Abstract
Background
Neuroinflammation-mediated epilepsy has become a focus of attention in recent years. Post-stroke epilepsy (PSE) is common, with most studies focusing on ischemic stroke rather than intracerebral hemorrhage (ICH) patients. Therefore, we aimed to determine the association between monocyte-to-lymphocyte ratio (MLR) levels and epilepsy after ICH. Additionally, we sought to develop a clinical score integrating MLR with the CAVE score to improve the risk stratification of PSE.
Methods
We retrospectively included consecutive ICH patients from January 2010 to July 2020. MLR was acquired from a routine blood test at admission. Multiple logistics and linear regression analyses were performed to identify risk factors for the PSE when MLR levels were divided into tertiles. Receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of epilepsy models based on the CAVE score and the MNCAVE score.
Results
Among the 834 patients with ICH, 47 (5.6%) patients had PSE. In multivariable logistics and linear regression analysis, elevated MLR levels were independently associated with the PSE after adjusting for several confounders (OR 2.545, 95% CI 1.036–6.255, P = 0.042). Six risk factors were selected to derive the MNCAVE score (higher MLR, severe stroke, cortical location, young age, large hematoma volume, and early seizures), which showed better prognostic performance than the conventional CAVE score (0.875 vs. 0.848, P = 0.041).
Conclusions
Elevated MLR levels were associated with PSE after ICH. We developed the MNCAVE score for predicting PSE, which could improve the management of patients after ICH. Inflammatory markers may provide personalized guidance for patients with varying degrees of neurological impairment.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12883-025-04562-1.
Keywords: Intracerebral hemorrhage, Epilepsy, Monocyte-to-lymphocyte ratio, Inflammation, Stroke severity, Score
Introduction
Epilepsy and seizures are common complications in stroke survivors. Recent advances in acute stroke therapy have significantly improved the outcomes for numerous patients. However, a consequent rise in the prevalence of epilepsy after stroke has been observed. It has been reported that the incidence of post-stroke epilepsy (PSE) ranges from 2.6% to 6.4%, and is even higher among patients with intracerebral hemorrhage (ICH) [1, 2]. Moreover, stroke survivors with epilepsy were more susceptible to suffer from worse functional outcomes and increased risk of death [3]. Large-scale studies have predominantly focused on epilepsy in patients with ischemic stroke, rather than those with ICH. Therefore, the imperative lies in discovering effective biomarkers for early recognition and prevention of PSE to improve ICH outcomes.
Extensive research has shown the relationship between inflammation and prognosis in cardiovascular diseases and neurological disorders [4]. Neuroinflammation-mediated epilepsy has become a focus of attention in recent years [5]. Neuroinflammation could lead to altered synaptic transmission, hyperexcitability, and neuronal loss through the dysfunction of neuronal and astroglial [6]. An increased risk of epilepsy has been reported in patients undergoing inflammation after stroke [7]. Elevated monocytes were found to be independently associated with the case fatality rate in patients with ICH [8]. Impaired regulation of inflammatory responses, particularly the activation and persistence of monocytes, in injured neuronal tissue may contribute to the development of epilepsy [9, 10].
The novel biomarker monocyte-to-lymphocyte ratio (MLR) has emerged as an indicator of inflammatory status and immune function in conditions such as cardiovascular disease, malignancy and systemic infection [11, 12]. A lower lymphocyte-to-monocyte ratio was associated with the poor prognosis of acute ischemic stroke treated with thrombolytic therapy [13]. Another study revealed that high MLR levels were independently associated with a higher risk of hematoma progression after cerebral contusion [14]. Nevertheless, the association between MLR and epilepsy following ICH has been seldom documented.
Given the role of inflammation in both stroke and epilepsy, the present study was designed to investigate the association between MLR levels and epilepsy after ICH and establish a clinical score including MLR to further predict PSE.
