVEDDI measured on TTE (r = 0.011, p = 0.879; r = 0.122, p = 0.111). The ability of the cardiothoracic ratio to predict LV enlargement (defined as LVEDDI > 30 mm/m2) was not statistically significant. The cardiothoracic ratio on chest X-ray is not a predictor of LV enlargement based on indexed linear measurements of the LV by TTE.

Prevalence of left ventricular systolic dysfunction by single echocardiographic view: towards an evidence-based point of care cardiac ultrasound scanning protocol.

Limited views are often obtained in the setting of cardiac ultrasound, however, the likelihood of missing left ventricular (LV) dysfunction based on a single view is not known. We sought to determine the echo views that were least likely to miss LV systolic dysfunction in consecutive transthoracic echocardiograms (TTEs). Structured data from TTEs performed at 2 hospitals from September 25, 2017, to January 15, 2019, were screened. Studies of interest were those with reported LV dysfunction. Views evaluated were the parasternal long-axis (PLAX), parasternal-short axis at mitral (PSAX M), papillary muscle (PSAX PM), and apical (PSAX A) levels, apical 2 (AP2), apical 3 (AP3), and apical 4 (AP4) chamber views. The probability that a view contained at least 1 abnormal segment was determined and analyzed with McNemar's test for 21 adjusted pair-wise comparisons. There were 4102 TTE studies included for analysis. TTEs on males comprised 72.7% of studies with a mean LV ejection fraction of 42.8 ± 9.7%. The echo view with the greatest likelihood of encompassing an abnormal segment was the AP2 view with a prevalence of 93.4% (p < 0.001, compared to all other views). The PLAX view performed the worst with a prevalence of 82.5% (p < 0.015, compared to all other views). The best parasternal view for the detection of abnormality was the PSAX PM view at 90.4%. In conclusions, a single echo view will contain abnormal segments > 82% of the time in the setting of LV systolic dysfunction, with a prevalence of up to 93.4% in the apical windows.

Fabry Cardiomyopathy: Current Practice and Future Directions.

Fabry disease (FD) is an X-linked lysosomal storage disorder caused by mutations in the galactosidase A (GLA) gene that result in deficient galactosidase A enzyme and subsequent accumulation of glycosphingolipids throughout the body. The result is a multi-system disorder characterized by cutaneous, corneal, cardiac, renal, and neurological manifestations. Increased left ventricular wall thickness represents the predominant cardiac manifestation of FD. As the disease progresses, patients may develop arrhythmias, advanced conduction abnormalities, and heart failure. Cardiac biomarkers, point-of-care dried blood spot testing, and advanced imaging modalities including echocardiography with strain imaging and magnetic resonance imaging (MRI) with T1 mapping now allow us to detect Fabry cardiomyopathy much more effectively than in the past. While enzyme replacement therapy (ERT) has been the mainstay of treatment, several promising therapies are now in development, making early diagnosis of FD even more crucial. Ongoing initiatives involving artificial intelligence (AI)-empowered interpretation of echocardiographic images, point-of-care dried blood spot testing in the echocardiography laboratory, and widespread dissemination of point-of-care ultrasound devices to community practices to promote screening may lead to more timely diagnosis of FD. Fabry disease should no longer be considered a rare, untreatable disease, but one that can be effectively identified and treated at an early stage before the development of irreversible end-organ damage.

Medication use during COVID-19: Review of recent evidence.

OBJECTIVE: To keep health care providers, in response to the ongoing coronavirus disease 2019 (COVID-19) pandemic, informed about the medications that have been proposed to treat the disease and the evidence supporting their use. QUALITY OF EVIDENCE: A narrative review of medications most widely used to treat COVID-19 was conducted, outlining the best available evidence for each pharmacologic treatment to date. Searches were performed in PubMed, EMBASE, and MEDLINE using key words COVID-19 and treatment, as well as related terms. Relevant research studies conducted in human populations and cases specific to patients with COVID-19 were included, as were relevant hand-searched papers and reviews. Only articles in English and Chinese were included. MAIN MESSAGE: While current management of patients with COVID-19 largely involves supportive care, without a widely available vaccine, practitioners have also resorted to repurposing medications used for other indications. This has caused considerable controversy, as many of these treatments have limited clinical evidence supporting their use and therefore pose implications for patient safety, drug access, and public health. For instance, medications such as hydroxychloroquine and chloroquine, lopinavir-ritonavir, nonsteroidal anti-inflammatory drugs, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers gained widespread media attention owing to hype, misinformation, or misinterpretation of research evidence. CONCLUSION: Given the severity of the pandemic and the potential broad effects of implementing safe and effective treatment, this article provides a narrative review of the current evidence behind the most widely used medications to treat COVID-19 in order to enable health care practitioners to make informed decisions in the care of patients with this life-threatening disease.

