
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39029027
MD-D-24-02480
00029
10.1097/MD.0000000000039041
3
3700
Research Article
Observational Study
Systemic immune-inflammation index and serum glucose–potassium ratio predict poor prognosis in patients with spontaneous cerebral hemorrhage: An observational study
Liu Yongqi MMed 1782277464@qq.com
a
Qiu Tianwen MMed qiutianwen@wmu.edu.cn
b
Fu Zhizhan MMed fzz0202@wmu.edu.cn
b
Wang Kewei MMed edward1998@foxmail.com
a
Zheng Huiwen MMed 884848853@qq.com
b
Li Meiying MMed limily99@163.com
b
https://orcid.org/0009-0002-8969-088X
Yu Guofeng MMed b*
a The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China
b The Quzhou Affiliated Hospital of Wenzhou Medical University, People’s Republic of China.
* Correspondence: Guofeng Yu, Department of Neurosurgery, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, People’s Republic of China (e-mail: 18905708948@163.com).
19 7 2024
19 7 2024
103 29 e3904109 3 2024
29 6 2024
01 7 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Recent studies have shown systemic inflammatory response, serum glucose, and serum potassium are associated with poor prognosis in spontaneous intracerebral hemorrhage (SICH). This retrospective study aimed to investigate the association of systemic immune-inflammatory index (SII) and serum glucose–potassium ratio (GPR) with the severity of disease and the poor prognosis of patients with SICH at 3 months after hospital discharge. We reviewed the clinical data of 105 patients with SICH, assessed the extent of their disease using Glasgow Coma Scale score, National Institutes of Health Stroke Scale (NIHSS) score, and hematoma volume, and categorized them into a good prognosis group (0–3 scores) and a poor prognosis group (4–6 scores) based on their mRS scores at 3 months after hospital discharge. Demographic characteristics, clinical, laboratory, and imaging data at admission were compared between the 2 groups, bivariate correlations were analyzed using Spearman’s correlation coefficients, multivariate logistic regression analysis was used to determine the independent risk factors for poor prognosis of patients with SICH, and finally, SII, GPR, and platelet/lymphocyte ratio (PLR) were examined using the subject’s work characteristics (ROC) curve, lymphocyte/monocyte ratio (LMR), and neutrophil/lymphocyte ratio (NLR) for their predictive efficacy for poor prognosis. Patients in the poor prognosis group had significantly higher SII and serum GPR than those in the good prognosis group, and Spearman analysis showed that SII and serum GPR were significantly correlated with the admission Glasgow Coma Scale score as well as the NIHSS score and that SII and GPR increased with the increase in mRS score. Multivariate logistic regression analysis showed that admission NIHSS score, hematoma volume SII, GPR, NLR, and PLR were independently associated with poor patient prognosis. Analysis of the subjects’ work characteristic curves showed that the areas under the SII, GPR, NLR, PLR, LMR, and coSII-GPR curves were 0.838, 0.837, 0.825, 0.718, 0.616, and 0.883. SII and GRP were significantly associated with disease severity and short-term prognosis in SICH patients 3 months after discharge, and SII and GPR had better predictive value compared with NLR, PLR, and LMR. In addition, coSII-GPR, a joint indicator based on SII and GPR, can improve the predictive accuracy of poor prognosis 3 months after discharge in patients with SICH.

biomarkers
prognosis
serum glucose–potassium ratio
spontaneous cerebral hemorrhage
systemic immune-inflammation index
OPEN-ACCESSTRUE
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pmc1. Introduction

