ABSTRACT
Objective
This study aimed to explore the associations between inflammatory markers and the severity of early neurological dysfunction and prognosis in patients with progressive stroke (PS) and evaluated the predictive value of inflammatory markers for PS.
Methods
Among 711 acute ischemic stroke (AIS) patients, 210 patients with PS and 501 patients without PS were included. Six inflammatory markers, including neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), systemic immune‐inflammation index (SII), systemic inflammatory response index (SIRI), and pan‐immune‐inflammation value (PIV), were measured and compared between two groups. Correlation analysis was used to analyze the correlation between inflammatory markers and early neurological dysfunction in patients with PS. Univariate and multivariate regression analyses were applied to screen the factors for the prognosis of PS patients. The receiver operating characteristic (ROC) curve was utilized to evaluate the predictive value for the prognosis of PS patients.
Results
Elevated levels of NLR, LMR, SII, and PIV were observed in PS patients. Correlation analysis revealed positive correlations between NLR, PLR, SII, SIRI, PIV, and early neurological deficits, while LMR showed a negative correlation in PS patients. Multivariate analysis identified LMR and the National Institutes of Health Stroke Score (NIHSS) as independent risk factors for poor outcome of PS patients. The predictive value of LMR alone was limited (AUC = 0.59), but combining it with NIHSS improved predictive accuracy (AUC = 0.73) (p < 0.05).
Conclusion
These findings suggest that inflammatory markers, particularly LMR, should be considered in PS management, and their combination with NIHSS enhances outcome prediction.
Keywords: acute ischemic stroke (AIS), inflammatory factors, lymphocyte to monocyte ratio (LMR), progressive stroke (PS)
1. Introduction
Acute ischemic stroke (AIS) is a common neurological disease in clinical, with the characteristics of high morbidity, high disability rate, and high mortality [1, 2]. The heavy economic burden it imposes on numerous families significantly impacts the patient's quality of life [3]. Progressive stroke (PS) occurs when cerebral infarction leads to further deterioration of neurological function over time. Presently, there is no unified definition of PS. In this study, an increase of ≥ 2 points in the National Institutes of Health Stroke Score (NIHSS) within 7 days of onset in AIS patients was defined as PS [4, 5, 6]. After AIS, an inflammatory cascade initiates in brain tissue, where ischemia triggers interactions between inflammatory cells, leading to further brain damage [7]. Blood cell‐based markers of systemic inflammation: neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), systemic immune‐inflammation index (SII), pan‐immune inflammation value (PIV), and systemic inflammatory response index (SIRI) are significantly important for AIS as reported [8, 9]. Han et al. [10] aimed to investigate NLR and PLR in patients with AIS and their relationship with prognosis, they found that NLR and PLR have certain predictive value for the prognosis of AIS patients receiving recombinant tissue plasminogen activator (rt‐PA) thrombolytic therapy and can be used as indicators for disease monitoring and prognosis evaluation of AIS patients. Tsalta‐Mladenov et al. [11] aimed to assess the correlation between NLR, PLR, and LMR with functional outcomes three months post‐AIS.They gathered modified Rankin Scale (mRS) scores from 141 AIS patients at discharge and again three months after the stroke.Their findings suggest that NLR and PLR are highly predictive biomarkers for clinical prognosis three months post‐AIS. AIS patients are prone to secondary infections, among which pulmonary infection is the most common. Studies have found that high PLR is a related factor for stroke‐associated pneumonia (SAP) in AIS patients and inflammatory biomarker PLR may be helpful for the diagnosis of high‐risk SAP patients [12]. A study enrolled 179 AIS patients undergoing mechanical thrombectomy (MT) and evaluated the potential use of complete blood count parameters, including metrics and ratios, in predicting clinical outcomes of MT in AIS patients, and verified LMR is an independent predictor of adverse clinical outcomes at 3 months in AIS patients undergoing MT [13]. SII and SIRI are the predictors in various diseases referred to as the prognosis. Ma et al. [9] divided AIS patients who underwent rt‐PA intravenous thrombolysis into two groups based on their prognosis: a good outcome group and a poor outcome group. They found that patients in the poor outcome group had higher SIRI, Inflammatory Prognosis Index (IPI), and SII values. Additionally, AIS patients who underwent intravenous thrombolysis and had high SIRI, IPI, and SII values were associated with a poor 90‐day prognosis. PIV, as a new comprehensive inflammatory marker, has gradually been unveiled and has become a research hotspot in recent years, has a predictive role in a variety of diseases, but there are still few studies on ischemic stroke [14, 15, 16]. So far, there is no related research on inflammatory markers in progressive stroke. This study mainly explores the expression and clinical significance of inflammatory factors in patients with progressive stroke.
