Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Computed tomography findings of COVID-19 pneumonia in Intensive Care Unit-patients.

BACKGROUND: In December 2019, a cluster of unknown etiology pneumonia cases occurred in Wuhan, China leading to identification of the responsible pathogen as SARS-coV-2. Since then, the coronavirus disease 2019 (COVID-19) has spread to the entire world. Computed Tomography (CT) is frequently used to assess severity and complications of COVID-19 pneumonia. The purpose of this study is to compare the CT patterns and clinical characteristics in intensive care unit (ICU) and non-ICU patients with COVID-19 pneumonia. DESIGN AND METHODS: This retrospective study included 218 consecutive patients (136 males; 82 females; mean age 63±15 years) with laboratory-confirmed SARS-coV-2. Patients were categorized in two different groups: (a) ICU patients and (b) non-ICU inpatients. We assessed the type and extent of pulmonary opacities on chest CT exams and recorded the information on comorbidities and laboratory values for all patients. RESULTS: Of the 218 patients, 23 (20 males: 3 females; mean age 60 years) required ICU admission, 195 (118 males: 77 females, mean age 64 years) were admitted to a clinical ward. Compared with non-ICU patients, ICU patients were predominantly males (60% versus 83% p=0.03), had more comorbidities, a positive CRP (p=0.04) and higher LDH values (p=0.008). ICU patients' chest CT demonstrated higher incidence of consolidation (p=0.03), mixed lesions (p=0.01), bilateral opacities (p<0.01) and overall greater lung involvement by consolidation (p=0.02) and GGO (p=0.001). CONCLUSIONS: CT imaging features of ICU patients affected by COVID-19 are significantly different compared with non-ICU patients. Identification of CT features could assist in a stratification of the disease severity and supportive treatment.