Skip to main content
Download PDF
  • Systematic Review
  • Open access
  • Published:

Key prognostic risk factors linked to poor functional outcomes in cerebral venous sinus thrombosis: a systematic review and meta-analysis

BMC Neurology volume 25, Article number: 52 (2025) Cite this article

Abstract

Background

Cerebral venous sinus thrombosis is a rare stroke with several clinical manifestations. Several studies have identified prognostic risk factors associated with poor functional outcomes and established predictive models. This systematic review and meta-analysis assessed the overall effect size of all prognostic risk factors.

Methods

A systematic review was conducted to explore all prognostic risk factors in studies published from the initial to June 2024 among 5 Databases included PubMed / Medline, Scopus, EBSCOhost, Web of Science, and Cochran Library. The quality of the methodology was analyzed using the Newcastle–Ottawa Scale. Data analysis was performed using the Statistical Package for Social Sciences (SPSS) version 29.

Results

Sixty-four studies involving 18,958 participants with a mean age of 38.46 years and females 63.03% were included in the quantitative meta-analysis. Functional outcomes were primarily measured using the Modified Rankin Scale (mRS), with scores ≥ 2 or ≥ 3 indicating poor outcomes in 35.00% and 60.00% of studies, respectively.

For general information, age (InOR = 0.98, 95% CI 0.53–1.43), intracranial hemorrhage (OR = 3.79, 95% CI 2.77–5.20), and ischemic infarction (OR = 3.18, 95% CI 2.40–4.23) were associated with poor functional outcomes. For general and neurological symptoms, headache (OR = 0.22, 95% CI 0.17–0.29), seizure (OR = 2.74, 95% CI 1.76–4.27), focal deficit (OR = 4.72, 95% CI 3.86–5.78), coma (OR = 11.60, 95% CI 6.12–21.98), and consciousness alteration (OR = 7.07, 95% CI 4.15–12.04) were outstanding factors. The blood biomarkers of NLR (log OR = 1.72, 95% CI 0.96–2.47), lymphocytes (Cohen’s d = -0.63, 95 CI -0.78—-0.47), and D-dimer (lnOR = 1.34, 95% CI 0.87–1.80) were the three most frequently reported factors. Parenchymal lesion (OR = 4.71, 95% CI 1.12–19.84) and deep cerebral venous thrombosis (OR = 6.30, 95% CI 2.92–13.63) in radiological images were two frequently reported factors. CVST patients with cancer (OR = 3.87, 95% CI 2.95–5.07) or high blood glucose levels (OR = 3.52, 95% CI 1.61–7.68) were associated with poor functional outcomes. In the meta-regression analysis, ischemic infarction (P = 0.032), consciousness alteration (P < 0.001), and NLR (P = 0.015) were associated with mRS prediction.

Conclusions

Pooled effect sizes revealed that ischemic infarction, headache, neurological focal deficit, lymphopenia, and cancer were significantly associated with poor functional outcomes, with low to moderate heterogeneity. Consciousness alterations/deterioration and deep cerebral venous thrombosis were also significant prognostic factors, albeit with substantial heterogeneity. The meta-regression analysis showed that the effect sizes of consciousness alterations/deterioration and NLR increased with worsening mRS scores. Other notable risk factors included age, intracranial hemorrhage, seizures, coma, D-dimer, parenchymal lesions, and hyperglycemia. This systematic review provides a comprehensive overview of the prognostic risk factors for poor functional outcomes in patients undergoing CVST, which can guide clinical decision-making and future research.

Trial registration

This systematic review and meta-analysis has been registered with INPLASY (International Platform of Registered Systematic Review and Meta-analysis Protocols), and the registration number is INPLASY202480072. The registration period is 14 August 2024.

Peer Review reports

Background

Cerebral venous sinus thrombosis (CVST) is a special type of cerebrovascular disease, that was observed only in 9 patients out of a series of 70 cases (12%) in a prospective population-based clinical study for ischemic stroke of unusual cause in 2001 [1]. The CVST counted 0.5–3% of all stroke subtypes [2,3,4]. The estimated incidence rate of CVST varied and increased, according to the reference, from 3–4 / 1,000,000 / annually in 2014 [5] of the stroke guideline to 13.9–20.2 / 1,000,000 / annually in 2020 in a population-based cohort study in the USA [6]. A wide range of factors can lead to thrombotic occlusion of the dural venous sinuses or cerebral veins, resulting in the development of various neurological symptoms. Blocking the drainage of intracranial blood circulation can leads to focal cerebral edema, venous cerebral infarction, intracranial hypertension, or even intracranial hemorrhage [7]. The patients’ presentations at the first onset of CVST varied from mild to severe, which challenged the accuracy and time of diagnosis [8]. The most frequently reported clinical manifestations include headache, seizures, neurological deficit, coma, consciousness/mental disorders, and even visual disorders provoked by clotting in the ophthalmic vein [9]. The median age at first onset was 41–50.9 years based on the latest nationwide or population-based research [10, 11]. Females, especially during pregnancy and postpartum or when taking contraceptive medication, were more likely to suffer from CVST than males [12], with a reported proportion of 75.3% [13]. There are different instrument tools for functional outcome measurement in CVST, including the Institutes of Health Stroke Scale (NIHSS), Modified Rankin Scale (mRS), Activities of Daily Living (ADL), and Glasgow Outcome Scale (GOS). The division scale of the dichotomy of functional outcome into good (favorable) and poor (unfavorable) was based on different studies. However, the epidemiology of CVST has demonstrated that the clinical or functional outcome of patients with CVST was 79%−91% in a favorable situation after long-term follow-up [5, 14]. Many studies have established models to identify potential risk factors for predicting functional outcomes of CVST during short- or long-term follow-up. Based on different research settings, sociodemographic backgrounds, complications involved, and perspectives, the output of the studies varied from one to another. However, systematic reviews, literature reviews, and meta-analyses that studied CVST mostly focused on diagnosis [15], different medical treatments [15, 16], incidence rate [11], or complications such as heparin-induced thrombocytopenia [17] /COVID-19 [18]. This systematic review and meta-analysis will help summarize all identified risk factors and provide a full perspective on CVST and functional outcome assessment.

Methods

This systematic review and meta-analysis followed the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement. The study protocol was registered in the INPLASY (International Platform of Registered Systematic Review and Meta-analysis Protocols) database (INPLASY202480072) [19].

Inclusion criteria

Studies that met the following criteria were included: (1) human studies were considered with patients aged ≥ 18 years. (2) patients diagnosed with CVST by any neuroradiological examination listed below (e.g., intra-arterial angiography (DSA), magnetic resonance imaging (MRI), MR angiography (MRA), MR venography (MRV), computed tomography venography (CTV), or CT angiography (CTA)). (3) functional outcome measurements should be reported in the research either in different assessment tools (for example, NIHSS, mRS, and ADL), which could reflect physical function in different aspects. (4) Cross-sectional studies, cohort studies, and case–control studies will be involved in the review. (5) Papers concerning the epidemiology, clinical manifestations, treatment, or prognosis, especially with either logistic regression, Cox regression, or any inferential-statistical analysis, were eligible. (6) All publications were written in English from the initial day to June 1, 2024.

