利用临床特征与生化指标建立细菌性脑膜炎转归不良的风险预测模型

doi:10.3760/cma.j.cn441417-20240913-04027

摘要/Abstract

摘要：

目的　分析细菌性脑膜炎患儿的临床特征与生化指标，构建转归不良的风险预测模型。方法　对陕西省人民医院2020年9月至2024年5月收治的274例细菌性脑膜炎患儿的临床资料展开回顾性分析，其中男156例、女118例，年龄范围0～18岁。根据患儿院内治疗结局将其分为转归不良组和转归良好组，分析其转归不良的危险因素。利用临床特征与生化指标建立细菌性脑膜炎转归不良的风险预测列线图模型，并进一步评价模型的区分度、预测性能及实用性。采用Mann-Whitney U、t、χ²检验进行统计比较，模型的区分度、预测性能、实用性分别采用受试者操作特征曲线（ROC）及曲线下面积（AUC）、校准曲线及Hosmer-Lemeshow检验、决策分析曲线进行评价。结果　274例细菌性脑膜炎患儿转归不良65例（23.72%）。反复惊厥发作、脑脊液蛋白>1 g/L、脑脊液葡萄糖/血糖降低、血/脑脊液细菌培养阳性是细菌性脑膜炎转归不良的危险因素（均P<0.05）。基于上述4个危险因素建立细菌性脑膜炎转归不良的风险预测列线图模型，ROC显示模型预测的AUC为0.855（95%置信区间0.808～0.895）；校准曲线显示模型预测的曲线与理想曲线走向相同且贴近，Hosmer-Lemeshow检验显示，模型预测转归不良概率与实际转归不良概率接近（P=0.394）；决策分析曲线显示，当高风险阈值在0.06～0.77时应用模型预测具有较好价值。结论　基于反复惊厥发作、脑脊液蛋白、脑脊液葡萄糖/血糖、血/脑脊液细菌培养4个危险因素构建细菌性脑膜炎转归不良的风险预测列线图模型具有良好的应用价值。

关键词:

细菌性脑膜炎, 儿童, 临床特征, 生化指标, 病情转归, 列线图模型

Abstract:

Objective　To analyze the clinical characteristics and biochemical indicators of children with bacterial meningitis, and to construct a risk prediction model for poor outcomes. Methods　A retrospective analysis was conducted on the clinical data of 274 children with bacterial meningitis treated at Shaanxi Provincial People's Hospital from September 2020 to May 2024. There were 156 boys and 118 girls. They were 0-18 years old. The children were divided into a poor outcome group and a good outcome group based on the in-hospital treatment outcomes. The clinical characteristics and biochemical indicators of the two groups were compared, and the risk factors for poor outcomes were analyzed. A nomogram model for predicting the risk of adverse outcomes in the children with bacterial meningitis was established using the clinical features and biochemical indicators. The model's discrimination, predictive performance, and practicality were evaluated. Mann-Whitney U test, t test, band χ² test were used for the statistical comparisons. The model's discriminability was evaluated by the receiver operating characteristic curve and the area under the curve. The model's performance was assessed by the calibration curve and Hosmer-Lemeshow test. The model's practicality was evaluated by the decision curve analysis. Results　Among the 274 children, 65 cases had poor outcomes, with a poor outcome rate of 23.72%. Repeated convulsive seizures, cerebrospinal fluid protein > 1 g/L, decreased cerebrospinal fluid glucose/blood glucose, and positive blood/cerebrospinal fluid bacterial culture were risk factors for the childrens poor outcomes (all P<0.05). A nomogram model was established to predict the risk of adverse outcomes in the children with bacterial meningitis based on the four risk factors mentioned above. The receiver operating characteristic curve showed that the area under the curve predicted by the model was 0.855 (95%CI 0.808-0.895). The calibration curve showed that the predicted curve of the model had the same and close direction as the ideal curve; the Hosmer-Lemeshow test showed that the predicted probability of poor outcomes was close to the actual probability of poor outcomes (P=0.394). The decision analysis curve showed that the application of the model prediction had good value when the high-risk threshold was between 0.06 and 0.77. Conclusions　A nomogram model for predicting poor outcomes of children with bacterial meningitis is constructed based on the four risk factors, including repeated convulsive seizure, cerebrospinal fluid protein, cerebrospinal fluid glucose/blood glucose, and blood/CSF bacterial culture; it is confirmed that the model has good application value for predicting poor outcomes.

