Invest Clin 66(3): 241 - 251, 2025 https://doi.org/10.54817/IC.v66n3a02

Correspondence author: Jing Chen. Department of Neurology, Tianjin Hospital, Tianjin 300211, China.

Email: cj19801210@163.com

Analysis of prognostic factors and

construction of a risk model for patients

with acute cerebral infarction treated with

dual antiplatelet therapy after optimal hyper

thrombolytic time window.

Jing Chen, Yuxiu Han and Meng Liu

Department of Neurology, Tianjin Hospital, Tianjin, China.

Keywords: brain infarction; platelet aggregation inhibitors; combination; aspirin

thrombolytic therapy; time factors; risk factors.

Abstract. The objective was to identify potential risk factors for the progno-

sis of dual antiplatelet therapy in patients with acute cerebral infarction (ACI)

treated beyond the optimal time window to prevent thrombolysis, and to con-

struct a nomogram to evaluate such risk. The clinical data of 300 ACI patients

treated outside the optimal hyperthrombolytic time window and admitted to

our hospital from January 2020 to May 2024 were analyzed retrospectively. The

association between potential risk factors for poor prognosis after dual anti-

platelet therapy was tested by logistic regression. A nomogram was constructed

to evaluate the risk of poor prognosis based on the results. The area under the

receiver operating characteristic (ROC) curve (AUC) was used to evaluate the

ability of the model to differentiate types of prognoses. A calibration curve

evaluated the consistency of the model, and the fitting of the model was evalu-

ated by the Hosmer-Lemeshow (HL) test. Of the 300 patients, 52 (17.3%) had a

poor prognosis. Old age, hypertension history, elevated homocysteine, elevated

fibrinogen level and carotid artery stenosis were risk factors associated with

poor prognosis in patients with ACI. A nomogram was built based on these

risk factors. The AUC, calibration curve, and HL test demonstrated that the

selected model was statistically capable of discriminating between good and

poor prognosis. In conclusion, advanced age, a history of hypertension, elevated

homocysteine, elevated fibrinogen, and carotid artery stenosis are risk factors

associated with a poor prognosis for patients with ACI treated beyond the op-

timal time window. If validated, the nomogram based on these five risk factors

could be used to distinguish between cases with poor prognosis and those with

good prognosis among these patients.

242 Chen et al.

Investigación Clínica 66(3): 2025

Análisis de factores pronósticos y construcción de un modelo

de riesgo en pacientes con infarto cerebral agudo tratados

con doble terapia antiplaquetaria después del tiempo

óptimo para evitar hiper trombólisis.

Invest Clin 2025; 66 (3): 241 – 251

Palabras clave: infarto cerebral; inhibidores de la agregación plaquetaria; terapia

combinada; aspirina; terapia trombolítica; factores de tiempo;

factores de riesgo.

Resumen. El objetivo fue identificar factores asociados con el pronóstico

de pacientes con infarto cerebral agudo (ICA) tratados con doble terapia anti-

plaquetaria después del periodo óptimo para evitar trombólisis, y construir un

modelo de riesgo en forma de nomograma. Se analizaron retrospectivamente

los datos clínicos de 300 pacientes, tratados después del tiempo hipertrombo-

lítico, que fueron ingresados en nuestro hospital entre enero del 2020 y mayo

del 2024. Los factores asociados con el mal pronóstico tras la doble terapia con

antiplaquetas se analizaron con regresión logística. De acuerdo a los resultados,

se construyó un nomograma para modelar el riesgo de un mal pronóstico. Se

utilizó el área bajo la curva de la característica operativa del receptor (ABC) para

evaluar la capacidad del modelo para diferenciar entre tipos de pronósticos. La

consistencia del modelo se evaluó mediante una curva de calibración y el ajuste

del modelo se evaluó mediante la prueba de Hosmer-Lemeshow (HL). De los 300

pacientes, 52 (17,3%) tuvieron un mal pronóstico. El análisis logístico mostró

que la vejez, los antecedentes de hipertensión, el nivel elevado de homocisteína,

el nivel elevado de fibrinógeno y la estenosis de la arteria carótida fueron factores

