1 Introduction

Spontaneous intracerebral hemorrhage (sICH) refers to the rupture of the patient’s own arteriovenous and capillaries due to various reasons under non-traumatic conditions, resulting in the accumulation of blood in the brain parenchyma, and its mortality and disability rates remain high. The incidence of cerebral hemorrhage accounts for about 30% of acute cerebrovascular diseases in China, and the 3-month mortality rate is 20–30%, which is the highest in acute cerebrovascular diseases. sICH accounts for about 70% of hemorrhagic strokes[1]. A large number of studies have shown that the inflammatory immune response plays a major role in secondary brain injury, but the predictive value of related inflammatory markers on the prognosis of sICH remains controversial [2].

The secondary inflammatory response during sICH is complex. A single inflammatory variable cannot fully reflect the intricate nature of sICH, limiting their use in predictive models for severity and prognosis. Lymphocyte-to-monocyte ratio (LMR) is a comprehensive inflammatory index, which can evaluate the dynamic changes of lymphocytes and monocytes in a balanced manner [3]. The systemic immune inflammation index (SII) includes platelet counts in addition to lymphocytes and neutrophils [4]. They are easy to measure and acquire on admission, and the secondary damage caused by the associated inflammatory cells in the inflammatory pathway also make them good prognostic markers.

Under physiological conditions, the content of lymphocytes in the brain is very low. Neutrophils in the innate immune system are the first to respond after pathological damage caused by sICH. After 24 h, lymphocytes begin to multiply and activated lymphocytes can cross the blood–brain barrier (BBB) to trigger an inflammatory cascade [5]; monocytes can participate in the immune response by promoting the secretion of inflammatory factors, and a higher level of monocytes indicates a poor prognosis in sICH [6]; platelet counts also play an important role in the prognosis of sICH effect [7]. Previous studies have demonstrated that LMR and SII correlated with the prognosis of ischemic stroke [8, 9]. In addition, LMR and SII have shown good predictive value in cardiovascular, cancer and other diseases[10, 11]. However, there is controversial evidence suggesting that LMR and SII correlate with the prognosis of sICH. In this study, we aimed at investigating the prognostic value of LMR and SII in patients with sICH.

2 Materials and Methods

2.1 Study Population

In this study, we consecutively enrolled patients with sICH who were admitted at the First Affiliated Hospital of Zhengzhou University from September 2019 to July 2020. Inclusion criteria were as follows: Aged more than 18 years old and less than 60 years old; cerebral hemorrhage confirmed by brain computer tomography scan within 24 h of stroke-like symptoms; complete blood routine examination. Exclusion criteria were as follows: diagnosis of secondary ICH due to trauma, aneurysm rupture, hemorrhagic transformation of ischemic stroke, drugs, abnormal coagulation function or other causes found during hospitalization; history of infection during the previous 2 weeks; history of stroke during the previous 6 months; concomitant presence of tumors, liver disorders, kidney disorders and/or autoimmune disorders; past or current use of immunosuppressants drugs, such as azathioprine, methotrexate, cyclophosphamide, etc.

2.2 Clinical Data

The white blood cell count, absolute neutrophil count, absolute lymphocyte count, and platelet count within 24 h of admission were obtained by COULTER LH780 blood cell analyzer (Beckman Coulter, Inc, Orange County, CA, USA). Age, gender, onset time, past history, basic imaging data and clinical data were obtained from recorded medical documents. The volume of the hematoma on admission was calculated according to the CT image and the Tada formula, hematoma volume (cm3) = the longest diameter of the hematoma maximum bleeding plane × the longest diameter perpendicular to the longest diameter × hematoma height/2, height = hematoma layers × layer height, a large number of Bleeding was defined as hematoma volume greater than 30 cm3. Clinical scores reflecting the degree of neurological deficit in sICH, such as GCS score, ICH score, were used in this study. Unexplained fever (stroke-related pneumonia, urinary tract infection, etc.) in hospitalized patients within 14 days, CRP > 5 mg/L, PCT > 0.046 ng/ml is defined as post-stroke infection.

