Clinical impact of the right ventricular impairment in patients following transcatheter aortic valve replacement.

The right ventricular (RV) impairment can predict clinical adverse events in patients following transcatheter aortic valve replacement (TAVR) for severe aortic stenosis (AS). Limited reports have compared impact of the left ventricular (LV) and RV disorders. This retrospective study evaluated two-year major adverse cardiac and cerebrovascular events (MACCE) in patients following TAVR for severe AS. RV sphericity index was calculated as the ratio between RV mid-ventricular and longitudinal diameters during the end-diastolic phase. Of 239 patients, 2-year MACCE were observed in 34 (14%). LV ejection fraction was 58 ± 11%. Tricuspid annular plane systolic excursion (TAPSE) and RV sphericity index were 20 ± 3 mm and 0.36 (0.31-0.39). Although the univariate Cox regression analysis demonstrated that both LV and RV parameters predicted the outcomes, LV parameters no longer predicted them after adjustment. Lower TAPSE (adjusted hazard ratio per 1 mm, 0.84; 95% confidence interval, 0.75-0.93) and higher RV sphericity index (adjusted hazard ratio per 0.1, 1.94; 95% confidence interval, 1.17-3.22) were adverse clinical predictors. In conclusion, the RV structural and functional disorders predict two-year MACCE, whereas the LV parameters do not. Impact of LV impairment can be attenuated after development of RV disorders.

Rapid three-dimensional quantification of high-intensity plaques from coronary atherosclerosis T1-weighted characterization to predict periprocedural myocardial injury.

BACKGROUND: High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T1-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR). METHODS: In this prospective study, CATCH MRI was conducted before elective stent implantation in 137 lesions from 125 patients. On CATCH images, dedicated software automatically generated tubular three-dimensional volumes of interest on the dark-blood plaque images along the coronary vasculature, based on the precisely matched bright-blood coronary artery images, and subsequently computed PMR and HIP volume (HIPvol). Specifically, HIPvol was calculated as the volume of voxels with signal intensity exceeding that of the myocardium, weighted by their respective signal intensities. PMI was defined as post-PCI cardiac troponin-T > 5 × the upper reference limit. RESULTS: The entire analysis process was completed within 3 min per lesion. PMI occurred in 44 lesions. Based on the receiver operating characteristic curve analysis, HIPvol outperformed PMR for predicting PMI (C-statistics, 0.870 [95% CI, 0.805-0.936] vs. 0.787 [95% CI, 0.706-0.868]; p = 0.001). This result was primarily driven by the higher sensitivity HIPvol offered: 0.886 (95% CI, 0.754-0.962) vs. 0.750 for PMR (95% CI, 0.597-0.868; p = 0.034). Multivariable analysis identified HIPvol as an independent predictor of PMI (odds ratio, 1.15 per 10-µL increase; 95% CI, 1.01-1.30, p = 0.035). CONCLUSIONS: Our semi-automated method of analyzing coronary plaque using CATCH MRI provided rapid HIP quantification. Three-dimensional assessment using this approach had a better ability to predict PMI than conventional two-dimensional assessment.

Potential confounders of the obesity paradox in older patients following transcatheter aortic valve replacement.

PURPOSE: A higher body mass index (BMI) is associated with lower mortality in older patients following transcatheter aortic valve replacement (TAVR) for severe aortic valve stenosis. The current study aimed to investigate potential confounders of association between BMI and prognosis. METHODS: The retrospective single-center study included consecutive patients following TAVR and excluded those in whom subcutaneous fat accumulation (SFA), visceral fat accumulation (VFA), and major psoas muscle (MPM) volume were not assessed by computed tomography. Cachexia was defined as a combination of BMI < 20 kg/m2 and any biochemical abnormalities. RESULTS: After 2 patients were excluded, 234 (age, 86 ± 5 years; male, 77 [33%]; BMI, 22.4 ± 3.8 kg/m2; SFA, 109 (54-156) cm2; VFA, 71 (35-115) cm2; MPM, 202 (161-267) cm3; cachexia, 49 [21%]) were evaluated. SFA and VFA were strongly correlated with BMI (ρ = 0.734 and ρ = 0.712, respectively), whereas MPM was weakly correlated (ρ = 0.346). Two-year all-cause mortality was observed in 31 patients (13%). Higher BMI was associated with lower mortality (adjusted hazard ratio [aHR], 0.86; 95% confidence interval [CI], 0.77-0.95). A similar result was observed in the multivariate model including SFA (aHR in an increase of 20 cm2, 0.87; 95% CI, 0.77-0.98) instead of BMI, whereas VFA was not significant. Cachexia was a worse predictor (aHR, 2.51; 95% CI 1.11-5.65). CONCLUSIONS: Association of higher BMI with lower mortality may be confounded by SFA in older patients following TAVR. Cachexia might reflect higher mortality in patients with lower BMI.

