Artificial intelligence and telemedicine in epilepsy and EEG: A narrative review.

The emergence of telemedicine and artificial intelligence (AI) has set the stage for a possible revolution in the future of medicine and neurology including the diagnosis and management of epilepsy. Telemedicine, with its proven efficacy during the COVID-19 pandemic, offers the advantage of bridging the gap between patients in resource-limited areas and specialized care, where in one study telemedicine reduced the epilepsy treatment gap from 43 % to 9 %. AI innovations promise a transformation in epilepsy care by possibly enhancing the accuracy of electroencephalogram (EEG) interpretation and seizure prediction through machine and deep learning. In one study, abnormal EEG recordings were classified into different categories using a convolutional neural networks (CNN) model showing a specificity of 90 % and an accuracy of 88.3 %. Other models constructed to predict seizures have also achieved a sensitivity of 96.8 % and specificity of 95.5 %. Various machine learning (ML) models highlight the potential AI holds in identifying interictal biomarkers and localizing seizure onset zones aiding in epilepsy treatment decision and outcome prediction. An ML model highlighted in this review localized seizure onset zone with an accuracy reaching 73 % and predicted surgical outcomes with an accuracy reaching 79 % compared to the 43 % accuracy of clinicians. However, limitations and challenges hinder the application of such technologies to reach their full potential in epilepsy care. Limitations include access to compatible devices, integration into clinical workflows, data bias, and availability of sufficient data. Extensive validated research is needed to guide future clinical practice with the implementation of technology-enhanced epilepsy care. This narrative review article will explore the use of AI and telemedicine in EEG and epilepsy care, examining their individual and combined impacts in shaping the future of epilepsy care and discussing the challenges and limitations faced in their usage.

The efficacy and safety of cabozantinib in patients with metastatic or advanced renal cell carcinoma: a systematic review and meta-analysis.

Background: Cabozantinib, a new first-line treatment for advanced renal cell carcinoma (aRCC), targets essential tyrosine kinases and outperforms the established comparator (sunitinib) in various efficacy outcomes. This systematic review and meta-analysis aimed to assess the efficacy and safety of cabozantinib compared to other aRCC treatments. Methods: Following PRISMA and Cochrane guidelines, our protocol was registered in PROSPERO. A systematic search, without date limits, was conducted on PubMed, Cochrane, Web of Science, and EMBASE until October 8, 2023. Data extraction encompassed study details, baseline information, and outcomes. Hazard ratios (HR) and risk ratios (RR) with 95% confidence intervals were employed for each outcome, and a random-effects model was applied to account for expected heterogeneity. Results: Three studies, encompassing 967 patients, were included in our analysis. In terms of efficacy, the pooled rate for overall survival significantly favored cabozantinib. However, in subgroup analyses, cabozantinib was only statistically superior to everolimus. For progression-free survival and tumor objective response rate, cabozantinib outperformed both everolimus and sunitinib. In adverse events, compared to sunitinib, cabozantinib exhibited inferiority in nearly all evaluated aspects, except for nausea and stomatitis, which showed no difference between the two groups. Conversely, it demonstrated a comparable risk profile with everolimus across various side effects. Conclusion: Cabozantinib shows significant efficacy in extending overall survival, progression-free survival, and tumor objective response rate despite a potentially higher risk of adverse events compared to sunitinib. These findings support cabozantinib as a first-line therapy for aRCC, either as an initial treatment or after prior VEGFR-targeted therapies.

Comparative Analysis of Heart Failure with Preserved Vs Reduced Ejection Fraction: Patient Characteristics, Outcomes, Mortality Prediction, and Machine Learning Model Development in the JoHFR.

Background: Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction. Methodology: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction. Results: Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study. Conclusion: The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.

Impact of Diabetes Mellitus on Heart Failure Patients: Insights from a Comprehensive Analysis and Machine Learning Model Using the Jordanian Heart Failure Registry.

