Measuring the appropriateness of carbamazepine and valproic acid prescribing and utilization using a newly implemented online system in the Tabuk Region of Saudi Arabia

Epilepsy is a common neurologic disorder, which is efficiently treated with carbamazepine and valproic acid. Moreover, Saudi Ministry of Health implemented a new E-system for Poison Control Centers called Awtar to enhance technology utilization in ensuring patients’ satisfaction and to improve treatment outcomes. Therefore, we conducted this study to assess appropriateness of indication of requests and therapeutic levels of carbamazepine and valproic acid in Tabuk area, North West Saudi Arabia. This is a retrospective observational study conducted in Poison Control & Forensic Chemistry Center, Tabuk, Saudi Arabia. Patients’ data were obtained for years 2018 and 2019. The blood levels of carbamazepine and valproic acid were measured by Therapeutic Drug Monitoring (TDM) Unit. We selected patients treated with either valproic acid or carbamazepine alone without any history of drug allergy. Data of 264 patients were extracted from Awtar E-system. Serum carbamazepine levels were within therapeutic range in 114 patients (75.50%), above-therapeutic range in 13 patients (8.61%) and sub-therapeutic levels in 24 patients (15.89%). Regarding serum valproic acid, it is within therapeutic range in 62 patients (54.87%), above-therapeutic range in 11 patients (9.73%) and sub-therapeutic levels in 40 patients (35.40%). In conclusion, this study gives information about partial appropriateness of usage of carbamazepine and low level of appropriateness of valproic acid. However, more efforts are needed to improve results of appropriateness of indication of antiepileptic drugs.     

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout,with applications to quality of life measurements from clinical trials

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.     

Ocular dysfunctions and toxicities induced by antiepileptic medications: Types,pathogenic mechanisms,and treatment strategies

Introduction: Ocular dysfunctions and toxicities induced by antiepileptic drugs (AEDs) are rarely reviewed and not frequently received attention by treating physicians compared to other adverse effects (e.g. endocrinologic, cognitive and metabolic). However, some are frequent and progressive even in therapeutic concentrations or result in permanent blindness. Although some adverse effects are non-specific, others are related to the specific pharmacodynamics of the drug.

Areas covered: This review was written after detailed search in PubMed, EMBASE, ISI web, SciELO, Scopus, and Cochrane Central Register databases (from 1970 to 2019). It summarized the reported ophthalmologic adverse effects of the currently available AEDs; their risks and possible pathogenic mechanisms. They include ocular motility dysfunctions, retinopathy, maculopathy, glaucoma, myopia, optic neuropathy, and impaired retinal vascular autoregulation. In general, ophthalmo-neuro- or retino-toxic adverse effects of AEDs are classified as type A (dose-dependent), type B (host-dependent or idiosyncratic) or type C which is due to the cumulative effect from long-term use.

Expert opinion: Ocular adverse effects of AEDs are rarely reviewed although some are frequent or may result in permanent blindness. Increasing knowledge of their incidence and improving understanding of their risks and pathogenic mechanisms are crucial for monitoring, prevention, and management of patients’ at risk.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).     

Tibial acceleration and shock attenuation while running over different surfaces in a trail environment

ObjectivesIncreased tibial axial acceleration and reduced shock attenuation are associated with running injuries and are believed to be influenced by surface type. Trail running has increased in popularity and is thought to have softer surface properties than paved surface, but it is unclear if trail surfaces influence tibial acceleration and shock attenuation. The purpose of this study was to investigate peak triaxial and resultant tibial acceleration as well as axial and resultant shock attenuation among dirt, gravel, and paved surfaces.DesignFifteen recreational runners (12 females, 3 males, age = 27.7 ± 9.1 years) ran over dirt, gravel, and paved surfaces in a trail environment while instrumented with triaxial tibial and head accelerometers.MethodsDifferences between tri-planar peak tibial accelerations (braking, propulsion, axial, medial, lateral, and resultant) and shock attenuations (axial and resultant) among surface types were assessed with one-way ANOVAs with Bonferroni post-hoc tests.ResultsNo significant differences were found for tibial accelerations or shock attenuations among surface types (p > 0.05).ConclusionsDirt and gravel trail running surfaces do not have lower tibial accelerations or greater shock attenuation than paved surfaces. While runners are encouraged to enjoy the psychological benefits of trail running, trail surfaces do not appear to reduce loading forces associated with running-related injuries.