Ultra-short-term heart rate variability (HRV) analysis (< 5 min) has been extensively growing in the field of exercise performance for autonomic assessment. However, the validation of ultra-short-term HRV was unclear in the recovery period of exercise. This study aimed to elucidate the agreement between ultra-short-term HRV (0–30 s, 0–1 min, 0–2 min, 0–3 min, 0–4 min) and standard short-term HRV (5 min) and to explore the optimal recording duration under rest and post-exercise conditions.
69 participants were recruited to perform physical exercise on a treadmill with an intensity of 6 km/h, 9 km/h and 12 km/h, independently. The standard deviation of RR-interval (SDNN) and root mean square of successive differences of RR-intervals (RMSSD) were calculated by using ultra-short periods and standard period at rest condition (Pre-E) and three post-exercise trials, i.e., Post-E1, Post-E2 and Post-E3, respectively. One-way ANOVA with repeated-measures and Cohen’s d statistics were conducted, and Bland-Altman analysis and interclass correlation coefficients (ICC) were used to assess the levels of agreement.
For SDNN and RMSSD, the results of agreement analysis at rest condition were different from those at post-exercise. At Pre-E, SDNN and RMSSD were reliable for ultra-short-term HRV analysis at all ultra-short periods, i.e., 0–30 s, 0–1 min, 0–2 min, 0–3 min and 0–4 min, with most ICCs greater than 0.9 and Cohen’s d showing trivial differences (Cohen’s d = 0.024–0.117). However, at post-exercise, SDNN0–30s, SDNN0-1min, RMSSD0–30s and RMSSD0-1min showed significant differences with SDNN5min and RMSSD5min, respectively (p < 0.01), and the ICCs was not perfect (< 0.9). HRV analysis with time duration longer than 2 min showed nearly perfect reliability in all post-exercise trials, with trivial differences (Cohen’s d = −0.003–0.110) and perfect ICCs (ICCs = 0.916–0.998). Furthermore, the limits of the agreement became tighter as the period duration increased in Bland-Altman plots.
This study demonstrated that ultra-short-term HRV analysis was a good surrogate of standard HRV time-domain measures to reflect the autonomic regulation at rest and post-exercise. Specifically, ultra-short-term HRV0–30s or HRV0-1min was recommended at rest condition, whereas longer than 2 min recording period was reliable to obtain SDNN and RMSSD for the accuracy of HRV analysis.
ABSTRACT Resistance training (RT) has beneficial effects on the cardiovascular (CV) system and potentially can be an effective treatment for a variety of clinical conditions, such as heart disease (HD). However, the impacts of RT on cardiac risk factors in older men are less known .The current study was investigated the effect of RT on serum levels of NT-proBNP, GDF-15, and markers of cardiac damage (CK and CK-MB) in the elderly men.
24 elderly men (aged 72.1 ± 5.3 years, height 164.3 ± 5.5 cm, and BMI 27.2 ± 4.3 kg/m2 ) were randomly assigned to one of the two intervention groups: RT (n=12) and control (n=12). The RT protocol included eight movements (3 × 10 repetitions with ∼70% of one repetition maximum [1RM], 1-min rest intervals) for eight weeks and three sessions per week. Serum levels of NT-proBNP, GDF-15, CK and CK-MB were tested at baseline as well as after eight weeks of intervention. All analyses were performed with SPSS version 24 at a significance level of P≤ 0.05. Serum levels of NT-proBNP significantly decreased in the RT group after 8 weeks (p≤0.05). Moreover, resistance training significantly increased serum levels of CK and CK-MB (p≤0.05). However, GDF-15 changes were not significant after eight weeks of RT (p>0.05). Therefore, our data confirm that resistance training May be improve cardiac risk factors in older men.
Objectives This study sought to identify whether left atrial strain can predict new-onset atrial fibrillation (NOAF) in patients with heart failure (HF) and sinus rhythm.
Background Both HF and atrial fibrillation have common risk factors, and HF is a risk factor for the development of atrial fibrillation and vice versa.
Methods Among 4,312 consecutive patients with acute HF from 3 tertiary hospitals, 2,461 patients with sinus rhythm and peak atrial longitudinal strain (PALS) were included in the study. Reduced PALS was defined as PALS ≤18%, and the primary endpoint was 5-year NOAF.
Results During a 5-year follow-up, 397 (16.1%) patients developed NOAF. Patients with reduced PALS had higher NOAF than their counterparts (18.2% vs. 12.7%; p < 0.001). After adjustment for significant covariates, we identified 6 independent predictors of NOAF, including age >70 years (hazard ratio [HR]: 1.50; 95% confidence interval [CI]: 1.12 to 2.00), hypertension (HR: 1.45; 95% CI: 1.10 to 1.91), left atrial volume index ≥40 ml/m2 (HR: 2.03; 95% CI: 1.48 to 2.77), PALS <18% (HR: 1.60; 95% CI: 1.18 to 2.17), HF with preserved ejection fraction (HR: 1.47; 95% CI: 1.11 to 1.95), and no beta-blocker prescription at discharge (HR: 1.48; 95% CI: 1.14 to 1.92). A weighted score based on these variables was used to create a composite score, HAS-BAP (H = hypertension; A = age; S = PALS; B = no beta-blocker prescription at discharge; A = atrial volume index; P = HF with preserved ejection fraction [range 0 to 6] with a median of 3 [interquartile range: 2 to 4]). The probability of NOAF increased with HAS-BAP score.
