ABSTRACT
BACKGROUND: Global longitudinal strain (GLS) is reported to be more reproducible and prognostic than ejection fraction. Automated, transparent methods may increase trust and uptake. OBJECTIVES: The authors developed open machine-learning-based GLS methodology and validate it using multiexpert consensus from the Unity UK Echocardiography AI Collaborative. METHODS: We trained a multi-image neural network (Unity-GLS) to identify annulus, apex, and endocardial curve on 6,819 apical 4-, 2-, and 3-chamber images. The external validation dataset comprised those 3 views from 100 echocardiograms. End-systolic and -diastolic frames were each labelled by 11 experts to form consensus tracings and points. They also ordered the echocardiograms by visual grading of longitudinal function. One expert calculated global strain using 2 proprietary packages. RESULTS: The median GLS, averaged across the 11 individual experts, was -16.1 (IQR: -19.3 to -12.5). Using each case's expert consensus measurement as the reference standard, individual expert measurements had a median absolute error of 2.00 GLS units. In comparison, the errors of the machine methods were: Unity-GLS 1.3, proprietary A 2.5, proprietary B 2.2. The correlations with the expert consensus values were for individual experts 0.85, Unity-GLS 0.91, proprietary A 0.73, proprietary B 0.79. Using the multiexpert visual ranking as the reference, individual expert strain measurements found a median rank correlation of 0.72, Unity-GLS 0.77, proprietary A 0.70, and proprietary B 0.74. CONCLUSIONS: Our open-source approach to calculating GLS agrees with experts' consensus as strongly as the individual expert measurements and proprietary machine solutions. The training data, code, and trained networks are freely available online.
Subject(s)
Consensus , Echocardiography , Image Interpretation, Computer-Assisted , Machine Learning , Neural Networks, Computer , Predictive Value of Tests , Humans , Biomechanical Phenomena , Datasets as Topic , Global Longitudinal Strain , Myocardial Contraction , Observer Variation , Reproducibility of Results , United Kingdom , Ventricular Dysfunction, Left/diagnostic imaging , Ventricular Dysfunction, Left/physiopathology , Ventricular Function, LeftABSTRACT
BACKGROUND: Accurate measurements from cardiac magnetic resonance (CMR) images require precise positioning of scan planes and elimination of movement artefacts from arrhythmia or breathing. Unidentified or incorrectly managed artefacts degrade image quality, invalidate clinical measurements and decrease diagnostic confidence. Currently, radiographers must manually inspect each acquired image to confirm diagnostic quality and decide whether reacquisition or a change in sequences is warranted. We aimed to develop an artificial intelligence (AI) to provide continuous quality scores across different quality domains, and from these, determine whether cines are clinically adequate, require replanning, or warrant a change in protocol. METHODS: A three-dimensional convolutional neural network was trained to predict cine quality graded on a continuous scale by a level 3 CMR expert, focusing separately on planning and movement artefacts. It incorporated four distinct output heads for the assessment of image quality in terms of: (a, b, c)2-, 3- and 4-chamber misplanning, and (d)long- and short-axis arrhythmia/breathing artefact. Back-propagation was selectively performed across these heads based on the labels present for each cine. Each image in the testing set was reported by four level 3 CMR experts, providing a consensus on clinical adequacy. The AI's assessment of image quality and ability to identify images requiring replanning or sequence changes were evaluated with Spearman's rho and the area under receiver operating characteristic (AUROC) respectively. RESULTS: A total of 1940 cines across 1387 studies were included. On the test set of 383 cines, AI-judged image quality correlated strongly with expert judgement, with Spearman's rho of 0.84, 0.84, 0.81 and 0.81 for 2-, 3- and 4-chamber planning quality and the extent of arrhythmia or breathing artefacts, respectively. The AI also showed high efficacy in flagging clinically inadequate cines (AUROC 0.88, 0.93, 0.93 and for identifying replanning of 2-, 3- and 4-chamber cines, and 0.90 for identifying movement artefacts). CONCLUSION: AI can assess distinct domains of CMR cine quality and provide continuous quality scores that correlate closely with a consensus of experts. These ratings could be used to identify cases where reacquisition is warranted, and guide corrective actions to optimise image quality, including replanning, prospective gating, or real-time imaging.
ABSTRACT
Impairment of left ventricular (LV) diastolic function is common amongst those with left heart disease and is associated with significant morbidity. Given that, in simple terms, the ventricle can only eject the volume with which it fills and that approximately one half of hospitalisations for heart failure (HF) are in those with normal/'preserved' left ventricular ejection fraction (HFpEF) (Bianco et al. in JACC Cardiovasc Imaging. 13:258-271, 2020. 10.1016/j.jcmg.2018.12.035), where abnormalities of ventricular filling are the cause of symptoms, it is clear that the assessment of left ventricular diastolic function (LVDF) is crucial for understanding global cardiac function and for identifying the wider effects of disease processes. Invasive methods of measuring LV relaxation and filling pressures are considered the gold-standard for investigating diastolic function. However, the high temporal resolution of trans-thoracic echocardiography (TTE) with widely validated and reproducible measures available at the patient's bedside and without the need for invasive procedures involving ionising radiation have established echocardiography as the primary imaging modality. The comprehensive assessment of LVDF is therefore a fundamental element of the standard TTE (Robinson et al. in Echo Res Pract7:G59-G93, 2020. 10.1530/ERP-20-0026). However, the echocardiographic assessment of diastolic function is complex. In the broadest and most basic terms, ventricular diastole comprises an early filling phase when blood is drawn, by suction, into the ventricle as it rapidly recoils and lengthens following the preceding systolic contraction and shortening. This is followed in late diastole by distension of the compliant LV when atrial contraction actively contributes to ventricular filling. When LVDF is normal, ventricular filling is achieved at low pressure both at rest and during exertion. However, this basic description merely summarises the complex physiology that enables the diastolic process and defines it according to the mechanical method by which the ventricles fill, overlooking the myocardial function, properties of chamber compliance and pressure differentials that determine the capacity for LV filling. Unlike ventricular systolic function where single parameters are utilised to define myocardial performance (LV ejection fraction (LVEF) and Global Longitudinal Strain (GLS)), the assessment of diastolic function relies on the interpretation of multiple myocardial and blood-flow velocity parameters, along with left atrial (LA) size and function, in order to diagnose the presence and degree of impairment. The echocardiographic assessment of diastolic function is therefore multifaceted and complex, requiring an algorithmic approach that incorporates parameters of myocardial relaxation/recoil, chamber compliance and function under variable loading conditions and the intra-cavity pressures under which these processes occur. This guideline outlines a structured approach to the assessment of diastolic function and includes recommendations for the assessment of LV relaxation and filling pressures. Non-routine echocardiographic measures are described alongside guidance for application in specific circumstances. Provocative methods for revealing increased filling pressure on exertion are described and novel and emerging modalities considered. For rapid access to the core recommendations of the diastolic guideline, a quick-reference guide (additional file 1) accompanies the main guideline document. This describes in very brief detail the diastolic investigation in each patient group and includes all algorithms and core reference tables.
ABSTRACT
BACKGROUND: In stable coronary artery disease, 30% to 60% of patients remain symptomatic despite successful revascularization. Perhaps not all symptoms reported by a patient with myocardial ischemia are, in fact, angina. OBJECTIVES: This study sought to determine whether independent symptom verification using a placebo-controlled ischemic stimulus could distinguish which patients achieve greatest symptom relief from percutaneous coronary intervention (PCI). METHODS: ORBITA-STAR was a multicenter, n-of-1, placebo-controlled study in patients undergoing single-vessel PCI for stable symptoms. Participants underwent 4 episodes (60 seconds each) of low-pressure balloon occlusion across their coronary stenosis, randomly paired with 4 episodes of placebo inflation. Following each episode, patients reported the similarity of the induced symptom in comparison with their usual symptom. The similarity score ranged from -10 (placebo replicated the symptom more than balloon occlusion) to +10 (balloon occlusion exactly replicated the symptom). The primary endpoint was the ability of the similarity score to predict symptom relief with PCI. RESULTS: Fifty-one patients were recruited, aged 62.9 ± 8.6 years. The median fractional flow reserve was 0.68 (Q1-Q3: 0.57-0.79), and the instantaneous wave-free ratio was 0.80 (Q1-Q3: 0.48-0.89). The median similarity score was 3 (Q1-Q3: 0.875-5.25). The similarity score was a strong predictor of symptom improvement following PCI: a patient with an upper quartile similarity score of 5.25 was significantly more likely to have lower angina frequency at follow-up (OR: 8.01; 95% credible interval: 2.39-15.86) than a patient with a lower quartile similarity score of 0.875 (OR: 1.31; 95% credible interval: 0.71-1.99), Pr(difference) >99.9%. CONCLUSIONS: Similarity score powerfully predicted symptom improvement from PCI. These data lay the foundation for independent symptom mapping to target PCI to those patients most likely to benefit. (Systematic Trial of Angina Assessment Before Revascularization [ORBITA-STAR]; NCT04280575).
Subject(s)
Angina Pectoris , Percutaneous Coronary Intervention , Humans , Male , Middle Aged , Female , Percutaneous Coronary Intervention/methods , Angina Pectoris/therapy , Aged , Treatment OutcomeABSTRACT
BACKGROUND: Placebo-controlled evidence from ORBITA-2 (Objective Randomised Blinded Investigation with Optimal Medical Therapy of Angioplasty in Stable Angina-2) found that percutaneous coronary intervention (PCI) in stable coronary artery disease with little or no antianginal medication relieved angina, but residual symptoms persisted in many patients. The reason for this was unclear. OBJECTIVES: This ORBITA-2 secondary analysis investigates the relationship between presenting symptoms and disease severity (anatomic, noninvasive, and invasive ischemia) and the ability of symptoms to predict the placebo-controlled efficacy of PCI. METHODS: Prerandomization symptom severity and nature were assessed using the ORBITA smartphone application and symptom and quality of life questionnaires including the World Health Organization Rose angina questionnaire (Rose). Disease severity was assessed using quantitative coronary angiography, stress echocardiography, fractional flow reserve, and instantaneous wave-free ratio. Bayesian ordinal regression was used. RESULTS: At prerandomization, the median number of daily angina episodes was 0.8 (Q1-Q3: 0.4-1.6), 64% had Rose angina, quantitative coronary angiography diameter stenosis was 61% (Q1-Q3: 49%-74%), stress echocardiography score was 1.0 (Q1-Q3: 0.0-2.7), fractional flow reserve was 0.63 (Q1-Q3: 0.49-0.75), and instantaneous wave-free ratio was 0.78 (Q1-Q3: 0.55-0.87). There was little relationship between symptom severity and nature and disease severity: angina symptom score with quantitative coronary angiography ordinal correlation coefficient: 0.06 (95% credible interval [CrI]: 0.00-0.08); stress echocardiography: 0.09 (95% CrI: 0.02-0.10); fractional flow reserve: 0.04 (95% CrI: -0.03 to 0.07); and instantaneous wave-free ratio: 0.04 (95% CrI: -0.01 to 0.07). However, Rose angina and guideline-based typical angina were strong predictors of placebo-controlled PCI efficacy (angina symptom score: OR: 1.9; 95% CrI: 1.6-2.1; probability of interaction [PrInteraction] = 99.9%; and OR: 1.8; 95% CrI: 1.6-2.1; PrInteraction = 99.9%, respectively). CONCLUSIONS: Although symptom severity and nature were poorly associated with disease severity, the nature of symptoms powerfully predicted the placebo-controlled efficacy of PCI.
