Severe traumatic brain injury outcome

Severe traumatic brain injury outcome

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 1).


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 2).

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 algorithms 3).


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. 4).


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 5).


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 6).

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 7).


1)

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.
2)

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.
3)

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.
4)

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.
5)

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.
6)

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.
7)

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.

COVID-19 Outcome

COVID-19 Outcome

The possible risk factors that lead to death in critical inpatients with coronavirus disease 2019 (COVID-19) are not yet fully understood.

Old age (>70 years), neutrophiliaC-reactive protein greater than 100 mg/L and lactate dehydrogenase over 300 U/L are high-risk factors for mortality in critical patients with COVID-19Sinus tachycardia and ventricular arrhythmia are independent ECG risk factors for mortality from COVID-19 1).

While the disease itself is often mild, approximately 11% of cases require acute medical care, and this cohort quickly overwhelmed healthcare systems around the world 2).

In anticipation of such a demand, hospitals in many countries quickly stopped all nonurgent visits, procedures, and surgeries, freeing up beds, equipment, and workforce 3)


The mortality rate for COVID-19 is not as high (approximately 2-3%), but its rapid propagation has resulted in the activation of protocols to stop its spread. 4).

A total of 174 consecutive patients confirmed with COVID-19 were studied. Demographic data, medical history, symptoms and signs, laboratory findings, chest computed tomography (CT) as well we treatment measures were collected and analyzed.

Guo et al. found that COVID-19 patients without other comorbidities but with diabetes (n=24) were at higher risk of severe pneumonia, the release of tissue injury-related enzymes, excessive uncontrolled inflammation responses and hypercoagulable state associated with dysregulation of glucose metabolism. Furthermore, serum levels of inflammation-related biomarkers such as IL-6, C-reactive protein, serum ferritin, and coagulation index, D-dimer, were significantly higher (p< 0.01) in diabetic patients compared with those without, suggesting that patients with diabetes are more susceptible to an inflammatory storm eventually leading to rapid deterioration of COVID-19.

Data support the notion that diabetes should be considered as a risk factor for a rapid progression and bad prognosis of COVID-19. More intensive attention should be paid to patients with diabetes, in case of rapid deterioration 5).


see Racism and discrimination in COVID-19 responses 6).


1)

Li L, Zhang S, He B, Chen X, Wang S, Zhao Q. Risk factors and electrocardiogram characteristics for mortality in critical inpatients with COVID-19. Clin Cardiol. 2020 Oct 22. doi: 10.1002/clc.23492. Epub ahead of print. PMID: 33094522.
2)

Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet. 2020;395(10231):P1225-P1228.
3)

Wong J, Goh QY, Tan Z, et al. Preparing for a COVID-19 pandemic: a review of operating room outbreak response measures in a large tertiary hospital in Singapore. Can J Anaesth. 2020;395:497.
4)

Palacios Cruz M, Santos E, Velázquez Cervantes MA, León Juárez M. COVID-19, a worldwide public health emergency. Rev Clin Esp. 2020 Mar 20. pii: S0014-2565(20)30092-8. doi: 10.1016/j.rce.2020.03.001. [Epub ahead of print] Review. English, Spanish. PubMed PMID: 32204922.
5)

Guo W, Li M, Dong Y, Zhou H, Zhang Z, Tian C, Qin R, Wang H, Shen Y, Du K, Zhao L, Fan H, Luo S, Hu D. Diabetes is a risk factor for the progression and prognosis of COVID-19. Diabetes Metab Res Rev. 2020 Mar 31:e3319. doi: 10.1002/dmrr.3319. [Epub ahead of print] PubMed PMID: 32233013.
6)

Devakumar D, Shannon G, Bhopal SS, Abubakar I. Racism and discrimination in COVID-19 responses. Lancet. 2020 Apr 1. pii: S0140-6736(20)30792-3. doi: 10.1016/S0140-6736(20)30792-3. [Epub ahead of print] PubMed PMID: 32246915.

Non small cell lung cancer outcome

Non small cell lung cancer outcome

Epidermal growth factor receptor mutation, primary cancer pathology, and Recursive partitioning analysis class may be proposed as prognostic factors for intracranial progression-free survival (PFS) in non-small cell lung cancer (NSCLC) patients after GKRS for brain metastasis in a study 1).


