Daily Cardiology Research Analysis

BACKGROUND: Heart failure should be diagnosed as early as possible. Although deep learning models can predict one or more echocardiographic findings from electrocardiograms (ECGs), such analyses are not comprehensive. OBJECTIVES: This study aimed to develop a deep learning model for comprehensive prediction of echocardiographic findings from ECGs. METHODS: We obtained 229,439 paired ECG and echocardiography data sets from 8 centers. Six centers contributed to model development and 2 to external validation. We identified 12 echocardiographic findings related to left-sided cardiac abnormalities, valvular heart diseases, and right-sided cardiac abnormalities. These findings were predicted using convolutional neural networks, and a composite label was analyzed using logistic regression. A positive composite label indicated positivity in any of the 12 findings. RESULTS: For the composite findings label, the area under the receiver-operating characteristic curve was 0.80 (95% CI: 0.80-0.81) on hold-out validation and 0.78 (95% CI: 0.78-0.79) on external validation. The composite findings label applying logistic regression had an area under the receiver-operating characteristic curve of 0.80 (95% CI: 0.80-0.81) with accuracy of 73.8% (95% CI: 73.2-74.4), sensitivity of 81.1% (95% CI: 80.5-81.8), and specificity of 60.7% (95% CI: 59.6-61.8). CONCLUSIONS: We have developed convolutional neural network models that predict a wide range of echocardiographic findings, including left-sided cardiac abnormalities, valvular heart diseases, and right-sided cardiac abnormalities from ECGs and created a model to predict a composite findings label by logistic regression analysis. This model has potential to serve as an adjunct for early diagnosis and treatment of previously undetected cardiac disease.

BACKGROUND: Patients with acute myocardial infarction and angiographically obstructive non-culprit lesions are at high risk for recurrent major adverse cardiac events (MACEs). However, it remains largely unknown whether events are due to stenosis severity or due to the underlying high-risk lesion morphology. METHODS: Between January 2017 and December 2021, 1312 patients with acute myocardial infarction underwent optical coherence tomography of all the 3 main epicardial arteries after successful percutaneous coronary intervention. Patients and lesions were categorized according to the presence or absence of (1) 1 or more non-culprit angiographic obstructive stenoses with a visual diameter stenosis of ≥50% and (2) 1 or more lesions with an underlying high-risk morphology defined as an optical coherence tomography thin-cap fibroatheroma (TCFA). Patients were followed for up to 5 years (median 4.1 [interquartile range: 3.0-5.0] years). MACEs comprised cardiac death, non-fatal myocardial infarction, and unplanned coronary revascularization. RESULTS: Overall, 492 patients had at least 1 obstructive non-culprit lesion, 352 had a single lesion, and 140 had multiple obstructive non-culprit lesions. The presence and number of angiographic obstructive non-culprit lesions correlated with the proportion and number of optical coherence tomography-derived TCFAs. At the lesion level, the prevalence of TCFA was twice as high in obstructive lesions compared with nonobstructive lesions. Patients with obstructive non-culprit lesions had an increased risk of overall MACEs (17.7% versus 12.8%; hazard ratio, 1.39 [95% CI, 1.02-1.91]) and non-culprit lesion-related MACEs (8.7% versus 3.9%; HR, 2.13 [95% CI, 1.26-3.59). Results were similar when patients were categorized on the basis of the underlying TCFA. A proportionally higher rate of overall and non-culprit lesion-related MACEs was observed as the number of obstructive stenoses or TCFAs in non-culprit segments increased. The lesion-specific HRs for obstructive lesion and TCFA were 2.03 (95% CI, 1.06-3.89) and 2.39 (95% CI, 1.29-4.43), respectively. Optical coherence tomography-derived TCFA, but not angiographic obstructive stenosis, was independently predictive of recurrent MACEs in both patient-level and lesion-level multivariable models in which these 2 characteristics were introduced simultaneously. CONCLUSIONS: The long-term prognostic implications of the presence and extent of angiographic obstructive non-culprit lesions in patients with acute myocardial infarction are primarily due to their correlation with the underlying high-risk morphology, which confers an increased risk of recurrent MACEs.

BACKGROUND: Coronary physiology measured by fractional flow reserve (FFR) is superior to angiography for assessing the efficacy of percutaneous coronary intervention (PCI). Yet, the clinical adoption of post-PCI FFR is limited. Murray law-based quantitative flow ratio (μQFR) may represent a promising alternative, as it can quickly compute FFR from a single angiographic view. OBJECTIVES: The authors aimed to investigate the potential role of post-PCI μQFR in predicting clinical outcomes. METHODS: This was a post hoc blinded analysis of the FLAVOUR trial. Patients with angiographically intermediate lesions randomized 1:1 to receive FFR or intravascular ultrasound-guided PCI were included. Post-PCI μQFR was assessed in successfully stented vessels, blinded to clinical outcomes. Suboptimal physiological outcome post-PCI was defined a priori as post-PCI μQFR <0.90. The primary endpoint was 2-year target vessel failure, including cardiac death, target vessel myocardial infarction, and target vessel revascularization. Secondary endpoints included the diagnostic concordance of pre-PCI μQFR with FFR in the FFR-guidance arm. RESULTS: Post-PCI μQFR was successfully analyzed in 806 vessels from 777 participants (feasibility 97.0% [806 of 831]). Suboptimal physiological outcome post-PCI was identified in 24.7% (199 of 806) of vessels and post-PCI μQFR <0.90 was associated with higher risk of 2-year target vessel failure (6.1% [12 of 199] vs 2.7% [16 of 607]; HR: 2.45 [95% CI: 1.14-5.26]; CONCLUSIONS: In patients with intermediate lesions who underwent PCI with contemporary imaging or physiology guidance, lower post-PCI μQFR values predict subsequent adverse events. (Fractional FLow Reserve And IVUS for Clinical OUtcomes in Patients With InteRmediate Stenosis [FLAVOUR]; NCT02673424).