临床-炎症联合模型预测前循环急性大血管闭塞性缺血性卒中男性患者机械取栓术后的不良预后

doi:10.3969/j.issn.1674-8115.2026.03.007

上海交通大学学报（医学版） ›› 2026, Vol. 46 ›› Issue (3): 332-339.doi: 10.3969/j.issn.1674-8115.2026.03.007

• 论著 · 临床研究 • 上一篇

临床-炎症联合模型预测前循环急性大血管闭塞性缺血性卒中男性患者机械取栓术后的不良预后

梅子贤¹, 孟旭晨¹, 苏文静¹, 钟伟杰¹, 汤定中², 李轶¹()

^1.上海交通大学医学院附属第九人民医院神经外科，上海 200011
^2.上海交通大学医学院附属第六人民医院金山分院神经内科，上海 201500

收稿日期:2025-07-07 接受日期:2025-12-10 出版日期:2026-03-28 发布日期:2026-03-30
通讯作者: 李　轶，主任医师，博士；电子信箱：snailliyi@163.com。
作者简介:第一联系人：为共同第一作者（co-first authors）。
基金资助:
上海交通大学医学院附属第九人民医院“交叉”研究基金(JYJC202131)

Clinical-inflammatory combined model for predicting poor prognosis in male patients with anterior circulation acute ischemic stroke with large vessel occlusion after mechanical thrombectomy

Mei Zixian¹, Meng Xuchen¹, Su Wenjing¹, Zhong Weijie¹, Tang Dingzhong², Li Yi¹()

^1.Department of Neurosurgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
^2.Department of Neurology, Shanghai Sixth People's Hospital Jinshan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai 201500, China

Received:2025-07-07 Accepted:2025-12-10 Online:2026-03-28 Published:2026-03-30
Contact: Li Yi, E-mail: snailliyi@163.com.
Supported by:
Cross Disciplinary Research Fund of Shanghai Ninth People′s Hospital, Shanghai Jiao Tong University School of Medicine(JYJC202131)

摘要/Abstract

摘要：

目的·探讨接受机械取栓（mechanical thrombectomy，MT）治疗的前循环急性大血管闭塞性缺血性卒中（acute ischemic stroke with large vessel occlusion，AIS-LVO）男性患者术后90 d不良预后的影响因素，并基于临床特征与炎症标志物构建预测模型。方法·回顾性纳入2022年3月—2024年6月因前循环AIS-LVO于上海市2所医院接受MT治疗的126例男性患者，以术后90 d的改良Rankin量表（modified Rankin Scale，mRS）评分为结局指标，并据此将患者分为预后良好组与预后不良组。收集并比较2组患者的基线资料、围术期临床指标、入院实验室指标。采用单因素Logistic回归模型筛选与术后90 d不良预后相关的变量，而后采用多因素Logistic回归模型确定独立预测因子并以此构建预测模型。使用受试者操作特征曲线（receiver operator characteristic curve，ROC curve）评估该预测模型的效能。结果·根据术后90 d的mRS评分，MT术后前循环AIS-LVO男性患者被分为预后良好组（n=50）和预后不良组（n=76）。对2组患者的基线资料、围术期临床指标及入院实验室指标分析后发现，入院美国国立卫生研究院卒中量表（National Institutes of Health Stroke Scale，NIHSS）评分、既往卒中或短暂性脑缺血发作（transient ischemic attack，TIA）史及中性粒细胞‑淋巴细胞比率与纤维蛋白原‑白蛋白比率乘积（neutrophil-lymphocyte ratio multiplied by fibrinogen to albumin ratio，NMF）指数等指标的组间差异具有统计学意义（P<0.05）。单因素Logistic回归分析显示，NMF指数的升高与不良预后风险增加呈强相关（OR=6.944，95% CI 2.636~22.022，P<0.001）。多因素Logistic回归分析显示NMF指数为不良预后的独立预测因子（OR=6.153，95% CI 1.939~24.563，P=0.004），其他独立预测因子包括 NIHSS评分（P=0.003）、既往卒中或TIA史（P=0.034）、急性卒中治疗Org 10172试验（Trial of Org 10172 in Acute Stroke Treatment，TOAST）分型中的大动脉粥样硬化型（P=0.032）与不明原因型（P=0.006）以及白细胞计数（P=0.027）。ROC曲线分析显示模型对不良预后的预测效能良好，曲线下面积（area under the curve，AUC）为0.889，灵敏度为70%，特异度为96%。结论·NMF指数可能是MT术后前循环AIS-LVO男性患者不良预后的潜在生物标志物，将其与入院NIHSS评分、既往卒中或TIA史、TOAST分型、白细胞计数等独立预测因子联合应用，对预测该类患者的不良预后具有较高准确性。

