Investigating the Risk Factors for Medication Nonadherence in Myasthenia Gravis Patients: Establishing a Nomogram...

Area of Science:

• Medical Informatics
• Clinical Pharmacy
• Health Services Research

Background:

• Poor medication adherence is prevalent in Myasthenia Gravis (MG) patients, negatively impacting disease control, physical function, and healthcare costs.
• Effective prediction of nonadherence is crucial for targeted interventions and improved patient outcomes.

Purpose of the Study:

• To develop and validate a nomogram for predicting medication nonadherence in MG patients.
• To identify key risk factors associated with medication nonadherence in this population.

Main Methods:

• Prospective review of clinical and demographic data from 376 MG patients.
• Logistic regression analysis to identify risk factors for nonadherence.
• Nomogram construction and internal validation using Bootstrap method, C-index, ROC curve (AUC), calibration curves, DCA, and clinical impact curves.

Main Results:

• The study included 376 MG patients, with 51.60% exhibiting poor medication adherence.
• A nomogram was developed incorporating five factors: disease recurrence, insurance type, BMQ necessity, BIPQ cognition, and BIPQ emotion.
• The model achieved an AUC of 0.94 (95% CI: 0.91-0.97), with 87.5% sensitivity and 85.8% specificity.

Conclusions:

• A validated nomogram effectively predicts medication adherence in MG patients.
• The developed tool, based on five identified risk factors, can support home management strategies for MG patients.