基于Logistic回归分析颅脑手术后炎症指标动态变化及对颅内感染的诊断价值

doi:10.16460/j.issn1008-9969.2023.01.022

摘要/Abstract

摘要： 目的探究颅脑外科手术后患者体温、降钙素原、C反应蛋白、白细胞计数随时间的动态变化及其对颅内感染的诊断价值。方法分析1 308例经手术治疗的颅内病变患者临床资料,根据是否发生感染分为感染组与非感染组,使用广义线性混合模型分析体温、C反应蛋白、白细胞计数及白细胞计数平均水平随时间的变化,使用Logistic回归对是否感染进行预测。结果（1）术后第1、第2、第3、第5、第7、第9天2组颅脑手术患者体温比较,差异均有统计学意义。术后2 d内2组患者白细胞计数、C反应蛋白、降钙素原均无差异,第3、第5、第7、第9天3项指标比较,差异均有统计学意义;（2）使用患者第2天体温、白细胞计数、C反应蛋白及降钙素原进行联合检测的AUC为0.802（95%CI:0.760~0.845）,若增加第2天与第3天的差值来联合检测颅内感染可将AUC提高至为0.915（95%CI:0.887~0.949）。结论使用患者第2天各项指标及其与第3天指标的差值对感染进行预测可以大大提高预测准确性,提示临床应用中术后感染诊断时需要同时考虑生理指标及其变化趋势,可以有效提高神经外科术后患者的护理质量。

关键词: 颅内感染, 白细胞计数, 降钙素原, C反应蛋白, 预测模型

Abstract: Objective To investigate the dynamic changes over time of body temperature, procalcitonin, C-reactive protein and leukocyte count in patients after intracranial surgery and their diagnostic value for intracranial infections. Methods The clinical data of 1,308 patients with intracranial lesions treated by surgery were analyzed, and the patients were divided into infection group and non-infection group. The changes over time of body temperature, C-reactive protein, leukocyte count and mean level of leukocyte count were analyzed using generalized linear mixed model, and Logistic regression was used to predict the occurrence of infection. Results (1) The differences were statistically significant when comparing body temperature in the 2 groups on the 1st, 2nd, 3rd, 5th, 7th and 9th day after the surgery. There was no difference in leukocyte count, C-reactive protein and procalcitonin between the 2 groups within 2 days after surgery, and the differences were statistically significant when comparing these three indexes on the 3rd, 5th, 7th and 9th day after the surgery. (2) The AUC of the combined test of body temperature, leukocyte count, C-reactive protein and procalcitonin on the 2nd day was 0.802 (95% CI: 0.760 to 0.845), and the AUC could be increased to 0.915 (95% CI: 0.887 to 0.949) when the difference of the indicators between the 2nd and 3rd day were included. Conclusion The prediction accuracy can be greatly improved when the indicators and the difference of the indicators between day 2 and 3 are used for the prediction of intracranial infection, suggesting the necessity of considering both physiological indicators and their trends when diagnosing postoperative infection in clinical practice, which can also effectively improve the nursing quality of neurosurgical patients.

Key words: intracranial infection, leukocyte count, procalcitonin, C-reactive protein, prediction model

中图分类号:

R473.6

楚文强, 彭俊祥, 李丹玲. 基于Logistic回归分析颅脑手术后炎症指标动态变化及对颅内感染的诊断价值[J]. 护理学报, 2023, 30(1): 22-27.

CHU Wen-qiang, PENG Jun-xiang, LI Dan-ling. Dynamic changes of inflammatory indicators after cranial surgery and diagnostic value for intracranial infection: a Logistic regression analysis[J]. Journal of Nursing, 2023, 30(1): 22-27.

参考文献

[1] Yao J, Liu D.Logistic regression analysis of risk factors for intracranial infection after multiple traumatic craniotomy and preventive measures[J]. J Craniofac Surg, 2019,30(7):1946-1948.DOI:10.1097/SCS.0000000000004972.
[2] 王世华, 朱金君, 朱坤灿, 等. 开颅脑肿瘤术后颅内感染的特点与相关危险因素分析[J]. 中华肿瘤防治杂志, 2020,27(S1):8-9.
[3] Shi Z, Xu M, Wang Y, et al.Post-craniotomy intracranial infection in patients with brain tumors: a retrospective analysis of 5723 consecutive patients[J]. Brit J Neurosurg, 2017,31(1):5-9.DOI:10.1080/02688697.2016.1253827.
[4] Hu Y, He W, Yao D, et al.Intrathecal or intraventricular antimicrobial therapy for post-neurosurgical intracranial infection due to multidrug-resistant and extensively drug-resistant gram-negative bacteria: a systematic review and meta-analysis[J]. Int J Antimicrob Agents, 2019,54(5):556-561.DOI:10.1016/j.ijantimicag.2019.08.002.
[5] Lan G, Xiao-Liang Y, Hui-Kang Y, et al.Application value analysis of magnetic resonance imaging and computed tomography in the diagnosis of intracranial infection after craniocerebral surgery[J]. World J Clin Cases, 2020,8(23):5894-5901.DOI:10.12998/wjcc.v8.i23.5894.
[6] 简智恒. 神经外科术后炎症指标的变化趋势的临床研究[D]. 广州:南方医科大学, 2013.
[7] 胡云华, 李淑艳, 陈林芳, 等. 急诊开颅手术患者颅内感染的影响因素[J]. 中华医院感染学杂志, 2022,32(2):203-206.DOI:10.11816/cn.ni.2022-211788.
[8] 张立, 钱晓波, 叶经纬, 等. 重型颅脑损伤患者术后继发颅内感染的危险因素及血清PCT、CRP的预测价值[J]. 热带医学杂志,2020,20(12):1577-1580.DOI:10.3969/j.issn.1672-3619.2020.12.011.
[9] 周婧婧, 黄飞, 张旦旦, 等. 老年颅脑外伤术后颅内感染病原菌特点及多指标联合预警诊断模型建立[J]. 中国老年学杂志, 2022,42(6):1395-1398.DOI:10.3969/j.issn.1005-9202.2022.06.035.
[10] 张玉林, 杨龙, 金福强. C反应蛋白和降钙素原在脑出血术后合并感染中的作用研究[J]. 重庆医学, 2015,44(20):2849-2851.DOI:10.3969/j.issn.1671-8348.2015.20.041.
[11] Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and cox regression[J]. Am J Epidemiol,2007,165(6):710-718.DOI:10.1093/aje/kwk052.
[12] Riley RD, Ensor J, Snell K, et al.Calculating the sample size required for developing a clinical prediction model[J]. BMJ, 2020,368:441.DOI:10.1136/bmj.m441.
[13] 中华医学会神经外科学分会, 中国神经外科重症管理协作组.中国神经外科重症患者感染诊治专家共识(2017)[J]. 中华医学杂志, 2017,97(21):1607-1614.DOI:10.3760/cma.j.issn.0376-2491.2017.21.005.
[14] Douglas B, Martin M, Ben B, et al.Fitting linear mixed-effects models using lme4[J]. J Stat Softw, 2015,67(1):1-48.DOI:10.18637/jss.v067.i01.
[15] Lenth RV,Buerkner P, Herve M, et al. Emmeans: estimated marginal means, aka least-squares means[EB/OL]. (2022-06-05)[2022-09-09]. https://cran.r-project.org/web/packages/emmeans/.
[16] LeDell E, Petersen M, van der Laan M. Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates[J]. Electron J Stat, 2015, 9(1):1583-1607.DOI:10.1214/15-EJS1035.
[17] Jalali A, Alvarez-Iglesias A, Roshan D, et al.Visualising statistical models using dynamic nomograms[J]. PLoS One, 2019,14(11):e0225253.DOI:10.1371/journal.pone.0225253.
[18] 简智恒, 张喜安, 漆松涛, 等. 神经外科术后炎症指标的变化及意义[J]. 中华神经医学杂志, 2013,12(4):415-418.DOI: 10.3760/cma.j.issn.1671-8925.2013.04.021.
[19] 楚燕飞, 郑鲁, 姚智强, 等. 幕下开颅术后发热原因及处理[J]. 中国神经精神疾病杂志, 2011,37(5):260-265.DOI:10.3969/j.issn.1002-0152.2011.05.002.
[20] 卜娜分, 蔡海云, 黄涛, 等. 46例脑出血患者神经内镜下颅内血肿清除术的并发症观察及护理[J].护理学报, 2022,29(2):71-72.DOI:10.16460/j.issn1008-9969.2022.02.071.
[21] 张丹梅, 袁丽, 朱琪. 开颅手术后颅内感染风险预测模型构建及效果评价[J]. 中国感染控制杂志,2022,21(5):439-446.DOI:10.12138/j.issn.1671-9638.20222269.
[22] 邱锋, 蔡佩浩, 龚立, 等. 高血压脑出血微创穿刺术后颅内感染的临床特点及血清NLRP3、S-100b联合检测的预测价值[J]. 海南医学, 2022,33(5):550-553.DOI:10.3969/j.issn.1003-6350.2022.05.002.
[23] 孙占玉, 郭智霖, 王上桥. 血清PTX3和NLRP3联合检测在颅脑损伤术后颅内感染早期诊治中的应用研究[J]. 热带医学杂志,2022,22(6):837-841.DOI:10.3969/j.issn.1672-3619.2022.06.020.
[24] 顿士娟,邹琪,吴强,等.颅脑术后继发颅内感染患者脑脊液肝素结合蛋白水平的变化及意义[J].中华全科医学,2020,18(12):2017-2020. DOI:10.16766/j.cnki.issn.1674-4152.001677.

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