Methods
Participants
We retrospectively collected the data of patients diagnosed with ICH in our hospital from January 2010 to July 2020. Patients meeting the following criteria were included in our analysis: (1) diagnosis of primary ICH on computed tomography (CT); (2) age > 18; (3) availability of good quality baseline CT scans within 72 h after symptoms onset; (4) first-ever ICH. The exclusion criteria were as follows: (1) subarachnoid hemorrhage or isolated intraventricular hemorrhage; (2) diagnosed with active infection or pyrexia within two weeks before admission; (3) had seizures or epilepsy previously; (4) had hematologic disease or receiving permanent immunomodulatory treatment. This study was approved by the Clinical Application Management Committee for Medical Technologies at the First Affiliated Hospital of Wenzhou Medical University (Approval No. YLJS-2018-005), which serves as the institutional review board (IRB) for this study. Due to the anonymity and retrospective nature of the data, the need for written informed consent was waived by the same committee. All study procedures and protocols complied with the Declaration of Helsinki.
Data collection
We collected and documented the information from the electronic medical record system including demographic data, medical history, and clinical data. Using these variables, we calculated the CAVE score for each patient. The CAVE score has been proposed as an easy-to-use instrument to identify individuals at high risk of seizures or epilepsy after ICH, including cortical involvement, young age, large hematoma volume, and early seizures [15]. And the main reported risk factors for PSE were cortical involvement, stroke severity, and early seizures [16]. Early seizure was classified as early symptomatic if it occurred within 7 days after ICH. Stroke severity was assessed by the National Institutes of Health Stroke Scale (NIHSS) at admission and the Glasgow Coma Scale (GCS) [17, 18]. The ICH location and volume were analyzed by experienced neuroradiologists, and hematoma volume was calculated using the ABC/2 method, where A was the greatest hemorrhage diameter on CT, B was the diameter perpendicular to A, and C was the approximate number of CT slices with hemorrhage multiplied by the slice thickness [19]. The whole blood counts (including monocytes and lymphocytes) were obtained at admission in the Emergency Department (within 24 h after ICH onset, the vast majority within 2 h). MLR was calculated using the formula MLR = monocyte count/lymphocyte count.
Study outcomes
The primary endpoint was epilepsy after ICH. Post-stroke epilepsy (PSE) was defined as unprovoked seizure(s) occurring more than 7 days after stroke. PSE events were identified through the review of medical records and supplemented by tele-interview based on a modified standardized questionnaire (Supplemental Table 1) [20]. The secondary outcome was functional outcome at discharge. We assessed the functional outcomes of ICH patients using the modified Rankin Scale (mRS) [21], with scores evaluated by trained staff. Poor functional outcome was defined as mRS ≥ 3 [22].
Statistical analysis
The estimated sample size to achieve good statistical power was found to be 474. We classified patients into the PSE group and the non-PSE group. Continuous variables were presented as means ± standard deviation (SD) or as the median and interquartile range (IQR), and categorical variables were expressed as counts (with percentages). Mann–Whitney U/Student t and chi-square/Fisher exact tests were used properly to compare subgroups in terms of numerical and categorical variables, respectively. The Spearman test was used to assess the association between risk factors and PSE. To further explore the relationship between MLR and PSE, we divided all patients into three subgroups based on the tertile cut-off points of the admission MLR values: T1 (≤ 0.28), T2 (0.29–0.44), and T3 (≥ 0.45) [23, 24]. For the multivariable logistic regression models, we defined covariates prior to statistical analysis based on literature and P < 0.05. Multiple logistic regression analysis was then performed to identify risk factors for PSE [25], after adjustment of age, gender, hypertension, GCS score, early seizures, cortical involvement, hematoma volume and moderate to severe stroke. Additionally, we categorized stroke severity based on the NIHSS score into mild (NIHSS < 7), moderate (NIHSS 7–16), and severe (NIHSS >16) groups [26]. We examined the incidence of PSE across mild and moderate-to-severe stroke groups, as well as across different MLR levels. We also explored the risk factors associated with PSE. Subsequently, we constructed the MNCAVE score and evaluated the predictive accuracy of epilepsy models based on the CAVE score and the alternative MNCAVE score using ROC curves. The DeLong test was used to compare the AUC differences between different models.
