Next Article in Journal
The Potential of SGLT-2 Inhibitors in the Treatment of Polycystic Ovary Syndrome: The Current Status and Future Perspectives
Next Article in Special Issue
Cerebrospinal Fluid–Basic Concepts Review
Previous Article in Journal
Sulforaphane-Induced Cell Mitotic Delay and Inhibited Cell Proliferation via Regulating CDK5R1 Upregulation in Breast Cancer Cell Lines
Previous Article in Special Issue
Cerebrospinal Fluid Biomarkers in Differential Diagnosis of Multiple Sclerosis and Systemic Inflammatory Diseases with Central Nervous System Involvement
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Inflammatory Markers as Predictors of Shunt Dependency and Functional Outcome in Patients with Aneurysmal Subarachnoid Hemorrhage

1
Department of Neurosurgery, The Neuroscience Centre Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark
2
Department of Neuroanaesthesiology, The Neuroscience Centre Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark
3
Department of Neuroscience, University of Copenhagen, 2200 Copenhagen, Denmark
*
Author to whom correspondence should be addressed.
Biomedicines 2023, 11(4), 997; https://doi.org/10.3390/biomedicines11040997
Submission received: 24 January 2023 / Revised: 16 March 2023 / Accepted: 20 March 2023 / Published: 23 March 2023

Abstract

:
The mechanisms underlying post-hemorrhagic hydrocephalus (PHH) development following subarachnoid hemorrhage (SAH) are not fully understood, which complicates informed clinical decisions regarding the duration of external ventricular drain (EVD) treatment and prevents the prediction of shunt-dependency in the individual patient. The aim of this study was to identify potential inflammatory cerebrospinal fluid (CSF) biomarkers of PHH and, thus, shunt-dependency and functional outcome in patients with SAH. This study was a prospective observational study designed to evaluate inflammatory markers in ventricular CSF. In total, 31 Patients with SAH who required an EVD between June 2019 and September 2021 at the Department of Neurosurgery, Rigshospitalet, Copenhagen, Denmark, were included. CSF samples were collected twice from each patient and analyzed for 92 inflammatory markers via proximity extension assay (PEA), and the prognostic ability of the markers was investigated. In total, 12 patients developed PHH, while 19 were weaned from their EVD. Their 6-month functional outcome was determined with the modified Rankin Scale. Of the 92 analyzed inflammatory biomarkers, 79 were identified in the samples. Seven markers (SCF, OPG, LAP TGFβ1, Flt3L, FGF19, CST5, and CSF1) were found to be predictors of shunt dependency, and four markers (TNFα, CXCL5, CCL20, and IL8) were found to be predictors of functional outcome. In this study, we identified promising inflammatory biomarkers that are able to predict (i) the functional outcome in patients with SAH and (ii) the development of PHH and, thus, the shunt dependency of the individual patients. These inflammatory markers may have the potential to be employed as predictive biomarkers of shunt dependency and functional outcome following SAH and could, as such, be applied in the clinic.

1. Introduction

Non-traumatic subarachnoid hemorrhage (SAH) due to aneurism rupture is associated with high mortality and morbidity [1]. Despite recent advances in aneurysm treatment and neurocritical care, more than one third of SAH patients still develop an unfavorable long-term functional outcome [2]. Several complications contribute to the risk for an unfavorable outcome, including rebleeding, cerebral vasospasms, post-hemorrhagic hydrocephalus (PHH), seizures, delayed ischemic neurological deficits, cortical spreading depression, delayed cerebral ischemia, infections, cardiomyopathy, and pulmonary edema [2,3]. Shortly after the aneurysmal rupture, the intracranial pressure (ICP) rises [4,5], and blood extravasates into the cerebrospinal fluid (CSF) spaces, which can cause acute hydrocephalus with further elevation of ICP due to mechanical blockage to CSF circulation, CSF hypersecretion, or meningeal inflammation [6,7,8]. This condition can be treated with external ventricular drainage (EVD) [9,10]. The relief of CSF pressure through EVD is often necessary for many days, and it comes with a risk of bacterial central nervous system (CNS) infection through the percutaneous drain access; it has been shown that the risk of infection increases with the duration of EVD [11,12,13,14]. Intuitively, a CNS infection superimposed on the vascular event is likely to adversely affect the outcome, and the need for keeping the EVD for CSF pressure relief weighed against removal of the drainage as early as possible to reduce the infection risk is often a difficult balance in clinical practice. Accordingly, there is no consensus on recommendations for drainage duration or for EVD weaning via gradual increase in the drainage height vs. prompt closure of the EVD [9,10,15,16].
Although many of the SAH patients are successfully weaned from EVD due to the subsequent normalization of CSF flow dynamics, a considerable portion of these patients fail weaning from EVD and need to undergo shunt surgery due to development of PHH. This patient group is referred to as ‘shunt-dependent’. A recent review finds that the reported risk of shunt-dependency—i.e., PHH—after SAH varies between 8 and 63% [17]. This wide variation implies uncertainty about the prediction of the risk for chronic PHH requiring shunt insertion. Even though several publications have investigated clinical, radiological, and treatment features as predictive indicators for PHH following SAH, no biomarkers are available to actually predict shunt dependency [18,19,20,21,22,23].
Immediately after acute brain injury and hemorrhages such as SAH, local and systemic inflammatory responses trigger inflammatory signaling cascades accompanied by the activation and infiltration of immune cells of the brain, microglia, and astrocytes at the site of injury, as well as damage-associated pattern molecules (DAMPs) [24]. The induction of inflammatory cascades upon intracerebral hemorrhage may contribute significantly to the development of PHH, both via promotion of CSF hypersecretion in the CSF-secreting tissue in the ventricles, the choroid plexus, and via impaired reabsorption of the CSF as a result of scarring and obstruction of CSF drainage pathways [25]. In recent years, CSF biomarkers in SAH have come into interest, as several studies have found an increase in inflammatory markers in patients developing PHH secondary to SAH and other types of intracerebral hemorrhages [8,26,27], although these findings were often based on the analysis of only a small subset of inflammatory markers in their cohorts. A recent systematic review of such studies illustrated an increase in the inflammatory markers interleukin 6 (IL6), interleukin 18 (IL18), and vascular endothelial growth factor (VEGF) in patients with PHH compared to control subjects [26]. We subsequently employed a multiplex analysis to demonstrate a range of elevated inflammatory markers in the CSF from patients with SAH compared to control subjects undergoing clipping of non-ruptured aneurisms [8]. However, to our knowledge, whether CSF levels of inflammatory markers can predict the shunt dependency or outcome of patients suffering from SAH remains unresolved. [25,26].
In order to minimize the duration of EVD treatment and to find a clinical tool to support the prediction of shunt-dependency and/or the functional outcome of the patients, we aimed to identify potential inflammatory biomarkers in CSF samples that were obtained from patients with SAH in the acute phase of the disease and again before EVD removal or shunt insertion by analyzing the samples with a panel of 92 inflammation-related proteins.