Subject(s)
Coronary Artery Disease , Percutaneous Coronary Intervention , Humans , Male , Female , Percutaneous Coronary Intervention/methods , Middle Aged , Coronary Artery Disease/therapy , Coronary Artery Disease/diagnosis , Aged , Treatment Outcome , Coronary Angiography , Severity of Illness Index , Angina, Stable/therapy , Angina, Stable/diagnosis , Angina, Stable/physiopathology , Quality of LifeABSTRACT
BACKGROUND: The coronary sinus reducer (CSR) is proposed to reduce angina in patients with stable coronary artery disease by improving myocardial perfusion. We aimed to measure its efficacy, compared with placebo, on myocardial ischaemia reduction and symptom improvement. METHODS: ORBITA-COSMIC was a double-blind, randomised, placebo-controlled trial conducted at six UK hospitals. Patients aged 18 years or older with angina, stable coronary artery disease, ischaemia, and no further options for treatment were eligible. All patients completed a quantitative adenosine-stress perfusion cardiac magnetic resonance scan, symptom and quality-of-life questionnaires, and a treadmill exercise test before entering a 2-week symptom assessment phase, in which patients reported their angina symptoms using a smartphone application (ORBITA-app). Patients were randomly assigned (1:1) to receive either CSR or placebo. Both participants and investigators were masked to study assignment. After the CSR implantation or placebo procedure, patients entered a 6-month blinded follow-up phase in which they reported their daily symptoms in the ORBITA-app. At 6 months, all assessments were repeated. The primary outcome was myocardial blood flow in segments designated ischaemic at enrolment during the adenosine-stress perfusion cardiac magnetic resonance scan. The primary symptom outcome was the number of daily angina episodes. Analysis was done by intention-to-treat and followed Bayesian methodology. The study is registered with ClinicalTrials.gov, NCT04892537, and completed. FINDINGS: Between May 26, 2021, and June 28, 2023, 61 patients were enrolled, of whom 51 (44 [86%] male; seven [14%] female) were randomly assigned to either the CSR groupâ(n=25) or the placebo group (n=26). Of these, 50 patients were included in the intention-to-treat analysis (24 in the CSR group and 26 in the placebo group). 454 (57%) of 800 imaged cardiac segments were ischaemic at enrolment, with a median stress myocardial blood flow of 1·08 mL/min per g (IQR 0·77-1·41). Myocardial blood flow in ischaemic segments did not improve with CSR compared with placebo (difference 0·06 mL/min per g [95% CrI -0·09 to 0·20]; Pr(Benefit)=78·8%). The number of daily angina episodes was reduced with CSR compared with placebo (OR 1·40 [95% CrI 1·08 to 1·83]; Pr(Benefit)=99·4%). There were two CSR embolisation events in the CSR group, and no acute coronary syndrome events or deaths in either group. INTERPRETATION: ORBITA-COSMIC found no evidence that the CSR improved transmural myocardial perfusion, but the CSR did improve angina compared with placebo. These findings provide evidence for the use of CSR as a further antianginal option for patients with stable coronary artery disease. FUNDING: Medical Research Council, Imperial College Healthcare Charity, National Institute for Health and Care Research Imperial Biomedical Research Centre, St Mary's Coronary Flow Trust, British Heart Foundation.
Subject(s)
Angina, Stable , Coronary Artery Disease , Coronary Sinus , Percutaneous Coronary Intervention , Humans , Male , Female , Coronary Artery Disease/therapy , Angina, Stable/drug therapy , Coronary Sinus/diagnostic imaging , Bayes Theorem , Treatment Outcome , Percutaneous Coronary Intervention/adverse effects , Double-Blind Method , Ischemia , AdenosineABSTRACT
BACKGROUND: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward, but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artifact) or further acquisition of intersecting images, but this must take place before the scan finishes. Real-time quality assurance by humans is a complex task requiring training and experience, so being able to identify which images have an intermediate likelihood of LGE while the scan is ongoing, without the presence of an expert is of high value. This decision-support could prompt immediate image optimization or acquisition of supplementary images to confirm or refute the presence of genuine LGE. This could reduce ambiguity in reports. METHODS: Short-axis, phase-sensitive inversion recovery late gadolinium images were extracted from our clinical cardiac magnetic resonance (CMR) database and shuffled. Two, independent, blinded experts scored each individual slice for "LGE likelihood" on a visual analog scale, from 0 (absolute certainty of no LGE) to 100 (absolute certainty of LGE), with 50 representing clinical equipoise. The scored images were split into two classes-either "high certainty" of whether LGE was present or not, or "low certainty." The dataset was split into training, validation, and test sets (70:15:15). A deep learning binary classifier based on the EfficientNetV2 convolutional neural network architecture was trained to distinguish between these categories. Classifier performance on the test set was evaluated by calculating the accuracy, precision, recall, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Performance was also evaluated on an external test set of images from a different center. RESULTS: One thousand six hundred and forty-five images (from 272 patients) were labeled and split at the patient level into training (1151 images), validation (247 images), and test (247 images) sets for the deep learning binary classifier. Of these, 1208 images were "high certainty" (255 for LGE, 953 for no LGE), and 437 were "low certainty". An external test comprising 247 images from 41 patients from another center was also employed. After 100 epochs, the performance on the internal test set was accuracy = 0.94, recall = 0.80, precision = 0.97, F1-score = 0.87, and ROC AUC = 0.94. The classifier also performed robustly on the external test set (accuracy = 0.91, recall = 0.73, precision = 0.93, F1-score = 0.82, and ROC AUC = 0.91). These results were benchmarked against a reference inter-expert accuracy of 0.86. CONCLUSION: Deep learning shows potential to automate quality control of late gadolinium imaging in CMR. The ability to identify short-axis images with intermediate LGE likelihood in real-time may serve as a useful decision-support tool. This approach has the potential to guide immediate further imaging while the patient is still in the scanner, thereby reducing the frequency of recalls and inconclusive reports due to diagnostic indecision.
