Computed tomography texture analysis of carotid plaque as predictor of unfavorable outcome after carotid artery stenting: A preliminary study

Novel biomarkers are advocated to manage carotid plaques. Therefore, we aimed to test the association between textural features of carotid plaque at computed tomography angiography (CTA) and unfavorable outcome after carotid artery stenting (CAS). Between January 2010 and January 2021, were selected 172 patients (median age, 77 years; 112/172, 65% men) who underwent CAS with CTA of the supra-aortic vessels performed within prior 6 months. Standard descriptors of the density histogram were derived by open-source software automated analysis obtained by CTA plaque segmentation. Multiple logistic regression analysis, receiver operating characteristic (ROC) curve analysis and the area under the ROC (AUC) were used to identify potential prognostic variables and to assess the model performance for predicting unfavorable outcome (periprocedural death or myocardial infarction and any ipsilateral acute neurological event). Unfavorable outcome occurred in 17/172 (10%) patients (median age, 79 years; 12/17, 70% men). Kurtosis was an independent predictor of unfavorable outcome (odds ratio, 0.79; confidence interval, 0.65–0.97; p = 0.029). The predictive model for unfavorable outcome including CTA textural features outperformed the model without textural features (AUC 0.789 vs 0.695, p = 0.004). In patients with stenotic carotid plaque, kurtosis derived by CTA density histogram analysis is an independent predictor of unfavorable outcome after CAS

Auditory-perceptual speech features in children with down syndrome

Speech disorders occur commonly in individuals with Down syndrome (DS), although data regarding the auditory-perceptual speech features are limited. This descriptive study assessed 47 perceptual speech features during connected speech samples in 26 children with DS. The most severely affected speech features were: naturalness, imprecise consonants, hyponasality, speech rate, inappropriate silences, irregular vowels, prolonged intervals, overall loudness level, pitch level, aberrant oropharyngeal resonance, hoarse voice, reduced stress, and prolonged phonemes. These findings suggest that speech disorders in DS are due to distributed impairments involving voice, speech sound production, fluency, resonance, and prosody. These data contribute to the development of a profile of impairments in speakers with DS to guide future research and inform clinical assessment and treatment

Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients

Purpose: To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak. Methods: The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death. Results: The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2–3.85, P = 0.01), %high attenuation area – 700 HU > 35% (HR 2.17, 95% CI 1.2–3.94, P = 0.01), exudative consolidations (HR 2.85–2.93, 95% CI 1.61–5.05/1.66–5.16, P < 0.001), visual CAC score > 1 (HR 2.76–3.32, 95% CI 1.4–5.45/1.71–6.46, P < 0.01/P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92–2.03, 95% CI 1.01–3.67/1.06–3.9, P = 0.04/P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911–0.913, 95% CI 0.873–0.95/0.875–0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816–0.922; P = 0.04 for both models). Conclusions: In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model