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.

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.

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.

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.

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.

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.

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

OBJECTIVE

To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation.

STUDY DESIGN AND SETTING

We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n=556) and assessed the change in discrimination (dAUC) in external validation cohorts (n=1,147).

RESULTS

PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P<0.001).

CONCLUSION

High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews.

Comorbidity Burden and Adverse Outcomes After Transcatheter Aortic Valve Replacement

Background

Transcatheter aortic valve replacement (TAVR) has become the preferred treatment for symptomatic patients with aortic stenosis and elevated procedural risk. Many deaths following TAVR are because of noncardiac causes and comorbid disease burden may be a major determinant of postprocedure outcomes. The prevalence of comorbid conditions and associations with outcomes after TAVR has not been studied.

Methods and Results

This was a retrospective single‐center study of patients treated with TAVR from January 2015 to October 2018. The association between 21 chronic conditions and short‐ and medium‐term outcomes was assessed. A total of 341 patients underwent TAVR and had 1‐year follow‐up. The mean age was 81.4 (SD 8.0) years with a mean Society of Thoracic Surgeons predicted risk of mortality score of 6.7% (SD 4.8). Two hundred twenty (65%) patients had ≥4 chronic conditions present at the time of TAVR. There was modest correlation between Society of Thoracic Surgeons predicted risk of mortality and comorbid disease burden (r=0.32, P<0.001). After adjusting for Society of Thoracic Surgeons predicted risk of mortality, age, and vascular access, each additional comorbid condition was associated with increased rates of 30‐day rehospitalizations (odds ratio, 1.21; 95% CI, 1.02–1.44), a composite of 30‐day rehospitalization and 30‐day mortality (odds ratio, 1.20; 95% CI, 1.02–1.42), and 1‐year mortality (odds ratio, 1.29; 95% CI, 1.05–1.59).

Conclusions

Comorbid disease burden is associated with worse clinical outcomes in high‐risk patients treated with TAVR. The risks associated with comorbid disease burden are not adequately captured by standard risk assessment. A systematic assessment of comorbid conditions may improve risk stratification efforts.

What is Quality End-of-Life Care for Patients With Heart Failure? A Qualitative Study With Physicians

Background

Advanced heart failure (AHF) carries a morbidity and mortality that are similar or worse than many advanced cancers. Despite this, there are no accepted quality metrics for end‐of‐life (EOL) care for patients with AHF.

Methods and Results

As a first step toward identifying quality measures, we performed a qualitative study with 23 physicians who care for patients with AHF. Individual, in‐depth, semistructured interviews explored physicians' perceptions of characteristics of high‐quality EOL care and the barriers encountered. Interviews were analyzed using software‐assisted line‐by‐line coding in order to identify emergent themes. Although some elements and barriers of high‐quality EOL care for AHF were similar to those described for other diseases, we identified several unique features. We found a competing desire to avoid overly aggressive care at EOL alongside a need to ensure that life‐prolonging interventions were exhausted. We also identified several barriers related to identifying EOL including greater prognostic uncertainty, inadequate recognition of AHF as a terminal disease and dependence of symptom control on disease‐modifying therapies.

Conclusions

Our findings support quality metrics that prioritize receipt of goal‐concordant care over utilization measures as well as a need for more inclusive payment models that appropriately reflect the dual nature of many AHF therapies.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances

Background

More than 500 000 sudden cardiac arrests (SCAs) occur annually in the United States. Clinical predictive models (CPMs) may be helpful tools to differentiate between patients who are likely to survive or have good neurologic recovery and those who are not. However, which CPMs are most reliable for discriminating between outcomes in SCA is not known.

Methods and Results

We performed a systematic review of the literature using the Tufts PACE (Predictive Analytics and Comparative Effectiveness) CPM Registry through February 1, 2020, and identified 81 unique CPMs of SCA and 62 subsequent external validation studies. Initial cardiac rhythm, age, and duration of cardiopulmonary resuscitation were the 3 most commonly used predictive variables. Only 33 of the 81 novel SCA CPMs (41%) were validated at least once. Of 81 novel SCA CPMs, 56 (69%) and 61 of 62 validation studies (98%) reported discrimination, with median c‐statistics of 0.84 and 0.81, respectively. Calibration was reported in only 29 of 62 validation studies (41.9%). For those novel models that both reported discrimination and were validated (26 models), the median percentage change in discrimination was −1.6%. We identified 3 CPMs that had undergone at least 3 external validation studies: the out‐of‐hospital cardiac arrest score (9 validations; median c‐statistic, 0.79), the cardiac arrest hospital prognosis score (6 validations; median c‐statistic, 0.83), and the good outcome following attempted resuscitation score (6 validations; median c‐statistic, 0.76).

Conclusions

Although only a small number of SCA CPMs have been rigorously validated, the ones that have been demonstrate good discrimination.

Can Clinical Predictive Models Identify Patients Who Should Not Receive TAVR? A Systematic Review

Background

One third of high- and prohibitive-risk TAVR patients remain severely symptomatic or die 1 year after treatment. There is interest in identifying individuals for whom this procedure is futile and should not be offered.