2. Patients and Methods
2.1. Patients
Initially, a total of 1243 consecutive patients with AIS, who were admitted to the Suzhou Ninth People's Hospital from January 2020 to July 2024, were enrolled in this study. All enrolled patients with AIS had undergone imaging examination and were diagnosed and met the criteria of the Chinese Guidelines for the Diagnosis and Treatment of Acute Ischemic Stroke 2023 [17]. Exclusion criteria: (1) > 48 h the onset time of patients with acute ischemic stroke; (2) with serious illnesses such as heart renal insufficiency, malignant tumor, and unstable vital signs; (3) for nearly 3 months have a stroke, cerebral hemorrhage, such as cerebrovascular disease; (4) acute or chronic infectious diseases (within the past 3 months). According to whether the NIHSS increased by ≥ 2 points, those 711 AIS patients were divided into the PS group (n = 210) and non‐PS group (n = 501) (Figure 1).
FIGURE 1.
Flowchart of the study. AIS, acute ischemic stroke.
This study was approved by the Ethics Committee of the Suzhou Ninth People's Hospital and complied with the Declaration of Helsinki. As a retrospective study, the informed consent was waived.
2.2. Data Collection
Demographic data and laboratory results, including counts of leucocytes, erythrocytes, hemoglobin, platelets, lymphocytes, neutrophils, monocytes, eosinophils, and basophils, were gathered from medical records. All test samples were collected within 48 h of stroke. Patients with a modified Rankin Scale (mRS) at discharge of 0–2 points were considered as good outcome and patients whose mRS score at discharge was 3–6 points were considered as poor outcome. Thus, there were 85 patients with good outcome and 125 patients with poor outcomes in the PS group, while 456 patients with good outcome and 45 patients with poor outcomes in the non‐PS group. The NIHSS was utilized to assess the severity of neurological dysfunction at admission (0–4 points were classified as mild, 5–15 points as moderate, and 16–25 points as severe). Stroke‐associated pneumonia (SAP) refers to the new onset of lung infection within 7 days after acute stroke in non‐mechanically ventilated patients and is one of the most common complications in patients with acute ischemic stroke [18, 19]. The neutrophil to lymphocyte ratio (NLR) was defined as the ratio of count of neutrophil to lymphocyte in the peripheral blood; platelet to lymphocyte ratio (PLR) was defined as the ratio of platelet count to lymphocyte count; lymphocyte to monocyte ratio (LMR) was defined as the ratio of lymphocyte count to monocyte count; systemic immune‐inflammation index (SII) was calculated by platelet * neutrophil/lymphocyte; systemic inflammatory response index (SIRI) was calculated by neutrophil * monocyte/lymphocyte; and pan‐immune inflammation value (PIV) was calculated by platelet * monocyte * neutrophil/lymphocyte. The early severity of neurological dysfunction was assessed by the NIHSS and the prognosis was assessed through a modified Rankin scale (mRS). All measurements of laboratory examination were performed by professional laboratory technicians in our hospital (not authors).
2.3. Statistical Analysis
The SPSS 25.0 software was used for statistical analysis, and the GraphPad Prism 7.0 software was used for graphing. For normal distribution or close to the normal distribution, the value of each group of measurement data was represented by the mean ± standard deviation ( ± s). Groups were compared using a two‐independent samples t‐test. For measurement data that did not follow a normal distribution, these were represented as a median with an interquartile range [M (P25, P75)], and the Mann–Whitney U‐test was employed for statistical comparison. The count data were expressed as the number of cases and percentage (%), and the comparison was performed by χ2 test. Correlation analysis was conducted in the way of Spearman's correlation coefficient. (The test level α is 0.05, and the difference is significant statistically with p < 0.05.) Univariate and multivariate logistic regression analyses were used to screen the prognostic risk factors of progressive stroke. The receiver operating characteristic (ROC) curve was drawn and the sensitivity, specificity, and area under the ROC curve (AUC) of NIHSS, LMR, and combined factors in predicting the prognosis of PS patients were calculated. The statistical significance was considered as p < 0.05 (two‐tailed).