2. Materials and Methods

2.1. Patients and Sample Collection

This study was a prospective observational study that was designed to evaluate inflammatory markers in CSF. Patients with SAH who required an EVD between June 2019 and September 2021 at the Department of Neurosurgery, The Neuroscience Centre, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark, were sought and included. Oral and written informed consent were obtained from all patients or next of kin depending on the capacity of the patients. The study was approved by the ethics committee of the Capital Region of Denmark (H-19001474, 22 March 2019) and the Danish Data Protection Agency (VD-2019-210, 8 April 2019). Other analyses from a proportion of the samples have previously been reported [8,28]. CSF was collected from 31 patients (median age: 60 y; range: 27–77 y; F/M: 21/10) through the EVD into a sterile collection tube. The first sample (‘start sample’, approximately 5 mL CSF) was obtained in the acute phase, either within 24 h of ictus (n = 22) or as soon as possible thereafter (n = 9). The last sample (‘end sample’, 1–2 mL CSF) was obtained just before removal of the EVD, and days between the two samples were registered (average time 17.9 days, range 5–30 days). Upon collection, the CSF samples were centrifuged at 2000× g for 10 min immediately after sampling and aliquoted into polypropylene microtubes (Sarstedt, Nürnbrecht, Germany) before storage at −80 °C [29]. Nineteen patients did not develop chronic hydrocephalus and were weaned off EVD (‘weaned’ group), and twelve patients underwent ventriculoperitoneal shunt surgery due to PHH (‘shunt’ group). The functional outcome as classified by the modified Rankin Scale (mRS) was assessed for each patient six months after discharge from the neurosurgical department. Twelve patients had a favorable 6-month functional outcome (mRS 0–2), and twelve patients had an unfavorable functional outcome (mRS 3–6). Seven patients were lost to follow-up.

2.2. Inflammatory Panel

The CSF samples were analyzed for 92 inflammatory markers via a proximity extension assay (PEA; Olink Bioscience, Uppsala, Sweden, https://www.bioxpedia.com/wp-content/uploads/2020/04/1029-v1.3-Inflammation-Panel-Content.pdf, accessed on 15 March 2023) at BioXpedia A/S (Aarhus, Denmark) [30]. In brief, the PEA technique is a targeted protein screening and employs a pair of oligonucleotide-conjugated antibodies to detect each of the tested markers in a CSF volume of 1 µL. Each of the oligonucleotide antibody-pairs contains unique DNA sequences which allow hybridization only to each other. Upon detection, the oligonucleotides are brought into close proximity, hybridize, and enable DNA polymerization, which produces a PCR sequence. The PCR sequence is then amplified and quantified via real-time qPCR. Ct values from the qPCR are translated into a relative quantification unit, Normalized Protein eXpression (NPX) via computation. The NPX values are an arbitrary unit on a log2 scale. The limit of detection (LOD), which is the lowest measurable level of an individual inflammatory marker, was defined by three times the standard deviation of each marker over the background signal. The assay was performed in a blinded fashion regarding the patient groups and the study endpoint. Of the 92 inflammatory markers, 13 were not identified (below the LOD).

2.3. Statistical Analysis

Clinical characteristics were collected from the electronic patient chart and handled via REDCap version 12.0.33 (Research Electronic Data Capture, Vanderbilt University, Nashville, TN, USA). All data processing and statistical analyses were carried out by using R version 4.1.0 (R Core Team, Vienna, Austria). For each of the prediction models, patients were stratified into two groups, and only proteins that were detected in at least five patients of each group were included to obtain the predictive potential of each protein. The prognostic ability was investigated for the start sample, the end sample, and the average change between the start and end sample. Average change per day was calculated by identifying the change in the marker between those two samples for each participant and dividing it with the time between the start and end sample. The prognostic ability was investigated by using area under the curve (AUC) estimates that were calculated from a receiver operating characteristics (ROC) curve. The markers were labelled as acceptable predictors if the lower 95% confidence limit of the AUC was above 0.7 [31]. For those identified as acceptable predictors, the optimal cut-off was identified by using the Youden’s cut-off, which reflected the point where the combined sensitivity and specificity was the highest [32]. The cut-off, area under the curve, specificity, sensitivity, and corresponding 95% confidence intervals are presented for each of the inflammatory markers.

3. Results

The presence of inflammatory markers in the CSF obtained from SAH patients was detected with a proximity extension assay that consisted of a panel of 92 inflammatory markers [30], out of which 79 were identified in the patient CSF.

3.1. Shunt Dependency

To determine whether any of the identified inflammatory markers in the CSF obtained in either the first or the last sample could serve as a predictor of shunt dependency, we divided the patients into two groups: those who received a VP shunt (n = 12; F/M: 11/1; age 53–77 years) and those who were successfully weaned from EVD (n = 19; F/M: 10/9; age 27–74 years), and we compared the levels of quantified inflammatory markers between these two groups. In total, 57 inflammatory markers were detected in the start samples in at least 5 patients from both the VP-shunted group and the EVD group, 58 for the end samples, and 54 from both (Supplemental Table S1). Figure 1 illustrates AUC for the prediction of shunt dependency for each of the detected inflammatory markers. Overall, 7 of these reached a lower confidence limit ≥ 0.7 and could be considered valid predictors of shunt dependency (Table 1). For start samples, no markers were identified as valid predictors. For end samples, 6 markers were identified as valid predictors with AUCs (with 95% confidence interval (CI)) of 0.88 (0.77–1.00) for stem cell factor (SCF), 0.86 (0.74–0.99) for osteoprotegerin (OPG), 0.86 (0.72–0.99) for latency-associated peptide-transforming growth factor beta 1 (LAP TGFβ1), 0.86 (0.72–0.99) for FMS-related tyrosine kinase 3 ligand (Flt3L), 0.86 (0.73–1.00) for fibroblast growth factor 19 (FGF19), and 0.86 (0.72–0.99) for cystatin-D (CST5) (Table 1 and Figure 2). For the average change per day, (calculated from the change in the marker between the start and the end samples normalized to the number of days between the samples), only colony stimulating factor 1 (CSF1) was identified as a valid predictor with an AUC of 0.91 (0.79–1.00) (Table 1 and Figure 2).

3.2. Functional Outcome

To reveal potential biomarkers that could serve as predictors of the functional outcome following the hemorrhagic event, we quantified the inflammatory marker content of the start and end CSF samples and plotted these according to the functional outcome of the patient six months after ictus. In total, 7 of the 31 patients were lost to follow-up and were not included in this analysis. Again, only markers that were detected in at least five samples in both groups were included in the analysis. In total, 59 markers were detected in the start samples, 57 markers were detected in the end samples, and 54 markers showed up for both, thereby allowing computation of the average change per day (Supplemental Table S2). A total of 12 patients had a favorable functional outcome (mRS 0–2, see Section 2), and 12 had an unfavorable outcome (mRS 3–6). Figure 3 illustrates AUC for the prediction of the outcome for each of the detected inflammatory markers. Overall, 4 of these reached a lower confidence limit ≥ 0.7 and could be considered valid predictors of functional outcome following SAH (Table 2). For start samples, no markers were identified as valid predictors. For end samples, 3 markers were identified as valid predictors with AUCs (95% CI) of 0.86 (0.70–1.00) for tumor necrosis factor-alpha (TNFα), 0.91 (0.78–1) for C-X-C motif chemokine ligand 5 (CXCL5), and 0.92 (0.79–1.00) for chemokine C-C motif ligand 20 (CCL20) (Table 2 and Figure 4). For average change per day, only interleukin 8 (IL8) was identified as a valid predictor with an AUC of 0.86 (0.71–1.00) (Table 2 and Figure 4).

