Board-certified Cardiologists Research Articles

Non-research payments to board-certified cardiologists from pharmaceutical industry in Japan from 2016 to 2019: a retrospective analysis

ObjectivesTo evaluate the extent and trends of personal payments from pharmaceutical companies to cardiologists board-certified by the Japanese Circulation Society.DesignA retrospective analysis study using data from a publicly available database.SettingThe...

Effect of cross-platform variations on transthoracic echocardiography measurements and clinical diagnosis.

Accurate cardiac chamber quantification is essential for clinical decisions and ideally should be consistent across different echocardiography systems. This study evaluates variations between the Philips EPIQ CVx (version 9.0.3) and Canon Aplio i900 (version 7.0) in measuring cardiac volumes, ventricular function, and valve structures. In this gender-balanced, single-centre study, 40 healthy volunteers (20 females and 20 males) aged 40 years and older (mean age 56.75 ± 11.57 years) were scanned alternately with both systems by the same sonographer using identical settings for both 2D and 4D acquisitions. We compared left ventricular (LV) and right ventricular (RV) volumes using paired t-tests, with significance set at P < 0.05. Correlation and Bland-Altman plots were used for quantities showing significant differences. Two board-certified cardiologists evaluated valve anatomy for each platform. The results showed no significant differences in LV end-systolic volume and LV ejection fraction between platforms. However, LV end-diastolic volume (LVEDV) differed significantly (biplane: P = 0.018; 4D: P = 0.028). Right ventricular (RV) measurements in 4D showed no significant differences, but there were notable disparities in 2D and 4D volumes within each platform (P < 0.01). Significant differences were also found in the LV systolic dyssynchrony index (P = 0.03), LV longitudinal strain (P = 0.04), LV twist (P = 0.004), and LV torsion (P = 0.005). Valve structure assessments varied, with more abnormalities noted on the Philips platform. Although LV and RV volumetric measurements are generally comparable, significant differences in LVEDV, LV strain metrics, and 2D vs. 4D measurements exist. These variations should be considered when using different platforms for patient follow-ups.

Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction

ObjectiveAlthough the importance of primary percutaneous coronary intervention (PCI) has been emphasized for ST-segment elevation myocardial infarction (STEMI), the appropriateness of the cardiac catheterization laboratory (CCL) activation remains suboptimal. This study aimed to develop a precise artificial intelligence (AI) model for diagnosis of STEMI and accurate CCL activation. MethodsWe used electrocardiography (ECG) waveform data from a prospective PCI registry in Korea in this study. Two independent board-certified cardiologists established a criterion standard (STEMI or Not-STEMI) for each ECG based on corresponding coronary angiography data. We developed a deep ensemble model by combining five convolutional neural networks. In addition, we performed clinical validation based on a symptom-based ECG dataset, comparisons with clinical physicians, and external validation. ResultsWe used 18,697 ECGs for the model development dataset and 1,745 (9.3%) were STEMI. The AI model achieved an accuracy of 92.1%, sensitivity of 95.4% and specificity of 91.8 %. The performances of the AI model were well-balanced and outstanding in the clinical validation, comparison with clinical physicians, and the external validation. ConclusionsThe deep ensemble AI model showed a well-balanced and outstanding performance. As visualized with the Grad-CAM, the AI model has a reasonable explainability. Further studies with prospective validation regarding clinical benefit in a real-world setting should be warranted.

Personalizing cardiovascular risk prediction for patients with systemic lupus erythematosus

