Background and Aims:

In Trigeminal Neuralgia (TN) pain is localized in the distribution of one or more branches of the trigeminal nerve. A hallmark of TN is the presence of discrete skin areas able to trigger pain attacks when touched. In classical TN trigeminal reflexes are normal but dedicated MRI studies can recognize a vascular compression with morphological changes of trigeminal root. In this combined clinical, neuroimaging and neurophysiological study we aim to disclose trigeminal root somatotopic organization.

Methods:

We enrolled 53 patients with a definite diagnosis of classical TN. From MRI images we measured the polar coordinates of the impacting vessel on the trigeminal root circumference and then correlate it with pain distribution, trigger zones and latencies of the early components of the trigeminal reflexes.

Results:

Pain in V1, V2 and V3 is associated, respectively, with vascular compression in the medial, superior and lateral aspect of the nerve (p<0.05). Cutaneous trigger zones are associated with corresponding region of the circumference (p<0.05). Increased latency of the R1 component of the blink reflex is associated with medial compression, while increased latency of the SP1 component of the masseter inhibitory reflex is associated with inferomedial compression when the reflex is evoked from the infraorbital nerve, and with lateral compression when it is evoked from the mental nerve (p<0.05).

Conclusions:

In TN, pain distribution, trigger zones and increased latencies of trigeminal reflexes are correlated with specific sites of neurovascular compression along trigeminal root circumference, disclosing its somatotopic organization.

Background and Aims:

Patients with MND often develop respiratory muscle weakness. Once diaphragmatic muscle weakness is present, sudden deterioration and death can occur in a highly unpredictable way, often leading to crisis hospital admission.

Access to specialist respiratory physiotherapy for support with airway clearance and non invasive ventilation in normal place of residence can avoid the need for hospital admission. This paper reports place of death outcomes across a rural region in the East of England with access to an outreach specialist respiratory physiotherapist.

Methods:

Place of death data was collected over a 30 month period, alongside referral and activity data. Results were compared against reported data of regional averages for all deaths, alongside reported place of death data for people with MND elsewhere in the UK.

Results:

There were 123 deaths in the reporting period. 95 of these patients received at least one home assessment by the outreach service. 23 deaths were excluded from analysis as place of death data not available. Only 27% of patients died in a hospital, compared to regional average of 47% for all conditions and reported figures of 59% for patients with MND elsewhere in the region. 47% of patients died in their normal place of residence. Outreach intervention was estimated to have avoided hospital admission in 83% of cases.

Conclusions:

Specialist outreach respiratory physiotherapy has a role in supporting patients with Motor Neurone Disease to avoid hospital admissions towards the end of life and in enabling people with MND to die at home.

Background and Aims:

Glioblastoma (GBM) prevails in elderly patients, who often suffer from other comorbidities that may affect the outcome. The aim of our study was to investigate which clinical characteristics, preexisting comorbidities, and treatment-related complications may impact the outcome of GBM elderly patients.

Methods:

In this retrospective study we included GBM patients ≥ 65-year-old diagnosed with glioblastoma from 2015 to 2020. We retained information about presence of comorbidities (according to Charlson Comorbidity Index – CCI), Karnofsky prognostic score (KPS), MGMTp methylation, and occurrence of clinical complications during treatment or follow-up.

Results:

We included 160 patients. Median age was 72 years (65-88). Median time of follow-up was 9.25 months. mPFS and mOS were 5.84 and 9.67 months. In a multivariate analysis, factors affecting survival were: KPS after surgery ≥ 70 (mPFS: HR 0.24, 0.13-0.44; mOS: HR 0.43, 0.24–0.76. 95% CI), partial vs gross total resection (mPFS: HR 2.15, 1.23–3.77; mOS: HR 2.61, 1.34–5.07. 95% CI), MGMTp methylation (mPFS: HR 0.35, 0.22–0.55; mOS: HR 0.37, 0.24–0.76. 95% CI), and occurrence of complications after surgery (mPFS: HR 2.52, 1.39–4.55; mOS: HR 2.96, 1.63–5.40. 95% CI). Conversely, age and CCI were not significantly correlated to prognosis.

Conclusions:

For elderly patients with GBM, CCI may not correlate with outcome. Conversely, other factors (such as KPS and occurrence of clinical complications after surgery, as well as extent of resection and MGMTp methylation) retain a significant importance. Further studies are needed to standardise clinical prognostic scale in elderly GBM patients.

Background and Aims:

Glioblastoma multiforme(GBM) is the deadliest form of brain tumor with a dismal overall survival rate of 2.2% over 3 years. There is limited literature highlighting whole genome sequencing and its overall survival. The aim of this study is to analyse the genomic profile and epidemiological characteristics in GBM to define the survival rates at 12 and 24 months with common mutations.

