Advancing toward precision migraine treatment: Predicting responses to preventive medications with machine learning models based on patient and migraine features.

OBJECTIVE: To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. BACKGROUND: Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial-and-error approach is inefficient. METHODS: In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow-up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta-blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre-trained a deep neural network, "TabNet," using 145 variables, then employed TabNet-embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non-responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held-out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. RESULTS: Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta-blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta-blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. CONCLUSION: We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine-specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

Treatment patterns of patients with migraine eligible for anti-CGRP pathway monoclonal antibodies.

Introduction: Migraine is a debilitating neurological disorder, with a wide range of symptoms and disease burden, underscoring the heterogeneity of patients' disease characteristics and treatment needs. To characterize the profile of migraine patients in the US who may be eligible for preventive treatment with an anti-CGRP pathway mAb and to better understand treatment patterns and real-world use of acute and preventive medications for migraine, we conducted a retrospective cohort study of adult patients. Methods: These patients were identified as having migraine using diagnosis codes or migraine-specific medication use (first = index) in the IQVIA PharMetrics® Plus database. Patients were required to have ≥ 12 months of continuous enrollment in medical and pharmacy benefits prior to index (baseline) and after index (follow-up). Patients were stratified into chronic migraine (CM) and non-chronic migraine (non-CM) by diagnosis codes. Based on acute migraine-specific medication dispensing data in the follow-up period, non-CM patients were divided into 3 cohorts: highest, middle, and lowest tertile of total units of dispensed acute migraine-specific medication (gepants, ditans, ergot derivatives, and triptans). Migraine medication use was captured in the baseline and follow-up periods. Results: A total of 22,584 CM and 216,807 non-CM patients (72,269 patients in each tertile) were identified and included in the study. Over the follow-up, CM patients had a mean of 70 units of acute migraine-specific medications dispensed, while the highest, middle, and lowest tertile of non-CM patients had a mean of 92, 29, and 10 units, respectively. Anti-calcitonin gene-related peptide pathway mAbs were dispensed for 28.9% of CM patients, and for 6.9%, 4.1%, and 2.9% of non-CM patients in the highest, middle, and lowest tertiles, respectively. Conclusion: A lower proportion of non-CM patients had use of anti-calcitonin gene-related peptide pathway mAbs compared to CM patients, confirming the unmet need with appropriate preventive medication. There appears to be a persistent gap in management of patients without a diagnosis of CM who are dispensed high quantities of acute migraine-specific medications.

Application of Artificial Intelligence in the Headache Field.

PURPOSE OF REVIEW: Headache disorders are highly prevalent worldwide. Rapidly advancing capabilities in artificial intelligence (AI) have expanded headache-related research with the potential to solve unmet needs in the headache field. We provide an overview of AI in headache research in this article. RECENT FINDINGS: We briefly introduce machine learning models and commonly used evaluation metrics. We then review studies that have utilized AI in the field to advance diagnostic accuracy and classification, predict treatment responses, gather insights from various data sources, and forecast migraine attacks. Furthermore, given the emergence of ChatGPT, a type of large language model (LLM), and the popularity it has gained, we also discuss how LLMs could be used to advance the field. Finally, we discuss the potential pitfalls, bias, and future directions of employing AI in headache medicine. Many recent studies on headache medicine incorporated machine learning, generative AI and LLMs. A comprehensive understanding of potential pitfalls and biases is crucial to using these novel techniques with minimum harm. When used appropriately, AI has the potential to revolutionize headache medicine.

Pulsatile Tinnitus: Differential Diagnosis and Approach to Management.

PURPOSE OF REVIEW: The purpose of this review is to provide an updated approach to the evaluation and management of pulsatile tinnitus (PT), an uncommon but often treatable subtype of tinnitus. RECENT FINDINGS: Secondary PT can be due to either vascular or non-vascular etiologies, including, but not limited to: neoplasm, arteriovenous malformation or fistula, idiopathic intracranial hypertension, dural venous sinus stenosis, otoacoustic etiologies (e.g., otosclerosis, patulous eustachian tube) and bony defects (e.g., superior semicircular canal dehiscence). Computed tomography (CT) and magnetic resonance imaging (MRI) imaging have comparable diagnostic yield, though each may be more sensitive to specific etiologies. If initial vascular imaging is negative and a vascular etiology is strongly suspected, digital subtraction angiography (DSA) may further aid in the diagnosis. Many vascular etiologies of PT can be managed endovascularly, often leading to PT improvement or resolution. Notably, venous sinus stenting is an emerging therapy for PT secondary to idiopathic intracranial hypertension with venous sinus stenosis. Careful history and physical exam can help establish the differential diagnosis for PT and guide subsequent evaluation and management. Additional studies on the efficacy and long-term outcome of venous sinus stenting for venous stenosis are warranted.

