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

Klaudia Bartoszewicz, Mateusz Bartoszewicz, Samuel Stróż, Anna Stasiak-Barmuta, Piotr Kosiorek
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Abstract

Introduction. 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.Hypothesis. 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.Aim. 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.Methodology. The methods of analysis, synthesis, generalization and descriptive statistics were used to achieve this objective.Results. 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.Conclusion. 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.

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预测 COVID-19 病程严重程度的因素:人口统计学因素、临床症状和实验室指标。
导言。Coronavirus Disease 2019(COVID-19)大流行对全球医疗保健产生了重大影响,高死亡率和严重并发症仍然是一个主要问题。了解 COVID-19 严重程度的预测因素可改善患者管理和预后。虽然大量研究集中在病毒的发病机制和疫苗开发方面,但确定严重疾病的可靠人口、临床和实验室预测因素仍然至关重要。特定的人口统计学因素、临床症状和实验室标志物可以可靠地预测 COVID-19 的严重程度。综合分析这些预测因素可提供更准确的预后并指导及时干预。本研究旨在确定和评估可作为重症 COVID-19 可靠预测指标的人口学、临床和实验室因素,从而帮助预测和预防不良后果。为实现这一目标,采用了分析、综合、概括和描述性统计等方法。分析表明,60 岁以上和男性等人口统计学因素是严重 COVID-19 的重要预测因素。临床预测因素包括呼吸道症状,尤其是呼吸困难,以及高血压、冠心病、慢性阻塞性肺病、呼吸衰竭、哮喘、糖尿病和肥胖等合并症。具有较高预后价值的实验室指标包括 C 反应蛋白、白细胞介素-6、铁蛋白、中性粒细胞/淋巴细胞比率、d-二聚体、天冬氨酸氨基转移酶水平升高以及淋巴细胞计数减少。该研究得出结论,要准确预测 COVID-19 的严重程度,必须采用综合方法,将人口统计学、临床和实验室数据结合起来。这种综合模型有助于早期识别高危人群,及时采取有针对性的干预措施,从而大大改善患者的预后。研究结果凸显了对患者进行全面评估对于管理和减轻 COVID-19 影响的重要性。
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