de riesgo asociados con un mal pronóstico de los pacientes con ICA. Se construyó

un nomograma basado en estos factores. La ABC, la prueba de HL, y la curva de

calibración mostraron que el modelo seleccionado es estadísticamente capaz de

distinguir entre buenos y malos pronósticos. Como conclusión, la vejez, los an-

tecedentes de hipertensión, el nivel elevado de homocisteína, el nivel elevado de

fibrinógeno y la estenosis de la arteria carótida son factores de riesgo asociados

con un mal pronóstico en pacientes con ICA tratados después del período ópti-

mo. Si es validado, el nomograma basado en estos cinco factores de riesgo podría

ayudar a distinguir entre casos de buenos y malos pronósticos en estos pacientes.

Received: 27-02-2025 Accepted: 21-06-2025

INTRODUCTION

Stroke is a primary chronic noncom-

municable disease that seriously endangers

health. According to data from the World

Health Organization, the annual death rate of

stroke accounts for 10.7% of the global death

rate 1. In China, stroke has become one of the

major diseases causing death and disability

and is the first cause of death and disability

in adults, with five characteristics: high in-

cidence, high disability rate, high mortality

rate, high recurrence rate and high economic

burden 2. Stroke can cause neurological im-

pairment of patients, seriously affect their

long-term prognosis, and lead to the decline

Prognostic factors in acute cerebral infarction patients 243

Vol. 66(3): 241 - 251, 2025

of cognitive function and daily living ability

3, and some patients may have residual limb

dysfunction 4. The most common clinical

stroke is ischemic stroke, that is, ACI, which

is caused by a disturbance in the brain blood

supply, and the main clinical manifestations

are ischemic necrosis of localized brain tis-

sue and nerve function deficit. Currently,

the treatment of ACI primarily includes in-

travenous thrombolysis, intravascular inter-

vention, and antiplatelet therapy. For ACI

patients within 4.5 hours after onset, intra-

venous thrombolysis is the preferred method

for vascular recanalization 5. However, a con-

siderable number of patients have insufficient

understanding of acute cerebral infarction

and miss the time window for effective treat-

ment. When patients miss the optimal time

for thrombolytic therapy, clinical treatment

options are minimal. Therefore, dual-anti-

platelet therapy (i.e., aspirin combined with

antiplatelet drugs such as clopidogrel) within

the hypercoagulable time window has be-

come an important therapeutic method 6. By

inhibiting platelet aggregation and prevent-

ing thrombus formation and expansion, dual

antiplatelet therapy can improve brain blood

flow and promote nerve function recovery7.

However, its therapeutic effect and prognosis

are influenced by numerous factors, and re-

sponses vary significantly among patients. At

present, there are relatively few studies on the

prognostic factors of dual-antiplatelet ther-

apy in ACI patients after the optimal hyper-

thrombolytic time window. In addition, most

existing risk assessment models are based on

thrombolytic therapy, and the risk model for

hyperthrombolytic time-window dual therapy

is not perfect, which is why it is challenging

to meet the clinical demand for individualized

prognosis assessment. Therefore, the purpose

of this study was to analyze the prognostic

factors affecting dual-antiplatelet therapy in

ACI patients beyond the hyperthrombolytic

time window, and to construct a nomogram

which, if validated, will provide clinicians with

an individualized prognostic assessment tool,

optimize the treatment plan, improve the

treatment effect, and reduce the disability

rate and mortality.

MATERIALS AND METHODS

General information

Three hundred and ten patients out-

side the hyperthrombolytic time window,

admitted to the Department of Neurology

at our hospital from January 2020 to May

2024, were included as initial samples. How-

ever, ten patients with incomplete clinical

data were excluded, resulting in a total of

300 patients meeting the inclusion criteria.

Inclusion criteria: (1) Meet the diagnostic

criteria of ACI 8. (2) Admission within 24h

after onset but beyond the time window of

intravenous thrombolysis. (3) Aspirin com-

bined with clopidogrel was given within 24

hours after admission. (4) Age ≥18 years.

Exclusion criteria: (1) Patients with drug

allergies. (2) Patients with cerebral hemor-

rhage, acute infection and malignant tumor.

(3) Patients with coagulation dysfunction or

thrombocytopenia. (4) Incomplete clinical

data. This study was reviewed and approved

by the Tianjin Hospital Ethics Committee.

