Impact of Pressure Recovery Adjustment on Aortic Valve Area Classification of Disease Severity in Transcatheter Aortic Valve Replacement Patients.

OBJECTIVES: To determine the impact of pressure recovery (PR) adjustment on disease severity grading in patients with severe aortic stenosis. The authors hypothesized that accounting for PR would result in echocardiographic reclassification of aortic stenosis severity in a significant number of patients. DESIGN: A retrospective observational study between October 2013 and February 2021. SETTING: A single-center, quaternary-care academic center. PARTICIPANTS: Adults (≥18 years old) who underwent transcatheter aortic valve implantation (TAVI). INTERVENTIONS: TAVI. MEASUREMENTS AND MAIN RESULTS: A total of 342 patients were evaluated in this study. Left ventricle mass index was significantly greater in patients who continued to be severe after PR (100.47 ± 28.77 v 90.15 ± 24.03, p = < 0.000001). Using PR-adjusted aortic valve area (AVA) resulted in the reclassification of 81 patients (24%) from severe to moderate aortic stenosis (AVA >1.0 cm2). Of the 81 patients who were reclassified, 23 patients (28%) had sinotubular junction (STJ) diameters >3.0 cm. CONCLUSION: Adjusting calculated AVA for PR resulted in a reclassification of a significant number of adult patients from severe to moderate aortic stenosis. PR was significantly larger in patients who reclassified from severe to moderate aortic stenosis after adjusting for PR. PR appeared to remain relevant in patients with STJ ≥3.0 cm. Clinicians need to be aware of PR and how to account for its effect when measuring pressure gradients with Doppler.

A meta-analysis of transcarotid versus transfemoral transcatheter aortic valve replacement.

BACKGROUND: Carotid access has shown promise as an excellent delivery route for transcatheter aortic valve replacement (TAVR). We aimed to compare outcomes of transcarotid (TC) and transfemoral (TF) TAVR by conducting a search and analysis of the best evidence in the literature to shed light on its safety and effectiveness. METHODS: The PubMed/MEDLINE, Embase, and Cochrane library from inception to July 2020 were searched to identify articles reporting comparative data on TC versus TF approaches for TAVR. Patients' baseline characteristics and clinical outcomes were extracted from the articles and pooled for analysis. RESULTS: Five studies, including a total of 2470 patients, were included in the study with 1859 patients in the TF group and 611 patients in the TC group. The TC group had higher prevalence of peripheral vascular disease, while the patients in the TF group was older. Meta-analysis revealed that there was no significant differences between the two groups with regard to 30-day mortality (p = 0.09), stroke (p = 0.28), new dialysis (p = 0.58), major bleeding (p = 0.69), or pacemaker implantation (p = 0.44). The TF group had a higher incidence of vascular complications (3.9% vs. 2.3%; OR 2.22; 95% CI [1.13, 4.38]; p = 0.02). CONCLUSIONS: Compared with the TF approach, TC-TAVR is associated with comparable procedural and clinical outcomes. Our analysis found a lower rate of vascular complication in TC access compared with TF access. This supports consideration of such an alternative access when there are concerns over the feasibility of TF access.

Propensity Score-Based Methods in Comparative Effectiveness Research on Coronary Artery Disease.

This review examines the conduct and reporting of observational studies using propensity score-based methods to compare coronary artery bypass grafting (CABG), percutaneous coronary intervention (PCI), or medical therapy for patients with coronary artery disease. A systematic selection process identified 48 studies: 20 addressing CABG versus PCI; 21 addressing bare-metal stents versus drug-eluting stents; 5 addressing CABG versus medical therapy; 1 addressing PCI versus medical therapy; and 1 addressing drug-eluting stents versus balloon angioplasty. Of 32 studies reporting information on variable selection, 7 relied exclusively on statistical criteria for the association of covariates with treatment, and 5 used such criteria to determine whether product or nonlinear terms should be included in the propensity score model. Twenty-five (52%) studies reported assessing covariate balance using the estimated propensity score, but only 1 described modifications to the propensity score model based on this assessment. The over 400 variables used in the 48 propensity score models were classified into 12 categories and 60 subcategories; only 17 subcategories were represented in at least half of the propensity score models. Overall, reporting of propensity score-based methods in observational studies comparing CABG, PCI, and medical therapy was incomplete; when adequately described, the methods used were often inconsistent with current methodological standards.

Acute Hemodynamic Effects of Intra-aortic Balloon Counterpulsation Pumps in Advanced Heart Failure.

BACKGROUND: The utility of intra-aortic balloon counterpulsation pumps (IABPs) in low cardiac output states is unknown and no studies have explored the impact of IABP therapy on ventricular workload in patients with advanced heart failure (HF). For these reasons, we explored the acute hemodynamic effects of IABP therapy in patients with advanced HF. METHODS: We prospectively studied 10 consecutive patients with stage D HF referred for IABP placement before left ventricular assist device (LVAD) surgery and compared with 5 control patients with preserved left ventricular (LV) ejection fraction (EF) who did not receive IABP therapy. Hemodynamics were recorded using LV conductance and pulmonary artery catheters. Cardiac index (CI)-responder and CI-nonresponder status was assigned a priori as being "equal to or above" or below the median of the IABP effect on CI, respectively, within 24 hours after IABP activation. RESULTS: Compared with controls, patients with advanced HF had lower LVEF, lower LV end-systolic pressure, lower LV stroke work, and higher LV end-diastolic pressures and volumes before IABP activation. IABP activation reduced LV stroke work primarily by reducing end-systolic pressure. IABP therapy increased CI by a median of 20% as well as increased diastolic pressure time index and the myocardial oxygen supply:demand ratio. Compared with CI-nonresponders, CI-responders had higher systemic vascular resistance, lower right heart filling pressures, and a trend toward lower left heart filling pressures with improved indices of right heart function. Compared with CI-nonresponders, the diastolic pressure time index was increased among CI-responders. CONCLUSIONS: IABP therapy may be effective at reducing LV stroke work, increasing CI, and favorably altering the myocardial oxygen supply:demand ratio in patients with advanced HF, especially among patients with low right heart filling pressures and high systemic vascular resistance.

Drier air, lower temperatures, and triggering of paroxysmal atrial fibrillation.

BACKGROUND: The few previous studies on the onset of paroxysmal atrial fibrillation and meteorologic conditions have focused on outdoor temperature and hospital admissions, but hospital admissions are a crude indicator of atrial fibrillation incidence, and studies have found other weather measures in addition to temperature to be associated with cardiovascular outcomes. METHODS: Two hundred patients with dual chamber implantable cardioverter-defibrillators were enrolled and followed prospectively from 2006 to 2010 for new onset episodes of atrial fibrillation. The date and time of arrhythmia episodes documented by the implanted cardioverter-defibrillators were linked to meteorologic data and examined using a case-crossover analysis. We evaluated associations with outdoor temperature, apparent temperature, air pressure, and three measures of humidity (relative humidity, dew point, and absolute humidity). RESULTS: Of the 200 enrolled patients, 49 patients experienced 328 atrial fibrillation episodes lasting ≥30 seconds. Lower temperatures in the prior 48 hours were positively associated with atrial fibrillation. Lower absolute humidity (ie, drier air) had the strongest and most consistent association: each 0.5 g/m decrease in the prior 24 hours increased the odds of atrial fibrillation by 4% (95% confidence interval [CI]: 0%, 7%) and by 5% (95% CI: 2%, 8%) for exposure in the prior 2 hours. Results were similar for dew point but slightly weaker. CONCLUSIONS: Recent exposure to drier air and lower temperatures were associated with the onset of atrial fibrillation among patients with known cardiac disease, supporting the hypothesis that meteorologic conditions trigger acute cardiovascular episodes.

The RoPE Score and Right-to-Left Shunt Severity by Transcranial Doppler in the CODICIA Study.

BACKGROUND: For patients with cryptogenic stroke (CS) and patent foramen ovale (PFO), it is unknown whether the magnitude of right-to-left shunt (RLSh) measured by contrast transcranial Doppler (c-TCD) is correlated with the likelihood an identified PFO is related to CS as determined by the Risk of Paradoxical Embolism (RoPE) score. Additionally, for patients with CS, it is unknown whether PFO assessment by c-TCD is more sensitive for identifying RLSh compared with transesophageal echocardiography (TEE). Our aim was to determine the significance of RLSh grade by c-TCD in patients with PFO and CS. METHODS: We evaluated patients with CS who had RLSh quantified by c-TCD in the Multicenter Study into RLSh in Cryptogenic Stroke (CODICIA) to determine whether there is an association between c-TCD shunt grade and the RoPE Score. For patients who underwent c-TCD and TEE, we determined whether there is agreement in identifying and grading RLSh between these two modalities. RESULTS: The RoPE score predicted the presence versus the absence of RLSh documented by c-TCD (c-statistic = 0.66). For patients with documented RLSh by c-TCD, shunt severity was correlated with increasing RoPE score (rank correlation (r) = 0.15, p = 0.01). Among 293 patients who had both c-TCD and TEE performed, c-TCD was more sensitive (98.7%) for detecting RLSh. Of the 97 patients with no PFO identified on TEE, 28 (29%) had a large amount of RLSh seen on c-TCD. CONCLUSIONS: For patients with CS, severity of RLSh by c-TCD is positively correlated with the RoPE score, indicating that this technique for shunt grading identifies patients more likely to have pathogenic rather than incidental PFOs. c-TCD is also more sensitive in detecting RLSh than TEE. These findings suggest an important role for c-TCD in the evaluation of PFO in the setting of CS.

Racial and Ethnic Differences in the Prevalence of Severe Aortic Stenosis by Echocardiography.

BACKGROUND: Understanding the burden of aortic stenosis (AS) across diverse racial and ethnic populations is important to ensure equitable resource allocation. This study explored whether severe AS rate varies by race and ethnicity. METHODS: The rates of severe AS, stratified by race and ethnicity, were calculated among 615,038 adults with a transthoracic echocardiogram. Logistic regression analysis was performed to identify factors associated with severe AS. RESULTS: Severe AS rates ranged from 0.08% in adults < 50 years old to 3.8% in those ≥ 90 years old. Compared to non-Hispanic White and Asian American [adjusted odds ratio (aOR) = 0.47, 95% confidence interval (CI): 0.42-0.53] and non-Hispanic Black (aOR = 0.44, 95% CI: 0.39-0.50) patients were less likely to have severe AS, whereas Hispanic patients (aOR = 0.91, 95% CI: 0.87-0.98) had near similar likelihood. Age was the strongest risk factor for severe AS (compared to age < 50 years, aOR = 21.8, 95% CI: 17.8-26.6 for age 80-89 years, and aOR = 43.8, 95% 35.5-54.0 for age ≥ 90 years). Additional factors associated with severe AS included male sex (aOR = 1.38, 95% CI: 1.30-1.46) and diabetes (aOR = 1.23, 95% CI: 1.15-1.31). CONCLUSIONS: Asian American and non-Hispanic Black adults had lower rates of severe AS compared to White and Hispanic patients. The rate of severe AS progressively increases with age in all racial and ethnic groups, with higher rates in men compared with women. With a demographic shift toward an aging and more diverse population, the burden of AS is anticipated to rise. Ensuring adequate allocation of resources to meet the evolving needs of a diverse population remains a shared health care imperative.

Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach.

BACKGROUND: Valvular heart disease (VHD) is a leading cause of cardiovascular morbidity and mortality that poses a substantial health care and economic burden on health care systems. Administrative diagnostic codes for ascertaining VHD diagnosis are incomplete. OBJECTIVE: This study aimed to develop a natural language processing (NLP) algorithm to identify patients with aortic, mitral, tricuspid, and pulmonic valve stenosis and regurgitation from transthoracic echocardiography (TTE) reports within a large integrated health care system. METHODS: We used reports from echocardiograms performed in the Kaiser Permanente Southern California (KPSC) health care system between January 1, 2011, and December 31, 2022. Related terms/phrases of aortic, mitral, tricuspid, and pulmonic stenosis and regurgitation and their severities were compiled from the literature and enriched with input from clinicians. An NLP algorithm was iteratively developed and fine-trained via multiple rounds of chart review, followed by adjudication. The developed algorithm was applied to 200 annotated echocardiography reports to assess its performance and then the study echocardiography reports. RESULTS: A total of 1,225,270 TTE reports were extracted from KPSC electronic health records during the study period. In these reports, valve lesions identified included 111,300 (9.08%) aortic stenosis, 20,246 (1.65%) mitral stenosis, 397 (0.03%) tricuspid stenosis, 2585 (0.21%) pulmonic stenosis, 345,115 (28.17%) aortic regurgitation, 802,103 (65.46%) mitral regurgitation, 903,965 (73.78%) tricuspid regurgitation, and 286,903 (23.42%) pulmonic regurgitation. Among the valves, 50,507 (4.12%), 22,656 (1.85%), 1685 (0.14%), and 1767 (0.14%) were identified as prosthetic aortic valves, mitral valves, tricuspid valves, and pulmonic valves, respectively. Mild and moderate were the most common severity levels of heart valve stenosis, while trace and mild were the most common severity levels of regurgitation. Males had a higher frequency of aortic stenosis and all 4 valvular regurgitations, while females had more mitral, tricuspid, and pulmonic stenosis. Non-Hispanic Whites had the highest frequency of all 4 valvular stenosis and regurgitations. The distribution of valvular stenosis and regurgitation severity was similar across race/ethnicity groups. Frequencies of aortic stenosis, mitral stenosis, and regurgitation of all 4 heart valves increased with age. In TTE reports with stenosis detected, younger patients were more likely to have mild aortic stenosis, while older patients were more likely to have severe aortic stenosis. However, mitral stenosis was opposite (milder in older patients and more severe in younger patients). In TTE reports with regurgitation detected, younger patients had a higher frequency of severe/very severe aortic regurgitation. In comparison, older patients had higher frequencies of mild aortic regurgitation and severe mitral/tricuspid regurgitation. Validation of the NLP algorithm against the 200 annotated TTE reports showed excellent precision, recall, and F1-scores. CONCLUSIONS: The proposed computerized algorithm could effectively identify heart valve stenosis and regurgitation, as well as the severity of valvular involvement, with significant implications for pharmacoepidemiological studies and outcomes research.

Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning.

Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis. We find previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We further find that off-the-shelf attention-based multiple instance learning (MIL) performs poorly. We contribute a new end-to-end MIL approach with two key methodological innovations. First, a supervised attention technique guides the learned attention mechanism to favor relevant views. Second, a novel self-supervised pretraining strategy applies contrastive learning on the representation of the whole study instead of individual images as commonly done in prior literature. Experiments on an open-access dataset and a temporally-external heldout set show that our approach yields higher accuracy while reducing model size.

Blood pressure changes during routine transthoracic echocardiography.

Background: Increased afterload affects many of the flow dependent metrics assessed during transthoracic echocardiography (TTE) especially in the evaluation valvular disease. A single timepoint blood pressure (BP) may not accurately reflect the afterload present at the time of flow-dependent imaging and quantification. We assessed the magnitude of change in BP at discrete timepoints during routine TTE. Method: We conducted a prospective study where participants underwent automated BP measurement while undergoing a clinically indicated TTE. The first reading was obtained right after the patient lay supine and subsequent readings were taken at 10-min intervals during image acquisition. Result: We included 50 participants (66% were male, with a mean age of 64 years). After 10 min, 40 (80%) participants had a drop in systolic BP of >10 mmHg. Compared to the baseline, there was a significant drop in systolic BP (mean decrease 20.0 ± 12.8 mmHg; P < 0.05), and diastolic BP (mean decrease 15.7 ± 13.2 mmHg; P < 0.05) at 10 min. The systolic BP remained different from the baseline value throughout the duration of the study (average decrease from baseline to study end was 12.4 ± 16.0 mmHg, p < 0.05). Conclusion: BP recorded just prior to TTE does not accurately reflect the afterload present during most of the study. This finding has important implications for valvular heart disease imaging protocols that incorporate flow dependent metrics, where the presence or absence of hypertension may lead to under- or over-estimation of disease severity.

Left ventricular stroke volume index following transcatheter aortic valve replacement is an early predictor of 1-year survival.

BACKGROUND: Adverse cardiac events are common following transcatheter aortic valve replacement (TAVR). Our aim was to investigate the low left ventricular stroke volume index (LVSVI) 30 days after TAVR as an early echocardiographic marker of survival. HYPOTHESIS: Steady-state (30-day) LVSVI after TAVR is associated with 1-year mortality. METHODS: A single-center retrospective analysis of all patients undergoing TAVR from 2017 to 2019. Baseline and 30-day post-TAVR echocardiographic LVSVI were calculated. Patients were stratified by pre-TAVR transaortic gradient, surgical risk, and change in transvalvular flow following TAVR. RESULTS: This analysis focuses on 238 patients treated with TAVR. The 1-year mortality rate was 9% and 124 (52%) patients had normal flow post-TAVR. Of those with pre-TAVR low flow, 67% of patients did not normalize LVSVI at 30 days. The 30-day normal flow was associated with lower 1-year mortality when compared to low flow (4% vs. 14%, p = .007). This association remained significant after adjusting for known predictors of risk (adjusted odds ratio [OR] of 3.45, 95% confidence interval: 1.02-11.63 [per 1 ml/m2 decrease], p = .046). Normalized transvalvular flow following TAVR was associated with reduced mortality (8%) when compared to those with persistent (15%) or new-onset low flow (12%) (p = .01). CONCLUSIONS: LVSVI at 30 days following TAVR is an early echocardiographic predictor of 1-year mortality and identifies patients with worse intermediate outcomes. More work is needed to understand if this short-term imaging marker might represent a novel therapeutic target.

Automated Detection of Aortic Stenosis Using Machine Learning.

BACKGROUND: Aortic stenosis (AS) is a degenerative valve condition that is underdiagnosed and undertreated. Detection of AS using limited two-dimensional echocardiography could enable screening and improve appropriate referral and treatment of this condition. The aim of this study was to develop methods for automated detection of AS from limited imaging data sets. METHODS: Convolutional neural networks were trained, validated, and tested using limited two-dimensional transthoracic echocardiographic data sets. Networks were developed to accomplish two sequential tasks: (1) view identification and (2) study-level grade of AS. Balanced accuracy and area under the receiver operator curve (AUROC) were the performance metrics used. RESULTS: Annotated images from 577 patients were included. Neural networks were trained on data from 338 patients (average n = 10,253 labeled images), validated on 119 patients (average n = 3,505 labeled images), and performance was assessed on a test set of 120 patients (average n = 3,511 labeled images). Fully automated screening for AS was achieved with an AUROC of 0.96. Networks can distinguish no significant (no, mild, mild to moderate) AS from significant (moderate or severe) AS with an AUROC of 0.86 and between early (mild or mild to moderate AS) and significant (moderate or severe) AS with an AUROC of 0.75. External validation of these networks in a cohort of 8,502 outpatient transthoracic echocardiograms showed that screening for AS can be achieved using parasternal long-axis imaging only with an AUROC of 0.91. CONCLUSION: Fully automated detection of AS using limited two-dimensional data sets is achievable using modern neural networks. These methods lay the groundwork for a novel method for screening for AS.

Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models.

BACKGROUND: While clinical prediction models (CPMs) are used increasingly commonly to guide patient care, the performance and clinical utility of these CPMs in new patient cohorts is poorly understood. METHODS: We performed 158 external validations of 104 unique CPMs across 3 domains of cardiovascular disease (primary prevention, acute coronary syndrome, and heart failure). Validations were performed in publicly available clinical trial cohorts and model performance was assessed using measures of discrimination, calibration, and net benefit. To explore potential reasons for poor model performance, CPM-clinical trial cohort pairs were stratified based on relatedness, a domain-specific set of characteristics to qualitatively grade the similarity of derivation and validation patient populations. We also examined the model-based C-statistic to assess whether changes in discrimination were because of differences in case-mix between the derivation and validation samples. The impact of model updating on model performance was also assessed. RESULTS: Discrimination decreased significantly between model derivation (0.76 [interquartile range 0.73-0.78]) and validation (0.64 [interquartile range 0.60-0.67], P<0.001), but approximately half of this decrease was because of narrower case-mix in the validation samples. CPMs had better discrimination when tested in related compared with distantly related trial cohorts. Calibration slope was also significantly higher in related trial cohorts (0.77 [interquartile range, 0.59-0.90]) than distantly related cohorts (0.59 [interquartile range 0.43-0.73], P=0.001). When considering the full range of possible decision thresholds between half and twice the outcome incidence, 91% of models had a risk of harm (net benefit below default strategy) at some threshold; this risk could be reduced substantially via updating model intercept, calibration slope, or complete re-estimation. CONCLUSIONS: There are significant decreases in model performance when applying cardiovascular disease CPMs to new patient populations, resulting in substantial risk of harm. Model updating can mitigate these risks. Care should be taken when using CPMs to guide clinical decision-making.

Development and Validation of a Clinical Predictive Model for Identifying Hypertrophic Cardiomyopathy Patients at Risk for Atrial Fibrillation: The HCM-AF Score.

[Figure: see text].

External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature.

BACKGROUND: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. METHODS: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. RESULTS: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0-94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th-75th percentile [interquartile range (IQR)], 0.66-0.79), representing a median percent decrease in discrimination of -11.1% (IQR, -32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was -3.7% (IQR, -13.2 to 3.1) for closely related validations (n=123), -9.0 (IQR, -27.6 to 3.9) for related validations (n=862), and -17.2% (IQR, -42.3 to 0) for distantly related validations (n=717; P<0.001). CONCLUSIONS: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.