关键词: 缺血性卒中, 机械取栓, 血栓炎症, 预后

Abstract:

Objective ·To explore the factors influencing 90 d poor prognosis in male patients with anterior circulation acute ischemic stroke with large vessel occlusion (AIS-LVO) after mechanical thrombectomy (MT), and to develop a predictive model based on clinical characteristics and inflammatory markers. Methods ·This retrospective study enrolled 126 male patients who received MT for anterior circulation AIS-LVO at two hospitals in Shanghai from March 2022 to June 2024. The 90-day modified Rankin Scale (mRS) score after surgery was used as the outcome measure, based on which patients were divided into a good prognosis group and a poor prognosis group. Baseline data, perioperative clinical indicators, and admission laboratory indicators were collected and compared between the two groups. Univariate Logistic regression model was used to screen variables associated with 90-day poor prognosis, and multivariate Logistic regression model was subsequently performed to identify independent predictors and construct a predictive model. Receiver operator characteristic (ROC) curve was used to evaluate the performance of the predictive model. Results ·According to the 90-day mRS score, male patients with anterior circulation AIS-LVO after MT were divided into a good prognosis group (n=50) and a poor prognosis group (n=76). Analysis of baseline data, perioperative clinical indicators, and admission laboratory indicators between the two groups showed that there were statistically significant differences in admission National Institutes of Health Stroke Scale (NIHSS) score, history of previous stroke or transient ischemic attack (TIA), and neutrophil-lymphocyte ratio multiplied by fibrinogen-to-albumin ratio (NMF) index (P<0.05). Univariate Logistic regression analysis showed that an elevated NMF index was strongly correlated with an increased risk of poor prognosis (OR=6.944, 95% CI 2.636‒22.022, P<0.001). Multivariate Logistic regression analysis further confirmed that the NMF index was an independent predictor of poor prognosis (OR=6.153, 95% CI 1.939‒24.563, P=0.004). Other independent predictors included NIHSS score (P=0.003), history of previous stroke or TIA (P=0.034), large-artery atherosclerosis subtype (P=0.032) and undetermined etiology subtype (P=0.006) in the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, and white blood cell count (P=0.027). ROC curve analysis showed that the model exhibited excellent performance in predicting poor prognosis, with an area under the curve (AUC) of 0.889, a sensitivity of 70%, and a specificity of 96%. Conclusion ·NMF index may serve as a potential biomarker for predicting poor prognosis in male patients with anterior circulation AIS-LVO after MT. When combined with independent predictors such as admission NIHSS score, history of previous stroke or TIA, TOAST classification, and white blood cell count, it has high accuracy in predicting poor prognosis in the specific patient population.

Key words: ischemic stroke, mechanical thrombolysis (MT), thromboinflammation, prognosis

中图分类号:

R743.33

梅子贤, 孟旭晨, 苏文静, 钟伟杰, 汤定中, 李轶. 临床-炎症联合模型预测前循环急性大血管闭塞性缺血性卒中男性患者机械取栓术后的不良预后[J]. 上海交通大学学报（医学版）, 2026, 46(3): 332-339.