Methods

We performed a systematic review of the highest reported stratum of risk in TAVR clinical predictive models (CPMs). We explore whether currently available predictive models can identify patients for whom TAVR is futile, based on a quantitative futility definition and the observed and predicted outcomes for patients in the highest stratum of risk.

Results

17 TAVR CPMs representing 69,191 treated patients were published from 2013 to 2018. When reported, the median number of patients in the highest stratum of risk was 569 (range 1 to 1759). Observed mortality for this risk stratum ranged from 9% at 30 days to 59% at 1 year after TAVR. Statistical confidence in these observed event rates was low. The highest predicted event rates ranged from 11.0% for in-hospital mortality to 75.1% for the composite of mortality or high symptom burden 1 year after TAVR.

Conclusion

No high-risk TAVR group in currently available TAVR CPMs had an appropriate event rate and adequate statistical power to meet a quantitative definition of futility.

Enhanced American College of Cardiology/American Heart Association Strategy for Prevention of Sudden Cardiac Death in High-Risk Patients With Hypertrophic Cardiomyopathy

Importance

Strategies for reliable selection of high-risk patients with hypertrophic cardiomyopathy (HCM) for prevention of sudden cardiac death (SCD) with implantable cardioverter/defibrillators (ICDs) are incompletely resolved.

Objective

To assess the reliability of SCD prediction methods leading to prophylactic ICD recommendations to reduce the number of SCDs occurring in patients with HCM.

Design, Setting, and Participants

In this observational longitudinal study, 2094 predominantly adult patients with HCM consecutively evaluated over 17 years in a large HCM clinical center were studied. All patients underwent prospective ICD decision making relying on individual major risk markers derived from the HCM literature and an enhanced American College of Cardiology/American Heart Association (ACC/AHA) guidelines-based risk factor algorithm with complete clinical outcome follow-up. Data were collected from June 2017 to February 2018, and data were analyzed from February to July 2018.

Main Outcomes and Measures

Arrhythmic SCD or appropriate ICD intervention for ventricular tachycardia or ventricular fibrillation.

Results

Of the 2094 study patients, 1313 (62.7%) were male, and the mean (SD) age was 51 (17) years. Of 527 patients with primary prevention ICDs implanted based on 1 or more major risk markers, 82 (15.6%) experienced device therapy-terminated ventricular tachycardia or ventricular fibrillation episodes, which exceeded the 5 HCM-related SCDs occurring among 1567 patients without ICDs (0.3%), including 2 who declined device therapy, by 49-fold (95% CI, 20-119; P = .001). Cumulative 5-year probability of an appropriate ICD intervention was 10.5% (95% CI, 8.0-13.5). The enhanced ACC/AHA clinical risk factor strategy was highly sensitive for predicting SCD events (range, 87%-95%) but less specific for identifying patients without SCD events (78%). The C statistic calculated for enhanced ACC/AHA guidelines was 0.81 (95% CI, 0.77-0.85), demonstrating good discrimination between patients who did or did not experience an SCD event. Compared with enhanced ACC/AHA risk factors, the European Society of Cardiology risk score retrospectively applied to the study patients was much less sensitive than the ACC/AHA criteria (34% [95% CI, 22-44] vs 95% [95% CI, 89-99]), consistent with recognizing fewer high-risk patients.

Conclusions and Relevance

A systematic enhanced ACC/AHA guideline and practice-based risk factor strategy prospectively predicted SCD events in nearly all at-risk patients with HCM, resulting in prophylactically implanted ICDs that prevented many catastrophic arrhythmic events in this at-risk population.

Ridges and Pouches: A Case Series of Anomalous Atrial Septal Fusion

•

Left atrial ridge may affect planning of trans-septal approach for interventions.

•

Left atrial septal pouch may become a nidus for thrombus and source of embolus.

•

Complete interrogation of the atrial septum can identify these anatomic variants.

Clinical Prediction Models for Valvular Heart Disease

Background

While many clinical prediction models (CPMs) exist to guide valvular heart disease treatment decisions, the relative performance of these CPMs is largely unknown. We systematically describe the CPMs available for patients with valvular heart disease with specific attention to performance in external validations.

Methods and Results

A systematic review identified 49 CPMs for patients with valvular heart disease treated with surgery (n=34), percutaneous interventions (n=12), or no intervention (n=3). There were 204 external validations of these CPMs. Only 35 (71%) CPMs have been externally validated. Sixty‐five percent (n=133) of the external validations were performed on distantly related populations. There was substantial heterogeneity in model performance and a median percentage change in discrimination of −27.1% (interquartile range, −49.4%–−5.7%). Nearly two‐thirds of validations (n=129) demonstrate at least a 10% relative decline in discrimination. Discriminatory performance of EuroSCORE II and Society of Thoracic Surgeons (2009) models (accounting for 73% of external validations) varied widely: EuroSCORE II validation c‐statistic range 0.50 to 0.95; Society of Thoracic Surgeons (2009) Models validation c‐statistic range 0.50 to 0.86. These models performed well when tested on related populations (median related validation c‐statistics: EuroSCORE II, 0.82 [0.76, 0.85]; Society of Thoracic Surgeons [2009], 0.72 [0.67, 0.79]). There remain few (n=9) external validations of transcatheter aortic valve replacement CPMs.

Conclusions

Many CPMs for patients with valvular heart disease have never been externally validated and isolated external validations appear insufficient to assess the trustworthiness of predictions. For surgical valve interventions, there are existing predictive models that perform reasonably well on related populations. For transcatheter aortic valve replacement (CPMs additional external validations are needed to broadly understand the trustworthiness of predictions.

Guideline-Based Referral for Septal Reduction Therapy in Obstructive Hypertrophic Cardiomyopathy Is Associated With Excellent Clinical Outcomes

BACKGROUND

The outcome of medically refractory patients with obstructive hypertrophic cardiomyopathy treated according to the American College of Cardiology/American Heart Association consensus guideline recommendations is not known. The objectives of this study were to define the short- and long-term outcomes of medically refractory obstructive hypertrophic cardiomyopathy patients undergoing alcohol septal ablation (ASA) and surgical septal myectomy (SM) with patient management in accordance with these consensus guidelines, as well as to quantify procedural risk and burden of comorbid conditions at the time of treatment.

METHODS AND RESULTS

Patients with obstructive hypertrophic cardiomyopathy referred for either ASA or SM from 2004 to 2015 were followed for the primary end point of short- and long-term mortality and compared with respective age- and sex-matched US populations. Of 477 consecutive severely symptomatic patients, 99 underwent ASA and 378 SM. Compared with SM, ASA patients were older ( P<0.001), had a higher burden of comorbid conditions ( P<0.01), and significantly higher predicted surgical mortality ( P<0.005). Procedure-related mortality was 0.3% and similarly low in both groups (0% in ASA and 0.8% in SM). Over 4.0±2.9 years of follow-up, 95% of patients had substantial improvement in heart failure symptoms to New York Heart Association class I/II (96% in SM and 90% in ASA). Long-term mortality was similar between the 2 groups with no difference compared with age- and sex-matched US populations.

CONCLUSIONS

Guideline-based referral for ASA and SM leads to excellent outcomes with low procedural mortality, excellent long-term survival, and improvement in symptoms. These outcomes occur in ASA patients despite being an older cohort with significantly more comorbidities.

Abstract 111: External Validations of Clinical Predictive Models for Patients with Sudden Cardiac Arrest

Background: Sudden cardiac arrest is associated with high morbidity and mortality. A number of clinical predictive models (CPMs) have been introduced to help with patient-specific prognostication of survival and neurologic outcomes. Here, we evaluate the performance of these CPMs with attention to key variables and models with rigorous validation.

Methods: We performed a systematic review and citation search of cardiac arrest CPMs in the Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry and identified external validations of these models through September 2018. We extracted information on CPM performance from both original reports and external validations. For external validations, we calculated the percent change in discrimination.

Results: We identified 65 unique cardiac arrest CPMs (median n=611, IQR=781) published between 1981 and 2018. Thirty-eight of the 65 models (58%) reported a c-statistic (ROC AUC) (median=0.82, IQR=0.09). The median number of predictive variables was 4 (IQR=3), and the three most common variables were 1) initial cardiac rhythm (n=41 of 65; 63%), 2) age (n=33 of 65; 51%), and 3) witnessed arrest (n=24 of 65; 37%). We identified external validations for 26 of 65 (40%) CPMs. All validations (n=44) reported discrimination, but only 21 of 44 (48%) validations reported information regarding calibration. Of the CPMs that reported discrimination and were externally validated at least once (n=15), we noted a median percent change in discrimination of -1.9% (IQR = 11.3%). The three most rigorously validated cardiac arrest CPMs were 1) the OHCA score (n=5, median AUC=0.79), 2) the CAHP score (n=3, median AUC=0.85), and 3) the GO-FAR score (n=3, median AUC=0.82).

Conclusions: While few cardiac arrest CPMs have been externally validated, those that have demonstrate stable discriminatory power.

Relation between therapy-induced changes in natriuretic peptide levels and long-term therapeutic effects on mortality in patients with heart failure and reduced ejection fraction

AIMS

To assess whether natriuretic peptides (NPs) can be used to reliably predict long-term therapeutic effect on clinical outcomes for patients with heart failure and reduced ejection fraction (HFrEF).

METHODS AND RESULTS

HFrEF intervention trials with mortality data were identified. Subsequently, we identified trials assessing therapy-induced changes in NPs. We assessed the correlation between the average short-term placebo-corrected drug or device effect on NPs and the longer-term therapeutic effect on clinical outcomes. Of 35 distinct therapies with an identifiable mortality result (n = 105 062 patients), 20 therapies had corresponding data on therapeutic effect on NPs. No correlation was observed between therapy-induced placebo-corrected change in brain natriuretic peptide or N-terminal pro-brain natriuretic peptide and therapeutic effect on all-cause mortality (ACM) (Spearman r = -0.32, P = 0.18 and r = -0.20, P = 0.47, respectively). There was no correlation between therapy-induced placebo-corrected per cent change in NP and intervention effect on ACM or ACM-heart failure hospitalizations (r = -0.30, P = 0.11 and r = 0.10, P = 0.75, respectively).

CONCLUSIONS

Short-term intervention-induced changes in NP levels are not reliable predictors of therapeutic long-term effect on mortality or morbidity outcomes for patients with HFrEF.

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.