4. Discussion

4.1. Hydrocephalus and Shunt Dependency

The main finding of this study is that potential inflammatory biomarkers from an early point after the SAH bleed may predict the outcome related to shunt dependency and functional outcome. Although many of the SAH patients are successfully weaned from EVD due to the subsequent normalization of CSF flow dynamics, a considerable proportion of these patients fail weaning from EVD and need to undergo shunt surgery. Several publications have investigated clinical, radiological, and treatment features as predictive indicators for shunt-dependent hydrocephalus following SAH [18,19,20,21,22,23]. A recent review lists a large number of potential risk factors including patient age and gender; Glasgow Coma and Hunt-Hess scores at ictus; radiological signs of acute hydrocephalus; radiological assessment of bleeding severity including the extent of blood in the ventricular system; need for EVD insertion; CSF drainage volume; EVD weaning; vasospasm and/or cerebral infarction; and occurrence of fever [17]. It has also been proposed that surgical vs. endovascular aneurysm ligation could influence the risk for PHH, but a recent meta-analysis from 2019 disputes this [33]. Moreover, increased levels in the CSF of S100B [22] protein, erythrocyte count, and interleukin [34] might also predict the risk for shunt dependency after SAH. In recent years, neuroinflammation upon SAH as a contributing factor of PHH has come into focus, and a range of publications has demonstrated elevated levels of various inflammatory factors in CSF from patients with SAH compared to that obtained from healthy individuals [8,26,27]. Of these, IL1β, TNFα, and IL6/8 are key inflammatory markers that are often detected in the CSF from SAH patients [26,35,36,37,38,39] and may contribute to cerebral vasospasm [39]. Regrettably, IL1β is not included in the tested panel of inflammatory markers (https://www.bioxpedia.com/wp-content/uploads/2020/04/1029-v1.3-Inflammation-Panel-Content.pdf, accessed on 15 March 2023). Although we previously detected IL6 and IL8 (amongst other inflammatory markers) as elevated in CSF from SAH patients, TNFα was not significantly elevated following Bonferroni correction for multiple samples [8]. Nevertheless, none of these three biomarkers appears to serve as a predictor for shunt-dependency, and only IL8 and TNFα served as predictors for functional outcome in the present study. This finding contrasts that of an earlier study that indicated elevated IL6 levels in the early post-SAH period as a useful diagnostic tool for predicting shunt dependency in patients with acute PHH [40] and functional outcome [36].
In the present study, we found that the inflammatory markers SCF, OPG, LAP TGFβ1 Flt3L, FGF19, CST5, and CSF1 could be used as predictors of shunt dependency. In patients who received a shunt, the average levels were higher in the end samples for the majority of these markers (SCF, OPG, LAP TGFβ1 Flt3L, FGF19, CST5) compared to patients who could be weaned from CSF diversion. The average change in the levels of CSF1 from the start to the end sample differed between the patient group that developed PHH compared to the patient group that did not. This relationship suggests that the level of hemorrhage-related inflammation dictates the PHH formation and, thus, shunt dependency, which is in line with an earlier demonstration of the acute inflammatory responses occurring in intracerebral hemorrhage being a possible contributing factor of PHH [8,25,41].

4.2. Functional Outcome

Cerebral vasospasm and delayed cerebral ischemia (DCI) are major factors for survival and functional outcome following SAH and have, for many years, been the main focus in SAH research [42,43,44,45]. Clinical parameters such as Hunt and Hess scores, lower modified Fisher grade, the absence of intracerebral hematoma, intact pupillary light reflexes, and clinical improvement before aneurysm treatment can be used as early clinical predictors of functional outcome and favorable/non-favorable prognosis [46]. However, other factors such as inflammation have also been shown to be useful predictors of poor outcome in patients with SAH. Systemic inflammatory response syndrome score has been found to be an independent prognostic factor of poor functional outcome following SAH as assessed by mRs scores [47,48,49]. Of the inflammatory predictors, elevated CSF chemokine levels and inflammatory mediators showed an association with an unfavorable clinical outcome after SAH [35,50,51].
Here, we found that the inflammatory markers TNFα, CXCL5, CCL20, and IL8 were predictors of functional outcome. The first three markers were assessed as being at higher levels in the end sample of the group with an unfavorable functional outcome compared to those with a favorable functional outcome, and the last marker was assessed as having a larger average daily difference detected in the group with an unfavorable outcome.