Mei Zixian, Meng Xuchen, Su Wenjing, Zhong Weijie, Tang Dingzhong, Li Yi. Clinical-inflammatory combined model for predicting poor prognosis in male patients with anterior circulation acute ischemic stroke with large vessel occlusion after mechanical thrombectomy[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(3): 332-339.

图/表 4

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Variable	Good prognosis group (n=50)	Poor prognosis group (n=76)	P value
Age/year	66±12	67±12	0.893
MAP/mmHg	99±14	107±15	0.017
Blood glucose/(mmol·L^-1)	6.05 (5.80, 8.23)	7.95 (6.00, 10.13)	0.042
NIHSS score/score	9 (4, 14)	16 (10, 22)	0.001
GCS score/score	15 (12, 15)	11 (9, 15)	0.003
OTP/h	6.05 (4.31, 7.98)	7.22 (4.84, 9.90)	0.141
mTICI grade (≥2b)/n(%)	43 (86.00)	64 (84.21)	1.000
Vascular risk factors/n(%)
Hypertension	25 (50.00)	58 (76.32)	0.017
Atrial fibrillation	10 (20.00)	21 (27.63)	0.560
Atherosclerosis	5 (10.00)	6 (7.89)	0.868
Diabetes	8 (16.00)	21 (27.63)	0.342
Coronary heart disease	4 (8.00)	9 (11.84)	0.563
Previous history of stroke or TIA	3 (6.00)	19 (25.00)	0.007
Smoking history	9 (18.00)	29 (38.16)	0.042
Alcoholism history	4 (8.00)	19 (25.00)	0.042
TOAST subtypes/n(%)			0.560
Cardioembolism	21 (42.00)	24 (31.58)
Large-artery atherosclerosis	8 (16.00)	18 (23.68)
Undetermined etiology	21 (42.00)	34 (44.74)
Laboratory indicators
White blood cell count/(×10⁹/L)	8.55 (7.15, 10.23)	10.60 (7.35, 13.50)	0.020
Neutrophil count/(×10⁹/L)	7.15 (5.80, 8.07)	8.58 (6.40, 10.95)	0.015
Lymphocyte count/(×10⁹/L)	1.16 (0.90, 1.40)	1.16 (0.71, 1.51)	0.712
Platelet count/(×10⁹/L)	191 (156, 242)	207 (164, 230)	0.893
Hemoglobin/(g·L^-1)	134 (127, 145)	142 (128, 150)	0.554
Albumin/(g·L^-1)	39 (37, 42)	39 (36, 42)	0.141
Total cholesterol/(mmol·L^-1)	4.38 (3.80, 4.87)	4.32 (3.73, 4.87)	0.712
Triglycerides/(mmol·L^-1)	1.07 (0.77, 1.48)	1.21 (0.81, 1.87)	0.554
High density lipoprotein/(mmol·L^-1)	1.06 (0.91, 1.32)	1.08 (0.90, 1.41)	0.888
Creatinine/(μmol·L^-1)	75 (66, 89)	80 (66, 95)	0.560
APTT/s	26.50 (25.73, 28.63)	26.85 (25.10, 29.60)	0.750
D-dimer/(mg·L^-1)	1.13 (0.64, 2.00)	1.46 (0.66, 3.14)	0.226
Fibrinogen/(g·L^-1)	2.60 (2.31, 3.02)	2.92 (2.47, 3.57)	0.042
NMF index	0.42 (0.32, 0.57)	0.73 (0.41, 1.14)	0.003