Abstract 276: Identification of Echocardiographic Imaging View Using Deep Learning

Objectives: Echocardiography plays a central role in the identification and treatment of heart disease and image interpretation requires extensive experience and time. Deep learning techniques can identify complex patterns from large labeled datasets and have revolutionized image classification, object detection and segmentation. To date, state-of-the-art automated image interpretation has not been widely applied to echocardiogram interpretation. The first cognitive step in image interpretation is imaging view identification. The aim of this study is to create an automated processing pipeline for determining the echocardiographic imaging view and evaluate the accuracy of various deep learning algorithms.

Methods: Individual parasternal long-axis (PLAX) and non-PLAX (other) DICOM images were obtained and de-identified for testing. Images were individually sorted based on imaging view (PLAX vs other) by a board certified echocardiographer. Deep convolutional neural networks were trained to sort images in a similar fashion. The algorithm was trained on an 80% sample of images and accuracy was tested on the remaining 20%. The accuracy of each network architecture was compared to view identification by a blinded echcoardiogapher. Three separate network architectures (LeNet, VGG-16, and DenseNet) were implemented using the TensorFlow API in python. Input images to each network were resized to 224x224 pixels to balance resolution, memory, and compute.

Results: 42,459 individual parasternal long-axis (PLAX) and 301,557 non-PLAX (other) DICOM images were used for analysis. The accuracy of the LeNet (11.3 million parameters), VGG-16 (40.4 million parameters), and DenseNet (7 million parameters) networks on the validation set of echocardiogram images was 85.0%, 97.50%, and 99.94% respectively for image identification. The compute time for forward pass inference is comparable for each network taking only milliseconds.

Conclusion: Vendor independent deep learning networks can rapidly and accurately identify features on standard echocardiogram images. DenseNet network architecture matched human level performance. Deep learning has the potential for rapid, automated image interpretation and can improve the accuracy and efficiency of echocardiogram interpretation.

Abstract WP173: A Systematic Review of Clinical Prediction Models for Patients With Stroke

Introduction: Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision-making and individualize care. We aim to conduct a systematic study of published CPMs predicting mortality, functional outcome or stroke recurrence for patients with stroke.

Methods: The Tufts PACE CPM Registry is based on a systematic review of cerebrovascular and cardiovascular CPMs published in English-language articles from 1/1990-3/2015, and includes 1084 unique CPMs extracted from 747 articles. CPMs predicting outcomes for patients with stroke were characterized based on index condition (hemorrhagic, ischemic or all stroke) and outcome (mortality, functional outcome or stroke recurrence). We identified the most commonly occurring covariates in models grouped by index condition-outcome pair (I-O pair).

Results: Among 1084 total models in the registry, 116 (11%) predicted mortality, functional outcomes or stroke recurrence among patients with stroke. The top three most frequent models predicted functional outcomes among ischemic stroke patients (n=23), mortality among all stroke patients (n=19), and mortality among patients with hemorrhagic stroke (n=18). The median reported C statistic was 0.84 (among n=78 models reporting this measure). About half (45%) of models reported internal validations, with only 25% reporting external validations. The most commonly occurring covariates in the models were age (77%), stroke severity (51%), and functional status (26%) (see Figure). Neuroimaging findings were included relatively infrequently (21%), but were included in all 9 models predicting functional outcome among hemorrhagic stroke patients.

Conclusions: There is an abundance of CPMs to predict clinically important outcomes in stroke populations. More work is needed to understand how this prognostic information might be used to improve decision making and outcomes for stroke patients.

Tufts PACE Clinical Predictive Model Registry: update 1990 through 2015

BACKGROUND

Clinical predictive models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision-making and individualize care. The Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry is a comprehensive database of cardiovascular disease (CVD) CPMs. The Registry was last updated in 2012, and there continues to be substantial growth in the number of available CPMs.

METHODS

We updated a systematic review of CPMs for CVD to include articles published from January 1990 to March 2015. CVD includes coronary artery disease (CAD), congestive heart failure (CHF), arrhythmias, stroke, venous thromboembolism (VTE), and peripheral vascular disease (PVD). The updated Registry characterizes CPMs based on population under study, model performance, covariates, and predicted outcomes.

RESULTS

The Registry includes 747 articles presenting 1083 models, including both prognostic (n = 1060) and diagnostic (n = 23) CPMs representing 183 distinct index condition/outcome pairs. There was a threefold increase in the number of CPMs published between 2005 and 2014, compared to the prior 10-year interval from 1995 to 2004. The majority of CPMs were derived from either North American (n = 455, 42%) or European (n = 344, 32%) populations. The database contains 265 CPMs predicting outcomes for patients with coronary artery disease, 196 CPMs for population samples at risk for incident CVD, and 158 models for patients with stroke. Approximately two thirds (n = 701, 65%) of CPMs report a c-statistic, with a median reported c-statistic of 0.77 (IQR, 0.05). Of the CPMs reporting validations, only 333 (57%) report some measure of model calibration. Reporting of discrimination but not calibration is improving over time (p for trend < 0.0001 and 0.39 respectively).

CONCLUSIONS

There is substantial redundancy of CPMs for a wide spectrum of CVD conditions. While the number of CPMs continues to increase, model performance is often inadequately reported and calibration is infrequently assessed. More work is needed to understand the potential impact of this literature.

