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J Stroke > Volume 24(3); 2022 > Article
Klein, Shu, Nguyen, Siegler, Omran, Simpkins, Heldner, Havenon, Aparicio, Abdalkader, Psychogios, Vedovati, Paciaroni, Martial, Liebeskind, Sousa, Coutinho, Yaghi, and for the ACTION-CVT Study Group: Outcome Prediction in Cerebral Venous Thrombosis: The IN-REvASC Score

Abstract

Background

We identified risk factors, derived and validated a prognostic score for poor neurological outcome and death for use in cerebral venous thrombosis (CVT).

Methods

We performed an international multicenter retrospective study including consecutive patients with CVT from January 2015 to December 2020. Demographic, clinical, and radiographic characteristics were collected. Univariable and multivariable logistic regressions were conducted to determine risk factors for poor outcome, mRS 3-6. A prognostic score was derived and validated.

Results

A total of 1,025 patients were analyzed with median 375 days (interquartile range [IQR], 180 to 747) of follow-up. The median age was 44 (IQR, 32 to 58) and 62.7% were female. Multivariable analysis revealed the following factors were associated with poor outcome at 90- day follow-up: active cancer (odds ratio [OR], 11.20; 95% confidence interval [CI], 4.62 to 27.14; P<0.001), age (OR, 1.02 per year; 95% CI, 1.00 to 1.04; P=0.039), Black race (OR, 2.17; 95% CI, 1.10 to 4.27; P=0.025), encephalopathy or coma on presentation (OR, 2.71; 95% CI, 1.39 to 5.30; P=0.004), decreased hemoglobin (OR, 1.16 per g/dL; 95% CI, 1.03 to 1.31; P=0.014), higher NIHSS on presentation (OR, 1.07 per point; 95% CI, 1.02 to 1.11; P=0.002), and substance use (OR, 2.34; 95% CI, 1.16 to 4.71; P=0.017). The derived IN-REvASC score outperformed ISCVT-RS for the prediction of poor outcome at 90-day follow-up (area under the curve [AUC], 0.84 [95% CI, 0.79 to 0.87] vs. AUC, 0.71 [95% CI, 0.66 to 0.76], χ2 P<0.001) and mortality (AUC, 0.84 [95% CI, 0.78 to 0.90] vs. AUC, 0.72 [95% CI, 0.66 to 0.79], χ2 P=0.03).

Conclusions

Seven factors were associated with poor neurological outcome following CVT. The INREvASC score increased prognostic accuracy compared to ISCVT-RS. Determining patients at highest risk of poor outcome in CVT could help in clinical decision making and identify patients for targeted therapy in future clinical trials.

Introduction

Cerebral venous thrombosis (CVT) is a rare cause of stroke, with an estimated incidence of approximately 1.5 per 100,000 person-years [1,2]. The condition is more common in young patients, especially young women. CVT involves thrombus formation in the venous system of the brain including the deep veins, superficial veins, and sinuses. Occlusions may result in venous infarct, intracranial hemorrhage, and cerebral edema. Overall mortality is low with a case fatality rate of 5% to 10% [3-5]. In accordance with its rarity, CVT is less well understood than other major causes of stroke.
Prior studies of CVT have identified a number of factors associated with poor neurological outcome or disability including coma, cerebral edema, focal deficit, and thrombosis of the deep cerebral vessels [3,5-9]. However, many of these studies have been small and limited in the variables under study. To date, the most influential study of CVT patients is the International Study on Cerebral Vein and Dural Sinus Thrombosis (ISCVT) published in 2004 [5]. From this cohort, the ISCVT risk score (ISCVT-RS) for poor neurological outcome was derived and validated, and has subsequently seen more widespread use than similar scores [10-14]. Since the publication of ISCVT, there have been advances in the diagnosis and treatment of CVT patients [15-18]. However, it remains unclear whether these advances have impacted outcomes of CVT patients. Risk stratification of patients may help with clinical decision making and identify those who could benefit from further treatment and study.
In this large observational study, we sought to identify clinical, laboratory, and imaging factors associated with poor neurological outcome, modified Rankin Scale (mRS) 3-6, and to derive and validate a prognostic score for poor neurological outcome at 90 days following a diagnosis of CVT.

Methods

Institutional Review Board approval was obtained from each participating center to conduct the study. Written informed consent by the patients was waived due to the retrospective nature of this study. De-identified data are available upon reasonable request to the corresponding author. This study followed the TRIPOD reporting guidelines (Supplementary Table 1).

Patient population

The Anticoagulation in the Treatment of Cerebral Venous Thrombosis (ACTION-CVT) study was a large multicenter international retrospective cohort study that enrolled consecutive patients with a confirmed diagnosis of CVT from January 2015 through December 2020 [17,19,20]. Patients were included from 27 centers in the United States, Europe, and New Zealand. Participating centers were tertiary care academic medical centers spanning multiple national models of care delivery.

Outcomes

A single primary outcome and a single secondary outcome were defined a priori. The primary outcome was presence of poor neurological outcome, defined as mRS 3-6, at 90-day follow-up. The secondary outcome was all-cause mortality.

Inclusion criteria

A total of 1,025 patients were enrolled in ACTION-CVT. Potential patients were identified using previously validated International Classification of Diseases, ninth revision (ICD-9; 325.0, 437.6, and 671.5) and ICD-10 codes (I67.6) with appropriate sensitivity and specificity for CVT [21,22]. The diagnosis of CVT was confirmed by review of the patient’s medical record and imaging studies by participating sites. Inclusion for each analysis was based on the presence of the required outcome variables at the specified time point.

