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

doi:10.3969/j.issn.1674-8115.2026.03.007

Abstract

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

CLC Number:

R743.33

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.

Figures/Tables 4

TrendMD

References 40

[1]	Saini V, Guada L, Yavagal D R. Global epidemiology of stroke and access to acute ischemic stroke interventions[J]. Neurology, 2021, 97(20 Suppl 2): S6-S16.
[2]	Fan J H, Li X G, Yu X Y, et al. Global burden, risk factor analysis, and prediction study of ischemic stroke, 1990-2030[J]. Neurology, 2023, 101(2): e137-e150.
[3]	Berkhemer O A, Fransen P S S, Beumer D, et al. A randomized trial of intraarterial treatment for acute ischemic stroke[J]. N Engl J Med, 2015, 372(1): 11-20.
[4]	Goyal M, Menon B K, van Zwam W H, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials[J]. Lancet, 2016, 387(10029): 1723-1731.
[5]	Morsi R Z, Elfil M, Ghaith H S, et al. Endovascular thrombectomy for large ischemic strokes: a living systematic review and meta-analysis of randomized trials[J]. J Stroke, 2023, 25(2): 214-222.
[6]	Brott T, Adams H P Jr, Olinger C P, et al. Measurements of acute cerebral infarction: a clinical examination scale[J]. Stroke, 1989, 20(7): 864-870.
[7]	Warner J J, Harrington R A, Sacco R L, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke[J]. Stroke, 2019, 50(12): 3331-3332.
[8]	Wang Y J, Zhang S M, Zhang L Q, et al. Chinese guidelines for the secondary prevention of ischemic stroke and transient ischemic attack 2010[J]. CNS Neurosci Ther, 2012, 18(2): 93-101.
[9]	Farrell B, Godwin J, Richards S, et al. The United Kingdom transient ischaemic attack (UK-TIA) aspirin trial: final results[J]. J Neurol Neurosurg Psychiatry, 1991, 54(12): 1044-1054.
[10]	Banks J L, Marotta C A. Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis[J]. Stroke, 2007, 38(3): 1091-1096.
[11]	Qu D, Wang L, Huo M Y, et al. Focal TLR4 activation mediates disturbed flow-induced endothelial inflammation[J]. Cardiovasc Res, 2020, 116(1): 226-236.
[12]	Weinstein J R, Schulze J, Lee R V, et al. Functional polymorphisms in toll-like receptor 4 are associated with worse outcome in acute ischemic stroke patients[J]. Neuroreport, 2014, 25(8): 580-584.
[13]	Marriott I, Bost K L, Huet-Hudson Y M. Sexual dimorphism in expression of receptors for bacterial lipopolysaccharides in murine macrophages: a possible mechanism for gender-based differences in endotoxic shock susceptibility[J]. J Reprod Immunol, 2006, 71(1): 12-27.
[14]	Villa A, Gelosa P, Castiglioni L, et al. Sex-specific features of microglia from adult mice[J]. Cell Rep, 2018, 23(12): 3501-3511.
[15]	Ahnstedt H, McCullough L D. The impact of sex and age on T cell immunity and ischemic stroke outcomes[J]. Cell Immunol, 2019, 345: 103960.
[16]	Ahnstedt H, Patrizz A, Chauhan A, et al. Sex differences in T cell immune responses, gut permeability and outcome after ischemic stroke in aged mice[J]. Brain Behav Immun, 2020, 87: 556-567.
[17]	DeLong J H, Ohashi S N, O′Connor K C, et al. Inflammatory responses after ischemic stroke[J]. Semin Immunopathol, 2022, 44(5): 625-648.
[18]	Wang Y, Mulder I A, Westendorp W F, et al. Immunothrombosis in acute ischemic stroke[J]. Stroke, 2025, 56(2): 553-563.
[19]	Sharma D, Spring K J, Bhaskar S M M. Neutrophil-lymphocyte ratio in acute ischemic stroke: immunopathology, management, and prognosis[J]. Acta Neurol Scand, 2021, 144(5): 486-499.
[20]	Duan Z H, Wang H M, Wang Z, et al. Neutrophil-lymphocyte ratio predicts functional and safety outcomes after endovascular treatment for acute ischemic stroke[J]. Cerebrovasc Dis, 2018, 45(5/6): 221-227.