Exclusion criteria

We excluded studies with the following criteria: (1) studies that did not provide empirical data (e.g., reviews, commentaries, opinions, case reports, interviews, theoretical papers, conference abstracts, letters, and gray literature). (2) studies with retinal vein clotting, subarachnoid hemorrhage, or spontaneous intracerebral hemorrhage were excluded. (3) Other disease-related CVST included traumatic brain injury, COVID-19 vaccination, intracranial meningioma, meningitis, cerebral venous system hypoplasia, or malformation. (4) Other thrombus diseases, such as venous thromboembolism (VTE), include deep venous thrombosis (DVT) and pulmonary embolism (PE). (5) Studies looked at how different clinical treatments (medication, rehabilitation, nursing approaches, etc.) affected disease prognosis and consequences. (6) Studies with smaller sample sizes (e.g., fewer than 30 patients), whereas case reports or series were not considered. (7) The full text was not available.

Search strategy

The search strategy was designed in accordance with the “Cochrane Guidelines for Systematic Reviews of Health Promotion and Public Health Interventions.” There are 5 electronic databases, including PubMed / Medline, Scopus, EBSCOhost, Web of Science, and Cochran Library, were used to search for articles. The selected keywords were based on the PICO strategy. Using Scopus as an example, the search strategy was as follows: ((TITLE-ABS-KEY(("cerebral" OR "intracranial") AND ("venous" OR "vein*" OR "sinus*") AND ("thrombosis" OR "thrombus")) AND TITLE-ABS-KEY("prognosis" OR "prognostic" OR "risk factors" OR "predictive factors") AND TITLE-ABS-KEY("National Institutes of Health Stroke Scale" OR "NIHSS" OR "mRS" OR "Modified Rankin Scale" OR "ADL" or "activities of daily living" OR "Barthel index" OR "functional" OR "function" OR "scale") AND ALL("odds ratio" OR "risk ratio" OR "hazard ratio" OR "logistic regression" or "Cox" OR "regression")) AND ( LIMIT-TO ( DOCTYPE,"ar")) AND ( LIMIT-TO ( LANGUAGE, "English"). The search strategies were adjusted according to the different databases.

Study selection

All studies from the selected databases were imported into EndNote 20 for duplication checking. Title/abstract screening was independently performed by two authors (LL and SF). Any discrepancies were resolved by a consultant with the corresponding author as a third party. To ensure accuracy and consistency, Cohen’s kappa was reported as agreement for the full text involved in data extraction and quality evaluation before the consensus meeting.

Data extraction and synthesis

A data extraction form was designed in the EXCEL, including Country, Study ID, Author/Year, Subtype of Participants, Sample Size, Functional Outcome Measurement, Level of Cutoff Points between Good and Poor, and prognostic risk factors. Data from full-text articles were extracted by three authors independently (LL, SF, and WW). LL for double checking and reassuring the data.

The prognostic risk factors associated with poor functional outcomes were determined in patients undergoing CVST. All factors were calculated and reported as a percentage of studies in which each risk factor appeared, which hierarchically mapped prognostic risk factors across the included studies.

Statistical analysis

The data extracted from each study were analyzed using SPSS version 29.0. (IBM). More than three studies that reported the same outcome measures were included in the quantitative analysis; otherwise, a narrative approach was adopted.

Continuous variables were extracted as mean and standard deviation (SD). Any articles with data not reported as mean and SD but as median and IQR will be converted into mean and SD following the guidelines by Wan et al. [20].

Twelve statistics were used to determine the heterogeneity of the studies, and a threshold of 50% was applied to distinguish between homogeneity and heterogeneity. The fixed-effects model was used for I2 ≤ 50, and the random-effects model was used for I2 > 50 [21]. To visualize the pooled effect size, forest plots were generated. The quality of the bias is presented in the Funnel Plot. The Bubble Plot displays the meta-regression between particular prognostic risk factors and subtype analysis in functional outcome measurement. The effect sizes were indicated as minor (d = 0.2), medium (d = 0.5), and large (d = 0.8) based on hierarchical pooling. The output of P < 0.05 as statistically significant.

Quality analysis

LL and SF evaluated the quality of the included studies using the Newcastle–Ottawa scale as a guideline. The cohort, case–control, and cross-sectional studies were all included in the Newcastle–Ottawa scale. Stars were assigned to each observational category. All included studies were independently assessed by LL and SF, and any disagreements were discussed with the corresponding author as a third-party. The categories of quality in evidence were reported as very good (9–10 points), good (7–8 points), satisfactory (5–6 points), and unsatisfactory (0–4 points).

Results

Study selection

The five databases yielded 1117 studies, with an additional 16 discovered through citation searches. After EndNote duplication verification and manual double-checking, the 757 studies were left for title or abstract screening. Following full text reading, 64 publications met the eligibility criteria and were selected for final quantitative analysis in this systematic review (Fig. 1). The PRISMA flow diagram was based on the guideline [22]. The consistency between the two assessors was evaluated, and Cohen’s kappa was 0.929 (P < 0.001).

Fig. 1
figure 1

PRISMA flow diagram

Full size image

Study characteristics

Among the 64 included studies [4, 23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85], 12 were cross-sectional studies, 51 were cohort studies, and one was a case–control study. All studies had sample sizes ranging from 30 to 1,281 participants (a total of 18,957). The publication dates are from 2005 to 2024. The countries that implemented the research included 23 studies in China, 9 studies involving more than two countries for collaboration, 6 studies in India, and 6 studies in Turkey. Among the included studies, 41 were conducted in developing countries, and 23 were conducted in developed countries. No randomization method was used in any study (Table 1).

Table 1 Demographics and publication status
Full size table

Study population

Among the 64 included studies, 52 reported the mean age of the patient population [23, 24, 26, 28,29,30, 32,33,34,35,36,37,38, 41,42,43,44,45,46,47, 49,50,51,52,53,54,55,56, 59,60,61,62,63,64,65,66, 68, 70,71,72,73,74,75,76,77,78,79, 81,82,83,84,85], and 37 studies reported on the age difference between good and poor functional outcomes [23, 24, 28, 32,33,34,35,36,37, 39, 41,42,43,44,45,46, 49, 50, 52, 54, 59, 61, 64,65,66, 70,71,72, 75,76,77, 79, 80, 82,83,84,85]. The pooled mean age was 38.46 years (range 18–87), and the pooled effect size of age was 0.98 (95% CI: 0.53–1.43, I2 = 0.987, P = 0.00) (Fig. 2).

Fig. 2
figure 2

Meta-analysis with forest plot (a) and funnel plot (b) of pooled effect size on age

Full size image

Of the 57 studies reporting data on sex distribution, females accounted for 63.03%, and 6 studies reported males with pooled effect size in Odds Ratio (OR) of 2.14 (95% CI: 0.52–8.77, I2 = 0.922, P = 0.290) as a specific risk factor for poor functional outcomes but without statistical significant [28, 33, 52, 56, 65, 78].

Functional outcome measurements

All studies reported functional outcome measurements of patients using the mRS at discharge or short-term (e.g., 3 months) or long-term (12 months) follow-up, and dichotomized as good (favorable) or poor (unfavorable) according to the study design. The mRS was classified into 7 levels representing different severe neurological deficits in patients from different settings. Patients with mRS scores of 0 as no symptoms; 1 as mild symptoms but no disability (0–1 also considered as independent); 2 as minimal disability; 3 as moderate disability and could walk without help but required daily assistance; 4 as moderately severe disability and could not walk without help as well as need daily assistance; 5 as severe disability with bedridden and continuous nursing care (3–5 as dependent); and 6 as dead [39, 43]. Different studies have categorized the mRS measurement of functional outcomes into binary variables based on different score levels. The percentage of studies reporting an mRS score of ≥ 1, 2, 3, or 6 as the division of poor clinical outcomes was in 3.33%, 35.00%, 60.00%, and 8.33%, respectively (Table 1).