Treatment method

Patients received routine basic interven-

tions, including improving circulation, control-

ling blood pressure and blood sugar levels, and

stabilizing plaque. At the same time, Clopido-

grel (Sanofi Pharmaceutical Company, approv-

al number: H20171238, specification: 75 mg

x 7 tablets) was administered orally at a dose

of 75 mg once daily. Aspirin (Bayer Healthcare

Co., LTD., Sinopol: HJ20160685, specification:

100mg ×30 tablets) 100mg/ time, 1 time/day,

orally. A course of treatment lasts for seven

days. The patient persisted in the treatment

for two courses.

Data collection

Through consulting electronic medi-

cal records, clinical data of patients were

collected, including age, gender, history of

hypertension, diabetes, smoking history,

244 Chen et al.

Investigación Clínica 66(3): 2025

drinking history, atrial fibrillation history,

stroke history, homocysteine (HCY) and fi-

brinogen (Fib), high density lipoprotein,

C-reactive protein (CRP) levels and carotid

artery stenosis.

Outcome measurement

The primary measure of this study was

the prognosis of patients with ACI. The prog-

nosis was assessed 14 days after the onset

of the disease, and the National Institutes of

Health Stroke Scale (NIHSS) score was used

to evaluate the prognosis of patients after

treatment. The NIHSS score ranges from 0

to 42, with higher scores indicating more se-

vere nerve damage. The score is as follows:

0 to 1: normal or nearly normal; 1-4 scores:

mild stroke/minor stroke; 5~15 points:

moderate stroke; 15-20 points: moderate to

severe stroke; Scores 21-42: severe stroke. In

this study, a score of 0 to 4 was defined as a

good prognosis, and a score of 5 to 42 was

defined as a poor prognosis.

Statistical method

The SPSS 23.0 statistical software was

used for data analysis. Measurement data

were expressed as mean ± standard devia-

tion (x±sd), and inter-group comparison

was performed using the t-test of two inde-

pendent samples. Statistical data were ex-

pressed as cases and percentages [n (%)],

and the χ² test was used for comparison

between groups. Logistic regression analy-

sis was used to test for associated factors.

p<0.05 was considered statistically signifi-

cant. A sample of 70% of the 300 cases was

selected for training to construct the model,

while the remaining 30% was selected to

evaluate the model’s performance. The sta-

tistically significant factors were introduced

into RStudio software to construct a nomo-

gram. The model’s discrimination power was

evaluated using the ROC curve and calibra-

tion curve. The Hosmer-Lemeshow analysis

was used to evaluate the goodness of fit of

the nomogram model, and a p greater than

0.05 indicated good consistency.

RESULTS

General data comparison

According to the prognosis assess-

ment, 52 of the 300 patients (17.3%) had a

poor prognosis. There were significant dif-

ferences in age, history of hypertension, ho-

mocysteine, fibrinogen, and carotid artery

stenosis between the good prognosis group

and the poor prognosis group (all p<0.05).

No significant difference was observed in

the comparison of other indicators (all

p>0.05), as shown in Table 1.

Multifactor analysis

Variables with statistical significance in

univariate analysis were included as indepen-

dent variables, and whether there was a poor

prognosis was taken as the dependent vari-

able (yes =1, no =0). The variable assignment

table is shown in Table 2. The results showed

that old age, history of hypertension, elevated

homocysteine level, elevated fibrinogen level,

and carotid artery stenosis were risk factors

for poor prognosis of ACI patients treated with

dual antiplatelet therapy (Table 3).

Construction of a nomogram

of prognostic factors in ACI patients

Taking 210 patients in the training set as

samples, based on the results of multifactor

analysis, the above five statistically significant

factors were included in the risk assessment,

and a column-line risk model was established

(Fig. 1). The specific prediction model for-

mula is as follows: Logit (P)= -42.356 + 0.378

× (Age) + 1.391 × (Hypertension) + 1.503

× (Carotid stenosis) + 1.471 × (Homocyste-

ine) + 1.396 × (fibrinogen). The correspond-

ing scores can be obtained by projecting the

corresponding Points of each variable to the

“points” axis. The corresponding scores can be

added together, and the total scores obtained

can be used to asses prognosis.