Quantification of Low-Attenuation Plaque Burden from Coronary CT Angiography: A Head-to-Head Comparison with Near-Infrared Spectroscopy Intravascular US.

Purpose: To determine the association between low-attenuation plaque (LAP) burden at coronary CT angiography (CCTA) and plaque morphology determined with near-infrared spectroscopy intravascular US (NIRS-IVUS) and to compare the discriminative ability for NIRS-IVUS-verified high-risk plaques (HRPs) between LAP burden and visual assessment of LAP. Materials and Methods: This Health Insurance Portability and Accountability Act-compliant retrospective study included consecutive patients who underwent CCTA before NIRS-IVUS between October 2019 and October 2022 at two facilities. LAPs were visually identified as having a central focal area of less than 30 HU using the pixel lens technique. LAP burden was calculated as the volume of voxels with less than 30 HU divided by vessel volume. HRPs were defined as plaques with one of the following NIRS-IVUS-derived high-risk features: maximum 4-mm lipid core burden index greater than 400 (lipid-rich plaque), an echolucent zone (intraplaque hemorrhage), or echo attenuation (cholesterol clefts). Multivariable analysis was performed to evaluate NIRS-IVUS-derived parameters associated with LAP burden. The discriminative ability for NIRS-IVUS-verified HRPs was compared using receiver operating characteristic analysis. Results: In total, 273 plaques in 141 patients (median age, 72 years; IQR, 63-78 years; 106 males) were analyzed. All the NIRS-IVUS-derived high-risk features were independently linked to LAP burden (P < .01 for all). LAP burden increased with the number of high-risk features (P < .001) and had better discriminative ability for HRPs than plaque attenuation by visual assessment (area under the receiver operating characteristic curve, 0.93 vs 0.89; P = .02). Conclusion: Quantification of LAP burden improved HRP assessment compared with visual assessment. LAP burden was associated with the accumulation of HRP morphology.Keywords: Coronary CT Angiography, Intraplaque Hemorrhage, Lipid-Rich Plaque, Low Attenuation Plaque, Near-Infrared Spectroscopy Intravascular Ultrasound Supplemental material is available for this article. See also the commentary by Ferencik in this issue.© RSNA, 2023.

Diagnosis and Prognostic Value of the Underlying Cause of Acute Coronary Syndrome in Optical Coherence Tomography-Guided Emergency Percutaneous Coronary Intervention.

Background The prognostic impact of optical coherence tomography-diagnosed culprit lesion morphology in acute coronary syndrome (ACS) has not been systematically examined in real-world settings. Methods and Results This investigator-initiated, prospective, multicenter, observational study was conducted at 22 Japanese hospitals to identify the prevalence of underlying ACS causes (plaque rupture [PR], plaque erosion [PE], and calcified nodules [CN]) and their impact on clinical outcomes. Patients with ACS diagnosed within 24 hours of symptom onset undergoing emergency percutaneous coronary intervention were enrolled. Optical coherence tomography-guided percutaneous coronary intervention recipients were assessed for underlying ACS causes and followed up for major adverse cardiac events (cardiovascular death, myocardial infarction, heart failure, or ischemia-driven revascularization) at 1 year. Of 1702 patients with ACS, 702 (40.7%) underwent optical coherence tomography-guided percutaneous coronary intervention for analysis. PR, PE, and CN prevalence was 59.1%, 25.6%, and 4.0%, respectively. One-year major adverse cardiac events occurred most frequently in patients with CN (32.1%), followed by PR (12.4%) and PE (6.2%) (log-rank P<0.0001), primarily driven by increased cardiovascular death (CN, 25.0%; PR, 0.7%; PE, 1.1%; log-rank P<0.0001) and heart failure trend (CN, 7.1%; PR, 6.8%; PE, 2.2%; log-rank P<0.075). On multivariate Cox regression analysis, the underlying ACS cause was associated with 1-year major adverse cardiac events (CN [hazard ratio (HR), 4.49 [95% CI, 1.35-14.89], P=0.014]; PR (HR, 2.18 [95% CI, 1.05-4.53], P=0.036]; PE as reference). Conclusions Despite being the least common, CN was a clinically significant underlying ACS cause, associated with the highest future major adverse cardiac events risk, followed by PR and PE. Future studies should evaluate the possibility of ACS underlying cause-based optical coherence tomography-guided optimization.

Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.

OBJECTIVE: We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems. METHODS: A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death. RESULTS: The final population comprised 743 patients (mean age 65  ±â€¯ 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores. CONCLUSION: Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time. ADVANCES IN KNOWLEDGE: Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.

Complementary Roles of Near-Infrared Spectroscopy and Intravascular Ultrasound in the Prediction of Periprocedural Myocardial Injury.

BACKGROUND: Lipid-rich plaque detected by near-infrared spectroscopy (NIRS) and attenuated plaque detected by intravascular ultrasound (IVUS) predict periprocedural myocardial injury (MI) following percutaneous coronary intervention (PCI). Although echolucent plaque detected by IVUS was reported to be associated with a no-reflow phenomenon in acute myocardial infarction, it remains unclear whether echolucent plaque is predictive of periprocedural MI following elective PCI. We aimed to elucidate whether echolucent plaque is independently associated with periprocedural MI after elective PCI and whether the predictive ability for periprocedural MI is improved by the combination of NIRS and IVUS. METHODS: This retrospective study included 121 lesions of 121 patients who underwent elective NIRS-IVUS-guided stent implantation. Periprocedural MI was defined as post-PCI cardiac troponin T > 70 ng/L. A maximum 4-mm lipid core burden index > 457 was regarded as lipid-rich plaque. Echolucent plaque was defined as the presence on IVUS of an echolucent zone and attenuated plaque as an attenuation arc > 90°. RESULTS: Periprocedural MI occurred in 39 lesions. In multivariable analysis, echolucent plaque, attenuated plaque, and lipid-rich plaque were independent predictors of periprocedural MI. Adding echolucent plaque and attenuated plaque to lipid-rich plaque improved the predictive performance (C statistic 0.825 vs 0.688; P = 0.001). Periprocedural MI increased with the number of predictors: 3% [1/39], 29% [10/34], 47% [14/30], and 78% [14/18] for 0, 1, 2, and 3 predictors, respectively (P < 0.001). CONCLUSIONS: Echolucent plaque is a major predictor of periprocedural MI, independently from lipid-rich plaque and attenuated plaque. Compared with NIRS alone, the combination of NIRS with IVUS signatures improves the predictive ability.

Insufficient adenosine-induced hyperemia is a major determinant of discordance between non-hyperemic pressure ratio and fractional flow reserve.

Adenosine occasionally overestimates fractional flow reserve (FFR) values (i.e., insufficient adenosine-induced hyperemia), leading to low non-hyperemic pressure ratios (NHPR)-high FFR discordance. We investigated the impact of insufficient adenosine-induced hyperemia on NHPR-FFR discordance and the reclassification of functional significance. We measured resting distal-to-aortic pressure ratio (Pd/Pa) and FFR by using adenosine (FFRADN) and papaverine (FFRPAP) in 326 patients (326 vessels). FFRADN overestimation was calculated as FFRADN - FFRPAP. We explored determinants of low Pd/Pa - high FFRADN discordance (Pd/Pa ≤ 0.92 and FFRADN > 0.80) versus high Pd/Pa - low FFRADN discordance (Pd/Pa > 0.92 and FFRADN ≤ 0.80). Reclassification of functional significance was defined as FFRADN > 0.80 and FFRPAP ≤ 0.80. Multivariable analysis identified FFRADN overestimation (p = 0.002) and heart rate at baseline (p = 0.048) as independent determinants of the low Pd/Pa-high FFRADN discordance. In the low Pd/Pa-high FFRADN group (n = 26), papaverine produced a further decline in the FFR value in 21 vessels (81%) compared with FFRADN, and the reclassification was observed in 17 vessels (65%). Insufficient adenosine-induced hyperemia is a major determinant of the low resting Pd/Pa-high FFR discordance. Physicians should bear in mind that the presence of low NHPR-high FFR discordance may indicate a false-negative FFR result.

