Traumatic brain injury epidemiology in Europe

Traumatic brain injury epidemiology in Europe

In 2018 a systematic review provided a comprehensive, up-to-date summary of traumatic brain injury (TBI) epidemiology in Europe, describing incidence, mortality, age, and sex distribution, plus severity, mechanism of injury, and time trends. PubMed, CINAHL, EMBASE, and Web of Science were searched in January 2015 for observational, descriptive, English language studies reporting incidence, mortality, or case fatality of TBI in Europe. There were no limitations according to date, age, or TBI severity. Methodological quality was assessed using the Methodological Evaluation of Observational Research checklist. Data were presented narratively. Sixty-six studies were included in the review. Country-level data were provided in 22 studies, regional population or treatment center catchment area data were reported by 44 studies. Crude incidence rates varied widely. For all ages and TBI severities, crude incidence rates ranged from 47.3 per 100,000, to 694 per 100,000 population per year (country-level studies) and 83.3 per 100,000, to 849 per 100,000 population per year (regional-level studies). Crude mortality rates ranged from 9 to 28.10 per 100,000 population per year (country-level studies), and 3.3 to 24.4 per 100,000 population per year (regional-level studies.) The most common mechanisms of injury were traffic accidents and falls. Over time, the contribution of traffic accidents to total TBI events may be reducing. Case ascertainment and definitions of TBI are variable. Improved standardization would enable more accurate comparisons 1).


In 2016 aimed to estimate the hospital-based incidence, population-wide mortality, and the contribution of TBI to injury-related mortalities in European countries, and to provide European summary estimates for these indicators.

For this cross-sectional analysis, we obtained population data from Eurostat for hospital discharges and causes of death in European countries in 2012. Outcomes of interest were TBIs that required hospital admission or were fatal. We calculated age-adjusted hospital discharge rates and mortality rates and extrapolated data to 28 European Union countries and all 48 states in Europe. We present between-country comparisons, pooled age-adjusted rates, and comparisons with all-injury rates.

In 2012, 1 375 974 hospital discharges (data from 24 countries) and 33 415 deaths (25 countries) related to TBI were identified. The pooled age-adjusted hospital discharge rate was 287·2 per 100 000 (95% CI 232·9-341·5) and the pooled age-adjusted mortality rate was 11·7 per 100 000 (9·9-13·6). TBI caused 37% (95% CI 36-38) of all injury-related deaths in the analysed countries. Extrapolating our results, we estimate 56 946 (95% CI 47 286-66 099) TBI-related deaths and 1 445 526 (1 172 996-1 717 039) hospital discharges occurred in 2012 in the European Union (population 508·5 million) and about 82 000 deaths and about 2·1 million hospital discharges in the whole of Europe (population 737 million). We noted substantial between-country differences.

TBI is an important cause of death and hospital admissions in Europe. The substantial between-country differences observed warrant further study and suggest that the true burden of TBI in Europe has not yet been captured. Rigorous epidemiological studies are needed to fully quantify the effect of TBI on society. Despite a great degree of consistency in data reporting across countries already being achieved, further efforts in this respect could improve the validity of between-country comparisons 2).


In 2015 a total, 28 epidemiological studies on TBI from 16 European countries were identified in the literature. A great variation was found in case definitions and case ascertainment between studies. Falls and road traffic accidents (RTA) were the two most frequent causes of TBI, with falls being reported more frequently than RTA 3).

A search was conducted in the PubMed electronic database using the terms: epidemiology, incidence, brain injur*, head injur* and Europe. Only articles published in English and reporting on data collected in Europe between 1990 and 2014 were included. In total, 28 epidemiological studies on TBI from 16 European countries were identified in the literature. A great variation was found in case definitions and case ascertainment between studies. Falls and road traffic accidents (RTA) were the two most frequent causes of TBI, with falls being reported more frequently than RTA. In most of the studies a peak TBI incidence was seen in the oldest age groups. In the meta-analysis, an overall incidence rate of 262 per 100,000 for admitted TBI was derived.