Subject(s)
Contrast Media , Deep Learning , Image Interpretation, Computer-Assisted , Predictive Value of Tests , Humans , Contrast Media/administration & dosage , Reproducibility of Results , Image Interpretation, Computer-Assisted/standards , Databases, Factual , Myocardium/pathology , Male , Female , Magnetic Resonance Imaging, Cine/standards , Middle Aged , Heart Diseases/diagnostic imaging , Quality Assurance, Health Care/standards , Observer Variation , Aged , Magnetic Resonance Imaging/standardsABSTRACT
BACKGROUND: Cardiovascular magnetic resonance (CMR) imaging is an important tool for evaluating the severity of aortic stenosis (AS), co-existing aortic disease, and concurrent myocardial abnormalities. Acquiring this additional information requires protocol adaptations and additional scanner time, but is not necessary for the majority of patients who do not have AS. We observed that the relative signal intensity of blood in the ascending aorta on a balanced steady state free precession (bSSFP) 3-chamber cine was often reduced in those with significant aortic stenosis. We investigated whether this effect could be quantified and used to predict AS severity in comparison to existing gold-standard measurements. METHODS: Multi-centre, multi-vendor retrospective analysis of patients with AS undergoing CMR and transthoracic echocardiography (TTE). Blood signal intensity was measured in a â¼1 cm2 region of interest (ROI) in the aorta and left ventricle (LV) in the 3-chamber bSSFP cine. Because signal intensity varied across patients and scanner vendors, a ratio of the mean signal intensity in the aorta ROI to the LV ROI (Ao:LV) was used. This ratio was compared using Pearson correlations against TTE parameters of AS severity: aortic valve peak velocity, mean pressure gradient and the dimensionless index. The study also assessed whether field strength (1.5 T vs. 3 T) and patient characteristics (presence of bicuspid aortic valves (BAV), dilated aortic root and low flow states) altered this signal relationship. RESULTS: 314 patients (median age 69 [IQR 57-77], 64% male) who had undergone both CMR and TTE were studied; 84 had severe AS, 78 had moderate AS, 66 had mild AS and 86 without AS were studied as a comparator group. The median time between CMR and TTE was 12 weeks (IQR 4-26). The Ao:LV ratio at 1.5 T strongly correlated with peak velocity (r = -0.796, p = 0.001), peak gradient (r = -0.772, p = 0.001) and dimensionless index (r = 0.743, p = 0.001). An Ao:LV ratio of < 0.86 was 84% sensitive and 82% specific for detecting AS of any severity and a ratio of 0.58 was 83% sensitive and 92% specific for severe AS. The ability of Ao:LV ratio to predict AS severity remained for patients with bicuspid aortic valves, dilated aortic root or low indexed stroke volume. The relationship between Ao:LV ratio and AS severity was weaker at 3 T. CONCLUSIONS: The Ao:LV ratio, derived from bSSFP 3-chamber cine images, shows a good correlation with existing measures of AS severity. It demonstrates utility at 1.5 T and offers an easily calculable metric that can be used at the time of scanning or automated to identify on an adaptive basis which patients benefit from dedicated imaging to assess which patients should have additional sequences to assess AS.
Subject(s)
Aortic Valve Stenosis , Aortic Valve , Magnetic Resonance Imaging, Cine , Predictive Value of Tests , Severity of Illness Index , Ventricular Function, Left , Humans , Aortic Valve Stenosis/diagnostic imaging , Aortic Valve Stenosis/physiopathology , Female , Male , Retrospective Studies , Aged , Middle Aged , Aortic Valve/diagnostic imaging , Aortic Valve/physiopathology , Aortic Valve/pathology , Aortic Valve/abnormalities , Reproducibility of Results , Aorta/diagnostic imaging , Aorta/physiopathology , Image Interpretation, Computer-Assisted , Heart Ventricles/diagnostic imaging , Heart Ventricles/physiopathology , Regional Blood Flow , United StatesABSTRACT
The coronary sinus Reducer (CSR) is an hourglass-shaped device which creates an artificial stenosis in the coronary sinus. Whilst placebo-controlled data show an improvement in angina, these results are unreplicated and are the subject of further confirmatory research. The mechanism of action of this unintuitive therapy is unknown. The Coronary Sinus Reducer Objective Impact on Symptoms, MRI Ischaemia, and Microvascular Resistance (ORBITA-COSMIC) trial is a randomised, placebo-controlled, double-blind trial investigating the efficacy of the CSR. Patients with (i) established epicardial coronary artery disease, (ii) angina on maximally tolerated antianginal medication, (iii) evidence of myocardial ischaemia and (iv) no further options for percutaneous coronary intervention or coronary artery bypass grafting will be enrolled. Upon enrolment, angina and quality-of-life questionnaires, treadmill exercise testing and quantitative stress perfusion cardiac magnetic resonance (CMR) imaging will be performed. Participants will record their symptoms daily on a smartphone application throughout the trial. After a 2-week symptom assessment phase, participants will be randomised in the cardiac catheterisation laboratory to CSR or a placebo procedure. After 6 months of blinded follow-up, all prerandomisation tests will be repeated. A prespecified subgroup will undergo invasive coronary physiology assessment at prerandomisation and follow-up. The primary outcome is stress myocardial blood flow on CMR. Secondary outcomes include angina frequency, quality of life and treadmill exercise time. (ClinicalTrials.gov: NCT04892537).
