Next Article in Journal
Hematopoiesis, Inflammation and Aging—The Biological Background and Clinical Impact of Anemia and Increased C-Reactive Protein Levels on Elderly Individuals
Next Article in Special Issue
Dehydration Status at Admission Predicts Recurrence in Patients with Traumatic Chronic Subdural Hematoma
Previous Article in Journal
Clinical Course and Outcome of Non-Immune Fetal Hydrops in Singleton Pregnancies
Previous Article in Special Issue
Neutrophil-to-Lymphocyte Ratios and Infections after Traumatic Brain Injury: Associations with Hospital Resource Utilization and Long-Term Outcome
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 11
Issue 3
10.3390/jcm11030705
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Inflammatory Predictors of Prognosis in Patients with Traumatic Cerebral Haemorrhage: Retrospective Study

by
Piotr Defort
1,*,
Natalia Retkowska-Tomaszewska
1,
Marcin Kot
1,
Paweł Jarmużek
1,
Anna Tylutka
2 and
Agnieszka Zembron-Lacny
2
1
Neurosurgery Center University Hospital, Collegium Medicum University of Zielona Gora, 28 Zyty Str., 65-417 Zielona Gora, Poland
2
Department of Applied and Clinical Physiology, Collegium Medicum University of Zielona Gora, 28 Zyty Str., 65-417 Zielona Gora, Poland
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2022, 11(3), 705; https://doi.org/10.3390/jcm11030705
Submission received: 14 January 2022 / Accepted: 26 January 2022 / Published: 28 January 2022
(This article belongs to the Special Issue Traumatic Brain Injury: Diagnosis, Management, and Outcomes)
Browse Figures
Review Reports Versions Notes

Abstract

:
We aimed to evaluate the relationship between neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), systemic inflammation index (SII), and Glasgow Coma Scale (GCS) score in patients with traumatic intracerebral haemorrhage (TICH). We retrospectively investigated 95 patients with TICH hospitalised at the Neurosurgery Department in Zielona Gora from January 2017 to March 2021. Routine blood tests were performed 5 h after injury. NRL and SII were significantly higher in patients with GCS ≤ 8 than patients with GCS > 8 and exceeded reference values in 95% of patients. GCS was inversely correlated with NLR and SII. Receiver operating characteristic (ROC) analysis confirmed the value of NLR and SII regarding GCS score; Area Under the Curve (AUC) 0.748, 95% Confidence Interval (CI) 0.615–0.880. An optimised NLR cut-off value of 0.154 was identified with a sensitivity of 0.90 and specificity of 0.56. The value of SII regarding GCS was confirmed with ROC curves; AUC 0.816, 95% CI 0.696–0.935. An optimised NLR cut-off value of 0.118 was identified with a sensitivity of 0.95 and specificity of 0.57. NLR and SII are significantly related to GCS scores and are promising predictors of clinical prognosis in TICH patients.

1. Introduction

Traumatic brain injury (TBI) remains a major cause of death and disability worldwide. For all ages and TBI severities, crude incidence rates ranged from 47.3 to 694 per 100,000 population per year (country-level studies) and from 83.3 to 849 per 100,000 population per year (regional-level studies). Mortality rates ranged from 9 to 28.10 per 100,000 population per year (country-level studies) and from 3.3 to 24.4 per 100,000 population per year (regional-level studies). The most common mechanisms of injury were traffic accidents and falls [1]. Patients with TBI are most commonly categorised into mild, moderate, and severe based on Glasgow Coma Scale (GCS) [2], as well as findings from computed tomography (CT) for the head [3]. Magnetic resonance imaging (MRI) is routinely performed at a growing number of centres and is particularly helpful in the quantification of diffuse axonal TBI. The more recently developed diffusion tension DT-MRI can detect in even greater detail alterations in the microstructure of the white matter, and it shows considerable promise in the assessment of neuronal damage [4]. However, these neuroimaging techniques reveal little or no information regarding secondary injury processes such as excitotoxicity, neuroinflammation, blood–brain barrier breakdown, ischemic damage, and cell death. New biomarkers are a promising tool for clinicians for obtaining additional, patient-specific information.
A substantial subgroup of patients with TBI admitted to hospitals includes patients with traumatic intracerebral haemorrhage (TICH). In these cases, damage to the neuronal structure is apparent on admission CTs. The earliest consequences of TICH are increased vascular permeability, leading to brain oedema and complex inflammatory response [5,6,7,8]. Alterations in blood–brain barrier permeability may contribute to secondary brain injury through an abnormal passage of blood-borne substances into the extravascular space, increasing neuronal vulnerability [9,10]. Increased vascular permeability is associated with multiple post-traumatic inflammatory events [9,11]. Trauma-induced mechanical damage to vascular cell membranes results in endothelial cell necrosis and red blood cell extravasation [12,13]. Endothelial and leukocyte adhesion molecules are upregulated as a result of primary traumatic events, leading to the recruitment and extravasation of circulating leukocytes into the brain parenchyma [14]. Blood-borne circulating monocytes gain access to the brain parenchyma and may contribute to macrophage-induced secondary injury mechanisms. Moreover, a brain injury is often accompanied by multi-organ trauma, which is often severe and difficult to diagnose and treat and may influence prognosis [15].
The prognosis of patients with TICH is still unclear, and multiple studies have tried to identify predictors of outcome in these patients [9]. An assessment of the level of consciousness with the Glasgow Coma Scale at admission remains the most reliable prognostic factor, which unfortunately can be subject to human error. It may also be underestimated due to drug or alcohol intoxication [16]. Thus, the need for more objective prognostic factors should be addressed. The immunological studies demonstrate diagnostic potential and provide information regarding both the acute as well as long-term cerebrovascular consequences of brain injury leading to mortality, disability, and cognitive impairment [17]. In the past few years, many studies reported that the combinations of the haematological components of the systemic inflammatory responses, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), the lymphocyte-to-monocyte ratio (LMR), and systemic immune inflammation index (SII) were effective prognostic indicators in patients with a severe traumatic brain injury [10,17,18], neoplasms [19,20,21,22,23], obesity [24,25], coronary artery disease [26,27], diabetes [28], and acute ischaemic stroke [29]. The components of these easily calculated parameters are readily available, inexpensive, and routinely measured in daily practice as part of the complete blood count [30]. The calculation of these haematological components of the systemic inflammatory response may provide clinicians with a further valuable and objective tool that would support the clinical assessment of the head trauma patients in their management and prognosis. Therefore, the study was designed to assess the diagnostic suitability of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and systemic immune inflammation index compared to the severity of the brain injury according to the Glasgow Coma Scale in patients with traumatic cerebral haemorrhage.