Retrospective analysis of patients with Non small cell lung cancer (NSCLC) completely resected from lung found a 6.8 % first recurrence rate in the brain 2).

Prognosis better than small cell lung cancer (SCLC).

see also Non small cell lung cancer intracranial metastases outcome.

Between 25% and 30% of non-small cell lung cancer (NSCLC) patients will develop Non small cell lung cancer intracranial metastases.

Frequently they are the first site of recurrence in early-stage NSCLC patients treated with definitive therapies 3) 4) 5) 6) 7).

Non small cell lung cancer outcome is better than small cell lung cancer.

Given uniform staging, treatment, and socioeconomic status the overall survival rates for African American and White patients with NSLC are similar 8)

A significant excess mortality remains in lung cancer after years which may be explained by excess risk of death due to smoking-related comorbidity in these patients. Caregivers should use this information for planning optimal cancer surveillance and informing cancer survivors about their actual prognosis 9).

Retrospective analysis of patients with Non small cell lung cancer completely resected from lung found a 6,8 % first recurrence rate in the brain 10).


1)

Yang SH, Kim HY, Lee SI, Jin SJ. The Effect of Epidermal Growth Factor Receptor Mutation on Intracranial Progression-Free Survival of Non-Small Cell Lung Cancer Patients with Brain Metastasis Underwent Gamma Knife Radiosurgery. Brain Tumor Res Treat. 2020 Oct;8(2):103-108. doi: 10.14791/btrt.2020.8.e15. PMID: 33118342.
2)

Figlin RA, Piantadosi S, Feld R, et al. Intracranial Recurrence of Carcinoma After Complete Resection of Stage I, II, and III Non-Small-Cell Lung Cancer. N Engl J Med. 1988; 318:1300–1305
3)

Schettino C, Bareschino MA, Rossi A, Maione P, Sacco PC, Colantouni G, Rossi E, Gdidelli C. Targeting angiogenesis for treatment of NSCLC brain metastases. Curr Cancer Drugs Targets. 2012;12:289–99.
4)

Sperduto PW, Wang M, Robis HI, et al. A phase 3 trial of whole brain radiation therapy and stereotactic radiosurgery alone versus WBRT and SRS with temozolomide or erlotinib for non-small cell lung cancer and 1 to 3 brain metastases: Radiation Therapy Oncology Group 0320. Int J Radiat Oncol Biol Phys. 2013;85:1312–8.
5)

Iuchi T, Shingyoji M, Sakaida T, et al. Phase II trial of gefitinib alone without radiation therapy for Japanese patients with brain metastases from EGFR-mutant lung adenocarcinoma. Lung Cancer. 2013;82:282–7.
6)

Villalva C, Duranton-Tanneur V, Guilloteau K, et al. EGRF, KRAS, BRAF and HER-2 molecular status in brain metastases from 77 NSCLC patients. Cancer Med. 2013;2:296–304.
7)

Barlesi F, Khobta N, Tablet A, et al. Strategies de prise en charge des metastases cerebrales des maladies atteints de cancers bronchiques primitives. Bull Cancer. 2013;100:303–8.
8)

Bryant AS, Cerfolio RJ. Impact of race on outcomes of patients with non-small cell lung cancer. J Thorac Oncol. 2008 Jul;3(7):711-5. doi: 10.1097/JTO.0b013e31817c60c7. PubMed PMID: 18594315.
9)

Janssen-Heijnen ML, van Steenbergen LN, Steyerberg E, Visser O, De Ruysscher DK, Groen HJ. Long-term excess mortality for survivors of non-small cell lung cancer in the Netherlands. J Thorac Oncol. 2012 Mar;7(3):496-502. doi: 10.1097/JTO.0b013e318241f80b. PubMed PMID: 22246194.
10)

Figlin RA, Piantadosi S, Feld R; Lung Cancer Study Group. Intracranial recurrence of carcinoma after complete surgical resection of stage I, II, and III non-small-cell lung cancer. N Engl J Med. 1988 May 19;318(20):1300-5. PubMed PMID: 2834646.
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