Conclusions In patients with HF and sinus rhythm, 16.1% developed NOAF, and PALS could be used to predict the risk for NOAF. The HAS-BAP score allows determination of the risk of NOAF. (Strain for Risk Assessment and Therapeutic Strategies in Patients With Acute Heart Failure [STRATS-AHF] Registry; NCT03513653)
AI is a tool that will not replace sonographers but will help them be more efficient.
Artificial intelligence (AI) is emerging as a key component in diagnostic medical imaging, including echocardiography. AI with deep learning has already been used with automated view labeling, measurements, and interpretation. As the development and use of AI in echocardiography increase, potential concerns may be raised by cardiac sonographers and the profession. This report, from a sonographer’s perspective, focuses on defining AI, the basics of the technology, identifying some current applications of AI, and how the use of AI may improve patient care in the future.
Artificial intelligence (AI) is evolving into a major focus in medicine that can be applicable to echocardiography in addressing problems of inconsistency and inter- and intraobserver variability during image acquisition and interpretation.1 Compared with other modalities, such as computed tomography, nuclear imaging, and magnetic resonance imaging, echocardiography is often affected by interobserver variability and a strong dependence on experience level. Cardiovascular imaging, and echocardiography in particular, is increasing in demand and complexity. It is imperative to find ways to decrease variability among echocardiographers, improve efficiency, and decrease acquisition time. This is where AI could be extremely advantageous for the benefit of patients, sonographers, and cardiologists. The latest American Society of Echocardiography guideline on performing a comprehensive transthoracic examination recommends obtaining >100 images.2 It would be beneficial to have AI assist with adhering to these American Society of Echocardiography guidelines for standardizing views and measurements in echocardiography.
AI: Origins and Definitions
One of the earliest articles on machine learning was published in 1959 by Arthur Samuel, who coined the phrase “artificial intelligence,” when he published an article titled “Some Studies in Machine Learning Using the Game of Checkers.”3 Since AI’s inception >60 years ago, interest in the field has seen multiple rises and falls, but AI has now emerged with major breakthroughs.4 Starting in the early 1980s, advances in computer technology and the creation of advanced neural networks led to a rapid evolution in AI. Biological neural networks are circuits of neurons; the human brain has approximately 100 billion neurons. Artificial neural networks are computing systems that mimic the human brain by recognizing relationships in vast amounts of data.
“AI” is a broad term that covers any computer program (algorithms and models) that mimics human logic and intelligence. Many terms are used to describe different subfields and techniques within AI. Machine learning and deep learning are two subfields that serve as the basis of most AI functions4 (Figure 1).
Machine learning uses statistical techniques and data to learn from experiences in order to make predictions about new data. Machine learning involves programming a computer to store, learn, and analyze data. As a subset of AI, machine learning uses statistical methods to enable machines to improve with experience. Machine learning allows a computer to take data that are input, learn from these data, predict an outcome, and improve its own knowledge on how to react the next time it is presented with similar data. Machine learning has many applications today, including speech and natural language processing, image and video processing and recognition, autonomous vehicles, and game-playing computers.5,6 An easy-to-understand example in echocardiography is a machine’s ability to identify structures within an image and accurately label them, such as identifying an image as a parasternal long-axis view rather than an apical long-axis view.7
Machine learning can be developed by either supervised learning or unsupervised learning. The simplistic difference between the two is that unsupervised learning uses unlabeled data, whereas supervised learning uses labeled or known data. In unsupervised learning, the machine takes unlabeled data sets and detects previously unknown patterns. The term “supervised learning” is the process of giving algorithms known or labeled inputs along with desired outputs and is the most common type of machine learning used today. This method enables a machine to classify or predict objects, problems, or situations on the basis of labeled data fed into the machine. The goal of supervised learning is to have a machine receive new (untaught) input variables and correctly predict output variables.5 An example is a program that can analyze two apical echocardiographic views and produce an accurate value of ejection fraction (EF) without drawing borders and assessing ventricular volumes. Because the program was “trained” on thousands of images with known EFs, it is able to produce an EF value that is as accurate as that of an expert cardiologist with >20 years of experience.8
Deep learning is a subset of machine learning that is used in circumstances in which a very large amount of data must be processed. These deep learning networks are based on multilayered configurations called an artificial neural network to process data (Figure 2). These artificial neural networks, often hundreds of layers deep, can train themselves from large data sets and make accurate predictions on newly input unknown data. They also have the ability to learn from their experiences but require that the initial “training” data set be accurate and without bias. Training without bias is critically important and means that the initial data are free of any outside influences that might cloud the information, such as demographic information or additional medical information that may suggest a certain diagnosis. Without bias also means that the “training” data set is large enough to contain a wide variety of patients. For echocardiography, such a data set would include studies from both sexes along with a range of body mass index, ages, and image quality. This is imperative for the machine to learn patterns on the basis of the images alone. The components of an artificial neural network are loosely related to the components of a biological neuron consisting of multiple layers in which the data are processed consecutively until the final output is achieved.6,8 We can train a deep learning algorithm on a set of labeled echocardiographic views from patients with hypertrophic cardiomyopathy and use that algorithm to predict hypertrophic cardiomyopathy from new images.1 This is an example of AI, because the system recognizes a pattern an experienced sonographer might recognize: seeing a parasternal long-axis view of the heart and being suspicious that the patient might have hypertrophic cardiomyopathy. Table 1 lists the main differences between machine learning and deep learning.
