关键词: C-reactive protein Fasciitis bacterial infections case-control study diagnostic test lymphocytes necrotizing neutrophils sensitivity and specificity

来  源:   DOI:10.1080/00016489.2024.2384433

Abstract:
UNASSIGNED: Cervical necrotizing fasciitis (CNF) is a life-threatening bacterial infection with a diagnostic challenge. Currently, there is insufficient evidence on the diagnostic accuracy of inflammatory indicators in CNF.
UNASSIGNED: This study aims to identify key inflammatory indicators and assess their diagnostic accuracy for CNF.
UNASSIGNED: A diagnostic case-control study was conducted at a tertiary healthcare facility from January 2020 to December 2023. Laboratory data from patients with CNF and non-CNF at admission were evaluated. Key inflammatory indicators were identified through consistent outcomes from multivariable logistic regression and receiver operating characteristic curves analyses. The diagnostic accuracy of these indicators, with the results of combined tests, were calculated.
UNASSIGNED: CNF was confirmed in 21 of the 67 patients investigated. C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio (NLR) were identified as key inflammatory indicators, with sensitivities of 0.905 and 0.810, and specificities of 0.870 and 0.913, respectively, at CRP threshold of 165.0 mg/L and NLR of 15.8. Combining CRP and NLR in parallel and serial tests increased sensitivity to 0.952 and specificity to 1.0, respectively.
UNASSIGNED: CRP and NLR have been verified as key inflammatory indicators with satisfactory diagnostic abilities for CNF diagnosis, providing a strong foundation for future studies.
摘要:
宫颈坏死性筋膜炎(CNF)是一种威胁生命的细菌感染,具有诊断挑战。目前,CNF中炎性指标的诊断准确性证据不足.
本研究旨在确定关键炎症指标并评估其对CNF的诊断准确性。
于2020年1月至2023年12月在三级医疗机构进行了诊断性病例对照研究。评估入院时CNF和非CNF患者的实验室数据。通过多变量逻辑回归和受试者工作特征曲线分析的一致结果确定关键炎症指标。这些指标的诊断准确性,结合测试的结果,被计算。
在所调查的67例患者中有21例证实了CNF。C反应蛋白(CRP)和中性粒细胞与淋巴细胞比值(NLR)被确定为关键的炎症指标。灵敏度分别为0.905和0.810,特异性分别为0.870和0.913,CRP阈值为165.0mg/L,NLR为15.8。在并行和串行测试中组合CRP和NLR分别将敏感性提高到0.952和特异性提高到1.0。
CRP和NLR已被证实为关键炎症指标,对CNF诊断具有令人满意的诊断能力,为未来的研究奠定了坚实的基础。
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