2.3 Assessment of Outcomes

All patients were followed up for 3 months after ICH by telephone interview unless they died or were lost to follow-up. Functional outcomes were assessed using the modified Rankin Scale (mRS) score. The good prognosis group was defined as mRS 0–2, while the poor prognosis group was defined as mRS ≥ 3.

2.4 Statistical Analysis

The statistical software SPSS21.0, PASS15 and Medcalc18.2.1 were used for statistical analysis. The measurement data were tested for normality by Kolmogorov–Smirnov, and the continuous measurement data conforming to the normal distribution were expressed as mean ± standard deviation (SD), two independent samples t test was used for comparison between groups; Continuous measurement data with skewed distribution were described by median (lower quartile-upper quartile), and comparison between groups was performed by Mann–Whitney U test; count data were represented by percentage. Kendall's correlation analysis LMR, SII and hematoma location, hematoma volume, whether the hematoma broke into the ventricle or not and the prognosis. Significant variables were first screened out by univariate analysis (P < 0.10), and then the factors independently associated with the 3-month prognosis of spontaneous intracerebral hemorrhage were determined by multivariate Logistic regression analysis. In addition, the predictive value of the predictors was assessed by combining multiple indicators with the area under the receiver operating characteristic curve, and the predictive performance of the indicators was tested by Delong. P < 0.05 was considered to be statistically significant.

3 Results

3.1 Clinical Characteristics

A total of 171 patients with sICH were included in the study. At the baseline, the mean LMR was 2.82 and the mean SII was 2452.11. The baseline characteristics are displayed in Table 1. According to mRS scores, 53 patients (30.99%) were classified in the good prognosis group at 3 months. The mortality rate at 3 months was 16.96%.

Table 1 Baseline characteristics of 171 patients with sICH
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3.2 Correlation Analysis with the Hematoma Site and Intraventricular Hemorrhage and Bleeding Site

LMR and SII were not related to the location of the hematoma site and intraventricular hemorrhage and bleeding site (Table 2).

Table 2 Correlation analysis with the hematoma site and Intraventricular hemorrhage and bleeding site
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3.3 Univariate Analysis

The univariate analysis showed that the 3-month prognosis was related to gender (p = 0.064), surgery (p = 0.007), infection (p = 0.013), history of heart disease (p = 0.023), hematoma volume (p = 0.082), hematoma volume > 30 cm3 (p = 0.026), GCS scores (p < 0.001), ICH scores (p < 0.001), SII (p < 0.003) and LMR (p < 0.001) (Table 3).

Table 3 Univariate analysis of indicators of 3-month prognosis
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Considering the sample size, the collinearity index and different clinical aspects, p < 0.1 was also utilized in the multivariate analysis to avoid omission of relevant predictors. We adopted stepwise forward regression analysis to evaluate the relationship between the proposed variables. The following factors were independent predictors at 3 months: LMR (OR 0.392, 95% CI 0.281–0.547 p < 0.0001), GCS scores (OR 0.821 95% CI 0.747–0.902, p < 0.0001) and history of a cardiac disease (OR 6.307, 95% CI 1.381–30.196, p = 0.021). For every unit increase in LMR, the risk for a poor prognosis increased 0.392 times from the original (Table 4).

Table 4 Multivariate analysis of 3-month prognostic factors
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To further analyze the effect of clinical variables on the relationship between LMR and prognosis, we conducted subgroup and interaction analysis and found that LPR was inversely correlated with the prognosis of most variables. In addition, the interaction of LMR with other clinical variables was not significant (Table 5).