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure

Background

Heart failure clinical practice guidelines recommend applying validated clinical predictive models (CPMs) to support decision making. While CPMs are now widely available, the generalizability of heart failure CPMs is largely unknown.

Methods and Results

We identified CPMs derived in North America that predict mortality for patients with acute heart failure and validated these models in different world regions to assess performance in a contemporary international clinical trial (N=4133) of patients with acute heart failure treated with guideline‐directed medical therapy. We performed independent external validations of 3 CPMs predicting in‐hospital mortality, 60‐day mortality, and 1‐year mortality, respectively. CPM discrimination decreased in all regional validation cohorts. The median change in area under the receiver operating curve was −0.09 (range −0.05 to −0.23). Regional calibration was highly variable (90th percentile of absolute difference between smoothed observed and predicted values range <1% to >50%). Calibration remained poor after global recalibrations; however, region‐specific recalibration procedures significantly improved regional performance (recalibrated 90th percentile of absolute difference range <1% to 5% across all regions and all models).

Conclusions

Acute heart failure CPM discrimination and calibration vary substantially across different world regions; region‐specific (as opposed to global) recalibration techniques are needed to improve CPM calibration.

Abstract 129: Clinical Predictive Models for Valvular Heart Disease: A Systematic Review of the Literature

Objectives: Pre-procedure risk assessment is central to clinical decision making for patients with advanced valvular heart disease (VHD) and treatments are increasingly being offered to patients with elevated pre-procedure risk. While there are numerous clinical predictive models (CPMs) available for patients with VHD, the relative performance of these CPMs is largely unknown. Here we describe the performance of CPMs available for patients with VHD with specific attention to whether CPMs have been externally validated.

Methods: To identify CPMs for patients with VHD, we conducted a systematic review of the Tufts PACE CPM Registry, a comprehensive database of cardiovascular CPMs. For each identified CPM for patients with VHD, we performed a complete citation search using Scopus to identify any external validations of these models published in other articles. We extracted information on CPM performance in both the original report and also the external validations. For external validations we calculated the relative percent decrease in discrimination.

Results: We identified 41 CPMs predicting outcomes for patients with VHD. 33 (81%) predict outcomes following surgical intervention, 5 (12%) predict outcomes following percutaneous interventions, and 3 (7%) predict outcomes in the absence of intervention. Only 30/41 (73%) of the CPMs report a c-statistic. The median reported c- statistic was 0.77 [IQR, 0.04] for CPMs predicting outcomes following surgical interventions, 0.68 [IQR, 0.04] for CPMs for percutaneous interventions, and 0.83 [IQR, 0.07] for CPMs predicting outcomes in the absence of intervention. While a total of 69 external validations of these CPMs have been published, only 21 (51%) of the CPMs have ever been externally validated. For external validations that report c- statistics, we noted a median percent decrement in discrimination of -27.6% [IQR, -37.4] ( Figure) .

Conclusion: While there are numerous CPMs for patients with VHD, performance is often incompletely reported and half of these CPMs have never been externally validated. The CPMs that have been externally validated generally show substantially worse discrimination in external datasets compared to the derivation datasets.

Abstract 130: The Tufts PACE Clinical Predictive Model Registry: Update 1990 Through 2015

Background: Clinical Predictive Models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. The Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry is a comprehensive database of cardiovascular (CVD) CPMs. The Registry was last updated in 2012 and there has been substantial growth in the number of CPMs that are available.

Methods and Results: We updated a systematic review of CPMs for CVD to include articles published from January 1990 to March 2015. The Registry now includes prognostic (n=1047) and diagnostic (n = 27) CPMs. There was a 3-fold increase in the number of CPMs published between 2005 and 2014, when compared to 1995 and 2004 ( Figure) . There are 1074 models included in this database representing 68 distinct index/ outcome (I/O) pairings. 792 (72%) of the CPMs were derived from either North American (n = 448) or European (n = 344) populations. The database contains 265 CPMs predicting outcomes for patients with coronary artery disease, 187 CPMs for population samples at risk for incident CVD, and 158 models for patients with prior stroke. 697 (65%) CPMs report a c- statistic and overall the median reported c- statistic was 0.77 [IQR, 0.09]. Of the 10 most common index conditions, discrimination was highest for CPMs predicting outcomes following cardiac arrest (14/27 reporting, median c- statistic 0.83 [IQR, 0.08]). Discrimination was lowest for CPMs predicting outcomes for patients with other types of arrhythmias (16/22 reporting, median c -statistic 0.71 [IQR 0.07]). Of the CPMs included in this Registry only 422 (39%) report some measure of model calibration.

Conclusions: There is continued growth and substantial redundancy of CPMs for a wide spectrum of CVD conditions. While the number of CPMs continues to increase, model performance is often inadequately reported and calibration is infrequently assessed. More work is needed to understand the potential impact of this literature.

Field Synopsis of Sex in Clinical Prediction Models for Cardiovascular Disease

BACKGROUND

Several widely used risk scores for cardiovascular disease (CVD) incorporate sex effects, yet there has been no systematic summary of the role of sex in clinical prediction models (CPMs). To better understand the potential of these models to support sex-specific care, we conducted a field synopsis of sex effects in CPMs for CVD.