Collected variables

Demographic factors, in-hospital treatments, follow-up variables, clinical, imaging, and laboratory factors were collected. All variables were collected via manual chart abstraction by investigators at participating sites. Standardized variable definitions were used. Substance use was defined as active smoking or non-medical use of drugs other than alcohol and tobacco. Hypercoagulable factors were defined as one or more antiphospholipid antibodies, protein C <65, protein S <70, antithrombin <80, factor V Leiden mutation, or prothrombin gene mutation. Collected variables are further described in a previous publication [17].

Statistical analysis

To ensure data integrity and consistency, data verification was conducted with queries sent to participating sites. Death was carried forward to future timepoints. Missing data were not imputed. Patients were included in the analysis for each outcome if the required outcome variables were available at the specified time point.
Factors included in the univariable analysis were selected based on previously shown associations with CVT outcomes or based on biological plausibility. Factors with low prognostic value (e.g., tracheostomy during hospitalization), a lack of biological plausibility (e.g., coronavirus disease 2019 [COVID-19] testing), or reflective of clinician treatment decisions (e.g., neurosurgical treatment), were not included. Demographic variables were also included in the univariable analysis due to previously identified disparities in CVT and other forms of stroke [5,23]. Univariable analysis of focal deficit was performed in patients without global deficit (i.e., encephalopathy and coma). Colinear factors were dichotomized, discarded, or combined as appropriate. Odds ratios (ORs) for continuous variables were calculated as ratio per unit.
For both univariable and multivariable analyses, outcomes were dichotomized based upon mRS score at the specified time point for each outcome. Between-group univariable comparisons were done by two-sided t-test, chi-square test, Fisher’s exact test, or rank sum test as appropriate. The univariable analysis was carried out only based on the primary outcome. Factors found to be associated in univariable analysis were fed into the multivariable analysis. Statistical significance for all analyses was set at α=0.05.
A risk scoring model was generated based upon the results of the multivariable analysis. A nomogram was generated based on the multivariable regression result of all potential prognostic factors (P<0.20) from the multivariable analysis. This variable filtering step increases the number of events per predictor variable, reducing the likelihood of model overfitting. The National Institutes of Health Stroke Scale (NIHSS) was divided into five discrete categories. The Liu method for cut-point estimation [24] was used for dichotomization: age was dichotomized at 50 years, creatinine was dichotomized at >1.0 mg/dL, and hemoglobin was dichotomized at ≥12.0 g/dL. Nomogram generated point values were rounded to the nearest integer to create the IN-REvASC score model containing nine factors: intracranial hemorrhage, NIHSS, Black race, encephalopathy or coma on presentation, age, anemia, substance use, creatinine, and active cancer (Figure 1). The previously described ISCVT-RS model score was calculated [10]. The prognostic value of both models was tested using a receiver operating characteristic curve under non-parametric assumptions. The area under the curve (AUC) was used to determine prognostic accuracy. Internal validation of models was performed using the bootstrap method with 100 iterations. Models were compared using the chi-square test. All data analyses were carried out using Stata version 15.1 (StataCorp., College Station, TX, USA).

Results

Data were collected on 1,025 patients with CVT from 27 sites in the United States, Europe, and New Zealand. Baseline characteristics of the cohort are summarized in Table 1. The median age of included subjects was 44 years (interquartile range [IQR], 32 to 58), 62.7% (640) were women, and 15.7% (160) were Black. The median length of follow-up was 375 days (IQR, 180 to 747). Endovascular therapy was performed in 8.7% (89) of patients and 87.8% (900) patients were treated with oral anticoagulation [17]. There were 649 patients included in the primary analysis and 101 had poor outcome (Figure 2). In the univariable analysis, 11 factors were associated with poor outcome at 90 days and two factors were inversely associated with poor outcome at 90 days. These 13 factors were fed into the multivariable analyses (Table 2).

Poor outcome at 90-day follow-up

At 90-day follow-up, 15.6% (101/649) of patients had poor outcome, mRS 3-6. Active cancer (OR, 11.20; 95% confidence interval [CI], 4.62 to 27.14; P<0.001), age (OR, 1.02 per year; 95% CI, 1.00 to 1.04; P=0.039), Black race (OR, 2.17; 95% CI, 1.10 to 4.27; P=0.025), encephalopathy or coma on presentation (OR, 2.71; 95% CI, 1.39 to 5.30; P=0.004), decreased hemoglobin (OR, 1.16 per g/dL; 95% CI, 1.03 to 1.31; P=0.014), higher NIHSS on presentation (OR, 1.07 per point; 95% CI, 1.02 to 1.11; P=0.002), and substance use (OR, 2.34; 95% CI, 1.16 to 4.71; P=0.017) were associated with poor outcome at 90 days follow-up (Table 3). Among patients with zero prognostic factors, 2.9% (7/239) had poor outcome at 90 days. Poor outcome at 90 days was present in 12.9% (31/240), 26.1% (29/111), and 57.6% (28/59) of patients with 1, 2, or 3+ prognostic factors, respectively.