3. Results
3.1. Patient Characteristics and Laboratory Data
As shown in Table 1, patients in the PS group and non‐PS group were similar in age and gender (p > 0.05). There were 61 non‐PS patients with atrial fibrillation, 440 non‐PS patients without atrial fibrillation, 14 PS patients with atrial fibrillation, and 196 PS patients without atrial fibrillation. Compared to the PS patients, counts of leucocytes, platelets, neutrophils, NLR, LMR, SII, and PIV were higher in patients with PS (p < 0.05, Table 1). Besides, the mRS score, NIHSS, and the incidence of SAP were higher in PS patients, along with more severe neurological dysfunction (p < 0.05, Table 1). The count of eosinophils was lower in the PS patients than in non‐PS patients (p < 0.05, Table 1). Other clinical data and other laboratory parameters between PS patients and non‐PS patients showed no significant differences (p > 0.05, Table 1).
TABLE 1.
Baseline characteristics between PS patients and AIS patients.
| Baseline characteristics | PS patients (n = 210) | Non‐PS patients (n = 501) | t/z/x 2 | p |
|---|---|---|---|---|
| Sex, n (%) | ||||
| Male | 120 (57.14) | 286 (57.09) | 0.000 | 0.989 |
| Female | 90 (42.86) | 215 (42.91) | ||
| Age, years | 68.00 (57.75, 76.25) | 70.00 (60.00, 77.00) | −1.058 | 0.290 |
| Age cohorts | ||||
| < 45 years old | 8 (3.81) | 17 (3.39) | 1.687 | 0.430 |
| 45–60 years old | 77 (36.67) | 160 (31.94) | ||
| > 60 years old | 125 (59.52) | 324 (64.67) | ||
| Hypertension, n (%) | ||||
| Without | 33 (15.74) | 99 (19.76) | 1.602 | 0.206 |
| With | 177 (84.29) | 402 (80.24) | ||
| Diabetes, n (%) | ||||
| Without | 129 (61.43) | 336 (67.07) | 2.078 | 0.149 |
| With | 81 (38.57) | 105 (32.93) | ||
| Atrial fibrillation, n (%) | ||||
| Without | 196 (93.33) | 440 (87.82) | 4.759 | 0.029 |
| With | 14 (6.67) | 61 (12.18) | ||
| SAP, n (%) | ||||
| Without | 188 (89.52) | 477 (95.21) | 7.905 | 0.005 |
| With | 22 (10.48) | 24 (4.79) | ||
| mRS score | 3 (2, 4) | 1 (1, 2) | −12.956 | 0.000 |
| Outcome | ||||
| Good outcome | 85 (40.48) | 456 (91.02) | 207.769 | 0.000 |
| Poor outcome | 125 (59.52) | 45 (8.98) | ||
| NIHSS | 3 (1, 5) | 2 (1, 4) | −3.513 | 0.000 |
| Stroke category | ||||
| Mild (NIHSS of 0–4) | 148 (70.48) | 401 (80.04) | 8.379 | 0.015 |
| Moderate (NIHSS of 5–15) | 59 (28.10) | 92 (18.36) | ||
| Severe (NIHSS of 16–25) | 3 (1.43) | 8 (1.60) | ||
| Leucocytes (×109/L) | 7.49 (5.97, 8.94) | 6.75 (5.57, 8.06) | −4.756 | 0.000 |
| Erythrocytes (×109/L) | 4.59 (4.22, 4.87) | 4.52 (4.18, 4.86) | −1.178 | 0.239 |
| Hemoglobin (×109/L) | 139.00 (128.00, 150.00) | 136.00 (126.00, 148.00) | −1.567 | 0.117 |
| Platelets (×109/L) | 222.50 (184.00, 257.00) | 202.00 (165.00, 237.00) | −4.640 | 0.000 |
| Lymphocytes (×109/L) | 1.49 (1.20, 1.97) | 1.46 (1.11, 1.91) | −1.287 | 0.198 |