METHODS AND RESULTS

We identified CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry, a comprehensive database of CVD CPMs published from January 1990 to May 2012. We report the proportion of models including sex effects on CVD incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 592 CVD-related CPMs, 193 (33%) included sex as a predictor or presented sex-stratified models. Sex effects were included in 78% (53/68) of models predicting incidence of CVD in a general population, versus only 35% (59/171), 21% (12/58), and 17% (12/72) of models predicting outcomes in patients with coronary artery disease, stroke, and heart failure, respectively. Among sex-including CPMs, women with heart failure were at lower mortality risk in 8 of 8 models; women undergoing revascularization for coronary artery disease were at higher mortality risk in 10 of 12 models. Factors associated with the inclusion of sex effects included the number of outcome events and using cohorts at-risk for CVD (rather than with established CVD).

CONCLUSIONS

Although CPMs hold promise for supporting sex-specific decision making in CVD clinical care, sex effects are included in only one third of published CPMs.

Field Synopsis of the Role of Sex in Stroke Prediction Models

Background

Guidelines for stroke prevention recommend development of sex‐specific stroke risk scores. Incorporating sex in Clinical Prediction Models (CPMs) may support sex‐specific clinical decision making. To better understand their potential to guide sex‐specific care, we conducted a field synopsis of the role of sex in stroke‐related CPMs.

Methods and Results

We identified stroke‐related CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Database, a systematic summary of cardiovascular CPMs published from January 1990 to May 2012. We report the proportion of models including the effect of sex on stroke incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 92 stroke‐related CPMs, 30 (33%) contained a coefficient for sex or presented sex‐stratified models. Only 12/58 (21%) CPMs predicting outcomes in patients included sex, compared to 18/30 (60%) models predicting first stroke (P<0.0001). Sex was most commonly included in models predicting stroke among a general population (69%). Female sex was consistently associated with reduced mortality after ischemic stroke (n=4) and higher risk of stroke from arrhythmias or coronary revascularization (n=5). Models predicting first stroke versus outcomes among patients with stroke (odds ratio=5.75, 95% CI 2.18–15.14, P<0.001) and those developed from larger versus smaller sample sizes (odds ratio=4.58, 95% CI 1.73–12.13, P=0.002) were significantly more likely to include sex.

Conclusions

Sex is included in a minority of published CPMs, but more frequently in models predicting incidence of first stroke. The importance of sex‐specific care may be especially well established for primary prevention.

Abstract 107: A Field Synopsis of Sex in Clinical Prediction Models for Cardiovascular Disease

Introduction: Incorporating sex in Clinical Prediction Models (CPMs) may support sex-specific clinical decision making. Risk scores commonly used in CVD prevention, such as the Pooled Cohort Equation and the Framingham risk score for 10 year CVD risk, present sex-specific algorithms, yet to date, there has been no systematic summary of the role of sex across CPMs. To better understand the potential influence these models might have on sex-specific care, we conducted a field synopsis of the role of sex in CPMs for CVD.

Methods: We identified CPMs in the Tufts PACE CPM Database, a systematic review of CVD CPMs published from 1/1990-5/2012. We report the proportion of models including the effect of sex on CVD incidence or prognosis, summarize the directionality of sex effects (harm or protection associated with female sex), and explore factors influencing the inclusion of sex.

Results: Out of 592 CPMs with CVD as either an index condition or outcome, 173 (34%) contained a coefficient for sex and 27 (5%) presented sex-stratified models. Sex was over 2.5 times more likely to be included in models predicting CVD incidence in a general population sample versus models predicting prognostic outcomes among patients with known CVD (79% (54/68) vs. 29% (146/498), p<0.0001). Among the 366 CVD-related models that did not include sex as a covariate or stratification variable, 71% reported that sex had been considered as a candidate for inclusion based on clinical or statistical criteria. Being a woman was associated with lower risk of death in 8 of 8 models predicting mortality among patients with heart failure that included sex as a covariate (see figure), yet a higher risk of death among women undergoing revascularization procedures in 10 of 12 CPMs. In multivariable analysis, the number of outcome events (OR=2.6, 95% CI 1.6-4.4, p=0.0002) and a cohort defined as a population sample at risk for developing CVD (OR=6.2, 95% CI 2.7-14.1, p<0.0001) were significantly associated with inclusion of sex in CPMs.

Conclusions: Sex is included in about one third of published CPMs, but more frequently in models predicting incidence of CVD. The importance of sex-specific care may be especially well established for primary prevention. The rapidly growing literature on CPMs may yield important insights to guide sex-specific CVD prevention and treatment.

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.

Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database

BACKGROUND

Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described.

METHODS AND RESULTS

We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration.

CONCLUSIONS

There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood.

Abstract 164: A Field Synopsis of Gender Effects in Clinical Prediction Models for Cardiovascular Disease

Introduction: Gender differences in incidence, prognosis and treatment response have been observed across the spectrum of cardiovascular diseases (CVD). However, despite several decades of investigation, consistent findings regarding the magnitude and directionality of gender differences in CVD are elusive. We therefore conducted the first field synopsis of the role of gender on CVD conditions using a registry of clinical prediction models (CPMs).