Lacrimal Neuralgia: A Case Report and Comprehensive Review of the Literature.

PURPOSE OF REVIEW: Lacrimal neuralgia is a rare periorbital neuralgia. To date, only nine cases have been reported in the literature. Herein, we report a case and a comprehensive overview of the entity with a focus on the differential diagnosis of lacrimal neuralgia. Additionally, we propose putative diagnostic criteria for this rare neuralgia based on cases that have been reported. RECENT FINDINGS: Among the ten cases of lacrimal neuralgia reported (including the one in this review), seven out of ten were idiopathic, and the other three were considered secondary. Most patients reported stabbing and shooting pain that was either paroxysmal or continuous. The most effective therapy was nerve block for seven patients and pregabalin for three patients. The most important clues to differentiate lacrimal neuralgia from other causes of periorbital pain include pain topography and pain with features suggestive of neuralgia. The core feature of lacrimal neuralgia is neuralgic pain located in the area supplied by the lacrimal nerve, and the etiology could be primary or secondary. Responsiveness to anesthetic blockade might better serve as a confirmational, rather than mandatory, criterion for diagnosis.

Pharmacogenomic study-A pilot study of the effect of pharmacogenomic phenotypes on the adequate dosing of verapamil for migraine prevention.

OBJECTIVE: To investigate factors affecting the efficacy and tolerability of verapamil for migraine prevention using individual pharmacogenomic phenotypes. BACKGROUND: Verapamil has a wide range of dosing in headache disorders without reliable tools to predict the optimal doses for an individual. METHODS: This is a retrospective chart review examining adults with existing pharmacogenomic reports at Mayo Clinic who had used verapamil for migraine. Effects of six cytochrome P450 phenotypes on the doses of verapamil for migraine prevention were assessed. RESULTS: Our final analysis included 33 migraine patients (82% with aura). The mean minimum effective and maximum tolerable doses of verapamil were 178.2(20-320) mg and 227.9(20-480) mg. A variety of CYP2C9, CYP2D6, and CYP3A5 phenotypes were found, without significant association with the verapamil doses after adjusting for age, sex, body mass index, and smoking status. CONCLUSIONS: We demonstrated a wide range of effective and tolerable verapamil doses used for migraine in a cohort with various pharmacogenomic phenotypes.

A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

OBJECTIVE: To develop a natural language processing (NLP) algorithm that can accurately extract headache frequency from free-text clinical notes. BACKGROUND: Headache frequency, defined as the number of days with any headache in a month (or 4 weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional NLP algorithms. METHODS: This was a retrospective cross-sectional study with patients identified from two tertiary headache referral centers, Mayo Clinic Arizona and Mayo Clinic Rochester. All neurology consultation notes written by 15 specialized clinicians (11 headache specialists and 4 nurse practitioners) between 2012 and 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model, (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) model zero-shot, (3) GPT-2 QA model few-shot training fine-tuned on clinical notes, and (4) GPT-2 generative model few-shot training fine-tuned on clinical notes to generate the answer by considering the context of included text. RESULTS: The mean (standard deviation) headache frequency of our training and testing datasets were 13.4 (10.9) and 14.4 (11.2), respectively. The GPT-2 generative model was the best-performing model with an accuracy of 0.92 (0.91, 0.93, 95% confidence interval [CI]) and R2 score of 0.89 (0.87, 0.90, 95% CI), and all GPT-2-based models outperformed the ClinicalBERT model in terms of exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy of 0.27 (0.26, 0.28), it demonstrated a high R2 score of 0.88 (0.85, 0.89), suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. CONCLUSION: We developed a robust information extraction model based on a state-of-the-art large language model, a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT-2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub. Additional fine-tuning of the algorithm might be required when applied to different health-care systems for various clinical use cases.

Potential treatment targets for migraine: emerging options and future prospects.

