Predictors of the severity of the course of COVID-19: demographic factors, clinical signs and laboratory markers

<b>Introduction.</b> The Coronavirus Disease 2019 (COVID-19) pandemic has had a significant impact on global healthcare, with high mortality and severe complications remaining a major concern. Understanding the predictors of COVID-19 severity may improve patient management and outcomes. While considerable research has focused on the pathogenesis of the virus and vaccine development, the identification of reliable demographic, clinical and laboratory predictors of severe disease remains critical.<b>Hypothesis.</b> Specific demographic factors, clinical signs and laboratory markers can reliably predict the severity of COVID-19. A comprehensive analysis integrating these predictors could provide a more accurate prognosis and guide timely interventions.<b>Aim.</b> The aim of this study is to identify and evaluate the demographic, clinical and laboratory factors that can serve as reliable predictors of severe COVID-19, thereby aiding in the prediction and prevention of adverse outcomes.<b>Methodology.</b> The methods of analysis, synthesis, generalization and descriptive statistics were used to achieve this objective.<b>Results.</b> The analysis showed that demographic factors such as age over 60 and male sex are significant predictors of severe COVID-19. Clinical predictors include respiratory symptoms, especially dyspnoea, and comorbidities such as hypertension, coronary artery disease, chronic obstructive pulmonary disease, respiratory failure, asthma, diabetes mellitus and obesity. Laboratory markers with high prognostic value include elevated levels of C-reactive protein, interleukin-6, ferritin, neutrophil/lymphocyte ratio, d-dimer, aspartate aminotransferase enzyme and decreased lymphocyte count.<b>Conclusion.</b> The study concludes that a holistic approach incorporating demographic, clinical and laboratory data is essential to accurately predict the severity of COVID-19. This integrated model may significantly improve patient prognosis by facilitating early identification of high-risk individuals and allowing timely, targeted interventions. The results highlight the importance of comprehensive patient assessment in managing and mitigating the impact of COVID-19.

Authors

Institutions

Publication Details

Journal
Journal of Medical Microbiology
Published
2024-10-10
DOI
https://doi.org/10.1099/jmm.0.001911
Citations
4
Primary Topic
COVID-19 Clinical Research Studies
Type
article
Field-Weighted Citation Impact
1.56
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictors of the severity of the course of COVID-19: demographic factors, clinical signs and laboratory markers

Piotr Kosiorek, Anna Stasiak‐Barmuta, Samuel Stróż, Klaudia Bartoszewicz et al.
4 citations
Journal of Medical Microbiology
COVID-19 Clinical Research Studies
1.56
article

Predictors of the severity of the course of COVID-19: demographic factors, clinical signs and laboratory markers

Piotr Kosiorek, Anna Stasiak‐Barmuta, Samuel Stróż, Klaudia Bartoszewicz, Mateusz Bartoszewicz
article en
4 citations

Abstract

<b>Introduction.</b> The Coronavirus Disease 2019 (COVID-19) pandemic has had a significant impact on global healthcare, with high mortality and severe complications remaining a major concern. Understanding the predictors of COVID-19 severity may improve patient management and outcomes. While considerable research has focused on the pathogenesis of the virus and vaccine development, the identification of reliable demographic, clinical and laboratory predictors of severe disease remains critical.<b>Hypothesis.</b> Specific demographic factors, clinical signs and laboratory markers can reliably predict the severity of COVID-19. A comprehensive analysis integrating these predictors could provide a more accurate prognosis and guide timely interventions.<b>Aim.</b> The aim of this study is to identify and evaluate the demographic, clinical and laboratory factors that can serve as reliable predictors of severe COVID-19, thereby aiding in the prediction and prevention of adverse outcomes.<b>Methodology.</b> The methods of analysis, synthesis, generalization and descriptive statistics were used to achieve this objective.<b>Results.</b> The analysis showed that demographic factors such as age over 60 and male sex are significant predictors of severe COVID-19. Clinical predictors include respiratory symptoms, especially dyspnoea, and comorbidities such as hypertension, coronary artery disease, chronic obstructive pulmonary disease, respiratory failure, asthma, diabetes mellitus and obesity. Laboratory markers with high prognostic value include elevated levels of C-reactive protein, interleukin-6, ferritin, neutrophil/lymphocyte ratio, d-dimer, aspartate aminotransferase enzyme and decreased lymphocyte count.<b>Conclusion.</b> The study concludes that a holistic approach incorporating demographic, clinical and laboratory data is essential to accurately predict the severity of COVID-19. This integrated model may significantly improve patient prognosis by facilitating early identification of high-risk individuals and allowing timely, targeted interventions. The results highlight the importance of comprehensive patient assessment in managing and mitigating the impact of COVID-19.

Journal of Medical MicrobiologyVol. 73(10)
Medical University of Białystok (PL)
Good health and well-being
Openalex Percentile: Top 17%
COVID-19 Clinical Research Studies
1.56
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.