Background: Heart failure (HF) is a common final pathway of various insults to the heart, primarily from risk factors including diabetes mellitus (DM) type 2. This study analyzed the clinical characteristics of HF in a Jordanian population with a particular emphasis on the relationship between DM and HF. Methods: This prospective study used the Jordanian Heart Failure Registry (JoHFR) data. Patients with HF were characterized by DM status and HF type: HF with preserved ejection fraction (HFpEF) or HF with reduced ejection fraction (HFrEF). Demographics, clinical presentations, and treatment outcomes were collected. Statistical analyses and machine learning techniques were carried out for the prediction of mortality among HF patients: Recursive Feature Elimination with Cross-Validation (RFECV) and Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTEENN) were employed. Results: A total of 2007 patients with HF were included. Notable differences between diabetic and non-diabetic patients are apparent. Diabetic patients were predominantly male, older, and obese (p < 0.001 for all). A higher incidence of HFpEF was observed in the diabetes cohort (p = 0.006). Also, diabetic patients had significantly higher levels of cholesterol (p = 0.008) and LDL (p = 0.003), reduced hemoglobin levels (p < 0.001), and more severe renal impairment (eGFR; p = 0.006). Machine learning models, particularly the Random Forest Classifier, highlighted its superiority in mortality prediction, with an accuracy of 90.02% and AUC of 80.51%. Predictors of mortality included creatinine levels >115 µmol/L, length of hospital stay, and need for mechanical ventilation. Conclusion: This study underscores notable differences in clinical characteristics and outcomes between diabetic and non-diabetic heart failure patients in Jordan. Diabetic patients had higher prevalence of HFpEF and poorer health indicators such as elevated cholesterol, LDL, and impaired kidney function. High creatinine levels, longer hospital stays, and the need for mechanical ventilation were key predictors of mortality.

Machine Learning-Based Mortality Prediction in Chronic Kidney Disease among Heart Failure Patients: Insights and Outcomes from the Jordanian Heart Failure Registry.

Background and Objectives: Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on healthcare systems and adversely affects the quality of life of patients worldwide. Comorbidities such as chronic kidney disease (CKD), arterial hypertension, and diabetes mellitus (DM) are common among HF patients, as they share similar risk factors. This study aimed to identify the prognostic significance of multiple factors and their correlation with disease prognosis and outcomes in a Jordanian cohort. Materials and Methods: Data from the Jordanian Heart Failure Registry (JoHFR) were analyzed, encompassing medical records from acute and chronic HF patients attending public and private cardiology clinics and hospitals across Jordan. An online form was utilized for data collection, focusing on three kidney function tests, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), and creatinine levels, with the eGFR calculated using the Cockcroft-Gault formula. We also built six machine learning models to predict mortality in our cohort. Results: From the JoHFR, 2151 HF patients were included, with 644, 1799, and 1927 records analyzed for eGFR, BUN, and creatinine levels, respectively. Age negatively impacted all measures (p ≤ 0.001), while smokers surprisingly showed better results than non-smokers (p ≤ 0.001). Males had more normal eGFR levels compared to females (p = 0.002). Comorbidities such as hypertension, diabetes, arrhythmias, and implanted devices were inversely related to eGFR (all with p-values <0.05). Higher BUN levels were associated with chronic HF, dyslipidemia, and ASCVD (p ≤ 0.001). Higher creatinine levels were linked to hypertension, diabetes, dyslipidemia, arrhythmias, and previous HF history (all with p-values <0.05). Low eGFR levels were associated with increased mechanical ventilation needs (p = 0.049) and mortality (p ≤ 0.001), while BUN levels did not significantly affect these outcomes. Machine learning analysis employing the Random Forest Classifier revealed that length of hospital stay and creatinine >115 were the most significant predictors of mortality. The classifier achieved an accuracy of 90.02% with an AUC of 80.51%, indicating its efficacy in predictive modeling. Conclusions: This study reveals the intricate relationship among kidney function tests, comorbidities, and clinical outcomes in HF patients in Jordan, highlighting the importance of kidney function as a predictive tool. Integrating machine learning models into clinical practice may enhance the predictive accuracy of patient outcomes, thereby supporting a more personalized approach to managing HF and related kidney dysfunction. Further research is necessary to validate these findings and to develop innovative treatment strategies for the CKD population within the HF cohort.

Assessing the Impact of Oral Isotretinoin on the Menstrual Cycle: A Prospective Study on Predictors of Menstrual Irregularities.

