Searching for the Predictors of Response to BoNT-A in Migraine Using Machine Learning Approaches

Abstract

OnabotulinumtoxinA (BonT-A) reduces migraine frequency in a considerable portion of patients with migraine. So far, predictive characteristics of response are lacking. Here, we applied machine learning (ML) algorithms to identify clinical characteristics able to predict treatment response. We collected demographic and clinical data of patients with chronic migraine (CM) or high-frequency episodic migraine (HFEM) treated with BoNT-A at our clinic in the last 5 years. Patients received BoNT-A according to the PREEMPT (Phase III Research Evaluating Migraine Prophylaxis Therapy) paradigm and were classified according to the monthly migraine days reduction in the 12 weeks after the fourth BoNT-A cycle, as compared to baseline. Data were used as input features to run ML algorithms. Of the 212 patients enrolled, 35 qualified as excellent responders to BoNT-A administration and 38 as nonresponders. None of the anamnestic characteristics were able to discriminate responders from nonresponders in the CM group. Nevertheless, a pattern of four features (age at onset of migraine, opioid use, anxiety subscore at the hospital anxiety and depression scale (HADS-a) and Migraine Disability Assessment (MIDAS) score correctly predicted response in HFEM. Our findings suggest that routine anamnestic features acquired in real-life settings cannot accurately predict BoNT-A response in migraine and call for a more complex modality of patient profiling.

The effectiveness and predictors influencing the outcome of onabotulinumtoxinA treatment in chronic migraine: understanding from diverse patient profiles in a single session

“…Despite over half of the patients having received prior migraine prophylaxis there was no significant difference observed between previously treated and treatment-naive patients or between responders and non-responders to previous treatments and abortive medication in terms of MHDs and MMDs following onabotulinumtoxinA injection. In a study of 212 patients with CM and high-frequency episodic migraine receiving onabotulinumtoxinA no anamnestic characteristics differentiated responders from non-responders in the CM group ( 77 ). Similarly, a study by Pagola et al failed to identify any clinical feature in patients with refractory migraine that predicts a favorable response to onabotulinumtoxinA treatment ( 78 ).…”

The Clinical Relevance of Artificial Intelligence in Migraine

“…In the CM group, no clinical feature was able to discriminate between responders and non-responders, suggesting the increasing need for novel and multimodal biomarkers. However, in the HFEM group, four clinical features (i.e., age of migraine onset, opioid use, the anxiety subscore of the hospital anxiety and depression scale, and the Migraine Disability Assessment, or MIDAS) helped predict a good response to BoNT-A [56].…”

Comparison of Efficacy of Botulinum Toxin Injection and Conventional Oral Drugs for Treatment of Refractory Migraine

“…Conventional oral medications, a broad category that includes triptans, nonsteroidal antiinflammatory drugs (NSAIDs), beta-blockers, antiepileptic drugs, and antidepressants, primarily seek to prevent migraines or reduce their severity. Although essential to the treatment of refractory migraines and effective in most patients, a significant proportion do not respond or cannot tolerate adverse effects, necessitating the investigation of alternative therapies (11). Comparative investigations of BoNT-A injections and conventional oral medications have produced contradictory findings.…”