Klaudia Bartoszewicz, Mateusz Bartoszewicz, Samuel Stróż, Anna Stasiak-Barmuta, Piotr Kosiorek
{"title":"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。","authors":"Klaudia Bartoszewicz, Mateusz Bartoszewicz, Samuel Stróż, Anna Stasiak-Barmuta, Piotr Kosiorek","doi":"10.1099/jmm.0.001911","DOIUrl":null,"url":null,"abstract":"<p><p><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.</p>","PeriodicalId":94093,"journal":{"name":"Journal of medical microbiology","volume":"73 10","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictors of the severity of the course of COVID-19: demographic factors, clinical signs and laboratory markers.\",\"authors\":\"Klaudia Bartoszewicz, Mateusz Bartoszewicz, Samuel Stróż, Anna Stasiak-Barmuta, Piotr Kosiorek\",\"doi\":\"10.1099/jmm.0.001911\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><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.</p>\",\"PeriodicalId\":94093,\"journal\":{\"name\":\"Journal of medical microbiology\",\"volume\":\"73 10\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of medical microbiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1099/jmm.0.001911\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of medical microbiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1099/jmm.0.001911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictors of the severity of the course of COVID-19: demographic factors, clinical signs and laboratory markers.
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.