Ishii, Ryotaro

Abstract Objective To explore hidden Markov models (HMMs) as an approach for defining clinically meaningful headache‐frequency‐based groups in migraine. Background Monthly headache frequency in patients with migraine is known to vary over time. This variation has not been completely characterized and is not well accounted for in the classification of individuals as having chronic or episodic migraine, a diagnosis with potentially significant impacts on the individual. This study investigated variation in reported headache frequency in a migraine population and proposed a model for classifying individuals by frequency while accounting for natural variation. Methods The American Registry for Migraine Research (ARMR) was a longitudinal multisite study of United States adults with migraine. Study participants completed quarterly questionnaires and daily headache diaries. A series of HMMs were fit to monthly headache frequency data calculated from the diary data of ARMR. Results Changes in monthly headache frequency tended to be small, with 47% of transitions resulting in a change of 0 or 1 day. A substantial portion (24%) of months reflected daily headache with individuals ever reporting daily headache likely to consistently report daily headache. An HMM with four states with mean monthly headache frequency emissions of 3.52 (95% Prediction Interval [PI] 0–8), 10.10 (95% PI 4–17), 20.29 (95% PI 12–28), and constant 28 days/month had the best fit of the models tested. Of sequential month‐to‐month headache frequency transitions, 12% were across the 15‐headache days chronic migraine cutoff. Under the HMM, 38.7% of those transitions involved a change in the HMM state, and the remaining 61.3% of the time, a change in chronic migraine classification was not accompanied by a change in the HMM state. Conclusion A divide between the second and third states of this model aligns most strongly with the current episodic/chronic distinction, although there is a meaningful overlap between the states that supports the need for flexibility. An HMM has appealing properties for classifying individuals according to their headache frequency while accounting for natural variation in frequency. This empirically derived model may provide an informative classification approach that is more stable than the use of a single cutoff value.

Abstract Objective To investigate the impact of having headaches prior to traumatic brain injury (TBI) on headache features and long‐term patient health outcomes. Background and Methods This was an exploratory analysis of patients with TBI who were enrolled in the American Registry for Migraine Research (ARMR), a multicenter, prospective, longitudinal patient registry composed of patients with International Classification of Headache Disorders, 3rd edition (ICHD‐3)–defined headache diagnoses. The ARMR study enrolled 2,707 patients between February 1, 2016 and May 6, 2020, 565 of whom qualified for this analysis. Those with headaches prior to their TBI were compared to those without headaches prior to their TBI for ICHD‐3 diagnoses, headache frequency and intensity, headache‐related disability (Migraine Disability Assessment score), symptoms of anxiety (General Anxiety Disorder [GAD‐7]), depression (two items from Patient Health Questionnaire‐9), post‐traumatic stress disorder (PTSD), cutaneous allodynia (12‐item Allodynia Symptom Checklist [ASC‐12]), cognitive dysfunction (Migraine Attacks Subjective Cognitive Impairments Scale [Mig‐SCog]), pain interference (Patient‐Reported Outcomes Measurement Information System‐Pain Interference), and work productivity (Work Productivity and Activity Impairment). Results Among 565 participants with TBI, 350 had headaches prior to their TBI. Those with pre‐TBI headaches were less likely to receive a diagnosis of post‐traumatic headache (PTH; 14/350 [4.0%] vs. 21/215 [9.8%], p = 0.006), even though 25.7% reported new or worsening headaches within 7 days of their TBI. Those with pre‐TBI headaches had higher ASC‐12 scores (2.4 ± 3.5 vs. 1.8 ± 3.4, p = 0.030), Mig‐SCog scores (9.3 ± 4.7 vs. 8 ± 4.9, p = 0.004), and GAD‐7 scores (6.9 ± 5.1 vs. 6.2 ± 5.4, p = 0.039), and were more likely to have a migraine diagnosis (335/350 [95.7%] vs. 192/215 [89.3%], p = 0.003). Conclusions Those with headaches prior to TBI are less likely to receive a diagnosis of PTH. They have more severe symptoms of cutaneous allodynia, cognitive impairment, and generalized anxiety. This analysis suggests that pre‐TBI headaches might impact post‐TBI headache diagnoses and associated features.