Migraine is a leading cause of disability worldwide. Despite the recent approval of several calcitonin gene-related peptide-targeted therapies, many people with migraine do not achieve satisfactory headache improvement with currently available therapies and there continues to be an unmet need for effective and tolerable migraine-specific treatments. Exploring additional targets that have compelling evidence for their involvement in modulating migraine pathways is therefore imperative. Potential new therapies for migraine include pathways involved in nociception, regulation of homoeostasis, modulation of vasodilation, and reward circuits. Animal and human studies show that these targets are expressed in regions of the CNS and peripheral nervous system that are involved in pain processing, indicating that these targets might be regarded as promising for the discovery of new migraine therapies. Future studies will require assessment of whether targets are suitable for therapeutic modulation, including assessment of specificity, affinity, solubility, stability, efficacy, and safety.

Migrainous infarction.

Migrainous infarction is defined as a migraine attack occurring as migraine with aura, typical of the patient's previous attacks, except that one or more aura symptoms persist for >60min, and neuroimaging demonstrates ischemic infarct in the relevant area. To better understand migrainous infarction, one must disentangle the complex interactions between migraine and stroke. In this chapter, we first discuss the migraine-stroke association in sections including "Increased Risks of Stroke and Subclinical Infarcts in Patients With Migraine," "Migrainous Headache Cooccurring or Triggered by Ischemic Stroke," "Stroke Progression in Patients With Migraine," and "Clinic Conditions Associated With Higher Risks of Both Migraine and Stroke." As an extreme example of migraine-stroke association, the annual incidence of migrainous infarction was reported to be 0.80/100,000/year, with the incidence in females nearly twofold that of male patients. Patients diagnosed with migrainous infarction are typically younger (average age 29-39 in case series), have fewer traditional vascular risk factors, and have more favorable prognosis compared to strokes from traditional risk factors. Thorough evaluation is recommended to rule out other etiologies of stroke. Patients diagnosed with migrainous infarction should receive antiplatelet therapy and migraine preventive therapy to avoid future events. Vasoactive medications, including triptans and ergots, should be avoided.

Emerging and future directions of migraine research and treatment.

Despite many migraine-specific treatments that became available over the past 5 years, many patients still suffer from debilitating migraine. Emerging and future directions of migraine research and treatment should consider different aspects including revising the headache diagnostic criteria to reflect disease burden and prognosis, developing biomarkers, including genetic, serum, imaging, and deep phenotyping biomarkers to facilitate personalized medicine for headache treatment. Additionally, research should also emphasize identifying novel treatment targets for drug development. In this chapter, we provide an overview of current studies and controversies in the diagnosis of migraine and available research on potential migraine biomarkers. We also discuss potential treatment targets for migraine, including CGRP, PACAP, orexin, non-µ opioid receptors, nitric oxide, BKCa channel, KATP channel, amylin, TRP channels, prolactin, PAR-2, and other potential targets.

Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature.

The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner. While several closed-source summarization tools based on large language models (LLMs) now exist, rigorous and systematic evaluations of their outputs are lacking. Furthermore, there is a paucity of high-quality datasets and appropriate benchmark tasks with which to evaluate these tools. We address these issues with four contributions: we release Clinfo.ai, an open-source WebApp that answers clinical questions based on dynamically retrieved scientific literature; we specify an information retrieval and abstractive summarization task to evaluate the performance of such retrieval-augmented LLM systems; we release a dataset of 200 questions and corresponding answers derived from published systematic reviews, which we name PubMed Retrieval and Synthesis (PubMedRS-200); and report benchmark results for Clinfo.ai and other publicly available OpenQA systems on PubMedRS-200.

Simultaneous Comparisons of 25 Acute Migraine Medications Based on 10 Million Users' Self-Reported Records From a Smartphone Application.