Mortality

Mortality in the cohort was 6.6% (43/649) at 90-day follow-up and the in-hospital mortality was 3.9% (25/649) during this time period. Mortality was 6.8% (70/1,025) at last follow-up (median 375 days [IQR, 180 to 747]). The incidence rate for mortality was 5.13 per 100 person-years with a median follow-up of 375 days. Active cancer (OR, 12.17; 95% CI, 5.45 to 27.16; P<0.001), Black race (OR, 2.62; 95% CI, 1.20 to 5.69; P=0.015), and substance use (OR, 2.17; 95% CI, 1.03 to 4.60; P=0.042) were associated with mortality (Table 3).

IN-REvASC score

The IN-REvASC score used nine factors simplified from a nomogram regression: Intracranial hemorrhage, NIHSS, Black Race, Encephalopathy or coma at admission, Age, Anemia, Substance use, Creatinine, and active Cancer. Intracranial hemorrhage was assigned one point. Anemia, defined as hemoglobin less than 12.0 mg/dL, and creatinine greater than 1.0 mg/dL were assigned two points. Black race and age greater than 50 were assigned three points. Encephalopathy or coma on presentation and substance use were assigned four points. NIHSS of 1-10 was assigned two points, 11-20 was assigned four points, 21-30 was assigned six points, and greater than 30 was assigned eight points. Active cancer was assigned 10 points (Figure 1).
The IN-REvASC (AUC, 0.84; 95% CI, 0.78 to 0.89) score outperformed ISCVT-RS (AUC, 0.71; 95% CI, 0.66 to 0.77; χ2 P<0.001) for the prediction of poor outcome at 90-day follow-up. The probability of poor outcome in those with a low risk, IN-REvASC <10, was 5.1% (23/447) (Figure 1). For those with a moderate risk, IN-REvASC ≥10 to <20, the probability was 38.8% (40/103). For those with a high risk, IN-REvASC ≥20, the probability was 90.0% (9/10) (Figure 3).
The IN-REvASC score (AUC, 0.84; 95% CI, 0.78 to 0.90) outperformed ISCVT-RS (AUC, 0.72; 95% CI, 0.66 to 0.79; χ2 P=0.03) for the prediction of mortality. For those in the low (IN-REvASC <10), moderate (IN-REvASC ≥10 to <20), and high (IN-REvASC ≥20) risk categories, the mortality risks were 1.9% (12/642), 15.3% (24/157), and 37.5% (6/16), respectively.

Losses to follow-up

Of the total study population, 16.3% (167/1025) were lost to follow-up prior to 90 days. Those lost to follow-up were less likely to be female, more likely to have substance use, and had higher median NIHSS on presentation compared to those with 90-day or greater follow-up. However, there were no differences in rates of in-hospital interventions (intubation, neurosurgical treatment, percutaneous endoscopic gastrostomy placement, tracheostomy) suggesting a similar clinical course as those with follow-up (Table 4).