SPSS version 27.0, R version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria) and Prism version 8.0 (GraphPad Software, San Diego, CA, USA) were used for data analysis. The two-tailed value of P < 0.05 was considered statistically significant.
Results
Comparison of clinical characteristics
A total of 834 patients with first-time ICH were included in this study (Supplementary Fig. 1), 47 (5.6%) patients had PSE. The median age was 58.47 ± 11.02 years, and 569 (68.2%) were male. The general characteristics of the study participants were detailed in Table 1. Those who developed epilepsy had a significantly higher occurrence of cortical involvement, larger hematoma volume, as well as higher occurrence of early seizures (P < 0.001). Compared with those without PSE, patients with PSE had lower GCS scores (P < 0.001) and higher NIHSS scores (P = 0.026). Moreover, MLR levels were significantly associated with hematoma volume (r = 0.186, P < 0.001), GCS score (r = −0.161, P < 0.001), and NIHSS score (r = 0.143, P < 0.001) (Supplementary Fig. 2).
Table 1.
Baseline characteristics according to patients with and without PSE
| PSE (n = 47) |
Non-PSE (n = 787) |
P value | |
|---|---|---|---|
| Age (y), mean ± SD | 59.28 ± 13.4 | 58.42 ± 10.9 | 0.604 |
| Male, n (%) | 35 (74.5) | 534 (67.9) | 0.344 |
| Hematoma volume (ml), median (IQR) | 14.5 (7.5–26.5) | 6.2 (2.6–12.0) | < 0.001 |
| Cortical involvement, n (%) | 40 (85.1) | 240 (30.5) | < 0.001 |
| Intraventricular hemorrhage, n (%) | 11 (23.4) | 138 (17.5) | 0.308 |
| Early seizures, n (%) | 14 (29.8) | 15 (1.9) | < 0.001 |
| Anti-seizure medication, n (%) | 38 (80.9) | 41 (5.2) | < 0.001 |
| Smoking, n (%) | 19 (40.4) | 306 (38.9) | 0.833 |
| Alcohol consumption, n (%) | 17 (36.2) | 301 (38.2) | 0.776 |
| Hypertension, n (%) | 39 (83.0) | 736 (93.5) | 0.006 |
| Diabetes mellitus, n (%) | 10 (21.3) | 157 (19.9) | 0.825 |
| Admission GCS score, median (IQR) | 15 (13–15) | 15 (15–15) | < 0.001 |
| Initial NIHSS score, median (IQR) | 8 (3–14) | 6 (2–10) | 0.026 |
| Moderate to severe stroke, n (%) | 27 (57.4) | 358 (45.5) | 0.110 |
| mRS score at discharge, median (IQR) | 3 (2–4) | 2 (2–3) | 0.087 |
| Poor functional outcome, n (%) | 29 (61.7) | 388 (49.3) | 0.099 |
| MLR, median (IQR) | 0.5 (0.3–0.8) | 0.3 (0.2–0.5) | < 0.001 |
| MLR tertiles, n (%) | 0.001 | ||
| Tertile 1 (≤ 0.28) | 10 (21.3) | 288 (34.1) | 0.071 |
| Tertile 2 (0.29–0.44) | 10 (21.3) | 270 (34.3) | 0.066 |
| Tertile 3 (≥ 0.45) | 27 (57.4) | 249 (31.6) | < 0.001 |
PSE Post-stroke epilepsy, GCS Glasgow Coma Scale, NIHSS National Institutes of Health Stroke Scale, mRS Modified Rankin Scale, MLR Monocyte to lymphocyte ratio, IQR Interquartile range, SD Standard deviation
Association between MLR and PSE
Patients with PSE had higher MLR than those without PSE (0.5 (0.3–0.8) vs. 0.3 (0.2–0.5), P < 0.001) (Fig. 1). In univariable linear regression, MLR showed a modest linear association with PSE (r = 0.184, P < 0.001) (Supplementary Fig. 2). Multiple linear regression revealed that MLR were independently associated with a modest increased risk of PSE (β = 0.106, 95% CI 0.042–0.157, P = 0.001), after adjusting for age, gender, hypertension, GCS score, early seizures, cortical involvement, hematoma volume and moderate to severe stroke.
