Frontiers in Microbiology (Jul 2024)

Risk stratification and prediction of severity of COVID-19 infection in patients with preexisting cardiovascular disease

  • Stanislava Matejin,
  • Igor D. Gregoric,
  • Rajko Radovancevic,
  • Slobodan Paessler,
  • Vladimir Perovic

DOI
https://doi.org/10.3389/fmicb.2024.1422393
Journal volume & issue
Vol. 15

Abstract

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IntroductionCoronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is a highly contagious viral disease. Cardiovascular diseases and heart failure elevate the risk of mechanical ventilation and fatal outcomes among COVID-19 patients, while COVID-19 itself increases the likelihood of adverse cardiovascular outcomes.MethodsWe collected blood samples and clinical data from hospitalized cardiovascular patients with and without proven COVID-19 infection in the time period before the vaccine became available. Statistical correlation analysis and machine learning were used to evaluate and identify individual parameters that could predict the risk of needing mechanical ventilation and patient survival.ResultsOur results confirmed that COVID-19 is associated with a severe outcome and identified increased levels of ferritin, fibrinogen, and platelets, as well as decreased levels of albumin, as having a negative impact on patient survival. Additionally, patients on ACE/ARB had a lower chance of dying or needing mechanical ventilation. The machine learning models revealed that ferritin, PCO2, and CRP were the most efficient combination of parameters for predicting survival, while the combination of albumin, fibrinogen, platelets, ALP, AB titer, and D-dimer was the most efficient for predicting the likelihood of requiring mechanical ventilation.ConclusionWe believe that creating an AI-based model that uses these patient parameters to predict the cardiovascular patient’s risk of mortality, severe complications, and the need for mechanical ventilation would help healthcare providers with rapid triage and redistribution of medical services, with the goal of improving overall survival. The use of the most effective combination of parameters in our models could advance risk assessment and treatment planning among the general population of cardiovascular patients.

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