Impact of overestimation of fractional flow reserve by adenosine on anatomical-functional mismatch.

Adenosine occasionally results in overestimation of fractional flow reserve (FFR) values, compared with other hyperemic stimuli. We aimed to elucidate the association of overestimation of FFR by adenosine with anatomically significant but functionally non-significant lesions (anatomical-functional mismatch) and its influence on reclassification of functional significance. Distal-to-aortic pressure ratio (Pd/Pa) was measured using adenosine (Pd/PaADN) and papaverine (Pd/PaPAP) in 326 patients (326 vessels). The overestimation of FFR was calculated as Pd/PaADN-Pd/PaPAP. The anatomical-functional mismatch was defined as diameter stenosis > 50% and Pd/PaADN > 0.80. Reclassification was indicated by Pd/PaADN > 0.80 and Pd/PaPAP ≤ 0.80. The mismatch (n = 72) had a greater overestimation of FFR than the non-mismatch (n = 99): median 0.02 (interquartile range 0.01-0.05) versus 0.01 (0.00-0.04), p = 0.014. Multivariable analysis identified the overestimation of FFR (p = 0.003), minimal luminal diameter (p = 0.001), and non-left anterior descending artery (LAD) location (p < 0.001) as determinants of the mismatch. Reclassification was indicated in 29% of the mismatch and was more frequent in the LAD than in the non-LAD (52% vs. 20%, p = 0.005). The overestimation of FFR is an independent determinant of anatomical-functional mismatch. Anatomical-functional mismatch, specifically in the LAD, may suggest a false-negative result.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks.

Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 ± 0.07 ; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 ± 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95-0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.

Impact of small dense low-density lipoprotein cholesterol on cholesterol crystals in patients with acute coronary syndrome: An optical coherence tomography study.

BACKGROUND: The presence of cholesterol crystals (CCs) is recognized as a component of vulnerable atherosclerotic plaques at risk of rupture. The phagocytosis of atherogenic lipid factors by macrophages precedes and promotes the formation of vulnerable plaques, but it is not clear how these factors affect the formation of CC. OBJECTIVE: This study aimed to evaluate the relationship between lipid biomarkers such as small dense low-density lipoprotein cholesterol (sd-LDL-c) and CC detected by optical coherence tomography (OCT) in patients with acute coronary syndrome (ACS). METHODS: Serum samples were collected immediately before coronary angiography in consecutive 174 patients with ACS who did not take statins and underwent OCT imaging of the culprit lesion. The sd-LDL-c levels were measured using a direct homogenous assay. CC was defined as a thin linear structure with high reflectivity and low signal attenuation on the OCT images. RESULTS: CC was identified in 85 patients (48.9%). The prevalence of CC was significantly higher in lesions with ruptured plaques and greater macrophage grade. The sd-LDL-c levels were significantly higher in the patients with CC (41.6 vs. 31.2 mg/dL, p = 0.01) although there were no significant differences in the levels of LDL-c and apolipoprotein B. The CC group also had higher levels of apolipoprotein C3 and HbA1c levels. In multiple logistic regression analysis, sd-LDL-c was an independent risk factor of CC (odds ratio, 1.19 per 10 mg/dL; p = 0.03). CONCLUSIONS: sd-LDL may play an important role in the presence of CC in patients with ACS.

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study.

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0-5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm3 or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70-16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07-5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99-1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction. FUNDING: National Heart, Lung, and Blood Institute and the Miriam & Sheldon G Adelson Medical Research Foundation.

Coronary High-Intensity Plaques at T1-weighted MRI in Stable Coronary Artery Disease: Comparison with Near-Infrared Spectroscopy Intravascular US.

Background The histologic nature of coronary high-intensity plaques (HIPs) at T1-weighted MRI in patients with stable coronary artery disease remains to be fully understood. Coronary atherosclerosis T1-weighted characterization (CATCH) enables HIP detection by simultaneously acquiring dark-blood plaque and bright-blood anatomic reference images. Purpose To determine if intraplaque hemorrhage (IPH) or lipid is the predominant substrate of HIPs on T1-weighted images by comparing CATCH MRI scans with findings on near-infrared spectroscopy (NIRS) intravascular US (IVUS) images. Materials and Methods This study retrospectively included consecutive patients who underwent CATCH MRI before NIRS IVUS between December 2019 and February 2021 at two facilities. At MRI, HIP was defined as plaque-to-myocardium signal intensity ratio of at least 1.4. The presence of an echolucent zone at IVUS (reported to represent IPH) was recorded. NIRS was used to determine the lipid component of atherosclerotic plaque. Lipid core burden index (LCBI) was calculated as the fraction of pixels with a probability of lipid-core plaque greater than 0.6 within a region of interest. Plaque with maximum LCBI within any 4-mm-long segment (maxLCBI4 mm) greater than 400 was regarded as lipid rich. Multivariable analysis was performed to evaluate NIRS IVUS-derived parameters associated with HIPs. Results There were 205 plaques analyzed in 95 patients (median age, 74 years; interquartile range [IQR], 67-78 years; 75 men). HIPs (n = 42) at MRI were predominantly associated with an echolucent zone at IVUS (79% [33 of 42] vs 8.0% [13 of 163], respectively; P < .001) and a higher maxLCBI4 mm at NIRS (477 [IQR, 258-738] vs 232 [IQR, 59-422], respectively; P < .001) than non-HIPs. In the multivariable model, HIPs were independently associated with an echolucent zone (odds ratio, 24.5; 95% CI: 9.3, 64.7; P < .001), but not with lipid-rich plaque (odds ratio, 2.0; 95% CI: 0.7, 5.4; P = .20). Conclusion The predominant substrate of T1-weighed MRI-defined high-intensity plaques in stable coronary artery disease was intraplaque hemorrhage, not lipid. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Stuber in this issue.

Linear concentration-response relationship of serum caffeine with adenosine-induced fractional flow reserve overestimation: a comparison with papaverine.

BACKGROUND: Caffeine intake from one cup of coffee one hour before adenosine stress tests, corresponding to serum caffeine levels of 3-4 mg/L, is thought to be acceptable for non-invasive imaging. AIMS: We aimed to elucidate whether serum caffeine is independently associated with adenosine-induced fractional flow reserve (FFR) overestimation and their concentration-response relationship. METHODS: FFR was measured using adenosine (FFRADN) and papaverine (FFRPAP) in 209 patients. FFRADN overestimation was defined as FFRADN - FFRPAP. The locally weighted scatterplot smoothing (LOWESS) approach was applied to evaluate the relationship between serum caffeine level and FFRADN overestimation. Multiple regression analysis was used to determine independent factors associated with FFRADN overestimation. RESULTS: Caffeine was ingested at <12 hours in 85 patients, at 12-24 hours in 35 patients, and at >24 hours in 89 patients. Multiple regression analysis identified serum caffeine level as the strongest factor associated with FFRADN overestimation (p<0.001). The LOWESS curve demonstrated that FFRADN overestimation started from just above the lower detection limit of serum caffeine and increased approximately 0.01 FFR unit per 1 mg/L increase in serum caffeine level with a linear relationship. The 90th percentile of serum caffeine levels for the ≤12-hour, the 12-24-hour, and the >24-hour groups corresponded to FFRADN overestimations by 0.06, 0.03, and 0.02, respectively. CONCLUSIONS: Serum caffeine overestimates FFRADN values in a linear concentration-response manner. FFRADN overestimation occurs at much lower serum caffeine levels than those that were previously believed. Our results highlight that standardised caffeine control is required for reliable adenosine-induced FFR measurements.

Early Vascular Healing Following Bioresorbable-Polymer Sirolimus-Eluting Stent Placement Compared to That with Durable-Polymer Everolimus-Eluting Stent.

A recent thinner strut drug-eluting stent might facilitate early strut coverage after its placement. We aimed to investigate early vascular healing responses after the placement of an ultrathin-strut bioresorbable-polymer sirolimus-eluting stent (BP-SES) compared to those with a durable-polymer everolimus-eluting stent (DP-EES) using optical coherence tomography (OCT) imaging.This study included 40 patients with chronic coronary syndrome (CCS) who underwent OCT-guided percutaneous coronary intervention (PCI). Twenty patients each received either BP-SES or DP-EES implantation. OCT was performed immediately after stent placement (baseline) and at 1-month follow-up.At one month, the percentage of uncovered struts reduced significantly in both the BP-SES (80.9 ± 10.3% to 2.9 ± 1.7%; P < 0.001) and DP-EES (81.9 ± 13.0% to 5.7 ± 1.8%; P < 0.001) groups, and the percentage was lower in the BP-SES group than in the DP-EES group (P < 0.001). In the BP-SES group, the percentage of malapposed struts also decreased significantly at 1 month (4.9 ± 3.7% to 2.6 ± 3.0%; P = 0.025), which was comparable to that of the DP-EES group (2.5 ± 2.2%; P = 0.860). The optimal cut-off value of the distance between the strut and vessel surface immediately after the placement to predict resolved malapposed struts was ≤ 160 µm for BP-SES and ≤ 190 µm for DP-EES.Compared to DP-EES, ultrathin-strut BP-SES demonstrated favorable vascular responses at one month, with a lower rate of uncovered struts and a comparable rate of malapposed struts.

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in Coronavirus disease 2019 (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (LSTM) networks. Utilizing the expert annotations, model training was performed using 5-fold cross-validation to segment ground-glass opacity and high opacity (including consolidation and pleural effusion). The performance of the method was evaluated on CT data sets from 197 patients with positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score coefficient of 0.876 ± 0.005; excellent correlations of 0.978 and 0.981 for ground-glass opacity and high opacity volumes. In the external validation set of 67 patients, there was dice score coefficient of 0.767 ± 0.009 as well as excellent correlations of 0.989 and 0.996 for ground-glass opacity and high opacity volumes. Computations for a CT scan comprising 120 slices were performed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX graphics processing unit. Therefore, our deep learning-based method allows rapid fully-automated quantitative measurement of pneumonia burden from CT and may generate the big data with an accuracy similar to the expert readers.

Small Dense Low-Density Lipoprotein Cholesterol: A Residual Risk for Rapid Progression of Non-Culprit Coronary Lesion in Patients with Acute Coronary Syndrome.

AIM: This study investigated whether the small dense low-density lipoprotein cholesterol (sd-LDL-c) level is associated with the rapid progression (RP) of non-culprit coronary artery lesions and cardiovascular events (CE) after acute coronary syndrome (ACS). METHODS: In 142 consecutive patients with ACS who underwent primary percutaneous coronary intervention for the culprit lesion, the sd-LDL-c level was measured using a direct homogeneous assay on admission for ACS and at the 10-month follow-up coronary angiography. RP was defined as a progression of any pre-existing coronary stenosis and/or stenosis development in the initially normal coronary artery. CEs were defined as cardiac death, myocardial infarction, stroke, or coronary revascularization. RESULTS: Patients were divided into two groups based on the presence (n=29) or absence (n=113) of RP after 10 months. The LDL-c and sd-LDL-c levels at baseline were equivalent in both the groups. However, the sd-LDL-c, triglyceride, remnant lipoprotein cholesterol (RL-c), and apoC3 levels at follow-up were significantly higher in the RP group than in the non-RP group. The optimal threshold values of sd-LDL-c, triglyceride, RL-c, and apoC3 for predicting RP according to receiver operating characteristics analysis were 20.9, 113, 5.5, and 9.7 mg/dL, respectively. Only the sd-LDL-c level (≥ 20.9 mg/dL) was significantly associated with incident CEs at 31±17 months (log-rank: 4.123, p=0.043). CONCLUSIONS: The sd-LDL-c level on treatment was significantly associated with RP of non-culprit lesions, resulting in CEs in ACS patients. On-treatment sd-LDL-c is a residual risk and aggressive reduction of sd-LDL-c might be needed to prevent CEs.