Table 1. Machine learning versus deep learning
Can perform with smaller amounts of data
Benefits from large amounts of data
Can work on low-end machines
Requires a high-end machine to train but can be run on low-end machines (e.g., smart phones)
Depends on specific function to reach a conclusion
Can learn very complex functions to reach a conclusion
Important features must be identified by an expert
Neural network determines most important features, do not need to be identified by an expert
Short time to train
Long time to train
Uses statistical methods to improve with experience
Mimics functionality of human brain neural networks
Convolutional Neural Networks
Because of their ability to accurately perform pattern recognition, convolutional neural networks (CNNs) are a class of artificial neural networks often applied to analyze images. In a CNN, the multiple (deep) hidden layers (Figure 2), called convolutional layers, perform pattern analysis using a convolution operation. When an image passes through a layer of a CNN, it is divided into hundreds of small regions, and each region is analyzed by that layer of the CNN. Filters can detect characteristics of the image, like an edge or a corner. The image is scanned by each of the layers and passed on to the next layer.4 Initial layers can detect simple geometric boundaries or edges; deeper layers will detect a more complex pattern, such as ventricular chambers. The final output is a summation of the individual analyses performed by each layer of the CNN.
Current State of AI and Echocardiography
AI and Image Acquisition
There are ways that AI is already making cardiac imaging easier, faster, and more accurate. Some of these examples already validated are automated measurement features, including left ventricular EF, chamber dimensions, wall thickness, Doppler measurements, and so on.9,10Figures 3 and 4 are examples of current software available on echocardiographic machines that can perform some of these functions. Automated measurement packages and image optimization can save sonographers time during the study and help standardize reproducibility of the echocardiographic results for patient data reporting.6 In a 2019 study by Asch et al.,8 99 patients were studied using automatic left ventricular EF by machine learning compared with reference standards of conventional volume-derived EF by clinical readers, high consistency (r = 0.95) and excellent agreement (r = 0.94) were found.
Products have been developed to help guide a novice sonographer toward a technically correct image.10 These tools could be especially beneficial when a user is a nonexpert.11 This software uses an algorithm to guide the user to capture a diagnostic image using real-time positional directions. Deep learning software guides the user by recognizing incorrect or off-axis views and providing guidance on how to move the probe in order to obtain diagnostic images. Once the images are deemed diagnostic by the software, they are acquired automatically.10 These tools would lead to a shortened acquisition time, optimized images, and standardized measurements.8 With patient populations increasing in age and body mass, and an increasing demand for echocardiographic studies, sonographers are at risk for work-related musculoskeletal disorders.12 Automated acquisition might help improve the ergonomic burden for sonographers, decreasing the frequency of awkward musculoskeletal movements and examination length. The current technology used in commercial echocardiography is only beginning to scratch the surface of what AI can bring to the field. These products are extremely helpful to work flow, but the real benefit of machine learning will be in the interpretation of echocardiograms.
AI and Image Interpretation
Echocardiography is not only skill dependent in the acquisition of images, but the interpretation of echocardiographic images relies heavily on the highly evolved process of pattern recognition by the human brain.13 Pattern recognition develops with experience, yet interpretation of echocardiograms today can remain highly subjective. AI holds promise in echocardiographic interpretation, as it has the potential to extract information not readily apparent to the observer.
AI also has the potential to overcome the human limitations of fatigue or distraction, inter- and intraobserver variability, and the tedious, time-consuming interpretation of large data sets.6 Deep learning has already been used in the diagnose of structural heart disease from limited echocardiographic views.8 It has also been able to use cardiac landmarks to assess left ventricular endocardial border segments for assessment of wall motion and volumes.6,8 Current programs include AI-based EF, chamber size evaluation, valve mobility status, pericardial effusion presence, and many more areas of automated interpretation.14 A rapidly growing area of AI is in valvular modeling and segmentation. Computers are being used that will help with precise sizing and modeling of minimally invasive structural heart interventional devices, with an emphasis on real-time guidance.6
AI in Educational Settings
Echocardiographic machines equipped with automated protocols can help students or newly hired employees learn complex protocols using algorithms designed to assist with correct image acquisition and measurements. Modern-day echocardiography simulators offer a wide variety of cases with cardiovascular disease to develop skill in recognizing pathology as well as the ability to accurately measure images. Software on these simulators and on ultrasound machines can train novice users and sonographer students.11 The systems provide real-time feedback to imagers that they are obtaining diagnostic-quality images and instructions to correct acquisition if applicable. Medical schools have started to include point-of-care ultrasound into their curricula, even for first-year students, in order to better teach anatomy.15 Using AI along with handheld point-of-care ultrasound systems could speed up the learning curve for these medical students. In fact, wherever cardiac ultrasound is used, whether it is in emergency situations such as on the battlefield or screening for rheumatic heart disease in developing countries, pairing AI to assist in obtaining images or diagnosis would be of great benefit.16, 17, 18 At no other time has this been more apparent than in the current coronavirus disease 2019 pandemic. In an effort to reduce provider exposure, as well as limit the use of critical supplies, hospitals all over the country are trying to find ways to obtain real-time cardiac imaging by novice users on the front lines. Point-of-care ultrasound with AI is developing as a real-time solution to this complicated problem.
