AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients.

PURPOSE: To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients' risk stratification. MATERIAL AND METHODS: In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web-mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). RESULTS: The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816-0.867) on wave 1 and was used to build a 0-100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402-0.8766). CONCLUSIONS: AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

Secondary prevention and follow-up of patients with ACS and not-at-target LDL: An Italian real-world retro-prospective analysis by the inertia group.

Background: In patients with recent ACS, the latest ESC/EAS guidelines for management of dyslipidaemia recommend intensification of LDL-C-lowering therapy. Objective: Report a real-world picture of lipid-lowering therapy prescribed and cholesterol targets achieved in post-ACS patients before and after a specific educational program. Methods: Retrospective data collection prior to the educational course and prospective data collection after the course of consecutive very high-risk patients with ACS admitted in 2020 in 13 Italian cardiology departments, and with a non-target LDL-C level at discharge. Results: Data from 336 patients were included, 229 in the retrospective phase and 107 in the post-course prospective phase. At discharge, statins were prescribed in 98.1% of patients, alone in 62.3% of patients (65% of which at high doses) and in combination with ezetimibe in 35.8% of cases (52% at high doses). A significant reduction was obtained in total and LDL cholesterol (LDL-C) from discharge to the first control visit. Thirty-five percent of patients achieved a target LDL-C <55 mg/dL according to ESC 2019 guidelines. Fifty percent of patients achieved the <55 mg/dL target for LDL-C after a mean of 120 days from the ACS event. Conclusions: Our analysis, though numerically and methodologically limited, suggests that management of cholesterolaemia and achievement of LDL-C targets are largely suboptimal and need significant improvement to comply with the lipid-lowering guidelines for very high CV risk patients. Earlier high intensity statin combination therapy should be encouraged in patients with high residual risk.