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Dynamic changes of peripheral inflammatory markers link with disease severity and predict short-term poor outcome of
Yiyun Weng1, Jinrong Zhu1,2, Shengqi Li1,3
1Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Abstract:
The relationship between peripheral inflammatory markers, their dynamic changes, and the disease severity of myasthenia gravis (MG) is still not fully understood. Besides, the possibility of using it to predict the short-term poor outcome of MG patients have not been demonstrated. This study aims to investigate the relationship between peripheral inflammatory markers and their dynamic changes with Myasthenia Gravis Foundation of America (MGFA) classification (primary outcome) and predict the short-term poor outcome (secondary outcome) in MG patients. The study retrospectively enrolled 154 MG patients from June 2016 to December 2021. The logistic regression was used to investigate the relationship of inflammatory markers with MGFA classification and determine the factors for model construction presented in a nomogram. Finally, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were utilized to evaluate the incremental capacity. Logistic regression revealed significant associations between neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), aggregate index of systemic inflammation (AISI) and MGFA classification (p = 0.013, p = 0.032, p = 0.017, respectively). Incorporating dynamic changes of inflammatory markers into multivariable models improved their discriminatory capacity of disease severity, with significant improvements observed for NLR, systemic immune-inflammation index (SII) and AISI in NRI and IDI. Additionally, AISI was statistically associated with short-term poor outcome and a prediction model incorporating dynamic changes of inflammatory markers was constructed with the area under curve (AUC) of 0.953, presented in a nomograph. The inflammatory markers demonstrate significant associations with disease severity and AISI could be regarded as a possible and easily available predictive biomarker for short-term poor outcome in MG patients.
Insights
Peripheral inflammatory markers like NLR, PLR, and AISI correlate with myasthenia gravis (MG) severity. Dynamic changes in these markers, particularly AISI, can predict short-term poor outcomes in MG patients.
Area of Science:
- Immunology
- Neurology
- Clinical Medicine
Background:
- The link between peripheral inflammatory markers and myasthenia gravis (MG) severity, along with their predictive potential for patient outcomes, remains unclear.
- Existing research has not fully established the utility of dynamic changes in inflammatory markers for predicting short-term outcomes in MG.
Purpose of the Study:
- To investigate the association between peripheral inflammatory markers and their dynamic changes with Myasthenia Gravis Foundation of America (MGFA) classification.
- To predict short-term poor outcomes in MG patients using these inflammatory markers.
Main Methods:
- Retrospective study of 154 MG patients (June 2016 - December 2021).
- Logistic regression analysis to assess marker-MGFA classification relationships and identify predictive factors.
- Nomogram construction for outcome prediction, validated using Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI).
Main Results:
- Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and aggregate index of systemic inflammation (AISI) showed significant associations with MGFA classification.
- Dynamic changes in NLR, systemic immune-inflammation index (SII), and AISI improved the discrimination of disease severity.
- AISI was significantly associated with short-term poor outcomes; a prediction model achieved an AUC of 0.953.
Conclusions:
- Peripheral inflammatory markers are significantly associated with MG disease severity.
- AISI shows promise as an easily accessible predictive biomarker for short-term poor outcomes in MG patients.
- Dynamic assessment of inflammatory markers enhances prediction of MG severity and patient outcomes.
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