Background and Objectives: This study aims to evaluate the association between the use of oral isotretinoin and menstrual irregularities in acne patients with previously regular menstrual cycles. Materials and Methods: A prospective observational study was conducted on 58,599 female patients aged 14 to 36 at King Abdullah University Hospital in Irbid, Jordan. The patients were followed for a period of 4.5 to 8 months during treatment and for 2 months post-treatment. Menstrual cycle changes were documented, and statistical analysis was performed to identify any significant associations. Results: A total of 111 (37.1%) patients, who were previously known to have regular menstrual cycles, complained of menstrual changes while using oral isotretinoin. Ninety-nine of those patients who complained of menstrual changes had their cycles back to normal post-treatment. There is a significant difference in the total accumulative dose between those with changes in menses and those without; p-value [0.008]. The most common change that occurred was amenorrhea (p < 0.001), followed by oligomenorrhea and menorrhagia (p < 0.001 and p = 0.050, respectively). The duration of treatment was a significant predictor of menstrual irregularities, with an odds ratio (OR) of 5.106 (95% CI: 1.371-19.020, p = 0.015), indicating a higher likelihood of menstrual changes with increased treatment duration. The total accumulative dose was also significantly associated with menstrual irregularities (OR = 0.964; 95% CI: 0.939-0.990; p = 0.006). Additionally, a family history of PCOS significantly increased the odds of menstrual irregularities (OR = 3.783; 95% CI: 1.314-10.892; p = 0.014). Conclusions: The study identified that 37.1% of the participants experienced changes in their menstrual cycles while undergoing isotretinoin therapy, with the vast majority (89.2%) returning to normal within two months post-treatment. Our logistic regression analysis pinpointed the duration of isotretinoin treatment, the total accumulative dose, and a family history of PCOS as significant predictors of menstrual irregularities.

Identification of Hypoxia Prognostic Signature in Glioblastoma Multiforme Based on Bulk and Single-Cell RNA-Seq.

Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas-Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs' expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.

Predicting the Stone-Free Status of Percutaneous Nephrolithotomy with the Machine Learning System.

Purpose: The study aimed to create a machine learning model (MLM) to predict the stone-free status (SFS) of patients undergoing percutaneous nephrolithotomy (PCNL) and compare its performance to the S.T.O.N.E. and Guy's stone scores. Patients and Methods: This is a retrospective study that included 320 PCNL patients. Pre-operative and post-operative variables were extracted and entered into three MLMs: RFC, SVM, and XGBoost. The methods used to assess the performance of each were mean bootstrap estimate, 10-fold cross-validation, classification report, and AUC. Each model was externally validated and evaluated by mean bootstrap estimate with CI, classification report, and AUC. Results: Out of the 320 patients who underwent PCNL, the SFS was found to be 69.4%. The RFC mean bootstrap estimate was 0.75 and 95% CI: [0.65-0.85], 10-fold cross-validation of 0.744, an accuracy of 0.74, and AUC of 0.761. The XGBoost results were 0.74 [0.63-0.85], 0.759, 0.72, and 0.769, respectively. The SVM results were 0.70 [0.60-0.79], 0.725, 0.74, and 0.751, respectively. The AUC of Guy's stone score and the S.T.O.N.E. score were 0.666 and 0.71, respectively. The RFC external validation set had a mean bootstrap estimate of 0.87 and 95% CI: [0.81-0.92], an accuracy of 0.70, and an AUC of 0.795, While the XGBoost results were 0.84 [0.78-0.91], 0.74, and 0.84, respectively. The SVM results were 0.86 [0.80-0.91], 0.79, and 0.858, respectively. Conclusion: MLMs can be used with high accuracy in predicting SFS for patients undergoing PCNL. MLMs we utilized predicted the SFS with AUCs superior to those of GSS and S.T.O.N.E scores.

Compensatory Hyperhidrosis After Non-Surgical Treatment of Primary Focal Hyperhidrosis: Two-Year Single-Centered Prospective Study From Jordan.

BACKGROUND: Primary focal hyperhidrosis (PH) can be managed by a wide range of medical and surgical modalities. Compensatory hyperhidrosis (CH) is a well-documented complication of surgical treatment. We aimed to investigate the occurrence of compensatory hyperhidrosis (CH) in PH patients after nonsurgical treatment with botulinum toxin A (BTX- A) or iontophoresis. METHODOLOGY: We carried out a unicentric prospective study on PH patients from King Abdullah University Hospital (KAUH) in Jordan. PH patients were evaluated after 1-month of nonsurgical treatment. Patients who developed CH were re-assessed after 3-6 months through a telephone-based interview. RESULTS: A total of 86 patients with PH who underwent nonsurgical treatment with iontophoresis or botulinum toxin were recruited. Twenty-four (27.9%) patients developed subjective CH. It was mild in (75%), moderate in (21%), and severe in (4%) of patients affected, it was self-limiting within a few months in all patients. Patients with CH did not differ significantly in demographic or clinical variables from patients who did not develop CH except at the site of PH (p value = .05). CONCLUSION: The findings of this study indicate that more than quarter (27.9%) of patients with PH may develop minor compensatory sweating, however this didn't affect satisfaction with treatment.

