摘要：

目的: 测定急性一氧化碳中毒患者系统性炎症反应指数(SIRI)和血清脂蛋白相关磷脂酶A2(Lp-PLA2)水平，并探讨二者及其联合对急性一氧化碳中毒迟发性脑病(DEACMP)的预测价值。方法: 纳入2020年3月至2023年3月河北医科大学哈励逊国际和平医院、河北省衡水市第二人民医院和第四人民医院诊断与治疗的265例急性一氧化碳中毒患者，测定SIRI和血清Lp-PLA2水平，根据是否发生DEACMP分为DEACMP组(32例)和非DEACMP组(233例)，根据中毒程度分为轻度中毒组(20例)、中度中毒组(107例)和重度中毒组(138例)，单因素和多因素Logistic回归分析筛查急性一氧化碳中毒患者发生DEACMP的危险因素，绘制受试者工作特征(ROC)曲线，评估SIRI、Lp-PLA2及二者联合对DEACMP的预测效能。结果: DEACMP组患者SIRI(t = 13.068，P = 0.000)和血清Lp-PLA2水平(t = 8.208，P = 0.000)均高于非DEACMP组，且二者随中毒程度的加重而逐渐升高，重度中毒组和中度中毒组SIRI(t = 8.764，P = 0.000；t = 4.586，P = 0.000)和Lp-PLA2(t = 3.726，P = 0.000；t = 2.038，P = 0.044)高于轻度中毒组，重度中毒组亦高于中度中毒组(t = 10.294，P = 0.000；t = 2.700，P = 0.007)。Logistic回归分析显示，重度中毒(OR = 11.695，95%CI：4.893~39.994；P = 0.000)、SIRI增加(OR = 1.600，95%CI：1.033~2.476；P = 0.001)和血清Lp-PLA2水平升高(OR = 11.302，95%CI：1.486~38.933；P = 0.000)是急性一氧化碳中毒患者发生DEACMP的危险因素。ROC曲线显示，Lp-PLA2、SIRI及二者联合预测DEACMP的曲线下面积分别为0.82(95%CI：0.754~0.894，P = 0.000)、0.82(95%CI：0.739~0.895，P = 0.000)和0.87 (95%CI：0.805~0.934，P = 0.000)，灵敏度为0.66、0.72和0.84，特异度为0.85、0.88和0.90；Lp-PLA2联合SIRI预测DEACMP的效能优于单一Lp-PLA2(t = 2.198，P = 0.027)或SIRI(t = 2.268，P = 0.023)。结论: 急性一氧化碳中毒患者SIRI和血清Lp-PLA2水平较高时易发生DEACMP，二者联合检测可用于DEACMP的早期筛查。

关键词: 一氧化碳中毒, 脑疾病, 1-烷基-2-乙酰甘油磷酸胆碱酯酶, 炎症, 生物标记, 危险因素, Logistic模型, ROC曲线

Abstract:

Objective: To examine the levels of systemic inflammatory response index (SIRI) and serum lipoprotein-associated phospholipase A2 (Lp-PLA2) in patients with acute carbon monoxide poisoning (ACOP), and to explore the predictive value of SIRI, Lp-PLA2 and their combination for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP). Methods: Total 265 patients with ACOP diagnosed and treated in Harrison International Peace Hospital of Hebei Medical University, The Second People's Hospital of Hengshui and The No. 4 People's Hospital of Hengshui from March 2020 to March 2023 were included.SIRI and serum Lp-PLA2 levels were measured.According to the occurrence of DEACMP, they were divided into DEACMP group (n = 32) and non-DEACMP group (n = 233), while according to the degree of poisoning, they were divided into mild poisoning group (n = 20), moderate poisoning group (n = 107) and severe poisoning group (n = 138). Univariate and multivariate Logistic regression analyses were used to screen the risk factors of DEACMP in patients with ACOP, and the receiver operating characteristic (ROC) curve was drawn to evaluate the predictive efficacy of SIRI, Lp-PLA2 and their combination for DEACMP. Results: The levels of SIRI (t = 13.068, P = 0.000) and serum Lp-PLA2 (t = 8.208, P = 0.000) in DEACMP group were higher than those in non-DEACMP group, and their levels gradually increased with the severity of poisoning, the levels of SIRI (t = 8.764, P = 0.000; t = 4.586, P = 0.000) and Lp-PLA2 (t = 3.726, P = 0.000; t = 2.038, P = 0.044) in the severe poisoning group and moderate poisoning group were higher than those in the mild poisoning group, and the levels of SIRI and serum Lp-PLA2 in the severe poisoning group were also higher than those in the moderate poisoning group (t = 10.294, P = 0.000; t = 2.700, P = 0.007). Logistic regression analysis showed the severe poisoning (OR = 11. 695, 95%CI: 4.893-39.994; P = 0.000), SIRI increased (OR = 1.600, 95%CI: 1.033-2.476; P = 0.001) and Lp-PLA2 increased (OR = 11.302, 95%CI: 1.486-38.933; P = 0.000) were risk factors of DEACMP in patients with ACOP. ROC curve showed that area under the curve (AUC) predicted by Lp-PLA2, SIRI and their combination were 0.82 (95%CI: 0.754-0.894, P = 0.000), 0.82 (95%CI: 0.739-0.895, P = 0.000) and 0.87 (95%CI: 0.805-0.934, P = 0.000), sensitivity were 0.66, 0.72 and 0.84, specificity were 0.85, 0.88 and 0.90, respectively. The prediction efficiency of Lp-PLA2 combined with SIRI was better than that of Lp-PLA2 (t = 2.198, P = 0.027) or SIRI (t = 2.268, P = 0.023) alone. Conclusions: DEACMP is easy to occur when SIRI and serum Lp-PLA2 were high in patients with ACOP. The combined detection of Lp-PLA2 and SIRI can be used for early screening of DEACMP.

Key words: Carbon monoxide poisoning, Brain diseases, 1-Alkyl-2 -acetylglycerophosphocholine esterase, Inflammation, Biomarkers, Risk factors, Logistic models, ROC curve