ObjectiveCardiovascular disease (CVD) risk is increased in SLE and underestimated by general population prediction algorithms. We aimed to develop a novel SLE-specific prediction tool, SLECRISK, to provide a more accurate estimate of CVD risk in SLE. MethodsWe studied patients in the Brigham and Women's Hospital SLE cohort. We collected one-year baseline data including the presence of traditional CVD factors and SLE-related features at cohort enrollment. Ten-year follow-up for the first major adverse cardiovascular event (MACE; myocardial infarction (MI), stroke, or cardiac death) began at day +1 following the baseline period (index date). ICD-9/10 codes identified MACE were adjudicated by board-certified cardiologists. Least absolute shrinkage and selection operator regression selected SLE-related variables to add to the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Risk Equations 10-year risk Cox regression model. Model fit statistics and performance (sensitivity, specificity, positive/negative predictive value, c-statistic) for predicting moderate/high 10-year risk (≥7.5 %) of MACE were assessed and compared to ACC/AHA, Framingham risk score (FRS), and modified FRS (mFRS). Optimism adjustment internal validation was performed using bootstrapping. ResultsWe included 1,243 patients with 90 MACEs (46 MIs, 36 strokes, 19 cardiac deaths) over 8946.5 person-years of follow-up. SLE variables selected for the new prediction algorithm (SLECRISK) were SLE activity (remission/mild vs. moderate/severe), disease duration (years), creatinine (mg/dL), anti-dsDNA, anti-RNP, lupus anticoagulant, anti-Ro positivity, and low C4. The sensitivity for detecting moderate/high-risk (≥7.5 %) of MACE using SLECRISK was 0.74 (95 %CI: 0.65, 0.83), which was better than the sensitivity of the ACC/AHA model (0.38 (95 %CI: 0.28, 0.48)). It also identified 3.4-fold more moderate/high-risk patients than the ACC/AHA. Patients who were moderate/high-risk according to SLECRISK but not ACC/AHA, were more likely to be young women with severe SLE and few other traditional CVD risk factors. Model performance between SLECRISK, FRS, and mFRS were similar. ConclusionThe novel SLECRISK tool is more sensitive than the ACC/AHA for predicting moderate/high 10-year risk for MACE and may be particularly useful in predicting risk for young females with severe SLE. Future external validation studies utilizing cohorts with more severe SLE are needed.

Vertebral heart size and vertebral left atrial size reference ranges in healthy Miniature Schnauzers.

The inclusion of vertebral heart score (VHS) and, more recently, the inclusion of the vertebral left atrial size (VLAS) in radiographic evaluation have become important screening tools for identifying dogs with occult cardiac disease. Several recent papers have shown there are interbreed variations in the VHS reference range. Our hypothesis is that the Miniature Schnauzer would also have a higher reference range for its VHS. The electronic medical records of IDEXX Telemedicine Consultants were searched for Miniature Schnauzers undergoing thoracic radiographs between March 1, 2022, and February 28, 2023. Dogs were included if they had 3 view thoracic radiographs performed and no evidence of cardiopulmonary disease was detected. Dogs with incomplete radiographic studies or cardiac or extracardiac disease were excluded. The VHS and VLAS measurements were performed by 2 board-certified cardiologists independent of one another. A total of 1,000 radiographs were obtained of which 272 were included for the study. The overall range for the VHS in this cohort was 9.68 to 12.07 with a median of 10.9. For VLAS measurements, a range of 1.71 to 2.4 was documented with a median of 2.0. The VHS for Miniature Schnauzers without cardiac disease was confirmed to be higher than the canine reference range.

Lymphoscintigraphy Findings are Associated with Outcome in Children with Chylothorax After Cardiac Surgery.

Postoperative chylothorax in patients with congenital heart diseases (CHD) results in poor outcomes if anatomical and functional abnormalities of the lymphatic system are present. While these abnormalities are typically diagnosed by intranodal lymphangiography and dynamic contrast magnetic resonance lymphangiography, the usefulness of lymphoscintigraphy in these patients has not been evaluated. Between January 2019 and December 2021, 28 lymphoscintigraphies were performed in our institution for investigating prolonged pleural effusion after cardiac surgery. The images were assessed by three board-certified pediatric cardiologists retrospectively to determine the likelihood of a central lymphatic flow disorder. The likelihood was scored (range 1-3) based on structural abnormalities and congestive flow in the lymphatic system. Those scores were summed and the likelihood was categorized as low to intermediate (< 8 points) or high (8 or 9 points). Median age at lymphoscintigraphy was 129days (IQR, 41-412days), it was performed at a median of 22days (IQR, 17-43) after surgery, and median score was 6 points (IQR, 4-7.5). Kendall's coefficient of concordance (0.867; p < 0.05) indicated high inter-rater reliability. Overall survival at 6months after surgery was 92.5% in the low-to-intermediate group but 68.6% in the high group (p < 0.05), and duration of postoperative thoracic drainage was 27 and 58days, respectively (p < 0.05). Lymphatic abnormalities detected by lymphoscintigraphy were associated with poorer outcomes. Lymphoscintigraphy was thought to be useful in assessing anatomic and functional lymphatic abnormalities, despite its minimal invasiveness.

Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.

Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.

Diagnostic validation of vertebral heart score machine learning algorithm for canine lateral chest radiographs.

The vertebral heart score is a measurement used to index heart size relative to thoracic vertebra. Vertebral heart score can be a useful tool for identifying and staging heart disease and providing prognostic information. The purpose of this study is to validate the use of a vertebral heart score algorithm compared to manual vertebral heart scoring by three board-certified veterinary cardiologists. A convolutional neural network centred around semantic segmentation of relevant anatomical features was developed to predict heart size and vertebral bodies. These predictions were used to calculate the vertebral heart score. An external validation study consisting of 1200 canine lateral radiographs was randomly selected to match the underlying distribution of vertebral heart scores. Three American College of Veterinary Internal Medicine board-certified cardiologists were enrolled to manually score 400 images each using the traditional Buchanan method. Post-scoring, the cardiologists evaluated the algorithm for misaligned anatomic landmarks and overall image quality. The 95th percentile absolute difference between the cardiologist vertebral heart score and the algorithm vertebral heart score was 1.05 vertebrae (95% confidence interval: 0.97 to 1.20 vertebrae) with a mean bias of -0.09 vertebrae (95% confidence interval: -0.12 to -0.05 vertebrae). In addition, the model was observed to be well calibrated across the predictive range. We have found the performance of the vertebral heart score algorithm comparable to three board-certified cardiologists. While validation of this vertebral heart score algorithm has shown strong performance compared to veterinarians, further external validation in other clinical settings is warranted before use in those settings.

A World of Maximalist Medicine: Physician Perspectives on Palliative Care and End-of-life for Patients With Pulmonary Arterial Hypertension.

Physicians who specialize in pulmonary arterial hypertension (PAH) care for patients facing a serious, life-limiting illness. Palliative care is underutilized in patients with PAH, and little is known about how best to provide palliative care to this patient population. Using a qualitative approach, assess physicians' perspectives on barriers and facilitators to the use of palliative care in PAH. Participants were board-certified pulmonologists and cardiologists recruited from the Pulmonary Hypertension Association's list of physician specialists and academic center websites. We performed one-on-one semi-structured interviews that were recorded, transcribed, and analyzed using thematic analysis. Twelve physicians participated in the study, with a median age of 48.5 years and 20.5 years of clinical experience caring for patients with PAH. We identified the following themes and associated barriers and facilitators to effective implementation of palliative care for patients with PAH: a tailored approach to the individual patient; a PAH-specialist-led culture of care; effective collaboration with palliative care clinicians; and limitations imposed by health systems. PAH physicians are open to palliative care for their patients and are willing to partner with palliative care clinicians to implement this effectively and in the right setting. Areas for targeted improvement in enhancing palliative care for patients with PAH exist, especially enhancing collaboration between PAH physicians and palliative care specialists and navigating barriers in health systems.

Frequency of in-Stent Restenosis in patients having DES, with Stent Length Greater than 30mm Presenting to Tertiary Care Center

Objective: The purpose of this study was to calculate the incidence of in-stent restenosis in patients with DES and stent lengths higher than 30mm who presented to a tertiary care hospital. Study Design: A Cross-sectional study Place and Duration: This cross-sectional study was conducted at Department of Cardiology, Rashid Latif Medical College, Lahore in the duration from February, 2022 to July, 2022. Methods: There were 83 patients of coronary artery disease (CAD)were included. After obtaining written consent from participants, researchers collected information about their participants' ages, sexes, body mass indexes, and co-morbidities. Atherosclerosis risk variables were collected with baseline investigations and an echocardiography for ejection fraction determination. Imaging studies were reviewed by board-certified interventional cardiologists, who found evidence of ISR. Past angioplasty information and stent details were recorded. Results: In our study mean age of the presented cases was 54.13±7.44 years and mean BMI 25.2±13.87 kg/m2. Most common comorbidity was diabetes found in 25 (30.1%) followed by hypertension in 23 (27.7%) and dyslipidemia in 20 (24.1%) cases. We found that 40 (48.2%) patients were smokers. Mean LDL cholesterol of the patients was 68.6±1.97 mg/dl and mean uric acid was 6.2±2.35 mg/dl. Majority of the cases 45 (54.2%) had poor socio-economic status. We found that frequency of ISR was higher among cases of biomatrix stent 24 (28.9%), followed by Xience stent in 22 (26.5%) cases, Ultimaster stent in 19 (22.9%) and Xlimus stent in 18 (21.7%) cases after coronary angiograpghy. Our results showed that the stents longer than 30mm were associated with a higher incidence of In-Stent Restenosis (p=0.03). Conclusion: Based on the findings of this study, we came to the conclusion that a previous diagnosis of diabetes mellitus and a longer stent length are both significant risk factors for developing in-stent restenosis. In our research, we found that instances with biomatrix stents had a greater incidence of ISR. Keywords: CAD, Drug eluting stents (DES), Coronary angiography, In-Stent Restenosis