Royal jelly mediates fibrotic signaling, collagen cross-linking and cell proliferation in cardiac fibroblasts.

Royal jelly (RJ) is a multifunctional bee product with a unique composition and wide-ranging biological properties, including antioxidant, anti-inflammatory and antiproliferative activities. Still, little is known about the possible myocardial protective properties of RJ. Considering that sonication could enhance RJ bioactivity, this study aimed to assess the effects of non-sonicated (NS) and sonicated (S) RJ on fibrotic signaling, cell proliferation, and collagen production in cardiac fibroblasts. S-RJ was produced by ultrasonication at 20 kHz. Ventricular fibroblasts isolated from neonatal rats were cultured and treated with different concentrations of NS-RJ or S-RJ (0, 50, 100, 150, 200, and 250 µg/well). S-RJ significantly depressed the expression levels of transglutaminase 2 (TG2) mRNA across all the concentrations tested and was inversely associated with the expression of this profibrotic marker. S-RJ and NS-RJ displayed distinct dose-dependent effects on mRNA expression of several other profibrotic, proliferation, and apoptotic markers. Unlike NS-RJ, S-RJ elicited strong negative dose-dependent relationships with the expression of profibrotic markers (TG2, COL1A1, COL3A1, FN1, CTGF, MMP-2, α-SMA, TGF-ß1, CX43, periostin), as well as proliferation (CCND1) and apoptotic (BAX, BAX/BCL-2) markers, indicating that RJ dose-response effects were significantly modified by sonification. NS-RJ and S-RJ increased the content of soluble collagen, while decreasing collagen cross-linking. Collectively, these findings show that S-RJ has a greater range of action than NS-RJ for downregulating the expression of biomarkers associated with cardiac fibrosis. Reduced biomarker expression and collagen cross-linkages upon cardiac fibroblast treatment with specific concentrations of S-RJ or NS-RJ suggests putative roles and mechanisms by which RJ may confer some protection against cardiac fibrosis.

Limbic and cortical regions as functional biomarkers associated with emotion regulation in bipolar disorder: A meta-analysis of neuroimaging studies.

BACKGROUND: Bipolar disorder (BD) is a psychiatric disorder characterized by episodes of depression and mania, associated with impaired emotion processing. Several functional MRI (fMRI) studies have been used to investigate the structural and functional alteration in BD. Here, we aim to investigate the current fMRI findings of brain activation during emotion-regulation tasks between BD patients and healthy controls (HC). METHODS: A systematic search through PubMed database for fMRI studies on bipolar patients and HC yielded 685 studies. We performed an activation likelihood estimation (ALE) on 21 studies for emotion regulation in BD patients and HC. Furthermore, we performed subgroup analyses for task performances in response time and accuracy between bipolar patients and HC. RESULTS: The total sample included 21 fMRI studies, comprising 543 BD patients, compared to 565 HC. ALE maps for emotion-related tasks showed hyperactivation in BD patients in the caudate, amygdala, precentral gyrus, middle frontal gyri, and sub-gyrus. Whereas hypoactivation was seen in the inferior frontal gyrus and anterior cingulate gyrus. LIMITATIONS: We could not apply a correction for p-value thresholds, as it needs large number of foci. Second, functional abnormalities were investigated for adult BD patients only, as BD patients have functional differences correlated with age. CONCLUSIONS: Our results showed that limbic and cortical regions can represent a potential biomarker for the diagnosis and management of BD, by showing clustered brain regions of abnormal patterns of increased activation between BD patients and HC.

Meta-analytical evidence of functional and structural abnormalities associated with pain processing in migraine patients: An activation likelihood estimation.