BACKGROUND AND OBJECTIVES: Many acute treatment options exist for migraine. However, large-scale, head-to-head comparisons of treatment effectiveness from real-world patient experience reports are lacking. METHODS: This is a retrospective analysis of 10,842,795 migraine attack records extracted from an e-diary smartphone application between June 30, 2014, and July 2, 2020. We analyzed 25 acute medications among 7 classes-acetaminophen, nonsteroid anti-inflammatory drugs (NSAIDs), triptans, combination analgesics, ergots, antiemetics, and opioids. Gepants and ditan were not included in this analysis. Different doses and formulations of each medication, according to the generic names, were combined in this analysis. We used a 2-level nested logistic regression model to analyze the odds ratio (OR) of treatment effectiveness of each medication by adjusting concurrent medications and the covariance within the same user. Subgroup analyses were conducted for users in the United States, the United Kingdom, and Canada. RESULTS: Our final analysis included 4,777,524 medication-outcome pairs from 3,119,517 migraine attacks among 278,006 users. Triptans (mean OR 4.8), ergots (mean OR 3.02), and antiemetics (mean OR 2.67) were the top 3 classes of medications with the highest effectiveness, followed by opioids (mean OR 2.49), NSAIDs (other than ibuprofen, mean OR 1.94), combination analgesics (acetaminophen/acetylsalicylic acid/caffeine) (OR 1.69, 95% CI 1.67-1.71), others (OR 1.49, 95% CI 1.47-1.50), and acetaminophen (OR 0.83, 95% CI 0.83-0.84), using ibuprofen as the reference. Individual medications with the highest ORs were eletriptan (OR 6.1, 95% CI 6.0-6.3), zolmitriptan (OR 5.7, 95% CI 5.6-5.8), and sumatriptan (OR 5.2, 95% CI 5.2-5.3). The ORs of acetaminophen, NSAIDS, combination analgesics, and opioids were mostly around or less than 1, suggesting similar or lower reported effectiveness compared with ibuprofen. The ORs for 24 medications, except that of acetylsalicylic acid, achieved statistical significance with p < 0.0001, and our nested logistic regression model achieved an area under the curve (AUC) of 0.849. Country-specific subgroup analyses revealed similar ORs of each medication and AUC (United States 0.849, United Kingdom 0.864, and Canada 0.842), demonstrating the robustness of our analysis. DISCUSSION: Using a big data approach, we analyzed patient-generated real-time records of 10 million migraine attacks and conducted simultaneous head-to-head comparisons of 25 acute migraine medications. Our findings that triptans, ergots, and antiemetics are the most effective classes of medications align with the guideline recommendations and offer generalizable insights to complement clinical practice. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that for patients with migraine, selected acute medications (e.g., triptans, ergots, antiemetics) are associated with higher odds of user-rated positive response than ibuprofen.

A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records.

Background: Headache frequency, defined as the number of days with any headache in a month (or four weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional natural language processing (NLP) algorithms. Methods: This was a retrospective cross-sectional study with human subjects identified from three tertiary headache referral centers- Mayo Clinic Arizona, Florida, and Rochester. All neurology consultation notes written by more than 10 headache specialists between 2012 to 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) Model zero-shot (3) GPT-2 QA model few-shot training fine-tuned on Mayo Clinic notes; and (4) GPT-2 generative model few-shot training fine-tuned on Mayo Clinic notes to generate the answer by considering the context of included text. Results: The GPT-2 generative model was the best-performing model with an accuracy of 0.92[0.91 - 0.93] and R2 score of 0.89[0.87, 0.9], and all GPT2-based models outperformed the ClinicalBERT model in terms of the exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy 0.27[0.26 - 0.28], it demonstrated a high R2 score 0.88[0.85, 0.89], suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. Conclusion: We developed a robust model based on a state-of-the-art large language model (LLM)- a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub.

Novel grading system for CADASIL severity: A multicenter cross-sectional study.

Background: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an inherited progressive cerebral microangiopathy with considerable phenotypic variability. The purpose of this study was to describe the generalizability of a recently proposed grading system of CADASIL across multiple centers in the United States. Methods: Electronic medical records (EMR) of an initial neurological assessment of adult patients with confirmed CADASIL were reviewed across 5 tertiary referral medical centers with expertise in CADASIL. Demographic, vascular risk factors, and neuroimaging data were abstracted from EMR. Patients were categorized into groups according to the proposed CADASIL grading system: Grade 0 (asymptomatic), Grade 1 (migraine only), Grade 2 (stroke, TIA, or MCI), Grade 3 (gait assistance or dementia), and Grade 4 (bedbound or end-stage). Inter-rater reliability (IRR) of grading was tested in a subset of cases. Results: We identified 138 patients with a mean age of 50.9 ± 13.1 years, and 57.2% were female. The IRR was acceptable over 33 cases (κ=0.855, SD 0.078, p<0.001) with 81.8% being concordant. There were 15 patients (10.9%) with Grade 0, 50 (36.2%) with Grade 1, 61 (44.2%) with Grade 2, 12 (8.7%) with Grade 3, and none with Grade 4. Patients with a lower severity grade (grade 0 vs 3) tended to be younger (49.5 vs. 61.9 years) and had a lower prevalence of hypertension (50% vs. 20%, p = 0.027) and diabetes mellitus (0% vs. 25%, p = 0.018). A higher severity grade was associated with an increased number of vascular risk factors (p = 0.02) and independently associated with hypertension and diabetes (p<0.05). Comparing Grade 0 vs. 3, cortical thickness tended to be greater (2.06 vs. 1.87 mm; p = 0.06) and white matter hyperintensity volume tended to be lower (54.7 vs. 72.5 ml; p = 0.73), but the differences did not reach significance. Conclusion: The CADASIL severity grading system is a pragmatic, reliable system for characterizing CADASIL phenotype that does not require testing beyond that done in standard clinical practice. Higher severity grades tended to have a higher vascular risk factor burden. This system offers a simple method of categorizing CADASIL patients which may help to describe populations in observational and interventional studies.