Verification of the nomogram model

To further verify the predictive efficiency

of the model, ROC curves for the training set

Prognostic factors in acute cerebral infarction patients 245

Vol. 66(3): 241 - 251, 2025

and the test set were plotted separately (Fig.

2). The model had a high prediction accuracy

in both the training set and the test set, with

an ACU of 0.963 (95%CI: 0.920~1.000) and

0.969 (95%CI: 0.927~1.000), respectively.

The Hosmer-Lemeshow test showed a good

fit (χ2 =14.933, p = 0.060). The calibration

curve (Fig. 3) demonstrates good agreement

between the prediction probabilities of the

training set and the test set. In addition, the

decision curve shows a significant increase

in the net benefit of the nomogram (Fig. 4).

Table 1. Comparison of clinical data between the two groups.

Factors Good prognosis group

(n=248)

Poor prognosis group

(n=52) c2/t p

Age (y) 61.31±3.58* 68.27±5.26* 9.111 <0.001

Gender

female 104(41.94)** 20(38.46)** 0.214 0.644

male 144(58.06)** 32(61.54)**

Diabetes

No 166(66.94)** 29(55.77)** 2.356 0.125

Ye s 82(33.06)** 23(44.23)**

Hypertension

No 172(69.35)** 23(44.23)** 11.927 0.001

Ye s 76(30.65)** 29(55.77)**

Smoking history

No 130(52.42)** 27(51.92)** 0.004 0.948

Ye s 118(47.58)** 25(48.08)**

Drinking history

No 133(53.63)** 28(53.85)** 0.001 0.977

Ye s 115(46.37)** 24(46.15)**

Atrial fibrillation history

No 207(83.47)** 39(75.00)** 2.088 0.148

Ye s 41(16.53)** 13(25.00)**

Stroke history

No 168(67.74)** 29(55.77)** 2.733 0.098

Ye s 80(32.26)** 23(44.23)**

Carotid stenosis

No 184(74.19)** 20(38.46)** 25.223 <0.001

Ye s 64(25.81)** 32(61.54)**

Homocysteine (μmol/L) 6.56±0.98* 8.65±1.56* 9.268 <0.001

High density lipoprotein

(mmol/L) 1.31±0.21* 1.26±0.24* 1.506 0.133

C-reactive protein (mg/L) 5.39±0.99* 5.49±1.06* 0.681 0.497

Fibrinogen (g/L) 2.80±0.59* 3.29±0.77* 4.344 <0.001

Note: Data are represented as* mean ± standard deviation, and as ** n (%).

246 Chen et al.

Investigación Clínica 66(3): 2025

DISCUSSION

Previous studies have confirmed that

factors such as inflammation, oxidative

stress and ischemia-reperfusion injury can

induce acute cerebral infarction 9 and ag-

gravate the progression of the disease. Af-

ter acute cerebral infarction, most patients

present with limb weakness, sensory disor-

ders, swallowing disorders and cognitive

function decline, etc.

Table 2. Factor assignment table.

Factor Assign

Age (y) Original input

Hypertension 0=No,1=Yes

Carotid stenosis 0=No,1=Yes

Homocysteine (μmol/L) Original input

Fibrinogen (g/L) Original input

Table 3. Logistic regression analysis.

Factor B SE Wald pOR (95%CI)

Age 0.378 0.071 28.544 <0.001 1.46 (1.27-1.68)

Hypertension 1.391 0.600 5.370 0.020 4.02 (1.24-13.03)

Carotid stenosis 1.503 0.582 6.670 0.010 4.50 (1.44-14.07)

Homocysteine 1.471 0.268 30.079 <0.001 4.35 (2.57-7.37)

Fibrinogen 1.396 0.478 8.551 0.003 4.04 (1.59-10.30)

Constant -42.356 6.274 45.580 <0.001 -

Fig. 1. Nomogram model.

Fig. 2. ROC curve.

Prognostic factors in acute cerebral infarction patients 247

Vol. 66(3): 241 - 251, 2025

In severe cases, coma or even death

may occur. If adequate measures are not

taken as soon as possible, a large number

of neurons may be damaged 10, thus affect-

ing the neurological function of patients.

Intravenous thrombolytic therapy within

the time window is an effective means to

treat patients with acute cerebral infarc-

tion. However, some patients have passed

the time window and are not suitable for

thrombolytic therapy. Antiplatelet aggrega-

tion therapy is a common clinical interven-

tion for acute cerebral infarction 11. Howev-

er, its therapeutic effect and prognosis are

Note: A: Training set; B: Test set.

Fig. 3. Calibration curve analysis.

Note: A: Training set; B: Test set.

Fig. 4. Decision curve analysis of the nomogram model.

influenced by numerous factors, and the

responses of different patients vary signifi-

cantly. The results of this study showed that

52 cases (17.33%) of 300 ACI patients who

passed the hyperthrombolytic time window

had a poor prognosis after dual-antiplatelet

therapy, consistent with the report of Liu

et al. 12.

The results of this study showed that

old age, history of hypertension, elevated

homocysteine level, elevated fibrinogen

level, and carotid artery stenosis were risk

factors for poor prognosis of ACI patients

treated with dual antiplatelet therapy.

248 Chen et al.

Investigación Clínica 66(3): 2025

This study found that advanced age

was a risk factor for poor prognosis in ACI

patients, which was consistent with Zar-

rintan13, who pointed out that elderly pa-

tients had degraded body function, weak-

ened arterial elasticity, and were more likely

to suffer from arterial stenosis and aggra-

vate atherosclerosis. With the increase of

age, the structure and function of cerebral

vessels undergo significant changes, such as

the decrease of the elasticity of blood ves-

sel walls and the aggravation of atheroscle-

rosis 14. These changes make that in elderly

patients after ACI, the brain tissue’s toler-

ance to ischemia and hypoxia decrease, and

the ability to recover nerve function weak-

ens. In addition, the ability of cells to repair

and regenerate is reduced in older patients,

and the plasticity of neurons is diminished,

resulting in slower and less effective recov-

ery of neural function compared to younger

patients, thereby increasing the risk of a

poor prognosis. The history of hypertension

is also one of the risk factors affecting the

poor prognosis of ACI patients, which is con-

sistent with the results of Zheng’s study15.

Long-term hypertension leads to vascular

endothelial damage, platelet adhesion and

aggregation, and accelerates the formation

of atherosclerotic plaque16. In addition, hy-

pertension also promotes the proliferation

of vascular smooth muscle cells, which thick-

ens the vascular wall and narrows the lumen,

further damaging hemodynamic stability 17.

After a cerebral infarction, narrow and ath-

erosclerotic blood vessels severely impede

the supply of blood to ischemic brain tissue.

Even if platelet aggregation is inhibited by

dual antiplatelet therapy, established vascu-

lar lesions and disordered blood flow status

still seriously interfere with the restoration

of blood supply to the brain, thereby increas-

ing the risk of poor prognosis.

