Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence-powered analysis of 12-lead intake electrocardiogram.

BACKGROUND: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. OBJECTIVE: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). METHODS: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. RESULTS: A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. CONCLUSION: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.

Distribution of C-arm projections in native and bioprosthetic aortic valves cusps: Implication for BASILICA procedures.

OBJECTIVES: We sought to document aortic cusps fluoroscopic projections and their distributions using leaflet alignment which is a novel concept to optimize visualization of leaflets and for guiding BASILICA (bioprosthetic or native aortic scallop intentional laceration to prevent coronary artery obstruction) and determine whether these projections were feasible in catheter laboratory. BACKGROUND: Optimal fluoroscopic projections of aortic valve cusps have not been well described. METHODS: A total of 128 pre-transcatheter aortic valve replacement (pre-TAVR) computed tomographies (CT) (72 native valves and 56 bioprosthetic surgical valves) were analyzed. Using CT software (3Mensio, Pie medical imaging, the Netherlands), leaflet alignment was performed and the feasibility of these angles, which were defined as rate of obtainable with efforts (within LAO/RAO of 85° and CRA/CAU of 50°) were evaluated. RESULTS: High feasibility was seen in right coronary cusp (RCC) front view (100%) and left coronary cusp (LCC) side view (99.2%), followed by noncoronary cusp side view (95.3%). In contrast, low feasibility of RCC side view (7.8%) and LCC front view (47.6%) was observed. No statistical differences were seen between the distribution of native valves and bioprosthetic surgical valves. With patient/table tilt of 20°LAO and 10°CRA, the feasibility of RCC side view and LCC front view increased to 43.7 and 85.2%, respectively. CONCLUSION: Distributions of each cusp's leaflet alignment follows "sigmoid curve" which can provide better understanding of aortic valve cusp orientation in TAVR and BASILICA. RCC side view used in right cusp BASILICA is commonly unachievable in catheter laboratory and may improve with patient/table tilt.

Coronary ostial eccentricity in severe aortic stenosis: Guidance for BASILICA transcatheter leaflet laceration.

BACKGROUND: Eccentricity of coronary ostial positions in relation to the aortic valve cusp may influence the target laceration location in BASILICA (Bioprosthetic or native Aortic Scallop Intentional Laceration to prevent Coronary Artery obstruction). Eccentricity of the coronary ostia in relation to coronary cusps of native and valve-in-valve transcatheter aortic valve replacement (TAVR) was not well described before. METHODS: A total of 121 pre-TAVR patients' CT data (72 native valves TAVR and 49 bioprosthetic surgical valves TAVR) was included and coronary ostial eccentricity angles were measured and compared. Coronary ostial angles were measured between mid-cusp line to coronary ostium in CT perpendicular images. RESULTS: In the overall cohort, the right coronary artery (RCA) had an eccentric origin in the majority of cases, favoring the commissure between the right and the non coronary cusp (17.0°, IQR; 10-25). On the other hand, the left coronary artery (LCA) originated most commonly near center of the cusp position (0°, IQR; -8 -7.5) In comparison of native and bioprosthetic valves, RCA ostial angles were more eccentric in native valves (19.0°, IQR; 12-26) than in bioprosthetic valves (14.0°, IQR; 3-20) (p = 0.004). Whereas, LCA ostial angle has no significant differences between native valves (-2.0°, IQR;-7.75-5.75) and bioprosthetic valves (1°, IQR;-8-13), (p = 0.6). CONCLUSION: RCA ostia often have an eccentric origin towards the non-coronary cusp, especially in native aortic valves, while LCA ostia commonly originate near the center of the cusp. This finding may contribute to better performance of BASILICA procedures.