Automated Assessment of Cardiac Systolic Function From Coronary Angiograms With Video-Based Artificial Intelligence Algorithms.

Importance: Understanding left ventricular ejection fraction (LVEF) during coronary angiography can assist in disease management. Objective: To develop an automated approach to predict LVEF from left coronary angiograms. Design, Setting, and Participants: This was a cross-sectional study with external validation using patient data from December 12, 2012, to December 31, 2019, from the University of California, San Francisco (UCSF). Data were randomly split into training, development, and test data sets. External validation data were obtained from the University of Ottawa Heart Institute. Included in the analysis were all patients 18 years or older who received a coronary angiogram and transthoracic echocardiogram (TTE) within 3 months before or 1 month after the angiogram. Exposure: A video-based deep neural network (DNN) called CathEF was used to discriminate (binary) reduced LVEF (≤40%) and to predict (continuous) LVEF percentage from standard angiogram videos of the left coronary artery. Guided class-discriminative gradient class activation mapping (GradCAM) was applied to visualize pixels in angiograms that contributed most to DNN LVEF prediction. Results: A total of 4042 adult angiograms with corresponding TTE LVEF from 3679 UCSF patients were included in the analysis. Mean (SD) patient age was 64.3 (13.3) years, and 2212 patients were male (65%). In the UCSF test data set (n = 813), the video-based DNN discriminated (binary) reduced LVEF (≤40%) with an area under the receiver operating characteristic curve (AUROC) of 0.911 (95% CI, 0.887-0.934); diagnostic odds ratio for reduced LVEF was 22.7 (95% CI, 14.0-37.0). DNN-predicted continuous LVEF had a mean absolute error (MAE) of 8.5% (95% CI, 8.1%-9.0%) compared with TTE LVEF. Although DNN-predicted continuous LVEF differed 5% or less compared with TTE LVEF in 38.0% (309 of 813) of test data set studies, differences greater than 15% were observed in 15.2% (124 of 813). In external validation (n = 776), video-based DNN discriminated (binary) reduced LVEF (≤40%) with an AUROC of 0.906 (95% CI, 0.881-0.931), and DNN-predicted continuous LVEF had an MAE of 7.0% (95% CI, 6.6%-7.4%). Video-based DNN tended to overestimate low LVEFs and underestimate high LVEFs. Video-based DNN performance was consistent across sex, body mass index, low estimated glomerular filtration rate (≤45), presence of acute coronary syndromes, obstructive coronary artery disease, and left ventricular hypertrophy. Conclusion and relevance: This cross-sectional study represents an early demonstration of estimating LVEF from standard angiogram videos of the left coronary artery using video-based DNNs. Further research can improve accuracy and reduce the variability of DNNs to maximize their clinical utility.

Echocardiographic assessment of pulmonary arterial capacitance predicts mortality in pulmonary hypertension.

BACKGROUND: Pulmonary arterial capacitance (PAC) is one of the strongest predictors of clinical outcomes in patients with pulmonary hypertension (PH). We examined the value of an echocardiographic surrogate for PAC (ePAC) as a predictor of mortality in patients with PH. METHODS: We performed a retrospective study of 302 patients with PH managed at a PH comprehensive care center over a cumulative follow-up time of 858 patient-years. Charts from 2004 to 2018 were reviewed to identify patients in whom a right heart catheterization (RHC) was performed within two months of an echocardiogram. Standard invasive, non-invasive, functional, and biochemical prognostic markers were extracted from the time of RHC. The primary outcome was all-cause mortality. Cox proportional hazards models were used to model the time from RHC to the primary outcome or last medical contact. RESULTS: Variables associated with all-cause mortality included ePAC [standardized hazard ratio (HR) 0.68, 95% CI 0.48-0.98, p = 0.036], RHC-PAC (HR 0.68, 95% CI 0.48-0.96, p = 0.027), echocardiographic pulmonary vascular resistance (HR 1.29, 95% CI 1.05-1.60, p = 0.017), six-minute walk distance (HR 0.43, 95% CI 0.23-0.82, p = 0.01), and B-type natriuretic peptide (HR 1.29, 95% CI 1.03-1.62, p = 0.027). In multivariable-adjusted Cox analysis, ePAC predicted all-cause mortality independently of age, gender, and multiple comorbidities. There was a graded and stepwise association between low (<0.15 cm/mmHg), medium (0.15-0.25 cm/mmHg), and high (>0.25 cm/mmHg) tertiles of ePAC and all-cause mortality. CONCLUSIONS: We have demonstrated that ePAC is a readily available echocardiographic marker that independently predicts mortality in PH, and have provided clinically relevant ranges by which to risk-stratify patients and predict mortality.

