Department of Clinical Immunology, Medical University of Bialystok, Bialystok, Poland.
Department of Anesthesiology and Intensive Care, Medical University of Bialystok, Bialystok, Poland.
J Med Microbiol. 2024 Oct;73(10). doi: 10.1099/jmm.0.001911.
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. 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. 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. The methods of analysis, synthesis, generalization and descriptive statistics were used to achieve this objective. 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. 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.
2019 年冠状病毒病(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关注点。了解 COVID-19 严重程度的预测因素可能会改善患者管理和结局。虽然大量研究集中在病毒的发病机制和疫苗开发上,但确定可靠的人口统计学、临床和实验室严重疾病预测因素仍然至关重要。特定的人口统计学因素、临床体征和实验室标志物可可靠地预测 COVID-19 的严重程度。综合分析这些预测因素可以提供更准确的预后,并指导及时干预。本研究的目的是确定和评估可作为严重 COVID-19 可靠预测因素的人口统计学、临床和实验室因素,从而有助于预测和预防不良结局。采用分析、综合、概括和描述性统计的方法来实现这一目标。分析表明,年龄超过 60 岁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系统症状,特别是呼吸困难,以及合并症,如高血压、冠心病、慢性阻塞性肺疾病、呼吸衰竭、哮喘、糖尿病和肥胖症。具有高预后价值的实验室标志物包括 C 反应蛋白、白细胞介素-6、铁蛋白、中性粒细胞/淋巴细胞比值、D-二聚体、天冬氨酸转氨酶酶和淋巴细胞计数降低。研究得出结论,综合考虑人口统计学、临床和实验室数据对于准确预测 COVID-19 的严重程度至关重要。这种综合模型可以通过早期识别高风险个体并允许及时、有针对性的干预措施,显著改善患者的预后。研究结果强调了全面评估患者在管理和减轻 COVID-19 影响方面的重要性。