Identifying Migraine in Primary Care Workflows: Prevalence, Risk Signals, and Predictive Tools

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Publication Date

11-18-2025

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Presentation: 24:30

Abstract

OBJECTIVES: Migraine is a leading neurological disability worldwide; however, it is often underdiagnosed in primary care, leading to delays in treatment and an increased burden on patients. This study aimed to identify demographic, clinical, and social risk factors, and to evaluate predictive models for earlier detection in primary care.

METHODS: This study conducted a cross-sectional analysis of de-identified EMR data from 59,088 adult outpatient visits across Jefferson Health clinics. The predictor variables included demographics, comorbidities, vital signs, BMI, clinic location, and health-related social needs. Two models, an L1-regularized logistic regression and a Random Forest model, were trained with a 70/30 split and were subsequently evaluated with ROC-AUC, Precision–Recall AUC, calibration, Brier Score, and threshold-based diagnostic metrics.

RESULTS: Overall migraine prevalence was low (2.53%, n = 1,495) and higher among younger adults and females. Prevalence increased slightly (3.17%) among patients with one or more social needs. Psychiatric and pain-related conditions such as anxiety, depression, fibromyalgia, and asthma were strongly linked to migraine and ranked among the top predictive features along with age and sex. The models both displayed consistent but modest discrimination (ROC-AUC: 0.705–0.745) and low performance on precision–recall metrics (PR-AUC: 0.055–0.101), particularly due to class imbalance. Logistic regression demonstrated smoother calibration, and more reliable probability estimates than Random Forest. At a typical clinical threshold (0.05), specificity and negative predictive value stayed high (0.997 and 0.97, respectively), but sensitivity was low (< 0.03).

CONCLUSION: This indicates that the models are more appropriate for conservative screening rather than for diagnosis. Including clinical and social factors slightly enhances risk stratification, but identifying positives remains challenging due to low prevalence. These findings indicate that EMR-based prediction tools may assist in identifying at-risk patients who require follow-up. To improve future

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English

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