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG while at the same time preserving interpretability for medical decision-making. METHODS AND RESULTS: We identified 36 186 ECGs from the University of California, San Francisco database that would enable training of models for estimation of cardiac structure or function or detection of disease. We segmented the ECG into standard component waveforms and intervals using a novel combination of convolutional neural networks and hidden Markov models and evaluated this segmentation by comparing resulting electrical intervals against 141 864 measurements produced during the clinical workflow. We then built a patient-level ECG profile, a 725-element feature vector and used this profile to train and interpret machine learning models for examples of cardiac structure (left ventricular mass, left atrial volume, and mitral annulus e-prime) and disease (pulmonary arterial hypertension, hypertrophic cardiomyopathy, cardiac amyloid, and mitral valve prolapse). ECG measurements derived from the convolutional neural network-hidden Markov model segmentation agreed with clinical estimates, with median absolute deviations as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Models trained using patient-level ECG profiles enabled surprising quantitative estimates of left ventricular mass and mitral annulus e' velocity (median absolute deviation of 16% and 19%, respectively) with good discrimination for left ventricular hypertrophy and diastolic dysfunction as binary traits. Model performance using our approach for disease detection demonstrated areas under the receiver operating characteristic curve of 0.94 for pulmonary arterial hypertension, 0.91 for hypertrophic cardiomyopathy, 0.86 for cardiac amyloid, and 0.77 for mitral valve prolapse. CONCLUSIONS: Modern machine learning methods can extend the 12-lead ECG to quantitative applications well beyond its current uses while preserving the transparency that is so fundamental to clinical care.

Influences of general and traditional Chinese beliefs on the decision to donate blood among employer-organized and volunteer donors in Beijing, China.

BACKGROUND: For the past several decades, Chinese blood centers have relied on blood donations from employer-organized donors (blood donors who donate blood in groups with coworkers as prearranged by the employer and the local blood center). Recently the government has decided to phase out employer-organized donors and transition to the use of only volunteer donors (blood donors who donate individually independent of employers). Evaluating the beliefs and attitudes of employer-organized and volunteer donors is critical to maintain an adequate blood supply after this transition. STUDY DESIGN AND METHODS: The study population consisted of 431 volunteer donors and 527 employer-organized donors who completed a structured questionnaire in July 2005. RESULTS: Employer-organized donors tended to be older, male, and married, with higher education and higher income compared to volunteer donors. Volunteer donors were more often motivated by altruism (p < 0.001) and more likely to donate larger volumes (400 mL vs. 200 mL) of blood (volunteer 70.5% vs. employer-organized 7%; p < 0.001). Employer-organized donors were more inhibited by factors related to traditional Chinese beliefs, such as the belief that blood donation affects life energy "Qi" (volunteer 3.1% vs. employer-organized 12.7%; p < 0.001), and requested more time off from work after donating. Employer-organized donors also express a greater concern about contracting disease from donating blood. CONCLUSION: To recruit voluntary donors effectively in China and other countries with traditional cultures, efforts need to counteract traditional beliefs and perceptions of risk that discourage donation by emphasizing the benefits, safety mechanisms, physiology, and epidemiology of blood donation. In China, there is a rich opportunity to convert prior employer-organized donors into volunteer donors, and the institution of a confidential predonation screening system may help to facilitate truthful risk factor disclosure.