基于血清学炎性指标构建预测缺血性脑卒中患者预后的列线图模型

Development of a nomogram model based on serological inflammatory markers for predicting prognosis in patients with ischemic stroke

目的探讨血小板与淋巴细胞比值(platelet to lymphocyte ratio,PLR)、中性粒细胞与淋巴细胞比值(neutrophil to lymphocyte ratio,NLR)、白细胞介素-5(interleukin-5,IL-5)、白细胞介素-6(interleukin-6,IL-6)、白细胞介素-1β(interleukin-1β,IL-1β)、白细胞介素-8(interleukin-8,IL-8)与缺血性脑卒中患者预后的关系,并建立预测脑卒中预后的列线图模型。方法回顾性分析320例缺血性脑卒中患者病例资料。采用改良Rankin量表(modified Rankin Scale,mRS)评价预后,根据患者预后情况,分为预后良好组(n=280)与预后不良组(n=40)。采用Logistic回归分析确定危险因素,建立列线图模型。受试者工作特征(receiver operation characteristic,ROC)曲线,分析其预测价值。结果与预后不良组比较,预后良好组患者的发病至治疗时间、美国国立卫生院卒中量表(national institute of health stroke scale,NIHSS)评分≥5分、PLR、NLR、尿酸、IL-5、IL-6、IL-1β、IL-8,差异均有统计学意义(均P<0.05)。Logistic回归分析显示:发病至治疗时间延长,NIHSS评分≥5分,PLR升高、NLR升高、尿酸升高、IL-5升高、IL-6升高、IL-1β升高、IL-8升高均为脑卒中患者预后不良的危险因素(P<0.05)。列线图模型结果显示:IL-6是脑卒中患者预后不良的最强预测因子,其次是IL-1β、NLR、IL-8、IL-5、PLR、发病至治疗时间、尿酸、NIHSS评分≥5分。Hosmer-Lemeshow检验结果显示:列线图模型预测值与真实值相比无明显差异,预测效能良好(χ^(2)=1.43、P=0.754)。ROC曲线显示:列线图模型预测缺血性脑卒中患者预后不良的曲线下面积为0.786(95%CI:0.683~0.862),灵敏度为0.816,特异度为0.797,约登指数为0.653。提示该预测模型为中度区分度,预测效果良好。结论基于上述血清学炎性指标建立的列线图模型可准确预测缺血性脑卒中患者的预后不良风险。

Objective To investigate the relationship between platelet to lymphocyte ratio(PLR),neutrophil to lymphocyte ratio(NLR),interleukin-5(IL-5),interleukin-6(IL-6),interleukin-1β(IL-1β),interleukin-8(IL-8),and prognosis in patients with ischemic stroke,and to establish a nomogram model for predicting the prognosis of ischemic stroke.Methods The clinical data of 320 patients with ischemic stroke were analyzed retrospectively.The modified Rankin Scale(mRS)was used to evaluate the prognosis,and the patients were divided into a good prognosis group(mRS 0-2,n=280)and a poor prognosis group(mRS 3-5,n=40).Logistic regression analysis was used to identify risk factors,and a nomogram model was established.The receiver operating characteristic(ROC)curve was used to analyze the predictive value of the model.Results Compared with the poor prognosis group,the time from onset to treatment,National Institutes of Health Stroke Scale(NIHSS)score≥5,PLR,NLR,uric acid,IL-5,IL-6,IL-1β,and IL-8 levels were significantly different from the good prognosis group(all P<0.05).Logistic regression analysis revealed that time from onset to treatment was prolonged,NIHSS score≥5,elevated PLR,NLR,uric acid,IL-5,IL-6,IL-1β,and IL-8 were risk factors for poor prognosis in patients with ischemic stroke(P<0.05).The nomogram model showed that IL-6 was the strongest predictor of poor prognosis in stroke patients,followed by IL-1β,NLR,IL-8,IL-5,PLR,time from onset to treatment,uric acid,and NIHSS score≥5.The Hosmer-Lemeshow(H-L)test indicated no significant difference between the predicted and observed values of the nomogram model,demonstrating good predictive performance(χ^(2)=1.43,P=0.754).The ROC curve showed that the area under the curve(AUC)of the nomogram model for predicting poor prognosis in stroke patients was 0.786(95%CI:0.683-0.862),with a sensitivity of 0.816,a specificity of 0.797,and a Youden index of 0.653,indicating moderate discrimination and good predictive performance.Conclusions The nomogram model based on these serological inflammatory markers can accurately predict the risk of poor prognosis in patients with ischemic stroke.