Interpretation of published epidemiologic studies is confounded by differences in inclusion criteria and case ascertainment. Nevertheless, changes in epidemiological patterns are found: falls are now the most common cause of TBI, most notably in elderly patients. Improvement of the quality of standardised data collection for TBI is mandatory for reliable monitoring of epidemiological trends and to inform appropriate targeting of prevention campaigns 4).

In 2006 it was difficult to reach a consensus on all epidemiological findings across the 23 published European studies because of critical differences in methods employed across the reports 5).

In a retrospectivelongitudinal study of all TBI patients treated in ICU between 2013-2018, 77% (n=171) were male and the median age was 46 (Q1-Q3: 28-62). The most common mechanism of injury was fall from less than two meters (<2m) followed by road traffic accidents (RTA). The proportion of injuries due to RTA increased over the six-year period (p=0.006). 41.4% (n=92) of injuries had reported alcohol involvement. Patients with falls <2m had double the median age and double the rate of alcohol involvement compared to those suffering RTA (p<0.001, p<0.001). The neurosurgical intervention rate was 74% (n=165). The median duration of ICU admission and of intracranial pressure monitoring, advanced ventilation, and inotropic therapy increased over the six-year period (p=0.031, p=0.038, p=0.033, p<0.001). This study’s findings could inform precise and impactful public prevention measures. The increasing duration of ICU admission and of other interventions should be examined further for their effect on patient outcomes and resource consumption 6).

Traumatic brain injury epidemiology in Finland

A coordinated strategy to evaluate this public health problem in Romania would first of all rely on a related advanced monitoring system, to provide precise information about the epidemiology, clinical and paraclinical data, but concerning the social and economic connected consequences, too 7).

Traumatic brain injury epidemiology in Spain


1)

Brazinova A, Rehorcikova V, Taylor MS, Buckova V, Majdan M, Psota M, Peeters W, Feigin V, Theadom A, Holkovic L, Synnot A. Epidemiology of Traumatic Brain Injury in Europe: A Living Systematic Review. J Neurotrauma. 2018 Dec 19. doi: 10.1089/neu.2015.4126. Epub ahead of print. PMID: 26537996.
2)

Majdan M, Plancikova D, Brazinova A, Rusnak M, Nieboer D, Feigin V, Maas A. Epidemiology of traumatic brain injuries in Europe: a cross-sectional analysis. Lancet Public Health. 2016 Dec;1(2):e76-e83. doi: 10.1016/S2468-2667(16)30017-2. Epub 2016 Nov 29. PMID: 29253420.
3) , 4)

Peeters W, van den Brande R, Polinder S, Brazinova A, Steyerberg EW, Lingsma HF, Maas AI. Epidemiology of traumatic brain injury in Europe. Acta Neurochir (Wien). 2015 Oct;157(10):1683-96. doi: 10.1007/s00701-015-2512-7. Epub 2015 Aug 14. PubMed PMID: 26269030.
5)

Tagliaferri F, Compagnone C, Korsic M, Servadei F, Kraus J. A systematic review of brain injury epidemiology in Europe. Acta Neurochir (Wien). 2006 Mar;148(3):255-68; discussion 268. Review. PubMed PMID: 16311842.
6)

Forrest C, Healy V, Plant R. Temporal Trends in Traumatic Brain Injury. Ir Med J. 2022 May 25;115(5):597. PMID: 35696279.
7)

Popescu C, Anghelescu A, Daia C, Onose G. Actual data on epidemiological evolution and prevention endeavours regarding traumatic brain injury. J Med Life. 2015 Jul-Sep;8(3):272-7. Review. PubMed PMID: 26351526; PubMed Central PMCID: PMC4556905.

