Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients.

To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and respiratory (ROX index and PaO2/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization.

PaO/FiO ratio forecasts COVID-19 patients' outcome regardless of age: a cross-sectional, monocentric study.

We studied the predictive value of the PaO/FiO ratio for classifying COVID-19-positive patients who will develop severe clinical outcomes. One hundred fifty patients were recruited and categorized into two distinct populations ("A" and "B"), according to the indications given by the World Health Organization. Patients belonging the population "A" presented with mild disease not requiring oxygen support, whereas population "B" presented with a severe disease needing oxygen support.

The identification and management of interstitial lung disease in systemic sclerosis: evidence-based European consensus statements.

Background: Systemic sclerosis-associated interstitial lung disease (ILD) carries a high mortality risk; expert guidance is required to aid early recognition and treatment. We aimed to develop the first expert consensus and define an algorithm for the identification and management of the condition through application of well established methods.

Methods: Evidence-based consensus statements for systemic sclerosis-associated ILD management were established for six domains (ie, risk factors, screening, diagnosis and severity assessment, treatment initiation and options, disease progression, and treatment escalation) using a modified Delphi process based on a systematic literature analysis.