Non-Conventional Prognostic Markers in Life-Threatening COVID-19 Cases-When Less Is More

非常规预后指标在危重新冠肺炎病例中的应用——少即是多

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Abstract

Background/Objectives: In this study, we aimed to compare the predictive power of non-conventional (neutrophil/lymphocyte ratio-NLR; platelet/lymphocyte ratio-PLR) and conventional markers (C-reactive protein-CRP; procalcitonin-PCT; interleukin-6-IL-6) in terms of disease progression and mortality in severe SARS-CoV-2 patients. Methods: In this prospective observatory study, blood samples were collected daily, focusing on the established inflammatory markers. Critically ill COVID-19 patients who required ICU admission were included. Patient treatment followed established COVID-19 protocols, and the data analysis was performed using SPSS with non-normal distribution methods. The study cohort primarily included patients infected with the delta variant. Results: A mortality rate of 76.6% was observed among 167 patients during the study period. Significant differences in conventional and non-conventional markers between survivor and non-survivor groups were observed. The PCT levels were significantly elevated (p < 0.005) in the deceased group. Among the non-conventional markers, the NLR was consistently higher in non-survivors and emerged as a significant predictor of mortality, whereas the PLR was not elevated among the non-survivors. ROC analyses indicated that PCT and the NLR were the markers with the highest predictive power for mortality. The multivariate logistic regression analysis identified NLR, PCT, CRP, and IL-6 as significant predictors of mortality across different days. The NLR showed a consistent, though not always statistically significant, association with increased mortality risk, particularly on Days 2 and 5. Conclusions: The NLR's accessibility and simplicity of determination make it a valuable and practical tool for monitoring inflammatory processes in viral infections. Our findings suggest that incorporating NLR analysis into routine clinical practice could enhance the early identification of high-risk patients, thereby improving patient management and outcomes.

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