Médicaments utilisés durant la COVID-19: Examen des données probantes récentes.

OBJECTIF: En réponse à la pandémie actuelle de maladie à coronavirus 2019 (COVID-19), garder les médecins au fait des médicaments qui ont été proposés pour combattre la maladie, et des données probantes à l'appui de leur utilisation. SOURCES D'INFORMATION: Une revue narrative des médicaments les plus fréquemment utilisés pour combattre la COVID-19 a été réalisée, afin de souligner les meilleures données probantes disponibles concernant chaque traitement pharmacologique jusqu'ici. Des recherches ont été effectuées sur PubMed, EMBASE et MEDLINE à l'aide des mots-clés anglais COVID-19 et treatment, ainsi que d'autres mots-clés connexes. Ont été inclus les études pertinentes menées auprès de populations humaines et des cas de patients atteints de la COVID-19, ainsi que les articles et revues relevés à la main. Seuls les articles rédigés en anglais et en chinois ont été retenus. MESSAGE PRINCIPAL: Alors que la prise en charge actuelle des patients atteints de la COVID-19 consiste principalement en soins de soutien, sans accès aux vaccins, les praticiens se sont tournés vers des médicaments utilisés dans d'autres indications. Cela a causé une grande controverse, puisque des données cliniques limitées étayaient l'utilisation de beaucoup de ces traitements, et cela pouvait se répercuter sur la sécurité du patient, l'accès aux médicaments et la santé publique. Par exemple, les médicaments tels que l'hydroxychloroquine et la chloroquine, le lopinavir-ritonavir, les anti-inflammatoires non stéroïdiens, les inhibiteurs de l'enzyme de conversion de l'angiotensine et les antagonistes des récepteurs de l'angiotensine ont capté l'attention des médias en raison de la médiatisation, de la mésinformation ou de la mauvaise interprétation des données de recherche. CONCLUSION: Vu la gravité de la pandémie et les vastes effets éventuels de l'adoption de traitements sûrs et efficaces, cet article se veut être une revue narrative des données probantes actuelles étayant les médicaments les plus utilisés pour le traitement de la COVID-19 afin de permettre aux professionnels de la santé de prendre des décisions éclairées en matière de soins pour les patients qui sont atteints de cette maladie potentiellement mortelle.

A new transseptal solution for enabling left atrial access of large delivery sheaths.

BACKGROUND: Transseptal access for large sheaths may be encumbered by tissue resistance against the sheath-dilator stepped interface. The ExpanSure Large Access Transseptal Dilator (Baylis Medical) is designed as a single introducer and dilation device with a smooth sheath-dilator transition to support transseptal puncture. It may facilitate ease and efficiency of interatrial crossing. METHODS: This study experimentally evaluated the crossing force of ExpanSure relative to a conventional 8.5 F Swartz SL1 transseptal sheath and dilator in a benchtop septum model. Its ability to reduce the subsequent crossing force of a 14 F WATCHMAN delivery sheath was also tested. The clinical use of ExpanSure, including procedure time, was then validated in a series of left atrial appendage closure (LAAC) procedures. RESULTS: In a benchtop septum model (N = 12), less peak force (1.90 ± 0.08 N vs. 2.36 ± 0.09 N; p < .001) and overall work (17.3 ± 1.2 mJ vs. 28.0 ± 1.9 mJ; p < .001) were required to advance ExpanSure relative to a conventional SL1 transseptal sheath and dilator system. Peak force (2.34 ± 0.24 N vs. 2.65 ± 0.21 N; p < .003) and overall work (28.5 ± 3.9 mJ vs. 35.4 ± 2.1 mJ; p < .001) to advance a WATCHMAN sheath were also significantly lower after using ExpanSure than after using a conventional transseptal system. In 19 LAAC procedures, ExpanSure crossed the septum smoothly and integrated readily, which enabled efficient procedure completion (mean total procedure time 37.6 ± 13.5 min), with 100% success and no procedure-related complications. CONCLUSION: Experimental force measurements, combined with early clinical experience using ExpanSure, suggest that the tapered design with smooth transition without dilator-sheath step-up and the larger diameter, both facilitated ease and efficiency of interatrial crossing.

Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Impact of the updated diastolic function guidelines in the real world.

BACKGROUND: Echocardiographic assessment of diastolic function is complex but can aid in the diagnosis of heart failure, particularly in patients with preserved ejection fraction. In 2016, the American Society of Echocardiography (ASE) and European Association of Cardiovascular Imaging (EACVI) published an updated algorithm for the evaluation of diastolic function. The objective of our study was to assess its impact on diastolic function assessment in a real-world cohort of echo studies. METHODS: We retrospectively identified 71,727 consecutive transthoracic echo studies performed at a tertiary care center between February 2010 and March 2016 in which diastolic function was reported based on the 2009 ASE Guidelines. We then programmed a software algorithm to assess diastolic function in these echo studies according to the 2016 ASE/EACVI Guidelines. RESULTS: When diastolic function assessment based on the 2009 guidelines was compared to that using the 2016 guidelines, there were significant differences in proportion of studies classified as normal (23% vs. 32%) or indeterminate (43% vs. 36%) function, and mild (23% vs. 23%), moderate (10% vs. 8%), or severe (1% vs. 2%) diastolic dysfunction, with poor agreement between the two methods (Kappa 0.323, 95% CI 0.318-0.328). Furthermore, within the subgroup of studies with preserved ejection fraction and no evidence of myocardial disease, there was significant reclassification from mild diastolic dysfunction to normal diastolic function. CONCLUSION: The updated guidelines result in significant differences in diastolic function interpretation in the real world. Our findings have important implications for the identification of patients with or at risk for heart failure.

Left Atrial Strain in Evaluation of Heart Failure with Preserved Ejection Fraction.

BACKGROUND: Patients with heart failure with preserved ejection fraction (HFpEF) may have elevated left ventricular filling pressure with exercise (LVFP-ex), despite normal LVFP at rest. The aim of this study was to assess the diagnostic value of resting left atrial strain (LAS) in detecting elevated LVFP-ex in patients with dyspnea evaluated on exercise stress echocardiography. METHODS: Two-dimensional speckle-tracking analysis for LAS was performed in 669 consecutive patients (mean age, 64 ± 14 years; 53% men) who underwent treadmill echocardiographic evaluation and had left ventricular ejection fractions ≥ 50%. Assessment of LVFP at rest LVFP-ex was based on the 2016 American Society of Echocardiography guidelines for diastolic function assessment. An E/e' ratio ≥ 15 after exercise is considered to indicate elevated LVFP-ex. A continuous diagnostic score of HFpEF was calculated on the basis of the European Society of Cardiology HFA-PEFF diagnostic algorithm. RESULTS: LASreservoir was lowest in patients with elevated LVFP at rest (n = 81) and lower in those with normal resting filling pressure who developed elevated LVFP-ex (n = 108) compared with those who maintained normal LVFP-ex (29.0 ± 5.2% vs 33.1 ± 5.0% vs 39.3 ± 4.8%, P < .001). Lower LASreservoir was associated with worse exercise capacity as assessed by metabolic equivalents, exercise time, and functional aerobic capacity (multivariate-adjusted P values all < .05). In patients with normal or indeterminate LVFP at rest (n = 587), LASreservoir and preexercise HFA-PEFF score demonstrated areas under the curve of 0.82 and 0.7, respectively, for elevated LVFP-ex. There were 28% higher odds of developing elevated LVFP-ex per 1% decrease in LASreservoir (odds ratio, 0.78; 95% CI, 0.74-0.82). Among patients with intermediate scores (n = 461), 123 developed elevations in LVFP-ex and were classified as having HFpEF per the diagnostic algorithm. The addition of LASreservoir improved the diagnostic value of HFA-PEFF score for HFpEF (area under the curve increased from 0.71 to 0.80, P = .01). CONCLUSIONS: LASreservoir has potential to identify patients with intermediate scores for HFpEF who may develop elevated LVFP-ex only and is therefore a promising alternative to aid in diagnosis when exercise testing is not feasible.