Prognostic risk factors

The prognostic risk factors associated with poor functional outcomes were extracted from all included studies and are presented as percentages. The categories were organized by demographic setting, stroke type, clinical manifestation (general & neurological specific), examination of blood biomarkers, radiological images foreseen, and complications (Table 2).

Table 2 Prognostic risk factors associated with poor functional outcomes
Full size table

Intracranial hemorrhage and ischemic attack

There were 25 studies reported intracranial hemorrhage as a prognostic risk factor for poor function [24, 28, 30,31,32,33, 36, 39, 44,45,46, 49, 50, 55, 56, 62, 66, 68, 70, 76,77,78, 83,84,85], and 8 studies about the ischemic infarction [32, 39, 41, 62, 70, 78, 84, 85]. The pooled data show that the effect size (OR) of intracranial hemorrhage was 3.79 (95% CI: 2.77–5.20, I2 = 0.761, P = 0.00) and 3.18 (95% CI:2.40–4.23, I2 = 0.200, P = 0.00) in ischemic infarction for effect size, which was represented in the fix-effects model (Fig. 3) (Fig. 4a and b).

Fig. 3
figure 3

Meta-analysis with forest plot (a) and funnel plot (b) of pooled effect size on intracranial hemorrhage

Full size image
Fig. 4
figure 4

Meta-analysis with forest plot (a), funnel plot (b) and bubble plot (c) of pooled effect size on ischemic infarction

Full size image

Geneal vs. neurologically specific manifestations

Seizure and headache are the two most frequently reported clinical manifestations in patients undergoing CVST. The proportions were 21.88% (14/64) and 12.50% (8/64), respectively. The pooled effect size (OR) was 2.74 (95% CI: 1.76–4.27, I2 = 0.796, P = 0.00) in seizure [31, 33, 34, 38, 42, 44, 51, 54, 55, 57, 60,61,62, 70] (Fig. 5). The homogeneity test for headache was not statistically significant (P = 0.000), and the fix-effects model was used with a pooled effect size (OR) of 0.22 (95% CI: 0.17–0.29, I2 = 0.00, P = 0.00) [23, 32, 35, 65, 66, 75, 84, 85] (Appendix 1).

Fig. 5
figure 5

Meta-analysis with forest plot (a) and funnel plot (b) of pooled effect size on seizure

Full size image

The neurological specific manifestation list below was organized in percentage descent and included focal deficit (13/64) [23, 39, 44, 49, 51, 52, 56, 61, 62, 65, 66, 84, 85], coma, which was measured in the Glasgow Coma Scale (12/64) [31, 40, 43,44,45,46, 50, 56, 76, 77, 82, 83], consciousness disorder (change/deterioration) by physician examination (7/60) [28, 32, 39, 54, 61, 62, 66], and NIHSS Assessment (4/60) [33, 44, 50, 70]. The pooled effect size for focal deficit (OR = 4.72, 95% CI: 3.86–5.78, I2 = 0.388, P = 0.00) was calculated in the fix-effects model with lower heterogeneity (Fig. 6). Coma and consciousness were reported to have pooled effect size (OR) of 11.60 (95% CI: 6.12–21.98; I2 = 0.854, P = 0.00), and 7.07 (95% CI: 4.15–12.04, I2 = 0.740, P = 0.00), respectively (Appendix 2). The meta-regression analysis of consciousness disorder revealed that the effect size was increasing among the functional measurements in mRS (P < 0.001 in Wald Chi-Square) (Fig. 7). The pooled effect size (log OR) in the NIHSS Assessment was not statistically significant, with an output of 1.69 (95% CI: −0.09–3.47, I2 = 0.987, P = 0.060) in SPSS.

Fig. 6
figure 6

Meta-analysis with forest plot (a) and funnel plot (b) of pooled effect size on focal deficit

Full size image
Fig. 7
figure 7

Meta-analysis with forest plot (a), funnel plot (b) and bubble plot (c) of pooled effect size on consciousness

Full size image

Inflammatory biomarkers of blood

The neutrophil-to-lymphocyte ratio (NLR), absolute lymphocyte count, D-dimer level, and C-reactive protein (CRP)/High-sensitive C-reactive protein level, were four prognostic risk factors reported in more than 3 studies. The statistically significant pooled effect size in NLR (log OR = 1.72, 95% CI: 0.96–2.47, I2 = 0.963, P = 0.00) [26, 46, 48, 71, 72, 76, 82] (Fig. 8a and b), D-dimer (log OR = 1.34, 95% CI: 0.87–1.80, I2 = 0.778, P = 0.00) [30, 32, 71, 83], and lymphocyte (Cohen’s d = −0.63, 95% CI: −0.78 to −0.47, I2 = 0.184, P = 0.000) [71, 76, 77, 82, 83] were reported (Appendix 3 and 4). The meta-regression analysis showed that the effect size of NLR increased as mRS score worsened (Wald Chi-Square P = 0.015) (Fig. 8c).

Fig. 8
figure 8

Meta-analysis with forest plot (a), funnel plot (b) and bubble plot (c) of pooled effect size on nlr

Full size image

Radiology image

The position of the thrombus in a specific area of the cerebral venous system was detected during radiological examination. Deep cerebral vein (venous system) thrombosis has been reported in 8 studies [31, 40, 45, 46, 61,62,63, 76]. The pooled effect size (OR) was 6.30 (95% CI: 2.92–13.63, I2 = 0.719, P = 0.00) (Appendix 5). Parenchymal lesions were reported in 5 studies, and the pooled effect size (OR) was 4.71 (95% CI: 1.12–19.84, I2 = 0.900, P = 0.03) [28, 31, 37, 39, 54] (Appendix 6).

Complication

Cancer (6/64) and hyperglycemia (5/64) were statistically significant prognostic risk factors for poor functional outcome. The pooled effect size (OR) was 3.87 (95% CI: 2.95–5.07, I2 = 0.424, P = 0.00) for cancer (Fig. 9) [39, 50, 56, 63, 79] and 3.52 (95% CI: 1.61–7.68, I2 = 0.937, P = 0.02) for glucose [44, 56, 74, 81] (Appendix 7). The lower hemoglobin level/anemia (5/64), and higher blood pressure/hypertension (5/64) were the other two factors that were mostly reported. However, the pooled effect size was not statistically significant (P ≥ 0.05) (Table 2).

Fig. 9
figure 9

Meta-analysis with forest plot (a) and funnel plot (b) of pooled effect size on cancer

Full size image

Quality assessment

Of the 64 studies on CVST patients, 12 were cross-sectional studies, 51 were cohort studies, and 1 was a case–control study. The quality of each study was assessed by accumulating stars based on the description of evidence in each category, with a total score of 10. The percentage of good and satisfactory quality in the cross-sectional studies were 41.67% and 58.33%. In the cohort studies, the qualities of very good, good, and satisfactory were 5.88%, 82.35%, and 11.76%, respectively. There was only one case–control study and the quality was satisfactory (6/10) (Tables 3, 4 and 5).