| Neutrophils (×109/L) | 5.35 (4.04, 6.52) | 4.50 (3.59, 5.78) | −5.134 | 0.000 |
| Monocytes (×109/L) | 0.41 (0.31, 0.51) | 0.42 (0.33, 0.52) | −1.276 | 0.202 |
| Eosinophils (×109/L) | 0.07 (0.03, 0.13) | 0.08 (0.04, 0.16) | −2.459 | 0.014 |
| Basophils (×109/L) | 0.03 (0.02, 0.04) | 0.03 (0.02, 0.04) | −0.116 | 0.908 |
| NLR | 3.20 (2.48, 4.89) | 3.07 (2.27, 4.29) | −2.298 | 0.022 |
| PLR | 144.98 (109.41, 187.72) | 139.76 (106.04, 179.76) | −1.722 | 0.085 |
| LMR | 3.84 (2.86, 5.13) | 3.52 (2.76, 4.24) | −2.664 | 0.008 |
| SII | 734.87 (526.65, 1079.42) | 608.39 (431.42, 903.57) | −4.559 | 0.000 |
| SIRI | 1.30 (0.97, 2.11) | 1.25 (0.94, 1.86) | −1.114 | 0.265 |
| PIV | 291.88 (197.30, 459.17) | 254.20 (165.24, 402.44) | −3.192 | 0.001 |
Abbreviations: AIS, acute ischemic stroke; LMR, lymphocyte to monocyte ratio; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Score; NLR, neutrophil to lymphocyte ratio; PIV, pan‐immune‐inflammation value; PLR, platelet to lymphocyte ratio; PS, progressive stroke; SAP, stroke‐associated pneumonia; SII, systemic immune‐inflammation index; SIRI, systemic inflammatory response index.
3.2. Level of NLR, PLR, LMR, SII, SIRI, and PIV in PS Patients and in Non‐PS Patients
As is shown in the research, NLR, LMR, SII, and PIV in PS patients were increased (p < 0.05, Table 1, Figure 2), but no difference was observed in the two groups referred to PLR and SIRI (p > 0.05, Figure 2).
FIGURE 2.
Level of NLR, PLR, LMR, SII, SIRI, and PIV in PS patients and AIS patients. (A) The level of NLR increased in PS patients than in AIS patients. (B) The level of PLR was no different in both two groups of patients. (C) The level of LMR increased in AIS patients than in PS patients. (D) The level of SII was lower in AIS patients than in PS patients. (E) The level of SIRI was no different in the two groups. (F) The level of PIV increased in PS patients than in AIS patients (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). AIS, acute ischemic stroke; LMR, lymphocyte to monocyte ratio; NLR, neutrophil to lymphocyte ratio; PIV, pan‐immune‐inflammation value; PLR, platelet to lymphocyte ratio; PS, progressive stroke; SII, systemic immune‐inflammation index; SIRI, systemic inflammatory response index.
3.3. Correlation of Inflammatory Markers With the Severity of Early Neurological Dysfunction in Patients With PS
The results of Spearman's correlation analysis of inflammatory markers with NIHSS in patients with PS are presented in Figure 3. In this study, NLR, PLR, SII, SIRI, and PIV were positively correlated with NIHSS (r = 0.205, p < 0.001; r = 0.111, p = 0.003; r = 0.171, p < 0.001; r = 0.139, p < 0.001; r = 0.144, p < 0.001, respectively, Figure 3A,B,D–F). While LMR was found negatively related to NIHSS in PS patients (r = −0.082, p = 0.028, Figure 3C).
FIGURE 3.
