Montenegro, Monique M.

Abstract Purpose This study examines sense of belonging (belongingness) in a large population of medical students, residents, and fellows and associations with learner burnout, organizational recruitment retention indicators, and potentially modifiable learning environment factors. Method All medical students, residents, and fellows at Mayo Clinic sites were surveyed between October and November 2020 with items measuring sense of belonging in 3 contexts (school or program, organization, surrounding community), burnout (2 Maslach Burnout Inventory items), recruitment retention indicators (likelihood of recommending the organization and accepting a job offer), potentially modifiable learning environment factors, and demographics (age, gender, race and ethnicity, LGBTQ+ identification, disability, socioeconomic background). Results Of 2,257 learners surveyed, 1,261 (56%) responded. The percentage of learners reporting a somewhat or very strong sense of belonging was highest in the school or program (994 of 1,227 [81%]) followed by the organization (957 of 1,222 [78%]) and surrounding community (728 of 1,203 [61%]). In adjusted analyses, learners with very strong organization belongingness had lower odds of burnout (odds ratio [OR], 0.05; 95% CI, 0.02–0.12) and higher odds of being likely to recommend the organization (OR, 505.23; 95% CI, 121.54–2,100.18) and accept a job offer (OR, 38.68; 95% CI, 15.72–95.15; all P < .001). School or program and community belongingness also correlated strongly with these outcomes. In multivariable analyses, social support remained associated with higher odds of belongingness in all 3 contexts; favorable ratings of faculty relationships and leadership representation remained associated with higher odds of belongingness in 2 contexts (school or program and organization); and favorable ratings of diversity, equity, and inclusion learning climate remained associated with belongingness in 1 context (community). Conclusions Sense of belonging among medical students, residents, and fellows varies across contexts, correlates strongly with burnout and organizational recruitment retention indicators, and is associated with multiple potentially modifiable learning environment factors.

Abstract Objective To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. Background Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial‐and‐error approach is inefficient. Methods In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow‐up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta‐blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene‐related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre‐trained a deep neural network, “TabNet,” using 145 variables, then employed TabNet‐embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non‐responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held‐out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. Results Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta‐blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta‐blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. Conclusion We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine‐specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

Objective To review the clinical and radiographic characteristics of orthostatic headache following suboccipital craniectomy without CSF leak after encountering 2 such patients. Background Orthostatic headache may occur without CSF leak, suggesting alternative mechanisms for postural head pain in some patients. Methods Patients who were referred for orthostatic headache and suspected CSF leak within 1 year after suboccipital craniectomy but who had negative post‐operative head and spine MRI, normal radioisotope cisternography, and normal or elevated CSF opening pressure were identified and their medical records reviewed. Results Two patients satisfied all inclusion criteria. One underwent suboccipital craniectomy for treatment of Chiari malformation type I in adolescence; the same surgical approach was used to resect a posterior fossa meningioma in the second. Both patients had non‐orthostatic headache before surgery and newly developed orthostatic headache later. Delay from surgery to orthostatic headache onset was variable (2–9 months). Headaches were predominantly occipital and pressure‐like, worsened by upright posture, bending forward, and exertion. MRI consistently showed adequate decompression of the posterior fossa. Epidural blood patches were unhelpful in the one patient in whom they were performed. Conclusions Orthostatic headaches may develop after suboccipital craniectomy in the absence of CSF leak. Possible mechanisms include (1) scarring of the dura in the posterior fossa that leads to compensatory increased distensibility of lumbar dura and (2) sensitization of mechanosensitive dural nociceptors from altered skull‐dura apposition.