Table 3 Newcastle-Ottawa Scale for cross-sectional studies
Full size table
Table 4 Newcastle-Ottawa Scale for cohort studies
Full size table
Table 5 Newcastle-Ottawa Scale for case-control studies
Full size table

Discussion

Our study systematically collected prognostic risk factors and quantitatively summarized their effect sizes in on poor functional outcomes in patients undergoing CVST. Following a comprehensive search strategy, we identified 64 studies from the past 20 years that performed statistical analyses of prognostic risk factors to compare good (favorable) and poor (unfavorable) functional outcomes. The quality of each study was assessed using the Newcastle–Ottawa scale for cross-sectional, cohort, and case–control studies. Of these, 50 studies (78.13%) scored more than 7 out of 10 on the Newcastle–Ottawa scale, indicating good evidence of quality. Most studies collected adequate data from representative settings. In 75% (48/64) of the studies, the sample size exceeded 100, with only one study limited to 30 patients due to COVID-19 restrictions.

The pooled effect size, with low (I2: 0–25%) to moderate (I2: 25–50%) heterogeneity and statistical significance (P < 0.05), indicated that ischemic infarction, headache, neurological focal deficit, absolute lymphocyte count, and cancer were significant prognostic risk factors for poor functional outcomes in patients undergoing CVST. Consciousness impairment with substantial heterogeneity emerged, as a more significant risk factor for poor outcomes, a pattern also observed in patients with deep cerebral venous thrombosis.

Age and sex were consistently reported as important variables in many national and population-based epidemiological studies on CVST. Three recent publications since 2023 have highlighted these two variables. A national cohort study from Denmark reported a median age of 41 years for patients with CVST, with 67% being women [10]. A population-based systematic review and meta-analysis from Canada found a mean age of 50.9 years, with 55.4% women [79]. A multicenter multinational prospective observational study in the USA (2000–2018) reported a median age of 46 for men and 37 for women, with women comprising 75.32% of the cohort [13]. CVST is more common than conventional stroke. The global stroke burden study (November 2018 to December 2021) took age ≥ 65 years old as a crucial population as previous [86]. Another retrospective cohort study among 223,358 stroke survivors reported a mean age of 64.8 ± 10.9 years [87]. In our review, age was a statistically significant prognostic risk factor, although high heterogeneity persisted, even in subgroup analyses based on mRS scores. Different studies used different cutoff value to define poor functional outcomes. Younger patients may have better resilience following stroke, which could influence long-term outcomes after endovascular thrombectomy [88]. Although women were reported to be more frequently affected by CVST, gender was not a significant prognostic factor for poor outcomes due to high heterogeneity and limited reporting.

The effect size and number of studies were higher for in intracranial hemorrhage than for ischemic infarction. However, homogeneity was better in ischemic infarction. Intracranial hemorrhage was more prevalent among patients with neurological deficits, consistent with the findings of prior reviews and stroke guidelines [89, 90].

Headache and seizure are common initial symptoms of CVST, as reported in many studies, including the 2019 CVT update by Ferro & Susa [3]. In our systematic review, the pooled effect size (OR) was less than 1 with moderate heterogeneity (I2 = 0.52). According to the raw data, patients with headache had a statistically better functional outcome. This finding was not previously reported, potentially because of the variability in headache types and severity, which were not standardized across the studies [15]. All studies included in this review were not reported regarding specific degree or properties. In addition, patients without headache were more likely to experience seizure or hemiparalysis. Epileptic seizures after CVST were an independent risk factor, with an OR of 5.66 (95% CI 3.83–8.35) in a meta-analysis [91].

Specific neurological symptoms, including focal deficit, coma, and consciousness deterioration. The pooled effect size of focal neurological deficits was a stable prognostic risk factor associated with poor functional. Our systematic review was performed quantitatively in accordance with the updated research in CVT by Ferro et al. in 2019 [3].

Inflammation plays a crucial role in venous infarction, and several inflammatory markers are associated with CVST [92]. Absolute lymphocyte counts lower in patients with poor outcomes, whereas the neutrophil-to-lymphocyte ratio (NLR) is elevated [26, 71]. One study that compared inflammatory markers between CVST patients and CVST mimics (those with anatomical variants in the cerebral sinuses) found that the NLR was lower in CVST mimics [93]. The pooled effect size for CRP was significantly heterogenous and did not significantly differ despite its common use as a marker of systemic inflammation.

Thrombus in the deep cerebral venous system, distinct from the superior/inferior sagittal sinus, straight sinus, transversal sinus, and sigmoid sinus, had a pooled OR of 6.30 with high heterogeneity (I2 = 0.719). Calandrelli et al. [94]established a scoring system to evaluate the deep cerebral venous system (DCVT), involving the veins of Rosenthal, internal cerebral veins, and veins of Galen, as predictors of clinical features.

Malignancy and high blood glucose levels were also associated with poor functional outcomes in patients with CVST. The link between malignancy and mortality is well established. The SI2NCAL2C score system was developed [56] and externally validated [95] to predict poor outcomes in CVS, with cancer as a key factor. Cancer-associated thrombosis is common in various malignancies and driven by different coagulation pathways. Patients with active cancer have a higher risk of venous thromboembolism (VTE) than the general population [96]. Hematologic disorders, including hematologic malignancies, have been a commonly unrecognized cause of cerebral sinus venous thrombosis since the 1990s [97]. More studies have highlighted the importance of hematologic disorders as a risk factor for venous thrombosis (and CVST) [7, 47, 59, 75]. Based on the Virchow triad (the role of blood hypercoagulability, blood flow dynamics, and endothelial damage), changes in blood components were reported in many studies, for example, thrombophilia [98, 99], polycythemia [47, 59], and hyperhomocysteinemia [59, 61], which were associated with CVST or a risk factor of CVST, and both of them were subtypes of hematologic disorder. In considering functional outcomes, anemia was reported in 5 studies. However, the pooled effect size showed no significant P-value in the chi-square test between good and poor outcomes. Patients with hematologic disorders present with CVST. In this case, anticoagulation was symptomatic treatment. Guidelines for hematologic malignancy-associated venous thrombosis have been developed [96]. Studies focused on glucose metabolism also demonstrated hypoglycemia as a risk factor for deep vein thrombosis (DVT) based on preclinical mechanism research [100].

Limitations

This systematic review pioneered the exploration and quantitative analysis of prognostic risk factors for short- or long-term poor functional outcomes among patients undergoing CVST and facilitated evidence-based clinical practice and decision making. However, our review has some limitations.

The eligible articles written in English in this study could increase the possibility of losing some studies conducted and published in Non-English countries and journals. Further data collection from a wider range of studies is required.

The heterogeneity of the pooled effect size for some factors was high, even applied with subgroup analysis with different cutoff points in mRS for functional division in binary outcomes between good/favorable and poor/unfavorable. Although a higher I2 value in a meta-analysis should be cautiously accepted, it is important to note that many factors (such as different countries, specific demographic backgrounds, and lifestyle, et al.) are inherent to the nature of an observational study.

Conclusions

Our systematic review and meta-analysis identified multiple prognostic risk factors associated with poor functional outcomes in CVST. Ischemic infarction, neurological focal deficits, impaired consciousness, absolute lymphocyte count, and cancer were prominent factors related to poor outcomes. Furthermore, intracranial hemorrhage had a larger impact magnitude, and ischemic infarction demonstrated greater homogeneity across studies. inflammatory biomarkers, particularly the neutrophil-to -lymphocyte ratio (NLR), were also associated with poor outcomes; however, certain markers, such as CRP, showed heterogeneity. Headache and seizure, despite being frequent symptoms, had inconsistent relationships with outcomes, possibly due to heterogeneity in clinical presentation and study methodology. Further research is needed to refine prognostic models and tailor treatment strategies for at-risk populations.

