Observational Retrospective Study in Patients Treated with Galcanezumab as Preventive Treatment for Migraine: The ORYGAM Study.

INTRODUCTION: The efficacy of galcanezumab has been demonstrated in randomized controlled trials, but evidence about its use under clinical practice conditions is still limited. This study aimed to describe the characteristics of the patients treated with galcanezumab in routine clinical practice in Spain as well as treatment patterns, persistence, and effectiveness. METHODS: A retrospective chart review study was carried out in six hospitals. Information of adults with migraine, who started treatment with galcanezumab between November 2019 and September 2021, was analyzed until end or loss of follow-up. Continuous variables were described as mean (standard deviation, SD) and median (interquartile range, IQR), and categorical variables as frequency and percentages. Persistence to treatment was estimated using Kaplan-Meier analysis. RESULTS: A total of 314 patients were analyzed over median follow-up period of 17.5 months (13.8-20.7), with a mean age of 46.3 (12.6), 85% women, 80.6% chronic migraine, and reporting a mean of monthly migraine days of 16.7 (7.8). Overall, 72.9% had comorbid conditions, with anxiety and depression disorders being the most frequent. More than 60% had received ≥ 6 previous preventive drugs, the most common being antiepileptics, antidepressants, and botulinum toxin (95.2%, 89.8% and 84.1%, respectively). Overall, 60.3% of the patients with other preventive treatments maintained them after galcanezumab initiation. The median time on galcanezumab was 14.6 months (9.4-22.8); 95.7%, 82.0%, 76.2% and 59.8% of patients were persistent to treatment at 3, 6, 9 and 12 months, respectively. Of the patients who discontinued (151: 48.1%), 57.6% were due to lack of effectiveness and 31.1% were due to improvement in migraine. The average reduction of monthly migraine days at 3, 6, 9 and 12 months was 7.9 (7.2), 9.1 (7.5), 8.8 (6.6) and 9.0 (6.9) days, respectively. CONCLUSIONS: In real clinical practice, galcanezumab is an effective treatment and has a high persistence in patients with migraine, mostly chronic and with multiple use of previous preventive treatments.

Effectiveness, tolerability, and response predictors of preventive anti-CGRP mAbs for migraine in patients over 65 years old: a multicenter real-world case-control study.

OBJECTIVE: To evaluate clinical characteristics, effectiveness, and tolerability of preventive anti- calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) in the elderly. Anti-CGRP mAbs have demonstrated efficacy and safety in patients with migraine although there is limited information regarding the elderly. DESIGN: We performed a multicenter case-control study of cases (patients over 65 years old) and controls (sex-matched patients under 55 years old) with migraine receiving anti-CGRP mAbs. METHODS: We included the demographic characteristics, effectiveness-reduction in the number of monthly headache days (MHD) and monthly migraine days (MMD), 30%, 50%, and 75% responder rates-and treatment emergent adverse events (TEAEs). The primary endpoint was the 50% response rate regarding MHD at weeks 20-24; exploratory 50% response predictors in the elderly were evaluated. RESULTS: In total, 228 patients were included: 114 cases , 114 controls-. Among cases 84.2% (96/114) were women, 79.8% (91/114) CM; mean age of cases 70.1 years old (range: 66-86); mean age of controls was 42.9 years old(range: 38-49). Cases had a higher percentage of vascular risk factors (P < .05),older age of onset (P < .001) and more reported prior preventive treatments (P < .001). Regarding effectiveness in cases, 50% response rate was achieved by 57.5% (42/73) at 20-24 weeks, with lower reduction in the MHD at 8-12 weeks (5 [7.2], 8 [9.1]; P = .001) and a higher reduction in MMD at 20-24 weeks (10.7 [9.1], 9.2 [7.7]; P = .04) compared to the control group. The percentage of TEAEs was similar in the 2 groups. Diagnosis of episodic migraine (EM) (P = .03) and lower number of MHD at baseline (P = .001) were associated with a 50% response in the elderly in univariate analysis. CONCLUSIONS: Our study provides real world evidence of effectiveness and safety of anti-CGRP mAbs for migraine in patients without upper age-limit and possible predictors of anti-CGRP response in the elderly.

Machine-learning-based approach for predicting response to anti-calcitonin gene-related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study.

BACKGROUND AND PURPOSE: Several variables have been reported to be associated with anti-calcitonin gene-related peptide (CGRP) receptor or ligand antibody response, but with differing results. Our objective was to determine whether machine-learning (ML)-based models can predict 6-, 9- and 12-month responses to anti-CGRP receptor or ligand therapies among migraine patients. METHODS: We performed a multicenter analysis of prospectively collected data from patients with migraine receiving anti-CGRP therapies. Demographic and clinical variables were collected. Response rates in the 30% to 50% range, or at least 30%, in the 50% to 75% range, or at least 50%, and response rate of at least 75% regarding the reduction in the number of headache days per month at 6, 9 and 12 months were calculated. A sequential forward feature selector was used for variable selection and ML-based predictive models for the response to anti-CGRP therapies at 6, 9 and 12 months, with model accuracy not less than 70%, were generated. RESULTS: A total of 712 patients were included, 93% were women, and the mean (SD) age was 48 (11.6) years. Eighty-four percent of patients had chronic migraine. ML-based models using headache days/month, migraine days/month and the Headache Impact Test (HIT-6) yielded predictions with an F1 score range of 0.70-0.97 and an area under the receiver-operating curve score range of 0.87-0.98. SHAP (SHapley Additive exPlanations) summary plots and dependence plots were generated to evaluate the relevance of the factors associated with the prediction of the above-mentioned response rates. CONCLUSIONS: Our results show that ML models can predict anti-CGRP response at 6, 9 and 12 months. This study provides a predictive tool that can be used in a real-world setting.