2. Materials and Methods

2.1. Patients

This retrospective study was based on a database including 198 cases of traumatic injury brain at Neurosurgery Centre University Hospital in Zielona Gora from January 2017 to March 2021. The average time from head injury to admission ranged from 1 to 12 h. The diagnosis was based on a history of injury, clinical manifestations, and radiological findings. The inclusion criteria were as follows: traumatic intracerebral haemorrhage (TICH) and age ≥18 years. Patients admitted later than 12 h after the injury and patients with incomplete and missing data were excluded from the study. Eventually, 95 patients (females n = 16, males n = 79) were included in the project (Table 1). The study patients were allocated into two groups based on Glasgow Coma Scale (GCS) score: GCS > 8 group including patients with minimal, mild, and moderate head injuries (n = 75) and GCS ≤ 8 group including patients with severe and critical head injuries (n = 20) according to the classification described by Carney et al. [31] and Maas et al. [32]. Data regarding age, gender, haematological variables, injury mechanism, GCS score, indications for operative treatment, and accompanying injuries, diseases, and addictions were collected from electronic medical records at University Hospital in Zielona Gora. Patients with traumatic cerebral haemorrhage were diagnosed using head computed tomography (CT) at admission. The study protocol was approved by the Bioethics Commission at Regional Medical Chamber Zielona Gora, Poland (No. 02/131/2020), in accordance with the Helsinki Declaration.

2.2. Treatments

The treatment of patients with traumatic cerebral haemorrhage in Neurosurgery Center in Zielona Gora depends on TICH severity and findings from the admission CT and is managed according to Guidelines for the Management of Severe TBI [33,34]. Tracheal intubation and mechanical ventilation were adopted based on the patients’ level of consciousness (GCS ≤ 8) and arterial oxygen saturation. If a severe mass effect caused by enlargement of TICH or intracerebral hematoma developed during hospitalisation, a decompressive craniectomy and/or evacuation of mass lesions was performed.

2.3. Haematological Variables and White Blood Cells

Blood sampling for laboratory tests using S-Monovette-EDTA K2 tubes (Sarstedt AG & Co. KG, Nümbrecht, Germany) was limited to the timespan within 24 h after the onset of brain injury. Haematological parameters including total white cell count (WBC), red blood cell count (RBC), platelet count (PLT), differential white cell count (neutrophils, lymphocytes, monocytes, eosinophils, and basophils), and haemoglobin (HB) were determined by Sysmex XN-1000 (Europe Gmbh, Norderstedt, Germany). NLR, PLR, LMR, and SII were calculated and compared to references values according to Luo et al. [30]. The formulas are as follows: NLR = Neutrophil count (103/µL)/Lymphocyte count (103/µL); PLR = Platelet count (103/µL)/Lymphocyte count (103/µL); LMR = Lymphocyte count (103/µL)/Monocyte count (103/µL); SII = (Platelet count (103/µL) × (Neutrophil count (103/µL)/Lymphocyte count (103/µL)).

2.4. Statistical Analysis

Continuous variables were reported as mean values ± standard deviation (SD) and median with interquartile range (25–75%), while categorical variables were expressed as count and percentage. The statistical significance of intergroup differences (according to GCS) was compared through the Kruskal–Wallis non-parametric test and through Pearson’s χ2 test for categorical variables. Receiver operating characteristic (ROC) curves showed sensitivity vs. specificity such that the area under the curve (AUC) varied from 0.5 to 1.0, with increased values demonstrating higher discriminatory ability. Univariate logistic regression analyses were performed to separately examine the association between unfavourable outcomes and each of the indicators. Correlations between variables and GCS score were analysed through Spearman correlation tests (R Spearman rank correlation coefficient). The value p < 0.05 was considered to represent a statistically significant difference. All analyses were performed by using the R version 4.0.3 [35].