4.3. Neuroinflammation and Therapeutic Targets

An imbalance of CSF drainage and CSF production may promote hydrocephalus. Emerging evidence suggests that neuroinflammation can mediate both reduced CSF drainage and increased CSF production [50]. The impaired CSF drainage can arise from fibrosis in the brain tissue, e.g., the arachnoid granulations in adults, thereby obstructing the outflow routes [52] and disordered arterial pulsatility may further contribute to a decreased CSF flow and, thus, modulate the fine-tuned CSF clearance apparatus [53]. In recent years, it has come into focus that neuroinflammation following a hemorrhagic event might promote hypersecretion of CSF, thus contributing to the development of PHH [25]. In rat models, it was proposed that following intraventricular hemorrhage (IVH), the inflammatory cascade resulted in hyperactivation of the ion (and fluid) transporters in the choroid plexus tissue and, hence, increased fluid transportation into the ventricles [8,41,54]. Upon intracerebral hemorrhage, the brain tissue is exposed to blood components. This results in the activation of microglia, which are the resident immune cells within the brain, and the recruitment of peripheral leukocytes (macrophages) [50]. Signals from the damaged brain region can lead to the activation of the systemic immune system, which is then followed by immunosuppression [50]. The early activation of the immune system may lead to secondary injury, resulting in further activation of microglia; secretion of proinflammatory cytokines, ROS, and matrix metalloproteinases; and neuronal injury [25,55], and a similar cascade seems probable following SAH. Cytokines are a class of small proteins that act as signaling molecules that regulate inflammation and modulate cellular activities such as growth, survival, and differentiation [56]. Cytokines do not only act to increase neuroinflammation upon brain hemorrhage but also contribute to PHH through fibrosis and scarring of the leptomeninges and arachnoid granulations, as well as protein deposition in the periventricular tissue [57,58].
The majority of the predictive markers found in this study belong to the cytokine family (TGFβ1, SCF, Flt3L, CSF1, TNFα), with one receptor of tumor necrosis factor (TNF) receptor superfamily (OPG). We detected a small number of chemokines (CXCL5, CCL20, and IL8), which are responsible for the induction of cell migration of immune cells upon inflammation [56]; a proteinase inhibitor controlling proteolytic activity during inflammation (CST5); and a growth factor involved in the processes of the adaptive and innate immune system (FGF-19). The binding of a cytokine or chemokine ligand to its receptor results in the activation of the receptor, which, in turn, triggers a cascade of signaling events that regulates a variety of cellular functions connected to immune response and inflammation [56].
Our finding that the predictive markers are mostly increased in the unfavorable outcome-groups (VP-shunt and mRS3–6) suggests that there is increased inflammation in these patients compared to the patients with a more favorable outcome. Hence, the inflammatory machinery could be a pharmacological therapeutic target to improve the outcome for patients following SAH. Several clinical trials with the immunosuppressant statin simvastatin have been conducted on patients with SAH to improve the outcome, although meta-analyses conducted on trial data found no beneficial effects of the treatment [59,60]. The statins and another immunosuppressant, cyclosporine, which also failed to prevent the unfavorable outcome following SAH [61], may not target the immune pathways involved in the acute inflammatory response following SAH. Thus, these immunosuppressants do not reverse the acute inflammatory cascade following bleeding and thereby fail to prevent the downstream negative effects of SAH resulting in unfavorable outcome for the patients.
The predictive markers found in this study do not only relate to neuroinflammation but may be present during systemic inflammation in general. Although these markers are known to be involved in inflammatory and/or immune responses, some of the molecules are implicated in other pathophysiological processes such as fibrosis in inflammatory conditions, i.e., liver, myocardial, and pulmonary fibrosis [62,63,64,65]. We cannot rule out that some of the markers are elevated as a result of fibrotic tissue formation around the EVD and, therefore, may vary with the number of days with EVD insertion. The varying number of days between the start and the end samples, which is indicative of days with EVD insertion (17.9 days, range 5–30), is therefore a limitation of this study. The sampling times were chosen for ethical reasons to prevent infection by puncturing the sterile drain system, thus only sampling when the patients underwent intervention as a part of their treatment. The increased risk of drain/CSF infection by repeated CSF sampling precluded daily sampling, which would have been optimal for a time profile in marker development. Instead, we have chosen to calculate an average per-day change in marker levels, but the change in marker levels may not be evenly spread over the study days. Since our CSF analysis was conducted, customized PEA panels including additional important neuroinflammatory markers (e.g., IL1β and IL1-RA) have become available and may provide further insight into neuroinflammation in connection with various neurological conditions [66].

5. Conclusions

In conclusion, we report a distinct inflammatory profile of the CSF obtained from SAH patients receiving a shunt and those with an unfavorable outcome. These inflammatory markers may have the potential to be employed as predictive biomarkers of shunt dependency and functional outcome following SAH and could, as such, be applied in the clinic. Future validation in a larger cohort is required to establish the relevance of the detected predictors in this study. In addition, increased insight into the neuroinflammatory pathways and downstream effectuators associated with SAH could reveal if a more specific treatment, aiming to decrease inflammation in SAH patients, could benefit patients in terms of better outcome and fewer cases of PHH. We here focused on inflammatory markers in the CSF, but elevated BBB permeability to immune cells could be equally important for shunt dependency and unfavorable outcome and a relevant target for future studies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines11040997/s1, Supplemental Table S1: Predictors of Shunt Dependency (not reaching lower confidence limit of ≥0.7); Supplemental Table S2: Predictors of Functional Outcome (not reaching lower confidence limit of ≥0.7).

Author Contributions

Conceptualization, N.R., M.H.O., T.C., N.M. and M.J.; data curation, N.R., M.H.O. and T.C.; formal analysis, M.H.O., N.M. and M.J.; funding acquisition, N.M. and M.J.; methodology, N.R., M.H.O., T.C., N.M. and M.J.; writing—original draft, N.R., M.H.O., N.M. and M.J.; writing—review and editing, T.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Novo Nordisk Foundation (Tandem grant NNF17OC0024718 to N.M. and M.J.).

Institutional Review Board Statement

The study was approved by the Ethics Committee of the Capital Region of Denmark (H-19001474, 22 March 2019) and the Danish Data Protection Agency (VD-2019-210, 8 April 2019).

Informed Consent Statement

Consent was obtained from all subjects involved in the study.

Data Availability Statement

Anonymized data are available upon reasonable request to the corresponding author.

