Chronic Migraine May Be Associated With Postprandial Hypoglycemia in Adult Men: A Case Series.

Migraine is a common neurological disorder that significantly impacts patients around the world. In the United States, one in six individuals suffers from a migraine disorder. Despite its high prevalence, the etiology of migraine is not well understood. Multiple factors likely contribute to the development of both acute and chronic migraine, making the consensus as to the cause and treatment difficult. Presented here are three case studies involving adult males suffering from chronic migraine. Each subject provided a medical history and underwent physical, psychological, and neurological examinations. In addition, relevant bloodwork and cervical spine X-rays were obtained. Physical examination, laboratory studies, imaging, and psychological metrics were unremarkable with the notable exception of the three-hour oral glucose tolerance tests. All three patients displayed hypoglycemia at three hours. Furthermore, their symptoms markedly improved with the initiation of a ketogenic diet. These data are suggestive of a potential link between postprandial hypoglycemia and chronic migraine. Despite the small sample size, we feel that this report presents possible evidence for a connection between postprandial hypoglycemia and chronic migraine. Furthermore, properly controlled studies of larger sample sizes are required, but we suggest that clinicians consider screening patients for this easily overlooked metabolic disturbance, especially in the absence of other options.

An Overview of Machine Learning Applications in Sports Injury Prediction.

Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.

Implementation of Pharmacogenomics and Artificial Intelligence Tools for Chronic Disease Management in Primary Care Setting.

Chronic disease management often requires use of multiple drug regimens that lead to polypharmacy challenges and suboptimal utilization of healthcare services. While the rising costs and healthcare utilization associated with polypharmacy and drug interactions have been well documented, effective tools to address these challenges remain elusive. Emerging evidence that proactive medication management, combined with pharmacogenomic testing, can lead to improved health outcomes and reduced cost burdens may help to address such gaps. In this report, we describe informatic and bioanalytic methodologies that integrate weak signals in symptoms and chief complaints with pharmacogenomic analysis of ~90 single nucleotide polymorphic variants, CYP2D6 copy number, and clinical pharmacokinetic profiles to monitor drug-gene pairs and drug-drug interactions for medications with significant pharmacogenomic profiles. The utility of the approach was validated in a virtual patient case showing detection of significant drug-gene and drug-drug interactions of clinical significance. This effort is being used to establish proof-of-concept for the creation of a regional database to track clinical outcomes in patients enrolled in a bioanalytically-informed medication management program. Our integrated informatic and bioanalytic platform can provide facile clinical decision support to inform and augment medication management in the primary care setting.

Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen. In order for public health, care delivery systems, clinicians, and other stakeholders to be better prepared for the next wave of SARS-CoV-2 infections, which, at this point, seems inevitable, we need to better understand this disease-not only from a clinical diagnosis and treatment perspective-but also from a forecasting, planning, and advanced preparedness point of view. To predict the onset and outcomes of a next wave, we first need to understand the pathologic mechanisms and features of COVID-19 from the point of view of the intricacies of clinical presentation, to the nuances of response to therapy. Here, we present a novel approach to model COVID-19, utilizing patient data from related diseases, combining clinical understanding with artificial intelligence modeling. Our process will serve as a methodology for analysis of the data being collected in the ASAIO database and other data sources worldwide.