Severe traumatic brain injury outcome

Severe traumatic brain injury outcome

Females exhibited more favorable cerebral physiology post-Traumatic Brain Injury, particularly better mitochondrial function, and reduced excitotoxicity, but this did not translate into better clinical outcomes compared to males. Future studies need to further explore potential sex differences in secondary injury mechanisms in TBI 1).


deep learning model of head computed tomography and clinical information can be used to predict 6-month severe traumatic brain injury outcome 2).


Younger age, modified Fisher scale (mFS) score, and Intracerebral hemorrhage volume are associated with Intracranial pressure elevation in patients with a severe traumatic brain injury. Imaging features may stratify patients by their risk of subsequent ICP elevation 3).


There has been a secular trend towards reduced incidence of severe traumatic brain injury in the first world, driven by public health interventions such as seatbelt legislation, helmet use, and workplace health and safety regulations. This has paralleled improved outcomes following TBI delivered in a large part by the widespread establishment of specialised neurointensive care 4).

The impact of a moderate to severe brain injury depends on the following:

Severity of initial injury

Rate/completeness of physiological recovery

Functions affected

Meaning of dysfunction to the individual

Resources available to aid recovery

Areas of function not affected by TBI

see Effect of trauma center designation in severe traumatic brain injury outcome


Mortality or severe disability affects the majority of patients after severe traumatic brain injury (TBI). Adherence to the brain trauma foundation severe traumatic brain injury guidelines has overall improved outcomes; however, traditional as well as novel interventions towards intracranial hypertension and secondary brain injury have come under scrutiny after series of negative randomized controlled trials. In fact, it would not be unfair to say there has been no single major breakthrough in the management of severe TBI in the last two decades. One plausible hypothesis for the aforementioned failures is that by the time treatment is initiated for neuroprotection, or physiologic optimization, irreversible brain injury has already set in. Lazaridis et al., and others, have developed predictive models based on machine learning from continuous time series of intracranial pressure and partial pressure of brain tissue oxygen. These models provide accurate predictions of physiologic crises events in a timely fashion, offering the opportunity for an earlier application of targeted interventions. In a article, Lazaridis et al., review the rationale for prediction, discuss available predictive models with examples, and offer suggestions for their future prospective testing in conjunction with preventive clinical algorithm5).


Determining the prognostic significance of clinical factors for patients with severe head injury can lead to an improved understanding of the pathophysiology of head injury and to improvement in therapy. A technique known as the sequential Bayes method has been used previously for the purpose of prognosis. The application of this method assumes that prognostic factors are statistically independent. It is now known that they are not. Violation of the assumption of independence may produce errors in determining prognosis. As an alternative technique for predicting the outcome of patients with severe head injury, a logistic regression model is proposed. A preliminary evaluation of the method using data from 115 patients with head injury shows the feasibility of using early data to predict outcome accurately and of being able to rank input variables in order of their prognostc significance. 6).


A prospective and consecutive series of 225 patients with severe head injuries who were managed in a uniform way was analyzed to relate outcome to several clinical variables. Good recovery or moderate disability were achieved by 56% of the patients, 10% remained severely disabled or vegetative, and 34% died. Factors important in predicting a poor outcome included the presence of intracranial hematoma, increasing agemotor impairment, impaired or absent eye movements or pupillary light reflexes, early hypotensionhypoxemia or hypercarbia, and raised intracranial pressure over 20 mm Hg despite artificial ventilation. Most of these predictive factors were assessed on admission, but a subset of 158 patients was identified in whom coma was present on admission and was known to have persisted at least until the following day. Although the mortality in this subset (40%) was higher than in the total series, it was lower than in several comparable reported series of patients with severe head injury. Predictive correlations were equally strong in the entire series and in the subset of 158 patients with coma. A plea is made for inclusion in the definition of “severe head injury” of all patients who do not obey commands or utter recognizable words on admission to the hospital after early resuscitation 7).


Survival rate of isolated severe TBI patients who required an emergent neurosurgical intervention could be time dependent. These patients might benefit from expedited process (computed tomographic scan, neurosurgical consultation, etc.) to shorten the time to surgical intervention 8).