Methods: The Tufts PACE Center (CPM) Registry is based on a systematic review of cardiovascular CPMs published in English-language articles from 1/1990-5/2012. All included CPMs permit calculation of outcome probabilities from information provided in an equation, point score or nomogram. For the 15 most common unique index condition-outcome pair models, we calculated the proportion of models that included coefficients for the effect of gender on CVD incidence or prognosis, or presented gender-stratified models. The sample size, age distribution and proportion of females in the model development cohorts were summarized.

Results: Out of 579 CPMs with CVD as either an index condition or outcome, 169 (29%) contained a coefficient for gender and 33 (6%) presented gender-stratified models. Gender was more frequently included as a covariate or stratification variable in models predicting incident CVD versus prognosis for patients with known CVD. Gender was included in 60/74 (81%) models predicting morbidity and/or mortality among a population sample, yet in only 9/53 (17%) of models predicting morbidity and/or mortality among patients with stroke, and 9/53 (17%) of models predicting mortality among patients with congestive heart failure. Gender was more likely to be included in CPMs developed from cohorts with larger sample sizes (150/299 cohorts with n≥2000 versus 54/277 cohorts with n<2000, p<0.001). For each 10% increase in the proportion of women in the model development cohort, there was a 22% increased odds of including gender in the CPM (OR = 1.22, 95% CI 1.08-1.39, p=0.002).

Conclusions: Gender is an important prognostic factor in CVD, but is only included in about one third of published CPMs. Gender is much more frequently included as a predictor of incident CVD among the disease-free than of prognosis in those with established CVD.

Abstract 174: Clinical Prediction Models for Cardiovascular Disease: The Tufts PACE CPM Database

Background: Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease (CVD) there are numerous CPMs available though the extent of this literature is not well described.

Methods and Results: We conducted a systematic review for articles containing CPMs for CVD published between January 1990 through May 2012. CVD includes coronary artery disease (CAD), congestive heart failure (CHF), arrhythmias, stroke, venous thromboembolism (VTE) and peripheral vascular disease (PVD). We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. We included articles that describe newly developed CPMs that predict the risk of developing an outcome (prognostic models) or the probability of a specific diagnosis (diagnostic models). There are 796 models included in this database representing 31 distinct index conditions. 717 (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. There are 215 CPMs for patients with CAD, 168 CPMs for population samples at risk for incident CVD, and 79 models for patients with CHF (Figure). De novo CPMs predicting mortality were most commonly published for patients with known CAD (98 models) followed by HF (63 models) and stroke (24 models). There are 77 distinct index/ outcome (I/O) pairings and models are roughly evenly split between those predicting short term outcomes (< 3 months) and those predicting long term outcomes (< 6 months). There are 41 diagnostic CPMs included in this database, most commonly predicting diagnoses of CAD (11 models), VTE (10 models), and acute coronary syndrome (5 models). Of the de novo models in this database 450 (63%) report a c-statistic and 259 (36%) report either the Hosmer-Lemeshow statistic or show a calibration plot.

Conclusions: There is an abundance of CPMs available for many CVD conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood.

Prevention of recurrent stroke in patients with patent foramen ovale

Patent foramen ovale (PFO) is common and only rarely related to stroke. The high PFO prevalence in healthy individuals makes for difficult decision making when a PFO is found in the setting of a cryptogenic stroke, because the PFO may be an incidental finding. Recent clinical trials of device-based PFO closure have had negative overall summary results; these trials have been limited by low recurrence rates. The optimal antithrombotic strategy for these patients is also unknown. Recent work has identified a risk score that estimates PFO-attributable fractions based on individual patient characteristics, although whether this score can help direct therapy is unclear.

Abstract W P177: The Frequency and Directionality of the Effect of Sex in Prediction Models for Stroke

Introduction: There are apparent sex differences in stroke, with women having a higher lifetime risk and worse outcomes. However, there remain critical gaps in our understanding of sex differences in risk, treatment response and outcomes following stroke. We conducted the first systematic summary of the role of sex on stroke-related conditions using a registry of clinical prediction models (CPMs).

Methods: The Tufts PACE CPM Registry is based on a systematic review of cerebrovascular and cardiovascular CPMs published in English-language articles from 1/1990-5/2012, and includes 585 unique CPMs extracted from 506 articles. All included CPMs permit calculation of outcome probabilities from information provided in the form of an equation, point score or nomogram. We calculated the proportion of models with coefficients for the effect of sex on stroke incidence or prognosis, and summarized the directionality (harmful vs. protective) of the coefficients for sex.

Results: Out of 75 CPMs with stroke as either an index condition or outcome, 23 (31%) contained a coefficient for sex or presented sex-stratified models. Only 8/48 (17%) models of stroke prognosis included sex or presented sex-specific models, as compared to 14/24 (58%) of models predicting stroke incidence. In models categorized by unique index-outcome pairs, sex was most commonly included in models predicting stroke among a general population (67%). Female sex was associated with reduced risk of mortality after ischemic stroke and a higher risk of stroke from arrhythmias or CABG/PCI. In a general population, women typically had a lower risk of stroke, although stratified models suggest this depends on the presence or absence of other risk factors.