Discussion

In this large, multicenter, international cohort study, we found that in patients with CVT, active cancer, age, Black race, encephalopathy or coma on presentation, decreased hemoglobin, higher NIHSS on presentation, and substance use were associated with poor outcome, defined as mRS 3-6, at 90 days. In contrast to prior reports, sex, clot location, and birth control [3,5] use were not associated with poor outcomes in this study.
Two prognostic score models were tested in our cohort: IN-REvASC and ISCVT-RS. In line with previous publications, ISCVT-RS achieved an AUC of 0.71 for poor 90-day outcome and 0.72 for mortality when tested in our cohort. However, three of the six scoring factors used in ISCVT-RS, male gender, parenchymal hemorrhage, and deep thrombus location, were not associated with poor outcome in our study. Compared to ISCVT-RS, the IN-REvASC score offers increased ease of use and improvement in prognostic accuracy with an AUC of 0.84 for poor 90-day outcome and 0.84 for mortality. The IN-REvASC score uses a series of factors associated with poor outcome and readily available clinical data in usual clinical practice increasing the ease of use. The nomogram scoring method used to generate IN-REvASC allows for the evaluation of a complex regression without any calculation required. IN-REvASC score cutoffs stratify patients into low, moderate, and high-risk groups for both poor outcome at 90-day follow-up and mortality. For those in the low-risk group, the probability of poor outcome was 5.1% (23/447) and the probability of mortality was 1.9% (12/642). For the moderate-risk group, these probabilities were 38.8% (40/103) and 15.3% (24/157). For the high-risk group, these probabilities were 90.0% (9/10) and 37.5% (6/16).
Prior studies have demonstrated that the presence of neurological symptoms is associated with worse outcome following CVT [3,5,7,25,26]. This study more specifically characterizes the association, demonstrating that increasing severity of neurological symptoms, as measured by NIHSS, is associated with higher odds of poor neurological outcome at 90 days. Encephalopathy or coma on presentation were also associated with poor 90-day outcome suggesting that both the overall severity of neurological symptoms as well as their localization are important.
In this study, we noted that Black race was associated with poor outcomes following CVT. Specifically, Black race was associated with an increased rate of poor neurologic outcome at 90 days (OR, 2.17) and increased mortality (OR, 2.62). In prior ischemic stroke literature, Black race was also associated with poor outcomes indicating that this racial disparity in outcomes is present across multiple forms of stroke [23]. Additionally, the incidence of CVT has been observed to be significantly higher in Black populations [27]. Taken together, Black patients are disproportionately more likely to experience CVT and more likely to experience poor outcomes. This disparity is likely the result of differences in social determinants of health, inequities in healthcare access, and experiences of structural racism between racial groups [28-32]. Importantly, the inclusion of race as a factor in the IN-REvASC score is an acknowledgement of an observed disparity only, not a statement of an intrinsic biological difference in CVT between racial groups. Caution should be taken when using these results to inform the care of individual patients and when enrolling patients in future studies. Future research is essential to identify and address the factors leading to these disparities.
Active cancer was associated with poor outcome at 90-day follow-up and mortality, and was the strongest prognostic factor for both outcomes, in agreement with previous literature [5,6]. Anemia has been previously identified as a risk factor for poor outcome following both ischemic stoke [33] and CVT [34] and, in our study, decreased hemoglobin was associated with poor outcome at 90-day follow-up. Serum creatinine greater than 1.0 mg/dL was also associated with poor outcome implicating poor renal function or hydration status in recovery after CVT, the latter of which has been previously suggested as a driver of poor outcome in CVT patients [35].
Evidence for the role of sex in CVT outcomes is mixed. The ISCVT study noted a disparity in outcomes by sex, explained by the presence of sex-specific risk factors such as pregnancy and oral contraceptives, while multiple other studies failed to show this disparity [3,7,36,37]. In this study, no difference in 90-day outcome was observed by sex or by peripartum status, and the use of birth control was not associated with poor outcome at 90-day follow-up or mortality in the multivariable analyses. Thus, providers should not assume a better or worse prognosis following CVT provoked by sex-specific risk factors.
Mortality in the ACTION-CVT cohort was lower than in previous studies. Compared to ISCVT, baseline patient characteristics were similar, and the rate of poor neurological outcome was similar between the studies. However, death during follow-up was less frequent at a rate of 5.12 per 100 person-years in ACTION-CVT, compared to 5.38 per 100 person-years in ISCVT [5]. This mortality reduction may be due to improvements in stroke care and CVT diagnosis since the publication of ISCVT.
The prognostic factors identified in this study stratify patients into risk groups for poor outcome. The presence of zero risk factors identifies 36.8% (239/649) of the cohort with a much lower risk of poor outcome compared with the overall cohort (2.9% vs. 15.6%; 7/239 vs. 101/649; OR, 0.19). Meanwhile, the presence of three or more risk factors identifies a small subset of the cohort with a much higher risk of poor outcome compared with the overall cohort (57.6% vs. 15.6%; 28/59 vs. 101/649; OR, 3.70).
Risk stratification using the prognostic scores generated here may identify a group of patients with higher severity of illness who may benefit from new therapies, such as endovascular clot retrieval, in future trials. The recent Thrombolysis or Anti-coagulation for Cerebral Venous Thrombosis (TO-ACT) trial was stopped early as it was unable to meet its primary endpoint, despite an absolute reduction in the proportion of patients with a poor outcome at follow-up [38]. However, future trials may show a benefit with endovascular therapy if conducted in patients at the highest risk of poor outcome.
While this work represents the largest study of patients with CVT to date, it is limited due to its observational, retrospective design. Bias may exist in this data due to the lack of standardized treatment protocol and observed differences may be due to a treatment effect rather than the variable under study. Allcause mortality was used in this study and deaths observed may not have been due to CVT directly, especially in patients with comorbidities such as active cancer. However, in comparison to disease-specific mortality, all-cause mortality is not susceptible to misclassification bias. Standardized training for the assessment of mRS was not provided to participating centers. Although the mRS scores were generated by either the treating clinician or experienced researchers and stroke coordinators, previous research has noted only moderate inter-rater reliability of mRS scores, potentially decreasing statistical power [39]. Patients lost to follow-up prior to 90-day were a substantial minority of the study population. Based upon available data, those lost to follow-up likely had a similar clinical course as those with follow-up. The higher rate of substance use and higher median NIHSS would contribute a bias towards the null for an effect of these variables on both poor outcome and mortality. This study was conducted prior to the introduction of COVID-19 vaccinations, as a result, CVT cases caused by vaccine induced thrombotic thrombocytopenia were not captured. Although internal validation of models was conducted using the bootstrap method, the prognostic model of this study should be interpreted with caution. External validation of the IN-REvASC model is important for future study. It is unknown whether this score is generalizable to populations with substantially different patient characteristics or healthcare settings. Finally, the primary outcome of this study, mRS 3-6 at 90-day follow-up, was low at 15.6%. While this is consistent with prior studies [5,40], it is possible that other factors such as concomitant COVID-19 infection [41,42] are relevant in the prognostication of CVT but were undetected in this study due to a small effect size.

Conclusions

Active cancer, age, Black race, encephalopathy or coma on presentation, decreased hemoglobin, higher NIHSS on presentation, and substance use were associated with poor outcome at 90-day follow-up following CVT. Active cancer, Black race, and substance use were also associated with mortality. The racial disparity in CVT outcomes, while in alignment with other types of stroke, requires further study. In contrast to previous literature, sex, clot location, and birth control use were not associated with any primary or secondary outcome in this study. These factors may not confer as high prognostic value to CVT as previously thought. The derived IN-REvASC model offers increased prognostic accuracy for the prediction of poor outcome at 90- day follow-up and mortality, compared to ISCVT-RS. IN-REvASC score cutoffs stratified patients into groups with significantly different outcomes at 90-day follow-up. Improved prognostication may aid in clinical decision making and allow for identification of patients with the possibility of benefit in future clinical trials.