The impact of a moderate to severe brain injury can include:

Cognitive deficits including difficulties with:

Attention Concentration Distractibility Memory Speed of Processing Confusion Perseveration Impulsiveness Language Processing “Executive functions” Speech and Language

not understanding the spoken word (receptive aphasia) difficulty speaking and being understood (expressive aphasia) slurred speech speaking very fast or very slow problems reading problems writing Sensory

difficulties with interpretation of touch, temperature, movement, limb position and fine discrimination Perceptual

the integration or patterning of sensory impressions into psychologically meaningful data Vision

partial or total loss of vision weakness of eye muscles and double vision (diplopia) blurred vision problems judging distance involuntary eye movements (nystagmus) intolerance of light (photophobia) Hearing

decrease or loss of hearing ringing in the ears (tinnitus) increased sensitivity to sounds Smell

loss or diminished sense of smell (anosmia) Taste

loss or diminished sense of taste Seizures

the convulsions associated with epilepsy that can be several types and can involve disruption in consciousness, sensory perception, or motor movements Physical Changes

Physical paralysis/spasticity Chronic pain Control of bowel and bladder Sleep disorders Loss of stamina Appetite changes Regulation of body temperature Menstrual difficulties Social-Emotional

Dependent behaviors Emotional ability Lack of motivation Irritability Aggression Depression Disinhibition Denial/lack of awareness


Both single predictors from early clinical examination and multiple hospitalization variables/parameters can be used to determine the long-term prognosis of TBI. Predictive models like the IMPACT or CRASH prognosis calculator (based on large sample sizes) can predict mortality and unfavorable outcomes. Moreover, imaging techniques like MRI (Magnetic Resonance Imaging) can also predict consciousness recovery and mental recovery in severe TBI, while biomarkers associated with stress correlate with, and hence can be used to predict, severity and mortality. All predictors have limitations in clinical application. Further studies comparing different predictors and models are required to resolve limitations of current predictors 9).

Clinical outcome prediction following traumatic brain injury (TBI) is a widely investigated field of research. Several outcome prediction models have been developed for prognosis after TBI. There are two main prognostic models: International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) prognosis calculator and the Corticosteroid Randomization after Significant Head Injury (CRASH) prognosis calculator. The prognosis model has three or four levels:

(1) model A included age, motor GCS, and pupil reactivity

(2) model B included predictors from model A with CT characteristics

(3) model C included predictors from model B with laboratory parameters.

In consideration of the fact that interventions after admission, such as ICP management also have prognostic value for outcome predictions and may improve the models’ performance, Yuan F et al developed another prediction model (model D) which includes ICP. With the development of molecular biology, a handful of brain injury biomarkers were reported that may improve the predictive power of prognostic models, including neuron-specific enolase (NSE), glial fibrillary acid protein (GFAP), S-100β protein, tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), myelin basic protein (MBP), cleaved tau protein (C-tau), spectrin breakdown products (SBDPs), and ubiquitin C-terminal hydrolase-L1 (UCH-L1), and sex hormones. A total of 40 manuscripts reporting 11 biomarkers were identified in the literature. Many substances have been implicated as potential biomarkers for TBI; however, no single biomarker has shown the necessary sensitivity and specificity for predicting outcome. The limited number of publications in this field underscores the need for further investigation. Through fluid biomarker analysis, the advent of multi-analyte profiling technology has enabled substantial advances in the diagnosis and treatment of a variety of conditions. Application of this technology to create a bio-signature for TBI using multiple biomarkers in combination will hopefully facilitate much-needed advances. We believe that further investigations about brain injury biomarkers may improve the predictive power of the contemporary outcome calculators and prognostic models, and eventually improve the care of patients with TBI 10).


Injury site, injury type, and injury degree are the main risk factors for post-traumatic epilepsyTraumatic brain injury outcome can be affected by early post-traumatic epilepsy11).

