Improving the prediction of outcome in severe acute closed head injury by using discriminant function analysis of normal auditory brainstem response latencies and amplitudes

Risk Factors for Epilepsy After Thrombolysis for Ischemic Stroke: A Cohort Study

The effects of thrombolysis in seizure and epilepsy after acute ischemic stroke have been poorly explored. In this study, we examine risk factors and consequences of intravenous rt-PA for treatment of acute ischemic stroke. In a retrospective cohort study we evaluate risk factors for seizure and epilepsy after stroke thrombolysis, as well as the impact of seizures and epilepsy in outcome of stroke patients. In our cohort, mean age of patients was 67.2 years old (SD = 13.1) and 79 of them (51.6%) were male and. Initial NIHSS mean score were 10.95 (SD = 6.25). Three months NIHSS mean score was 2.09 (SD = 3.55). Eighty seven (56.9%) patients were mRS of 0–1 after thrombolysis. Hemorrhagic transformation was observed in 22 (14.4%) patients. Twenty-one (13.7%) patients had seizures and 15 (9.8%) patients developed epilepsy after thrombolysis. Seizures were independently associated with hemorrhagic transformation (OR = 3.26; 95% CI = 1.08–9.78; p = 0.035) and with mRS ≥ 2 at 3 months after stroke (OR = 3.51; 95% CI = 1.20–10.32; p = 0.022). Hemorrhagic transformation (OR = 3.55; 95% CI = 1.11–11.34; p = 0.033) and mRS ≥ 2 at 3 months (OR = 5.82; 95% CI = 1.45–23.42; p = 0.013) were variables independently associated with post-stroke epilepsy. In our study, independent risks factors for poor outcome in stroke thrombolysis were age (OR = 1.03; 95% CI = 1.01–1.06; p = 0.011), higher NIHSS (OR = 1.08; 95% CI = 1.03–1.14; p = 0.001), hemorrhagic transformation (OR = 2.33; 95% CI = 1.11–4.76; p = 0.024), seizures (OR = 3.07; 95% CI = 1.22–7.75; p = 0.018) and large cortical area (ASPECTS ≤ 7) (OR = 2.04; 95% CI = 1.04–3.84; p = 0.036). Concluding, in this retrospective cohort study, the neurological impairment after thrombolysis (but not before) and hemorrhagic transformation remained independent risk factors for seizures or post-stroke epilepsy after thrombolysis. Moreover, we observed that seizures emerged as an independent risk factor for poor outcome after thrombolysis therapy in stroke patients (OR = 3.07; 95% CI = 1.22–7.75; p = 0.018).

Effects of transcranial direct current stimulation on patients with disorders of consciousness after traumatic brain injury: study protocol for a randomized, double-blind controlled trial

Background

Disorders of consciousness (DOC) after traumatic brain injury (TBI) raise the mortality of patients, restrict the rehabilitation of patients with TBI, and increase the physical and economic burden that TBI imposes on patients and their families. Thus, treatment to promote early awakening in DOC after TBI is of vital importance. Various treatments have been reported, but there is no advanced evidence base to support them. Transcranial direct current stimulation (tDCS) has shown great potential in promoting neuroelectrochemical effects. This protocol is for a double-blind, randomized, controlled, clinical trial aiming to research the effects and safety of conventional rehabilitation combined with tDCS therapy in patients with DOC after TBI.

Methods/design

Eighty patients with DOC after TBI will be randomized into one of two groups receiving conventional rehabilitation combined with sham tDCS or conventional rehabilitation combined with active tDCS. The intervention period in each of the two groups will last 4 weeks (20 min per day, 6 days per week). Primary outcomes (Glasgow Outcome Scale (GOS)) will be measured at baseline and the end of every week from the first to the fourth week. Secondary outcomes will be measured at baseline and the end of the fourth week. Adverse events and untoward effects will be measured during each treatment.

Discussion

Patients with central nervous system lesions have received tDCS as a painless, non-invasive, easily applied and effective therapy for several decades, and there has been some evidence in recent years showing partial improvement on the level of consciousness of partial patients with DOC. However, reports mainly focus on the patients in a minimally conscious state (MCS), and there is a lack of large-sample clinical trials. This protocol presents an objective design for a randomized controlled trial that aims to study the effectiveness of conventional rehabilitation combined with tDCS therapy for DOC after TBI, to evaluate its safety, and to explore effective and economical therapeutic methods.

Trial registration

Chinese Clinical Trial Registry, ChiCTR1800014808. Registered on 7 February 2018.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3680-1) contains supplementary material, which is available to authorized users.

Long-term outcome prediction after a traumatic brain injury using early somatosensory and acoustic evoked potentials: analysis of the predictive value of the different single components of the potentials

PURPOSE

The prediction of the long-term outcome of comatose patients after severe traumatic brain injury (TBI) using early somatosensory and acoustic evoked potentials is controversial. It was our aim to examine the different single components of the evoked potentials regarding their predictive capacity in comatose patients.

METHODS

We examined the amplitude and latency of the wave N20, the amplitude differences between right and left hemisphere, the central conduction time (CCT), the amplitude ratio N20 left/N20 right, the amplitude and latency of peak V, the inter-peak latency I-V and the amplitude ratio V/I. The long-term clinical outcome of the patients was re-evaluated 3 years after their discharge and correlated with the different components.