Spontaneous cerebral hemorrhage (SICH) is a subtype of stroke, accounting for 9% to 27% of all strokes.[1]SICH has an acute onset and rapid progression, with symptoms such as headache, nausea and vomiting, hemiparesis, and impaired consciousness occurring within minutes to hours, and the case fatality rate can reach 30% to 40%, with only 20% of survivors able to regain the ability of self-care.[2] Brain injuries after SICH include primary and secondary, and primary brain injuries refer to blood aggregation and coagulation to form hematoma clusters that compress the surrounding brain tissues after SICH occurs and cause a rapid increase in intracranial pressure, ultimately resulting in neurological function of brain tissues.[3] Secondary brain damage is caused by various factors, such as cytotoxic reaction of blood and harmful substances such as blood cell lysis products, immune-inflammatory reaction, oxidative stress, autophagy, and apoptosis of cells.[4] Among them, the immune-inflammatory response starts immediately after hematoma formation in SICH, which is involved in the pathophysiological process of brain injury after SICH[5] and plays a key role. After the occurrence of SICH, the rapid activation of the immune system cascade response can lead to an increase in cytokines and chemokines, such as neutrophils and lymphocytes,[6] which in turn triggers secondary brain damage. In addition, after stroke occurs, the body produces a stress response and over-secretion of hormones such as catecholamines, glucagon, and corticosteroids, which leads to an increase in blood glucose[7–9]; on the other hand, due to the rise in blood glucose, the secretion of insulin is also increased, which leads to intracellular transfer of serum potassium.[10] It ultimately leads to the patient’s water-electrolyte disorders. Systemic immune-inflammation index (SII) is a kind of evaluation index for assessing patients’ inflammation and immune status, which has been mainly used to reflect the prognosis of tumor patients in the past,[11,12] and serum glucose–potassium ratio (GPR) is a kind of index that can reflect patients’ metabolic status, which is related to the prognosis of patients with craniocerebral injury.[13,14] There are few studies on the correlation between SII and GPR in predicting the prognosis of SICH patients. Therefore, this study was conducted to analyze the value of SII and GPR in assessing the condition and prognosis prediction of SICH patients through a retrospective study of the clinical data of SCIH patients.

2. Materials and methods

2.1. Patient section

The study protocol was approved by the Ethics Review Committee of Quzhou People’s Hospital and complied with the ethical standards outlined in the 1964 World Medical Assembly Declaration of Helsinki and its subsequent amendments. As this study was retrospective and did not involve commercial interests, the signing of individual informed consent was waived by Chinese law and the requirements of relevant organizations. Patients with SICH admitted to Quzhou People’s Hospital between July 2021 and July 2023 were retrospectively included.

2.2. Inclusion and exclusion criteria

Inclusion criteria: age ≥ 18 years; compliance with the 2022 AHA/ASA Guidelines for the Management of Spontaneous Cerebral Hemorrhage[15]; onset time ≤ 12 h; and completion of routine blood and blood biochemistry tests within 12 h of admission.

Exclusion criteria: age < 18 years; cerebral hemorrhage caused by traumatic brain injury, brain tumor, cerebral arteriovenous malformation, etc; patients with a combination of serious diseases that affect the body’s metabolism, such as hyperthyroidism, hypothyroidism, severe malnutrition, extensive burns, and Cushing syndrome; combination of hematologic severe disorders, such as idiopathic thrombocytopenic purpura, leukemia, and multiple myeloma; usual use of hormones or combination of diabetes mellitus; surgical treatment at another hospital or our hospital; (7) refusal of treatment after admission to the hospital.

2.3. Data collection and definition

Patient demographic characteristics, clinical, laboratory, and imaging data including age, gender, height, weight, history of smoking, history of alcohol consumption, systolic blood pressure, diastolic blood pressure on admission, Glasgow Coma Scale (GCS) score, National Institutes of Health Stroke Scale (NIHSS) score, hematoma volume, whether or not the hematoma had broken into a ventricle, whether or not it was supratentorial, and history of hypertension were collected and recorded, history of stroke, history of hypertension medication, history of statin medication, history of antiplatelet medication, history of anticoagulant medication, length of hospitalization, hemoglobin (Hb), red blood cell count (RBC), neutrophil count (NEU), lymphocyte count (LY), monocyte count (MO), platelet count (PLT), C-reactive protein (CRP), serum glucose (GLU), serum potassium (K), sodium (Na), calcium (Ca), chloride (Cl), and magnesium (Mg), and SII, GPR, NLR, PLR, and LMR were calculated on admission.

2.4. Follow-up

All discharged patients were followed up regularly by professional staff through phone calls, text messages, and outpatient visits. The prognosis of the patients was assessed by a modified Rankin Scale[16] (mRS), and the specific scoring criteria of GOS score were 0 points, i.e., no symptoms or bleeding lesions found during physical examination of the disease; 1 point, i.e., there are some clinical symptoms, but the work and duties that can be done daily are completely unaffected; 2 points, i.e., there are symptoms, cannot completely 2 symptomatic points, cannot fully complete the daily work done, life is basically self-care, everyday needs of things can be done alone without help; 3 points that are the clinical symptoms are apparent, can still walk on their own without the need for external use; 4 points that is severely disabled, cannot walk independently, cannot take care of their own body, bowel function is normal; 5 points that are hardly disabled, completely bedridden, incontinence; 6 points that is death. Patients were categorized into a good prognosis group (0–3 points) and a poor prognosis group (4–6 points) according to their mRS scores 3 months after discharge.