Early Vascular Response to Ultrathin Biodegradable Polymer Sirolimus-Eluting Stents for the Treatment of ST-Elevation Myocardial Infarction After Plaque Rupture.

Recent clinical studies suggest that newer-generation drug-eluting stents that combine ultrathin struts and nanocoating (biodegradable polymer sirolimus-eluting stents, BP-SES) could improve long-term clinical outcomes in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI). However, the early vascular response to BP-SES in these patients has not been investigated so far.We examined this response in 20 patients with STEMI caused by plaque rupture using frequency-domain optical coherence tomography (OCT) to understand the underlying mechanisms. Plaque rupture was diagnosed by OCT before PCI with BP-SES implantation was performed. OCT was again performed before the final angiography (post-PCI) and after 2 weeks (2W-OCT).BP-SES placement caused protrusion of atherothrombotic material into the stent lumen and incomplete stent apposition in all patients. After 2 weeks, incomplete stent apposition was significantly reduced (% malapposed struts: post-PCI 4.7 ± 3.3%; 2W-OCT 0.9 ± 1.2%; P < 0.0001), and the percentage of uncovered struts also significantly decreased (% uncovered struts: post-PCI; 69.8 ± 18.3%: 2W-OCT; 29.6 ± 11.0%, P < 0.0001). The maximum protrusion area of the atherothrombotic burden was significantly reduced (post-PCI 1.36 ± 0.70 mm2; 2W-OCT 0.98 ± 0.55 mm2; P = 0.004).This study on the early vascular responses following BP-SES implantation showed rapid resolution of atherothrombotic material and progression of strut apposition and coverage. (UMIN000041324).

Non-lipid-rich low attenuation plaque with intraplaque haemorrhage assessed by multimodality imaging: a case report.

BACKGROUND: The lipid-rich necrotic core is a major pathological hallmark of acute coronary syndrome. Low attenuation plaque (LAP) on coronary computed tomography angiography (CCTA), defined as plaque CT attenuation of <30 Hounsfield units, is commonly believed to correspond to the lipid component. This report presents a non-lipid-rich LAP with intraplaque haemorrhage of the left main coronary artery (LM), as assessed by CCTA, near-infrared spectroscopy (NIRS), and non-contrast magnetic resonance imaging (MRI) using coronary atherosclerosis T1-weighted characterization with integrated anatomical reference technique, recently developed by our group. CASE SUMMARY: A 75-year-old woman presented with chest discomfort on exertion. Coronary computed tomography angiography revealed severe stenosis of the mid-left circumflex coronary artery and minimal stenosis with a large eccentric LM plaque. The LM lesion had an LAP, with a minimum plaque attenuation of 25 Hounsfield units. On non-contrast T1-weighted MRI, a high-intensity plaque with a plaque-to-myocardium signal intensity ratio of 3.02 was observed within the vessel wall, indicating intraplaque haemorrhage. Near-infrared spectroscopy categorized the lesion as non-lipid-rich, with a maximum lipid core burden index in 4 mm of 169. DISCUSSION: Intraplaque haemorrhage is a key feature of plaque instability, which is different from the lipid-rich necrotic core. Non-contrast T1-weighted MRI is ideal for detecting intraplaque haemorrhage with short T1 values. The imaging findings suggest that LAP on CCTA may represent not only lipid-rich plaques but also intraplaque haemorrhage. Magnetic resonance imaging provides a unique insight into plaque vulnerability from a different perspective than lipid assessment. Multimodality imaging, including MRI, facilitates the understanding of complicated plaque morphologies. KEYWORDS: Atherosclerosis • Case report • Computed tomography • Intraplaque haemorrhage • Lipid-rich plaque • Magnetic resonance imaging • Near-infrared spectroscopy-intravascular ultrasound.