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 to predict anti-CGRP response in patients with migraine: multicenter Spanish study

BACKGROUND

To date, several variables have been associated with anti-CGRP receptor or ligand-antibody response with disparate results. Our objective is to determine whether machine learning (ML)-based models can predict 6, 9 and 12 months response to anti-CGRP receptor or ligand therapies among migraine patients.

METHODS

We performed a multicenter analysis of a prospectively collected data cohort of patients with migraine receiving anti-CGRP therapies. Demographic and clinical variables were collected. Response rate defined in the 30% to 50% range -or at least 30%-, in the 50% to 75% range -or at least 50%-, and response rate over 75% reduction in the number of headache days per month at 6, 9 and 12 months. A sequential forward feature selector was used for variable selection and ML-based predictive models response to anti-CGRP therapies at 6, 9 and 12 months, with models' accuracy not less than 70%, were generated.

RESULTS

A total of 712 patients were included, 93% women, aged 48 years (SD=11.7). Eighty-three percent had chronic migraine. ML models using headache days/month, migraine days/month and HIT-6 variables yielded predictions with a F1 score range of 0.70-0.97 and AUC (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

According to our study, ML models can predict anti-CGRP response at 6, 9 and 12 months. This study provides a predictive tool to be used in a real-world setting.