Faculty, Staff and Student Publications

Language

English

Publication Date

1-1-2024

Journal

Frontiers in Oncology

DOI

10.3389/fmicb.2024.1422393

PMID

39119143

PMCID

PMC11306936

PubMedCentral® Posted Date

7-25-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Introduction: Coronavirus 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.

Methods: We 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.

Results: Our 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.

Conclusion: We 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.

Keywords

COVID-19, SARS-CoV-2, cardiovascular diseases, machine learning, prediction of survival

Published Open-Access

yes

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