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隐球菌性脑膜炎预后不良危险因素分析及列线图预测模型构建

Analysis of risk factors for dismal prognosis of cryptococcal meningitis and construction of a Nomogram predictive model
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摘要 目的筛查隐球菌性脑膜炎预后不良危险因素,并基于危险因素构建风险预测列线图(Nomogram)模型。方法纳入2010年1月至2022年8月遵义医科大学附属医院收治的100例隐球菌性脑膜炎患者,均予以抗隐球菌治疗,根据住院期间脑脊液隐球菌培养结果以及出院时临床症状与体征分为预后良好组(19例)和预后不良组(81例),单因素和多因素逐步法Logistic回归分析筛查隐球菌性脑膜炎患者预后不良危险因素,并基于危险因素构建Nomogram模型,绘制受试者工作特征(ROC)曲线和校准曲线并行Hosmer-Lemeshow拟合优度检验。结果预后不良组患者入院时营养风险筛查2002(NRS 2002)评分(Z=-3.898,P=0.000)、脑脊液压力>250 mm H_(2)O比例(χ^(2)=9.512,P=0.002)、抗真菌治疗时间<14 d比例(χ^(2)=17.847,P=0.000)高于预后良好组,血常规红细胞计数(t=-2.802,P=0.006)和淋巴细胞计数(Z=-2.878,P=0.004)、血浆白蛋白(t=-4.332,P=0.000)、应用两性霉素B比例(χ^(2)=4.597,P=0.032)低于预后良好组。Logistic回归分析显示,入院时NRS 2002评分高(OR=3.258,95%CI:1.337~7.940;P=0.009)、脑脊液压力>250 mm H_(2)O(OR=0.108,95%CI:0.018~0.659;P=0.016)、抗真菌治疗时间<14 d(OR=0.092,95%CI:0.011~0.742;P=0.025)是隐球菌性脑膜炎预后不良的危险因素。根据上述3项危险因素构建Nomogram模型,ROC曲线下面积为0.927(95%CI:0.873~0.980,P=0.000),该模型预测隐球菌性脑膜炎预后不良的截断值为53.50分;校准曲线(一致性良好)、Hosmer-Lemeshow拟合优度检验(χ^(2)=2.694,P=0.912)表明该模型具有良好的区分度、校准度和稳定性。结论入院时NRS 2002评分高、颅内压>250 mm H_(2)O、抗真菌治疗<14 d的隐球菌性脑膜炎患者预后较差,据此构建的Nomogram模型具有较高的预后不良风险预测价值。 Objective To analyze the risk factors for dismal prognosis in patients with cryptococcal meningitis and construct a prediction scoring system Nomogram model.Methods A total of 100 patients with cryptococcal meningitis who treated with anticryptococcal therapy admitted to Affiliated Hospital of Zunyi Medical University from January 2010 to August 2022 were selected.The patients were divided into favorable prognosis group(n=19)and dismal prognosis group(n=81)according to the results of cerebrospinal fluid(CSF)cryptococcal culture during hospitalization and clinical symptoms and signs at the time of discharge.Risk factors were screened by using univariate and multivariate stepwise Logistic regression analyses.A Nomogram model was constructed based on the risk factors,the receiver operating characteristic(ROC)curve and calibration curves of the model were plotted,and Hosmer-Lemeshow goodness-of-fit test was performed.Results The proportion of patients in the dismal prognosis group with Nutritional Risk Screening 2002(NRS 2002)score(Z=-3.898,P=0.000),CSF pressure>250 mm H_(2)O(χ^(2)=9.512,P=0.002)and duration of antifungal treatment<14 d(χ^(2)=17.847,P=0.000)on admission were higher than those in the favorable prognosis group,and the blood routine red blood cell count(t=-2.802,P=0.006)and lymphocyte count(Z=-2.878,P=0.004),plasma albumin(t=-4.332,P=0.000),and the proportion of amphotericin B application(χ^(2)=4.597,P=0.032)were lower than those in the favorable prognosis group.Logistic regression analysis showed the admission high NRS 2002 score(OR=3.258,95%CI:1.337-7.940;P=0.009),CBF pressure>250 mm H_(2)O(OR=0.108,95%CI:0.018-0.659;P=0.016),and the duration of antifungal treatment<14 d(OR=0.092,95%CI:0.011-0.742;P=0.025)were risk factors for dismal prognosis of cryptococcal meningitis.A Nomogram model was constructed based on the above 3 risk factors,and the area under the R0C curve was 0.927(95%CI:0.873-0.980,P=0.000),which predicted a cut-off value of 53.50 points for dismal prognosis in cryptococcal meningitis;the calibration curve(with good consistency),and the Hosmer-Lemeshow goodness-of-fit test(χ^(2)=2.694,P=0.912)indicated that the model had good discrimination,calibration and stability.Conclusions Patients with cryptococcal meningitis with a high NRS 2002 score,CSF pressure>250 mm H_(2)O,and antifungal treatment<14 d had a dismal prognosis,and the Nomogram model constructed accordingly has a high predictive value of dismal prognostic risk.
作者 熊冰婕 曹飘 张骏 张海清 XIONG Bing-jie;CAO Piao;ZHANG Jun;ZHANG Hai-qing(Department of Neurology,Affiliated Hospital of Zunyi Medical University,Zunyi 563000,Guizhou,China)
出处 《中国现代神经疾病杂志》 CAS 北大核心 2024年第4期230-237,共8页 Chinese Journal of Contemporary Neurology and Neurosurgery
基金 国家自然科学基金青年科学基金资助项目(项目编号:82101527) 贵州省科技厅基金资助项目(项目编号:黔科合基础-ZK[2021]一般408) 贵州省卫生健康委科学技术基金项目(项目编号:gzwjkj2020-1-010)。
关键词 脑膜炎 隐球菌性 两性霉素B 预后 危险因素 Logistic模型 列线图 Meningitis,cryptococcal Amphotericin B Prognosis Risk factors Logistic models Nomograms
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