Acknowledgments

We are grateful for the contribution from all the clinical staff at the Department of Neurosurgery and the Neurointensive Unit, Rigshospitalet, who participated in the collection of CSF samples.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Olsen, M.H.; Orre, M.; Leisner, A.C.W.; Rasmussen, R.; Bache, S.; Welling, K.; Eskesen, V.; Møller, K. Delayed cerebral ischaemia in patients with aneurysmal subarachnoid haemorrhage: Functional outcome and long-term mortality. Acta Anaesthesiol. Scand. 2019, 63, 1191–1199. [Google Scholar] [CrossRef] [PubMed]
  2. Claassen, J.; Park, S. Spontaneous subarachnoid haemorrhage. Lancet 2022, 400, 846–862. [Google Scholar] [CrossRef] [PubMed]
  3. Suarez, J.I.; Tarr, R.W.; Selman, W.R. Aneurysmal subarachnoid hemorrhage. N. Engl. J. Med. 2006, 354, 387–396. [Google Scholar] [CrossRef] [PubMed]
  4. Cahill, J.; Cahill, W.J.; Calvert, J.W.; Calvert, J.H.; Zhang, J.H. Mechanisms of early brain injury after subarachnoid hemorrhage. J. Cereb. Blood Flow Metab. 2006, 26, 1341–1353. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Grote, E.; Hassler, W. The critical first minutes after subarachnoid hemorrhage. Neurosurgery 1988, 22, 654–661. [Google Scholar] [CrossRef] [PubMed]
  6. Papaioannou, V.; Czosnyka, Z.; Czosnyka, M. Hydrocephalus and the neuro-intensivist: CSF hydrodynamics at the bedside. Intensive Care Med. Exp. 2022, 10, 20. [Google Scholar] [CrossRef] [PubMed]
  7. Chen, S.; Luo, J.; Reis, C.; Manaenko, A.; Zhang, J. Hydrocephalus after Subarachnoid Hemorrhage: Pathophysiology, Diagnosis, and Treatment. Biomed. Res. Int. Hindawi 2017, 2017, 8584753. [Google Scholar] [CrossRef] [Green Version]
  8. Lolansen, S.D.; Rostgaard, N.; Barbuskaite, D.; Capion, T.; Olsen, M.H.; Norager, N.H.; Vilhardt, F.; Andreassen, S.N.; Toft-Bertelsen, T.L.; Ye, F.; et al. Posthemorrhagic hydrocephalus associates with elevated inflammation and CSF hypersecretion via activation of choroidal transporters. Fluids Barriers CNS 2022, 19, 62. [Google Scholar] [CrossRef]
  9. Capion, T.; Lilja-Cyron, A.; Juhler, M.; Mathiesen, T.I.; Wetterslev, J. Prompt closure versus gradual weaning of external ventricular drainage for hydrocephalus in adult patients with aneurysmal subarachnoid haemorrhage: A systematic review. BMJ Open 2020, 10, e040722. [Google Scholar] [CrossRef]
  10. Capion, T.; Lilja-Cyron, A.; Bartek, J.; Forsse, A.; Logallo, N.; Juhler, M.; Mathiesen, T. Discontinuation of External Ventricular Drainage in Patients with Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage—A Scandinavian Multi-institutional Survey. Acta Neurochir. 2020, 162, 1363–1370. [Google Scholar] [CrossRef]
  11. Zhu, Y.; Wen, L.; You, W.; Wang, Y.; Wang, H.; Li, G.; Chen, Z.; Yang, X. Influence of Ward Environments on External Ventricular Drain Infections: A Retrospective Risk Factor Analysis. Surg. Infect. 2021, 22, 211–216. [Google Scholar] [CrossRef] [PubMed]
  12. Champey, J.; Mourey, C.; Francony, G.; Pavese, P.; Gay, E.; Gergele, L.; Manet, R.; Velly, L.; Bruder, N.; Payen, J.-F. Strategies to reduce external ventricular drain–related infections: A multicenter retrospective study. J. Neurosurg. 2019, 130, 2034–2039. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Walek, K.W.; Leary, O.P.; Sastry, R.; Asaad, W.F.; Walsh, J.M.; Horoho, J.; Mermel, L.A. Risk factors and outcomes associated with external ventricular drain infections. Infect. Control. Hosp. Epidemiol. 2022, 43, 1859–1866. [Google Scholar] [CrossRef] [PubMed]
  14. Camacho, E.F.; Boszczowski, I.; Basso, M.; Jeng, B.C.P.; Freire, M.P.; Guimarães, T.; Teixeira, M.J.; Costa, S.F. Infection rate and risk factors associated with infections related to external ventricular drain. Infection 2011, 39, 47–51. [Google Scholar] [CrossRef] [PubMed]
  15. Palasz, J.; D’Antona, L.; Farrell, S.; Elborady, M.A.; Watkins, L.D.; Toma, A.K. External ventricular drain management in subarachnoid haemorrhage: A systematic review and meta-analysis. Neurosurg. Rev. 2021, 45, 365–373. [Google Scholar] [CrossRef] [PubMed]
  16. Rao, S.S.; Chung, D.Y.; Wolcott, Z.; Sheriff, F.; Khawaja, A.M.; Lee, H.; Guanci, M.M.; Leslie-Mazwi, T.M.; Kimberly, W.T.; Patel, A.B.; et al. Intermittent CSF drainage and rapid EVD weaning approach after subarachnoid hemorrhage: Association with fewer VP shunts and shorter length of stay. J. Neurosurg. 2019, 132, 1583–1588. [Google Scholar] [CrossRef]
  17. Yang, Y.-C.; Yin, C.-H.; Chen, K.-T.; Lin, P.-C.; Lee, C.-C.; Liao, W.-C.; Chen, J.-S. Prognostic Nomogram of Predictors for Shunt-Dependent Hydrocephalus in Patients with Aneurysmal Subarachnoid Hemorrhage Receiving External Ventricular Drain Insertion: A Single-Center Experience and Narrative Review. World Neurosurg. 2021, 150, e12–e22. [Google Scholar] [CrossRef]
  18. Dorai, Z.; Hynan, L.S.; Kopitnik, T.A.; Samson, D. Factors Related to Hydrocephalus after Aneurysmal Subarachnoid Hemorrhage. Neurosurgery 2003, 52, 763–771; discussion 769–771. [Google Scholar] [CrossRef]
  19. Brisman, J.L.; Berenstein, A. Factors Related to Hydrocephalus after Aneurysmal Subarachnoid Hemorrhage. Neurosurgery 2004, 54, 1031. [Google Scholar] [CrossRef]
  20. Chan, M.; Alaraj, A.; Calderon, M.; Herrera, S.R.; Gao, W.; Ruland, S.; Roitberg, B.Z. Prediction of ventriculoperitoneal shunt dependency in patients with aneurysmal subarachnoid hemorrhage. J. Neurosurg. 2009, 110, 44–49. [Google Scholar] [CrossRef] [Green Version]
  21. Rincon, F.; Gordon, E.; Starke, R.M.; Buitrago, M.M.; Fernandez, A.; Schmidt, J.M.; Claassen, J.; Wartenberg, K.E.; A Frontera, J.; Seder, D.; et al. Predictors of long-term shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. Clinical article. J Neurosurg. 2010, 113, 774–780. [Google Scholar] [CrossRef] [PubMed]
  22. Brandner, S.; Xu, Y.; Schmidt, C.; Emtmann, I.; Buchfelder, M.; Kleindienst, A. Shunt-dependent hydrocephalus following subarachnoid hemorrhage correlates with increased S100B levels in cerebrospinal fluid and serum. Acta Neurochir. Suppl. 2012, 114, 217–220. [Google Scholar] [PubMed]
  23. Lai, L.; Morgan, M.K. Predictors of in-hospital shunt-dependent hydrocephalus following rupture of cerebral aneurysms. J. Clin. Neurosci. 2013, 20, 1134–1138. [Google Scholar] [CrossRef]