Key words:

Bacterial meningitis, Children, Clinical features, Biochemical indicators, Disease progression, Nomogram model

李博迟洁高蒙.

利用临床特征与生化指标建立细菌性脑膜炎转归不良的风险预测模型 [J]. 国际医药卫生导报, 2025, 31(4): 663-668.

Li Bo, Chi Jie, Gao Meng.

Establishing a risk prediction model for adverse outcomes of bacterial meningitis using clinical features and biochemical indicators [J]. International Medicine and Health Guidance News, 2025, 31(4): 663-668.

参考文献

[1] Bor M, Çokuğraş H. Factors associated with early complications in inpatients who were treated in our clinic between 1992 and 2011 with a diagnosis of acute bacterial meningitis[J]. Turk Pediatri Ars, 2020,55(2):149-156. DOI: 10.14744/TurkPediatriArs.2019.34445.
[2] Johansson Kostenniemi U, Bazan A, Karlsson L, et al. Psychiatric Disabilities and other long-term consequences of childhood bacterial meningitis[J]. Pediatr Infect Dis J, 2021,40(1):26-31. DOI: 10.1097/INF.0000000000002908.
[3] Zarak Khan M, Waheed A, Fazal F, et al. Outcomes, symptomatology, and mortality of children presenting with Bacterial Meningitis at Allied Hospitals of Rawalpindi Medical University, Pakistan: a cross-sectional study[J]. Cureus, 2024,16(3):e56107. DOI: 10.7759/cureus.56107.
[4] Garg A, Fotedar S, Bharti, et al. Clinical and laboratory profile of patients with presumptive clinical diagnosis of acute bacterial meningitis and its short-term complications[J]. J Family Med Prim Care, 2022,11(10):6274-6279. DOI: 10.4103/jfmpc.jfmpc_429_22.
[5] 陈彤岩,史一君,姜文灿,等. 脑脊液与血多指标联合诊断模型对神经外科细菌性脑膜炎早期诊断的价值[J]. 标记免疫分析与临床,2024,31(4):589-592,608. DOI:10.11748/bjmy.issn.1006-1703.2024.04.001.
[6] 许海东,刁红梅,朱晓莉,等. 急性脑膜炎病原学及临床特征[J]. 中华医院感染学杂志,2021,31(7):1102-1106. DOI:10.11816/cn.ni.2021-202059.
[7] Tubiana S, Varon E, Biron C, et al. Community-acquired bacterial meningitis in adults: in-hospital prognosis, long-term disability and determinants of outcome in a multicentre prospective cohort[J]. Clin Microbiol Infect, 2020,26(9):1192-1200. DOI: 10.1016/j.cmi.2019.12.020.
[8] 梁森全,莫庆仪,梁桂明,等. CRP/PA、LDH预测儿童细菌性脑膜炎预后的效能分析[J]. 分子诊断与治疗杂志,2021,13(6):998-1001. DOI:10.3969/j.issn.1674-6929.2021.　06.038.
[9] 中华医学会儿科学分会神经学组. 儿童社区获得性细菌性脑膜炎诊断与治疗专家共识[J]. 中华儿科杂志,2019,57(8):584-591. DOI:10.3760/cma.j.issn.0578‐1310.2019.　08.003.
[10] Parikh SR, Campbell H, Bettinger JA, et al. The everchanging epidemiology of meningococcal disease worldwide and the potential for prevention through vaccination[J]. J Infect, 2020,81(4):483-498. DOI: 10.1016/j.jinf.2020.05.079.
[11] 杨鹏,杨琳,段敏敏,等. 脑脊液NPY和Caveolin-1及VE-cadherin与细菌性脑膜炎患儿脑功能关系及其对预后的预测价值[J]. 中华医院感染学杂志,2022,32(20):3171-3175. DOI:10.11816/cn.ni.2022-220335.
[12] 刘雪梅,吴鹏,周晋,等. 南京地区儿童细菌性脑膜炎病原菌特征和近期预后危险因素[J]. 南京师大学报(自然科学版),2018,41(4):115-119. DOI:10.3969/j.issn.1001-4616. 2018.04.018.