Fig. 1.
The MLR levels between patients with PSE and those without PSE. PSE Post-stroke epilepsy, MLR Monocyte to lymphocyte ratio
After stratifying MLR into three groups (tertile 1, ≤ 0.28; tertile 2, 0.29–0.44; and tertile 3, ≥ 0.45), we observed that the incidence of PSE was significantly higher in the highest MLR group (T3) compared to the middle (T2) and low (T1) MLR groups (9.8% vs. 3.6% vs. 3.6%, P = 0.001) among all patients. Significant differences were observed among the three groups in NIHSS score (P = 0.001), GCS score (P < 0.001), mRS score (P = 0.041), presence of intraventricular hemorrhage (P = 0.010), and hematoma volume (P < 0.001) (Table 2). To further elucidate the relationship between MLR and PSE, we categorized patients into two subgroups according to the severity of neurological impairment based on NIHSS scores. In the moderate-to-severe stroke group (NIHSS ≥ 7), the T1 MLR group had a higher incidence of PSE compared to the T2 and T3 MLR groups (11.2% vs. 5.6% vs. 3.2%, P = 0.027), while this association was not significant in the mild stroke group (NIHSS < 7) (Fig. 2).
Table 2.
Clinical characteristics according to tertiles of MLR levels
| Tertile 1(≤ 0.28) n = 278 |
Tertile 2 (0.29–0.44) n = 280 |
Tertile 3 (≥ 0.45) n = 276 |
P value | |
|---|---|---|---|---|
| PSE, n (%) | 10 (3.6) | 10 (3.6) | 27 (9.8) | 0.001 |
| Hematoma volume (ml), mean ± SD | 5.4 (1.5–10.8) | 6.5 (3.8–12.1) | 7.9 (3.3–15.6) | < 0.001 |
| Cortical involvement, n (%) | 79 (28.4) | 97 (34.6) | 104 (37.7) | 0.062 |
| Intraventricular hemorrhage, n (%) | 44 (15.8) | 40 (14.3) | 65 (23.6) | 0.010 |
| Early seizures, n (%) | 10 (3.6) | 7 (2.5) | 12 (4.3) | 0.489 |
| Hypertension, n (%) | 258 (92.8) | 259 (92.5) | 258 (93.5) | 0.900 |
| Diabetes mellitus, n (%) | 61 (21.9) | 50 (17.9) | 56 (20.3) | 0.479 |
| Admission GCS score, median (IQR) | 15 (15–15) | 15 (15–15) | 15 (14–15) | < 0.001 |
| Initial NIHSS score, median (IQR) | 5 (2–9) | 6 (2–10) | 7 (3–11) | 0.001 |
| Moderate to severe stroke, n (%) | 107 (38.5) | 126 (45.0) | 152 (55.1) | < 0.001 |
| mRS score at discharge, median (IQR) | 2 (2–3) | 2 (2–3) | 3 (2–4) | 0.041 |
| Poor functional outcome, n (%) | 129 (46.4) | 131 (46.8) | 157 (56.9) | 0.020 |
MLR Monocyte to lymphocyte ratio, PSE Post-stroke epilepsy, GCS Glasgow Coma Scale, NIHSS National Institutes of Health Stroke Scale, mRS Modified Rankin Scale, IQR Interquartile range, SD Standard deviation
Fig. 2.
