Prognostic value of coronary CT angiography on long-term follow-up of 6.9 years

Long term follow-up of coronary CT angiography (CCTA) is scarce. The aim of the present study was to assess the prognostic value of CCTA over a follow-up period of more than 6 years. 218 Patients were included undergoing 64-slice CCTA. Images were analysed with regard to the presence of nonobstructive (<50 %) or obstructive (50 % stenosis) coronary artery disease (CAD). Major adverse cardiovascular events (MACE) were defined as death, nonfatal myocardial infarction or urgent coronary revascularization. CCTA revealed normal coronaries in 49, nonobstructive lesions in 94, and obstructive CAD in 75 patients. During a median follow-up period of 6.9 years, MACE occurred in 45 patients (21 %). Annual MACE rates were 0.3, 2.7, and 6.0 % (p = 0.001), for patients with normal CCTA, nonobstructive, and obstructive CAD, respectively. Multivariate Cox regression analysis identified the number of segments with plaques [hazard ratio (HR) 1.18, p = 0.002] as well as the presence of obstructive lesions (HR 2.28, p = 0.036) as independent predictors of MACE. The present study extends the predictive value of CCTA over more than 6 years. Patients with normal coronary arteries of CCTA continue to have an excellent cardiac prognosis, while outcome is progressively worse in patients with nonobstructive and obstructive CAD

Design of, and enrollment in, the palliative care communication research initiative: A direct-observation cohort study

© 2015 Gramling et al.Background: Understanding the characteristics of communication that foster patient-centered outcomes amid serious illness are essential for the science of palliative care. However, epidemiological cohort studies that directly observ

Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction

IMPORTANCE Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.Objectives To assess the utility of machine learning systems for automatic discrimination of TTS and AMI.Design, Settings, and Participants This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021.Exposure Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed.Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis.Results In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%).Conclusions and Relevance In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application

Assessment of Artificial Intelligence in Echocardiography Diagnostics in Differentiating Takotsubo Syndrome From Myocardial Infarction

IMPORTANCE Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.Objectives To assess the utility of machine learning systems for automatic discrimination of TTS and AMI.Design, Settings, and Participants This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021.Exposure Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed.Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis.Results In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%).Conclusions and Relevance In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application.Cardiolog

First in vivo head-to-head comparison of high-definition versus standard-definition stent imaging with 64-slice computed tomography

The aim of this study was to compare image quality characteristics from 64-slice high definition (HDCT) versus 64-slice standard definition CT (SDCT) for coronary stent imaging. In twenty-five stents of 14 patients, undergoing contrast-enhanced CCTA both on 64-slice SDCT (LightSpeedVCT, GE Healthcare) and HDCT (Discovery HD750, GE Healthcare), radiation dose, contrast, noise and stent characteristics were assessed. Two blinded observers graded stent image quality (score 1 = no, 2 = mild, 3 = moderate, and 4 = severe artefacts). All scans were reconstructed with increasing contributions of adaptive statistical iterative reconstruction (ASIR) blending (0, 20, 40, 60, 80 and 100 %). Image quality was significantly superior in HDCT versus SDCT (score 1.7 ± 0.5 vs. 2.7 ± 0.7; p < 0.05). Image noise was significantly higher in HDCT compared to SDCT irrespective of ASIR contributions (p < 0.05). Addition of 40 % ASIR or more reduced image noise significantly in both HDCT and SDCT. In HDCT in-stent luminal attenuation was significantly lower and mean measured in-stent luminal diameter was significantly larger (1.2 ± 0.4 mm vs. 0.8 ± 0.4 mm; p < 0.05) compared to SDCT. Radiation dose from HDCT was comparable to SDCT (1.8 ± 0.7 mSv vs. 1.7 ± 0.7 mSv; p = ns). Use of HDCT for coronary stent imaging reduces partial volume artefacts from stents yielding improved image quality versus SDCT at a comparable radiation dose