2.5. Statistical analysis

IBM SPSS Statistics for Windows, version 25.0 (IBM Corp., Armonk, NY, USA) SPSS25.0 statistical software was used for statistical analysis. Graphs were plotted using GraphPad Prism version 9.5.1 (GraphPad Software Inc., La Jolla, CA, USA), normality of measures was tested using the Shapiro–Wilk test (Shapiro–Wilk test), and conformity of measures to normal distribution was described using mean ± standard deviation Measures that did not conform to normal distribution were described by median and quartiles, and comparisons between groups were analyzed using the Mann–Whitney U test; count data were described by frequency (n) and percentage (%), and comparisons between groups were made using the Chi-square test or Fisher test; and 2-way correlation coefficients were analyzed using the Spearman correlation coefficient, and 2-way correlation coefficients were analyzed using the Shapiro–Wilk test. Spearman correlation coefficient to analyze bivariate correlations; variables of significance in univariate analyses were included in multivariate logistic regression equations to analyze independent risk factors for poor prognosis of patients with SICH; and subject work characteristics (ROC) curves were constructed to determine the value of the SII and the GPR for predicting the prognosis of patients with SICH, and the ROC curves were used to establish the sum of the sensitivities and the specificities with the highest optimal cutoff value. All tests were considered statistically significant at P < .05.

3. Results

3.1. Patient characteristics

A total of 105 patients with SICH cases were collected, of which 70 (66.7%) were male and 35 (33.3%) were female. About 105 patients were included in the follow-up. 61 (58%) patients with poor prognosis (mRS score 4–6), of which 8 (13.1%) were fatal, and 44 (42%) patients with good prognosis (mRS score 0–3).

3.2. Univariate analysis of prognostic influences in patients with SICH

Univariate analysis showed that hematoma volume, admission GCS score, admission NIHSS score, SII, GPR, NLR, PLR, and LMR were the influencing factors for the prognosis of SCIH patients (P < .05), as shown in Table 1.

Table 1 Baseline characteristics of the 105 patients with SICH.