[21]	Wang Y F, Bai L, Li X H, et al. Fibrinogen-to-albumin ratio and clinical outcomes in patients with large artery atherosclerosis stroke[J]. J Am Heart Assoc, 2023, 12(24): e030837.
[22]	Sun S F, Cheng Y Q, Li L, et al. A high fibrinogen-to-albumin ratio on admission is associated with early neurological deterioration following intravenous thrombolysis in patients with acute ischemic stroke[J]. J Inflamm Res, 2024, 17: 4151-4161.
[23]	Tariq M B, Lee J, McCullough L D. Sex differences in the inflammatory response to stroke[J]. Semin Immunopathol, 2023, 45(3): 295-313.
[24]	Diener H C, Hankey G J. Primary and secondary prevention of ischemic stroke and cerebral hemorrhage JACC focus seminar[J]. J Am Coll Cardiol, 2020, 75(15): 1804-1818.
[25]	Zaidat O O, Yoo A J, Khatri P, et al. Recommendations on angiographic revascularization grading standards for acute ischemic stroke: a consensus statement[J]. Stroke, 2013, 44(9): 2650-2663.
[26]	LeCouffe N E, Kappelhof M, Treurniet K M, et al. 2B, 2C, or 3: what should be the angiographic target for endovascular treatment in ischemic stroke [J]. Stroke, 2020, 51(6): 1790-1796.
[27]	Kwah L K, Diong J. National institutes of health stroke scale (NIHSS)[J]. J Physiother, 2014, 60(1): 61.
[28]	Mohan K M, Wolfe C D, Rudd A G, et al. Risk and cumulative risk of stroke recurrence: a systematic review and meta-analysis[J]. Stroke, 2011, 42(5): 1489-1494.
[29]	Touzé E, Varenne O, Chatellier G, et al. Risk of myocardial infarction and vascular death after transient ischemic attack and ischemic stroke: a systematic review and meta-analysis[J]. Stroke, 2005, 36(12): 2748-2755.
[30]	Cao J Y, Roth S, Zhang S J, et al. DNA-sensing inflammasomes cause recurrent atherosclerotic stroke[J]. Nature, 2024, 633(8029): 433-441.
[31]	Kang L J, Yu H L, Yang X, et al. Neutrophil extracellular traps released by neutrophils impair revascularization and vascular remodeling after stroke[J]. Nat Commun, 2020, 11(1): 2488.
[32]	Huo X C, Sun D P, Raynald, et al. Endovascular treatment in acute ischemic stroke with large vessel occlusion according to different stroke subtypes: data from ANGEL-ACT registry[J]. Neurol Ther, 2022, 11(1): 151-165.
[33]	Grau A J, Weimar C, Buggle F, et al. Risk factors, outcome, and treatment in subtypes of ischemic stroke: the German stroke data bank[J]. Stroke, 2001, 32(11): 2559-2566.
[34]	Dawson S L, Manktelow B N, Robinson T G, et al. Which parameters of beat-to-beat blood pressure and variability best predict early outcome after acute ischemic stroke [J]. Stroke, 2000, 31(2): 463-468.
[35]	Tikhonoff V, Zhang H F, Richart T, et al. Blood pressure as a prognostic factor after acute stroke[J]. Lancet Neurol, 2009, 8(10): 938-948.
[36]	Willmot M, Leonardi-Bee J, Bath P M. High blood pressure in acute stroke and subsequent outcome: a systematic review[J]. Hypertension, 2004, 43(1): 18-24.
[37]	Ferrari F, Villa R F. Brain bioenergetics in chronic hypertension: risk factor for acute ischemic stroke[J]. Biochem Pharmacol, 2022, 205: 115260.
[38]	Martin S S, Aday A W, Almarzooq Z I, et al. 2024 Heart Disease And Stroke Statistics: a report of US and global data from the American Heart Association[J]. Circulation, 2024, 149(8): e347-e913.
[39]	Henderson E, Rodriguez Guerrero L A, Continente X, et al. Measurement of airborne nicotine, as a marker of secondhand smoke exposure, in homes with residents who smoke in 9 European countries[J]. Environ Res, 2023, 219: 115118.
[40]	Larsson S C, Wallin A, Wolk A, et al. Differing association of alcohol consumption with different stroke types: a systematic review and meta-analysis[J]. BMC Med, 2016, 14(1): 178.

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