[13] Larsen FTBD, Brandt CT, Larsen L, et al. Risk factors and prognosis of seizures in adults with community-acquired bacterial meningitis in Denmark: observational cohort studies[J]. BMJ Open, 2019,9(7):e030263. DOI: 10.1136/bmjopen-2019-030263.
[14] 邓学灵,聂仙娟,莫星枝,等. 新生儿化脓性脑膜炎神经系统并发症的影响因素分析[J]. 国际医药卫生导报,2022,28(24):3526-3530. DOI:10.3760/cma.j.issn.1007- 1245.2022.24.026.
[15] Ataei Nakhaei A, Bakhtiari E, Ghahremani S, et al. Prevalence and risk factors of seizure in children with acute bacterial meningitis: updating previous evidence using an epidemiological design[J]. Iran J Child Neurol, 2021,15(3):47-54. DOI: 10.22037/ijcn.v15i2.22250.
[16] 漆晓东,秦琴,秦蔚. 脑脊液蛋白、脑脊液神经肽Y和脑源性神经营养因子水平对细菌性脑膜炎患儿神经后遗症的预测价值[J]. 中国临床医生杂志,2021,49(8):982-985. DOI:10.3969/j.issn.2095-8552.2021.08.032.
[17] 胡古月,杨静,黄瑞. 细菌性脑膜炎患者脑脊液、血清lncRNA NEAT1水平与病情及血脑屏障破坏程度相关性分析[J]. 中国病原生物学杂志,2023,18(2):207-210. DOI:10.13350/j.cjpb.230216.
[18] Reiber H. Blood-cerebrospinal fluid (CSF) barrier dysfunction means reduced CSF flow not barrier leakage - conclusions from CSF protein data[J]. Arq Neuropsiquiatr, 2021,79(1):56-67. DOI: 10.1590/0004- 282X- anp- 2020-0094.
[19] 熊星宇,赵培丽,朱慧花. 脑脊液糖及脑脊液蛋白对新生儿GBS化脓性脑膜炎预后的预测价值[J]. 中国优生与遗传杂志,2023,31(1):27-32. DOI:10.13404/j.cnki.cjbhh. 2023.01.040.
[20] Ye L, Ji X, Song Z, et al. Clinical value of glycan changes in cerebrospinal fluid for evaluation of post-neurosurgical bacterial meningitis with hemorrhagic stroke patients[J]. Diagnostics (Basel), 2023,13(2):187. DOI: 10.3390/diagnostics13020187.
[21] Gong Q, Liu S, Xiao Z, et al. Elevated blood and cerebrospinal fluid glucose levels affect the severity and short-term prognosis of Guillain-Barré syndrome[J]. Neurol Res, 2022,44(2):121-127. DOI: 10.1080/01616412.2021.1965337.
[22] Chekrouni N, Kroon M, Drost EHGM, et al. Characteristics and prognostic factors of bacterial meningitis in the intensive care unit: a prospective nationwide cohort study[J]. Ann Intensive Care, 2023,13(1):124. DOI: 10.1186/s13613-023-01218-6.
[23] Sharma S, Acharya J, Caugant DA, et al. Detection of Streptococcus pneumoniae, Neisseria meningitidis and Haemophilus influenzae in culture negative cerebrospinal fluid samples from meningitis patients using a multiplex polymerase chain reaction in Nepal[J]. Infect Dis Rep, 2021,13(1):173-180. DOI: 10.3390/idr13010019.
[24] 黄一灵,王珏,张晓慧,等. 病原学阳性成人细菌性脑膜炎121例临床特征及预后分析[J]. 中国感染与化疗杂志,2024,24(1):1-7. DOI:10.16718/j.1009-7708.2024.01.001.
[25] Wang H, Zhu X. Cerebrospinal fluid culture-positive bacterial meningitis increases the risk for neurologic damage among neonates[J]. Ann Med, 2021,53(1):2199-2204. DOI: 10.1080/07853890.2021.2004318.