The correlation between NIHSS and NLR, PLR, LMR, SII, SIRI, and PIV in PS patients. (A, B, D–F) NLR, PLR, SII, SIRI, and PIV were positively correlated with NIHSS in PS patients. (C) LMR was negatively correlated with NIHSS in PS patients (*p < 0.05, **p < 0.01, ***p < 0.001). LMR, lymphocyte to monocyte ratio; NIHSS, National Institutes of Health Stroke Score; NLR, neutrophil to lymphocyte ratio; PIV, pan‐immune‐inflammation value; PLR, platelet to lymphocyte ratio; PS, progressive stroke; SII, systemic immune‐inflammation index; SIRI, systemic inflammatory response index.
3.4. Characteristics of Outcome in PS Patients
To explore the influence factor of outcome in PS patients, we got two groups according to the mRS score at discharge: the specific mRS score of 0–2 was defined as good outcome and the mRS score of 3–6 was defined as poor outcome. The mRS score specific score between the PS patients and non‐PS patients indicates the dichotomization of a favorable functional outcome in different categories of stroke patients. Figure 4 illustrates the distribution of mRS scores in the AIS patients (Figure 4). The characteristics of PS patients with different outcomes are listed in Table 2. In PS patients, patients with good outcome were younger than those with poor outcome and had lower mRS and NIHSS. In addition, other than LMR was higher in the good outcome group, NLR, SII, SIRI, and PIV were all higher in the poor outcome group.
FIGURE 4.
mRS score in different categories of stroke patients. AIS, acute ischemic stroke; mRS, modified Rankin scale; PS, progressive stroke.
TABLE 2.
Characteristics of outcome in PS patients.
| Characteristics | Good outcome (n = 85) | Poor outcome (n = 125) | t/z/x 2 | p |
|---|---|---|---|---|
| Sex, n (%) | ||||
| Male | 49 (57.65) | 71 (56.80) | 0.015 | 0.903 |
| Female | 36 (42.35) | 54 (43.20) | ||
| Age, years | 67 (50.00, 74.50) | 70 (59, 78) | −1.956 | 0.050 |
| Age cohorts | ||||
| < 45 years old | 2 (2.35) | 6 (4.80) | 3.331 | 0.189 |
| 45–60 years old | 37 (43.53) | 40 (32.00) | ||
| > 60 years old | 46 (54.12) | 79 (63.20) | ||
| Hypertension, n (%) | ||||
| Without | 13 (15.29) | 20 (16.00) | 0.019 | 0.890 |
| With | 72 (84.71) | 105 (84.00) | ||
| Diabetes, n (%) | ||||
| Without | 49 (57.65) | 80 (64.00) | 0.862 | 0.353 |
| With | 36 (42.35) | 45 (36.00) | ||
| Atrial fibrillation, n (%) | ||||
| Without | 78 (91.76) | 118 (94.40) | 0.565 | 0.452 |
| With | 7 (8.24) | 7 (5.60) | ||
| SAP, n (%) | ||||
| Without | 79 (92.94) | 109 (87.20) | 1.778 | 0.182 |
| With | 6 (7.06) | 16 (12.08) | ||
| mRS score | 1 (1,2) | 4 (3, 4) | −11. 727 | 0.000 |
| NIHSS | 2 (1, 3) | 4 (2, 6) | −4.612 | 0.000 |
| Stroke category | ||||
| Mild (NIHSS of 0–4) | 76 (89.41) | 72 (57.60) | 25.071 | 0.000 |
| Moderate (NIHSS of 5–15) | 8 (9.41) | 51 (40.80) | ||
| Severe (NIHSS of 16–25) | 1 (1.18) | 2 (1.60) | ||
| Leucocytes (×109/L) | 7.37 (5.83, 8.47) | 7. 77 (6.39, 9.15) | −1.448 | 0.148 |
| Erythrocytes (×109/L) | 4.59 (4.34, 4.79) | 4.56 (4.17, 4.90) | −0.133 | 0.894 |
| Hemoglobin (×109/L) | 139 (131.00, 148.00) | 141 (126.00, 152.00) | −0.511 | 0.609 |
| Platelets (×109/L) | 224 (185.50, 262.00) | 220 (182.50, 255.50) | −0.341 | 0.733 |
| Lymphocytes (×109/L) | 1.55 (1.30, 2.05) | 1.43 (1.15, 1.90) | −1.667 | 0.096 |
| Neutrophils (×109/L) | 4.87 (3.98, 5.97) | 5.78 (4.13, 6.79) | −2.216 | 0.027 |