BACKGROUND: Migraine is a primary headache disorder that causes debilitating throbbing pain. Several functional MRI (fMRI) and voxel-based morphometry (VBM) studies have been used to investigate the structural and functional alteration in migraine. Here, we aim to study the converged brain regions of functional and structural abnormalities in gray matter volume (GMV) associated with pain processing and management in migraineurs and healthy controls (HC). METHODS: A systematic search through PubMed and Sleuth was carried out for peer-reviewed functional and structural neuroimaging studies on migraine patients and HC yielded a total of 1136 studies. We performed an activation likelihood estimation (ALE) meta-analysis on VBM and pain stimulation task-based fMRI studies to investigate the converged areas of GMV and functional abnormalities between migraineurs and HC. We performed two subgroup analyses between migraine with aura (MwA) and migraine without aura (MwoA) relative to HC, and between chronic migraine (CM) and episodic migraine (EM) compared to HC. RESULTS: The total sample included 16 fMRI and 22 VBM studies, consisting of 1295 migraine patients, compared to 995 HC. In fMRI analysis, ALE maps for pain stimulation tasks revealed hyperactivation in migraineurs in the substantia nigra compared to HC, whereas hypoactivation was seen in the cerebellum. For the VBM analysis, ALE clusters of increased GMV in migraineurs were observed in the parahippocampus and putamen nucleus. Whereas clusters of reduced GMV in migraineurs were seen in the frontal gyri. Compared to HC, MwoA patients showed a GMV reduction in the insula, and anterior cingulate, whereas MwA patients showed GMV reduction in the cerebellum, cingulate gyrus, and insula. CM patients showed decreased GMV in the precentral gyrus, whereas EM patients showed decreased GMV in the parahippocampus, and inferior frontal gyrus when compared to HC. CONCLUSIONS: Our findings represent a potential biomarker for the diagnosis and management of migraine, by showing clustered brain regions of abnormal patterns of activation and GMV changes between migraineurs and HC which might be associated with hyposensitivity to pain in migraineurs. Further studies are required to determine disease progression or therapeutic interventions' effect on migraine.

Genomic and Transcriptomic Predictors of Response to Immune Checkpoint Inhibitors in Melanoma Patients: A Machine Learning Approach.

Immune checkpoint inhibitors (ICIs) became one of the most revolutionary cancer treatments, especially in melanoma. While they have been proven to prolong survival with lesser side effects compared to chemotherapy, the accurate prediction of response remains to be an unmet gap. Thus, we aim to identify accurate clinical and transcriptomic biomarkers for ICI response in melanoma. We also provide mechanistic insight into how high-performing markers impose their effect on the tumor microenvironment (TME). Clinical and transcriptomic data were retrieved from melanoma studies administering ICIs from cBioportal and GEO databases. Four machine learning models were developed using random-forest classification (RFC) entailing clinical and genomic features (RFC7), differentially expressed genes (DEGs, RFC-Seq), survival-related DEGs (RFC-Surv) and a combination model. The xCELL algorithm was used to investigate the TME. A total of 212 ICI-treated melanoma patients were identified. All models achieved a high area under the curve (AUC) and bootstrap estimate (RFC7: 0.71, 0.74; RFC-Seq: 0.87, 0.75; RFC-Surv: 0.76, 0.76, respectively). Tumor mutation burden, GSTA3, and VNN2 were the highest contributing features. Tumor infiltration analyses revealed a direct correlation between upregulated genes and CD8+, CD4+ T cells, and B cells and inversely correlated with myeloid-derived suppressor cells. Our findings confirmed the accuracy of several genomic, clinical, and transcriptomic-based RFC models, that could further support the use of TMB in predicting response to ICIs. Novel genes (GSTA3 and VNN2) were identified through RFC-seq and RFC-surv models that could serve as genomic biomarkers after robust validation.

An in Depth Look Into Intracranial Abscesses and Empyemas: a Ten-year Experience in a Single Institute.