3. Results

3.1. Patients

A total of 95 patients were included in this study, with 83% being males; the median age of patients was 51 years (Table 1). TICH was mainly caused by falls (42% of all patients: 43.5% in GCS > 8 group and 30% in GCS ≤ 8 group). The traffic accidents were the cause of TICH in 19% of patients; most of them (63%) had GCS ≤ 8 at admission. This group of patients constituted 55% of all patients in GCS ≤ 8 group. The seizures and violence were also important causes of TBI in GCS > 8 group of the studied patients. There were no patients in GCS ≤ 8 group with TBI caused by seizures. The other or unknown causes of TICH accounted for 19%; most of these patients were allocated to GCS > 8 group of the study (89%). The patients with severe and critical head injuries (GCS ≤ 8 group) were significantly (p < 0.01) younger than patients with minimal, mild, and moderate head injuries. Thirty-seven per cent of all patients with traumatic cerebral haemorrhage (TICH) had suffered the injury under the influence of alcohol. Fifteen per cent of patients were operated on during the first 24 h after the cerebral injury, 9.3% (n = 7) of patients in GCS > 8 group, and 35% (n = 7) of patients in GCS ≤ 8 group. There were three cases of deaths during hospitalisation; two of them were classified as brain death and subjected to the organ transplant procedure.

3.2. Haematological Variables and White Blood Cells

The haematological markers were within the referential ranges, and they did not significantly differ between groups (Table 2). However, glucose concentration was significantly higher in patients with GCS ≤ 8 but it was not related to causes of TICH. The counts of WBC, neutrophils, and monocytes were above reference values in both groups, whereby a 2-fold increase was recorded in patients with ≤8 GCS (Table 3). Similarly, NRL and SII were significantly higher in patients with GCS ≤ 8 and exceeded reference values in 95% of patients. GCS was inversely correlated with NLR and SII (Table 4). There was no significant correlation between GCS score and PLR and LMR counts. ROC analysis confirmed the value of NLR regarding GCS score (AUC 0.748, 95% confidence interval CI, 0.615–0.880, p < 0.001) (Table 5). An optimised NLR cut-off value (Youden Index) of 0.154 was identified with a sensitivity of 0.90 and specificity of 0.56. The value of SII regarding GCS score was also confirmed with ROC curves (AUC 0.816; 95% CI; 0.696–0.935, p < 0.0001). An optimised NLR cut-off value of 0.118 was identified with a sensitivity of 0.95 and specificity of 0.57 (Figure 1 and Figure 2).

4. Discussion

Traumatic brain injury, particularly TICH, is one of the most complex and diverse pathological medical conditions. According to the epidemiological investigation in Poland in 2009–2012 [33], the predominant external causes of TICH were traffic accidents (40%) followed by falls (33%). Our study shows the opposite trend, i.e., TICH was mainly caused by falls (42%), and traffic accidents contributed to TICH in 19% of patients. The shift from road traffic incidents to falls as the most common mechanism of injury in Europe was observed in recent studies [1]. There is also an apparent trend for increasing the percentage of falls as a cause of TICH as the age of patients advances. We observed a similar shift in our study.
Age and sex breakdown were reported in the majority of studies. However, it is difficult to compare the distribution of TICH across populations, as many studies report the data concerning only selected parts of the population (children or older adults, or adults only, etc.). According to a systemic review by Brazinova et al. [1], the lowest reported mean age was 26.7 years in the Republic of San Marino, and the highest value reached 44.5 years in Austria in 2009–2011. The reported proportion of males was always greater than that of females (irrespective of age, severity, and mechanism of injury), ranging from 55% in Sweden in 2001 to 80% in Ireland in 2005–2007. This may reflect the fact that the major causes of traumatic brain injury are related to more male-dominated activities. However, the proportion of men in TBI studies decreased with age. In our study, males constituted over 80% of patients, and they were significantly younger, i.e., 37.8 ± 19.2 years for GCS ≤ 8 and 53.9 ± 18.6 years for GCS > 8 group. The difference persisted when the whole population was taken into account. This might be due to a significantly higher percentage of falls as a cause of head trauma in the older population in comparison to high-energy traffic accidents, which dominate in younger patients.
In the Polish study by Miekisiak et al. [36], the most common diagnosis was TICH (82 × 105 per year), and the most prevalent injury requiring surgery was subdural haematomas (15 × 105 per year). In our study, patients diagnosed with subdural and intracerebral haematoma at admission were excluded from the study; however, there were 13 patients (14%) requiring evacuation of intracerebral haematomas, which developed 12 h after the admission. The prognosis in patients who have not required surgical treatment and those who have undergone surgery is still unclear, and multiple studies have tried to identify predictors of outcome in these patients [37]. In our group, every patient, who needed surgical intervention later in the course of treatment, was found to have high values of NLR and SII above reference levels. Fourteen patients were operated on during the first 24 h after the cerebral injury. However, due to the small sample size, no conclusion can be made for NLR and SII as prognostic factors for the development of intracerebral haematoma and/or progression of contusions, as was presented in the study by Zhuang et al. [18]. The authors demonstrated that NRL allowed identifying the risk factors for surgical intervention among patients with the growth of TICH.
The inflammatory response to brain injury is observed following ischemic, traumatic, or excitotoxic brain injury or seizure and often contributes to a long-term disability. At sites of brain injury, damaged cells release the factors that trigger the inflammatory cascade, along with chemokines and growth factors, which attract neutrophils and monocytes. The first cells attracted to the site of injury include neutrophils, followed by monocytes, lymphocytes, and mast cells. The migratory activity of neutrophils allows them to accumulate at sites of brain injury within a few hours [38]. Accordingly, neutrophils increase oxidative stress, exacerbate blood–brain barrier damage, and promote neuronal cell death. This is manifested by a poor functional outcome and an increased mortality rate in patients with various degrees of TBI. The role of T lymphocytes in TBI remains largely unknown. T cells are recruited by the reactive oxygen species released from neutrophils, but experimental data show that T cells play no significant role in the pathogenesis of early TBI [39]. In our study, the count of neutrophils was above reference values in all patients, whereas it was approx. 35% higher in GCS ≤ 8 than in GCS > 8 group. There was no difference in the lymphocyte count. Although neutrophils seem to play a pathogenic role during TBI, their advantageous role in the wound-healing process should not be neglected [40].
The changes in NRL and SII are strictly dependent on neutrophils, which represent from 50% to 70% of the total circulating leukocytes [36]. In our study, NLR and SII were significantly higher in patients with GCS ≤ 8 than GCS >8. Similarly, Chen et al. [17] observed the peak NRL in patients with severe brain injury and indicated NRL as a promising predictor for 1-year outcomes. Siwicka-Gieroba et al. [41] showed that NRL elevation predicted mortality in severe TBI patients. According to Acar et al. [42], NLR can suggest brain pathologies on CT scans of patients with isolated minor head trauma and with a GCS score of 15 when the need for a CT scan is not clear. Recently Reznik et al. [43] demonstrated that early NLR elevation also predicted delayed-onset delirium, potentially implicating systemic inflammation as a contributory delirium mechanism. There is much less research reporting the analyses of SII in brain injury. Trifan and Testai [44] concluded that SII was an independent predictor of poor outcomes at hospital discharge in patients with spontaneous intracerebral haemorrhage. We demonstrated that SII was even 2-fold higher in patients with GCS ≤ 8 including patients with severe and critical head injuries. In contrast, some authors did not confirm the sensitivity of NLR or SII in predicting poor outcomes in TBI patients, thereby suggesting that other haematological parameters have a higher sensitivity as prognostic markers [45,46]. However, most studies generally support the view of the predictive value of NLR and SII in patients with brain injury.
NLR and SII, inversely correlated with GCS, are considered to be the best method for the initial classification of TBI patients and to define the route of administration. However, ethanol exposure may confound the initial assessment of GCS, leading to the exposed patients being assessed as more severe cases. The percentage of patients being intoxicated at the time of admission ranges between 30% and 60% in different studies [16]. According to Xu et al. [47], alcohol may alleviate the TBI-induced pro-inflammatory response. In our study, 37% of all the patients with TICH were under the influence of alcohol, but their values of NLR and SII did not differ when compared to other patients. Ethanol intoxication increases the risk of traumatic brain injury, but, on the other hand, the presence of ethanol following isolated moderate–severe traumatic brain injury may be neuroprotective. Therefore, further studies are recommended to investigate the interaction between ethanol exposure and brain injury and the potential of ethanol as a therapeutic agent [16].