| Monocytes (×109/L) | 0.38 (0.31, 0.48) | 0.43 (0.31, 0.53) | −1.243 | 0.214 |
| Eosinophils (×109/L) | 0.07 (0.03, 0.13) | 0.07 (0.03, 0.14) | −0.137 | 0.891 |
| Basophils (×109/L) | 0.03 (0.02, 0.04) | 0.03 (0.02, 0.04) | −0.451 | 0.652 |
| NLR | 2.82 (2.30, 4.41) | 3.65 (2.67, 5.10) | −2.839 | 0.005 |
| PLR | 142.14 (104.77, 178.27) | 150.36 (114.38, 188.72) | −1.544 | 0.123 |
| LMR | 4.22 (2.98, 5.38) | 3.44 (2.74, 4.74) | −2.199 | 0.028 |
| SII | 643.84 (518.60, 921.00) | 800.60 (544.90, 1205.08) | −2.498 | 0.013 |
| SIRI | 1.14 (0.87, 1.77) | 1.44 (1.05, 2.32) | −3.076 | 0.002 |
| PIV | 266.50 (181.36, 399.72) | 313.88 (219.48, 539.26) | −2.574 | 0.010 |
Abbreviations: LMR, lymphocyte to monocyte ratio; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Score; NLR, neutrophil to lymphocyte ratio; PIV, pan‐immune‐inflammation value; PLR, platelet to lymphocyte ratio; PS, progressive stroke; SAP, stroke‐associated pneumonia; SII, systemic immune‐inflammation index; SIRI, systemic inflammatory response index.
3.5. Risk Factors and Predictors for the Short‐Time Prognosis of PS Patients
Univariate regression analysis and multivariate regression analysis showed that LMR and NIHSS were independent risk factors for poor outcome in patients with PS (OR, 0.685; 95% CI, 0.499–0.976) (Tables 3 and 4, Figure 5). ROC curve results showed that when LMR was 4.33, the AUC for predicting the outcome was 0.59, the sensitivity was 69.60%, and the specificity was 48.24%. The AUC of the NIHSS is 0.72, the sensitivity was 42.40%, and the specificity was 90.59%. The AUC of combined factors (NIHSS combined with LMR) is up to 0.73, the sensitivity was 53.60%, and the specificity was 84.71% (p < 0.05) (Figure 6).
TABLE 3.
Univariate regression analysis of factors for predicting the outcome.
| Characteristics | OR‐value | OR 95% CI | p |
|---|---|---|---|
| Sex | 1.035 | 0.593–1.807 | 0.903 |
| Male/Female | |||
| Age | 1.021 | 0.998–1.043 | 0.073 |
| Age cohorts | 1.227 | 0.757–1.987 | 0.407 |
| < 45/45–60/> 60 | |||
| Hypertension | 0.948 | 0.443–2.027 | 0.890 |
| Yes/No | |||
| Diabetes | 0.766 | 0.435–1.346 | 0.354 |
| Yes/No | |||
| Atrial fibrillation | 0.661 | 0.223–1.958 | 0.455 |
| Yes/No | |||
| SAP | 1.933 | 0.724–5.160 | 0.188 |
| Yes/No | |||
| NIHSS score | 1.387 | 1.209–1.591 | 0.000 |
| Stroke category | 5.187 | 2.477–10.860 | 0.000 |
| Mild/Moderate/Severe | |||
| Leucocytes | 1.112 | 0.965–1.281 | 0.142 |
| Erythrocytes | 0.977 | 0.566–1.686 | 0.933 |
| Hemoglobin | 1.004 | 0.987–1.022 | 0.634 |
| Platelets | 0.999 | 0.994–1.004 | 0.702 |
| Lymphocytes | 0.664 | 0.425–1.038 | 0.073 |
| Neutrophils | 1.197 | 1.018–1.407 | 0.030 |
| Monocytes | 2.929 | 0.382–22.478 | 0.301 |
| Eosinophils | 0.760 | 0.395–1.465 | 0.413 |
| Basophils | 0.032 | 0.000–45.969 | 0.354 |
| NLR | 1.242 | 1.145–1.347 | 0.000 |
| PLR | 1.008 | 1.005–1.011 | 0.000 |
| LMR | 0.931 | 0.820–1.056 | 0.265 |
| SII | 1.001 | 1.001–1.002 | 0.000 |
| SIRI | 1.480 | 1.235–1.775 | 0.000 |
| PIV | 1.002 | 1.001–1.003 | 0.000 |
Abbreviations: CI, confidence interval; LMR, lymphocyte to monocyte ratio; NIHSS, National Institutes of Health Stroke Score; NLR, neutrophil to lymphocyte ratio; OR, odds ratio; PIV, pan‐immune‐inflammation value; PLR, platelet to lymphocyte ratio; SAP, stroke‐associated pneumonia; SII, systemic immune‐inflammation index; SIRI, systemic inflammatory response index.