Subject(s)
Angina, Stable , Coronary Artery Disease , Coronary Sinus , Percutaneous Coronary Intervention , Humans , Angina, Stable/diagnosis , Quality of Life , Coronary Sinus/surgery , Treatment Outcome , Coronary Artery Disease/therapyABSTRACT
BACKGROUND: Percutaneous coronary intervention (PCI) is frequently performed to reduce the symptoms of stable angina. Whether PCI relieves angina more than a placebo procedure in patients who are not receiving antianginal medication remains unknown. METHODS: We conducted a double-blind, randomized, placebo-controlled trial of PCI in patients with stable angina. Patients stopped all antianginal medications and underwent a 2-week symptom assessment phase before randomization. Patients were then randomly assigned in a 1:1 ratio to undergo PCI or a placebo procedure and were followed for 12 weeks. The primary end point was the angina symptom score, which was calculated daily on the basis of the number of angina episodes that occurred on a given day, the number of antianginal medications prescribed on that day, and clinical events, including the occurrence of unblinding owing to unacceptable angina or acute coronary syndrome or death. Scores range from 0 to 79, with higher scores indicating worse health status with respect to angina. RESULTS: A total of 301 patients underwent randomization: 151 to the PCI group and 150 to the placebo group. The mean (±SD) age was 64±9 years, and 79% were men. Ischemia was present in one cardiac territory in 242 patients (80%), in two territories in 52 patients (17%), and in three territories in 7 patients (2%). In the target vessels, the median fractional flow reserve was 0.63 (interquartile range, 0.49 to 0.75), and the median instantaneous wave-free ratio was 0.78 (interquartile range, 0.55 to 0.87). At the 12-week follow-up, the mean angina symptom score was 2.9 in the PCI group and 5.6 in the placebo group (odds ratio, 2.21; 95% confidence interval, 1.41 to 3.47; P<0.001). One patient in the placebo group had unacceptable angina leading to unblinding. Acute coronary syndromes occurred in 4 patients in the PCI group and in 6 patients in the placebo group. CONCLUSIONS: Among patients with stable angina who were receiving little or no antianginal medication and had objective evidence of ischemia, PCI resulted in a lower angina symptom score than a placebo procedure, indicating a better health status with respect to angina. (Funded by the National Institute for Health and Care Research Imperial Biomedical Research Centre and others; ORBITA-2 ClinicalTrials.gov number, NCT03742050.).
Subject(s)
Angina, Stable , Percutaneous Coronary Intervention , Aged , Female , Humans , Male , Middle Aged , Acute Coronary Syndrome , Angina, Stable/drug therapy , Angina, Stable/surgery , Cardiovascular Agents/therapeutic use , Fractional Flow Reserve, Myocardial , Health Status , Percutaneous Coronary Intervention/methods , Treatment Outcome , Double-Blind Method , Myocardial IschemiaABSTRACT
AIMS: Left bundle branch pacing (LBBP) can deliver physiological left ventricular activation, but typically at the cost of delayed right ventricular (RV) activation. Right ventricular activation can be advanced through anodal capture, but there is uncertainty regarding the mechanism by which this is achieved, and it is not known whether this produces haemodynamic benefit. METHODS AND RESULTS: We recruited patients with LBBP leads in whom anodal capture eliminated the terminal R-wave in lead V1. Ventricular activation pattern, timing, and high-precision acute haemodynamic response were studied during LBBP with and without anodal capture. We recruited 21 patients with a mean age of 67 years, of whom 14 were males. We measured electrocardiogram timings and haemodynamics in all patients, and in 16, we also performed non-invasive mapping. Ventricular epicardial propagation maps demonstrated that RV septal myocardial capture, rather than right bundle capture, was the mechanism for earlier RV activation. With anodal capture, QRS duration and total ventricular activation times were shorter (116 ± 12 vs. 129 ± 14â ms, P < 0.01 and 83 ± 18 vs. 90 ± 15â ms, P = 0.01). This required higher outputs (3.6 ± 1.9 vs. 0.6 ± 0.2â V, P < 0.01) but without additional haemodynamic benefit (mean difference -0.2 ± 3.8â mmHg compared with pacing without anodal capture, P = 0.2). CONCLUSION: Left bundle branch pacing with anodal capture advances RV activation by stimulating the RV septal myocardium. However, this requires higher outputs and does not improve acute haemodynamics. Aiming for anodal capture may therefore not be necessary.