5. Conclusions

Inflammation is frequently a key element in the pathological progression of traumatic brain injury; therefore, the analysis of changes in NLR and SII as exponents of the systemic inflammatory response can be an important supplement to conventional methods in determining prognosis and possibly selecting patients requiring closer monitoring.

6. Limitations

The limitations of the study include a relatively small number of patients, especially the group with ≤8 points in GCS score, and no observations in the following days after TICH.

Author Contributions

N.R.-T. and M.K.: conception and design, blood sample collection, and analysis of the data. A.T.: statistical analysis. P.D.: drafting of the paper, critical review, and approval of the final version submitted for publication. P.J. and A.Z.-L.: conception and design, analysis and interpretation of the data, critical review, and approval of the final version submitted for publication. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by funds from the University of Zielona Gora.

Institutional Review Board Statement

The study protocol was approved by the Bioethics Commission at Regional Medical Chamber Zielona Gora, Poland (No. 02/131/2020), in accordance with the Helsinki Declaration.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brazinova, A.; Rehorcikova, V.; Tayloro, M.S.; Buckova, V.; Majdan, M.; Psota, M.; Peeters, W.; Feigin, V.; Teadom, A.; Halkovic, L.; et al. Epidemiology of Traumatic Brain Injury in Europe: A living systematic review. J. Neurotrauma 2015, 38, 1411–1440. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Teasdale, G.; Murray, G.; Parker, L.; Jennett, B. Adding up the Glasgow Coma Score. Acta Neurochir. Suppl. 1979, 28, 13–16. [Google Scholar] [PubMed]
  3. Hagbood, I.R.; Mass, D.K.; Menon, E.W.; Stayeberg, G.; Citerio, F.; Lecky, G.T.; Manley, S.; Hill, V.; Legrand, A. Sorgner: Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI): A prospective longitudinal observational study. Neurosurgery 2015, 76, 67–80. [Google Scholar]
  4. Salomond, C.H.; Menon, D.K.; Chatfield, D.A.; Williams, G.B.; Pena, A.; Sahakian, B.J.; Pickard, J.D. Diffusion tensor imaging in chronic head injury survivors: Correlations with learning and memory indices. Neuroimage 2006, 29, 117–124. [Google Scholar] [CrossRef] [PubMed]
  5. Tanno, H.; Nockels, R.P.; Pitts, L.H.; Noble, L.J. Breakdown of the blood-brain barrier after fluid percussion brain injury in the rat: Part 2: Effect of hypoxia on permeability to plasma proteins. J. Neurotrauma 1992, 9, 335–347. [Google Scholar] [CrossRef] [PubMed]
  6. Dietrich, W.D.; Alonso, O.; Halley, M. Early microvascular and neuronal consequences of traumatic brain injury: A light and electron microscopic study in rats. J. Neurotrauma 1994, 11, 289–301. [Google Scholar] [CrossRef]
  7. Habgood, M.D.; Bye, M.N.; Dzięgielewska, K.M.; Ek, C.J.; Lane, M.A.; Potter, A.; Morganti-Kossmann, C.; Sauders, N.R. Changes in blood-brain barrier permeability to large and small molecules following traumatic brain injury in mice. Eur. J. Neurosci. 2007, 25, 231–238. [Google Scholar] [CrossRef]
  8. Lotocki, G.; Pablo de Rivero Vaccari, J.; Rerez, E.R.; Sanchez-Molano, J.; Furones-Alonso, O.; Bramlett, H.M.; Dietrich, W.D. Alterations in blood-brain barrier permeability to large and small molecules and leukocyte accumulation after traumatic brain injury: Effects of post-traumatic hypothermia. J. Neurotrauma 2009, 26, 1123–1134. [Google Scholar] [CrossRef] [Green Version]
  9. Stahel, P.F.; Shohami, E.; Younis, F.M.; Kariya, K.; Otto, V.I.; Lenzlinger, P.M.; Grosjean, M.B.; Eugster, H.P.; Trentz, O.; Kossmann, T.; et al. Experimental closed head injury: Analysis of neurological outcome, blood-brain barrier dysfunction, intracranial neutrophil infiltration, and neuronal cell death in mice deficient in genes for pro-inflammatory cytokines. J. Cereb. Blood Flow Metab. 2000, 20, 369–380. [Google Scholar] [CrossRef] [Green Version]
  10. Lucas, S.-M.; Rothwell, N.J.; Gibson, R.M. The role of inflammation in CNS injury and disease. Br. J. Pharmacol. 2006, 147 (Suppl. 1), 232–240. [Google Scholar] [CrossRef] [Green Version]
  11. Chatzipanteli, K.; Alonso, O.F.; Kraydieh, S.; Dietrich, W.D. Importance of posttraumatic hypothermia and hyperthermia on the inflammatory response after fluid percussion brain injury: Biochemical and immunocytochemical studies. J. Cereb. Blodd Flow Metab. 2000, 20, 531–542. [Google Scholar] [CrossRef] [PubMed]
  12. Dietrich, W.D.; Alonso, O.; Busto, R.; Globus, M.Y.; Ginsberg, M.D. Post-traumatic brain hypothermia reduces histopathological damage following concussive brain injury in the rat. Acta Neuropathol. 1994, 87, 250–258. [Google Scholar] [CrossRef] [PubMed]
  13. Kinoshita, K.; Chatzipanteli, K.; Olonso, O.F.; Howard, M.; Dietrich, W.D. The effect of brain temperature on hemoglobin extravasation after traumatic brain injury. J. Neurosurg. 2002, 97, 945–953. [Google Scholar] [CrossRef] [PubMed]
  14. Schoettle, R.J.; Kochanek, P.M.; Magargee, M.J.; Uhl, M.W.; Nemoto, E.M. Early polymorphonuclear leukocyte accumulation correlates with the development of posttraumatic cerebral edema in rats. J. Neurotrauma 1990, 7, 207–217. [Google Scholar] [CrossRef] [PubMed]
  15. Bramlett, H.M.; Dietrich, W.D. Long-Term Consequences of Traumatic Brain Injury: Current Status of Potential Mechanisms of Injury and Neurological Outcomes. J. Neurotrauma 2015, 32, 1834–1848. [Google Scholar] [CrossRef]
  16. Brennan, J.H.; Bernard, S.; Cameron, P.A.; Rosenfeld, J.V.; Mitra, B. Ethanol and isolated traumatic brain injury. J. Clin. Neurosci. 2015, 22, 1375–1381. [Google Scholar] [CrossRef]
  17. Chen, J.; Qu, X.; Li, Z.; Zhang, D.; Hou, L. Peak Neutrophil-to-Lymphocyte Ratio Correlates with Clinical Outcomes in Patients with Severe Traumatic Brain Injury. Neurocritical Care 2019, 30, 334–339. [Google Scholar] [CrossRef]
  18. Zhuang, D.; Sheng, J.; Peng, G.; Li, T.; Cai, S.; Din, F.; Li, L.; Huang, M.; Tian, F.; Li, K.; et al. Neutrophil to lymphocyte ratio predicts early growth of traumatic intracerebral haemorrhage. Ann. Clin. Trans. Neur 2021, 8, 1601–1609. [Google Scholar] [CrossRef]
  19. Haugdahl Nost, T.; Alcala, K.; Ubarova, I.; Byrne, K.S.; Guida, F.; Sandanger, T.M.; Johansson, M. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur. J. Epidemiol. 2021, 36, 841–848. [Google Scholar] [CrossRef]
  20. Kim, E.Y.; Lee, J.W.; Yoo, H.M.; Park, C.H.; Song, K.Y. The Platelet-to-Lymphocyte Ratio Versus Neutrophil-to-Lymphocyte Ratio: Which is Better as a Prognostic Factor in Gastric Cancer? Gastrointest. Oncol. 2015, 22, 4363–4370. [Google Scholar] [CrossRef]
  21. Sahin, F.; Yildiz, P. Serum platelet, MPV, PCT and PDW values, neutrophil to lymphocyte and platelet to lymphocyte ratios in lung cancer diagnosis. Eur. Resp. J. 2015, 46, PA4279. [Google Scholar]
  22. Tang, J.-N.; Goyal, H.; Yu, S.; Luo, H. Prognostic value of systemic immune-inflammation index (SII) in cancers: A systematic review and meta-analysis. J. Lab. Precis. Med. 2018, 3, 29. [Google Scholar] [CrossRef]
  23. Templeton, A.J.; McNamara, M.G.; Seruga, B.; Vera-Badillo, F.E.; Aneja, P.; Ocana, A.; Leibowitz-Amit, R.; Sonpavde, G.; Knox, J.J.; Tran, B.; et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: A systematic review and meta-analysis. J. Natl. Cancer Inst. 2014, 106, dju124. [Google Scholar] [CrossRef] [Green Version]
  24. Erdal, E.; Inanir, M. Platelet-to-lymphocyte ratio (PLR) and Plateletcrit (PCT) in young patients with morbid obesity. Rev. Assoc. Médica Bras. 2019, 65, 1182–1187. [Google Scholar] [CrossRef] [PubMed]
  25. Furuncuiglu, Y.; Tulgar, S.; Dogan, A.N.; Cakar, S.; Tulgar, Y.K.; Cakiroglu, B. How obesity affects the neutrophil/lymphocyte and platelet/lymphocyte ratio, systemic immune-inflammatory index and platelet indices: A retrospective study. Eur. Rev. Med. Pharmacol. Sci. 2016, 20, 1300–1306. [Google Scholar]
  26. Gawaz, M.; Langer, H.; May, A.E. Platelets in inflammation and atherogenesis. J. Clin. Investig. 2005, 115, 3378–3384. [Google Scholar] [CrossRef] [Green Version]
  27. Kurtul, M.; Yarlioglues, S.N.; Murat, G.; Ergun, M.; Duran, H.A.; Kasapkara, M.B.; Demircelik, M.; Cetin, A.H. Ocek: Usefulness of the platelet-to-lymphocyte ratio in predicting angiographic reflow after primary percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction. Am. J. Cardiol. 2014, 114, 342–347. [Google Scholar] [CrossRef]
  28. Schneider, D.J. Factors Contributing to Increased Platelet Reactivity in People with Diabetes. Diabetes Care 2009, 32, 525–527. [Google Scholar] [CrossRef] [Green Version]
  29. Cheng, Y.; Ying, A.; Lin, Y.; Yu, J.; Luo, J.; Zeng, Y.; Lin, Y. Neutrophil-to-lymphocyte ratio, hyperglycemia, and outcomes in ischemic stroke patients treated with intravenous thrombolysis. Brain Behav. 2020, 10, e01741. [Google Scholar] [CrossRef]
  30. Luo, H.; He, L.; Zhang, G.; Yu, J.; Chen, Y.; Yin, H.; Goyal, H.; Zhang, G.; Xiao, Y.; Gu, C.; et al. Normal Reference Intervals of Neutrophil-To-Lymphocyte Ratio, Platelet-To-Lymphocyte Ratio, Lymphocyte-To-Monocyte Ratio, and Systemic Immune Inflammation Index in Healthy Adults: A Large Multi-Center Study from Western China. Clin. Lab. 2019, 65, 255–265. [Google Scholar] [CrossRef]
