Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure.

ABSTRACT: Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pneumopathy is characterized by a complex clinical picture and heterogeneous pathological lesions, both involving alveolar and vascular components. The severity and distribution of morphological lesions associated with SARS-CoV-2 and how they relate to clinical, laboratory, and radiological data have not yet been studied systematically. The main goals of the present study were to objectively identify pathological phenotypes and factors that, in addition to SARS-CoV-2, may influence their occurrence. Lungs from 26 patients who died from SARS-CoV-2 acute respiratory failure were comprehensively analysed. Robust machine learning techniques were implemented to obtain a global pathological score to distinguish phenotypes with prevalen

SUBMITTER: Calabrese F 

PROVIDER: S-EPMC8014445 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature