<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Background</h3>
<p>The increasing incidence of acute ischemic stroke (AIS) necessitates a comprehensive understanding of the related factors. Hemorrhagic transformation (HT), a severe complication of AIS, is influenced by platelet-induced inflammation and lymphocyte levels.</p>
</sec>
<sec><h3>Objective</h3>
<p>To measure the predictive value of platelet-to-lymphocyte ratio (PLR) in the occurrence of HT in young AIS patients.</p>
</sec>
<sec><h3>Methods</h3>
<p>Data of young AIS patients (n = 157) admitted to the hospital for the first time were retrospectively collected. The patients were divided into HT (63 patients) and non-HT groups (94 patients) on the basis of whether HT had occurred after admission. The National Institute of Health stroke scale (NIHSS) score was used to determine the severity of clinical symptoms. The relationship between PLR and HT and NIHSS scores was analyzed to evaluate the predictive value of PLR in the occurrence of HT using receiver operating characteristic (ROC) and area under the curve (AUC).</p>
</sec>
<sec><h3>Results</h3>
<p>Multivariate analysis showed that PLR and NIHSS are independent risk factors of HT. The PLR value of the observation group was positively associated with the NIHSS score (<italic>r</italic> = 0.8075, <italic>P</italic> &lt; 0.0001). According to the PLR prediction about the occurrence of HT, an AUC of 0.713 (95% CI, 0.652–0.781), a cut-off value of 109.073, and a sensitivity and specificity of 0.806 and 0.674, respectively, were obtained.</p>
</sec>
<sec><h3>Conclusions</h3>
<p>PLR value can predict the possibility of HT in young AIS patients to a certain extent. To take effective measures to prevent HT in advance has crucial clinical significance according to PLR value.</p>
</sec>
</abstract>

Background
The increasing incidence of acute ischemic stroke (AIS) necessitates a comprehensive understanding of the related factors. Hemorrhagic transformation (HT), a severe complication of AIS, is influenced by platelet-induced inflammation and lymphocyte levels.

Objective
To measure the predictive value of platelet-to-lymphocyte ratio (PLR) in the occurrence of HT in young AIS patients.

Methods
Data of young AIS patients (n = 157) admitted to the hospital for the first time were retrospectively collected. The patients were divided into HT (63 patients) and non-HT groups (94 patients) on the basis of whether HT had occurred after admission. The National Institute of Health stroke scale (NIHSS) score was used to determine the severity of clinical symptoms. The relationship between PLR and HT and NIHSS scores was analyzed to evaluate the predictive value of PLR in the occurrence of HT using receiver operating characteristic (ROC) and area under the curve (AUC).

Results
Multivariate analysis showed that PLR and NIHSS are independent risk factors of HT. The PLR value of the observation group was positively associated with the NIHSS score (r = 0.8075, P < 0.0001). According to the PLR prediction about the occurrence of HT, an AUC of 0.713 (95% CI, 0.652–0.781), a cut-off value of 109.073, and a sensitivity and specificity of 0.806 and 0.674, respectively, were obtained.

Conclusions
PLR value can predict the possibility of HT in young AIS patients to a certain extent. To take effective measures to prevent HT in advance has crucial clinical significance according to PLR value.

The early predictive value of platelet-to-lymphocyte ratio to hemorrhagic transformation of young acute ischemic stroke

Asian Biomedicine

This work is licensed under the Creative Commons Attribution 4.0 International License.

Background
The increasing incidence of acute ischemic stroke (AIS) necessitates a comprehensive understanding of the related factors. Hemorrhagic...

Basic factors	Non-HT group (n = 94)	HT group (n = 63)	t/x²	P
Sex (male, %)	59/60.8	39/61.9	0.003	0.958
Age (year)	35.85 ± 6.9	33.75 ± 8.1	0.372	0.666
Smoking, n (%)	24 (25.53)	16 (25.39)	0.116	0.142
Alcohol, n (%)	23 (23/93)	10 (10/63)	1.112	0.292
FBG (mmol/L)	7.427 ± 2.660	7.365 ± 3.131	0.139	0.1128
Atrial fibrillation	11 (11/93)	15 (15/63)	2.814	0.093
WBC (×109)	7.266 ± 2.547	7.237 ± 2.863	0.073	0.2543
PLT (×109)	279.2 ± 52.80	275.3 ± 49.89	0.4653	0.6423
NEUT (×109)	4.262 ± 0.8894	4.390 ± 0.8848	0.9808	0.374
LYM (×109)	1.254 ± 0.3528	1.311 ± 0.374	0.966	0.336
TC (mmol/L)	4.410 ± 0.4997	4.311 ± 0.7162	1.015	0.312
TG (mmol/L)	1.441 ± 0.2841	1.489 ± 0.2812	1.029	0.3052
LDL-C (mmol/L)	3.224 ± 0.8313	3.119 ± 0.498	0.903	0.368
HCY (μmol/L)	17.615 ± 4.2904	16.914 ± 5.4401	0.90	0.370
FIB (g/L)	3.163 ± 0.6877	3.382 ± 0.809	1.747	0.083
D-D (mg/L)	0.5832 ± 0.3270	0.2484 ± 0.07191	0.219	0.827
PLR	100.654 ± 47.667	139.219 ± 54.945	4.672	<0.0001
NIHSS	9.0745 ± 4.467	10.667 ± 4.276	2.226	0.027

Variables	Univariate analysis			Multivariate analysis

	B (beta values)	P	95% CI (Confidence interval)	B (beta values)	P	95% CI (Confidence interval)
PLR	0.14	<0.0001	0.979–0.992	0.31	<0.0001	0.956–0.984
NIHSS	0.082	0.029	6.032	0.202	0.014	1.042–1.438

The early predictive value of platelet-to-lymphocyte ratio to hemorrhagic transformation of young acute ischemic stroke

Article Category: Original article

Published Online: Dec 28, 2023

Page range: 267 - 272

DOI: https://doi.org/10.2478/abm-2023-0069

Keywords
blood–brain barrier, cerebral hemorrhage, cerebral infarction, magnetic resonance imaging, platelet count

© 2023 Huijun Wen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Figure 2.

Comparison of baseline data between the observation group and control group

Univariate and multivariate Logistic Regression Analysis for Factors Associated with hemorrhagic transformation

The early predictive value of platelet-to-lymphocyte ratio to hemorrhagic transformation of young acute ischemic stroke

Article Category: Original article

Published Online: Dec 28, 2023

Page range: 267 - 272

DOI: https://doi.org/10.2478/abm-2023-0069

Keywordsblood–brain barrier, cerebral hemorrhage, cerebral infarction, magnetic resonance imaging, platelet count

© 2023 Huijun Wen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Figure 2.

Comparison of baseline data between the observation group and control group

Univariate and multivariate Logistic Regression Analysis for Factors Associated with hemorrhagic transformation

Keywords
blood–brain barrier, cerebral hemorrhage, cerebral infarction, magnetic resonance imaging, platelet count