Insurance and racial disparities continue to exist for TBI patients. Insurance status appears to have an impact on short- and long-term outcomes to a greater degree than patient race 12).

CRASH

IMPACT

Traumatic brain injury mortality.

see Quality of Life after Brain Injury.

Traumatic brain injury complications.

Statins have been shown to improve traumatic brain injury outcome in animal models. The aim of a study was to determine the effect of preinjury statins on outcomes in TBI patients.

Lokhandwala et al. performed a 4-y (2014-2017) review of a TBI database and included all patients aged ≥18 y with severe isolated TBI. Patients were stratified into those who were on statins and those who were not and were matched (1:2 ratio) using propensity score matching. The primary outcome was in-hospital mortality. The secondary outcomes were skilled nursing facility disposition, Glasgow Outcome Scale-extended score, and hospital and intensive care unit length of stay (LOS).

They identified 1359 patients, of which 270 were matched (statin: 90, no-statin: 180). Mean age was 55 ± 8y, median Glasgow Coma Scale was 10 (8-12), and median head-abbreviated injury scale was 3 (3-5). Matched groups were similar in age, mechanism of injury, Glasgow Coma Scale, Injury Severity Score, neurosurgical intervention, type and size of intracranial hemorrhage, and preinjury anticoagulant or antiplatelet use. The overall in-hospital mortality rate was 18%. Patients who received statins had lower rates of in-hospital mortality (11% versus 21%, P = 0.01), skilled nursing facility disposition (19% versus 28%; P = 0.04), and a higher median Glasgow Outcome Scale-extended (11 [9-13] versus 9 [8-10]; P = 0.04). No differences were found between the two groups in terms of hospital LOS (6 [4-9] versus 5 [3-8]; P = 0.34) and intensive care unit LOS (3 [3-6] versus 4 [3-5]; P = 0.09).

Preinjury statin use in isolated traumatic brain injury patients is associated with improved outcomes. This finding warrants further investigations to evaluate the potential beneficial role of statins as a therapeutic drug in a TBI 13).


1)

Svedung Wettervik TM, Hånell A, Howells T, Enblad P, Lewén A. Females Exhibit Better Cerebral Pressure Autoregulation, Less Mitochondrial Dysfunction, and Reduced Excitotoxicity following Severe Traumatic Brain Injury. J Neurotrauma. 2022 May 19. doi: 10.1089/neu.2022.0097. Epub ahead of print. PMID: 35587145.
2)

Pease M, Arefan D, Barber J, Yuh E, Puccio A, Hochberger K, Nwachuku E, Roy S, Casillo S, Temkin N, Okonkwo DO, Wu S; TRACK-TBI Investigators. Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans. Radiology. 2022 Apr 26:212181. doi: 10.1148/radiol.212181. Epub ahead of print. PMID: 35471108.
3)

Murray NM, Wolman DN, Mlynash M, Threlkeld ZD, Christensen S, Heit JJ, Harris OA, Hirsch KG. Early Head Computed Tomography Abnormalities Associated with Elevated Intracranial Pressure in Severe Traumatic Brain Injury. J Neuroimaging. 2020 Nov 4. doi: 10.1111/jon.12799. Epub ahead of print. PMID: 33146933.
4)

Khellaf A, Khan DZ, Helmy A. Recent advances in traumatic brain injury. J Neurol. 2019 Sep 28. doi: 10.1007/s00415-019-09541-4. [Epub ahead of print] PubMed PMID: 31563989.
5)

Lazaridis C, Rusin CG, Robertson CS. Secondary Brain Injury: Predicting and Preventing Insults. Neuropharmacology. 2018 Jun 6. pii: S0028-3908(18)30279-X. doi: 10.1016/j.neuropharm.2018.06.005. [Epub ahead of print] Review. PubMed PMID: 29885419.
6)