Variable	Good prognosis group (n=50)	Poor prognosis group (n=76)	P value
Age/year	66±12	67±12	0.893
MAP/mmHg	99±14	107±15	0.017
Blood glucose/(mmol·L^-1)	6.05 (5.80, 8.23)	7.95 (6.00, 10.13)	0.042
NIHSS score/score	9 (4, 14)	16 (10, 22)	0.001
GCS score/score	15 (12, 15)	11 (9, 15)	0.003
OTP/h	6.05 (4.31, 7.98)	7.22 (4.84, 9.90)	0.141
mTICI grade (≥2b)/n(%)	43 (86.00)	64 (84.21)	1.000
Vascular risk factors/n(%)
Hypertension	25 (50.00)	58 (76.32)	0.017
Atrial fibrillation	10 (20.00)	21 (27.63)	0.560
Atherosclerosis	5 (10.00)	6 (7.89)	0.868
Diabetes	8 (16.00)	21 (27.63)	0.342
Coronary heart disease	4 (8.00)	9 (11.84)	0.563
Previous history of stroke or TIA	3 (6.00)	19 (25.00)	0.007
Smoking history	9 (18.00)	29 (38.16)	0.042
Alcoholism history	4 (8.00)	19 (25.00)	0.042
TOAST subtypes/n(%)			0.560
Cardioembolism	21 (42.00)	24 (31.58)
Large-artery atherosclerosis	8 (16.00)	18 (23.68)
Undetermined etiology	21 (42.00)	34 (44.74)
Laboratory indicators
White blood cell count/(×10⁹/L)	8.55 (7.15, 10.23)	10.60 (7.35, 13.50)	0.020
Neutrophil count/(×10⁹/L)	7.15 (5.80, 8.07)	8.58 (6.40, 10.95)	0.015
Lymphocyte count/(×10⁹/L)	1.16 (0.90, 1.40)	1.16 (0.71, 1.51)	0.712
Platelet count/(×10⁹/L)	191 (156, 242)	207 (164, 230)	0.893
Hemoglobin/(g·L^-1)	134 (127, 145)	142 (128, 150)	0.554
Albumin/(g·L^-1)	39 (37, 42)	39 (36, 42)	0.141
Total cholesterol/(mmol·L^-1)	4.38 (3.80, 4.87)	4.32 (3.73, 4.87)	0.712
Triglycerides/(mmol·L^-1)	1.07 (0.77, 1.48)	1.21 (0.81, 1.87)	0.554
High density lipoprotein/(mmol·L^-1)	1.06 (0.91, 1.32)	1.08 (0.90, 1.41)	0.888
Creatinine/(μmol·L^-1)	75 (66, 89)	80 (66, 95)	0.560
APTT/s	26.50 (25.73, 28.63)	26.85 (25.10, 29.60)	0.750
D-dimer/(mg·L^-1)	1.13 (0.64, 2.00)	1.46 (0.66, 3.14)	0.226
Fibrinogen/(g·L^-1)	2.60 (2.31, 3.02)	2.92 (2.47, 3.57)	0.042
NMF index	0.42 (0.32, 0.57)	0.73 (0.41, 1.14)	0.003

Variable	OR	95% CI	P value
Age	1.006	0.976‒1.038	0.677
MAP	1.038	1.012‒1.068	0.006
Blood glucose	1.138	1.018‒1.302	0.038
NIHSS score	1.102	1.051‒1.163	<0.001
GCS score	0.793	0.687‒0.900	0.001
OTP	1.014	0.969‒1.077	0.575
mTICI grade (≥2b vs. <2b)	0.868	0.302‒2.338	0.784
Hypertension	3.222	1.511‒7.034	0.003
Atrial fibrillation	1.527	0.661‒3.715	0.332
Atherosclerosis	0.771	0.220‒2.818	0.683
Diabetes	2.005	0.833‒5.225	0.133
Coronary heart disease	1.545	0.472‒5.976	0.490
Previous history of stroke or TIA	5.222	1.653‒23.193	0.011
Smoking history	2.811	1.229‒6.931	0.018
Alcoholism history	3.833	1.330‒13.924	0.022
TOAST subtypes	1.969	0.726‒5.662	0.192
White blood cell count	1.203	1.072‒1.369	0.003
Neutrophil count	1.229	1.079‒1.419	0.003
Lymphocyte count	1.010	0.571‒1.830	0.973
Platelet count	1.001	0.995‒1.006	0.850
Hemoglobin	1.001	0.993‒1.025	0.388
Albumin	0.953	0.896‒1.005	0.091
Total cholesterol	0.948	0.683‒1.317	0.746
Triglycerides	1.440	0.904‒2.597	0.173
High density lipoprotein	1.175	0.373‒3.812	0.784
Creatinine	1.004	0.989‒1.020	0.622
APTT	1.022	0.966‒1.100	0.488
D-dimer	1.060	0.976‒1.191	0.230
Fibrinogen	1.763	1.175‒2.868	0.012
NMF index	6.944	2.636‒22.022	<0.001

