Faculty Bibliography

Objective: Network analysis is the utilization of mathematical techniques to analyze the relationships of information within a network. We conducted a network analysis of the International Classification of Headache Disorders, 3rd Edition (ICHD3). Our goal is to better understand individual disease entity based on the interconnectedness inherent in the ICHD hierarchy.
Method(s): A network is defined by a set of objects, called "nodes", and the connections between them. If two nodes are connected, an "edge" exists between them. We define a node as a headache diagnosis identified by at least one ICHD3 diagnosis criterium. An edge between two headache disorders exists if one disorder is mentioned explicitly by the other in the "notes" or "comments" section of the ICHD3. We identify key nodes in a network by measuring mathematically a node's interconnectedness in three ways: degree centrality, between-ness centrality, and closeness centrality. To examine how hierarchy affects classification, we developed two models for the ICHD3, a non-hierarchical model and a hierarchical model. In the non-hierarchical model, only cross-references in the subsections qualify as edges. In the hierarchical model, the structure of the ICHD3 is taken into account by establishing additional edges between sections and their subsections.
Result(s): There are 396 nodes in both of our models. In the non-hierarchical model, there are 718 edges with average degree of separation of 3.63. In the hierarchical model, there are 1385 edges with average degree of 6.99. In both models, migraine and medication-overuse headache (MOH) are in the top 10 diagnoses according to the three centrality measurements. The choice of nonhierarchical or hierarchical model affects which diagnoses occupy the top 10 centrality nodes; specifically, there are more secondary headache diagnoses in the top 10 position in the hierarchy model compared to the non-hierarchical model.
Conclusion(s): Migraine and MOH are the most well connected nodes in ICHD3. Diagnostic hierarchy allows for unification of secondary headaches that would otherwise be considered isolated diagnoses. Once connected in a hierarchical fashion, secondary headache diagnoses form a majority of the most well-connected nodes in our field

Progressive muscle relaxation (PMR) is an under-utilized Level A evidence-based treatment for migraine prevention. We studied the feasibility and acceptability of smartphone application (app)-based PMR for migraine in a neurology setting, explored whether app-based PMR might reduce headache (HA) days, and examined potential predictors of app and/or PMR use. In this single-arm pilot study, adults with ICHD3 migraine, 4+ HA days/month, a smartphone, and no prior behavioral migraine therapy in the past year were asked to complete a daily HA diary and do PMR for 20 min/day for 90 days. Outcomes were: adherence to PMR (no. and duration of audio plays) and frequency of diary use. Predictors in the models were baseline demographics, HA-specific variables, baseline PROMIS (patient-reported outcomes measurement information system) depression and anxiety scores, presence of overlapping pain conditions studied and app satisfaction scores at time of enrollment. Fifty-one patients enrolled (94% female). Mean age was 39 ± 13 years. The majority (63%) had severe migraine disability at baseline (MIDAS). PMR was played 22 ± 21 days on average. Mean/session duration was 11 ± 7 min. About half (47%) of uses were 1+ time/week and 35% of uses were 2+ times/week. There was a decline in use/week. On average, high users (PMR 2+ days/week in the first month) had 4 fewer days of reported HAs in month 2 vs. month 1, whereas low PMR users (PMR < 2 days/week in the first month) had only 2 fewer HA days in month 2. PROMIS depression score was negatively associated with the log odds of using the diary at least once (vs. no activity) in a week (OR = 0.70, 95% CI = [0.55, 0.85]) and of doing the PMR at least once in a week (OR = 0.77, 95% CI = [0.68, 0.91]). PROMIS anxiety was positively associated with using the diary at least once every week (OR = 1.33, 95% CI = [1.09, 1.73]) and with doing the PMR at least once every week (OR = 1.14 [95% CI = [1.02, 1.31]). In conclusion, about half of participants used smartphone-based PMR intervention based upon a brief, initial introduction to the app. App use was associated with reduction in HA days. Higher depression scores were negatively associated with diary and PMR use, whereas higher anxiety scores were positively associated.

OBJECTIVES/OBJECTIVE:To provide a unique perspective on geriatric headache and a number of novel treatment options that are not well known outside of the headache literature. DESIGN/METHODS:Review of the most current and relevant headache literature for practitioners specializing in geriatric care. RESULTS:Evaluation and management of headache disorders in older adults requires an understanding of the underlying pathophysiology and how it relates to age-related physiological changes. To treat headache disorders in general, the appropriate diagnosis must first be established, and treatment of headaches in elderly adults poses unique challenges, including potential polypharmacy, medical comorbidities, and physiological changes associated with aging. CONCLUSION/CONCLUSIONS:The purpose of this review is to provide a guide to and perspective on the challenges inherent in treating headaches in older adults.

Background: There are limited data on the adherence of migraine patients to recommendations for evidence-based behavioral treatments. Among patients seen by a headache specialist, we sought to determine rates of adherence to recommended behavioral treatments and barriers to adherence. We also sought to determine whether psychosocial factors such as migraine related disability, locus of control and self-efficacy were associated with adherence to migraine behavioral treatment recommendations. Methods: We conducted a prospective study of consecutive patients presenting to four headache specialists who were diagnosed with migraine at our Headache Center from 2016-2017 to examine whether they adhered to the recommendation to receive behavioral treatment. The primary outcome was whether patients had scheduled at least one visit for behavioral treatment. Descriptive statistics were reported. Patients who made an appointment for behavioral treatment were compared to those who did not across multiple categories including demographics, migraine characteristics, and personal beliefs with ANOVA and chi-square tests. Qualitative analyses were also done for open ended survey questions. Results: Of the 234 eligible patients, 69 (29.5%) were referred for behavioral treatment. Fifty-three (76.8%) patients referred for behavioral treatment were successfully reached by phone. Mean duration from time of referral to follow-up was 76 days (median 76, SD5 45). Just over half of patients (56.6%, N530) adhered to the recommendation for behavioral treatment. Patients who had previously seen a psychologist for their migraines were more likely to adhere to the behavioral treatment recommendation than patients who had not. Time constraints were the most common barrier cited for not scheduling a behavioral treatment appointment. Conclusion: Less than one third of eligible patients were referred for behavioral treatment and only about half adhered to the recommendation to schedule an appointment for behavioral treatment. More research should assess factors which might play a role in adherence to migraine behavioral treatment recommendations