TABLE 4.
Single regression analysis of factors for predicting the outcome.
| Characteristics | OR‐value | OR 95% CI | p |
|---|---|---|---|
| NIHSS | 1.252 | 1.006–1.559 | 0.044 |
| Stroke category | 1.792 | 0.534–6.010 | 0.345 |
| Mild/Moderate/Severe | |||
| Neutrophils | 1.259 | 0.908–1.762 | 0.170 |
| NLR | 1.112 | 0.444–2.783 | 0.821 |
| LMR | 0.706 | 0.507–0.983 | 0.039 |
| SII | 1.000 | 0.996–1.004 | 0.900 |
| SIRI | 0.364 | 0.041–3.254 | 0.366 |
| PIV | 1.002 | 0.992–1.012 | 0.656 |
Abbreviations: CI, confidence interval; LMR, lymphocyte to monocyte ratio; NIHSS, National Institutes of Health Stroke Score; NLR, neutrophil to lymphocyte ratio; OR, odds ratio; PIV, pan‐immune‐inflammation value; SII, systemic immune‐inflammation index; SIRI, systemic inflammatory response index.
FIGURE 5.
Odds ratio for the outcome in risk factors. AIS, acute ischemic stroke; CI, confidence interval; LMR, lymphocyte to monocyte ratio; NIHSS, National Institutes of Health Stroke Score; NLR, neutrophil to lymphocyte ratio; OR, odds ratio; PIV, pan‐immune‐inflammation value; PS, progressive stroke; SII, systemic immune‐inflammation index; SIRI, systemic inflammatory response index.
FIGURE 6.
ROC curve was used to evaluate the accuracy of LMR, NIHSS and combined factors to predict functional outcomes in PS patients. LMR, lymphocyte to monocyte ratio; NIHSS, National Institutes of Health Stroke Score; PS, progressive stroke; ROC, receiver operator characteristic.
4. Discussion
With time elapsing, the brain tissue will undergo ischemia and hypoxia after the onset of AIS, which will lead to neurological deficits [20, 21]. AIS was considered to be PS with worsening neurological deficit symptoms along with an increase in NIHSS of more than 2 points within the first week after onset in the study [22]. PS is a type of cerebrovascular disease and accounts for exceeded 30% of acute ischemic strokes; it can lead to a more serious outcome [23]. The accumulation of inflammatory factors in the area of cerebral infarction and the occurrence of inflammatory cascade reactions may lead to further aggravation of cerebral infarction, which may be one of the reasons for the progression of AIS to PS [24, 25]. As an inflammatory disease, finding inflammatory markers with high sensitivity and specificity for predicting the occurrence of PS is of great value for early clinical intervention and improvement of clinical outcomes of patients.
In order to investigate whether early neurological prognosis before and after thrombolysis is correlated with NLR, PLR, and LMR, a study found that NLR before thrombolysis is associated with early neurological improvement after thrombolysis. In addition, NLR and PLR may have the ability to predict early neurological deterioration after thrombolysis [26]. This study explored the relationship between a number of inflammatory markers early neurological deficits and short‐term prognosis in PS patients. Correlation analysis showed that early neurological deficits in PS patients were positively correlated with NLR, SII, SIRI, PIV, and mRS Score, and negatively correlated with LMR, while PLR was not related to early neurological deficits.