Supplementary materials

Supplementary materials related to this article can be found online at https://doi.org/10.5853/jos.2022.01606.
Supplementary Table 1.
TRIPOD reporting guidelines checklist
jos-2022-01606-suppl1.pdf

Notes

Disclosure
Thanh Nguyen reports research support from Medtronic and the Society of Vascular and Interventional Neurology. Maurizio Paciaroni reports speaker honoraria from Sanofi-Aventis; Boehringer-Ingelheim, Bayer, BMS, Daiichi-Sankyo, Pfizer. Diana Aguiar de Sousa reports speaker fees from Bayer, travel support from Boehringer Ingelheim, participating in an advisory board for AstraZeneca, and DSMB participation for the SECRET trial, outside the submitted work.

Acknowledgments

This work has been partially supported by the Italian Ministry of Health Ricerca Corrente-IRCCS Multimedica. The complete list of members and affiliations can be found in Appendix 1.

Figure 1.
Prognostic models for poor outcome at 90-day follow-up. AUC, area under the curve; ISCVT-RS, International Study on Cerebral Vein and Dural Sinus Thrombosis risk score.
jos-2022-01606f1.jpg
Figure 2.
Study flow chart. ACTION-CVT, Anticoagulation in the Treatment of Cerebral Venous Thrombosis; mRS, modified Rankin Scale.
jos-2022-01606f2.jpg
Figure 3.
Distribution of scores on the modified Rankin Scale (mRS) at 90 days according to IN-REvASC risk groups.
jos-2022-01606f3.jpg
Table 1.
Demographic and baseline clinical characteristics of the ACTION-CVT study population
Factor No./total no. (%) Median (IQR)
Age (yr) 44 (32-58)
Race
 White 710/1,018 (69.7)
 Black 160/1,018 (15.7)
 Asian 40/1,018 (3.9)
 Other 108/1,018 (10.6)
Ethnicity
 Hispanic 102/1,014 (10.1)
Sex
 Female 643/1,025 (62.7)
BMI (kg/m2) 28.00 (23.88-33.10)
Medical history
 Chronic kidney disease 39/1,023 (3.8)
 Active cancer 67/1,023 (6.5)
 Prior DVT 101/1,024 (9.9)
 Prior PE 57/1,023 (5.6)
 Antiphospholipid syndrome 11/1,001 (1.1)
 COVID-19 tested 148/1,025 (14.4)
 COVID-19 positive 8/148 (5.4)
 12 Weeks postpartum 38/1,016 (3.7)
 Recent mastoiditis or sinusitis 89/1,025 (8.7)
 Recent lumbar puncture 46/1,025 (4.5)
 Recent head trauma 89/1,024 (8.7)
 Smoking 146/1,019 (14.3)
 Alcohol abuse 84/1,019 (8.2)
 Substance use 59/1,019 (5.8)
 Family history of VTE 101/1,018 (9.9)
Medications
 Antiplatelet 104/1,024 (10.2)
 Warfarin 39/1,025 (3.8)
 DOAC 32/1,025 (3.1)
 Birth control 235/1,007 (23.3)
Days from symptoms to diagnosis 4 (1-10)
Previously discharged from ED with missed diagnosis 157/1,024 (15.3)
Symptoms
 Headache 762/1,023 (74.5)
 Focal deficit 401/1,024 (39.2)
 Seizure 243/1,024 (23.7)
 Encephalopathy 209/1,024 (20.4)
 Coma 29/1,024 (2.8)
 Papilledema 100/958 (10.4)
 NIHSS 0.00 (0.00-3.00)
Imaging features
 Venous infarct 273/1,025 (26.6)
 Edema 318/1,025 (31.0)
 Any hemorrhage 390/1,025 (38.0)
CVT location
 Superficial veins 745/1,023 (72.8)
 Deep veins 118/1,023 (11.5)
 Cortical vein only 30/1,023 (2.9)
 Both superficial and deep veins 130/1,023 (12.7)
Laboratory features
 1+ Antiphospholipid antibody positive 82/839 (9.8)
 Protein C <65 60/571 (10.5)
 Protein S <70 120/559 (21.5)
 Antithrombin <80 82/572 (14.3)
 Factor V mutation 47/679 (6.9)
 Prothrombin mutation 32/635 (5.0)
 White blood count (103/mL) 9.60 (7.40-12.50)
 Hemoglobin (g/dL) 13.30 (11.70-14.70)
 Platelet count (103/mL) 247.00 (200.00-309.00)
 Creatinine (mg/dL) 0.80 (0.69-0.95)
ACTION-CVT, Anticoagulation in the Treatment of Cerebral Venous Thrombosis; IQR, interquartile range; BMI, body mass index; DVT, deep vein thrombosis; PE, pulmonary embolism; COVID-19, coronavirus disease 2019; VTE, venous thromboembolism; DOAC, direct oral anticoagulation; ED, emergency department; NIHSS, National Institutes of Health Stroke Scale; CVT, cerebral venous thrombosis.
Table 2.