Table 5 Effects of clinical variables on the relationship between LMR and prognosis
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3.4 Predictive Ability of LMR and SII

The predictors screened by binary regression were used to draw the ROC curves. The predictive performance of AUC was expressed under the line. Binary logistic was selected to obtain the predicted probability. Combining the results obtained by each indicator, the final curve showed an AURC of 0.850, indicating a satisfactory predictive performance. The sensitivity of LMR was 93.07% (95% CI 86.2–97.2), the specificity was 52.86% (95% CI 40.6–64.9), and the Youden index was 0.4593. Post hoc sample size calculations indicated that 104 cases were required to achieve statistical significance in terms of specificity and 35 cases were required to achieve statistical significance in terms of sensitivity. The data obtained is reasonable considering the current number of cases is 171, the best cutoff value was 3.6, and the AUC was 0.799 (p < 0.001, 95% CI 0.729–0.825). The sensitivity of GCS scores was 76.77% (95% CI 67.2–84.7), while the specificity was 60.29% (95% CI 47.7–72.05), the Youden index was 0.3706 and the best cutoff value was 10, and the AUC was 0.696 (p < 0.0001, 95% CI 0.620–0.764). The sensitivity of SII was 91.09% (95% CI 83.8–95.8), the specificity was 37.14% (95% CI 25.9–49.5), and the Youden index was 0.2823. The best cut-off value was 850.23, the AURC was 0.656 (p = 0.0002, 95% CI 0.583–0.730). The Delong test showed a statistically significant difference between the area under the curve of LMR and SII around 0.132, and the Z statistic was 3.913, p = 0.0001. Considering the 3-month prognosis, the LMR at 24 h after admission was better than SII (Fig. 1).

Fig. 1
figure 1

The ROC model and each index ROC curve

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4 Discussion

This study retrospectively analyzed the relationship between LMR and SII collected within 24 h of admission and the prognosis at 3 months in patients with sICH. The results showed that: (1) Lower LMR was an independent predictor of poor prognosis at 3 months. The best cutoff value was 3.6; (2) A lower GCS score measured at 24 h after admission was an independent predictor of 3-month prognosis. The best cutoff value was 10; (3) SII measured at 24 h of admission was not an independent predictor of 3-month prognosis. Our findings described the predictive value of LMR in patients with sICH, revealing a potential predictive role of systemic inflammatory responses.

After sICH, neutrophils are the earliest recruited cells in the central nervous system [12]. Neutrophils infiltrate the brain parenchyma, facilitate the hematoma resolution and release multiple inflammatory factors [13]. A higher neutrophil count on admission was previously associated with a reduced risk of hematoma expansion, possibly through the activation of platelets, stabilizing fibrin clots, increasing the production of thrombin and favoring the release of tissue factor, coagulation factor X and XII [14]. Hematoma expansion has been proven to be an independent risk factor for poor long-term prognosis [15]. After stroke, the inflammatory response at the injury site disrupts the hemostasis in the brain. Changes in lymphocyte counts reflect the impact of hemorrhagic stroke on the entire body [16]. In addition, the lymphocytes are involved in other activities. In a mouse model of intracerebral hemorrhage, the infiltration of T lymphocytes damaged the blood–brain barrier [5]. Specific subtypes of lymphocytes are involved in neuroprotection, promoting the release of anti-inflammatory cytokines, such as interleukin 10 (IL-10), and the inhibition of proinflammatory cytokines, such as interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) [8]. Monocytes and platelets also play a role during stroke. Six hours after the occurrence of ICH, the decrease of T lymphocytes and the increase of monocytes in the blood reflect the activation of the immune system. The elevation of monocytes in peripheral blood, mainly as intermediate mononuclear cells, is one of the most prominent features at the early stage of ICH. When activated, monocytes secrete cytokines and enhance phagocytic capacity [6]. Platelets are activated by several inflammatory mediators released at the injury site, including thrombin, oxygen free radicals, histamine and complement complexes. Platelets are involved in the coagulation cascade, inducing edema after intracerebral hemorrhage by interacting with thrombin [17]. A high platelet count reduced the risk of cerebral hemorrhage, but increased the risk of ischemic stroke [18].

Inflammation is closely related to the progression and prognosis of intracerebral hemorrhage, which is the basis of the correlation between LMR and disease prognosis.