Data availability

Datasets are available through the corresponding author, upon reasonable request.

Abbreviations

CVST:

Cerebral venous sinus thrombosis

NIHSS:

Institutes of Health Stroke Scale (NIHSS)

mRS:

Modified Rankin Scale

ADL:

Activities of Daily Living

GOS:

Glasgow Outcome Scale

OR:

Odds Ratio

NLR:

Neutrophil-to-lymphocyte ratio

CRP:

C-reactive protein

References

  1. Arboix A, Bechich S, Oliveres M, Garcia-Eroles L, Massons J, Targa C. Ischemic stroke of unusual cause: clinical features, etiology and outcome. Eur J Neurol. 2001;8(2):133–9.

    Article  CAS  PubMed  Google Scholar 

  2. Capecchi M, Abbattista M, Martinelli I. Cerebral venous sinus thrombosis. J Thromb Haemost. 2018;16(10):1918–31.

    Article  CAS  PubMed  Google Scholar 

  3. Ferro JM, Aguiar de Sousa D. Cerebral Venous Thrombosis: an Update. Curr Neurol Neurosci Rep. 2019;19(10):74.

  4. Liu K, Song B, Gao Y, Zhao L, Fang H, Wang Y, et al. Long-Term Outcomes in Patients with Anemia And Cerebral Venous Thrombosis. Neurocrit Care. 2018;29(3):463–8.

    Article  PubMed  Google Scholar 

  5. Kernan WN, Ovbiagele B, Black HR, Bravata DM, Chimowitz MI, Ezekowitz MD, et al. Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2014;45(7):2160–236.

    Article  PubMed  Google Scholar 

  6. Otite FO, Patel S, Sharma R, Khandwala P, Desai D, Latorre JG, et al. Trends in incidence and epidemiologic characteristics of cerebral venous thrombosis in the United States. Neurology. 2020;95(16):e2200–13.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bajko Z, Motataianu A, Stoian A, Barcutean L, Andone S, Maier S, et al. Gender Differences in Risk Factor Profile and Clinical Characteristics in 89 Consecutive Cases of Cerebral Venous Thrombosis. J Clin Med. 2021;10(7).

  8. Bushnaq SA, Qeadan F, Thacker T, Abbas M, Carlson AP. High-Risk Features of Delayed Clinical Progression in Cerebral Venous Thrombosis: A Proposed Prediction Score for Early Intervention. Interventional Neurology. 2018;7(6):297–307.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Borhani-Haghighi A, Hooshmandi E. Cerebral venous thrombosis: a practical review. Postgrad Med J. 2024;100(1180):68–83.

    Article  PubMed  Google Scholar 

  10. Skajaa N, Vandenbroucke JP, Simonsen CZ, Sorensen HT, Adelborg K. Thromboembolic events, bleeding, and mortality in patients with cerebral venous thrombosis: a nationwide cohort study. Blood Adv. 2023;7(10):2070–81.

    Article  PubMed  Google Scholar 

  11. Zhou LW, Yu AYX, Ngo L, Hill MD, Field TS. Incidence of Cerebral Venous Thrombosis: A Population-Based Study, Systematic Review, and Meta-Analysis. Stroke. 2023;54(1):169–77.

    Article  PubMed  Google Scholar 

  12. Cantu-Brito C, Arauz A, Aburto Y, Barinagarrementeria F, Ruiz-Sandoval JL, Baizabal-Carvallo JF. Cerebrovascular complications during pregnancy and postpartum: Clinical and prognosis observations in 240 Hispanic women. Eur J Neurol. 2011;18(6):819–25.

    Article  CAS  PubMed  Google Scholar 

  13. Ranjan R, Ken-Dror G, Martinelli I, Grandone E, Hiltunen S, Lindgren E, et al. Age of onset of cerebral venous thrombosis: the BEAST study. Eur Stroke J. 2023;8(1):344–50.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Green M, Styles T, Russell T, Sada C, Jallow E, Stewart J, et al. Non-genetic and genetic risk factors for adult cerebral venous thrombosis. Thromb Res. 2018;169:15–22.

    Article  CAS  PubMed  Google Scholar 

  15. Luo Y, Tian X, Wang X. Diagnosis and Treatment of Cerebral Venous Thrombosis: A Review. Front Aging Neurosci. 2018;10:2.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bose G, Graveline J, Yogendrakumar V, Fergusson D, Dowlatshahi D. Direct oral anticoagulants in treatment of cerebral venous thrombosis: a systematic review protocol. Syst Rev. 2019;8(1):99.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Aguiar de Sousa D, Romoli M, Sanchez Van Kammen M, Heldner MR, Zini A, Coutinho JM, et al. Cerebral Venous Thrombosis in Patients With Heparin-Induced Thrombocytopenia a Systematic Review. Stroke. 2022;53(6):1892–903.

  18. Algarni SA, NS AL, Alharbi MS, Albarrak A, Alanezi AA, Al Shehri HM. Sex Differences and Clinical Outcomes of Patients with Coronavirus Disease,. Infection and Cerebral Venous Sinus Thrombosis: A Systematic Review. Clin Appl Thromb Hemost. 2019;2024(30):10760296241240748.

    Google Scholar 

  19. Lin L. Protocol-The Impact of Prognostic Risk Factors on Functional Outcome in Patients with Cerebral Venous Sinus Thrombosis-Systematic Review and Meta-analysis. 2024.

  20. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14:135.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods. 2010;1(2):97–111.

    Article  PubMed  Google Scholar 

  22. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372: n71.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Aarju G, Birinder Singh P, Vipin K, Alisha S, Gunchan P. Neurological Predictors of Functional Outcome in Cortical Venous Sinus Thrombosis. Journal of Neurosciences in Rural Practice. 2022;13(2):290–4.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Aguiar De Sousa D, Lucas Neto L, Arauz A, Sousa AL, Gabriel D, Correia MA, et al. Early Recanalization in Patients With Cerebral Venous Thrombosis Treated With Anticoagulation. Stroke. 2020;51(4):1174–81.

  25. Aguiar De Sousa D, Lucas Neto L, Jung S, Penas S, Panos L, Heldner MR, et al. Brush Sign Is Associated with Increased Severity in Cerebral Venous Thrombosis. Stroke. 2019;50(6):1574–7.

  26. Aguiar de Sousa D, Pereira-Santos MC, Serra-Caetano A, Lucas Neto L, Sousa AL, Gabriel D, et al. Blood biomarkers associated with inflammation predict poor prognosis in cerebral venous thrombosis:: a multicenter prospective observational study. Eur J Neurol. 2021;28(1):202–8.

  27. Bakradze E, Shu L, Henninger N, Prabhakaran S, Siegler JE, De Marchis GM, et al. Delayed Diagnosis in Cerebral Venous Thrombosis: Associated Factors and Clinical Outcomes. J Am Heart Assoc. 2023;12(19).

  28. Barboza MA, Chiquete E, Arauz A, Merlos-Benitez M, Quiroz-Compeán A, Barinagarrementería F, et al. A practical score for prediction of outcome after cerebral venous thrombosis. Frontiers in Neurology. 2018;9(OCT).