RESULTS

Only the central conduction time (CCT) and the latency of the wave N20 indicated a statistical correlation with the later outcome (p = 0.0366). The amplitude ratio of wave V/I of the EAEP did not reveal a significant statistical difference between the various outcome groups.

CONCLUSIONS

In this study, the use of single components of the SSEP and EAEP per se could not predict the long-term clinical outcome after TBI. Combined systems such as the Riffel Score are necessary in order to achieve this goal.

Early prediction of outcome in very severe closed head injury

BACKGROUND

People with severe head injury and admission Glasgow Coma Scale (GCS) score < or =5 have a poor outcome and greatly strain limited resources.

AIM

To identify patients with the best chances of survival, using routine clinical measures.

METHODS

People attending the trauma intensive care unit, who had isolated blunt head injury and GCS< or =5 and who had survived > or =4h, were included in the study, resuscitated and clinically assessed. The GCS score was followed serially after admission. Bivariate analysis of various parameters with outcome was performed using the chi-square test. Serial GCS scores were compared with admission GCS by paired t-testing.

RESULTS

Of the 102 patients who were studied prospectively, 78 (76.5%) died and 24 (23.5%) survived. Age, gender, pre-hospital delay and admission GCS scores were comparable between the two groups. Adequate spontaneous respiration, brisk pupillary light reactivity on admission and increase in GCS by at least 2 at 24h after admission significantly affected the outcome (p<0.05). In the presence of all these factors, the survival rate increased from 6.1% to 57.1% (p<0.001).

CONCLUSIONS

People with GCS score < or =5 still have a reasonable chance of survival, so all patients should be aggressively managed initially. Better survival was observed among those with adequate spontaneous respiration, good pupillary reaction and improvement in GCS of at least 2 at 24h. These clinical parameters can help to predict survival and thus make best use of limited resources.

Hybrid Outcome Prediction Model for Severe Traumatic Brain Injury

Numerous studies addressing different methods of head injury prognostication have been published. Unfortunately, these studies often incorporate different head injury prognostication models and study populations, thus making direct comparison difficult, if not impossible. Furthermore, newer artificial intelligence tools such as machine learning methods have evolved in the field of data analysis, alongside more traditional methods of analysis. This study targets the development of a set of integrated prognostication model combining different classes of outcome and prognostic factors. Methodologies such as discriminant analysis, logistic regression, decision tree, Bayesian network, and neural network were employed in the study. Several prognostication models were developed using prospectively collected data from 513 severe closed head-injured patients admitted to the Neurocritical Unit at National Neuroscience Institute of Singapore, from April 1999 to February 2003. The correlation between prognostic factors at admission and outcome at 6 months following injury was studied. Overfitting error, which may falsely distinguish different outcomes, was compared graphically. Tenfold cross-validation technique, which reduces overfitting error, was used to validate outcome prediction accuracy. The overall prediction accuracy achieved ranged from 49.79% to 81.49%. Consistently high outcome prediction accuracy was seen with logistic regression and decision tree. Combining both logistic regression and decision tree models, a hybrid prediction model was then developed. This hybrid model would more accurately predict the 6-month post-severe head injury outcome using baseline admission parameters.

Koma

BACKGROUND

The prognosis after traumatic coma is often unclear. We investigated the prognostic value of somatosensory (SSEP) and early acoustic (EAEP) evoked potentials on comatose patients in the intensive care unit regarding long-term outcome. Different evaluation systems were investigated.

METHODS

This was a retrospective analysis of 100 patients. SSEP and EAEP were examined at different times and analysed according to the Riffel score. Combinations of the different types of potentials were evaluated regarding possible improvement of outcome prediction.

RESULTS

The positive predictive value of at least one missing peak V of the EAEP regarding a fatal prognosis was 83%. The negative predictive value of the EAEP was 96%. A good outcome (GOS 4+5) could be predicted by bilateral normal SSEP and EAEP with a positive predictive value of 98%.

CONCLUSIONS

Early evaluation of SSEP and EAEP allows reliable prognostic predictions regarding a later outcome in patients with severe traumatic brain injury and should therefore be used more often for intensive care patients.

Over-Complete Discrete Wavelet Transformation of the Normal Auditory Brainstem Response Improves Prediction of Outcome following Severe Acute Closed Head Injury

Previous research has shown that complex statistical analysis (discriminant function analysis) of a 'normal' auditory brainstem response (ABR) result can improve this measure's ability to predict subject outcome following severe acute closed head injury (ACHI). We hypothesized that adding the ABR's time-frequency information to such an analysis would improve this predictive value even further. 'Normal' ABR results were sampled from 69 severe ACHI subjects (22 of whom died and 47 of whom lived) and their time-frequency information extracted using an over-complete discrete wavelet transformation (OCDWT). A series of logistic regression analyses then showed correct predictions of death and survival as follows: ABR measures only 72 and 89% (respectively), ABR OCDWT measures only 82 and 89% (respectively), and ABR and ABR OCDWT measures combined 86 and 93% (respectively). These results showed that the addition of time-frequency information can improve the ability of the 'normal' ABR result to predict outcome following severe ACHI.