Variables		mRS(0-3) (n = 44)	mRS(4-6) (n = 61)	P value	
Demographics	Sex [n (%)male]	28 (0.64)	42 (0.69)	.576	
	Age [M(P25,P75) yr]	69 (59, 76)	72 (63, 78)	.419	
	Height [ (x¯±s) cm]	166.6 ± 5.8	165.6 ± 5.8	.889	
	Weight [M(P25,P75) kg]	57 (55, 62)	58 (56, 62)	.499	
	Smoking [n (%)]	16 (0.36)	24 (0.36)	.756	
	Drinking [n (%)]	15 (0.34)	16 (0.26)	.537	
Medical history	Hypertension [n (%)]	24 (0.55)	36 (0.59)	.648	
	Stroke [n (%)]	16 (0.36)	20 (0.33)	.523	
Medication history	Hypertensive drugs [n (%)]	19 (0.43)	31 (0.51)	.439	
	Statins [n (%)]	5 (0.11)	4 (0.07)	.912	
	Antiplatelet drugs [n (%)]	6 (0.14)	4 (0.07)	.378	
	Anticoagulant [n (%)]	4 (0.09)	5 (0.06)	.825	
Clinical features	Hematoma volume [M(P25,P75) °C]	5.03 (1.83, 13.30)	12.28 (4.06, 26.10)	.004	
	Intraventricular hemorrhage [n (%)]	18 (0.41)	29 (0.48)	.500	
	Supratentorial haemorrhage [n (%)]	33 (0.75)	43 (0.71)	.610	
	SBP [(x¯±s) mmgh]	167 ± 32	161 ± 37	.358	
	DBP [(x¯±s) mmgh]	90 ± 15	92 ± 20	.671	
	GCS [M(P25,P75)]	14 (13, 14)	11 (8, 12)	<.001	
	NIHSS [M(P25,P75)]	2 (2, 5)	10 (7, 21)	<.001	
	Hospital stays [M(P25,P75) d]	14 (8, 19)	13 (7, 17)	.173	
Laboratory date	Hb [(x¯±s) g/L]	135.72 ± 17.86	127.02 ± 19.46	.072	
	RBC [(x¯±s)×1012·L−1]	4.39 ± 0.55	4.23 ± 0.63	.168	
	NEU [M(P25,P75) × 109·L−1]	4.13 (3.16, 5.42)	7.12 (4.11, 8.90)	.064	
	LY [M(P25,P75) × 109·L−1]	1.59 (1.22, 2.35)	1.34 (0.93, 1.98)	.054	
	MO [M(P25,P75) × 109·L−1]	0.50 (0.36, 0.61)	0.49 (0.34, 0.67)	.763	
	PLT [M(P25,P75) × 109·L−1]	178 (136, 208)	204 (174, 230)	.079	
	CRP [M(P25,P75) mg/dL]	4.53 (2.90, 6.18)	3.92 (2.58, 9.85)	.778	
	GLU [M(P25,P75) mmol/L]	5.67 (4.81, 7.49)	8.50 (7.40, 9.90)	.083	
	K [M(P25,P75) mmol/L]	3.61 (3.32, 3.93)	3.67 (3.30, 3.93)	.610	
	Na [(x¯±s) mmol/L]	139.86 ± 2.58	138.95 ± 3.15	.136	
	Ca [(x¯±s) mmol/L]	2.30 ± 0.10	2.25 ± 0.12	.248	
	Mg [M(P25,P75) mmol/L]	0.83 (0.81, 0.88)	0.83 (0.79, 0.89)	.971	
	Cl [M(P25,P75) mmol/L]	106.25 (104.60, 107.70)	104.7 (102.1, 106.9)	.108	
	SII [M(P25,P75)]	380.75 (259.33,601.88)	955.84 (698.40, 1472.10)	<.001	
	GPR [M(P25,P75)]	1.53 (1.27,2.01)	2.28 (1.93,2.86)	<.001	
	NLR [M(P25,P75)]	2.55 (1.88,3.40)	4.69 (3.50,6.83)	<.001	
	LMR [M(P25,P75)]	3.98 (2.55,5.68)	2.81 (1.91,4.33)	.044	
	PLR [M(P25,P75)]	93.50 (62.48,130.71)	141.29 (98.96,224.29)	<.001	
Ca = calcium, CI = chloride, CRP = C-reactive protein, DBP = diastolic blood pressure, GCS = Glasgow Coma Scale, GLU = serum glucose, GPR = serum glucose/serum potassium, Hb = hemoglobin, K = serum potassium, LMR = lymphocyte/monocyte ratio, LY = lymphocyte count, Mg = magnesium, MO = monocyte count, mRS = modified rankin scale, Na = sodium, NEU = neutrophil count, NHISS = national institute of health stroke scale, NLR = neutrophil/lymphocyte ratio, PLR = platelet/lymphocyte ratio, PLT = platelet count, RBC = red blood cell count, SBP = systolic blood pressure, SII = systemic immune-inflammation index.

3.3. SII and GPR and severity of disease in patients with SICH

As shown in Figure 1, the median SII and GPR were significantly higher in the poor prognosis group than in the good prognosis group (SII: 955.84 vs 380.75, P < .05), (GPR: 2.28 vs 1.53, P < .05).SII was significantly correlated with the admission GCS score (r = −0.397, P < .05), admission NIHSS score (r = 0.395, P < .05), as well as GPR correlated with the admission GCS score (r = −0.439, P < .05), and admission NIHSS score (r = 0.439, P < .05). In addition, SII correlated with hematoma volume in patients with SICH (r = 0.241, P < .05), and GPR correlated with hematoma volume in patients with SICH (r = 0.314, P < .05).

Figure 1. (A) Differences in the systemic immune-inflammation index between mRS(0-3) and mRS(4-6) with spontaneous intracerebral hemorrhage. (B) Differences in the serum glucose/potassium ratio between mRS(0-3) and mRS(4-6) with spontaneous intracerebral hemorrhage.

3.4. Multivariate analysis of factors influencing the prognosis of patients with SICH

Variables with significance in univariate analysis were substituted into multivariate logistic regression equations for analysis, and variables with P < .05 were retained. The results of the analysis showed that NIHSS score, Hematoma volume, SII, GPR, NLR, and PLR were independent risk factors for poor prognosis in patients with SICH (P < .05) (Table 2). Patients were categorized into the good prognosis group (0–3 points) and poor prognosis group (4–6) based on mRS score at 3 months after discharge. There were 61 patients (58%) with poor prognosis (mRS score 4–6), including 8 patients (13.1%) who died, and 44 patients (42%) with good prognosis (mRS score 0–3). In addition, SII and GPR increased with increasing mRS scores (Fig. 2), indicating a correlation between SII and GPR and mRS scores.