  24. Chaudhry, S.R.; Hafez, A.; Jahromi, B.R.; Kinfe, T.M.; Lamprecht, A.; Niemelä, M.; Muhammad, S. Role of Damage Associated Molecular Pattern Molecules (DAMPs) in Aneurysmal Subarachnoid Hemorrhage (aSAH). Int. J. Mol. Sci. 2018, 19, 2035. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Karimy, J.K.; Reeves, B.C.; Damisah, E.; Duy, P.Q.; Antwi, P.; David, W.; Wang, K.; Schiff, S.J.; Limbrick, D.D., Jr.; Alper, S.L.; et al. Inflammation in acquired hydrocephalus: Pathogenic mechanisms and therapeutic targets. Nat. Rev. Neurol. 2020, 16, 285–296. [Google Scholar] [CrossRef]
  26. Lolansen, S.D.; Rostgaard, N.; Oernbo, E.K.; Juhler, M.; Simonsen, A.H.; MacAulay, N. Inflammatory Markers in Cerebrospinal Fluid from Patients with Hydrocephalus: A Systematic Literature Review. Dis. Markers 2021, 2021, 8834822. [Google Scholar] [CrossRef]
  27. Jabbarli, R.; Pierscianek, D.; Oppong, M.D.; Sato, T.; Dammann, P.; Wrede, K.H.; Kaier, K.; Köhrmann, M.; Forsting, M.; Kleinschnitz, C.; et al. Laboratory biomarkers of delayed cerebral ischemia after subarachnoid hemorrhage: A systematic review. Neurosurg. Rev. 2020, 43, 825–833. [Google Scholar] [CrossRef]
  28. Toft-Bertelsen, T.L.; Barbuskaite, D.; Heerfordt, E.K.; Lolansen, S.D.; Andreassen, S.N.; Rostgaard, N.; Olsen, M.H.; Norager, N.H.; Capion, T.; Rath, M.F.; et al. Lysophosphatidic acid as a CSF lipid in posthemorrhagic hydrocephalus that drives CSF accumulation via TRPV4-induced hyperactivation of NKCC1. Fluids Barriers CNS 2022, 19, 69. [Google Scholar] [CrossRef]
  29. del Campo, M.; Mollenhauer, B.; Bertolotto, A.; Engelborghs, S.; Hampel, H.; Simonsen, A.H.; Kapaki, E.; Kruse, N.; Le Bastard, N.; Lehmann, S.; et al. Recommendations to standardize preanalytical confounding factors in Alzheimer’s and Parkinson’s disease cerebrospinal fluid biomarkers: An update. Biomark. Med. 2012, 6, 419–430. [Google Scholar] [CrossRef]
  30. Assarsson, E.; Lundberg, M.; Holmquist, G.; Björkesten, J.; Thorsen, S.B.; Ekman, D.; Eriksson, A.; Dickens, E.R.; Ohlsson, S.; Edfeldt, G.; et al. Homogenous 96-Plex PEA Immunoassay Exhibiting High Sensitivity, Specificity, and Excellent Scalability. PLoS ONE 2014, 9, e95192. [Google Scholar] [CrossRef] [Green Version]
  31. Swets, J.A. Measuring the accuracy of diagnostic systems. Science 1988, 240, 1285–1293. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Youden, W.J. Index for rating diagnostic tests. Cancer 1950, 3, 32–35. [Google Scholar] [CrossRef] [PubMed]
  33. Zeng, J.; Qin, L.; Wang, D.; Gong, J.; Pan, J.; Zhu, Y.; Sun, T.; Xu, K.; Zhan, R. Comparing the Risk of Shunt-Dependent Hydrocephalus in Patients with Ruptured Intracranial Aneurysms Treated by Endovascular Coiling or Surgical Clipping: An Updated Meta-Analysis. World Neurosurg. 2019, 121, e731–e738. [Google Scholar] [CrossRef] [PubMed]
  34. Lenski, M.; Biczok, A.; Huge, V.; Forbrig, R.; Briegel, J.; Tonn, J.-C.; Thon, N. Role of Cerebrospinal Fluid Markers for Predicting Shunt-Dependent Hydrocephalus in Patients with Subarachnoid Hemorrhage and External Ventricular Drain Placement. World Neurosurg. 2019, 121, e535–e542. [Google Scholar] [CrossRef] [PubMed]
  35. Lenski, M.; Huge, V.; Briegel, J.; Tonn, J.-C.; Schichor, C.; Thon, N. Interleukin 6 in the Cerebrospinal Fluid as a Biomarker for Onset of Vasospasm and Ventriculitis After Severe Subarachnoid Hemorrhage. World Neurosurg. 2017, 99, 132–139. [Google Scholar] [CrossRef]
  36. Chaudhry, S.R.; Stoffel-Wagner, B.; Kinfe, T.M.; Güresir, E.; Vatter, H.; Dietrich, D.; Lamprecht, A.; Muhammad, S. Elevated systemic IL-6 levels in patients with aneurysmal subarachnoid hemorrhage is an unspecific marker for post-SAH complications. Int. J. Mol. Sci. 2017, 18, 2580. [Google Scholar] [CrossRef] [Green Version]
  37. Kaestner, S.; Dimitriou, I. TGF Beta1 and TGF Beta2 and Their Role in Posthemorrhagic Hydrocephalus Following SAH and IVH. J. Neurol. Surg. Part A Central Eur. Neurosurg. 2013, 74, 279–284. [Google Scholar] [CrossRef]
  38. Lv, S.Y.; Wu, Q.; Liu, J.P.; Shao, J.; Wen, L.; Xue, J.; Zhang, X.S.; Zhang, Q.R.; Zhang, X. Levels of Interleukin-1β, Interleukin-18, and Tumor Necrosis Factor-α in Cerebrospinal Fluid of Aneurysmal Subarachnoid Hemorrhage Patients May Be Predictors of Early Brain Injury and Clinical Prognosis. World Neurosurg. 2018, 111, e362–e373. [Google Scholar] [CrossRef]
  39. Lucke-Wold, B.; Dodd, W.; Motwani, K.; Hosaka, K.; Laurent, D.; Martinez, M.; Dugan, V.; Chalouhi, N.; Lucke-Wold, N.; Barpujari, A.; et al. Investigation and modulation of interleukin-6 following subarachnoid hemorrhage: Targeting inflammatory activation for cerebral vasospasm. J. Neuroinflamm. 2022, 19, 228. [Google Scholar] [CrossRef]
  40. Wostrack, M.; Reeb, T.; Martin, J.; Kehl, V.; Shiban, E.; Preuss, A.; Ringel, F.; Meyer, B.; Ryang, Y.-M. Shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage: The role of intrathecal interleukin-6. Neurocrit. Care 2014, 21, 78–84. [Google Scholar] [CrossRef]
  41. Karimy, J.K.; Zhang, J.; Kurland, D.B.; Theriault, B.C.; Duran, D.; Stokum, J.A.; Furey, C.G.; Zhou, X.; Mansuri, M.S.; Montejo, J.; et al. Inflammation-dependent cerebrospinal fluid hypersecretion by the choroid plexus epithelium in posthemorrhagic hydrocephalus. Nat. Med. 2017, 23, 997–1003. [Google Scholar] [CrossRef] [PubMed]
  42. Mahlamäki, K.; Rautalin, I.; Korja, M. Case Fatality Rates of Subarachnoid Hemorrhage Are Decreasing with Substantial between-Country Variation: A Systematic Review of Population-Based Studies between 1980 and 2020. Neuroepidemiology 2022, 56, 402–412. [Google Scholar] [CrossRef] [PubMed]
  43. Dayyani, M.; Sadeghirad, B.; Grotta, J.C.; Zabihyan, S.; Ahmadvand, S.; Wang, Y.; Guyatt, G.H.; Amin-Hanjani, S. Prophylactic Therapies for Morbidity and Mortality After Aneurysmal Subarachnoid Hemorrhage: A Systematic Review and Network Meta-Analysis of Randomized Trials. Stroke 2022, 53, 1993–2005. [Google Scholar] [CrossRef] [PubMed]
  44. Ma, X.; Lan, F.; Zhang, Y. Associations between C-reactive protein and white blood cell count, occurrence of delayed cerebral ischemia and poor outcome following aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis. Acta Neurol. Belg. 2021, 121, 1311–1324. [Google Scholar] [CrossRef] [PubMed]