Background: As the incidence of intracranial infections increase due to diagnostic procedures improvement, more real-life data is needed to reach a more solid informed management approach. Objective: This study aims to describe and analyse clinical features of intracranial abscesses patients treated at a tertiary hospital in North Jordan during a 10-year period. Methods: We retrospectively identified 37 patients treated at King Abdullah University Hospital (KAUH) from 2011 to 2020 in Irbid, North Jordan. Treatment consisted of either aspiration, open craniotomy excision (OCE) or conservative therapy. Extracted variables included demographic data such (age, gender), clinical presentation, lab findings, radiological findings as well as management plan. Retrieved data was compared between the patients who underwent a single operation and those who underwent reoperation after the initial procedure. Results: Thirty-seven patients with 55 intracerebral abscesses were identified, 29 of whom had intraparenchymal brain abscesses, 4 patients had epidural empyema, and 4 had subdural empyema. The mean age was 28.8 (± 20.7) years, with a male predominance (78.4%). Sixteen patients underwent open craniotomy excision (OCE), 14 patients were treated by aspiration and 7 patients were treated conservatively. When comparing the single operation and the reoperation groups, there was no statistically significant difference across variables. Conclusion: Our study presents valuable insight from a tertiary hospital in north Jordan on intracranial abscesses and empyemas. Our findings confirm that good recovery can be established after aspiration or OCE in the majority of patients. Similar results were obtained when comparing the SOP and the ROP groups.

Functional outcomes of COVID-19 patients with acute ischemic stroke: A prospective, observational, single-center study in North Jordan.

We assessed whether stroke severity, functional outcome, and mortality in patients with ischemic stroke differed between patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and those without. We conducted a prospective, single-center cohort study in Irbid, North Jordan. All patients diagnosed with ischemic stroke and SARS-CoV-2 infection were consecutively recruited from October 15, 2020, to October 16, 2021. We recorded demographic data, vascular risk factors, National Institutes of Health Stroke Scale (NIHSS) score, stroke subtype according to the Trial of ORG 10172 in Acute Stroke Treatment Criteria (TOAST), treatments at admission, and laboratory variables for all patients. The primary endpoint was the functional outcome at 3 months assessed using the modified Rankin Score. Secondary outcomes involved in-hospital mortality and mortality at 3 months. We included 178 patients with a mean (standard deviation) age of 67.3 (12), and more than half of the cases were males (96/178; 53.9%). Thirty-six cases were coronavirus disease 2019 (COVID-19) related and had a mean (standard deviation) age of 70 (11.5). When compared with COVID-19-negative patients, COVID-19-positive patients were more likely to have a higher median NIHSS score at baseline (6 vs 11; P = .043), after 72 hours (6 vs 12; P = .006), and at discharge (4 vs 16; P < .001). They were also more likely to have a higher median modified Rankin Score after 3 months of follow-up (P < .001). NIHSS score at admission (odds ratio = 1.387, 95% confidence interval = 1.238-1.553]; P < .001) predicted having an unfavorable outcome after 3 months. On the other hand, having a concomitant SARS-CoV-2 infection did not significantly impact the likelihood of unfavorable outcomes (odds ratio = 1.098, 95% confidence interval = 0.270-4.473; P = .896). The finding conclude that SARS-CoV-2 infection led to an increase in both stroke severity and in-hospital mortality but had no significant impact on the likelihood of developing unfavorable outcomes.

Management of traumatic posterior fossa epidural hematomas in pediatrics: our experience and review of the literature.

BACKGROUND AND AIMS: Conservative management of posterior fossa epidural hematoma in the pediatric age group has been increasingly considered in the last decade with good clinical outcomes and comparable results to surgical intervention in carefully selected patients. The purpose of this study is to evaluate the outcome of observation in the management of pediatric patients with posterior fossa epidural hematoma (PFEDH) in our tertiary hospital and present a literature review on PFEDH pediatric patients. METHODS: We conducted a retrospective observational study at King Abdullah University Hospital (KAUH), a tertiary hospital in North Jordan. All pediatric patients (≤ 18 years) who were admitted with a diagnosis of PFEDH from January 2010 to December 2020 were included. Demographic data, trauma type, clinical signs and symptoms on admission, CT findings, treatment type, and outcomes were collected and assessed. The outcome was measured using the Glasgow outcome scale (GOS) on discharge from the hospital. RESULTS: A total of 16 patients were identified and included in this study. Nine patients were managed conservatively and 7 surgically. The mean age was 7.7 ± 6 years ranging from 1 to 18 years. Falls were the most common cause of injury. Vomiting was the most frequent presenting symptom. Except for 1 patient, 14 patients had good outcomes with a GOS of 5. One case of mortality was seen in our series. CONCLUSION: Posterior fossa epidural hematoma is a rare clinical condition among the pediatric age group. Early and consecutive CT scans must be obtained for all suspected cases. Successful conservative management can be achieved depending on multiple factors such as hematoma thickness or volume, neurological status on admission, and other radiological findings as shown in our study. The overall prognosis was good in our patients.