Table 2 Multivariate logistic regression analysis for risk factors of poor outcome of spontaneous intracerebral hemorrhage.

	B value	Odds ratio (95% CI)	P value	
NIHSS	0.843	2.323 (1.552–3.477)	.031	
Hematoma volume	0.025	1.025 (0.983–1.069)	.024	
SII	0.027	1.002 (0.999–1.006)	.020	
GPR	1.352	3.867 (1.543–9.690)	.017	
NLR	0.112	0.894 (0.394–2.030)	.043	
PLR	0.013	1.013 (1.001–1.026)	.039	
GPR = serum glucose/serum potassium, NHISS = national institute of health stroke scale, NLR = neutrophil/lymphocyte ratio, PLR = platelet/lymphocyte ratio, SII = systemic immune-inflammation index.

Figure 2. (A) Association of mRS scores with systemic immune-inflammation index after spontaneous intracerebral hemorrhage. (B) Association of mRS scores with serum glucose/potassium ratio after spontaneous intracerebral hemorrhage. mRS = modified Rankin Scale.

3.5. Predictive value of each index for the prognosis of patients with SICH

ROC curve analysis showed that the areas under the curve of SII, GPR, NLR, PLR, LMR, and coSII-GPR for predicting the prognosis of SICH patients were SII = 0.838 (95% CI: 0.756–0.920), GPR = 0.837 (95% CI: 0.752–0.922), NLR = 0.825 (95% CI: 0.740–0.910), PLR = 0.718 (95% CI: 0.620–0.815), LMR = 0.616 (95% CI: 0.508–0.723), and coSII-GPR = 0.883 (95% CI: 0.809–0.957) (Table 3). Compared with other independent predictors, the area under the curve of coSII-GPR, a joint indicator, was the largest, suggesting that coSII-GPR was the best predictor of poor prognosis in SICH patients at 3 months after discharge from the hospital (Fig. 3).

Table 3 Predictive value of each index for the prognosis of patients with SICH.

	AUC (95% CI)	Cutoff point	Sensitivity	Specificity	
SII	0.838 (0.756–0.920)	619.49	0.803	0.818	
GPR	0.837 (0.752–0.922)	1.835	0.934	0.682	
NLR	0.825 (0.740–0.910)	3.870	0.721	0.886	
PLR	0.718 (0.620–0.815)	81.93	0.885	0.455	
LMR	0.616 (0.508–0.723)	3.22	0.607	0.614	
coSII-GPR	0.883 (0.809–0.957)	0.459	0.918	0.818	
coSII-GPR = consociation systemic immune-inflammation index-serum glucose/serum potassium ratio, GPR = serum glucose/serum potassium ratio, LMR = lymphocyte/monocyte ratio, NLR = neutrophil/lymphocyte ratio, PLR = platelet/lymphocyte ratio, SII = systemic immune-inflammation index.

Figure 3. Areas under the curves of SII, GPR, NLR, PLR, LMR, and coSII-GPR. (A) The area under the curve of SII was 0.838. (B) The area under the curve of GPR was 0.837. (C) The area under the curve of NLR was 0.825. (D) The area under the curve of PLR was 0.718. (E) The area under the curve of LMR was 0.616. (F) The area under the curve of coSII-GPR was 0.883. (G) Compared with other independent predictive indicators, the area under the curve of coSII-GPR in the combined group was largest, thus indicating that coSII-GPR was the most accurate and reliable indicator for predicting the prognosis of patients with SICH.

4. Discussion

The results of this study showed that NIHSS score, hematoma volume, SII, GPR, NLR, and PLR were associated with patient prognosis, and these indices, including SII and GPR, were clinically accessible and validated, and higher SII and GPR in early stage were predictors of poor prognosis for patients with SCIH, and SII and GPR had a better predictive value. In addition, SII and GPR were significantly higher in the poor prognosis group (mRS 4–6) compared with the good prognosis group (mRS 0–3), thus SII and GPR helped to predict poor outcome in SICH patients.