Stablein DM, Miller JD, Choi SC, Becker DP. Statistical methods for determining prognosis in severe head injury. Neurosurgery. 1980 Mar;6(3):243-8. PubMed PMID: 6770283.
7)

Miller JD, Butterworth JF, Gudeman SK, Faulkner JE, Choi SC, Selhorst JB, Harbison JW, Lutz HA, Young HF, Becker DP. Further experience in the management of severe head injury. J Neurosurg. 1981 Mar;54(3):289-99. PubMed PMID: 7463128.
8)

Matsushima K, Inaba K, Siboni S, Skiada D, Strumwasser AM, Magee GA, Sung GY, Benjaminm ER, Lam L, Demetriades D. Emergent operation for isolated severe traumatic brain injury: Does time matter? J Trauma Acute Care Surg. 2015 Aug 28. [Epub ahead of print] PubMed PMID: 26317818.
9)

Gao L, Wu X. Prediction of clinical outcome in severe traumatic brain injury. Front Biosci (Landmark Ed). 2015 Jan 1;20:763-771. PubMed PMID: 25553477.
10)

Gao J, Zheng Z. Development of prognostic models for patients with traumatic brain injury: a systematic review. Int J Clin Exp Med. 2015 Nov 15;8(11):19881-5. eCollection 2015. Review. PubMed PMID: 26884899; PubMed Central PMCID: PMC4723744.
11)

Liu Z, Chen Q, Chen Z, Wang J, Tian D, Wang L, Liu B, Zhang S. Clinical analysis on risk factors and prognosis of early post-traumatic epilepsy. Arq Neuropsiquiatr. 2019 Jul 15;77(6):375-380. doi: 10.1590/0004-282×20190071. PubMed PMID: 31314838.
12)

Schiraldi M, Patil CG, Mukherjee D, Ugiliweneza B, Nuño M, Lad SP, Boakye M. Effect of Insurance and Racial Disparities on Outcomes in Traumatic Brain Injury. J Neurol Surg A Cent Eur Neurosurg. 2015 Mar 23. [Epub ahead of print] PubMed PMID: 25798799.
13)

Lokhandwala A, Hanna K, Gries L, Zeeshan M, Ditillo M, Tang A, Hamidi M, Joseph B. Preinjury Statins Are Associated With Improved Survival in Patients With Traumatic Brain Injury. J Surg Res. 2019 Aug 16;245:367-372. doi: 10.1016/j.jss.2019.07.081. [Epub ahead of print] PubMed PMID: 31425877.

Chronic subdural hematoma

Chronic subdural hematoma

Chronic subdural hematoma (CSDH) is an encapsulated collection of old blood, mostly or totally liquefied and located between the dura mater and the arachnoid mater.

They are arbitrarily defined as those hematomas presenting 21 days or more after injury. These numbers are not absolute, and a more accurate classification of a subdural hematoma usually is based on imaging characteristics.

The first description of a chronic subdural hematoma was made in 1658 by J.J. Wepfer, followed in 1761 by Morgagni. A possible case was described by Honoré de Balzac in 1840 including its traumatic origin and surgical treatment.

Virchow, in 1857, denied a traumatic origin, and gave the name of “pachymeningitis hemorrhagica interna” to this pathology which he explained by inflammatory processes.

The traumatic etiology of chronic subdural hematoma was recognized in the XXth century, especially by Trotter in 1914. Pathophysiology was considered later on in the XXth century.

It was first described by Rudolf Ludwig Karl Virchow, in 1857, as “an internal hemorrhagic pachymeningitis” 1).

Later, in 1914, Trotter launched the theory of traumatic brain injury and the consecutive lesion of the “bridging veins”, as being the cause of what he called “hemorrhagic subdural cyst” 2).

Chronic subdural hematoma epidemiology.

see Chronic subdural hematoma etiology.

Chemokines in Chronic Subdural Hematoma

see Chronic subdural hematoma classification.

