Bos, Daniël

Abstract Background and aims Carotid calcification features are readily quantifiable by CTA, yet their relationship with plaque vulnerability and rupture remains controversial. We investigated whether calcium score, volume, density, and residual (volume-normalized) calcium density are associated with carotid plaque vulnerability, symptomatic status, and recurrent ipsilateral ischemic stroke and transient ischemic attack (TIA). Methods Patients with recent TIA or ischemic stroke and ≥2mm carotid plaque with <70% stenosis underwent baseline CTA and MRI. Carotid calcifications were segmented on CTA (≥130 Hounsfield units), yielding calcium score, volume, and density. Volume-normalized residual calcium density was calculated as the standardized residuals from a log-transformed calcium density-volume model (Figure 1). Vulnerable plaque features (intraplaque hemorrhage, thin-or-ruptured fibrous cap, and lipid-rich necrotic core, and ulceration) were linked to calcium metrics. Associations between calcium metrics and recurrent ipsilateral TIA or ischemic stroke were assessed using Cox regression and adjusted for age, sex, stenosis. Results Among 193 patients (mean age 67.8±8.5 years, 144 (75%) men), 26 experienced ipsilateral ischemic stroke or TIA over median 5-year follow-up. Lower calcium density and residual calcium density were independently associated with vulnerable plaques (adjusted ORs: 0.40-0.60; Figure 2). Residual calcium density remained independently associated with symptomatic side (adjusted OR: 0.64, 95%CI: 0.47-0.88) and with recurrent TIA/stroke (adjusted HR: 0.53, 95%CI: 0.29-0.95). Calcium score, volume, and density were not independently associated with symptomatic side or recurrent events (Figure 3). Conclusions Residual calcium density is a robust, independent marker of carotid plaque vulnerability and ipsilateral ischemic stroke and TIA risk in patients with symptomatic carotid artery disease. Conflict of interest Juul Bierens: nothing to disclose; Daniel Bos: nothing to disclose; Paul J. Nederkoorn: nothing to disclose; Pim A. de Jong: nothing to disclose; Robert J. van Oostenbrugge: nothing to disclose; Luc J.M. Smits: nothing to disclose; Jae W. Song: nothing to disclose; Giuseppe Lanzino: nothing to disclose; M. Eline Kooi: nothing to disclose; Luca Saba: nothing to disclose. Figure 1 - belongs to Methods Figure 2 - belongs to Results Figure 3 - belongs to Conclusions

Photon-counting detector CT offers improved assessment of small stents and higher diagnostic accuracy than conventional CT through enhanced spatial resolution and spectral reconstructions.

Abstract Objective Determine if information regarding hypertrophic cardiomyopathy (HCM) can be accurately retrieved from cardiac magnetic resonance (CMR) reports using natural language processing (NLP). Background CMR imaging is used for diagnosis and risk stratification of HCM. Manual annotation of information from CMR is time-consuming. NLP is an artificial intelligence method for automating extraction of information from narrative text. Methods We identified 200 HCM patients who had CMR reports from 1998 to 2018. These patients were randomly allocated into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports). An NLP system with 2 tiers was developed; the first extracted information regarding HCM diagnosis while second extracted categorical or numeric concepts for HCM classification. NLP performance was compared with gold-standard manual annotation. Results NLP algorithms achieved very high performance across all concepts with mean positive predictive value (PPV) = 0.96. An outlier was the performance for abstracting the presence of an apical pouch from CMR reports, which had noticeably lower PPV= 0.78 which be attributed to the low number of cases with this finding. Conclusions The algorithms developed can be translated to clinical decision support systems to increase efficiency and contribute to improved quality of care. Funding Acknowledgement Type of funding source: Other. Main funding source(s): Study supported by the National Heart, Lung and Blood Institute of National Institutes of Health (K01HL124045), the Mayo Clinic Center for Clinical and Translational Science (CCaTS), and the Mayo Clinic K2R award. Content is solely the responsibility of authors and does not necessarily represent official views of the National Institutes of Health.