  45. Oka, F.; Chung, D.Y.; Suzuki, M.; Ayata, C. Delayed Cerebral Ischemia After Subarachnoid Hemorrhage: Experimental-Clinical Disconnect and the Unmet Need. Neurocrit. Care 2020, 32, 238–251. [Google Scholar] [CrossRef] [PubMed]
  46. de Winkel, J.; Cras, T.Y.; Dammers, R.; van Doormaal, P.-J.; van der Jagt, M.; Dippel, D.W.J.; Roozenbeek, B. Early predictors of functional outcome in poor-grade aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis. BMC Neurol. 2022, 22, 239. [Google Scholar] [CrossRef]
  47. Rass, V.; Gaasch, M.; Kofler, M.; Schiefecker, A.J.; Ianosi, B.-A.; Rhomberg, P.; Beer, R.; Pfausler, B.; Gizewski, E.R.; Thomé, C.; et al. Systemic Inflammatory Response Syndrome as Predictor of Poor Outcome in Nontraumatic Subarachnoid Hemorrhage Patients. Crit. Care Med. 2018, 46, e1152–e1159. [Google Scholar] [CrossRef]
  48. Yoshimoto, Y.; Tanaka, Y.; Hoya, K. Acute systemic inflammatory response syndrome in subarachnoid hemorrhage. Stroke 2001, 32, 1989–1993. [Google Scholar] [CrossRef] [Green Version]
  49. Yu, T.; Wang, Z. Use of A Systemic Inflammatory Response Index to Predict Non-Traumatic Non-Aneurysmal Subarachnoid Hemorrhage Patient Outcomes. J. Stroke Cerebrovasc. Dis. 2022, 31, 106863. [Google Scholar] [CrossRef]
  50. Holste, K.G.; Xia, F.; Ye, F.; Keep, R.F.; Xi, G. Mechanisms of neuroinflammation in hydrocephalus after intraventricular hemorrhage: A review. Fluids Barriers CNS 2022, 19, 28. [Google Scholar] [CrossRef]
  51. Sokół, B.; Woźniak, A.; Jankowski, R.; Jurga, S.; Wąsik, N.; Shahid, H.; Grześkowiak, B. HMGB1 Level in Cerebrospinal Fluid as a Marker of Treatment Outcome in Patients with Acute Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage. J. Stroke Cerebrovasc. Dis. 2015, 24, 1897–1904. [Google Scholar] [CrossRef] [PubMed]
  52. Massicotte, E.M.; Del Bigio, M.R. Human arachnoid villi response to subarachnoid hemorrhage: Possible relationship to chronic hydrocephalus. J. Neurosurg. 1999, 91, 80–84. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Wagshul, M.E.; Eide, P.K.; Madsen, J.R. The pulsating brain: A review of experimental and clinical studies of intracranial pulsatility. Fluids Barriers CNS 2011, 8, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Robert, S.M.; Reeves, B.C.; Kiziltug, E.; Duy, P.Q.; Karimy, J.K.; Mansuri, M.S.; Marlier, A.; Allington, G.; Greenberg, A.B.; DeSpenza, T.; et al. The choroid plexus links innate immunity to CSF dysregulation in hydrocephalus. Cell 2023, 186, 764–785.e21. [Google Scholar] [CrossRef]
  55. Keep, R.F.; Hua, Y.; Xi, G. Intracerebral haemorrhage: Mechanisms of injury and therapeutic targets. Lancet Neurol. 2012, 11, 720–731. [Google Scholar] [CrossRef] [Green Version]
  56. Ramesh, G.; MacLean, A.G.; Philipp, M.T. Cytokines and chemokines at the crossroads of neuroinflammation, neurodegeneration, and neuropathic pain. Mediators Inflamm. 2013, 2013, 480739. [Google Scholar] [CrossRef] [Green Version]
  57. Liao, F.; Li, G.; Yuan, W.; Chen, Y.; Zuo, Y.; Rashid, K.; Zhang, J.H.; Feng, H.; Liu, F. LSKL peptide alleviates subarachnoid fibrosis and hydrocephalus by inhibiting TSP1-mediated TGF-β1 signaling activity following subarachnoid hemorrhage in rats. Exp. Ther. Med. 2016, 12, 2537–2543. [Google Scholar] [CrossRef] [Green Version]
  58. Kuo, L.-T.; Huang, A. The Pathogenesis of Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage. Int. J. Mol. Sci. 2021, 22, 5050. [Google Scholar] [CrossRef]
  59. Lin, J.; Liu, H.; Jiang, J.; Jia, C.; Zhang, B.; Gao, X. Clinical evidence of efficacy of simvastatin for aneurysmal subarachnoid hemorrhage. J. Int. Med. Res. 2017, 45, 2128–2138. [Google Scholar] [CrossRef] [Green Version]
  60. Liu, J.; Chen, Q. Effect of statins treatment for patients with aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis of observational studies and randomized controlled trials. Int. J. Clin. Exp. Med. 2015, 8, 7198–7208. [Google Scholar]
  61. Manno, E.M.; Gress, D.R.; Ogilvy, C.S.; Stone, C.M.; Zervas, N.T. The safety and efficacy of cyclosporine A in the prevention of vasospasm in patients with Fisher grade 3 subarachnoid hemorrhages: A pilot study. Neurosurgery 1997, 40, 289–293. [Google Scholar] [CrossRef] [PubMed]
  62. Habibie, H.; Adhyatmika, A.; Schaafsma, D.; Melgert, B.N. The role of osteoprotegerin (OPG) in fibrosis: Its potential as a biomarker and/or biological target for the treatment of fibrotic diseases. Pharmacol Ther. 2021, 228, 107941. [Google Scholar] [CrossRef]
  63. Howe, M.D.; Furr, J.W.; Munshi, Y.; Roy-O’Reilly, M.A.; Maniskas, M.E.; Koellhoffer, E.C.; d’Aigle, J.; Sansing, L.; McCullough, L.; Urayama, A. Transforming growth factor-β promotes basement membrane fibrosis, alters perivascular cerebrospinal fluid distribution, and worsens neurological recovery in the aged brain after stroke. GeroScience 2019, 41, 543–559. [Google Scholar] [CrossRef] [PubMed]
  64. Justet, A.; Ghanem, M.; Boghanim, T.; Hachem, M.; Vasarmidi, E.; Jaillet, M.; Vadel, A.; Joannes, A.; Mordant, P.; Bonniaud, P.; et al. FGF19 Is Downregulated in Idiopathic Pulmonary Fibrosis and Inhibits Lung Fibrosis in Mice. Am. J. Respir. Cell Mol. Biol. 2022, 67, 173–187. [Google Scholar] [CrossRef] [PubMed]
  65. Kazakov, A.; Meier, T.; Werner, C.; Hall, R.; Klemmer, B.; Körbel, C.; Lammert, F.; Maack, C.; Böhm, M.; Laufs, U. C-kit + resident cardiac stem cells improve left ventricular fibrosis in pressure overload. Stem Cell Res. 2015, 15, 700–711. [Google Scholar] [CrossRef] [Green Version]
  66. Dyhrfort, P.; Wettervik, T.S.; Clausen, F.; Enblad, P.; Hillered, L.; Lewén, A. A Dedicated 21-Plex Proximity Extension Assay Panel for High-Sensitivity Protein Biomarker Detection Using Microdialysis in Severe Traumatic Brain Injury: The Next Step in Precision Medicine? Neurotrauma Rep. 2023, 4, 25–40. [Google Scholar] [CrossRef]
Figure 1. Inflammatory markers as predictors of shunt dependency. The figure illustrates AUC and 95% CI for prediction of shunt dependency for each of the detected inflammatory markers. The markers (highlighted/black bars) that reached the required level of ≥0.7 of the lower confidence limit were accepted as prognostic markers of development of hydrocephalus following SAH. EVD weaned: n = 19; VP-shunt. n = 12; AUC: area under the curve; CI: confidence interval.