  31. Carney, N.; Totten, A.M.; O’Reilly, C.; Ullman, J.S.; Hawryluk, G.W.; Bell, M.J.; Bratton, S.L.; Chesnut, R.; Harris, O.A.; Kissoon, N.; et al. Guidelines for the management of severe traumatic brain injury 4th edition. Neurosurgery 2017, 80, 6–15. [Google Scholar] [CrossRef] [PubMed]
  32. Maas, A.; Stocchetti, N.; Bullock, R. Moderate and severe traumatic brain injury in adults. Lancet Neurol. 2008, 7, 728–741. [Google Scholar] [CrossRef]
  33. Dijkland, S.A.; Foks, K.A.; Polinder, S.; Dippel, D.W.J.; Maas, A.I.R.; Lingsma, H.F.; Steyerberg, E.W. Prognosis in Moderate and Severe Traumatic Brain Injury: A Systematic Review of Contemporary Models and Validation Studies. J. Neurotrauma 2020, 37, 1–13. [Google Scholar] [CrossRef] [PubMed]
  34. Hawryluk, G.W.; Manley, G.T. Classification of traumatic brain injury: Past, present, and future. Handb. Clin. Neurol. 2015, 127, 15–21. [Google Scholar] [PubMed]
  35. R Core Team. A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://www.R-project.org (accessed on 9 October 2021).
  36. Miekisiak, G.; Czyż, M.; Tykocki, T.; Kaczmarczyk, J.; Załuski, R.; Łątka, D. Traumatic brain injury in Poland from 2009–2012: A national study on incidence. Brain Injury 2016, 30, 79–82. [Google Scholar] [CrossRef] [PubMed]
  37. Feinberg, M.; Mai, J.C.; Ecklund, J. Neurosurgical management in traumatic brain injury. Semin. Neurol. 2015, 35, 50–56. [Google Scholar] [PubMed]
  38. Chen, L.; Deng, H.; Cui, H.; Fang, J.; Zuo, Z.; Deng, J.; Li, Y.; Wang, X.; Zhao, L. Inflammatory responses and inflammation-associated diseases in organs. Oncotarget 2018, 9, 7204–7218. [Google Scholar] [CrossRef] [Green Version]
  39. Lämmermann, T.; Afonso, P.V.; Angermann, B.R.; Wang, J.M.; Kastenmüller, W.; Parent, C.A.; Germain, R.N. Neutrophil swarms require LTB4 and integrins at sites of cell death in vivo. Nature 2020, 498, 371–375. [Google Scholar] [CrossRef]
  40. Sabouri, E.; Majdi, A.; Jangjui, P.; Aghsan, S.R.; Alavi, S.A.N. Neutrophil-to-Lymphocyte Ratio and Traumatic Brain Injury: A Review Study. World Neurosurg 2020, 140, 142–147. [Google Scholar] [CrossRef]
  41. Siwicka-Gieroba, D.; Malodobry, K.; Biernawska, J.; Robba, C.; Bohatyrewicz, R.; Rola, R.; Dabrowski, W. The Neutrophil/Lymphocyte Count Ratio Predicts Mortality in Severe Traumatic Brain Injury Patients. J. Clin. Med. 2019, 8, 1453. [Google Scholar] [CrossRef] [Green Version]
  42. Acar, E.; Demir, A.; Alatas, Ö.D.; Beydilli, H.; Yıldırım, B.; Kırlı, U.; Hazer, D.B.; Kılınç, M.R.; Karagöz, Ü.; Derin, S. Evaluation of hematological markers in minor head trauma in the emergency room. Eur. J. Trauma Emerg. Surg. 2016, 42, 611–616. [Google Scholar] [CrossRef] [PubMed]
  43. Reznik, M.E.; Kalagara, R.; Moody, S.; Drake, J.; Margolis, S.A.; Cizginer, S.; Mahta, A.; Rao, S.S.; Stretz, C.; Wendell, L.C.; et al. Affiliations expand Common biomarkers of physiologic stress and associations with delirium in patients with intracerebral haemorrhage. J. Crit. Care 2021, 64, 62–67. [Google Scholar] [CrossRef] [PubMed]
  44. Trifan, G.; Testai, F.D. Systemic Immune-Inflammation (SII) index predicts poor outcome after spontaneous supratentorial intracerebral haemorrhage. J. Stroke Cereb. Dis. 2020, 29, 105057. [Google Scholar] [CrossRef] [PubMed]
  45. Corbett, J.-M.; Ho, K.M.; Honeybul, S. Prognostic significance of abnormal hematological parameters in severe traumatic brain injury requiring decompressive craniectomy. J. Neurosurg. 2019, 132, 545–551. [Google Scholar] [CrossRef]
  46. Herbert, J.P.; Guillotte, A.R.; Hammer, R.D.; Litofsky, N.S. Coagulopathy in the Setting of Mild Traumatic Brain Injury: Truths and Consequences. Brain Sci. 2017, 7, 92. [Google Scholar] [CrossRef]
  47. Xu, B.; Chandrasekar, A.; Heuvel, F.; Powerski, M.; Nowak, A.; Noack, L.; Omari, J.; Huber-Lang, M.; Roselli, F.; Relja, B. Ethanol Intoxication Alleviates the Inflammatory Response of Remote Organs to Experimental Traumatic Brain Injury. Int. J. Mol. Sci. 2020, 21, 8181. [Google Scholar] [CrossRef]
Jcm 11 00705 g001 550
Figure 1. ROC curve analysis for admission GCS score and NLR; FPR—false positive rate, TPR—true positive rate.
Figure 1. ROC curve analysis for admission GCS score and NLR; FPR—false positive rate, TPR—true positive rate.
Jcm 11 00705 g001
Jcm 11 00705 g002 550
Figure 2. ROC curve analysis for admission GCS score and SII; FPR—false positive rate, TPR—true positive rate.
Figure 2. ROC curve analysis for admission GCS score and SII; FPR—false positive rate, TPR—true positive rate.
Jcm 11 00705 g002
Table