Chronic subdural hematoma pathophysiology.

cSDHs have a tendency to persist and gradually increase in volume over time. The disease is thought to be related to a cycle of chronic inflammation and angiogenesis. An original hemorrhage forms and fibrinolysis ensues with the liquefaction of the initial clot. The subsequent blood breakdown products stimulate inflammation and thickening of the inner dural layer (ie, ‘dural border cells). This process incites angiogenesis with the ingrowth of immature capillaries, which chronically leak blood. These microhemorrhages result in the progressive enlargement of the collection with increased fibrinolytic activity, inflammation, and further angiogenesis, membrane formation, and vessel proliferation. The rate of accumulation of blood products outpaces physiological reabsorption and the collection gradually enlarges. Thus the entire basis for the pathology is the formation of leaky vascular membranes, which incite a positive feedback cycle of continued hemorrhage, inflammation, and angiogenesis 3) 4)


Chronic subdural hematoma (CSDH) is characterized by an “old” encapsulated collection of blood and blood breakdown products between the brain and its outermost covering (the dura).

It is delimited by an outer and inner membrane. In between are bloodplasmacerebrospinal fluid, membranes, and a mixture of inflammatory angiogenic fibrinolytic and coagulation factors. These factors maintain a self-perpetuating cycle of bleeding, lysis, and growing of neo-membranes and neo-capillaries 5).

see Chronic subdural hematoma clinical features.

Glasgow Coma Scale.

Markwalder grading score.

Modified Rankin Scale.

Chronic subdural hematoma diagnosis

The association between the biomarkers of inflammation and angiogenesis, and the clinical and radiological characteristics of CSDH patients, need further investigation. The high number of biomarkers compared to the number of observations, the correlation between biomarkers, missing data and skewed distributions may limit the usefulness of classical statistical methods.

Pripp et al. explored lasso regression to assess the association between 30 biomarkers of inflammation and angiogenesis at the site of lesions, and selected clinical and radiological characteristics in a cohort of 93 patients. Lasso regression performs both variable selection and regularization to improve the predictive accuracy and interpretability of the statistical model. The results from the lasso regression showed analysis exhibited lack of robust statistical association between the biomarkers in hematoma fluid with age, gender, brain infarct, neurological deficiencies and volume of hematoma. However, there were associations between several of the biomarkers with postoperative recurrence requiring reoperation. The statistical analysis with lasso regression supported previous findings that the immunological characteristics of CSDH are local. The relationship between biomarkers, the radiological appearance of lesions and recurrence requiring reoperation have been inclusive using classical statistical methods on these data, but lasso regression revealed an association with inflammatory and angiogenic biomarkers in hematoma fluid. They suggest that lasso regression should be a recommended statistical method in research on biological processes in CSDH patients 6).

Chronic subdural hematoma (CSDH) is a disease of the meninges and is to be distinguished from hygroma and subdural empyema.

Subdural effusion in the setting of dural metastases is very rare and may be difficult to be distinguished from chronic subdural hematoma. Such lesions could be missed and could be the cause of recurrence in CSDH. A contrast-enhanced brain CT scan is recommended to diagnose dural metastases.

Rosai–Dorfman disease may be mistaken for a CSDH on imaging. This disease is an uncommon, benign systemic histioproliferative disease characterized by massive lymphadenopathy, particularly in the head and neck region, and is often associated with extranodal involvement. CSDH can also develop in multifocal fibrosclerosis (MFS) which is a rare disorder of unknown etiology, characterized by chronic inflammation with dense fibrosis and lymphoplasmacytic infiltration into the connective tissue of various organs. The mechanism of the formation of CSDH is presumed to involve reactive granular membrane together with subdural collection. On the other hand, the extramedullary erythropoiesis within CSDH can be confused with metastatic malignant tumors, such as lymphoma, carcinoma, and malignant melanoma 7).

