Prognostic Nutritional Index and Systemic Immune-inflammation Index: Possible New Parameters for COVID-19 Severity

Suyoso Suyoso, Amaylia Oehadian, Alfreda Amelia Khotijah, Marthoenis Marthoenis
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引用次数: 0

Abstract

Background: The Prognostic Nutritional Index (PNI) parameter has been widely used in estimating the severity of COVID-19. In contrast, investigating the role of the Systemic Immune-inflammation Index (SII) in determining the COVID-19 severity is prospective. This study aimed to investigate the potential of PNI and SII parameters to distinguish the severity of symptoms of COVID-19. Methods: A retrospective observational study was conducted among 209 hospitalized patients with COVID-19. Data were collected from August 2021 to February 2022 in Indonesia's general COVID-19 referral hospital. Demographic and laboratory data, including PNI and SII, were analyzed and compared between the severe and non-severe symptoms of COVID-19 patients. The statistical analysis of the receiver operating characteristic curve (ROC) and area under curve (AUC) was conducted to predict the potential of these parameters in distinguishing the COVID-19 severity. Results: More than half of this study's patients (54.55%) were non-severe COVID-19. The SII values in patients with severe symptoms were significantly higher than in those with non-severe symptoms (2445.24 vs. 1423.28, p=0.005). In contrast, the PNI value in patients with severe COVID-19 symptoms was significantly lower than those with non-severe symptoms (38.04 vs. 33.93, p<0.001). The area under the curve (AUC) value of PNI was 0.694, while the SII was 0.635. The optimum cut-off for the PNI was <35,407, while the SII was >2212,787. PNI and SII were the potential new diagnostic parameters for COVID-19 severity. Conclusion: PNI and SII parameters can potentially distinguish the severity of symptoms of COVID-19.
预后营养指数和全身免疫炎症指数:COVID-19严重程度可能的新参数
背景:预后营养指数(PNI)参数已被广泛用于评估COVID-19的严重程度。相比之下,研究全身免疫炎症指数(SII)在确定COVID-19严重程度中的作用具有前瞻性。本研究旨在探讨PNI和SII参数在区分COVID-19症状严重程度方面的潜力。方法:对209例新冠肺炎住院患者进行回顾性观察研究。数据于2021年8月至2022年2月在印度尼西亚的COVID-19综合转诊医院收集。分析和比较COVID-19患者重症和非重症症状的人口学和实验室数据,包括PNI和SII。对受试者工作特征曲线(ROC)和曲线下面积(AUC)进行统计分析,预测这些参数在区分COVID-19严重程度方面的潜力。结果:超过一半(54.55%)的患者为非重症COVID-19。重度症状组SII值明显高于非重度症状组(2445.24 vs 1423.28, p=0.005)。相比之下,症状严重的患者的PNI值明显低于症状不严重的患者(38.04 vs. 33.93, p<0.001)。PNI曲线下面积(AUC)为0.694,SII为0.635。PNI的最佳截止值为35407,而SII的最佳截止值为2212787。PNI和SII是COVID-19严重程度的潜在新诊断参数。结论:PNI和SII参数可以潜在地区分COVID-19症状的严重程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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