Variable	OR	95% CI	P value
Age	1.006	0.976‒1.038	0.677
MAP	1.038	1.012‒1.068	0.006
Blood glucose	1.138	1.018‒1.302	0.038
NIHSS score	1.102	1.051‒1.163	<0.001
GCS score	0.793	0.687‒0.900	0.001
OTP	1.014	0.969‒1.077	0.575
mTICI grade (≥2b vs. <2b)	0.868	0.302‒2.338	0.784
Hypertension	3.222	1.511‒7.034	0.003
Atrial fibrillation	1.527	0.661‒3.715	0.332
Atherosclerosis	0.771	0.220‒2.818	0.683
Diabetes	2.005	0.833‒5.225	0.133
Coronary heart disease	1.545	0.472‒5.976	0.490
Previous history of stroke or TIA	5.222	1.653‒23.193	0.011
Smoking history	2.811	1.229‒6.931	0.018
Alcoholism history	3.833	1.330‒13.924	0.022
TOAST subtypes	1.969	0.726‒5.662	0.192
White blood cell count	1.203	1.072‒1.369	0.003
Neutrophil count	1.229	1.079‒1.419	0.003
Lymphocyte count	1.010	0.571‒1.830	0.973
Platelet count	1.001	0.995‒1.006	0.850
Hemoglobin	1.001	0.993‒1.025	0.388
Albumin	0.953	0.896‒1.005	0.091
Total cholesterol	0.948	0.683‒1.317	0.746
Triglycerides	1.440	0.904‒2.597	0.173
High density lipoprotein	1.175	0.373‒3.812	0.784
Creatinine	1.004	0.989‒1.020	0.622
APTT	1.022	0.966‒1.100	0.488
D-dimer	1.060	0.976‒1.191	0.230
Fibrinogen	1.763	1.175‒2.868	0.012
NMF index	6.944	2.636‒22.022	<0.001

Variable	OR	95% CI	P value
Age	1.031	0.984‒1.083	0.212
MAP	1.028	0.994‒1.067	0.117
NIHSS score	1.112	1.040‒1.201	0.003
Blood glucose	1.045	0.900‒1.238	0.582
Hypertension	1.620	0.520‒5.056	0.401
Previous history of stroke or TIA	5.823	1.287‒35.884	0.034
Smoking history	3.056	0.723‒14.125	0.136
Alcoholism history	2.373	0.389‒17.022	0.364
TOAST subtypes (vs. Cardioembolism)
Large-artery atherosclerosis	5.324	1.206‒26.534	0.032
Undetermined etiology	6.153	1.797‒24.563	0.006
OTP	0.995	0.943‒1.068	0.871
White blood cell count	1.194	1.025‒1.413	0.027
NMF index	6.153	1.939‒24.563	0.004

临床-炎症联合模型预测前循环急性大血管闭塞性缺血性卒中男性患者机械取栓术后的不良预后

Clinical-inflammatory combined model for predicting poor prognosis in male patients with anterior circulation acute ischemic stroke with large vessel occlusion after mechanical thrombectomy

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