Background: Epicardial adipose tissue (EAT) volume (cm 3 ) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenuation measurements quantified from noncontrast cardiac computed tomography. Methods: Our study included 2068 asymptomatic subjects (56±9 years, 59% male) from the EISNER trial (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary artery calcium measurement. EAT volume and mean attenuation were quantified using automated deep learning software from noncontrast cardiac computed tomography. MACE was defined as myocardial infarction, late (>180 days) revascularization, and cardiac death. EAT measures were compared to coronary artery calcium score and atherosclerotic cardiovascular disease risk score for MACE prediction. Results: At 14±3 years, 223 subjects suffered MACE. Increased EAT volume and decreased EAT attenuation were both independently associated with MACE. Atherosclerotic cardiovascular disease risk score, coronary artery calcium, and EAT volume were associated with increased risk of MACE (hazard ratio [95%CI]: 1.03 [1.01–1.04]; 1.25 [1.19–1.30]; and 1.35 [1.07–1.68], P <0.01 for all) and EAT attenuation was inversely associated with MACE (hazard ratio, 0.83 [95% CI, 0.72–0.96]; P =0.01), with corresponding Harrell C statistic of 0.76. MACE risk progressively increased with EAT volume ≥113 cm 3 and coronary artery calcium ≥100 AU and was highest in subjects with both ( P <0.02 for all). In 1317 subjects, EAT volume was correlated with inflammatory biomarkers C-reactive protein, myeloperoxidase, and adiponectin reduction; EAT attenuation was inversely related to these biomarkers. Conclusions: Fully automated EAT volume and attenuation quantification by deep learning from noncontrast cardiac computed tomography can provide prognostic value for the asymptomatic patient, without additional imaging or physician interaction.

Aims Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and automated epicardial adipose tissue (EAT) quantification, for the prediction of long-term risk of myocardial infarction (MI) and cardiac death in asymptomatic subjects. Methods and results Our study included 1912 asymptomatic subjects [1117 (58.4%) male, age: 55.8 ± 9.1 years] from the prospective EISNER trial with long-term follow-up after CAC scoring. EAT volume and density were quantified using a fully automated deep learning method. ML extreme gradient boosting was trained using clinical co-variates, plasma lipid panel measurements, risk factors, CAC, aortic calcium, and automated EAT measures, and validated using repeated 10-fold cross validation. During mean follow-up of 14.5 ± 2 years, 76 events of MI and/or cardiac death occurred. ML obtained a significantly higher AUC than atherosclerotic cardiovascular disease (ASCVD) risk and CAC score for predicting events (ML: 0.82; ASCVD: 0.77; CAC: 0.77, P &lt; 0.05 for all). Subjects with a higher ML score (by Youden’s index) had high hazard of suffering events (HR: 10.38, P &lt; 0.001); the relationships persisted in multivariable analysis including ASCVD-risk and CAC measures (HR: 2.94, P = 0.005). Age, ASCVD-risk, and CAC were prognostically important for both genders. Systolic blood pressure was more important than cholesterol in women, and the opposite in men. Conclusions In this prospective study, machine learning used to integrate clinical and quantitative imaging-based variables significantly improves prediction of MI and cardiac death compared with standard clinical risk assessment. Following further validation, such a personalized paradigm could potentially be used to improve cardiovascular risk assessment.

BackgroundThe study aimed to explore the sensitivity and specificity of a novel fast 16S rDNA PCR and sequencing assay for the improved diagnosis of infective endocarditis (IE) in patients with suspected native or prosthetic heart valve (HV) infection over a multi-year period at our cardiovascular center.MethodsSixty-eight patients were prospectively enrolled who underwent HV replacement for suspected or confirmed IE between February 1, 2009 and September 1, 2014. Patient demographics, medical co-morbidities, Duke’s criteria, culture results, and antibiotic therapy were collected by detailed chart reviews. Dual-priming oligonucleotide primers targeted to 500 bps of the V1-V3 region of the 16S rRNA gene were used to perform fast broad-range 16S rDNA PCR and Sanger sequencing on ribosomal DNA extracted from HV tissues. The performance/diagnostic efficiency of the molecular test was evaluated against blood cultures and Gram stain and culture of HV tissue in patients’ with definite IE according to Duke’s criteria.ResultsFifty patients (73.5 %) had definite IE and another 8 (11.8 %) had possible IE according to Duke’s criteria. Cardiac surgery was delayed an average of 15.4 days from the time of the patient’s last positive blood culture, and appropriate antibiotic therapy was given in the pre-operative period. While 44/50 (88 %) patients had a positive blood culture, HV tissue culture was only positive in 23 (46 %) of them. Molecular testing of all HV tissues had sensitivity, specificity, NPV and PPV of 92, 77.8, 77.8 and 92 % compared to 44, 100, 39.1 and 100 % respectively for culture for diagnosis of definite IE. For prosthetic HV tissue, 16S rDNA PCR had sensitivity of 93 % and specificity of 83 % compared to 35 and 100 % respectively for culture. A literature review showed that the diagnostic accuracy of our novel fast broad-range 16S rDNA PCR assay was similar or better than that of previously published studies.ConclusionsThis novel fast broad-range 16S rDNA PCR/sequencing test had superior sensitivity compared to tissue Gram stain and culture for identifying underlying bacterial pathogen in both native and prosthetic valve endocarditis.