Rinawati Weny, Machin Abdulloh, Aryati Aryati
Doctoral Program of Medical Science, Faculty of Medicine, Universitas Airlangga, Surabaya 60132, Indonesia.
Laboratory and Blood Bank, Department of Clinical Pathology, National Brain Center Hospital Prof. Dr. dr. Mahar Mardjono, Jakarta 13630, Indonesia.
Medicina (Kaunas). 2024 Dec 18;60(12):2076. doi: 10.3390/medicina60122076.
: Although a wide range of hematological parameters are used as blood-based inflammatory biomarkers, the role of complete blood count-derived inflammatory biomarkers in infection after acute ischemic stroke (AIS) is modest. Therefore, this study aimed to explore complete blood count-derived inflammatory biomarkers as predictors of infection after AIS. : A single-center retrospective cross-sectional study was carried out at the National Brain Center Hospital Prof. Dr. dr. Mahar Mardjono, Jakarta, Indonesia, between 1 October 2023, and 31 March 2024, using medical records of hospitalized first-ever ischemic stroke patients who underwent a complete blood count within 24 h of admission. Based on complete blood count-derived inflammatory biomarkers, this study included absolute numbers and related ratios or indices. : In total, 163 patients met the study criteria. The diagnosis of infection after AIS was established using reliable clinical symptoms and/or guidelines of the disease. According to the status of infection after AIS, the subjects were categorized into two groups, including 24 patients in the infection group and 139 patients in the non-infection group. Biomarkers that had significant accuracy (higher sensitivity and specificity, respectively) in predicting infection were the leukocyte count (LC; 70.8%, 74.1%, < 0.001), absolute neutrophil count (ANC; 66.7%, 79.9%, < 0.001), absolute monocyte count (AMC; 75.0%, 63.3%, = 0.001), neutrophil to lymphocyte ratio (NLR; 62.5%, 71.9%, = 0.003), derivative NLR (dNLR; 50.0%, 78.4%, = 0.003), monocyte-granulocyte to lymphocyte ratio (MGLR; 62.5%, 73.0%, = 0.003), systemic inflammatory response index (SIRI; 62.5%, 79.0%, = 0.001), and systemic immune inflammation index (SII; 87.5%, 44.0%, = 0.012) with chances of 74.4%, 75.4%, 71.0%, 69.0%, 68.7%, 69.3%, 73.4%, and 66.2%, respectively. : Considering the overall ROC curve used to evaluate the complete blood count-derived inflammatory biomarkers, ANC has a better ability to predict infection in AIS patients, as denoted by the highest AUC, suggesting a 75.4% chance of correctly discriminating patients with infection after stroke.
尽管多种血液学参数被用作基于血液的炎症生物标志物，但全血细胞计数衍生的炎症生物标志物在急性缺血性卒中（AIS）后感染中的作用有限。因此，本研究旨在探索全血细胞计数衍生的炎症生物标志物作为AIS后感染的预测指标。
在印度尼西亚雅加达的国家脑中心医院Prof. Dr. dr. Mahar Mardjono进行了一项单中心回顾性横断面研究，研究时间为2023年10月1日至2024年3月31日，使用首次住院的缺血性卒中患者在入院24小时内进行全血细胞计数的医疗记录。基于全血细胞计数衍生的炎症生物标志物，本研究纳入了绝对数值以及相关比例或指数。
共有163名患者符合研究标准。AIS后感染的诊断依据可靠的临床症状和/或疾病指南确定。根据AIS后感染状况，将研究对象分为两组，感染组24例，非感染组139例。在预测感染方面具有显著准确性（分别具有较高的敏感性和特异性）的生物标志物包括白细胞计数（LC；70.8%，74.1%，P<0.001）、绝对中性粒细胞计数（ANC；66.7%，79.9%，P<0.001）、绝对单核细胞计数（AMC；75.0%，63.3%，P = 0.001）、中性粒细胞与淋巴细胞比值（NLR；62.5%，71.9%，P = 0.003）、衍生NLR（dNLR；50.0%，78.4%，P = 0.003）、单核细胞-粒细胞与淋巴细胞比值（MGLR；62.5%，73.0%，P = 0.003）、全身炎症反应指数（SIRI；62.5%，79.0%，P = 0.001）和全身免疫炎症指数（SII；87.5%，44.0%，P = 0.012），其正确预测感染的概率分别为74.4%、75.4%、71.0%、69.0%、68.7%、69.3%、73.4%和66.2%。
考虑用于评估全血细胞计数衍生的炎症生物标志物的总体受试者工作特征曲线（ROC曲线），ANC预测AIS患者感染的能力更佳，其曲线下面积（AUC）最高，表明正确区分卒中后感染患者的概率为75.4%。
