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This cohort study of 495 COVID-19 patients hospitalized in Lithuania during 2020-2021 identified key predictors of severe disease requiring oxygen therapy. The strongest independent predictors were older age, obesity, lymphopenia (low lymphocyte count), and elevated levels of C-reactive protein (CRP) and lactate dehydrogenase (LDH). CRP showed the highest predictive value with an area under the curve of 0.84, followed by LDH at 0.80.
Why it matters
These findings provide a practical risk stratification tool that clinicians can use upon hospital admission to identify patients at highest risk for severe COVID-19, enabling more effective triage, earlier therapeutic intervention, and better allocation of medical resources. The study also contributes valuable data from an underrepresented Eastern European region where population characteristics may differ from more extensively studied areas.
Understand the Science
by Ieva Kubiliute, Edgaras Zaboras, Fausta Majauskaite, Jurgita Urboniene, Birute Zablockiene, Giedre Gefenaite, Aukse Mickiene, Ligita Jancoriene
Background
Since its emergence, the COVID-19 infection has led to significant morbidity and mortality worldwide. Early identification of patients at risk for severe disease is essential for more effective triage, timely therapeutic intervention, and optimal resource allocation. Differences in population characteristics may contribute to variability in disease outcomes, which emphasizes the need for regional-level data, especially from underrepresented regions. The main aim of this study was to identify the demographic, clinical, and laboratory predictors of severe COVID-19, defined as the need for oxygen therapy, in Lithuania.
Materials and methods
We conducted an ambispective observational cohort study at Vilnius University Hospital Santaros Klinikos in Vilnius, Lithuania, from March 2020 to December 2021. Adult patients with a confirmed diagnosis of COVID-19 and hospitalized longer than 24 hours were included in this study. Data were collected from the electronic medical records and patient interviews. To identify predictors of severe COVID-19 course, a multivariable binary logistic regression model was performed.
Results
Among 495 patients, 52.9% were male, the median age was 55 years, and 61.2% had at least one underlying condition. The most common symptoms on admission were malaise (77.1%), subfebrile fever (65.9%), and cough (69.7%). CRP demonstrated the highest predictive value for severe COVID-19 (AUC = 0.84), followed by LDH (AUC = 0.80). Older age (OR 1.04 per year, 95% CI 1.00–1.08), obesity (OR 3.55, 95% CI 1.35–9.30), lymphopenia (OR 3.70, 95% CI 1.37–9.99), higher LDH (OR 1.008, 95% CI 1.00–1.01) and CRP (OR 1.021, 95% CI 1.01–1.04) levels were identified as the strongest predictors for severe COVID-19 disease course.
Conclusion
Older age, obesity, lymphopenia, and higher CRP and LDH were associated with developing severe COVID-19 disease, indicating that combining patient history and laboratory parameters can provide a practical risk stratification approach to help clinicians identify high-risk patients early upon hospitalisation.