The incidence of PSE according to MLR levels, in the total sample, and stratified according to stroke severity. PSE Post-stroke epilepsy, MLR Monocyte to lymphocyte ratio
High MLR (≥ 0.45) was an independent predictor of PSE (OR 2.545, 95% CI 1.036–6.255, P = 0.042), after adjusting for potential confounders such as age, gender, hypertension, GCS score, early seizures, cortical involvement, hematoma volume and moderate to severe stroke. In addition, hematoma volume, early seizures, and cortical involvement were also presented as independent risk factors for predicting the occurrence of PSE (Table 3).
Table 3.
Multivariate logistic models for risk factors of PSE
| Univariable | Multivariable | |||
|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | |
| MLR | 0.002 | 0.030 | ||
| Tertile 3 | 2.906 (1.379–6.126) | 0.005 | 2.555 (1.041–6.273) | 0.041 |
| Early seizures | 21.834 (9.739–48.953) | < 0.001 | 10.906 (4.116–28.894) | < 0.001 |
| Cortical involvement | 13.024 (5.752–29.498) | < 0.01 | 5.727 (2.259–14.520) | < 0.001 |
| Hematoma volume | 1.091 (1.063–1.120) | < 0.001 | 1.049 (1.014–1.086) | 0.006 |
| Moderate to severe stroke | 1.618 (0.892–2.933) | 0.113 | 0.765 (0.344–1.700.344.700) | 0.511 |
In the multivariable analysis, the ORs were adjusted for age, gender, hypertension, GCS score and the variables listed in the table
PSE Post-stroke epilepsy, MLR Monocyte to lymphocyte ratio, Tertile 3 means MLR level ≥ 0.4, OR Odds ratio, CI Confidence interval
The predictive models of PSE
Two models based on the results of multivariate logistic regression were constructed. Model 1: early seizures + hematoma volume + cortical involvement; Model 2: high MLR levels + moderate to severe stroke + early seizures + hematoma volume + cortical involvement. The c-statistic for model 2 was 0.874, compared with 0.869 for model 1 (P = 0.498). To further explore the role of MLR and stroke severity in the predictive value of PSE, the highest MLR quartile and severe stroke at admission were applied in the following models. We performed ROC curve analysis to evaluate the predictive value of the MNCAVE score based on the CAVE score. MNCAVE score was composed of six factors: higher MLR (> 0.50), severe stroke (NIHSS > 16), cortical location, young age, large hematoma volume, and early seizures. Compared with those without PSE, patients with PSE had higher CAVE score (2 (1–3) vs. 2 (1–2), P = 0.015), as well as MNCAVE score (3 (2–4) vs. 2 (2–3), P < 0.001) (Table 4). Higher MLR levels, severe stroke, cortical location, and early seizures were significant risk factors for PSE (Fig. 3). The MNCAVE score showed a significantly higher c-statistic of 0.875 (95% CI 0.851–0.897), comparable to the CAVE score of 0.848 (95% CI 0.821–0.871) (P = 0.041) (Fig. 4).
Table 4.
The factors of MNCAVE score and CAVE score in patients with and without PSE
| PSE (n = 47) |
Non-PSE (n = 787) |
P value | |
|---|---|---|---|
| Cortical involvement | 40 (85.1) | 240 (30.5) | < 0.001 |
| Age < 65 years | 32 (68.1) | 553 (70.3) | 0.751 |
| Hematoma volume > 10 ml | 15 (31.9) | 532 (67.6) | < 0.001 |
| Early seizures | 14 (29.8) | 15 (1.9) | < 0.001 |
| CAVE score | 2 (1–3) | 2 (1–2) | 0.015 |
| MLR > 0.50 | 22 (46.8) | 167 (21.2) | < 0.001 |
| NIHSS > 16 | 11 (23.4) | 37 (4.7) | < 0.001 |
| MNCAVE score | 3 (2–4) | 2 (2–3) | < 0.001 |
PSE Post-stroke epilepsy, MLR Monocyte to lymphocyte ratio, NIHSS National Institutes of Health Stroke Scale
Fig. 3.