Intracerebral hemorrhage (ICH) refers to primary parenchymal brain hemorrhage, which has an acute-phase morbidity and mortality rate of 30% to 40%, the highest among acute cerebrovascular diseases,[17] and the incidence rate increases year by year, and the number of hospitalizations of patients with ICH has increased by 18% globally.[18] In recent years, the improvement of living standards and the development of medical technology have had a significant impact on the morbidity and mortality of SICH.[19] SICH, as a common neurological disease, has a high incidence, disability, and lethality, which imposes a huge burden on the family and society. Therefore, it is important to study the pathophysiological mechanisms that lead to brain injury after the occurrence of SICH, and to identify early the impact of the condition and prognosis, which can help to adjust the clinical treatment strategy, is of great significance to improve the prognosis of patients.

Brain injury caused by ICH includes a series of secondary injuries triggered by hematoma pushing, compression, and blood cell lysis. Among them, systemic inflammatory response plays an important role in the process of secondary brain injury, and after the occurrence of SICH, fibrinogen, thrombin, hemoglobin, and other substances within the hematoma activate microglia and astrocytes and initiate an inflammatory response.[20] Microglia are activated within minutes and induce macrophage and T-lymphocyte infiltration, which subsequently leads to the release of inflammatory cytokines (e.g., interleukin IL-1β, IL-7, IL-2, TNF-α), chemokines, TOLL-like receptor-4, and other immunomodulatory molecules such as free radicals,[21] which are interacting with each other through nuclear transcription factor-кB (NF-кB) coordination.[22] Microglia contain 2 cellular phenotypes (M1-type and M2-type), and in the pre-ICH phase, M1-type microglia are activated and release proinflammatory cytokines and free radicals, which disrupt the blood–brain barrier and cause brain damage,[23,24] whereas in the late phase of ICH, M1-type microglia are transformed into M2-type microglia, which attenuate brain inflammation by enhancing phagocytosis and promote brain tissue repair and regeneration.[25] In turn, IL-10 inhibits the expression of proinflammatory cytokines such as TNF-α and IL-6 and exerts antiinflammatory effects by up-regulating the expression of proinflammatory factor antagonists.[26]

After the occurrence of SICH, neutrophils are the first inflammatory cells to reach the hemorrhage site and participate in the neuroinflammatory response, which recruits monocytes by increasing the expression of adhesion molecules and can increase monocyte adhesion and migration.[27] Lymphocytes, as an important component of the immune system, play an important role in the sustained inflammatory response after SICH, which is involved in the neuroinflammatory process through 2 groups of proinflammatory/antiinflammatory functioning lymphocytes that are helper T cells 1 (Th1) and Th2, as well as regulatory T cells.[28] Immunosuppression and perihematomatous lymphocyte inactivation and apoptosis following SICH can combine to cause A decrease in the number of lymphocytes, reducing the ability of neuronal cells to respond to the inflammatory response, leading to infections and other complications. In addition, under the stimulation of the inflammatory response, platelets in the bone marrow rapidly mature and are released into the peripheral blood, resulting in a hypercoagulable state of the peripheral blood, and platelets release chemical mediators such as thromboxane A2, serotonin, and adenosine diphosphate, which may be associated with brain damage and poor prognosis in patients.[29] SII, a composite inflammatory indicator that contains neutrophils, lymphocytes, and platelets, was proposed by Hu et al[30] in 2014 based on the prognostic score of inflammation/immunity, which is valuable in assisting disease diagnosis and predicting disease outcome and is more stable than individual blood cell parameters. Trifan et al[31] found that SII was a good predictor of poor prognosis in patients with supratentorial SICH by retrospectively analyzing the clinical data of 239 patients with supratentorial SICH and performing ROC curve analysis, which for the first time confirms and indicates that early elevation of SII is a predictor of poor prognosis in patients with supratentorial SICH at discharge. In addition, Li et al[32] investigated the prognostic value of SII in patients with acute-phase ICH and reported for the first time the dynamic changes of SII and found that SII can rise to the highest level within 24 to 48 h after the onset of ICH and that the SII value on day 1 correlate with the patient’s prognosis after 3 months. In addition, Wang et al[33] included 320 patients with ICH and found that SII was associated with the development of stroke-associated pneumonitis and neurological function at discharge in patients with ICH. In the present study, we found that SII was associated with the severity of illness and short-term prognosis at 3 months postdischarge in patients with SICH and predicted poor prognosis at 3 months in patients with SICH, which is partially in line with the findings of previous studies.