Subject(s)
Bundle of His , Cardiac Pacing, Artificial , Male , Humans , Aged , Female , Cardiac Pacing, Artificial/methods , Heart Conduction System , Hemodynamics , Heart Ventricles , Electrocardiography/methodsABSTRACT
Aims: Coronary flow reserve (CFR) assessment has proven clinical utility, but Doppler-based methods are sensitive to noise and operator bias, limiting their clinical applicability. The objective of the study is to expand the adoption of invasive Doppler CFR, through the development of artificial intelligence (AI) algorithms to automatically quantify coronary Doppler quality and track flow velocity. Methods and results: A neural network was trained on images extracted from coronary Doppler flow recordings to score signal quality and derive values for coronary flow velocity and CFR. The outputs were independently validated against expert consensus. Artificial intelligence successfully quantified Doppler signal quality, with high agreement with expert consensus (Spearman's rho: 0.94), and within individual experts. Artificial intelligence automatically tracked flow velocity with superior numerical agreement against experts, when compared with the current console algorithm [AI flow vs. expert flow bias -1.68 cm/s, 95% confidence interval (CI) -2.13 to -1.23 cm/s, P < 0.001 with limits of agreement (LOA) -4.03 to 0.68 cm/s; console flow vs. expert flow bias -2.63 cm/s, 95% CI -3.74 to -1.52, P < 0.001, 95% LOA -8.45 to -3.19 cm/s]. Artificial intelligence yielded more precise CFR values [median absolute difference (MAD) against expert CFR: 4.0% for AI and 7.4% for console]. Artificial intelligence tracked lower-quality Doppler signals with lower variability (MAD against expert CFR 8.3% for AI and 16.7% for console). Conclusion: An AI-based system, trained by experts and independently validated, could assign a quality score to Doppler traces and derive coronary flow velocity and CFR. By making Doppler CFR more automated, precise, and operator-independent, AI could expand the clinical applicability of coronary microvascular assessment.
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BACKGROUND: The use of left bundle branch area pacing (LBBAP) for bradycardia pacing and cardiac resynchronization is increasing, but implants are not always successful. We prospectively studied consecutive patients to determine whether septal scar contributes to implant failure. METHODS: Patients scheduled for bradycardia pacing or cardiac resynchronization therapy were prospectively enrolled. Recruited patients underwent preprocedural scar assessment by cardiac MRI with late gadolinium enhancement imaging. LBBAP was attempted using a lumenless lead (Medtronic 3830) via a transeptal approach. RESULTS: Thirty-five patients were recruited: 29 male, mean age 68 years, 10 ischemic, and 16 non-ischemic cardiomyopathy. Pacing indication was bradycardia in 26% and cardiac resynchronization in 74%. The lead was successfully deployed to the left ventricular septum in 30/35 (86%) and unsuccessful in the remaining 5/35 (14%). Septal late gadolinium enhancement was significantly less extensive in patients where left septal lead deployment was successful, compared those where it was unsuccessful (median 8%, IQR 2%-18% vs. median 54%, IQR 53%-57%, p < .001). CONCLUSIONS: The presence of septal scar appears to make it more challenging to deploy a lead to the left ventricular septum via the transeptal route. Additional implant tools or alternative approaches may be required in patients with extensive septal scar.
Subject(s)
Ventricular Septum , Humans , Male , Aged , Ventricular Septum/diagnostic imaging , Bradycardia , Cicatrix , Contrast Media , GadoliniumABSTRACT
Background: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. Methods: Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Results: After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's κ=0.77). Conclusions: We present proof of concept that, given the same clinician labelling effort, comparing multiple images side-by-side using a 'multiple-image-ranking' strategy achieves ground truth labels for DL more accurately than by classifying images individually. We demonstrate a potential clinical application: the automatic removal of unrequired CMR images. This leads to increased efficiency by focussing human and machine attention on images which are needed to answer clinical questions.
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BACKGROUND: Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospitalizations. Treatment optimization and admission avoidance rely on frequent symptom reviews and monitoring of vital signs. Remote monitoring (RM) aims to prevent admissions by facilitating early intervention, but the impact of noninvasive, smartphone-based RM of vital signs on secondary health care use and costs in the months after a new diagnosis of HFrEF is unknown. OBJECTIVE: The purpose of this study is to conduct a secondary care health use and health-economic evaluation for patients with HFrEF using smartphone-based noninvasive RM and compare it with matched controls receiving usual care without RM. METHODS: We conducted a retrospective study of 2 cohorts of newly diagnosed HFrEF patients, matched 1:1 for demographics, socioeconomic status, comorbidities, and HFrEF severity. They are (1) the RM group, with patients using the RM platform for >3 months and (2) the control group, with patients referred before RM was available who received usual heart failure care without RM. Emergency department (ED) attendance, hospital admissions, outpatient use, and the associated costs of this secondary care activity were extracted from the Discover data set for a 3-month period after diagnosis. Platform costs were added for the RM group. Secondary health care use and costs were analyzed using Kaplan-Meier event analysis and Cox proportional hazards modeling. RESULTS: A total of 146 patients (mean age 63 years; 42/146, 29% female) were included (73 in each group). The groups were well-matched for all baseline characteristics except hypertension (P=.03). RM was associated with a lower hazard of ED attendance (hazard ratio [HR] 0.43; P=.02) and unplanned admissions (HR 0.26; P=.02). There were no differences in elective admissions (HR 1.03, P=.96) or outpatient use (HR 1.40; P=.18) between the 2 groups. These differences were sustained by a univariate model controlling for hypertension. Over a 3-month period, secondary health care costs were approximately 4-fold lower in the RM group than the control group, despite the additional cost of RM itself (mean cost per patient GBP £465, US $581 vs GBP £1850, US $2313, respectively; P=.04). CONCLUSIONS: This retrospective cohort study shows that smartphone-based RM of vital signs is feasible for HFrEF. This type of RM was associated with an approximately 2-fold reduction in ED attendance and a 4-fold reduction in emergency admissions over just 3 months after a new diagnosis with HFrEF. Costs were significantly lower in the RM group without increasing outpatient demand. This type of RM could be adjunctive to standard care to reduce admissions, enabling other resources to help patients unable to use RM.