  29. Barboza MA, Mejías C, Colin-Luna J, Quiroz-Compean A, Arauz A. Intracranial venous collaterals in cerebral venous thrombosis: Clinical and imaging impact. J Neurol Neurosurg Psychiatry. 2015;86(12):1314–8.

    Article  PubMed  Google Scholar 

  30. Billoir P, Siguret V, Fron EM, Drouet L, Crassard I, Marlu R, et al. C-reactive protein and D-dimer in cerebral vein thrombosis: Relation to clinical and imaging characteristics as well as outcomes in a French cohort study. Research and Practice in Thrombosis and Haemostasis. 2023;7(3).

  31. Canhão P, Ferro JM, Lindgren AG, Bousser MG, Stam J, Barinagarrementeria F. Causes and predictors of death in cerebral venous thrombosis. Stroke. 2005;36(8):1720–5.

    Article  PubMed  Google Scholar 

  32. Chen YC, Chang CW, Wu HC, Chen CM, Chang CH, Chang KH. Clinical Characteristics and Prognostic Factors of Non-Infectious Cerebral Venous Sinus Thrombosis. Journal of Clinical Medicine. 2022;11(20).

  33. Chu X, Zhang J, Zhang B, Zhao Y. Analysis of Age and Prevention Strategy on Outcome after Cerebral Venous Thrombosis. Biomed Res Int. 2020;2020:6637692.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Colò F, Brunetti V, Di Muro M, Rossi E, Bartolomei F, Alexandre AM, et al. Predicting Factors for Seizures after Cerebral Venous Thrombosis: A Retrospective Single Center Cohort Study. Life. 2023;13(1).

  35. Coutinho JM, Stam J, Canhão P, Barinagarrementeria F, Bousser MG, Ferro JM. Cerebral venous thrombosis in the absence of headache. Stroke. 2015;46(1):245–7.

    Article  PubMed  Google Scholar 

  36. Dinç Y, Özpar R, Bakar M, Hakyemez B. Evaluation of Clinical, Radiological, and Demographic Characteristics of Juxtacortical Hemorrhages in Cerebral Venous Thrombosis. Turkish Journal of Neurology. 2021;27(4):371–6.

    Article  Google Scholar 

  37. Dinc Y, Özpar R, Hakyemez B, Bakar M. The relationship between early neurological deterioration, poor clinical outcome, and venous collateral score in cerebral venous sinus thrombosis. Neurological Sciences and Neurophysiology. 2021;38(3):158–65.

    Article  Google Scholar 

  38. Ding HY, Xie YN, Li LX, Chu HL, Tang YP, Dong Q, et al. Clinical features of seizures after cerebral venous sinus thrombosis and its effect on outcome among Chinese Han population. Stroke Vasc Neurol. 2017;2(4):184–8.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Duman T, Uluduz D, Midi I, Bektas H, Kablan Y, Goksel BK, et al. A Multicenter Study of 1144 Patients with Cerebral Venous Thrombosis: The VENOST Study. J Stroke Cerebrovasc Dis. 2017;26(8):1848–57.

    Article  PubMed  Google Scholar 

  40. Ferro JM, Canhão P, Bousser MG, Stam J, Barinagarrementeria F, Stolz E. Cerebral venous thrombosis with nonhemorrhagic lesions: Clinical correlates and \prognosis. Cerebrovasc Dis. 2010;29(5):440–5.

    Article  PubMed  Google Scholar 

  41. Foschi M, Pavolucci L, Rondelli F, Amore G, Spinardi L, Rinaldi R, et al. Clinicoradiological Profile and Functional Outcome of Acute Cerebral Venous Thrombosis: A Hospital-Based Cohort Study. Cureus Journal of Medical Science. 2021;13(9).

  42. Gazioglu S, Yildirim A, Kokturk EG, Seker D, Altunayoglu Cakmak V, Velioglu SK. Acute Seizures in Cerebral Venous Sinus Thrombosis: Risk Factors and Prognosis. Neurologist. 2020;25(5):126–30.

    Article  PubMed  Google Scholar 

  43. Gupta S, Soni R, Dhull P, Somasekharan M, Sreen A. Glasgow Coma Scale ≤ 12 at Admission is a Predictor of Poor Functional Outcome (mRS 2–6) at One Year in Patients with Cerebral Venous Thrombosis. J Stroke Cerebrovasc Dis. 2021;30(7).

  44. Hu Y, Meng R, Zhang X, Guo L, Li S, Wu Y, et al. Serum neuron specific enolase may be a marker to predict the severity and outcome of cerebral venous thrombosis. J Neurol. 2018;265(1):46–51.

    Article  CAS  PubMed  Google Scholar 

  45. Hu YY, Zhang SZ, Wang GJ, Wang ZG, Yuan H. Clinical characteristics and risk factors for in-hospital death due to cerebral venous sinus thrombosis in three medical centers over a 10-year period. American Journal of Translational Research. 2023;15(4):2773–82.

    PubMed  PubMed Central  Google Scholar 

  46. Karahan SZ, Gazioglu S, Dilaver I, Boz C. The role of thrombo-inflammatory biomarkers in the prognosis of cerebral venous sinus thrombosis. Curr Neurovasc Res. 2021;18(2):237–43.

    CAS  PubMed  Google Scholar 

  47. Karthik Vanukuri N, Pedapati R, Shanmugam S, Hazeena P, Rangasami R, Venkatasubramanian S. Effect of recanalization on clinical outcomes in patients with cerebral venous thrombosis – An ambispective study. European Journal of Radiology. 2022;153.

  48. Khambholja J, Panchal SS, Gami JI. A Study of Cerebral Venous Sinus Thrombosis with Special Reference to Newer Risk Predictors. Journal of Association of Physicians of India. 2024;72(5):45–56.

    PubMed  Google Scholar 

  49. Khan M, Arauz A, Uluduz D, Barboza MA, Duman T, Cano-Nigenda V, et al. Predictors of Mortality and Functional Outcome in Pregnancy and Puerperium-Related Cerebral Venous Thrombosis. Cerebrovasc Dis. 2023;52(4):393–400.

    Article  CAS  PubMed  Google Scholar 

  50. Klein P, Shu L, Nguyen TN, Siegler JE, Omran SS, Simpkins AN, et al. Outcome Prediction in Cerebral Venous Thrombosis: The IN-REvASC Score. J Stroke. 2022;24(3):404–16.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Korathanakhun P, Sathirapanya P, Geater SL, Petpichetchian W. Predictors of hospital outcome in patients with cerebral venous thrombosis. J Stroke Cerebrovasc Dis. 2014;23(10):2725–9.

    Article  PubMed  Google Scholar 

  52. Krajíčková D, Král J, Herzig R, Klzo Ľ, Krajina A, Havelka J, et al. Factors influencing therapy choice and clinical outcome in cerebral venous sinus thrombosis. Sci Rep. 2020;10(1).

  53. Li M, Pan L, Gao X, Hou J, Meng R, Ji X. Low Diastolic Blood Pressure Predicts Good Clinical Outcome in Patients With Cerebral Venous Thrombosis. Frontiers in Neurology. 2021;12.

  54. Li M, Zhang B, Xie J, Meng R, Ji X. Prediction Score for Clinical Outcome of Chinese Patients with Cerebral Venous Thrombosis. Int J Gen Med. 2023;16:4099–107.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Liang ZW, Gao WL, Feng LM. Clinical characteristics and prognosis of cerebral venous thrombosis in Chinese women during pregnancy and puerperium. Sci Rep. 2017;7.

