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The severity and distribution of morphological lesions associated with SARS\u2010CoV\u20102 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\u2010CoV\u20102, may influence their occurrence. Lungs from 26 patients who died from SARS\u2010CoV\u20102 acute respiratory failure were comprehensively analysed. Robust machine learning techniques were implemented to obtain a global pathological score to distinguish phenotypes with prevalent vascular or alveolar injury. The score was then analysed to assess its possible correlation with clinical, laboratory, radiological, and tissue viral data. Furthermore, an exploratory random forest algorithm was developed to identify the most discriminative clinical characteristics at hospital admission that might predict pathological phenotypes of SARS\u2010CoV\u20102. Vascular injury phenotype was observed in most cases being consistently present as pure form or in combination with alveolar injury. Phenotypes with more severe alveolar injury showed significantly more frequent tracheal intubation; longer invasive mechanical ventilation, illness duration, intensive care unit or hospital ward stay; and lower tissue viral quantity (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Furthermore, in this phenotype, superimposed infections, tumours, and aspiration pneumonia were also more frequent (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Random forest algorithm identified some clinical features at admission (body mass index, white blood cells, D\u2010dimer, lymphocyte and platelet counts, fever, respiratory rate, and PaCO<jats:sub>2<\/jats:sub>) to stratify patients into different clinical clusters and potential pathological phenotypes (a web\u2010app for score assessment has also been developed; <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/r-ubesp.dctv.unipd.it\/shiny\/AVI-Score\/\">https:\/\/r-ubesp.dctv.unipd.it\/shiny\/AVI-Score\/<\/jats:ext-link>). In SARS\u2010CoV\u20102 positive patients, alveolar injury is often associated with other factors in addition to viral infection. Identifying phenotypical patterns at admission may enable a better stratification of patients, ultimately favouring the most appropriate management. \u00a9 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/path.5653","type":"journal-article","created":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T14:13:07Z","timestamp":1614175987000},"page":"173-184","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Machine learning\u2010based analysis of alveolar and vascular injury in <scp>SARS\u2010CoV<\/scp>\u20102 acute respiratory failure"],"prefix":"10.1002","volume":"254","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5351-9226","authenticated-orcid":false,"given":"Fiorella","family":"Calabrese","sequence":"first","affiliation":[{"name":"Department of Cardiac, Thoracic, Vascular Sciences, and Public Health University of Padua Medical School  Padua Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Federica","family":"Pezzuto","sequence":"additional","affiliation":[{"name":"Department of Cardiac, Thoracic, Vascular Sciences, and 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