Personalized LSTM Models for ECG Lead Transformations Led to Fewer Diagnostic Errors Than Generalized Models: Deriving 12-Lead ECG from Lead II, V2, and V6.

The prevalence of chronic cardiovascular diseases (CVDs) has risen globally, nearly doubling from 1990 to 2019. ECG is a simple, non-invasive measurement that can help identify CVDs at an early and treatable stage. A multi-lead ECG, up to 15 leads in a wearable form factor, is desirable. We seek to derive multiple ECG leads from a select subset of leads so that the number of electrodes can be reduced in line with a patient-friendly wearable device. We further compare personalized derivations to generalized derivations. Long-Short Term Memory (LSTM) networks using Lead II, V2, and V6 as input are trained to obtain generalized models using Bayesian Optimization for hyperparameter tuning for all patients and personalized models for each patient by applying transfer learning to the generalized models. We compare quantitatively using error metrics Root Mean Square Error (RMSE), R2, and Pearson correlation (ρ). We compare qualitatively by matching ECG interpretations of board-certified cardiologists. ECG interpretations from personalized models, when corrected for an intra-observer variance, were identical to the original ECGs, whereas generalized models led to errors. Mean performance values for generalized and personalized models were (RMSE-74.31 µV, R2-72.05, ρ-0.88) and (RMSE-26.27 µV, R2-96.38, ρ-0.98), respectively. Diagnostic accuracy based on derived ECG is the most critical validation of ECG derivation methods. Personalized transformation should be sought to derive ECGs. Performing a personalized calibration step to wearable ECG systems and LSTM networks could yield ambulatory 15-lead ECGs with accuracy comparable to clinical ECGs.

Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation.

Atrial fibrillation affects approximately 4% of the world's population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation. We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch. Eligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram). A total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm. The algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch's single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care. ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386.

Artificial intelligence for automated evaluation of aortic measurements in 2D echocardiography: Feasibility, accuracy, and reproducibility.

This study sought to examine the feasibility, accuracy and reproducibility of a novel, fully automated 2D transthoracic echocardiography (2D TTE) parasternal long axis (PLAX) view aortic measurements quantification software compared to board-certified cardiologists in controlled clinical setting. Aortic Annulus (AoA), Aortic Sinus (AoS), Sinotubular Junction (STJ) and Proximal Ascending Aorta (AAo) diameter measurements were performed retrospectively on each of 58 subjects in two different ways: twice using a fully automated software (Ligence Heart version 2) and twice manually by three cardiologists (ORG) and one expert cardiologist (EC). Out of 58 studies AoA was measured in 54 (93%), AoS in 55 (95%), STJ in 55 (95%) and AAo in 54 (93%) studies. Automated measurements had a stronger correlation with EC when compared to ORG with the largest correlation difference of .1 for STJ measurements and lowest difference of .01 for AoS measurements. Automated software was in higher agreement with ground truth intervals (ORG measurements mean +- SEM) in three out of four measurements. Fully automated 2D TTE PLAX view aortic measurements using a novel AI-based quantification software are feasible and yield results that are in close agreement with what experienced readers measure manually while providing better reproducibility. This approach may prove to have important clinical implications in the automation of the aortic root and ascending aorta assessment to improve workflow efficiency.

Device agnostic AI-based analysis of ambulatory ECG recordings

Deep Convolutional Neural Networks (DCNNs) have been shown to provide improved performance over traditional heuristic algorithms for the detection of arrhythmias from ambulatory ECG recordings. However, these DCNNs have primarily been trained and tested on device-specific databases with standardized electrode positions and uniform sampling frequencies. This work explores the possibility of training a DCNN for Atrial Fibrillation (AF) detection on a database of single‑lead ECG rhythm strips extracted from resting 12‑lead ECGs. We then test the performance of the DCNN on recordings from ambulatory ECG devices with different recording leads and sampling frequencies.We developed an extensive proprietary resting 12‑lead ECG dataset of 549,211 patients. This dataset was randomly split into a training set of 494,289 patients and a testing set of the remaining 54,922 patients. We trained a 34-layer convolutional DCNN to detect AF and other arrhythmias on this dataset. The DCNN was then validated on two Physionet databases commonly used to benchmark automated ECG algorithms (1) MIT-BIH Arrhythmia Database and (2) MIT-BIH Atrial Fibrillation Database. Validation was performed following the EC57 guidelines, with performance assessed by gross episode and duration sensitivity and positive predictive value (PPV). Finally, validation was also performed on a selection of rhythm strips from an ambulatory ECG patch that a committee of board-certified cardiologists annotated.On MIT-BIH, The DCNN achieved a sensitivity of 100% and 84% PPV in detecting episodes of AF. and 100% sensitivity and 94% PPV in quantifying AF episode duration. On AFDB, The DCNN achieved a sensitivity of 94% and PPV of 98% in detecting episodes of AF, and 98% sensitivity and 100% PPV in quantifying AF episode duration. On the patch database, the DCNN demonstrated performance that was closely comparable to that of a cardiologist.The results indicate that DCNN models can learn features that generalize between resting 12‑lead and ambulatory ECG recordings, allowing DCNNs to be device agnostic for detecting arrhythmias from single‑lead ECG recordings and enabling a range of clinical applications.

The Inverse Correlation Between the Duration of Lifetime Occupational Radiation Exposure and the Prevalence of Atrial Arrhythmia.

ObjectiveAdvancements in fluoroscopy-assisted procedures have increased radiation exposure among cardiologists. Radiation has been linked to cardiovascular complications but its effect on cardiac rhythm, specifically, is underexplored.MethodsDemographic, social, occupational, and medical history information was collected from board-certified cardiologists via an electronic survey. Bivariate and multivariable logistic regression analyses were performed to assess the risk of atrial arrhythmias (AA).ResultsWe received 1,478 responses (8.8% response rate) from cardiologists, of whom 85.4% were male, and 66.1% were ≤65 years of age. Approximately 36% were interventional cardiologists and 16% were electrophysiologists. Cardiologists > 50 years of age, with > 10,000 hours (h) of radiation exposure, had a significantly lower prevalence of AA vs. those with ≤10,000 h (11.1% vs. 16.7%, p = 0.019). A multivariable logistic regression was performed and among cardiologists > 50 years of age, exposure to > 10,000 radiation hours was significantly associated with a lower likelihood of AA, after adjusting for age, sex, diabetes mellitus, hypertension, and obstructive sleep apnea (adjusted OR 0.57; 95% CI 0.38–0.85, p = 0.007). The traditional risk factors for AA (age, sex, hypertension, diabetes mellitus, and obstructive sleep apnea) correlated positively with AA in our data set. Cataracts, a well-established complication of radiation exposure, were more prevalent in those exposed to > 10,000 h of radiation vs. those exposed to ≤10,000 h of radiation, validating the dependent (AA) and independent variables (radiation exposure), respectively.ConclusionAA prevalence may be inversely associated with radiation exposure in Cardiologists based on self-reported data on diagnosis and radiation hours. Large-scale prospective studies are needed to validate these findings.

Interobserver Variation in Echocardiographic Measurements and Grading of Tricuspid Regurgitation Based on a Novel Web-Based Assessment Environment

The primary aim of this study was to assess interobserver variability in grading tricuspid regurgitation (TR) severity. The authors' secondary goals were to delineate which transesophageal echocardiographic (TEE) parameters best correlate with severity and how consistent the participants were at grading severity. This was a prospective cohort study of how clinicians evaluated previously acquired TEE images and videos. The 19 TEE studies of patients with TR were recorded by 4 senior echocardiographers across 4 US academic institutions. The participants evaluated these cases on a novel, web-based, assessment environment designed specifically for this study. Twenty-nine fellowship-trained and board-certified cardiologists and cardiothoracic anesthesiologists volunteered to participate in the study as observers from 19 different institutions. No interventions were performed on the participants. For each case, participants measured the vena contracta (VC), proximal isovelocity surface area (PISA), and jet area before giving a final classification on the severity of TR. Variation was highest for effective regurgitant orifice area and lowest for VC and PISA. The coefficient of variation, defined as the standard deviation from the mean divided by the mean, for all cases of trace, mild, moderate and severe TR were as follows: Jet Area-111%, 46%, 48%, 76%; VC-67%, 44%, 43%, 36%; PISA-52%, 48%, 31%, 35%; and effective regurgitant orifice area-127%, 95%, 66%, 58%. The interobserver variation in quantifying TEE parameters for TR is high, suggesting these may be difficult to measure reliably in a busy perioperative setting. Of the parameters assessed, VC and PISA radius had the highest interobserver agreement and the highest correlation with severity.

Performance of Different Echocardiographic Measurements of Left Atrial Size in Dogs by Observers with Different Levels of Experience.

Simple SummaryAssessing enlargement of the left atrium (one of the four cardiac chambers) is extremely important for gaining information about dogs’ heart disease, their prognosis, and directing treatment. However, people with different levels of experience may be required to make this assessment, and we don’t know how observers with different experiences perform in making this assessment. In this study, five observers with different levels of experience evaluated the left atrium of 36 dogs in a blinded fashion (i.e., unaware of each other measurements, or of the identity of the dog), compared to two cardiologists. We then used statistical analysis to evaluate repeatability, reproducibility, accuracy of the measurements, and the capacity of correctly identifying left atrial enlargement. We found that the measurements performed by observers with more experience where more similar to the cardiologists’ measurements, and that combining more than one technique for measuring the left atrium can improve accuracy of the identification of left atrial enlargement.Assessment of left atrial (LA) sizes in dogs informs clinical staging, risk assessment, treatment decisions, and prognosis. The objective of this study was to assess the diagnostic performance of observers with different levels of experience measuring the LA with three different techniques. Echocardiographic images from 36 dogs with different degrees of left atrial enlargement (LAE) were retrospectively retrieved, anonymized and measured in a blinded fashion by a veterinary student, a first-year cardiology resident, a third-year cardiology resident, and two board-certified veterinary cardiologists. The LA to aortic root ratio (LA:Ao), LA antero-postero diameter indexed to body weight (LAiAPD) and left atrial area were measured. Inter- and intra-observer intraclass correlation coefficients (ICCs) were calculated for all three variables. Bland–Altman plots and accuracy in identification of LAE were calculated for the three least experienced observers using LA:Ao and LAiAPD. Intra- and interobserver ICCs were greater than 0.9 for every variable. The observer with least experience had significant positive bias and a tendency to overestimate larger measurements using LA:Ao, but not using LAiAPD. The accuracy of identification of LAE also increased with the increasing level of experience and was higher for LAiAPD compared to LA:Ao. Combining both methods for identification of LAE, further increased accuracy.

Regional disparities in cardiac rehabilitation volume throughout Japan based on open data from a National Database of Health Insurance Claims.

Objective: This study assessed the regional disparities and the associated factors in the implementation of cardiac rehabilitation in Japan. Materials and Methods: Regional disparities were investigated by comparing the number of cardiac rehabilitation units in each of 47 prefectures in Japan based on the National Database of Health Insurance Claims Open Data published by the Ministry of Health, Labour, and Welfare. The relationships between the numbers of inpatient and outpatient cardiac rehabilitation units and the numbers of registered instructors of cardiac rehabilitation, board-certified physiatrists, and board-certified cardiologists were examined. Results: The region with the highest and lowest numbers of inpatient units showed 11,620.5 and 1,650.2 population-adjusted cardiac rehabilitation units adjusted per 100,000 population, respectively, corresponding to a 7.0-fold difference. Meanwhile, 4,865.3 and 238.6 units were present in the regions with the highest and lowest numbers of outpatient units, respectively, corresponding to a 20.4-fold regional disparity. Our analysis showed that the population-adjusted number of inpatient cardiac rehabilitation units was significantly associated with the population-adjusted numbers of registered instructors of cardiac rehabilitation (r=0.647, P<0.001) and board-certified cardiologists (r=0.445, P=0.002) but only marginally associated with the population-adjusted number of board-certified physiatrists (r=0.329, P=0.024). Moreover, the population-adjusted number of outpatient cardiac rehabilitation units was significantly associated with the population-adjusted numbers of registered instructors of cardiac rehabilitation (r=0.406, P=0.005) and board-certified cardiologists (r=0.450, P=0.002) but not with the population-adjusted number of board-certified physiatrists (r=0.078, P=0.603). Conclusion: Large regional disparities were observed during the implementation of cardiac rehabilitation. Increased numbers of cardiac rehabilitation instructors and cardiac rehabilitation practices are expected to eliminate these regional differences in cardiac rehabilitation practices.