Abstract Objective To evaluate whether the 15‐day threshold of headache days per month adequately reflects substantial differences in disability across the full spectrum of migraine. Background The monthly frequency of headache days defines migraine subtypes and has crucial implications for epidemiological and clinical research as well as access to care. Methods The patients with migraine ( N = 836) who participated in the American Registry for Migraine Research, which is a multicenter, longitudinal patient registry, between February 2016 and March 2020, were divided into four groups based on monthly headache frequency: Group 1 (0–7 headache days/month, n = 286), Group 2 (8–14 headache days/month, n = 180), Group 3 (15–23 headache days/month, n = 153), Group 4 (≥24 headache days/month, n = 217). Disability (MIDAS), Pain intensity (NRS), Work Productivity and Activity Impairment (WPAI), Pain Interference (PROMIS‐PI), Patient Health Questionnaire‐4 (PHQ‐4), and General Anxiety Disorder‐7 (GAD‐7) scores were compared. Results Mean (standard deviation [SD]) age was 46 (13) years (87.9% [735/836] female). The proportion of patients in each group was as follows: Group 1 (34.2% [286/836]), Group 2 (21.5% [180/836]), Group 3 (18.3% [153/836]), and Group 4 (26.0% [217/836]). There were significant relationships with increasing disability, lost productive time, and pain interference in higher headache frequency categories. There were no significant differences between Group 2 and Group 3 for most measures (NRS, all WPAI scores, PROMIS‐PI, GAD‐7, and PHQ‐4), although MIDAS scores differed (median [interquartile range (IQR)]; 38 [20–58] vs. 55 [30–90], p < 0.001). Patients in Group 1 had significantly lower MIDAS (median [IQR];16 [7–30], p < 0.001), WPAI‐% total active impairment (mean (SD): Group 1 [30.9 (26.8)] vs. Group 2 [39.2 (24.5), p = 0.017], vs. Group 3 [45.9 (24.1), p < 0.001], vs. Group 4 [55.3 (23.0), p < 0.001], and PROMIS‐PI‐T score (Group 1 [60.3 (7.3)] vs. Group 2 [62.6 (6.4), p = 0.008], vs. Group 3 [64.6 (5.6), p < 0.001], vs. Group 4 [66.8 (5.9), p < 0.001]) compared to all other groups. Patients in Group 4 had significantly higher MIDAS (median (IQR): Group 4 [90 (52–138)] vs. Group 1 [16 (7–30), p < 0.001], vs. Group 2 [38 (20–58), p < 0.001], vs. Group 3 [55 (30–90), p < 0.001], WPAI‐%Presenteeism (Group 4 [50.4 (24.4)] vs. Group 1 [28.8 (24.9), p < 0.001], vs. Group 2 [34.9 (22.3), p < 0.001], vs. Group 3 [40.9 (22.3), p = 0.048], WPAI‐% total work productivity impairment (Group 4 [55.9 (26.1)] vs. Group 1 [32.1 (37.6), p < 0.001], vs. Group 2 [38.3 (24.0), p < 0.001], vs. Group 3 [44.6 (24.4), p = 0.019]), and WPAI‐%Total activity impairment (Group 4 [55.3 (23.0)] vs. Group 1 [30.9 (26.8), p < 0.001], vs. Group 2 [39.2 (24.5), p < 0.001], vs. Group 3 [45.9 (24.1), p = 0.025]) scores compared with all other groups. Conclusion Our data suggest that the use of a 15 headache day/month threshold to distinguish episodic and chronic migraine does not capture the burden of illness nor reflect the treatment needs of patients. These results have important implications for future refinements in the classification of migraine.