Univariable prognostic factors of poor neurological outcome at 90-day follow-up
Factor mRS 0-2 mRS 3-6 OR 95% CI P
Age (yr) 43.95±16.0 51.95±16.79 1.03 1.02-1.04 <0.001
Race
 White 393/546 (72.0) 54/99 (54.5) 0.47 0.30-0.72 0.001
 Black 83/546 (15.2) 29/99 (29.3) 2.31 1.41-3.78 0.001
 Asian 21/546 (3.8) 2/99 (2.0) 0.52 0.12-2.23 0.557
 Other 49/546 (9.0) 14/99 (14.1) 1.67 0.88-3.16 0.111
Ethnicity
 Hispanic 61/547 (11.2) 6/98 (6.1) 0.52 0.22-1.24 0.133
Sex
 Female 352/548 (64.2) 58/101 (57.4) 0.75 0.49-1.16 0.192
Medical history
 Active cancer 15/548 (2.7) 23/100 (23.0) 10.61 5.31-21.22 <0.001
 Prior VTE 58/548 (10.6) 18/101 (17.8) 1.83 1.03-3.27 0.038
 Antiphospholipid syndrome 7/548 (1.3) 2/97 (2.1) 1.63 0.33-7.95 0.631
 COVID-19 positive 2/72 (2.8) 2/20 (10.0) 3.89 0.51-29.53 0.205
 12 Weeks postpartum 20/539 (3.7) 2/101 (2.0) 0.52 0.12-2.28 0.555
 Recent mastoiditis or sinusitis 47/548 (8.6) 10/101 (9.9) 1.17 0.57-2.40 0.666
 Recent lumbar puncture 24/548 (4.4) 6/101 (5.9) 1.38 0.55-3.46 0.492
 Recent head trauma 52/548 (9.5) 15/101 (14.9) 1.66 0.90-3.09 0.104
 Substance use 77/542 (14.2) 25/100 (25.0) 2.01 1.21-3.36 0.007
 Family history of VTE 66/543 (12.2) 6/100 (6.0) 0.46 0.19-1.10 0.073
Medications
 Warfarin 14/548 (2.6) 3/101 (3.0) 1.17 0.33-4.14 0.737
 DOAC 14/548 (2.6) 4/101 (4.0) 1.57 0.51-4.88 0.504
 Birth control 138/534 (25.8) 12/101 (11.9) 0.39 0.21-0.73 0.002
Days from symptoms to diagnosis 4 (1-10) 3 (0-8) 1.00 0.99-1.01 0.055
Previously discharged from ED with missed diagnosis 85/548 (15.5) 13/101 (12.9) 0.81 0.43-1.51 0.496
Symptoms
 Focal deficit 147/450 (32.7) 14/43 (32.6) 1.00 0.51-1.94 0.988
 Seizure 135/548 (24.6) 28/101 (27.7) 1.17 0.73-1.89 0.511
 Encephalopathy or coma 98/548 (17.9) 58/101 (57.4) 6.19 3.95-9.72 <0.001
 NIHSS 0 (0-2) 3.5 (1-12) 1.11 1.07-1.14 <0.001
Imaging features
 Venous infarct 145/548 (26.5) 28/101 (27.7) 1.07 0.66-1.72 0.792
 Edema 161/548 (29.4) 46/101 (45.5) 2.01 1.30-3.10 0.001
 Any hemorrhage 207/548 (37.8) 52/101 (51.5) 1.75 1.14-2.68 0.010
CVT location
 Superficial veins 385/547 (70.4) 73/101 (72.3) 1.10 0.68-1.76 0.701
 Deep veins 71/547 (13.0) 12/101 (11.9) 0.90 0.47-1.74 0.761
 Cortical vein only 14/547 (2.6) 2/101 (2.0) 0.77 0.17-3.44 1.000
 Both superficial and deep veins 77/547 (14.1) 14/101 (13.9) 0.98 0.53-1.81 0.954
Laboratory features
 1+ Hypercoagulable factor 198/548 (36.1) 22/101 (21.8) 0.49 0.30-0.82 0.005
 White blood count (103/mL) 10.04±3.97 10.77±4.66 1.04 0.99-1.10 0.103
 Hemoglobin (g/dL) 13.06±2.35 12.08±2.52 0.85 0.78-0.93 <0.001
 Platelet count (103/mL) 269.12±102.14 255.45±117.82 1.00 1.00-1.00 0.234
 PTT (sec) 30.41±21.07 28.65±9.92 0.99 0.98-1.01 0.430
 Creatinine (mg/dL) 0.84±0.26 0.9±0.37 1.95 1.00-3.81 0.048
Values are presented as mean±standard deviation or number/total number (%).
mRS, modified Rankin Scale; OR, odds ratio; CI, confidence interval; VTE, venous thromboembolism; COVID-19, coronavirus disease 2019; DOAC, direct oral anticoagulation; ED, emergency department; NIHSS, National Institutes of Health Stroke Scale; CVT, cerebral venous thrombosis; PTT, partial thromboplastin time.
Table 3.
Multivariable prognostic factors of poor outcome at 90-day follow-up and mortality
Factor Poor outcome
Mortality
OR 95% CI P OR 95% CI P
Active cancer 11.20 4.62-27.14 <0.001 12.17 5.45-27.16 <0.001
Age 1.02 1.00-1.04 0.039 1.02 1.00-1.04 0.083
Birth control 1.17 0.52-2.65 0.700 0.32 0.07-1.47 0.143
Black race 2.17 1.10-4.27 0.025 2.62 1.20-5.69 0.015
Creatinine 1.97 0.72-5.33 0.185 1.40 0.68-2.86 0.361
Encephalopathy or coma 2.71 1.39-5.30 0.004 1.72 0.79-3.76 0.170
Hemoglobin 0.86 0.77-0.97 0.014 0.97 0.85-1.10 0.618
1+ Hypercoagulable factor 0.88 0.46-1.69 0.694 0.93 0.40-2.14 0.857
Cerebral edema on imaging 0.89 0.46-1.72 0.726 0.90 0.41-1.96 0.789