The Future of AI in Echocardiography
With the increasing use of three-dimensional echocardiography, carefully developed tools with supervised learning algorithms have the potential to determine whether structures can be seen and then guide acquisition for a more diagnostic clip. This would help produce better quality three-dimensional images and improved diagnostic value. Besides optimizing echocardiographic images, AI could identify in real time those patients needing additional views or the need for an image enhancement agent.
The future of AI in echocardiography may involve using cluster analysis, which combines both clinical and imaging data, to better characterize disease and predict outcomes. This combination of data may lead to the creation of therapies that are personalized for each individual patient, on the basis of a machine-created prediction of risk.6
AI in echocardiographic interpretation offers the likelihood of increasing not only reading accuracy but also timeliness. A physician reading an echocardiogram that was obtained to assess mitral regurgitation before an intervention could ask a program to retrieve all views related to mitral regurgitation. This would save the physician time in sifting through a study with possibly hundreds of images by allowing the physician to quickly visualize all relevant information. This would be especially helpful in collaboration between echocardiographers and surgeons, interventionalists, and/or anesthesiologists.
Another potential application of AI could include comparing a real-time image with an image from a previous echocardiographic examination, allowing the interpreting physician to compare “apples to apples.” This would greatly facilitate laboratory accreditation requirements of comparing the current echocardiogram with previous studies.
Limitations and Challenges
There are many challenges that need to be considered before relying on AI for interpretation. Even in the setting of a perfect algorithm, if the data being input are of poor quality or are biased, the interpretation will also be of poor quality.7,8 Additionally, emphasis must be placed on creating uniform standards that are consistent across vendors, thus allowing integration between the different algorithms and allowing the algorithms to run on different equipment. It would be helpful to have a set of standards for AI data management, similar to the picture archiving and communication system and the Digital Imaging and Communications in Medicine formats. These standards for AI management would include standardized nomenclature used to create a uniform system for data storage and retrieval.
Concerns of Sonographers
Cardiac sonographers take pride in obtaining high-quality diagnostic echocardiographic studies, and they recognize the difficulties and technical expertise required. These skills and techniques are learned over many years and vary greatly from patient to patient. It is understandable that sonographers may have some concerns when reading about the implementation of AI techniques in echocardiography.
They may wonder if echocardiographic examinations could become so automated that sonographers become obsolete. They may question if all health care providers could have transducers that connect to their cell phones and, after obtaining a few images, reach a diagnosis. They may be concerned that they could be replaced by remote scanning systems using robotics, similar to how some surgical procedures are now performed.19 Although it is not clear what the future holds, it seems unlikely that AI will lead to the replacement of sonographers but rather will help them become more efficient while using their knowledge and skill sets to focus on more complex patients. In the world of gaming, although a computer is able to beat a human chess master, a chess master combined with a computer will always beat the computer. AI is a tool that, once integrated into the daily work flow of the clinical echocardiography laboratory, will support our clinical work and improve laboratory work flow, laboratory quality, and echocardiographic interpretation, all resulting in better patient care.
AI has made its way into several aspects of health care, and echocardiography is no exception. There are many areas in echocardiography that have already witnessed AI’s impact on imaging, measurements, and diagnosis. Although there are concerns among sonographers and echocardiographers, the likelihood remains that AI will improve the jobs of sonographers and decrease the variability currently present in echocardiography. It is important to remain steadfast in the use of critical thinking skills and understand that only in the combination of properly developed AI and experienced human eyes will this marriage of technology and humanity be successful.
