AI Summary & Key Takeaways
AI InsightLastly, according to the results obtained from the GBTM analysis and physicians viewpoints, a migraine-specific comorbidity index was developed and validated.
📌 3 Key Takeaways
Özet
Abstract Objective: To develop and validate a comorbidity index to estimate the prognosis of migraine, defined as the severity of headache measured longitudinally in a heterogeneous population. Methods: The study data were collected from a computer-based Turkish Headache Database with 15-year’s follow-up data. The primary outcome was defined as the severity of headache [visual analog scale (VAS)] obtained from baseline to the 7th visit. The procedure was multistage: First, latent subgroups were determined using group-based trajectory modeling (GBTM) because the change in outcomes over time were different for each patient. Second, group-based trajectory modeling analysis was applied with the purpose of understanding how to evaluate comorbidities. Lastly, according to the results obtained from the GBTM analysis and physicians viewpoints, a migraine-specific comorbidity index was developed and validated. Results: Out of all weighting methods to evaluate comorbidities, the three-group model and quadratic form of all groups fitted the data best. After deciding the number of groups and functional form, the information criteria and minimum group percentage of the weighting methods were compared. The best method was the posterior probabilities obtained from latent class analysis (LCA) taken as weights. At the same time, age was effective in the separation of the second and third groups from the first group for severity (p=0.047, p=0.007). Sex difference had no effect on the prognosis of migraine (p=0.99, p=0.16). Conclusion: According to these results, an index formula was developed to evaluate the effect of covariates on migraine severity prognosis. A migraine-specific comorbidity index called the Migraine Comorbidity Index (MCI) was created by applying the formula.