Hemorrhage on imaging 1.62 0.85-3.09 0.145 1.73 0.79-3.78 0.171
NIHSS 1.07 1.02-1.11 0.002 1.02 0.98-1.07 0.338
Prior VTE 1.64 0.71-3.77 0.246 0.91 0.35-2.36 0.845
Substance use 2.34 1.16-4.71 0.017 2.17 1.03-4.60 0.042
OR, odds ratio; CI, confidence interval; NIHSS, National Institutes of Health Stroke Scale; VTE, venous thromboembolism.
Table 4.
Comparison of patients with and without 90-day follow-up
Factor <90-day follow-up ≥90-day follow-up P
Age (yr) 47.19±17.77 45.19±16.64 0.163
Race
 White 109/165 (66.1) 577/812 (71.1) 0.201
 Black 23/165 (13.9) 126/812 (15.5) 0.607
 Asian 10/165 (6.1) 29/812 (3.6) 0.136
 Other 23/165 (13.9) 80/812 (9.9) 0.119
Ethnicity
 Hispanic 19/162 (11.7) 83/811 (10.2) 0.571
Sex
 Female 94/167 (56.3) 531/817 (65.0) 0.033
BMI (kg/m2) 28.17±6.76 29.64±7.72 0.024
Medical history
 Chronic kidney disease 11/167 (6.6) 25/815 (3.1) 0.027
 Active cancer 13/167 (7.8) 38/815 (4.7) 0.098
 Prior VTE 18/167 (10.8) 96/817 (11.8) 0.721
 Antiphospholipid syndrome 1/158 (0.6) 10/805 (1.2) 1.000
 COVID-19 tested 40/167 (24.0) 98/817 (12.0) 0.000
 COVID-19 positive 2/40 (5.0) 4/98 (4.1) 1.000
 12 Weeks postpartum 8/165 (4.8) 28/810 (3.5) 0.388
 Recent mastoiditis or sinusitis 19/167 (11.4) 68/817 (8.3) 0.205
 Recent lumbar puncture 10/167 (6.0) 34/817 (4.2) 0.298
 Recent head trauma 16/167 (9.6) 67/816 (8.2) 0.562
 Smoking 35/167 (21.0) 102/811 (12.6) 0.004
 Alcohol abuse 21/167 (12.6) 58/811 (7.2) 0.019
 Substance use 40/167 (24.0) 120/810 (14.8) 0.016
 Family history of VTE 13/167 (7.8) 87/810 (10.7) 0.251
Medications
 Antiplatelet 13/167 (7.8) 85/816 (10.4) 0.301
 Warfarin 10/167 (6.0) 28/817 (3.4) 0.118
 DOAC 5/167 (3.0) 25/817 (3.1) 1.000
 Birth control 40/163 (24.5) 192/803 (23.9) 0.864
Days from symptoms to diagnosis 3.0 (1.0-7.0) 4.0 (1.0-10.0) 0.029
Previously discharged from ED with missed diagnosis 27/167 (16.2) 126/816 (15.4) 0.813
Symptoms
 Focal deficit 42/127 (33.1) 230/657 (35.0) 0.675
 Seizure 39/167 (23.4) 198/816 (24.3) 0.802
 Encephalopathy or coma 40/167 (24.0) 159/816 (19.5) 0.191
 Papilledema 7/154 (4.5) 88/766 (11.5) 0.010
 NIHSS 1.0 (0.0-4.0) 0.0 (0.0-2.0) 0.004
Imaging features
 Venous infarct 47/167 (28.1) 214/817 (26.2) 0.603
 Edema 54/167 (32.3) 247/817 (30.2) 0.591
 Any hemorrhage 62/167 (37.1) 309/817 (37.8) 0.866
CVT location
 Superficial veins 125/167 (74.9) 587/815 (72.0) 0.456
 Deep veins 19/167 (11.4) 93/815 (11.4) 0.990
 Cortical vein only 5/167 (3.0) 25/815 (3.1) 1.000
 Both superficial and deep veins 18/167 (10.8) 110/815 (13.5) 0.342
Laboratory features
 1+ Antiphospholipid antibody positive 8/123 (6.5) 72/690 (10.4) 0.178
 Protein C <65 10/67 (14.9) 48/492 (9.8) 0.193
 Protein S <70 15/64 (23.4) 103/484 (21.3) 0.693
 Antithrombin <80 11/66 (16.7) 69/498 (13.9) 0.538
 Factor V mutation 5/94 (5.3) 42/571 (7.4) 0.663
 Prothrombin mutation 2/87 (2.3) 30/535 (5.6) 0.294
 1+ Hypercoagulable factor 37/167 (22.2) 277/817 (33.9) 0.003
 White blood count (103/mL) 10.72±4.12 10.05±4.12 0.059
 Hemoglobin (g/dL) 13.07±2.38 13.02±2.42 0.842
 Platelet count (103/mL) 259.88±95.19 266.32±103.66 0.463
 Creatinine (mg/dL) 0.94±0.81 0.85±0.33 0.025
Hospital course
 Intubation 20/167 (12.0) 90/816 (11.0) 0.724
 Tracheostomy 2/167 (1.2) 21/817 (2.6) 0.403
 PEG tube insertion 6/167 (3.6) 25/817 (3.1) 0.719
 Neurosurgical treatment 6/167 (3.6) 45/817 (5.5) 0.309
Values are presented as mean±standard deviation, number/total number (%), or median (interquartile range).
BMI, body mass index; DVT, deep vein thrombosis; COVID-19, coronavirus disease 2019; DOAC, direct oral anticoagulation; ED, emergency department; NIHSS, National Institutes of Health Stroke Scale; CVT, cerebral venous thrombosis; PEG, percutaneous endoscopic gastrostomy.