1J. Zhang, S. Gajjala, P. Agrawal, G. Tison, L. Hallock, L. Beussink-Nelson, et al.Fully automated echocardiogram interpretation in clinical practiceCirculation, 138 (2018), pp. 1623-1635CrossRefView Record in ScopusGoogle Scholar2C. Mitchell, P.S. Rahko, L.A. Blauwet, B. Canaday, J.A. Finstuen, M.C. Foster, et al.Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: recommendations from the American Society of EchocardiographyJ Am Soc Echocardiogr, 32 (2019), pp. 1-64ArticleDownload PDFCrossRefView Record in ScopusGoogle Scholar3A.L. SamuelSome studies in machine learning using the game of checkersIBM J, 3 (1959), pp. 535-554Google Scholar4J. He, S.L. Baxter, J. Xu, J. Xu, X. Zhou, K. ZhangThe practical implementation of artificial intelligence technologies in medicineNature Medicine, 25 (2019), pp. 30-36CrossRefView Record in ScopusGoogle Scholar5D. Dey, P.J. Slomka, P.L. Leeson, D. Comaniciu, S. Shrestha, P.P. Sengupta, et al.Artificial intelligence in cardiovascular imagingJ Am Coll Cardiol, 73 (2019), pp. 1317-1335ArticleDownload PDFView Record in ScopusGoogle Scholar6M. Alsharqi, W.J. Woodward, J.A. Mumith, D.C. Markham, R. Upton, P. LeesonArtificial intelligence and echocardiographyEchocardiogr Res Pract, 5 (2018), pp. R115-R125CrossRefView Record in ScopusGoogle Scholar7S. Gandhi, W. Mosleh, J. Shen, C. ChowAutomation, machine learning, and artificial intelligence in echocardiography: a brave new worldEchocardiography, 35 (2018), pp. 1402-1418CrossRefView Record in ScopusGoogle Scholar8F.M. Asch, N. Poilvert, T. Abraham, M. Jankowski, J. Cleve, M. Adams, et al.Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expertCirc Cardiovasc Imaging, 12 (2019), p. e009303Google Scholar9D. Medvedofsky, V. Mor-Avi, M. Amzulescu, C. Ferandez-Golfin, R. Hinojar, M.J. Monaghan, et al.Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation studyEurHeart J Cardiovasc Imaging, 19 (2018), pp. 47-58CrossRefView Record in ScopusGoogle Scholar10B.S. Cheema, C. Hsieh, D. Adams, A. Narang, J. ThomasAutomated guidance and image capture of echocardiographic views using a deep learning-derived technology[poster]Circulation, 140 (suppl 1) (2019)Google Scholar11A. Narang, H. Hong, C. Hsieh, A. Chaudhry, S. Guttas, A. Pearlman, et al.Evaluation of a deep-learning model designed to aid novice scanners in obtaining diagnostic quality echocardiograms[abstract]J Am Soc Echocardiogr, 32 (2019), p. B118View Record in ScopusGoogle Scholar12B.J. Roberts, D.A. Adams, J.P. BakerThe pandemic of work-related musculoskeletal disorders: can we stem the tide in cardiac sonography?J Am Soc Echocardiogr, 32 (2019), pp. 1147-1150ArticleDownload PDFView Record in ScopusGoogle Scholar13C. Krittanawong, K.W. Johnson, R.S. Rosenson, Z. Wang, M. Aydar, U. Baber, et al.Deep learning for cardiovascular medicine: a practical primerEur Heart J, 40 (2019), pp. 2058-2073CrossRefGoogle Scholar14N. Poilvert, H. Hong, N. Romano, D. Adams, C. Cadieu, R.Y. Bae, et al.Deep learning algorithm for fully-automated left ventricular ejection fraction measurement[abstract]J Am Soc Echocardiogr, 31 (2018), p. 6View Record in ScopusGoogle Scholar15A.M. Johri, J. Durbin, J. Newbigging, R. Tanzola, R. Chow, S. De, et al.Cardiac point-of-care ultrasound state-of-the-art in medical school educationJ Am Soc Echocardiogr, 31 (2018), pp. 749-760ArticleDownload PDFView Record in ScopusGoogle Scholar16L. Gharahbaghian, K.L. Anderson, V. Lobo, R.W. Huang, C.M. Poffberger, P.D. NguyenPoint-of-Care ultrasound in austere environmentsEmerg Med Clin North Am, 35 (2017), pp. 409-441ArticleDownload PDFView Record in ScopusGoogle Scholar17M. Ploutz, J.C. Lu, J. Scheel, C. Webb, G.J. Ensing, T. Aliku, et al.Handheld echocardiographic screening for rheumatic heart disease by non-expertsHeart, 102 (2016), pp. 35-39CrossRefView Record in ScopusGoogle Scholar18J.C. Lu, C. Sable, G.J. Ensing, C. Webb, J. Scheel, T. Aliku, et al.Simplified rheumatic heart disease screening criteria for handheld echocardiographyJ Am Soc Echocardiogr, 28 (2015), pp. 463-469ArticleDownload PDFView Record in ScopusGoogle Scholar19A.M. Priester, S. Natarajan, M.O. CuljatRobotic ultrasound systems in medicineIEEE Trans Ultrason Ferroelectr Freq Control, 60 (2013), pp. 507-523View Record in ScopusGoogle Scholar
A Potential Marker for Ventricular Arrhythmia in Ischemic Heart Disease
Rachel Bastiaenen, PHD, Hanney Gonna, MB, Navin Chandra, MD, Ahmed Merghani, MB, Oswaldo Valencia, MD, A. John Camm, MD, Mark M. Gallagher, MD
The purpose of this study was to determine the potential value of a novel marker for the severity of structural heart disease and the risk of arrhythmia.
The ventricular ectopic QRS interval (VEQSI) has been shown to identify structural heart disease and predict mortality in an unselected population. In ischemic heart disease (IHD), risk stratification for sudden death is imperfect. We hypothesized that VEQSI would identify patients with prior myocardial infarction (MI) compared with healthy subjects and distinguish IHD patients who have suffered life-threatening events from those without prior significant ventricular arrhythmia.
The 12-lead Holter recordings from 189 patients with previous MI were analyzed: 38 with prior ventricular tachycardia/ventricular fibrillation (MI-VT/VF) (66 ± 9 years; 92% male); 151 without prior significant ventricular arrhythmia (MI-no VT/VF) (64 ± 11 years; 74% male). These were compared with 60 healthy controls (62 ± 7 years; 70% male). All ventricular ectopic beats were reviewed and maximal VEQSI duration (VESQI max) was recorded as the duration of the longest ventricular ectopic beat.
VEQSI max was longer in post-MI patients compared with normal controls (185 ±26 ms vs. 164± 16 ms; p < 0.001) and in MI-VT/VF patients with prior life-threatening events compared with MI-no VT/VF patients without prior life-threatening events (214 ±20 ms vs. 177 ± 22 ms; p < 0.001). Multivariate analysis established VEQSI max as the strongest independent marker for prior serious ventricular arrhythmia. VEQSI max >198 ms had 86% sensitivity, 85% specificity, 62% positive predictive value, and 96% negative predictive value for identifying patients with prior life-threatening events (odds ratio: 37.4; 95% confidence interval: 13.0 to 107.5).