Table 1. The demographic and clinical data of the TICH patients.
Table 1. The demographic and clinical data of the TICH patients.
Patients GCS > 8Patients GCS ≤ 8
Characteristics
Number of subjects7520
Median age (years)51 (21–100)29 (18–85)
Mean ages (years)53.9 ± 18.637.8 ± 19.2
Number of females, n (%)12 (16)4 (20)
Number of males, n (%)63 (84)16 (80)
Surgery within the first 24 h, n (%)7 (9.3)7 (35)
Injury mechanisms, n (%)
Falls34 (45.3)6 (30)
Traffic accident7 (9.3)12 (55)
Seizures12 (16)0 (0)
Violence6 (8)1 (5)
Others/unknown16 (21.3)2 (10)
Abbreviations: GCS—Glasgow Coma Scale, TICH—traumatic cerebral haemorrhage.
Table
Table 2. Haematological variables and glucose.
Table 2. Haematological variables and glucose.
Reference ValuesPatients GCS > 8 Patients GCS ≤ 8
Mean ± SDMed
(iqr 25–75%)		Mean ± SDMed
(iqr 25–75%)		p-Value
RBC (106/µL)4.2–6.54.3 ± 0.84.4 (3.9–4.7)4.3 ± 0.74.3 (3.8–4.8)0.823
HB (g/dL)12.0–18.013.7 ± 1.814.1 (12.6–14.9)13.2 ± 1.813.4 (12.2–4.6)0.308
HCT (%)38.0–54.042.2 ± 10.242.1 (38.1–44.4)39.0 ± 5.239.7 (35.8–42.7)0.111
MCV (fL)80.0–97.093.7 ± 11.594.8 (90.9–99.0)89.3 ± 12.789.6 (86.8–96.9)0.008
MCH (pg/RBC)26.0–32.032.1 ± 2.031.7 (30.6–33.6)33.8 ± 12.631.1 (29.2–33.0)0.124
MCHC (g/dL)31.0–36.033.4 ± 2.633.6 (32.9–34.4)33.6 ± 1.633.2 (32.4–34.8)0.658
RDW (%)11.5–14.813.0 ± 2.112.2 (11.3–14.0)13.3 ± 5.612.4 (11.0–14.2)0.161
Glucose (mg/dL)60.0–99.0132.4 ± 40.5124 (107–155)164.1 ± 88.2155 (127–195)p < 0.05
Abbreviations: GCS—Glasgow Coma Scale score, SD—standard deviation, Med—median, iqr—interquartile range, RBC—red blood cells, HB—haemoglobin, HCT—haematocrit, MCV—mean cell volume, MCH—mean corpuscular haemoglobin, MCHC—mean corpuscular haemoglobin concentration, RDW—red cell distribution width.
Table
Table 3. White blood cells and platelets counts.
Table 3. White blood cells and platelets counts.
Reference ValuesPatients GCS > 8 Patients GCS ≤ 8
Mean ± SDMed
(iqr 25–75%)		Mean ± SDMed
(iqr 25–75%)		p-Value
WBC (103/µL)4.0–10.212.1 ± 4.811.2 (8.7–14.3)22.3 ± 3.716.0 (12.2–22.3)p < 0.001
Neutrophils (103/µL)2.0–6.99.9 ± 3.18.7 (6.0–12.0)13.5 ± 4.812.7 (9.5–17.3)p < 0.01
Lymphocytes (103/µL)0.6–3.41.9 ± 1.21.6 (1.2–2.2)3.4 ± 2.92.5 (1.3–2.8)0.451
Monocytes (103/µL)0.00–0.900.8 ± 0.50.6 (0.5–0.9)2.0 ± 0.50.9 (0.6–1.2)p < 0.05
Platelets (103/µL)140–420211 ± 74207 (159–247)226 ± 103221 (190–247)0.067
NLR (103/µL)0.87–4.155.4 ± 3.35.3 (3.1–6.4)9.6 ± 6.48.3 (5.3–9.7)p < 0.001
PLR (103/µL)47–198119 ± 44119 (92–144)146 ± 71142 (86–162)0.340
LMR (103/µL)2.45–8.773.1 ± 1.93.1 (1.8–3.4)2.9 ± 2.32.6 (1.3–3.2)0.287
SII (103/µL)142–808995 ± 535977 (588–1216)2081 ± 12441970 (1188–2234)p < 0.0001
Abbreviations: GCS—Glasgow Coma Scale score, SD—standard deviation, Med—median, iqr—interquartile range, WBC—white blood cells, NLR—neutrophil/lymphocyte ratio, PLR—platelet/lymphocyte ratio, LMR—lymphocyte/monocyte ratio, SII—systemic immune inflammation index.
Table
Table 4. Significant correlations between GCS, NLR, PLR, LMR, and SII.
Table 4. Significant correlations between GCS, NLR, PLR, LMR, and SII.
NLRPLRLMRSII
GCSR = −0.318
p < 0.0001		R = −0.047
0.621		R = 0.153
0.105		R = −0.357
p < 0.0001
R Spearman rank correlation coefficient.
Table
Table 5. ROC analysis.
Table 5. ROC analysis.
AUC (95% CI)Cut-OffSensitivitySpecificity
NLR0.748 (0.615–0.880)0.1540.900.56
PLR0.570 (0.424–0.715)0.2440.400.82
LMR0.422 (0.285–0.559)0.2270.350.84
SII0.816 (0.696–0.935)0.1180.950.57
Abbreviations: AUC—area under the curve, CI—confidence interval, NLR—neutrophil/lymphocyte ratio, PLR—platelet/lymphocyte ratio, LMR—lymphocyte/monocyte ratio, SII—systemic immune inflammation index.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Defort, P.; Retkowska-Tomaszewska, N.; Kot, M.; Jarmużek, P.; Tylutka, A.; Zembron-Lacny, A. Inflammatory Predictors of Prognosis in Patients with Traumatic Cerebral Haemorrhage: Retrospective Study. J. Clin. Med. 2022, 11, 705. https://doi.org/10.3390/jcm11030705

AMA Style

Defort P, Retkowska-Tomaszewska N, Kot M, Jarmużek P, Tylutka A, Zembron-Lacny A. Inflammatory Predictors of Prognosis in Patients with Traumatic Cerebral Haemorrhage: Retrospective Study. Journal of Clinical Medicine. 2022; 11(3):705. https://doi.org/10.3390/jcm11030705

Chicago/Turabian Style

Defort, Piotr, Natalia Retkowska-Tomaszewska, Marcin Kot, Paweł Jarmużek, Anna Tylutka, and Agnieszka Zembron-Lacny. 2022. "Inflammatory Predictors of Prognosis in Patients with Traumatic Cerebral Haemorrhage: Retrospective Study" Journal of Clinical Medicine 11, no. 3: 705. https://doi.org/10.3390/jcm11030705

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop