目的：探讨多因素联合预测对急性缺血性卒中（AIS）精准用药患者预后的影响，并通过建立多因素 联合模型预测精准用药AIS患者预后。方法：采用前瞻性队列研究设计，于2019年3月1日至2020年12月 30日在本院神经内科进行病例招募，在出院后第90 天进行随访。根据90 d随访的改良Rankin量表（mRS） 分为预后良好组（mRS评分0～2分）和预后不良组（mRS评分3～6分），使用多因素Logistic 回归分析影响 精准用药AIS患者预后的标志物；采用ROC曲线验证Logistic回归模型的诊断效度，采用Delong检验评价 曲线下面积（AUC）的差异。结果：共招募240例患者，221例随访，预后良好组168例，预后不良组53例。 多因素Logistic回归分析显示白细胞介素-6（IL-6）水平及入院NIHSS评分是影响患者预后的独立危险因 素，其联合预测值高于单一变量预测效果。结论：血清IL-6和入院NIHSS评分可作为预测精准用药AIS患 者预后的因素，并且通过多因素联合预测模型可以更好地预测AIS患者90 d的预后。

To investigate effect of combined multifactorial prediction on the prognosis of patients with acute ischemic stroke (AIS) on the precise use of medication, and to predict the prognosis of patients with AIS on the precise use of medication by developing a combined multifactorial model. Methods: A prospective cohort study design was used to recruit patients at department of neurology in our hospital from March 1, 2019 to December 30, 2020, with a follow-up at the 90th d after hospital discharge. The modified Rankin Scale (mRS) at 90-d follow-up was divided into a good prognosis group (mRS score 0～2) and poor prognosis group (mRS score 3～6), and markers affecting the prognosis of patients with precision medication AIS were analyzed using multifactorial Logistic regression; ROC curves were used to verify the diagnostic validity of the Logistic regression model, and Delong test was performed to evaluate the difference in the area under the curve. Results: A total of 240 patients were recruited for the study and 221 patients completed the follow-up; of these, 168 and 53 were in the good and bad prognosis groups, respectively. Multi-factor Logistic regression analysis showed that interleukin-6 (IL-6) levels and admission NIHSS scores were independent risk factors for patient prognosis, and their combined predictive value was higher than the single variable predictive effect. Conclusion: Serum IL-6 and admission NIHSS scores can be used as predictors of prognosis for patients with precision medication AIS and can better predict the prognosis of AIS patients at 90 d by a combined multifactorial prediction model for early assessment of prognosis in AIS patients.