Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/Funder: Al-Alfi FoundationFunder: Magdi Yacoub Heart FoundationFunder: Rosetrees and Stoneygate Imperial College Research FellowshipFunder: National Health and Medical Research Council (Australia)Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/Funder: Al-Alfi FoundationFunder: Magdi Yacoub Heart FoundationFunder: Rosetrees and Stoneygate Imperial College Research FellowshipFunder: National Health and Medical Research Council (Australia)Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity

When Do Athletes Benefit from Cardiac Genetic Testing?

Describes when cardiac genetic testing is indicated for athlete

The use of smartphone ECGs in sports and exercise medicine.

Researchers Jessica Orchard, John Orchard, Lis Neubeck and Chris Semsarian look at the development of new devices making Electrocardiogram (ECG) screening much easier in the sports medicine field

The use of smartphone ECGs in sports and exercise medicine.

Researchers Jessica Orchard, John Orchard, Lis Neubeck and Chris Semsarian look at the development of new devices making Electrocardiogram (ECG) screening much easier in the sports medicine field

Cardiac screening of athletes: consensus needed for clinicians on indications for follow-up echocardiography testing

Suggested framework for discussion about indications for use of follow-up echocardiography testing for athletes who undergo cardiac screenin

Misclassification of cricket in the American College of Cardiology (ACC) Task Force classification of sports

Argument and data supporting the need to reclassify the cardiac demands of cricke

Comparison of left atrial phasic function in hypertrophic cardiomyopathy versus systemic hypertension using strain rate imaging

The aim of this study was to determine if left atrial (LA) phasic function evaluated by Doppler tissue imagingderived strain and strain rate would be differentially decreased in patients with hypertrophic cardiomyopathy (HC) compared to patients with hypertension and to normal controls. Thirty-seven patients with HC were compared to 44 patients with systemic hypertension (SH) and 65 normal controls using transthoracic echocardiography. Maximal and minimal LA volume and LA volume just before active atrial contraction (pre-P LA volume) were measured, and phasic LA volumes were calculated. Global and segmental systolic strain rate, early diastolic strain rate, and late diastolic strain rate (A-Sr) and strain were measured from Doppler tissue imaging. Left ventricular mass was increased in the HC and SH groups compared to normal controls, but diastolic dysfunction was greater in the HC group. LA volumes were increased in patients with HC compared to those with SH and to normal controls, with corresponding reductions in A-Sr and atrial strain in the HC group. In contrast, only early diastolic strain rate was decreased in the SH group compared to controls. A-Sr remained reduced in patients with HC compared to the SH group, even after adjusting for left ventricular mass. When left ventricular mass, parameters of diastolic function (peak E and E′ velocity), and the effect of patient group (SH vs HC) were examined in a stepwise regression model, patient group (SH vs HC) was the only independent determinant of A-Sr. In conclusion, HC results in LA enlargement with reduced LA phasic function that is reflected in reductions in A-Sr and atrial strain. Atrial enlargement is a likely consequence of the greater diastolic dysfunction in the HC group

Cardiovascular Screening of Elite Athletes by Sporting Organizations in Australia: A Survey of Chief Medical Officers

OBJECTIVE: To compare cardiovascular screening policies of Australian elite sporting organizations. DESIGN: Online survey. SETTING: Elite/professional sports in Australia. PARTICIPANTS: Chief medical officers (CMOs) of elite/professional sports in Australia, including rugby union and league, cricket, tennis, Australian football, and cycling. ASSESSMENT OF VARIABLES: Survey questions about each sport's cardiac screening policy: which screening components were included [eg, history and physical (H&P), resting 12-lead electrocardiogram (ECG)], whether screening was mandatory, whether the policy applied to elite junior and/or adult players, and which criteria were used to interpret ECGs. MAIN OUTCOME MEASURES: Which sports had a formal cardiac screening policy, which athletes the policy applied to, components of screening, ECG interpretation criteria used. RESULTS: Chief medical officers for 22/31 (71%) sports responded, representing >5000 athletes. Of these, 19/22 (86%) perform regular screening (100% H&P; 89% included ECG) with international cyclists also having routine echocardiograms and stress testing. Thirty-three percent of CMOs used the 2017 International Criteria for athlete ECG interpretation. Screening was mandatory with enforcement (26%), mandatory without enforcement (48%), and opt-out (26%). All screened adult elite athletes, and 68% screened junior elite athletes. Forty-two percent indicated athletes were required to pay for screening tests, and 63% required athletes to pay for follow-up tests. Almost all (94%) sports with a sports physician as the CMO screened athletes. CONCLUSIONS: Most sports have a screening policy, with reasonable uniformity of components. All included H&P, and almost all included ECG. Only one sport included an echocardiogram and stress test as a standard (international players only). Promoting the latest ECG interpretation criteria may reduce false-positives and cost. Future work should explore cardiac emergency plans, screening infrastructure, cost, and long-term follow-up