Using Artificial Intelligence in Predicting Ischemic Stroke Events After Percutaneous Coronary Intervention.

BACKGROUND: Ischemic stroke (IS) is an uncommon but severe complication in patients undergoing percutaneous coronary intervention (PCI). Despite significant morbidity and economic cost associated with post PCI IS, a validated risk prediction model is not currently available. AIMS: We aim to develop a machine learning model that predicts IS after PCI. METHODS: We analyzed data from Mayo Clinic CathPCI registry from 2003 to 2018. Baseline clinical and demographic data, electrocardiography (ECG), intra/post-procedural data, and echocardiographic variables were abstracted. A random forest (RF) machine learning model and a logistic regression (LR) model were developed. The receiver operator characteristic (ROC) analysis was used to assess model performance in predicting IS at 6-month, 1-, 2-, and 5-years post-PCI. RESULTS: A total of 17,356 patients were included in the final analysis. The mean age of this cohort was 66.9 ± 12.5 years, and 70.7% were male. Post-PCI IS was noted in 109 patients (.6%) at 6 months, 132 patients (.8%) at 1 year, 175 patients (1%) at 2 years, and 264 patients (1.5%) at 5 years. The area under the curve of the RF model was superior to the LR model in predicting ischemic stroke at 6 months, 1-, 2-, and 5-years. Periprocedural stroke was the strongest predictor of IS post discharge. CONCLUSIONS: The RF model accurately predicts short- and long-term risk of IS and outperforms logistic regression analysis in patients undergoing PCI. Patients with periprocedural stroke may benefit from aggressive management to reduce the future risk of IS.

Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media.

Migraine is a highly prevalent and disabling neurological disorder. However, information about migraine management in real-world settings is limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by those with migraine; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts, and used them for training and evaluating supervised machine learning methods. Our best system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

Narrative review of migraine management in patients with renal or hepatic disease.

OBJECTIVES/BACKGROUND: Treatment of migraine in the setting of either renal or hepatic disease can be daunting for clinicians. Not only does the method of metabolism have to be considered, but also the method of elimination/excretion of the parent drug and any active or toxic metabolites. Furthermore, it is difficult to think about liver or kidney disease in isolation, as liver disease can sometimes contribute to impaired renal function and renal disease can sometimes impair hepatic metabolism, through the cytochrome P450 system. METHODS: A detailed search for terms related to liver disease, renal disease, and migraine management was performed in PubMed, Ovid Medline, Embase, and the Cochrane Library.For each medication, product labels were retrieved and reviewed using the US FDA website, with additional review of IBM Micromedex, LiverTox, and the Renal Drug Handbook. RESULTS: This manuscript provides an overview of migraine drug metabolism and how it can be affected by liver and renal impairment. It reviews the standard terminology recommended by the US Food and Drug Administration for the different stages of hepatic and renal failure. The available evidence regarding the use of abortive and preventative medicines in the setting of organ failure is discussed in detail, including more recent therapies such as lasmiditan, gepants, and calcitonin gene-related peptide antibodies. CONCLUSIONS: For acute therapy, the use of NSAIDS should be limited, as these carry risk for both severe hepatic and renal disease. Triptans can be selectively used, often with dose guideline adjustments. Ubrogepant may be used in severe hepatic disease with dose adjustment and lasmiditan can be used in end stage renal disease. Though non-medicine strategies may be the most reasonable initial approach, many preventative medications can be used in the setting of hepatic and renal disease, often with dose adjustment. This review provides tables of guidelines, including reduced dosing recommendations, for the use of abortive and preventative migraine medications in hepatic and renal failure.

Acute Treatment of Headache (Focus on Migraine).

Acute treatments for migraine and cluster headache are necessary to abort attacks, relieve pain and associated symptoms, and restore an individual's ability to function. Acute headache treatments consist of a variety of medication and nonmedication options. In this article, we discuss the approach to acute treatment of migraine and cluster headache. We summarize the level of evidence to support each acute medication class according to recent systematic reviews and meta-analyses, as well as guideline recommendations from the American Headache Society, American Academy of Neurology, and European Federation of Neurological Society.