  56. Lindgren E, Krzywicka K, de Winter MA, Sánchez Van Kammen M, Heldner MR, Hiltunen S, et al. A scoring tool to predict mortality and dependency after cerebral venous thrombosis. European Journal of Neurology. 2023;30(8):2305–14.

  57. Lindgren E, Silvis SM, Hiltunen S, Heldner MR, Serrano F, De Scisco M, et al. Acute symptomatic seizures in cerebral venous thrombosis. Neurology. 2020;95(12):E1706–15.

    Article  CAS  PubMed  Google Scholar 

  58. Liu K, Pei L, Gao Y, Zhao L, Fang H, Bunda B, et al. Dehydration Status Predicts Short-Term and Long-Term Outcomes in Patients with Cerebral Venous Thrombosis. Neurocrit Care. 2019;30(2):478–83.

    Article  PubMed  Google Scholar 

  59. Madineni KU, Naveen Prasad SV, Bhuma V. A study of the prognostic significance of platelet distribution width, mean platelet volume, and plateletcrit in cerebral venous sinus thrombosis. Journal of Neurosciences in Rural Practice. 2023;14(3):418–23.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Mu S, Li J, Lin K, Fang Y, Lin F, Li Z, et al. Predictive Factors for Early-Onset Seizures in Patients With Cerebral Venous Sinus Thrombosis. Frontiers in Neurology. 2022;13.

  61. Narayan D, Kaul S, Ravishankar K, Suryaprabha T, Bandaru VC, Mridula KR, et al. Risk factors, clinical profile, and long-term outcome of 428 patients of cerebral sinus venous thrombosis: insights from Nizam’s Institute Venous Stroke Registry, Hyderabad (India). Neurol India. 2012;60(2):154–9.

    Article  PubMed  Google Scholar 

  62. Pan L, Ding J, Ya J, Zhou D, Hu Y, Fan C, et al. Risk factors and predictors of outcomes in 243 Chinese patients with cerebral venous sinus thrombosis: A retrospective analysis. Clin Neurol Neurosurg. 2019;183.

  63. Rezoagli E, Martinelli I, Poli D, Scoditti U, Passamonti SM, Bucciarelli P, et al. The effect of recanalization on long-term neurological outcome after cerebral venous thrombosis. J Thromb Haemost. 2018;16(4):718–24.

    Article  CAS  PubMed  Google Scholar 

  64. Ruiz-Sandoval JL, Chiquete E, Bañuelos-Becerra LJ, Torres-Anguiano C, González-Padilla C, Arauz-Góngora AA, et al. Cerebral venous thrombosis in a Mexican multicenter registry of acute cerebrovascular disease: The RENAMEVASC study. J Stroke Cerebrovasc Dis. 2012;21(5):395–400.

    Article  PubMed  Google Scholar 

  65. Salottolo K, Wagner J, Frei DF, Loy D, Bellon RJ, McCarthy K, et al. Epidemiology, Endovascular Treatment, and Prognosis of Cerebral Venous Thrombosis: US Center Study of 152 Patients. J Am Heart Assoc. 2017;6(6).

  66. Shakibajahromi B, Haghighi AB, Salehi A, Vardanjani HM, Ghaedian M, Safari A, et al. Clinical and radiological characteristics and predictors of outcome of cerebral venous sinus thrombosis, a hospital-based study. Acta Neurol Belg. 2020;120(4):845–52.

    Article  PubMed  Google Scholar 

  67. Silvis SM, Reinstra E, Hiltunen S, Lindgren E, Heldner MR, Mansour M, et al. Anaemia at admission is associated with poor clinical outcome in cerebral venous thrombosis. Eur J Neurol. 2020;27(4):716–22.

    Article  CAS  PubMed  Google Scholar 

  68. Simaan N, Molad J, Peretz S, Filioglo A, Auriel E, Hallevi H, et al. Characteristics of Cerebral Sinus Venous Thrombosis Patients Presenting with Intracerebral Hemorrhage. Journal of Clinical Medicine. 2022;11(4).

  69. Song B, Liu K, Gao Y, Zhao L, Fang H, Wang Y, et al. Gender-specific relationship between uric acid levels and prognosis after cerebral venous thrombosis. Curr Neurovasc Res. 2018;15(4):292–7.

    Article  CAS  PubMed  Google Scholar 

  70. Stolz E, Rahimi A, Gerriets T, Kraus J, Kaps M. Cerebral venous thrombosis: An all or nothing disease? Prognostic factors and long-term outcome. Clin Neurol Neurosurg. 2005;107(2):99–107.

    Article  PubMed  Google Scholar 

  71. Sun R, Huang F, Wu W, Yin G, Ding Q, Gu Z, et al. Association of neutrophil to lymphocyte ratio and D-dimer with functional outcome in patients with cerebral venous sinus thrombosis. BMC Neurol. 2023;23(1).

  72. Wang L, Duan J, Bian T, Meng R, Wu L, Zhang Z, et al. Inflammation is correlated with severity and outcome of cerebral venous thrombosis. J Neuroinflammation. 2018;15(1):329.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wei H, Jiang H, Zhou Y, Liu L, Zhou C, Ji X. Intracranial hypertension after cerebral venous thrombosis—Risk factors and outcomes. CNS Neurosci Ther. 2023;29(9):2540–7.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Wu Y, Zhou L, Yao M, Zhu Y, Ni J, Cui L, et al. Elevated fasting blood glucose is predictive of the severity and poor outcome in nondiabetic patients with cerebral venous thrombosis. J Neurol Sci. 2020;417.

  75. Zhang Y, Liu Y, Qin B, Tang S, Liang Z. Characteristics of Poor Prognosis in Patients with Cerebral Venous Sinus Thrombosis: A Multicenter Retrospective Study. Neuropsychiatr Dis Treat. 2023;19:1417–26.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zhao J, Liu K, Li S, Gao Y, Zhao L, Liu H, et al. Neutrophil-to-lymphocyte ratio predicts the outcome of cerebral venous thrombosis. Curr Neurovasc Res. 2021;18(2):204–10.

    CAS  PubMed  Google Scholar 

  77. Zhao J, Liu K, Li S, Gao Y, Zhao L, Liu H, et al. Prognostic nutritional index predicts clinical outcomes in patients with cerebral venous sinus thrombosis. BMC Neurol. 2021;21(1).

  78. Zhao Y, Su X, Liu Z, Zhou C, Yu J, Wang X, et al. Effect of intracranial venous collaterals on neurological outcomes in cerebral venous thrombosis. J Clin Neurosci. 2022;102:95–100.

    Article  PubMed  Google Scholar 

  79. Zhou LW, Hennawy M, Ngo L, Field TS. Prognosis after cerebral venous thrombosis: Mortality during initial admission and at 30 days and one year after discharge in a large Canadian population-based cohort. Thromb Res. 2024;233:145–52.

    Article  CAS  PubMed  Google Scholar 

  80. Zuurbier SM, Hiltunen S, Lindgren E, Silvis SM, Jood K, Devasagayam S, et al. Cerebral Venous Thrombosis in Older Patients. Stroke. 2018;49(1):197-+.

  81. Zuurbier SM, Hiltunen S, Tatlisumak T, Peters GM, Silvis SM, Haapaniemi E, et al. Admission Hyperglycemia and Clinical Outcome in Cerebral Venous Thrombosis. Stroke. 2016;47(2):390–6.

