Association between gait video information and general cardiovascular diseases: a prospective cross-sectional study.

Aims: Cardiovascular disease (CVD) may not be detected in time with conventional clinical approaches. Abnormal gait patterns have been associated with pathological conditions and can be monitored continuously by gait video. We aim to test the association between non-contact, video-based gait information and general CVD status. Methods and results: Individuals undergoing confirmatory CVD evaluation were included in a prospective, cross-sectional study. Gait videos were recorded with a Kinect camera. Gait features were extracted from gait videos to correlate with the composite and individual components of CVD, including coronary artery disease, peripheral artery disease, heart failure, and cerebrovascular events. The incremental value of incorporating gait information with traditional CVD clinical variables was also evaluated. Three hundred fifty-two participants were included in the final analysis [mean (standard deviation) age, 59.4 (9.8) years; 25.3% were female]. Compared with the baseline clinical variable model [area under the receiver operating curve (AUC) 0.717, (0.690-0.743)], the gait feature model demonstrated statistically better performance [AUC 0.753, (0.726-0.780)] in predicting the composite CVD, with further incremental value when incorporated with the clinical variables [AUC 0.764, (0.741-0.786)]. Notably, gait features exhibited varied association with different CVD component conditions, especially for peripheral artery disease [AUC 0.752, (0.728-0.775)] and heart failure [0.733, (0.707-0.758)]. Additional analyses also revealed association of gait information with CVD risk factors and the established CVD risk score. Conclusion: We demonstrated the association and predictive value of non-contact, video-based gait information for general CVD status. Further studies for gait video-based daily living CVD monitoring are promising.

Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography.

BACKGROUND: Current approaches for initial coronary artery disease (CAD) assessment rely on pretest probability (PTP) based on risk factors and presentations, with limited performance. Infrared thermography (IRT), a non-contact technology that detects surface temperature, has shown potential in assessing atherosclerosis-related conditions, particularly when measured from body regions such as faces. We aim to assess the feasibility of using facial IRT temperature information with machine learning for the prediction of CAD. METHODS: Individuals referred for invasive coronary angiography or coronary CT angiography (CCTA) were enrolled. Facial IRT images captured before confirmatory CAD examinations were used to develop and validate a deep-learning IRT image model for detecting CAD. We compared the performance of the IRT image model with the guideline-recommended PTP model on the area under the curve (AUC). In addition, interpretable IRT tabular features were extracted from IRT images to further validate the predictive value of IRT information. RESULTS: A total of 460 eligible participants (mean (SD) age, 58.4 (10.4) years; 126 (27.4%) female) were included. The IRT image model demonstrated outstanding performance (AUC 0.804, 95% CI 0.785 to 0.823) compared with the PTP models (AUC 0.713, 95% CI 0.691 to 0.734). A consistent level of superior performance (AUC 0.796, 95% CI 0.782 to 0.811), achieved with comprehensive interpretable IRT features, further validated the predictive value of IRT information. Notably, even with only traditional temperature features, a satisfactory performance (AUC 0.786, 95% CI 0.769 to 0.803) was still upheld. CONCLUSION: In this prospective study, we demonstrated the feasibility of using non-contact facial IRT information for CAD prediction.

Impact of High Lipoprotein(a) on Long-Term Survival Following Coronary Artery Bypass Grafting.

BACKGROUND: Lipoprotein(a) is a possible causal risk factor for atherosclerosis and related complications. The distribution and prognostic implication of lipoprotein(a) in patients undergoing coronary artery bypass grafting remain unknown. This study aimed to assess the impact of high lipoprotein(a) on the long-term prognosis of patients undergoing coronary artery bypass grafting. METHODS AND RESULTS: Consecutive patients with stable coronary artery disease who underwent isolated coronary artery bypass grafting from January 2013 to December 2018 from a single-center cohort were included. The primary outcome was all-cause death. The secondary outcome was a composite of major adverse cardiovascular and cerebrovascular events. Of the 18 544 patients, 4072 (22.0%) were identified as the high-lipoprotein(a) group (≥50 mg/dL). During a median follow-up of 3.2 years, primary outcomes occurred in 587 patients. High lipoprotein(a) was associated with increased risk of all-cause death (high lipoprotein(a) versus low lipoprotein(a): adjusted hazard ratio [aHR], 1.31 [95% CI, 1.09-1.59]; P=0.005; lipoprotein(a) per 1-mg/dL increase: aHR, 1.003 [95% CI, 1.001-1.006]; P=0.011) and major adverse cardiovascular and cerebrovascular events (high lipoprotein(a) versus low lipoprotein(a): aHR, 1.18 [95% CI, 1.06-1.33]; P=0.004; lipoprotein(a) per 1-mg/dL increase: aHR, 1.002 [95% CI, 1.001-1.004]; P=0.002). The lipoprotein(a)-related risk was greater in patients with European System for Cardiac Operative Risk Evaluation <3, and tended to attenuate in patients receiving arterial grafts. CONCLUSIONS: More than 1 in 5 patients with stable coronary artery disease who underwent coronary artery bypass grafting were exposed to high lipoprotein(a), which is associated with higher risks of death and major adverse cardiovascular and cerebrovascular events. The adverse effects of lipoprotein(a) were more pronounced in patients with clinically low-risk profiles or not receiving arterial grafts.