The results of this study showed that in-

creased homocysteine levels was a risk factor

for poor prognosis of patients with ACI treat-

ed with dual antiplatelet. Relevant studies

have shown that the plasma homocysteine

level of ACI patients is significantly higher

than that of the general population 18. The

possible reason is that homocysteine is a sul-

phur-containing amino acid, and its elevated

level can damage vascular endothelial cells,

promote inflammation and oxidative stress,

and accelerate the formation of atheroscle-

rotic plaque. In addition, homocysteine can

also directly activate coagulation factors,

increase blood coagulation and promote

the formation of thrombus 19. During the

hyperthrombolytic time window therapy for

cerebral infarction, this tendency of hyper-

coagulation interacts with vascular lesions,

hindering the reperfusion process of isch-

emic brain tissue, aggravating nerve injury,

and leading to poor prognosis. Carotid artery

stenosis is a risk factor for poor prognosis of

ACI patients treated with dual antiplatelet

therapy. It has been reported that plasma

homocysteine level is positively correlated

with the occurrence and severity of carotid

artery stenosis 20. Carotid artery stenosis

significantly reduces the blood supply to the

brain, leaving the brain tissue in a fragile

state of chronic ischemia and hypoxia. Dur-

ing the occurrence of ACI, the blood supply

of ischemic penumbra is severely limited due

to carotid artery stenosis, which cannot ef-

fectively meet the oxygen and nutrients re-

quired for the repair of damaged brain tis-

sue, thus expanding the scope of brain tissue

injury and increasing the difficulty of nerve

function recovery 21. When carotid artery

stenosis and homocysteine level increase,

the two cooperate to aggravate vascular

disease and blood hypercoagulability. In

the course of dual antiplatelet therapy, this

combined effect will weaken the therapeutic

effect and make brain tissue ischemia and

hypoxia continue to worsen, thereby jointly

increasing the risk of poor prognosis of pa-

tients. Increased fibrinogen level is also a

risk factor for poor prognosis of ACI patients

treated with dual antiplatelet therapy, which

is consistent with the results of the study 22.

The possible reason is that fibrinogen is a

key protein in the process of blood coagula-

Prognostic factors in acute cerebral infarction patients 249

Vol. 66(3): 241 - 251, 2025

tion, and its increased level will enhance the

coagulation ability of blood and promote the

formation and stability of thrombosis. In ACI

patients, elevated fibrinogen levels not only

increase the burden of cerebral thrombosis

and hinder the recovery of cerebral blood

flow, but also further damage brain tissue

by activating coagulation factors and inflam-

matory response 23. Fibrinogen can also bind

to platelets, enhancing the aggregation and

activation of platelets, weakening the effect

of dual-antiplatelet therapy, and making the

disease of cerebral infarction difficult to

control effectively.

Additionally, this study constructs a

nomogram risk model based on the associ-

ated factors. The nomogram can integrate

additional clinicopathological parameters to

achieve more individualized predictions. It is

a calculation chart, rather than complex for-

mulas, presenting the results of regression

analysis in an intuitive graphical way. The re-

sults of this study showed that the AUC values

of the area under the ROC curve of the test

set and validation data set of the nomogram

model were 0.963 (95%CI: 0.920~1.000)

and 0.969 (95%CI: 0.920~1.000), respec-

tively, and the AUC values of the two datasets

were >0.75, indicating that the nomogram

had good differentiation. The results of this

study demonstrate that a model for predict-

ing poor prognosis in ACI patients treated

beyond the hyperthrombolytic time window

has been successfully established. Following

validation in other populations, this nomo-

gram can aid clinicians in informed decision-

making regarding ACI.

However, this study still has certain

limitations. First, this study is a retrospec-

tive analysis with a small sample size, and

the conclusions may be biased. Second,

due to limitations in the data source, we

were unable to include all possible influ-

encing factors, which may limit the com-

prehensiveness of the constructed model.

Future studies can further enhance the re-

liability of the findings by increasing the

sample size and incorporating additional

potential influencing factors. At the same

time, the nomogram model established in

this study exhibits a high degree of dif-

ferentiation and fit, providing a valuable

tool for clinicians in treatment decision-

making.

In summary, the factors that affect the

prognosis of patients with ACI after the hy-

perthrombolytic time window include ad-

vanced age, history of hypertension, elevat-

ed homocysteine level, elevated fibrinogen

level, and carotid artery stenosis. The nomo-

gram model, established based on multiple

influencing factors, exhibits a high degree of

differentiation and fit, providing clinicians

with tools to optimize treatment and reduce

disability and mortality.

Acknowledgment

None

Consent to publish

The manuscript has neither been previ-

ously published nor is it under consideration

by any other journal. The authors have all ap-

proved the content of the paper.

Consent to participate

We secured a signed informed consent

form from every participant.

Ethic approval

The Tianjin Hospital Ethics Committee

approved this study.

Funding

None

Conflicts of interest

The authors declare that they have no

financial conflicts of interest.

250 Chen et al.

Investigación Clínica 66(3): 2025

ORCID numbers of authors

• Jing Chen (JC):

0009-0009-4436-7609

• Yuxiu Han (YH):

0009-0005-9372-6365

• Meng Liu (ML):

0009-0007-6191-2176

Author contribution

JC, developed and planned the study,

interpreted the results and edited and re-

fined the manuscript with a focus on critical

intellectual contributions. YH, participated

in collecting, assessing, and interpreting

the data and made significant contributions

to the data interpretation and manuscript

preparation. ML, provided substantial intel-

lectual input during the drafting and revi-

sion of the manuscript.

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