Methods:

We utilized cBioPortal cancer genomics [The Cancer Genome Atlas(TCGA) PanCancer Atlas and TCGA Firehose Legacy] to study the genetic mutations associated with GMB. Epidemiological characteristics and genetic profile were evaluated and a query was generated to calculate 12 and 24 months survival rate with most common mutations. Log rank test and Kaplan–Meier estimator were used to analyse the survival function. Patients with 2 or more overlapping mutations (715) were excluded.

Results:

We identified 2041 GBM patients, with 1032(51.1%) males and 656(32.5%) females. 759(37.6%) were white, 82(4.1%) were black, and 17(0.8%) were asian. Majority of patients were between 50-70 years. 1,592(78.8%) were deceased. 1940(95.1%) had primary and 62(3.0%) had recurrent cancer. The common mutations associated with reduced survival were TTN:156(7.6%), MUC16:117(5.7%), NF1:156(7.6%), PIK3R1:97(4.7%), IDH1:59(2.8%), EGFR:305(14.9%), PTEN:341(16.7%), and TP53:362(17.7%). Overall survival after initial diagnosis following 12 and 24 months was 58.9% and 24.2%, respectively. Survival was lowest amongst TTN[30%; 0%], PTEN[36.4%; 9%], CDKN2A[42.3%; 15.4%], MDM2[53.6%; 24.7%], EGFR[56.1%; 22.4%], MDM4[71.4%; 0%], and TP53[84.2%; 45.3%] respectively.(p=3.26e-10)

Conclusions:

GBM with TTN, PTEN, and MDM4 mutations were associated with worst overall prognosis. These new insights may provide the foundation for developing new future therapies for these patients.

Background and Aims:

We propose a supplemental map to Penfield motor homunculus in the pre-central cortex and a novel subcortical map of the cortico-spinal tract that are accurate and essential for safe surgery in patients with eloquent brain lesions.

Methods:

A single-institution prospective cohort study of patients who underwent craniotomy for motor eloquent lesions with intraoperative motor neuromonitoring (cortical and subcortical) between 2015 and 2020 was performed. All the positive cortical and subcortical stimulation points were taken into account and cartographic maps were produced to demonstrate cortical and subcortical areas of motor representation and their configuration.

Results:

180 patients (58.4% male, 41.6% female) were included in the study with 81.6% asleep craniotomies and 18.4% awake craniotomies for motor eloquent lesions (high grade gliomas 57.7%, low grade gliomas 23%, metastases 13.8%, vascular malformation 1.1%) with intraoperative cortical and subcortical motor mapping. Based on the data, we propose a supplemental clinical cortical and a novel subcortical motor map to the original Penfield’s motor homunculus, including demonstration of localisation of intercostal muscles both in the cortex and subcortex which has not been previously described.

Conclusions:

The supplementary clinical cortical and novel subcortical motor maps of the homunculus presented here have been derived from a large cohort of patients undergoing direct cortical and subcortical brain mapping. The information will have direct relevance for improving the safety and outcome of patients undergoing resection of motor eloquent brain lesions.

Background and Aims:

Previous studies on the association between etiological factors and hemorrhagic stroke (HS) yielded inconsistent results. A proper risk stratification requires a multivariative analysis of predictors including clinical risk factors, ethnicity, age, sex, weather.

We aimed to stratify a risk of moderate and high severity of HS in desert climate.

Methods:

For analysis, we used a large public hospital’s stroke registry (4 years; 160 cases) and meteorological data acquisitions from Al-Ain city station, UAE.

To elucidate associations between multiple weather parameters, demographic, clinical risk factors and HS incidence we calculated Pearson’s correlation coefficients and constructed barplots that represented regional circannual weather changes and HS morbidity rates. We also examined the immediate and delayed effects of multiple weather parameters and daily changes on HS incidence by building distributed lag nonlinear models.

To study an interaction of climatic and clinical risk factors with HS severity alone or in combination, we constructed ML models predicting the stoke severity (NIHSS >4 or ≤4).

Results:

HS incidence is associated significantly (p < 0.05) with changes in temperature, humidex, atmosphere pressure and relative humidity. The highest risk of HS is observed on day four after the weather event. The models that combine demographic and clinical factors in association with weather-related parameters showed the best performance to predict NIHSS severity with 87.5% sensitivity, 89% specificity.

Conclusions:

Accurate risk stratification of HS is possible with the employment of AI-algorithms that combine demographic, clinical, and weather-related parameters. Proposed predictive models may optimize stroke management practices.