Figure 1. Inflammatory markers as predictors of shunt dependency. The figure illustrates AUC and 95% CI for prediction of shunt dependency for each of the detected inflammatory markers. The markers (highlighted/black bars) that reached the required level of ≥0.7 of the lower confidence limit were accepted as prognostic markers of development of hydrocephalus following SAH. EVD weaned: n = 19; VP-shunt. n = 12; AUC: area under the curve; CI: confidence interval.
Biomedicines 11 00997 g001
Figure 2. Valid predictors of shunt dependency. The abundance of the inflammatory markers in the start vs. end sample is shown in the left column, with AUCs (AUC: arbitrary units with 95% CI), and the daily change in abundance illustrated in the right column). Dashed lines show Youden’s threshold as a cutoff for sensitivity and specificity for the given marker. EVD weaned: n = 19; VP-shunt: n = 12; AUC: area under the curve; CI: confidence interval.
Figure 2. Valid predictors of shunt dependency. The abundance of the inflammatory markers in the start vs. end sample is shown in the left column, with AUCs (AUC: arbitrary units with 95% CI), and the daily change in abundance illustrated in the right column). Dashed lines show Youden’s threshold as a cutoff for sensitivity and specificity for the given marker. EVD weaned: n = 19; VP-shunt: n = 12; AUC: area under the curve; CI: confidence interval.
Biomedicines 11 00997 g002
Figure 3. Inflammatory markers as predictors of functional outcome. The figure illustrates AUC and 95% CI for prediction of functional outcome for each of the detected inflammatory markers. The markers (highlighted/black bars) that reached the required level of ≥0.7 of the lower confidence limit were accepted as prognostic markers of functional outcome following SAH. Favorable functional outcome (mRS 0–2): n = 12; unfavorable functional outcome (mRS 3–6) n = 12; AUC: area under the curve; CI: confidence interval.
Figure 3. Inflammatory markers as predictors of functional outcome. The figure illustrates AUC and 95% CI for prediction of functional outcome for each of the detected inflammatory markers. The markers (highlighted/black bars) that reached the required level of ≥0.7 of the lower confidence limit were accepted as prognostic markers of functional outcome following SAH. Favorable functional outcome (mRS 0–2): n = 12; unfavorable functional outcome (mRS 3–6) n = 12; AUC: area under the curve; CI: confidence interval.
Biomedicines 11 00997 g003
Figure 4. Valid predictors of functional outcome. The abundance of the inflammatory markers in the start vs. end sample is shown in the left column, with AUCs (AUC: arbitrary units with 95% CI), and the daily change in abundance illustrated in the right column). Dashed lines show Youden’s threshold as a cutoff for sensitivity and specificity for the given marker. Favorable functional outcome (mRS 0–2): n = 12; unfavorable functional outcome (mRS 3–6): n = 12; AUC: area under the curve; CI: confidence interval.
Figure 4. Valid predictors of functional outcome. The abundance of the inflammatory markers in the start vs. end sample is shown in the left column, with AUCs (AUC: arbitrary units with 95% CI), and the daily change in abundance illustrated in the right column). Dashed lines show Youden’s threshold as a cutoff for sensitivity and specificity for the given marker. Favorable functional outcome (mRS 0–2): n = 12; unfavorable functional outcome (mRS 3–6): n = 12; AUC: area under the curve; CI: confidence interval.
Biomedicines 11 00997 g004
Table 1. Acceptable predictors of hydrocephalus measured by the Olink 96 inflammation panel. Inflammatory markers with AUC values above 0.7. AUC: area under the curve; CI: confidence interval; SD: standard deviation.
Table 1. Acceptable predictors of hydrocephalus measured by the Olink 96 inflammation panel. Inflammatory markers with AUC values above 0.7. AUC: area under the curve; CI: confidence interval; SD: standard deviation.
NameTimeEVD Weaned Mean (SD) [n]VP-Shunt
Mean (SD) [n]
AUC (95% CI)Cut-OffSensitivitySpecificity
SCFEnd4.18 (0.74) [19]5.29 (0.65) [12]0.88 (0.77–1.00)4.331.000.63
OPGEnd9.37 (1.13) [19]10.83 (0.80) [12]0.86 (0.74–0.99)10.200.830.79
LAP TGFβ1End3.83 (0.63) [19]4.65 (0.57) [12]0.86 (0.72–0.99)4.051.000.68
Flt3LEnd7.90 (0.76) [19]8.76 (0.37) [12]0.86 (0.72–0.99)8.490.830.79
FGF19End4.42 (0.63) [19]5.31 (0.61) [12]0.86 (0.73–1.00)4.970.830.89
CST5End5.29 (0.25) [19]5.51 (0.06) [12]0.86 (0.72–0.99)5.381.000.68
CSF1Daily Change0.05 (0.04) [19]−0.02 (0.03) [12]0.91 (0.79–1.00)0.010.920.89
Table 2. Acceptable predictors of functional outcome measured by the Olink 96 inflammation panel. Inflammatory markers with AUC values above 0.7. AUC: area under the curve; CI: confidence interval; SD: standard deviation.
Table 2. Acceptable predictors of functional outcome measured by the Olink 96 inflammation panel. Inflammatory markers with AUC values above 0.7. AUC: area under the curve; CI: confidence interval; SD: standard deviation.
NameTimemRS 0–2 -
Mean (SD) [n]
mRS 3–6 -
Mean (SD) [n]
AUC (95% CI)Cut-OffSensitivitySpecificity
TNFαEnd1.84 (0.88) [12]3.26 (2.01) [12]0.86 (0.7–1.00)1.661.000.67
CXCL5End8.08 (1.23) [12]10.56 (1.75) [12]0.91 (0.78–1.00)9.110.920.83
CCL20End6.33 (0.85) [11]8.12 (1.69) [12]0.92 (0.79–1.00)7.260.920.91
IL8Daily Change0.01 (0.06) [12]−0.08 (0.06) [12]0.86 (0.71–1.00)−0.011.000.58
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Rostgaard, N.; Olsen, M.H.; Capion, T.; MacAulay, N.; Juhler, M. Inflammatory Markers as Predictors of Shunt Dependency and Functional Outcome in Patients with Aneurysmal Subarachnoid Hemorrhage. Biomedicines 2023, 11, 997. https://doi.org/10.3390/biomedicines11040997

AMA Style

Rostgaard N, Olsen MH, Capion T, MacAulay N, Juhler M. Inflammatory Markers as Predictors of Shunt Dependency and Functional Outcome in Patients with Aneurysmal Subarachnoid Hemorrhage. Biomedicines. 2023; 11(4):997. https://doi.org/10.3390/biomedicines11040997

Chicago/Turabian Style

Rostgaard, Nina, Markus Harboe Olsen, Tenna Capion, Nanna MacAulay, and Marianne Juhler. 2023. "Inflammatory Markers as Predictors of Shunt Dependency and Functional Outcome in Patients with Aneurysmal Subarachnoid Hemorrhage" Biomedicines 11, no. 4: 997. https://doi.org/10.3390/biomedicines11040997

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