Conclusions: Sex is an important prognostic factor in CVD but is inconsistently included in stroke CPMs. Sex is more frequently included in models of stroke incidence than models of prognosis. Being female seems protective for some outcomes, but harmful for others.

Controversies in cardioembolic stroke

OPINION STATEMENT

Cardioembolic (CE) stroke mechanisms account for a significant number of ischemic strokes; however, the true burden is likely underestimated. It is critically important to identify patients with CE strokes because these individuals have high recurrence rates and represent a subgroup of patients who may benefit from targeted therapy in the form of anticoagulation or device based treatments. Current guidelines offer recommendations for diagnosis and treatment of these patients; however, important questions remain. First, appropriate cardiac testing in the setting of CE must be individualized and the optimal duration of electrocardiographic monitoring to rule out atrial fibrillation (AF) is unclear. Second, risk stratification tools for AF remain understudied, and there is controversy about which anticoagulant agents are most appropriate. Lastly, important potential CE sources of stroke such as patent foramen ovale have garnered significant attention recently, and debate regarding how to manage these patients persists. In this review, we discuss some of the important controversies in diagnosing and treating patients with possible CE stroke, pointing to areas where future research might be particularly valuable.

Transesophageal echocardiography in cryptogenic stroke and patent foramen ovale: analysis of putative high-risk features from the risk of paradoxical embolism database

BACKGROUND

Patent foramen ovale (PFO) is associated with cryptogenic stroke (CS), although the pathogenicity of a discovered PFO in the setting of CS is typically unclear. Transesophageal echocardiography features such as PFO size, associated hypermobile septum, and presence of a right-to-left shunt at rest have all been proposed as markers of risk. The association of these transesophageal echocardiography features with other markers of pathogenicity has not been examined.

METHODS AND RESULTS

We used a recently derived score based on clinical and neuroimaging features to stratify patients with PFO and CS by the probability that their stroke is PFO-attributable. We examined whether high-risk transesophageal echocardiography features are seen more frequently in patients more likely to have had a PFO-attributable stroke (n=637) compared with those less likely to have a PFO-attributable stroke (n=657). Large physiologic shunt size was not more frequently seen among those with probable PFO-attributable strokes (odds ratio [OR], 0.92; P=0.53). The presence of neither a hypermobile septum nor a right-to-left shunt at rest was detected more often in those with a probable PFO-attributable stroke (OR, 0.80; P=0.45; OR, 1.15; P=0.11, respectively).

CONCLUSIONS

We found no evidence that the proposed transesophageal echocardiography risk markers of large PFO size, hypermobile septum, and presence of right-to-left shunt at rest are associated with clinical features suggesting that a CS is PFO-attributable. Additional tools to describe PFOs may be useful in helping to determine whether an observed PFO is incidental or pathogenically related to CS.

Drug and Device Effects on Peak Oxygen Consumption, 6-Minute Walk Distance, and Natriuretic Peptides as Predictors of Therapeutic Effects on Mortality in Patients With Heart Failure and Reduced Ejection Fraction

Background—

Although peak oxygen consumption (peak V o 2 ), 6-minute walk distance (6MW), and natriuretic peptides (BNP and NT-proBNP) are predictors of mortality in heart failure (HF) patients, it is not known whether therapy-induced changes in these measures can predict therapeutic effect on mortality. The objective of this analysis is to quantitatively assess the relationship between therapeutic effects on commonly proposed short-term markers in HF trials and therapeutic effects on long-term outcome in patients with HF and left ventricular dysfunction.

Methods and Results—

We identified drug or device therapies for which there exists at least 1 randomized, controlled trial (RCT) assessing mortality over at least 6 months in at least 500 patients. For each of these therapies, we identified RCTs assessing the short-term changes in V o 2 , 6MW, BNP, and NT-proBNP (few of the mortality RCTs assessed the short-term changes in markers). For each intervention, we calculated the odds ratio for mortality (using random effect meta-analysis when necessary), as well as the trial level average drug- or device-induced change in the markers. We assessed the correlation between the odds ratio for death with the placebo-corrected change in the functional parameter or biomarker across the interventions. We identified mortality RCTs of 27 distinct therapies (n=73 267 patients) with a median follow-up of 19 months, that directed the search for RCTs of the effect of those interventions on the functional markers and biomarkers. There were 54 peak V o 2 trials (n=4646 patients), 34 6MW trials (n=6995 patients), 15 BNP trials (n=7233), and 6 NT-proBNP trials (n=1946) included in this analysis. There was no significant correlation between the average therapy-induced placebo-corrected change in peak V o 2 and the odds ratio for mortality ( r =0.158, P =0.26). Increased drug or device-induced average change in 6MW was correlated with increased odds ratio for mortality ( r =0.373, P =0.036). There was no significant correlation between the average therapy-induced, placebo-corrected change in the natriuretic peptides and the odds ratio for mortality (BNP: r =−0.065, P =0.82, NT-proBNP: r =−0.667, P =0.15). There was no apparent relation between change in the functional parameter or biomarker and categorical effect on mortality.

Conclusions—

This analysis, limited to trial level data from different therapeutic eras, suggests that drug- or device-induced effects on peak V o 2 , 6MW, and natriuretic peptides found in short-term trials do not predict the corresponding average long-term therapeutic effects on mortality for patients with HF and left ventricular dysfunction.