A lot of studies have demonstrated that the aggravation of early neurological deficit will lead to poor short‐term prognosis of patients with AIS, and the identification of high‐risk factors for AIS progression in the short term may improve the poor prognosis of patients [27]. In 2023, the Zhu et al. [28] team retrospectively analyzed the correlation of NLR and neutrophil/high‐density lipoprotein cholesterol ratio (NHR) with the severity and short‐term prognosis of 136 AIS patients in the Department of Neurology, Affiliated Hospital of Nantong University. The results showed that the high level of NLR at admission influenced the short‐term prognosis of patients, and NHR was not an independent risk factor. However, the combined index of NHR and NLR was more strongly associated with disease severity and short‐term prognosis than NLR or NHR alone. Several studies have shown that PLR has good predictive value in patients with AIS and is correlated with disease severity and functional outcome [11, 29, 30]. SII and SIRI have been studied in some cardiovascular diseases at once [31, 32], while it has been studied more in cerebrovascular diseases. To investigate the association of SII and SIRI with early neurological deterioration in AIS patients treated with rt‐PA, Wang et al. [33] found that high SII, but not SIRI, was an independent predictor of poor functional outcome and risk of neurological deterioration within 90 days. Similar but not identical studies have shown that higher levels of SIRI are associated with further worsening of severity in AIS patients [34].
In order to clarify the relationship between inflammatory factors and the short‐term prognosis of PS patients, regression analysis of the study screened out two independent risk factors affecting the short‐term prognosis of patients: NIHSS and LMR. Recent research showed that the levels of PLR and LMR in the blood of AIS patients after thrombolysis were measured based on time; PLR and LMR collected at 48 h (there were four‐time points, one of them being 48 h) were closely related to good prognosis (mRS = 0–1) at discharge [35]. A research collected 1005 AIS patients, of which 99 patients had hemorrhagic transformation (HT), the study found that the lower the LMR, the higher the risk of HT. This indicated that timely detection of the blood cell level at admission and calculation of the LMR level can help to reduce the possibility of HT in patients with AIS [36]. Few studies have explored the relationship between inflammatory markers and PS; a recent study devoted to exploring the relationship between LMR and PS. This study statistically analyzed a variety of laboratory indicators and finally found that LMR was an independent predictor of PS in AIS patients [37]. Finally, the ROC curve showed the value of the two risk factors in predicting the short‐term prognosis of PS patients in this study.
5. Conclusions
The levels of inflammatory factors NLR, LMR SII, and PIV are higher in PS patients. LMR is associated with early neurological deficit and is an independent risk factor for the prognosis of PS patients. These inflammatory parameters in this study are easy to obtain in clinical practice, which can be used to judge the prognosis of the disease conveniently and quickly and guide clinical medication when necessary. However, there are some limitations of this study. Considering this study is a single‐center study, multicenter cooperation is needed to further expand the sample size. The follow‐up time of this study is short, and the follow‐up time will be further extended to predict the relationship between inflammatory factors and the long‐term prognosis and survival of PS patients. There are some studies on imaging‐related indicators and early neurological deterioration [38]. Following, we can even combine imaging parameters to further find risk factors for the prognosis of PS patients.
Author Contributions
Yingying Wang: software, conceptualization, writing – original draft. Zhouao Zhang: methodology, software, writing – review and editing. Xiaoyu Hang: data curation, supervision. Wei Wang: investigation, validation.
Ethics Statement
This study was approved by the Ethics Committee of the Suzhou Ninth People's Hospital and complied with the Declaration of Helsinki.
Conflicts of Interest
All authors declare no conflicts of interest.
Acknowledgments
We are grateful to all staff in the Department of Laboratory Medicine, the Department of Neurology, Suzhou Ninth People's Hospital for their technical support.
Funding: This work was supported by the funding for research projects (YK202419).
Data Availability Statement
The data that support the findings of this study are openly available at [https://doi.org/10.1111/ene.70080].
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are openly available at [https://doi.org/10.1111/ene.70080].