VEQSI max >198 ms distinguishes post-MI patients with prior life-threatening events from those without prior significant ventricular arrhythmia. This may be a useful additional index for risk stratification in IHD.
Performing abdominal aorta screenings during routine echocardiographic examination can be useful for quick detection of asymptomatic abdominal aortic aneurysms (AAA) without additional cost. Furthermore, detection of any atherosclerosis of the aorta during this screening would qualify the patient for statin therapy with potential to improve outcome. The goal of our study was to evaluate the effect of routine screening of abdominal aorta during echocardiographic examination.
Recently, we started performing routine AAA screening during routine echocardiographic examinations. We retrospectively studied a total of 727 patients with successful screening between the ages of 33 and 96 with a median age of 72.4. We evaluate the presence of atherosclerosis of aorta and its effect on lipid therapy and detection of asymptomatic AAA.
We found 18 (2.4%) asymptomatic AAA’s and 468 (64.3%) cases of atherosclerosis of abdominal aorta. Retrospectively, data was collected on preventative lipid therapy. Of the 468 patients that had detected atherosclerosis of aorta, 414 patients had clinical follow up. 240 (57.9%) of patients were already treated with a statin due to another indications. However, 38 (9.1%) of these patients had been started on statin drugs for the first time, 85 (20.5%) were set a new lower LDL goal, and 41 (9.9%) had an intensified statin treatment.
Mortality in high-risk severe MR patients treated with MitraClip was associated with baseline GLS values.
Reduced GLS at baseline was associated with 1-year all-cause mortality.
Results may improve current risk stratification in patients considered for MitraClip therapy.
Transcatheter mitral valve repair (TMVr) using edge-to-edge mitral valve clip is effective for patients with mitral regurgitation (MR) and high or prohibitive surgical risk. Global longitudinal strain (GLS) allows evaluation of subclinical myocardial dysfunction, but its incremental clinical utility into risk stratification, beyond traditional clinical parameters, is unknown in patients treated with TMVr. We sought to evaluate the association of baseline GLS with 1-year all-cause mortality in patients treated with TMVr using edge-to-edge mitral valve clip.
We analyzed 155 patients who underwent transcatheter edge-to-edge mitral valve clip implantation (mean age, 83 ± 7 years; 48% were women; mean left ventricular ejection fraction, 56% ± 10%, Society of Thoracic Surgeons Predicted Risk of Mortality score for repair, 6.62% ± 5.22%). Baseline left ventricular GLS was obtained by two-dimensional speckle-tracking echocardiography, averaging 18 segments from three apical views. Receiver operating characteristic analyses were used to assess the GLS cut point associated with all-cause mortality. Multivariable models with Cox regression tested its relationship after adjustment for baseline comorbidities.
During a median follow-up of 316 days, all-cause deaths occurred in 30 patients at a median of 156 days after TMVr. The area under the curve of preoperative GLS associated with the outcome was 0.60, with a cutoff point of −14.5%. Baseline GLS > −14.5% was associated with 1-year mortality (hazard ratio = 2.50; 95% CI, 1.20-5.21; P = .02) before and after adjustment for baseline characteristics. After accounting for baseline characteristics, patients with GLS > −14.5% had worse 1-year mortality than those with GLS ≤ −14.5% (χ2P < .001). In nested Cox proportional hazards models, the addition of baseline GLS to Society of Thoracic Surgeons Predicted Risk of Mortality score, left ventricular ejection fraction, and the etiology of MR significantly increased the model χ2 value (χ2 = 12.32).
Baseline GLS is independently associated with 1-year all-cause mortality in patients who undergo TMVr, and its assessment improves risk stratification in these patients.
DMR Degenerative mitral regurgitation; FMR Functional mitral regurgitation; GLS Global longitudinal strain; LV Left ventricular; LVEF Left ventricular ejection fraction; LVESD Left ventricular end-systolic diameter; MR Mitral regurgitation; MV Mitral valve; STS-PROM Society of Thoracic Surgeons Predicted Risk of Mortality; TMVr Transcatheter mitral valve repair; TTE Transthoracic echocardiography
2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: The Task Force for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC)
Diagnosis. Chest discomfort without persistent ST-segment elevation (NSTE-ACS) is the leading symptom initiating the diagnostic and therapeutic cascade. The pathological correlate at the myocardial level is cardiomyocyte necrosis, measured by troponin release, or, less frequently, myocardial ischaemia without cell damage (unstable angina). Individuals with unstable angina have a substantially lower risk of death and derive less benefit from an aggressive pharmacological and invasive approach.
Troponin assays. High-sensitivity troponin assays measurements are recommended over less sensitive ones, as they provide higher diagnostic accuracy at identical low cost. It should be noted that many cardiac pathologies other than MI also result in cardiomyocyte injury and, therefore, cardiac troponin elevations.
Other biomarkers. Other biomarkers may have clinical relevance in specific clinical settings when used in combination with non hs-cTn T/I. CK-MB shows a more rapid decline after MI and may provide added value for detection of early reinfarction. The routine use of copeptin as an additional biomarker for the early rule-out of MI is recommended in the increasingly uncommon setting where hs-cTn assays are not available.