Potassium in the human body is mainly stored intracellularly, and its transportation is mainly accomplished through cell membrane uptake by active cells as well as active uptake by the sodium/potassium adenosine triphosphate pump (Na+/K+-ATPase) and β2-adrenergic hormones and insulin can act on the Na+/K+-ATPase leading to a decrease in serum potassium levels.[34] The incidence of hypokalemia after the craniocerebral disease is reported to be 43.7% to 65.5%, while the overall incidence of hyperkalemia is 1.3% to 5.7%.[35,36] After the occurrence of SICH, the body is in a state of stress, and it will secrete excessive amounts of hormones, such as catecholamines, glucagon, and corticosteroid hormones, with catecholamines being particularly important, which directly or indirectly elevate blood levels by increasing glucagon secretion and inhibiting insulin secretion directly or indirectly elevates blood glucose.[37,38] Post-stroke hyperglycemia has been shown to be associated with poor prognosis in patients with acute SICH and may increase the risk of early death in patients with SICH.[39,40] Wu et al,[41] for the first time, investigated the correlation between serum GPR and the severity of hemorrhage in patients with ICH as well as the long-term prognostic outcomes and found that GPR had a weak correlation with the severity of hemorrhage but was independently correlated with the 90-day poor outcome in patients with cerebral hemorrhage, in addition, the ratio significantly enhanced the prognostic, predictive power of hematoma volume. GPR has also been found to be valuable in the assessment and prognostic prediction of patients with subarachnoid hemorrhage.[34] In the present study, we found that GPR level was significantly associated with the neurological condition of SICH patients on admission and had good predictive value in terms of poor prognosis of patients 3 months after discharge.

However, we must recognize several limitations of this study. First, the study had a small sample size, and the patients were all from the same tertiary hospital, which may have led to selection bias. Second, the SICH patients selected for this study had onset within 12 h. The differences in the measurement time of relevant indicators such as SII and GPR may have affected the study. In addition, this study only investigated the relationship between SII and GPR on day 1 of admission and the 3-month clinical prognosis of SICH patients and did not observe and record the relationship between dynamic changes in relevant inflammatory and metabolic indices in the course of the disease development and the prognosis of patients. Finally, the correlation between SII and GPR and the prognosis of SICH patients is rare, and large-scale clinical studies are needed to confirm the specific findings.

5. Conclusion

In this study, we discussed and confirmed that SII and GRP were significantly associated with disease severity and short-term prognosis at 3 months postdischarge in patients with SICH and that SII and GPR had better predictive value compared with NLR, PLR, and LMR, and coSII-GPR, a joint indicator based on SII and GPR could be used to improve the predictive accuracy of poor prognosis at 3 months postdischarge in patients with SICH.

Author contributions

Conceptualization: Yongqi Liu, Guofeng Yu.

Methodology: Yongqi Liu, Guofeng Yu.

Writing – original draft: Yongqi Liu.

Writing – review & editing: Yongqi Liu, Guofeng Yu.

Data curation: Tianwen Qiu, Zhizhan Fu, Kewei Wang.

Formal analysis: Huiwen Zheng, Meiying Li.

Software: Huiwen Zheng, Meiying Li.

Abbreviations:

Ca calcium

CI chloride

CRP C-reactive protein

DBP diastolic blood pressure

GCS Glasgow Coma Scale

GLU serum glucose

GOS Glasgow Outcome Scale

GPR serum glucose/serum potassium ratio

Hb hemoglobin

K serum potassium

LMR lymphocyte/monocyte ratio

LY lymphocyte count

Mg magnesium

MO monocyte count

mRS Modified Rankin Scale

NA sodium

NEU neutrophil count

NHISS National Institute of Health stroke scale

NLR neutrophil/lymphocyte ratio

PLR platelet/lymphocyte ratio

PLT platelet count

RBC red blood cell count

ROC Receiver Operating Characteristic

SBP systolic blood pressure

SICH spontaneous intracerebral hemorrhage

SII platelets × neutrophils/lymphocytes

The authors have no funding and conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

How to cite this article: Liu Y, Qiu T, Fu Z, Wang K, Zheng H, Li M, Yu G. Systemic immune-inflammation index and serum glucose–potassium ratio predict poor prognosis in patients with spontaneous cerebral hemorrhage: An observational study. Medicine 2024;103:29(e39041).
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