Resources for cardiovascular healthcare associated with 30-day mortality in acute myocardial infarction with cardiogenic shock.

AimsAlthough primary percutaneous coronary intervention (PCI) and mechanical circulatory support (MCS), such as extracorporeal membrane oxygenation (ECMO) or intra-aortic balloon pumping (IABP), have been widely used for acute myocardial infarction (AMI) patients with cardiogenic shock (AMICS), their in-hospital mortality remains high. This study aimed to investigate the association of cardiovascular healthcare resources with 30-day mortality in AMICS.Methods and resultsThis was an observational study using a Japanese nationwide administrative data (JROAD-DPC) of 260 543 AMI patients between April 2012 and March 2018. Of these, 45 836 AMICS patients were divided into three categories based on MCS use: with MCS (ECMO with/without IABP), IABP only, or without MCS. Certified hospital density and number of board-certified cardiologists were used as a metric of cardiovascular healthcare resources. We estimated the association of MCS use, cardiovascular healthcare resources, and 30-day mortality. The 30-day mortality was 71.2% for the MCS, 23.9% for IABP only, and 37.8% for the group without MCS. The propensity score-matched and inverse probability-weighted Cox frailty models showed that primary PCI was associated with a low risk for mortality. Higher hospital density and larger number of cardiologists in the responsible hospitals were associated with a lower risk for mortality.ConclusionAlthough the 30-day mortality remained extremely high in AMICS, indication of primary PCI and improvement in providing cardiovascular healthcare resources associated with the short-term prognosis of AMICS.

Heart failure with preserved ejection fraction after left-sided valve surgery: prevalent and relevant.

AimsTo investigate the epidemiological and prognostic relationship between heart failure with preserved ejection fraction (HFpEF) and left‐sided valve surgery using all‐cause mortality as a primary endpoint.Methods and resultsWe studied a total of 973 patients, of whom 673 had undergone left‐sided valve surgery (time from surgery to enrolment 50 ± 30 months after valve surgery) and 300 patients with HFpEF without prior surgery served as control group. Among patients after surgery, 67.4% fulfilled all criteria of HFpEF according to current guideline recommendations, 20.6% had no heart failure (HF), and 12.0% had HF with mid‐range or reduced ejection fraction (HFmrEF/HFrEF). During 83 ± 39 months of follow‐up, a total of 335 (34.4%) patients died. Compared to surgical patients with no subsequent HF, patients with HFpEF and HFmrEF/HFrEF after surgery showed significantly higher all‐cause mortality rates [hazard ratio (HR) 1.80, 95% confidence interval (CI) 1.25–2.57, P = 0.001; and HR 1.86, 95% CI 1.16–2.98, P = 0.010, respectively]. This increased mortality rate was similar to the control HFpEF group without surgery (HR 2.05, 95% CI 1.38–3.02, P < 0.001). Results remained consistent after adjustment for clinical and imaging risk factors and when using the established HFA‐PEFF risk score for HFpEF diagnosis. Notably, only 12.5% of HFpEF patients after surgery were diagnosed with HF despite regular follow‐up visits by board‐certified cardiologists. In contrast, 92.1% of HFmrEF/HFrEF patients after surgery were diagnosed correctly.ConclusionsHeart failure with preserved ejection fraction following left‐sided valve surgery is highly prevalent, associated with unfavourable outcomes, but rarely recognized.