After a stroke, the homeostasis in the brain is disrupted, and the inflammatory response at the initial injury site affects the whole body through the peripheral immune system [16].Lymphocyte counts reflect the significant impact of hemorrhagic stroke on the systemic system. Lymphocyte subsets show dynamic changes during the development of cerebral hemorrhage. In the early stage of cerebral hemorrhage (7d), the level of peripheral lymphocyte subsets decreases. Due to the activation of the innate immune system at the injury site in the brain, peripheral T lymphocytes are attracted to enrich and migrate. To the surrounding area of injury, a strong immune inflammatory response consumes a large number of lymphocytes [5]. On the other hand, stroke-induced activation of the hypothalamic–pituitary–adrenal axis and sympathetic pathway leads to a reduction in cell-mediated systemic immunosuppression. Peripheral lymphocyte counts, and this adaptive immune response, on the one hand, inhibited the excessive activation of inflammatory responses in the brain, but the excessive immunosuppression also increased the possibility of infection after stroke, suggesting a poor prognosis [19, 20]. Hyperactivation of the parasympathetic nervous system leads to atrophy of the spleen, and the degree of atrophy also reflects the link between the severity of intracerebral hemorrhage and immunosuppression [21]. As the disease progresses, the lymphocyte subsets tend to increase gradually, and the time to reach normal levels is closely related to the severity of intracerebral hemorrhage [22]. Recent studies have found that not only the dynamic changes of lymphocyte counts reflect the duality of the immune response after stroke, but also the lymphocytes themselves play a dual role. The infiltration of T lymphocytes in the experimental mouse model promoted the damage of the blood–brain barrier, and the damage was alleviated after the inhibition of T lymphocytes [5]. The role of lymphocytes and monocytes in the inflammatory response is the basis for the relationship between LMR and the prognosis of intracerebral hemorrhage, and previous studies have also demonstrated this relationship [3, 23].

Regarding sICH, there are limited data. In our study, we investigated the prognostic ability of LMR in patients with sICH, but not SII. After multiple regression analysis, LMR, GCS scores and a history of heart disease showed a high independent predictive value. Consistently with previous research, a lower LMR at the admission were associated with a worse prognosis at 3 months. SII was also shown in our study to correlate with disease severity and prognosis in patients with intracerebral hemorrhage. However, due to the sample size, SII was not found to be an independent prognostic factor in this multivariate analysis. Considering the results of previous studies, SII was still included in the ROC model, the results show good predictive ability, and the sample size and external verification model ability can be improved in the future. A history of heart disease has also been identified as a risk factor for cerebral hemorrhage [24]. Intracranial hemorrhage is a serious complication of anticoagulation therapy, reducing the International Normalized Ratio to < 1.3 and the systolic blood pressure to 160 mmHg within 4 h after hospitalization improved the prognosis of cerebral hemorrhage [24]. In addition, hypertension, diabetes, NIHSS scores, initial hematoma volume and different treatment methods [26] are also a risk factor in the long-term, but it was not investigated in our study. At the same time, this study showed the predictive value of admission LMR and GCS scores and SII for poor 3-month prognosis in patients with acute spontaneous intracerebral hemorrhage through ROC curve analysis. Although the specificity of LMR was low, the prediction of the model was verified by Delong test. capability, which can be subsequently verified by external inspections. The possible correlation between LMR and SII and the prognosis of intracerebral hemorrhage is determined by the important role of the involved inflammatory cells in the inflammatory pathway. Whether LMR and SII can accurately predict the outcome of spontaneous intracerebral hemorrhage requires multicenter, large-scale, prospective research to verify.

This study has several limitations. First, this is a retrospective, observational and single-hospital study. The multiple logistic regression analysis might be associated with residual confounding bias. Second, we enrolled only patients between 18 and 60 years old. Future studies will investigate the prognosis in different age groups of patients with sICH. In addition, common residual confounders, including Body Mass Index, smoking, consumption of alcohol and blood pressure variability, were not included in the logistic regression analysis. New studies evaluating the indicators influencing the prognosis of sICH will require more detailed multidimensional analyses conducted in multicenter studies.

5 Conclusion

To sum up, lower LMR at 24 h of admission in slCH patients was a risk factor for poor 3-month prognosis with a best cut-off point of 3.6, and LMR has a high predictive power for the prognosis of sICH.