Rapid ‘rule-in’ and ‘rule-out’ algorithms. Due to the higher sensitivity and diagnostic accuracy for the detection of MI at presentation, the time interval to the second cTn assessment can be shortened with the use of hs-cTn assays. It is recommended to use the 0 h/1 h algorithm (best option, blood draw at 0 h and 1 h) or the 0 h/2 h algorithm (second-best option, blood draw at 0 h and 2 h). Optimal thresholds for rule-out and rule-in were selected to allow for a minimal sensitivity and NPV of 99% and a minimal PPV of 70%. Used in conjunction with clinical and ECG findings, the 0 h/1 h and 0 h/2 h algorithm allows the identification of appropriate candidates for early discharge and outpatient management.
Confounders of hs-cTn. Beyond the presence or absence of MI, four clinical variables affect hs-cTn concentrations. The effect of age (differences in concentration between healthy very young vs. ‘healthy’ very old individuals up to 300%), renal dysfunction (differences in concentration between otherwise healthy patients with very high vs. very low eGFR up to 300%), and chest pain onset (>300%) is substantial, and modest for sex (≈40%).
Ischaemic risk assessment. Initial cTn levels add prognostic information in terms of short- and long-term mortality to clinical and ECG variables. The higher the hs-cTn levels, the greater the risk of death. Serum creatinine and eGFR should also be determined in all patients with NSTE-ACS because they affect prognosis and are key elements of the GRACE risk score, which assessment is superior to (subjective) physician assessment for the occurrence of death or MI. Natriuretic peptides may provide incremental prognostic information and may help in risk stratification.
Bleeding risk assessment. ARC-HBR is a pragmatic approach that includes the most recent trials performed in HBR patients, who were previously excluded from clinical trials of DAPT duration or intensity. The PRECISE-DAPT score may be used to guide and inform decision making on DAPT duration with a modest predictive value for major bleeding. Their value in improving patient outcomes remains unclear.
Non-invasive imaging. Even after the rule-out of MI, elective non-invasive or invasive imaging may be indicated according to clinical assessment. CCTA may be an option in patients with low-to-modest clinical likelihood of unstable angina as a normal scan excludes CAD. CCTA has a high NPV to exclude ACS (by excluding CAD) and an excellent outcome in patients presenting to the emergency department with low-to-intermediate pre-test probability for ACS and a normal CCTA. In addition, upfront imaging with CCTA reduces the need for ICA in high risk patients. Stress imaging by cardiac magnetic resonance imaging, stress echocardiography, or nuclear imaging may also be an option based on risk assessment.
Risk stratification for an invasive approach. An early routine invasive approach within 24 h of admission is recommended for NSTEMI based on hs-cTn measurements, GRACE risk score >140, and dynamic new, or presumably new, ST-segment changes as it improves major adverse cardiac events and possibly early survival. Immediate invasive angiography is required in highly unstable patients according to hemodynamic status, arrythmias, acute heart failure, or persistent chest pain. In all other clinical presentation, a selective invasive approach may be performed according to non-invasive testing or clinical risk assessment.
Revascularization strategies. The principal technical aspects of PCI in NSTE-ACS patients do not differ from the invasive assessment and revascularization strategies for other manifestations of CAD. Radial access is recommended as the preferred approach in NSTE-ACS patients undergoing invasive assessment with or without PCI. Multivessel disease is frequent in NSTE-ACS, timing and completeness of revascularization should be decided according to functional relevance of all stenoses, age, general patient condition, comorbidities, and left ventricular function.
Myocardial infarction with non-obstructive coronary arteries. MINOCA incorporates a heterogeneous group of underlying causes that may involve both coronary and non-coronary pathological conditions, with the latter including cardiac and extra-cardiac disorders. It excludes by consensus myocarditis and Takotsubo syndrome. Cardiac magnetic resonance imaging is one of the key diagnostic tools as it identifies the underlying cause in more than 85% of patients and the subsequent appropriate treatment.
Spontaneous coronary artery dissection. Defined as a non-atherosclerotic, non-traumatic, or iatrogenic separation of the coronary arterial tunics secondary to vasa vasorum hemorrhage or intimal tear, it accounts for up to 4% of all ACS, but the incidence is reported to be much higher (22–35% of ACS) in women <60 years of age. Intracoronary imaging is very useful for the diagnosis and treatment orientation. Medical treatment remains to be established.
Pre-treatment with P2Y12 receptor inhibitors. Routine pre-treatment with a P2Y12 receptor inhibitor in NSTE-ACS patients in whom coronary anatomy is not known and an early invasive management is planned is not recommended given the lack of established benefit. However, it may be considered in selected cases and according to the bleeding risk of the patient.
Post-treatment antiplatelet therapy. DAPT consisting of a potent P2Y12 receptor inhibitor in addition to aspirin is generally recommended for 12 months, irrespective of the stent type, unless there are contraindications. New scenarios have been implemented. DAPT duration can be shortened (<12 months), extended (>12 months), or modified by switching DAPT or de-escalation. These decisions depend on individual clinical judgment being driven by the patient’s ischaemic and bleeding risk, the occurrence of adverse events, comorbidities, co-medications, and the availability of the respective drugs.
Triple antithrombotic therapy. In at least 6–8% of patients undergoing PCI, long-term oral anticoagulation is indicated and should be continued. NOACs are preferred over VKAs in terms of safety when patients are eligible. DAT with a NOAC at the recommended dose for stroke prevention and SAPT (preferably clopidogrel, chosen in more than 90% of cases in available trials) is recommended as the default strategy up to 12 months after a short period up to 1 week of TAT (with NOAC and DAPT). TAT may be prolonged up to 1 month when the ischaemic risk outweighs the bleeding risk.
