Author:
Sozio Emanuela,Hannemann Juliane,Fabris Martina,Cifù Adriana,Ripoli Andrea,Sbrana Francesco,Cescutti Demetrio,Vetrugno Luigi,Fapranzi Stefano,Bassi Flavio,Sponza Massimo,Curcio Francesco,Tascini Carlo,Böger Rainer
Abstract
AbstractWe aimed to assess the potential role of Asymmetric dimethylarginine (ADMA) in conditioning respiratory function and pulmonary vasoregulation during Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) infection. Within 72 h from admission, samples from 90 COVID-19 patients were assessed for ADMA, SDMA, L-arginine concentrations. In addition to classical statistics, patients were also clustered by a machine learning approach according to similar features. Multivariable analysis showed that C-reactive protein (OR 1.012), serum ADMA (OR 4.652), white blood cells (OR = 1.118) and SOFA (OR = 1.495) were significantly associated with negative outcomes. Machine learning-based clustering showed three distinct clusters: (1) patients with low severity not requiring invasive mechanical ventilation (IMV), (2) patients with moderate severity and respiratory failure whilst not requiring IMV, and (3) patients with highest severity requiring IMV. Serum ADMA concentration was significantly associated with disease severity and need for IMV although less pulmonary vasodilation was observed by CT scan. High serum levels of ADMA are indicative of high disease severity and requirement of mechanical ventilation. Serum ADMA at the time of hospital admission may therefore help to identify COVID-19 patients at high risk of deterioration and negative outcome.
Funder
JH and RB received funding by the Joachim Herz Foundation, Hamburg, Germany, as well as by the German Federal Ministry of Education and Research under grant no.
Publisher
Springer Science and Business Media LLC
Cited by
3 articles.
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