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Appendices

Appendix 1.

The ACTION-CVT study group

Author Affiliation
Ekaterina Bakradze Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
James A. Giles Department of Neurology, Washington University, Saint Louis, MO, USA
Jordan Y. Amar Department of Neurology, Washington University, Saint Louis, MO, USA
Nils Henninger Department of Neurology, University of Massachusetts, Worcester, MA, USA; Department of Psychiatry, University of Massachusetts, Worcester, MA, USA
Marwa Elnazeir Department of Neurology, University of Massachusetts, Worcester, MA, USA
Ava L. Liberman Department of Neurology, Weill Cornell Medical Center, New York, NY, USA
Khadean Moncrieffe Department of Neurology, Montefiore Medical Center, New York, NY, USA
Jenny Lu Department of Neurology, Montefiore Medical Center, New York, NY, USA
Richa Sharma Department of Neurology, Yale University, New Haven, CT, USA
Yee Cheng Department of Neurology, Yale University, New Haven, CT, USA
Adeel S. Zubair Department of Neurology, Yale University, New Haven, CT, USA
Grace T. Li Department of Neurology, University of Florida, Gainesville, FL, USA
Justin Chi Kung Department of Neurology, University of Florida, Gainesville, FL, USA
Dezaray Perez Department of Neurology, University of Florida, Gainesville, FL, USA
Adrian Scutelnic Department of Neurology, Inselspital Universitätsspital, Bern, Switzerland
David Seiffge Department of Neurology, Inselspital Universitätsspital, Bern, Switzerland
Bernhard Siepen Department of Neurology, Inselspital Universitätsspital, Bern, Switzerland
Aaron Rothstein Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
Ossama Khazaal Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
David Do Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
Sami Al Kasab Department of Neurology, Medical University of South Carolina, Charleston, SC, USA; Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, USA
Line Abdul Rahman Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
Eva A. Mistry Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Deborah Kerrigan Department of Neurology, Vanderbilt University, Nashville, TN, USA
Hayden Lafever Department of Neurology, Vanderbilt University, Nashville, TN, USA
Jennifer Frontera Department of Neurology, New York University, New York, NY, USA
Lindsey Kuohn Department of Neurology, New York University, New York, NY, USA
Shashank Agarwal Department of Neurology, New York University, New York, NY, USA
Christoph Stretz Department of Neurology, Brown University, Providence, RI, USA
Narendra Kala Department of Neurology, Brown University, Providence, RI, USA
Sleiman El Jamal Department of Neurology, Brown University, Providence, RI, USA
Alison Chang Department of Neurology, Brown University, Providence, RI, USA
Shawna Cutting Department of Neurology, Brown University, Providence, RI, USA
Han Xiao Department of Biostatistics, University of California Santa Barbara, Santa Barbara, CA, USA
Varsha Muddasani Department of Neurology, University of Utah, Salt Lake City, UT, USA
Teddy Wu Department of Neurology, Christchurch Hospital, Christchurch, New Zealand
Duncan Wilson Department of Neurology, Christchurch Hospital, Christchurch, New Zealand
Amre Nouh Department of Neurology, Hartford Hospital, Hartford, CT, USA
Syed Daniyal Assad Department of Neurology, Hartford Hospital, Hartford, CT, USA
Abid Qureshi Department of Neurology, University of Kansas, Kansas City, KS, USA
Justin Moore Department of Neurology, University of Kansas, Kansas City, KS, USA
Pooja Khatri Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Yasmin Aziz Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Bryce Casteigne Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
Muhib Khan Department of Neurology, Spectrum Health, Michigan State University, Grand Rapids, MI, USA
Yao Cheng Department of Neurology, Spectrum Health, Michigan State University, Grand Rapids, MI, USA
Brian Mac Grory Department of Neurology, Duke University, Durham, NC, USA
Martin Weiss Department of Neurology, Duke University, Durham, NC, USA
Dylan Ryan Department of Neurology, Duke University, Durham, NC, USA
Scott Kamen Department of Neurology, Cooper University, Camden, NJ, USA
Siyuan Yu Department of Neurology, Cooper University, Camden, NJ, USA
Christopher R. Leon Guerrero Department of Neurology, George Washington University, District of Columbia, USA
Eugenie Atallah Department of Neurology, George Washington University, District of Columbia, USA
Gian Marco De Marchis Department of Neurology, University Hospital Basel and University of Basel, Basel, Switzerland
Alex Brehm Department of interventional and diagnostic Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
Tolga Dittrich Department of Neurology, University Hospital Basel and University of Basel, Basel, Switzerland
Ronald Alvarado-Dyer Department of Neurology, University of Chicago, Chicago, IL, USA
Tareq Kass-Hout Department of Neurology, University of Chicago, Chicago, IL, USA
Shyam Prabhakaran Department of Neurology, University of Chicago, Chicago, IL, USA
Tristan Honda Department of Neurology, University of California at Los Angeles, Los Angeles, CA, USA
Karen Furie Department of Neurology, Brown University, Providence, RI, USA


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