    Article  CAS  PubMed  Google Scholar 

  82. Li S, Liu K, Gao Y, Zhao L, Zhang R, Fang H, et al. Prognostic value of systemic immune-inflammation index in acute/subacute patients with cerebral venous sinus thrombosis. Stroke Vasc Neurol. 2020;5(4):368–73.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Li S, Liu K, Zhang R, Gao Y, Fang H, Liu X, et al. Lower lymphocyte to monocyte ratio is a potential predictor of poor outcome in patients with cerebral venous sinus thrombosis. Stroke Vasc Neurol. 2019;4(3):148–53.

    Article  PubMed  Google Scholar 

  84. Ortega-Gutierrez S, Holcombe A, Aksan N, Dai B, Shaban A, Lazarre L, et al. Association of admission clinical predictors and functional outcome in patients with Cerebral Venous and Dural Sinus Thrombosis. Clin Neurol Neurosurg. 2020;188.

  85. Simaan N, Hallevi H, Seyman E, Rotschild O, Assayag EB, Filioglo A, et al. Prognosis and Outcome of Cerebral Sinus Venous Thrombosis—A Multicenter Cohort Study. Acta Neurol Scand. 2023;2023.

  86. Thayabaranathan T, Kim J, Cadilhac DA, Thrift AG, Donnan GA, Howard G, et al. Global stroke statistics 2022. Int J Stroke. 2022;17(9):946–56.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Lee D, Cho IY, Chang WH, Yoo JE, Choi HL, Park J, et al. Fracture Risk Among Stroke Survivors According to Poststroke Disability Status and Stroke Type. Stroke. 2024;55(6):1498–506.

    Article  CAS  PubMed  Google Scholar 

  88. Winkelmeier L, Kniep H, Faizy T, Heitkamp C, Holtz L, Meyer L, et al. Age and Functional Outcomes in Patients With Large Ischemic Stroke Receiving Endovascular Thrombectomy. JAMA Netw Open. 2024;7(8): e2426007.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Yamutai S, Sornplaeng P, Sanghan N, Khumtong R, Setthawatcharawanich S, Sathirapanya P, et al. Factors associated with the initial intracranial hemorrhage occurrence in patients with cerebral venous thrombosis. Clin Neurol Neurosurg. 2024;236: 108109.

    Article  PubMed  Google Scholar 

  90. Greenberg SM, Ziai WC, Cordonnier C, Dowlatshahi D, Francis B, Goldstein JN, et al. 2022 Guideline for the Management of Patients With Spontaneous Intracerebral Hemorrhage: A Guideline From the American Heart Association/American Stroke Association. Stroke. 2022;53(7):e282–361.

    Article  CAS  PubMed  Google Scholar 

  91. Gasparini S, Neri S, Brigo F, Cianci V, Mammì A, Pascarella A, et al. Late epileptic seizures following cerebral venous thrombosis: a systematic review and meta-analysis. Neurol Sci. 2022;43(9):5229–36.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Zhou Y, Jiang H, Wei H, Liu L, Zhou C, Ji X. Venous stroke–a stroke subtype that should not be ignored. Frontiers in Neurology. 2022;13.

  93. Ding JY, Pan LQ, Lan D, Chen ZY, Wang ZA, Zou M, et al. Inflammatory Markers Differentiate Cerebral Venous Sinus Thrombosis from Mimics. Thromb Haemost. 2023;123(03):326–35.

    Article  PubMed  Google Scholar 

  94. Calandrelli R, Colo F, Broccolini A, Della Marca G, Frisullo G, Colosimo C, et al. Deep cerebral venous system involvement in patients with cerebral sinus thrombosis. A proposal of neuroradiological score systems useful for clinical assessment. Neurol Sci. 2023;44(6):2049–60.

  95. Klein P, Shu L, Lindgren E, de Winter MA, Siegler JE, Simpkins AN, et al. External validation of the SI2NCAL2C score for outcomes following cerebral venous thrombosis. J Stroke Cerebrovasc Dis. 2024;33(6).

  96. Van Cutsem E, Mahe I, Felip E, Agnelli G, Awada A, Cohen A, et al. Treating cancer-associated venous thromboembolism: A practical approach. Eur J Cancer. 2024;209: 114263.

    Article  PubMed  Google Scholar 

  97. Arboix A, Besses C. Cerebrovascular disease as the initial clinical presentation of haematological disorders. Eur Neurol. 1997;37(4):207–11.

    Article  CAS  PubMed  Google Scholar 

  98. Pires GS, Ribeiro DD, Oliveira JAQ, Freitas LC, Vaez R, Annichino-Bizzacchi JM, et al. Risk factors associated with recurrent venous thromboembolism after a first cerebral venous thrombosis event: A cohort study. Thromb Res. 2019;178:85–90.

    Article  CAS  PubMed  Google Scholar 

  99. Sassi SB, Touati N, Baccouche H, Drissi C, Romdhane NB, Hentati F. Cerebral Venous Thrombosis: A Tunisian Monocenter Study on 160 Patients. Clin Appl Thromb Hemost. 2017;23(8):1005–9.

    Article  PubMed  Google Scholar 

  100. Cao Z, Jiang X, He Y, Zheng X. Metabolic landscape in venous thrombosis: insights into molecular biology and therapeutic implications. Ann Med. 2024;56(1):2401112.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The co-author of Senfeng Liu got a program supported by Natural Science Foundation of Xiamen Municipality with NO.3502Z20227317 as reference number. The authors gratefully acknowledge this funding, which made it possible to conduct this systematic review and meta-analysis.

Funding

Natural Science Foundation of Xiamen Municipality, NO.3502Z20227317.

Author information

Author notes

  1. Lili Lin and Senfeng Liu contributed equally to this work.

Authors and Affiliations

  1. Faculty of Medicine and Health Sciences, University Putra Malaysia, 43400, Serdang, Selangor, Malaysia

    Lili Lin, Muhammad Hibatullah Romli & Ruthpackiavathy Rajen Durai

  2. Faculty of Environment and Public Health, Xiamen Huaxia University, Xiamen, 361000, People’s Republic of China

    Lili Lin & Senfeng Liu

  3. Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China

    Wei Wang

  4. Department of Neurology, The Fifth Xiamen Hospital, Xiamen, 361000, People’s Republic of China

    Xiao-kuo He

Authors
  1. Lili Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Senfeng Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wei Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xiao-kuo He
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Muhammad Hibatullah Romli
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Ruthpackiavathy Rajen Durai
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

The authors’ contributions were as follows: LLL: designed the research, screening process, data extraction, quality evaluation, and initial draft of the manuscript; SFL: performed the screening process, data extraction, quality evaluation, and data analysis; As a co-first-author, SFL contribute equally to this study as LLL does. WW: clinical reasoning, data extraction, statistical analysis, result interpretation; XKH: problem solving for any discrepancy in screening process; MHR: critically revised the manuscript; RRD: manuscript polish, and has primary responsibility for final content; and all authors contributed to critically reviewing the manuscript, and read and approved the final manuscript.

Corresponding authors

Correspondence to Wei Wang, Muhammad Hibatullah Romli or Ruthpackiavathy Rajen Durai.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, L., Liu, S., Wang, W. et al. Key prognostic risk factors linked to poor functional outcomes in cerebral venous sinus thrombosis: a systematic review and meta-analysis. BMC Neurol 25, 52 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12883-025-04059-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12883-025-04059-x

Keywords

BMC Neurology

ISSN: 1471-2377

Contact us