Scarlata Giuseppe Guido Maria, Quirino Angela, Costache Carmen, Toc Dan Alexandru, Marascio Nadia, Pantanella Marta, Leucuta Daniel Corneliu, Ismaiel Abdulrahman, Dumitrascu Dan Lucian, Abenavoli Ludovico
Department of Health Sciences, University of Catanzaro "Magna Graecia", 88100 Catanzaro, Italy.
Unit of Clinical Microbiology, Department of Health Sciences, University of Catanzaro "Magna Graecia", 88100 Catanzaro, Italy.
Antibiotics (Basel). 2024 Aug 15;13(8):769. doi: 10.3390/antibiotics13080769.
infection (CDI) is a significant cause of mortality, especially in healthcare environments. Reliable biomarkers that can accurately predict mortality in CDI patients are yet to be evaluated. Our study aims to evaluate the accuracy of several inflammatory biomarkers and hemogram-derived ratios in predicting mortality in CDI patients, such as the neutrophil-to-lymphocyte ratio (NLR), the systemic immune-inflammation index (SII), the platelet-to-neutrophil ratio (PNR), the derived neutrophil-to-lymphocyte ratio (dNLR), C-reactive protein (CRP), the platelet-to-lymphocyte ratio (PLR), and procalcitonin (PCT).
NLR showed a sensitivity of 72.5% and a specificity of 58.42% with an area under curve (AUC) = 0.652. SII had a sensitivity of 77.5%, a specificity of 54.74%, and an AUC = 0.64. PNR, neutrophils, dNLR, and lymphocytes had lower AUCs which ranged from 0.595 to 0.616, with varied sensitivity and specificity. CRP, leukocytes, and platelets showed modest predictive values with AUCs below 0.6. PCT had a sensitivity of 100%, a low specificity of 7.41%, and an AUC = 0.528.
We conducted a retrospective analysis of CDI patients from two different hospital settings in Italy and Romania during the COVID-19 pandemic, from 1 January 2020 to 5 May 2023. Statistical analyses included -tests, Wilcoxon rank-sum tests, χ2 tests, and multivariate logistic regression to identify predictors of mortality. ROC analysis assessed the accuracy of biomarkers and hemogram-derived ratios. A value < 0.05 was considered significant.
Neutrophils, dNLR, NLR, SII, and PNR are valuable biomarkers for predicting mortality in CDI patients. Understanding these predictors can improve risk stratification and clinical outcomes for CDI patients.
艰难梭菌感染（CDI）是导致死亡的重要原因，尤其是在医疗环境中。能够准确预测CDI患者死亡率的可靠生物标志物尚未得到评估。我们的研究旨在评估几种炎症生物标志物和血常规衍生比值在预测CDI患者死亡率方面的准确性，如中性粒细胞与淋巴细胞比值（NLR）、全身免疫炎症指数（SII）、血小板与中性粒细胞比值（PNR）、衍生中性粒细胞与淋巴细胞比值（dNLR）、C反应蛋白（CRP）、血小板与淋巴细胞比值（PLR）和降钙素原（PCT）。
NLR的敏感性为72.5%，特异性为58.42%，曲线下面积（AUC）=0.652。SII的敏感性为77.5%，特异性为54.74%，AUC = 0.64。PNR、中性粒细胞、dNLR和淋巴细胞的AUC较低，范围为0.595至0.616，敏感性和特异性各不相同。CRP、白细胞和血小板的预测价值中等，AUC低于0.6。PCT的敏感性为100%，特异性低至7.41%，AUC = 0.528。
我们对2020年1月1日至2023年5月5日新冠疫情期间意大利和罗马尼亚两家不同医院的CDI患者进行了回顾性分析。统计分析包括t检验、Wilcoxon秩和检验、χ2检验和多因素逻辑回归，以确定死亡率的预测因素。ROC分析评估了生物标志物和血常规衍生比值的准确性。P值<0.05被认为具有统计学意义。
中性粒细胞、dNLR、NLR、SII和PNR是预测CDI患者死亡率的有价值的生物标志物。了解这些预测因素可以改善CDI患者的风险分层和临床结局。