Forest plot demonstrating factors influencing the post-stroke epilepsy in multivariable logistic regression analysis. CI Confidence interval, MLR Monocyte to lymphocyte ratio
Fig. 4.
ROC curves of the MNCAVE and CAVE scores in the derivation cohort
Discussion
In this study, we examined the association between MLR and PSE, with the aim of enhancing the prognostic assessment for stroke survivors. Baseline MLR levels were significantly higher in patients with PSE. It was presented that in the moderate-to-severe stroke group (NIHSS ≥ 7), the highest MLR group had a higher incidence of PSE compared to the middle and low MLR groups, while this association was not significant in the mild stroke group (NIHSS < 7). Finally, we introduced a novel, user-friendly scoring system for predicting the epilepsy after ICH, which showed better predictive accuracy compared to the CAVE score.
Our research results were in keeping with the previous analysis of PSE. The incidence rate of PSE was about 5.6%, consistent with earlier studies [1]. Consistent with previous studies, our study found that patients with high MLR levels had larger hematoma volume, more severe stroke, and worse functional outcomes [27, 28]. Nevertheless, these associations were statistically significant but weak and should not be over-interpreted as indicating strong predictive power.
The inflammatory response plays an important role in various stages following ICH. Innate and adaptive immune responses were activated after ICH, causing the release of a variety of cytokines and chemokines [29]. Changes in the inflammatory cell profiles may lead to hematoma progression through the impact on the blood-brain barrier permeability and neuronal apoptosis [4, 30]. Additionally, a review highlighted that unregulated focal or systemic inflammatory responses following ICH could contribute to aberrant neural connectivity and hyperexcitable neuronal networks, potentially involved in the onset and progression of epilepsy [9].
The relationship between MLR and epilepsy after ICH may be explained by the following pathological mechanisms. The MLR has been reported to reflect the relative balance between innate and adaptive immunity [11]. Monocytes, an immunological relative to microglia, were correlated with neurological disorders including stroke and epilepsy [31]. Monocytes and activated macrophages could enter and adhere to the inner surface of the arterial wall by secreting various proinflammatory and prooxidant mediators [32]. One animal experiment showed that reduced numbers of monocytes exhibited a significant reduction in the infiltration of neutrophils and mortality after traumatic brain injury [33]. Monocytes may penetrate the brain parenchyma and to differentiate into microglia-like cells, thus playing a part in the pathogenesis of epilepsy [34]. Furthermore, another animal experiment revealed that direct injection of monocytes into the brain may exacerbate the seizure burden of certain virus-infected mice [35]. The modulation of the chemokine ligand 2-chemokine receptor type 2 axis has been demonstrated to reduce monocyte infiltration and improve seizure-related outcome [10]. In addition, a decrease in lymphocyte count often indicates the inflammatory response as lymphocytes participate in the immune response in the body. Specific subsets of regulatory lymphocytes could modulate immunity and exert protective effect through limiting the autoreactive cells and the inflammatory-induced damage in the brain [36]. One vivo experiment indicated that a CD8 + lymphocyte subsets–initiated attack on hippocampal neurons could induce a sequence of structural and cellular dynamics manifesting hallmarks of limbic encephalitis that induces an epileptogenic process [37]. Given above, it could be implicated that high MLR levels may reflect increased inflammatory response or immune dysfunction, contributing to more severe brain tissue damage and the development of PSE.
Among the current prediction models of epilepsy after stroke, only few models like the CAVE score serve to predict the epilepsy after ICH [15, 38]. In the present study, a new scoring model was created to predict the epilepsy after ICH, which showed good prognostic performance than the CAVE score. The MNCAVE score was composed of six risk factors (higher MLR, severer stroke, cortical location, young age, large hematoma volume and early seizures). The MNCAVE score is easy to use because the above factors are available in clinical practice. Cortical involvement, hematoma volume, stroke severity and early seizures have been indicated as risk factors for epilepsy in stroke survivors [16]. One research in cellular and molecular mechanisms of epileptogenesis reported that excessive excitatory circuits and loss of functional inhibitory inputs after cortical injury may be involved in the development of epilepsy [39].