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BACKGROUND: Community-based management by heart failure specialist nurses (HFSNs) is key to improving self-care in heart failure with reduced ejection fraction. Remote monitoring (RM) can aid nurse-led management, but in the literature, user feedback evaluation is skewed in favor of the patient rather than nursing user experience. Furthermore, the ways in which different groups use the same RM platform at the same time are rarely directly compared in the literature. We present a balanced semantic analysis of user feedback from patient and nurse perspectives of Luscii, a smartphone-based RM strategy combining self-measurement of vital signs, instant messaging, and e-learning. OBJECTIVE: This study aims to (1) evaluate how patients and nurses use this type of RM (usage type), (2) evaluate patients' and nurses' user feedback on this type of RM (user experience), and (3) directly compare the usage type and user experience of patients and nurses using the same type of RM platform at the same time. METHODS: We performed a retrospective usage type and user experience evaluation of the RM platform from the perspective of both patients with heart failure with reduced ejection fraction and the HFSNs using the platform to manage them. We conducted semantic analysis of written patient feedback provided via the platform and a focus group of 6 HFSNs. Additionally, as an indirect measure of tablet adherence, self-measured vital signs (blood pressure, heart rate, and body mass) were extracted from the RM platform at onboarding and 3 months later. Paired 2-tailed t tests were used to evaluate differences between mean scores across the 2 timepoints. RESULTS: A total of 79 patients (mean age 62 years; 35%, 28/79 female) were included. Semantic analysis of usage type revealed extensive, bidirectional information exchange between patients and HFSNs using the platform. Semantic analysis of user experience demonstrates a range of positive and negative perspectives. Positive impacts included increased patient engagement, convenience for both user groups, and continuity of care. Negative impacts included information overload for patients and increased workload for nurses. After the patients used the platform for 3 months, they showed significant reductions in heart rate (P=.004) and blood pressure (P=.008) but not body mass (P=.97) compared with onboarding. CONCLUSIONS: Smartphone-based RM with messaging and e-learning facilitates bilateral information sharing between patients and nurses on a range of topics. Patient and nurse user experience is largely positive and symmetrical, but there are possible negative impacts on patient attention and nurse workload. We recommend RM providers involve patient and nurse users in platform development, including recognition of RM usage in nursing job plans.
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BACKGROUND: Patients hospitalized with COVID-19 suffer thrombotic complications. Risk factors for poor outcomes are shared with coronary artery disease. OBJECTIVES: To investigate the efficacy of an acute coronary syndrome regimen in patients hospitalized with COVID-19 and coronary disease risk factors. METHODS: A randomized controlled, open-label trial across acute hospitals (United Kingdom and Brazil) added aspirin, clopidogrel, low-dose rivaroxaban, atorvastatin, and omeprazole to standard care for 28 days. Primary efficacy and safety outcomes were 30-day mortality and bleeding. The key secondary outcome was a daily clinical status (at home, in hospital, on intensive therapy unit admission, or death). RESULTS: Three hundred twenty patients from 9 centers were randomized. The trial terminated early due to low recruitment. At 30 days, there was no significant difference in mortality (intervention vs control, 11.5% vs 15%; unadjusted odds ratio [OR], 0.73; 95% CI, 0.38-1.41; p = .355). Significant bleeds were infrequent and were not significantly different between the arms (intervention vs control, 1.9% vs 1.9%; p > .999). Using a Bayesian Markov longitudinal ordinal model, it was 93% probable that intervention arm participants were more likely to transition to a better clinical state each day (OR, 1.46; 95% credible interval [CrI], 0.88-2.37; Pr [beta > 0], 93%; adjusted OR, 1.50; 95% CrI, 0.91-2.45; Pr [beta > 0], 95%) and median time to discharge to home was 2 days shorter (95% CrI, -4 to 0; 2% probability that it was worse). CONCLUSION: Acute coronary syndrome treatment regimen was associated with a reduction in the length of hospital stay without an excess in major bleeding. A larger trial is needed to evaluate mortality.