Rural and indigenous populations are disproportionately affected by cardiovascular disease,1,2 with a higher prevalence of cardiovascular risk factors than urban populations, as well as harsher environmental conditions, reduced access to services, and greater difficulty in attracting and retaining health professionals.3,4 Patients therefore wait longer and travel larger distances to access diagnostic services, or they forgo treatment.
We compared waiting and reporting times and patient travel distances for exercise stress testing and 24-hour Holter monitoring over 12-month periods before (retrospective analysis) and after (prospective analysis) implementation of a telemedicine program (Tele-Cardiac Investigations) in two rural and remote regions in Australia with a referral population of 44,400 and a geographic area of 696,650 km2.
The telemedicine program enabled cardiology specialists at a metropolitan location (Royal Brisbane and Women’s Hospital [RBWH]) to work with local staff to conduct exercise stress tests and Holter monitoring remotely at 11 facilities (see the Supplementary Appendix, available at NEJM.org). For exercise stress testing, a live video feed of the electrocardiographic monitor at the rural facility allowed the telemedicine team to view patient data in real time. Local staff performed the exercise stress test with guidance from the telemedicine team, or, alternatively, the exercise stress test system was remotely controlled by the telemedicine team. The test results were immediately reported by the telemedicine team with the use of remote access software. For Holter monitoring, rural staff applied the device with guidance from the telemedicine team. The telemedicine team then remotely accessed the initialization software to program and start the recording. After the recording was complete, the telemedicine team remotely transferred the data to analysis software at RBWH for reporting.Table 1.Effect of Implementation of Telemedicine for Cardiac Testing.
Implementation of the telemedicine program was associated with a 42% increase in the number of tests performed over 12 months (516 in the 12 months before implementation vs. 734 in the 12 months after), with an even greater proportional increase in the number of patients from indigenous populations undergoing testing (63 before implementation vs. 127 after implementation) (Table 1). There were substantial reductions in waiting times to have tests conducted (17.71 fewer days [44.6% reduction]) and to have results reported (35.82 fewer days [99.2% reduction]), resulting in a significant reduction in the total time from referral to reporting (56.66 fewer days [71.1% reduction]; P<0.001). Round-trip travel was reduced by 502 km per patient, on average, for patients requiring Holter monitoring, with telemedicine allowing 91.3% of patients to receive testing without having to travel away from their local health facilities.
Adam C. Scott, Ph.D. Alice McDonald, G.Dip.Cardiac. Tiffany Roberts, B.E.S.S. Curtis Martin, B.E.S.S. Timothy Manns, B.E.S.S. Meghan Webster, B.E.S.S. Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia firstname.lastname@example.org
David Walker, M.B., B.S. Longreach Hospital, Longreach, QLD, Australia
Alan Sandford, M.B., B.S. Mount Isa Hospital, Mount Isa, QLD, Australia
Paul Scuffham, Ph.D. Griffith University, Brisbane, QLD, Australia
John J. Atherton, Ph.D., M.B., B.S. Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia
Nobuyuki Kagiyama, Marco Piccirilli, Naveena Yanamala, Sirish Shrestha, Peter D. Farjo, Grace Casaclang-Verzosa, Wadea M. Tarhuni, Negin Nezarat, Matthew J. Budoff, Jagat Narula and Partho P. Sengupta
Journal of the American College of Cardiology Volume 76, Issue 8, August 2020 DOI: 10.1016/j.jacc.2020.06.061
Background Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise.
Objectives This study sought to develop machine-learning models that quantitatively estimate myocardial relaxation using clinical and electrocardiography (ECG) variables as a first step in the detection of LV diastolic dysfunction.
Methods A multicenter prospective study was conducted at 4 institutions in North America enrolling a total of 1,202 subjects. Patients from 3 institutions (n = 814) formed an internal cohort and were randomly divided into training and internal test sets (80:20). Machine-learning models were developed using signal-processed ECG, traditional ECG, and clinical features and were tested using the test set. Data from the fourth institution was reserved as an external test set (n = 388) to evaluate the model generalizability.
Results Despite diversity in subjects, the machine-learning model predicted the quantitative values of the LV relaxation velocities (e’) measured by echocardiography in both internal and external test sets (mean absolute error: 1.46 and 1.93 cm/s; adjusted R2 = 0.57 and 0.46, respectively). Analysis of the area under the receiver operating characteristic curve (AUC) revealed that the estimated eʹ discriminated the guideline-recommended thresholds for abnormal myocardial relaxation and diastolic and systolic dysfunction (LV ejection fraction) the internal (area under the curve [AUC]: 0.83, 0.76, and 0.75) and external test sets (0.84, 0.80, and 0.81), respectively. Moreover, the estimated eʹ allowed prediction of LV diastolic dysfunction based on multiple age- and sex-adjusted reference limits (AUC: 0.88 and 0.94 in the internal and external sets, respectively).
Conclusions A quantitative prediction of myocardial relaxation can be performed using easily obtained clinical and ECG features. This cost-effective strategy may be a valuable first clinical step for assessing the presence of LV dysfunction and may potentially aid in the early diagnosis and management of heart failure patients.