The present study has some limitations. Firstly, it is essential to acknowledge that this study is limited by its observational and retrospective design nature, which may introduce selection and information biases. In order to minimize these biases, we employed layered analysis based on the severity of neurological deficits and multivariate logistic regression analysis to adjust for potential confounding variables, and more rigorous prospective studies are needed in the future. Secondly, this study was conducted at a single large tertiary hospital in Southern Zhejiang. The findings may have limited generalizability to other hospitals in China, particularly smaller community hospitals that care for ICH patients. Thirdly, monocyte and lymphocyte counts were measured once at admission. Dynamic monitoring for MLR levels may help further explore the relationship between the MLR level fluctuation and the development of PSE. Fourthly, the routine blood test provides only a gross metric of monocytes and lymphocytes, lacking the precision to distinguish specific subsets associated with neurological deterioration following ICH [40]. Future efforts should focus on developing more refined diagnostic approaches to better characterize these immune cell populations and their roles in disease progression. Lastly, the associations between MLR and some stroke severity measures were statistically significant but relatively weak, which may not be over-interpreted as indicating strong predictive power.
Conclusion
In conclusion, our study has demonstrated that high MLR at admission was an independent risk factor for the development of PSE. Understanding the association between MLR levels and PSE could provide comprehensive insights into the pathological mechanisms of epilepsy, and would be helpful to facilitate new treatments to prevent or modify the development of PSE. Nevertheless, future multicenter prospective studies are still needed to validate the role of monocytes and lymphocytes in the development of epilepsy and long-term outcomes after ICH. In addition, the MLR substantially improves the performance of the conventional CAVE score for epilepsy after ICH. We have developed the MNCAVE score for predicting PSE, which could improve the management of patients after ICH.
Supplementary Information
Acknowledgements
Not applicable.
Abbreviations
- PSE
Post Stroke Epilepsy
- ICH
Intracerebral Hemorrhage
- MLR
Monocyte to Lymphocyte Ratio
- OR
Odds Ratio
- CI
Confidence Interval
- CT
Computed Tomography
- NIHSS
National Institutes of Health Stroke Scale
- GCS
Glasgow Coma Scale
- mRS
Modified Rankin Scale
- SD
Standard Deviation
- IQR
Interquartile Range
- ROC
Receiver Operating Characteristic
Authors’ contributions
Weilei He: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. Mengmeng Xu: Data curation, Formal analysis, Methodology, Validation, Writing – originaldraft, Writing – review & editing. Jie Lin: Conceptualization, Project administration, Resources, Validation, Writing – review & editing. Yunjun Yang: Conceptualization, Funding acquisition, Project administration, Resources, Writing – review & editing. Ru Lin, Shu Wang, Wenjie Huang and Haisong Chen: Data curation, Investigation, Visualization. All authors contributed to and have approved the final manuscript.
Funding
This study was supported by the Wenzhou Major Program of Science and Technology Innovation (Grant No. ZY2020012), Key Laboratory of Novel Nuclide Technologies on Precision Diagnosis and Treatment & clinical Transformation of Wenzhou City (Grant No. 2023HZSY0012).
Data availability
Data are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Clinical Application Management Committee for Medical Technologies at the First Affiliated Hospital of Wenzhou Medical University (Approval No. YLJS-2018-005), which serves as the institutional review board (IRB) for this study. Due to the anonymity and retrospective nature of the data, the need for written informed consent was waived by the same committee. All study procedures and protocols complied with the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Weilei He and Mengmeng Xu are Co-first authors.
Contributor Information
Yunjun Yang, Email: yangyunjun@wmu.edu.cn.
Jie Lin, Email: linjie@wmu.edu.cn.
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Data Availability Statement
Data are available from the corresponding author on reasonable request.