Subject(s)
Acute Coronary Syndrome , COVID-19 , Humans , SARS-CoV-2 , Acute Coronary Syndrome/diagnosis , Acute Coronary Syndrome/drug therapy , Bayes Theorem , Aspirin/therapeutic use , Hemorrhage/chemically induced , Hemorrhage/drug therapy , Treatment OutcomeABSTRACT
BACKGROUND: patients hospitalised with covid-19 suffer thrombotic complications. risk factors for poor outcomes are shared with coronary artery disease. Objectives: to investigate efficacy of an acute coronary syndrome regimen in patients hospitalised with covid-19 and coronary disease risk factors. PATIENTS/METHODS: a randomised controlled open-label trial across acute hospitals (uk and brazil) added aspirin, clopidogrel, low-dose rivaroxaban, atorvastatin, and omeprazole to standard care for 28-days. primary efficacy and safety outcomes were 30-day mortality and bleeding. the key secondary outcome was a daily clinical status (at home, in hospital, on intensive therapy unit admission, death). RESULTS: 320 patients from 9 centres were randomised. the trial terminated early due to low recruitment. at 30 days there was no significant difference in mortality (intervention: 11.5% vs control: 15%, unadjusted or 0.73, 95%ci 0.38 to 1.41, p=0.355). significant bleeds were infrequent and not significantly different between the arms (intervention: 1.9% vs control 1.9%, p>0.999). using a bayesian markov longitudinal ordinal model, it was 93% probable that intervention arm participants were more likely to transition to a better clinical state each day (or 1.46, 95% cri 0.88 to 95 2.37, pr(beta>0) =93%; adjusted or 1.50, 95% cri 0.91 to 2.45, pr(beta>0) =95%) and median time to discharge home was two days shorter (95% cri -4 to 0, 2% probability that it was worse). CONCLUSIONS: acute coronary syndrome treatment regimen was associated with a 99 reduction in the length of hospital stay without an excess in major bleeding. a larger trial is needed to evaluate mortality.
Subject(s)
Acute Coronary Syndrome , COVID-19ABSTRACT
AIMS: Left bundle branch area pacing (LBBAP) is a promising method for delivering cardiac resynchronization therapy (CRT), but its relative physiological effectiveness compared with His bundle pacing (HBP) is unknown. We conducted a within-patient comparison of HBP, LBBAP, and biventricular pacing (BVP). METHODS AND RESULTS: Patients referred for CRT were recruited. We assessed electrical response using non-invasive mapping, and acute haemodynamic response using a high-precision haemodynamic protocol. Nineteen patients were recruited: 14 male, mean LVEF of 30%. Twelve had time for BVP measurements. All three modalities reduced total ventricular activation time (TVAT), (ΔTVATHBP -43 ± 14 ms and ΔTVATLBBAP -35 ± 20 ms vs. ΔTVATBVP -19 ± 30 ms, P = 0.03 and P = 0.1, respectively). HBP produced a significantly greater reduction in TVAT compared with LBBAP in all 19 patients (-46 ± 15 ms, -36 ± 17 ms, P = 0.03). His bundle pacing and LBBAP reduced left ventricular activation time (LVAT) more than BVP (ΔLVATHBP -43 ± 16 ms, P < 0.01 vs. BVP, ΔLVATLBBAP -45 ± 17 ms, P < 0.01 vs. BVP, ΔLVATBVP -13 ± 36 ms), with no difference between HBP and LBBAP (P = 0.65). Acute systolic blood pressure was increased by all three modalities. In the 12 with BVP, greater improvement was seen with HBP and LBBAP (6.4 ± 3.8 mmHg BVP, 8.1 ± 3.8 mmHg HBP, P = 0.02 vs. BVP and 8.4 ± 8.2 mmHg for LBBAP, P = 0.3 vs. BVP), with no difference between HBP and LBBAP (P = 0.8). CONCLUSION: HBP delivered better ventricular resynchronization than LBBAP because right ventricular activation was slower during LBBAP. But LBBAP was not inferior to HBP with respect to LV electrical resynchronization and acute haemodynamic response.
Subject(s)
Cardiac Resynchronization Therapy , Heart Failure , Humans , Male , Bundle of His , Cardiac Resynchronization Therapy/adverse effects , Cardiac Resynchronization Therapy/methods , Bundle-Branch Block/diagnosis , Bundle-Branch Block/therapy , Electrocardiography/methods , Treatment Outcome , Heart Failure/diagnosis , Heart Failure/therapy , Hemodynamics , Cardiac Pacing, Artificial/methodsABSTRACT
Purpose: To develop an artificial intelligence (AI) solution for automated segmentation and analysis of joint cardiac MRI short-axis T1 and T2 mapping. Materials and Methods: In this retrospective study, a joint T1 and T2 mapping sequence was used to acquire 4240 maps from 807 patients across two hospitals between March and November 2020. Five hundred nine maps from 94 consecutive patients were assigned to a holdout testing set. A convolutional neural network was trained to segment the endocardial and epicardial contours with use of an edge probability estimation approach. Training labels were segmented by an expert cardiologist. Predicted contours were processed to yield mapping values for each of the 16 American Heart Association segments. Network segmentation performance and segment-wise measurements on the testing set were compared with those of two experts on the holdout testing set. The AI model was fully integrated using open-source software to run on MRI scanners. Results: A total of 3899 maps (92%) were deemed artifact-free and suitable for human segmentation. AI segmentation closely matched that of each expert (mean Dice coefficient, 0.82 ± 0.07 [SD] vs expert 1 and 0.86 ± 0.06 vs expert 2) and compared favorably with interexpert agreement (Dice coefficient, 0.84 ± 0.06 for expert 1 vs expert 2). AI-derived segment-wise values for native T1, postcontrast T1, and T2 mapping correlated with expert-derived values (R 2 = 0.96, 0.98, and 0.87, respectively, vs expert 1, and 0.97, 0.99, and 0.92 vs expert 2) and fell within the range of interexpert reproducibility (R 2 = 0.97, 0.99, and 0.90, respectively). The AI model has since been deployed at two hospitals, enabling automated inline analysis. Conclusion: Automated inline analysis of joint T1 and T2 mapping allows accurate segment-wise tissue characterization, with performance equivalent to that of human experts.Keywords: MRI, Neural Networks, Cardiac, Heart Supplemental material is available for this article. © RSNA, 2022.