A 44-year old woman with gastric adenocarcinoma was presented with headache and a hypodense subdural collection in right fronto-parietal in brain CT. Burr-hole irrigation was performed with the impression of chronic subdural hematoma, but nonhemorrhagic xantochromic fluid was evacuated without malignant cell. Brain CT on the 11th day depicted fluid re-accumulation and noticeable midline shift, necessitating craniotomy and removing the affected dura.

Because the affected dura can be supposed as the main source of subdural effusion, resection of the involved dura is obligatory for the appropriate palliative management of such patients 8).

see Chronic subdural hematoma treatment.

Routine postoperative CT

Routine post-operative CT brain for burr hole drainage of CSDH may be unnecessary in view of the good predictive value of pre-operative volume, and also because it is not predictive of the clinical outcome 9).

Scheduled postoperative cranial imaging with indwelling drains was not shown to be beneficial and misses information of intracranial damage inflicted by removal of drains. Brokinkel et al recommend CT-scanning after drainage removal 10).

see Chronic subdural hematoma surgery complications.

see Chronic subdural hematoma recurrence.

see Chronic subdural hematoma outcome.

A study aimed to quantify the heterogeneity of data elements in the pre-operative, operative, and post-operative phases of care, and build the basis for the development of a set of common data elements (CDEs) for CSDH. This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and was registered with the PROSPERO register of systematic reviews (CRD42014007266). All full-text English studies with more than 10 patients (prospective) or more than 100 patients (retrospective) published after 1990 examining clinical outcomes in CSDH were eligible for inclusion. One hundred two eligible studies were found. Only 40 studies (39.2%) reported the main presenting symptom/feature and 24 (23.5%) reported additional symptoms/features. Admitting neurological/functional status was classified by the Glasgow Coma Scale (25 studies; 24.5%), the Markwalder Score (26 studies; 25.5%) and the modified Rankin Scale (three studies; 2.9%). Fifty-four studies (52.9%) made some mention of patient comorbidities and 58 studies (56.9%) reported the proportion or excluded patients on anticoagulant medication. Eighteen studies (17.6%) reported baseline coagulation status. Sixty-four studies (62.7%) stratified or assessed severity based on radiological findings, although the methods used varied widely. There was variable reporting of surgical technique and post-operative care; 32 studies (31.4%) made no mention of whether the operations were performed under general or local anesthetic. This study, a part of the Core Outcomes and Common Data Elements in CSDH (CODE-CSDH) project, confirms and quantifies the heterogeneity of data elements collected and reported in CSDH studies to date. It establishes the basis for the consensus-based development of a set of common data elements, facilitating robust cross-study comparisons and resulting improvements in patient outcomes 11).

see Chronic subdural hematoma case series.

see Chronic subdural hematoma case reports.

Attempts to create CSDH have been made in mice, rats, cats, dogs and monkeys. Methods include injection or surgical implantation of clotted blood or various other blood products and mixtures into the potential subdural space or the subcutaneous space. No intracranial model produced a progressively expanding CSDH. Transient hematoma expansion with liquification could be produced by subcutaneous injections in some models. Spontaneous subdural blood collections were found after creation of hydrocephalus in mice by systemic injection of the neurotoxin, 6-aminonicotinamide. The histology of the hematoma membranes in several models resembles the appearance in humans. None of the models has been replicated since its first description.

D’Abbondanza et al. did not find a report of a reproducible, well-described animal model of human CSDH 12).


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Guénot M. [Chronic subdural hematoma: historical studies]. Neurochirurgie. 2001 Nov;47(5):461-3. French. PubMed PMID: 11915757.
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Ito H , Yamamoto S , Komai T , et al . Role of local hyperfibrinolysis in the etiology of chronic subdural hematoma. J Neurosurg 1976;45:26–31.doi:10.3171/jns.1976.45.1.0026
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Edlmann E , Giorgi-Coll S , Whitfield PC , et al . Pathophysiology of chronic subdural haematoma: inflammation, angiogenesis and implications for pharmacotherapy. J Neuroinflammation 2017;14:108.doi:10.1186/s12974-017-0881-y
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