Severe traumatic brain injury outcome

Severe traumatic brain injury outcome

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 1).

Effect of trauma center designation in severe traumatic brain injury outcome

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 algorithms 2).


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. 3).


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 age, motor impairment, impaired or absent eye movements or pupillary light reflexes, early hypotension, hypoxemia 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 4).


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 5).

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 6).

References

1)

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.
2)

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.
3)

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.
4)

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.
5)

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.
6)

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.

Transnasal Endoscopic Skull Base and Brain Surgery Surgical Anatomy and its Applications

Transnasal Endoscopic Skull Base and Brain Surgery Surgical Anatomy and its Applications

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This fully revised and updated second edition of Transnasal Endoscopic Skull Base and Brain Surgery: Surgical Anatomy and its Applications builds on the acclaimed first edition, focusing on the correlation between endoscopic skull base anatomy and state-of-the-art clinical applications. Among these are the transplanum/transtuberculum, transcribrifom, transclival, and craniocervical junction surgical approaches.

Renowned skull base surgeon Aldo Stamm and leading worldwide experts have compiled a comprehensive multidisciplinary textbook with 72 chapters in 14 sections, didactically organized by regions and diseases. Detailed descriptions of sinonasal, orbital, cranial base, and intracranial anatomy, imaging modalities, and in-depth surgical navigation techniques form the foundation of this remarkable book. The content reflects significant knowledge and diverse perspectives from masters in neurosurgery, otorhinolaryngology, head and neck surgery, neuroendocrinology, intensive care, neuro-anesthesiology, and other disciplines.

Key Highlights

Chapter summaries and highlights facilitate understanding and retention of complex concepts 1,500 beautiful anatomical, operative, and dissection illustrations and photographs enhance understanding of impacted areas 18 accompanying videos provide guidance on endoscopic transnasal approaches in patients with diverse skull base diseases Pearls, pitfalls, and nuances throughout this book provide invaluable insights on achieving optimal outcomes Neurosurgeons, otolaryngologists–head and neck surgeons, and others will greatly benefit from the step-by-step endoscopic procedural guidance and tips in this quintessential skull base surgical reference.

This book includes complimentary access to a digital copy on https://medone.thieme.com.

Rechargeable deep brain stimulation implantable pulse generator

Rechargeable deep brain stimulation implantable pulse generator

The Activa PC implantable pulse generator (IPG) demonstrates a significantly reduced batterylife of 2.1 years, with a median battery life of 4.5 years in comparison to 6.6 years in the KinetraIPG. Future technology developments should therefore be focused on improving the battery life of the newer IPG systems 1).

Nonrechargeable deep brain stimulation implantable pulse generators (IPGs) for movement disorders require surgical replacement every few years due to battery depletion. Rechargeable IPGs reduce frequency of replacement surgeries and inherent risks of complications but require frequent recharging 2).

Rechargeable deep brain stimulation implantable pulse generator for movement disorders are well received by patients as initial therapy and after conversion. Mild reduction in stimulation parameters might be allowed after conversion to RC IPG 3).

However, there is now a choice between fixed-life and rechargeable batteries, with each having their own advantages and disadvantages.

Most patients in a adult cohort with movement disorders chose the fixed-life battery. The better lifestyle associated with a fixed-life battery is a major factor influencing their choice. Rechargeable batteries may be more acceptable if the recharging process is improved, more convenient, and discreet 4).


Mitchell et al., evaluated patient experience with rechargeable IPGs and define predictive characteristics for higher satisfaction.

They contacted all patients implanted with rechargeable IPGs at a single center in a survey-based study. They analyzed patient satisfaction with respect to agediagnosis, target, charging duration, and body mass index. They tabulated hardware-related adverse events.

Dystonia patients had significantly higher satisfaction than Parkinson’s disease patients in recharging, display, programmer, and training domains. Common positive responses were “fewer surgeries” and “small size.” Common negative responses were “difficulty finding the right position to recharge” and “need to recharge every day.” Hardware-related adverse events occurred in 21 of 59 participants.

Patient experience with rechargeable IPGs was largely positive; however, frustrations with recharging and adverse events were common. Dystonia diagnosis was most predictive of high satisfaction across multiple categories, potentially related to expected long disease duration with need for numerous IPG replacements 5).


Hitti et al., implanted rechargeable stimulators in 206 patients undergoing DBS surgery, and demonstrated the cost-effectiveness and high patient satisfaction associated with this procedure 6).

References

1)

Fisher B, Kausar J, Garratt H, Hodson J, White A, Ughratdar I, Mitchell R. Battery Longevity Comparison of Two Commonly Available Dual Channel Implantable Pulse Generators Used for Subthalamic Nucleus Stimulation in Parkinson’s Disease. Stereotact Funct Neurosurg. 2018;96(3):151-156. doi: 10.1159/000488684. Epub 2018 Jun 19. PubMed PMID: 29920479.
2) , 5)

Mitchell KT, Volz M, Lee A, San Luciano M, Wang S, Starr PA, Larson P, Galifianakis NB, Ostrem JL. Patient Experience with Rechargeable Implantable Pulse Generator Deep Brain Stimulation for Movement Disorders. Stereotact Funct Neurosurg. 2019 Jul 9:1-7. doi: 10.1159/000500993. [Epub ahead of print] PubMed PMID: 31288242.
3)

Waln O, Jimenez-Shahed J. Rechargeable deep brain stimulation implantable pulse generators in movement disorders: patient satisfaction and conversion parameters. Neuromodulation. 2014 Jul;17(5):425-30; discussion 430. doi: 10.1111/ner.12115. Epub 2013 Sep 24. PubMed PMID: 24112630.
4)

Khaleeq T, Hasegawa H, Samuel M, Ashkan K. Fixed-Life or Rechargeable Battery for Deep Brain Stimulation: Which Do Patients Prefer? Neuromodulation. 2019 Jun;22(4):489-492. doi: 10.1111/ner.12810. Epub 2018 Aug 22. PubMed PMID: 30133071.
6)

Hitti FL, Vaughan KA, Ramayya AG, McShane BJ, Baltuch GH. Reduced long-term cost and increased patient satisfaction with rechargeable implantable pulse generators for deep brain stimulation. J Neurosurg. 2018 Sep 1:1-8. doi: 10.3171/2018.4.JNS172995. [Epub ahead of print] PubMed PMID: 30265199.
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