| Literature DB >> 32577893 |
Federico Geraldini1, Alessandro De Cassai2, Christelle Correale2, Giulio Andreatta2, Marzia Grandis2, Paolo Navalesi2, Marina Munari2.
Abstract
BACKGROUND: Subarachnoid hemorrhage is a severe subtype of hemorrhagic stroke, and deep-vein thrombosis is a frequent complication detected in these patients. In addition to other well-established risk factors, the early activation of coagulation systems present in patients with subarachnoid hemorrhage could potentially play a role in the incidence of deep-vein thrombosis. This study aims to identify possible predictors for deep-vein thrombosis related to subarachnoid hemorrhage.Entities:
Keywords: D-dimer; Deep-vein thrombosis; Intracerebral hemorrhage; Retrospective study; Risk assessments; Subarachnoid hemorrhages
Mesh:
Substances:
Year: 2020 PMID: 32577893 PMCID: PMC7311113 DOI: 10.1007/s00701-020-04455-x
Source DB: PubMed Journal: Acta Neurochir (Wien) ISSN: 0001-6268 Impact factor: 2.216
Univariate analysis of classic predictors of deep-vein thrombosis. DVT deep-vein thrombosis, BMI body mass index, CKD chronic kidney disease
| DVT ( | No DVT ( | ||
|---|---|---|---|
| Male sex | 10 (34.50%) | 46 (32.60%) | 0.846 |
| Age (years) | 63(53–72) | 58(48–69) | 0.155 |
| Cancer | 0 (0.00%) | 2 (1.40%) | 0.518 |
| Diabetes mellitus | 2 (6.90%) | 9 (6.40%) | 0.918 |
| Liver disease | 0 (0.00%) | 3 (2.10%) | 0.428 |
| BMI > 30 kg/cm2 | 0 (0.00%) | 2 (1.40%) | 0.518 |
| Hypertension | 16 (55.20%) | 58 (41.10%) | 0.165 |
| CKD (%) | 0 (0.00%) | 1 (0.70%) | 0.649 |
| Tobacco use | 6 (20.70%) | 32 (22.70%) | 0.813 |
*Statistically significant results
Univariate analysis of possible predictors of deep-vein thrombosis linked to subarachnoid hemorrhage.
| DVT ( | No DVT ( | ||
|---|---|---|---|
| ICA | 0 (0.00%) | 18 (13.47%) | 0.046* |
| MCA | 7 (24.13%) | 25 (17.73%) | 0.467 |
| ACA | 3 (10.34%) | 10 (7.09%) | 0.388 |
| ACoA | 12 (41.37%) | 48 (34.04%) | 1.000 |
| CP | 6 (20.68%) | 30 (21.98%) | 0.437 |
| SM | 1 (3.44%) | 10 (7.80%) | 0.693 |
| ICH | 11 (37.90%) | 27 (19.10%) | 0.027* |
| IVH | 14 (48.30%) | 71 (50.40%) | 0.838 |
| Vasospasm | 9 (31.00%) | 41 (29.10%) | 0.833 |
| Surgical clipping | 14 (48.30%) | 45 (31.90%) | 0.091 |
| Dec craniectomy | 5 (17.20%) | 12 (8.5%) | 0.153 |
| WFNS scale | 2 (1–5) | 3 (2–5) | 0.996 |
| Fisher scale | 4 (3–4) | 4 (3–4) | 0.168 |
| D-dimer at hosp (mcg/L) | 965 (488–2166) | 543 (266–1107) | 0.002* |
| PT start (days) | 4 (3–5) | 3 (2–4) | 0.010* |
| Ventilation days (days) | 14.17 ± 11.46 | 11.06 ± 12.31 | 0.196 |
| Infection (%) | 8 (27.59%) | 35 (24.82%) | 0.755 |
| Motor deficit (%) | 17 (58.62%) | 47 (33.33%) | 0.010* |
ICA internal carotid artery, MCA middle cerebral artery, ACA anterior cerebral artery, ACoA anterior communicating artery, CP posterior cerebral artery, SM sine materia, ICH intraparenchymal cerebral hemorrhage, IVH intraventricular hemorrhage, WFNS World Federation of Neurological Surgeons, PT pharmacologic thromboprophylaxis
*Statistically significant results
Logistic regression and fitted model of possible predictors of deep-vein thrombosis linked to subarachnoid hemorrhage.
| Logistic regression | Fitted model | |||||
|---|---|---|---|---|---|---|
| OR | 95% CI | OR | 95% CI | |||
| ICA (%) | 6.26 * 10−8 | 3.5.83 * 10−50–2.68 * 1024 | 0.990 | |||
| MCA(%) | 0.99 | 0.18–6.35 | 0.997 | |||
| ACA(%) | 1.15 | 0.34–4.32 | 0.935 | |||
| ACoA(%) | 1.40 | 0.29–8.49 | 0.688 | |||
| CP(%) | 1.25 | 0.23–8.32 | 0.802 | |||
| SM(%) | 0.65 | 0.02–9.61 | 0.769 | |||
| ICH(%) | 2.59 | 0.81–8.39 | 0.106 | 2.78 | 1.07–7.12 | 0.032* |
| IVH(%) | 0.99 | 0.33–2.99 | 0.986 | |||
| Vasospasm (%) | 1.11 | 0.38–3.05 | 0.840 | |||
| Surgical clipping (%) | 0.49 | 0.16–1.42 | 0.194 | |||
| Dec craniectomy (%) | 1.65 | 0.34.-7.75 | 0.520 | |||
| WFNS scale | 0.71 | 0.43–1.10 | 0.147 | 0.73 | 0.50–1.02 | 0.086 |
| Fisher | 0.97 | 0.001–1.11 | 0.819 | |||
| D-dimer at hosp (mcg/L) | 1.003 | 1.001–1.005 | 0.028* | 1.002 | 1.001–1.003 | 0.042* |
| PT start (days) | 1.16 | 0.90–1.05 | 0.226 | |||
| Ventilation days (days) | 1.004 | 0.95–1.05 | 0.864 | |||
| Infection (%) | 0.71 | 0.20–2.44 | 0.597 | |||
| Motor deficit (%) | 6.3 | 1.77–24.2 | 0.005* | 3.46 | 1.37–9.31 | 0.010* |
ICA internal carotid artery, MCA middle cerebral artery, ACA anterior cerebral artery, ACoA anterior communicating artery, CP posterior cerebral artery, SM sine materia, ICH intraparenchymal cerebral hemorrhage, IVH intraventricular hemorrhage, WFNS World Federation of Neurological Surgeons, PT pharmacologic thromboprophylaxis
*Statistically significant results
Fig. 1Receiving operator curve for d-dimer level at hospitalization
Fig. 2Combined ROC curve for d-dimer, motor deficit, and ICH
Fig. 3Kaplan-Meier curve for deep-vein thrombosis and intraparenchymal cerebral hemorrhage (p value 0.003). Black continuous line represents population without intraparenchymal cerebral hemorrhage while blue dotted line represents population with intraparenchymal cerebral hemorrhage
Fig. 4Kaplan-Meier curves for deep-vein thrombosis (p value 0.006). Black continuous line represents population with d-dimer below 687 mcg/L while blue dotted line represents population with d-dimer above 687 mcg/L