作者：田继文1 ，方晨光1 ，刘小菲1 ，王芳1 ，陶飞2

单位：

1.247000安徽省池州市，皖南医学院附属池州医院 池州市人民医院急诊内科　2.247000安徽省池州市，皖 南医学院附属池州医院 池州市人民医院神经内科

Units：

1.Department of Emergency Medicine, Chizhou Hospital Attached to Wannan Medical College/the People's Hospital of Chizhou, Chizhou 247000, China 2.Department of Neurology, Chizhou Hospital Attached to Wannan Medical College/the People's Hospital of Chizhou, Chizhou 247000, China

关键词：

缺血性卒中；重组组织型纤溶酶原激活剂；静脉溶栓治疗；预后；影响因素分析；列线图；网络 计算器

Keywords：

Ischaemic stroke; Recombinant tissue plasminogen activator; Intravenous thrombolytic therapy; Prognosis; Root cause analysis; Nomograms; Web calculator

CLC：

R 743.3

DOI：

10.12114/j.issn.1008-5971.2024.00.089

Funds：

安徽省重点研究与开发计划项目（2022e07020058）；2022年度安徽省卫生健康科研项目（AHWJ2022c044）

摘要：

目的　探讨急性缺血性脑卒中（AIS）患者重组组织型纤溶酶原激活剂（rt-PA）静脉溶栓治疗后 预后的影响因素，构建其风险预测列线图模型并进行验证，同时开发网络计算器。方法　选取2021年1月—2023年6 月在皖南医学院附属池州医院行rt-PA静脉溶栓治疗的AIS患者192例为研究对象。收集患者临床资料，对患者随访3 个月，根据改良Rankin量表（mRS）评分将患者预后分为预后良好（mRS评分0～2分）及预后不良（mRS评分3～6 分）。构建血小板指数评分（PIS）；采用多因素Logistic回归分析探讨AIS患者rt-PA静脉溶栓治疗后预后的影响因 素；采用regplot包构建AIS患者rt-PA静脉溶栓治疗后预后不良的风险预测列线图模型；采用Bootstrap法重复抽样1 000 次进行内部验证，计算一致性指数；采用Hosmer-Lemeshow拟合优度检验和校准曲线评价该列线图模型的拟合程 度，采用ROC曲线分析该列线图模型对AIS患者rt-PA静脉溶栓治疗后预后不良的预测价值，采用决策曲线评价该列 线图模型的临床有效性；使用DynNom包将列线图模型发布至网络中并开发网络计算器。结果　192例患者中，失访9 例，最终完成研究183例，其中预后不良48例（26.2%）。预后不良患者年龄、平均血小板体积（MPV）大于预后良 好患者，合并心房颤动者占比、美国国立卫生研究院卒中量表（NIHSS）评分、卒中预测工具-Ⅱ（SPI-Ⅱ）评分、 CRP、D-二聚体（D-D）高于预后良好患者，心率快于预后良好患者，DBP、格拉斯哥昏迷量表（GCS）评分、Hb、 PLT、血小板分布宽度（PDW）低于预后良好患者（P＜0.05）。多因素Logistic回归分析结果显示，NIHSS评分、 SPI-Ⅱ评分、CRP、PIS是AIS患者rt-PA静脉溶栓治疗后预后的独立影响因素（P＜0.05）。基于NIHSS评分、SPI-Ⅱ 评分、CRP、PIS构建AIS患者rt-PA静脉溶栓治疗后预后不良的风险预测列线图模型。该列线图模型的一致性指数为 0.894〔95%CI（0.765～0.913）〕。Hosmer-Lemeshow拟合优度检验结果显示，该列线图模型拟合较好（χ 2 =2.531， P=0.960）。ROC曲线分析结果显示，该列线图模型预测AIS患者rt-PA静脉溶栓治疗后预后不良的AUC为0.899〔95%CI （0.846～0.939）〕。决策曲线分析结果显示，当阈值概率为0.070～0.924时，净获益率＞0。基于AIS患者rt-PA静脉 溶栓治疗后预后不良风险预测列线图模型开发网络计算器（https://npmcls.shinyapps.io/DynNomappP/）。结论　NIHSS 评分、SPI-Ⅱ评分、CRP、PIS是AIS患者rt-PA静脉溶栓治疗后预后的独立影响因素，基于上述因素构建的风险预测列 线图模型对AIS患者rt-PA静脉溶栓治疗后预后不良具有较好的区分能力、校准度及一定预测价值。开发的网络计算器 能高效地协助临床医生做出诊疗决策和预后评估。

Abstract：

Objective To explore the influencing factors of the prognosis of patients with acute ischemic stroke (AIS) after recombinant tissue plasminogen activator (rt-PA) intravenous thrombolytic therapy, and to construct and validate the nomogram model for predicting its risk, and develop the web calculator. Methods A total of 192 AIS patients undergoing rt-PA intravenous thrombolytic therapy admitted to Chizhou Hospital Attached to Wannan Medical College from January 2021 to June 2023 were selected as the research subjects. The clinical data of the patients were collected, the patients were followed up for 3 months, and the prognosis of patients were divided into good prognosis [modified Rankin Scale (mRS) score was 0-2] and poor prognosis (mRS score was 3-6) based on the mRS score. Platelet Index Score (PIS) was constructed. Multivariate Logistic regression analysis was used to explore the influencing factors of prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy. The nomogram model for predicting the risk of poor prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy was constructed by using the regplot package. Bootstrap method was used to repeat sample 1 000 times for internal verification, and the consistency index was calculated. Hosmer-Lemeshow goodness of fit test and calibration curve were used to evaluate the fitting degree of the nomogram model. ROC curve was used to analyze the predictive value of the nomogram model for poor prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy. The decision curve was drawn to evaluate the clinical effectiveness of the nomogram model. The DynNom package was used to publish the nomogram model to the Web and develop a Web calculator. Results Among 192 patients, 9 were lost to follow-up, and 183 cases were ultimately completed in the study, of which 48 cases (26.2%) had poor prognosis. The age, mean platelet volume (MPV) in the patients with poor prognosis were greater than those in the patients with good prognosis, proportion of patients with atrial fibrillation, National Institutes of Health Stroke Scale (NIHSS) score, Stroke Prognostic Instrument-Ⅱ (SPI-Ⅱ) score, CRP, D-dimer (D-D) were higher than those in the patients with good prognosis, heart rate was fast than that in the patients with good prognosis, DBP, Glasgow Coma Scale (GCS) score, Hb, PLT, and platelet distribution width (PDW) were lower than those in the patients with good prognosis (P < 0.05) . Multivariate Logistic regression analysis showed that NIHSS score, SPI-Ⅱ score, CRP, PIS were the independent influencing factors of prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy (P < 0.05) . The nomogram model for poor prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy constructed based on NIHSS score, SPI-Ⅱ score, CRP, and PIS. The consistency index of the nomogram model was 0.894 [95% CI (0.765-0.913) ] . The results of Hosmer-Lemeshow goodness of fit test showed that the nomogram model fitted well (χ 2 =2.531, P=0.960) . The results of ROC curve analysis showed that the AUC of the nomogram model for predicting poor prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy was 0.899 [95%CI (0.846-0.939) ] . The results of decision curve analysis showed that when the threshold probability was 0.070-0.924, the net benefit rate was > 0. A web calculator (https://npmcls.hinyapps. io/DynNomappP/) was developed based on the nomogram model for predicting poor prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy. Conclusion NIHSS score, SPI-Ⅱ score, CRP, PIS are the independent influencing factors of prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy. The nomogram model constructed based on the above influencing factors has a high degree of discrimination and calibration and certain predictive value for poor prognosis of patients with AIS after rt-PA intravenous thrombolytic therapy. The developed web calculator can efficiently assist clinicians in making diagnostic decisions and prognostic evaluation.

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