ArticleCNS neuroscience & therapeutics2024
Clinico-sero-pathological profiles and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different perifascicular changes.
Article in CNS neuroscience & therapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating Machine Learning into Myositis Research: a Systematic Review.Clinical reviews in allergy & immunology · 2025Pooled it
- Integration of clinical and serological biomarkers in a nomogram for predicting interstitial lung disease in idiopathic inflammatory myopathies.BMC rheumatology · 2025Article
- Clinico-sero-pathological profiles and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different perifascicular changes.CNS neuroscience & therapeutics · 2024Article
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11 authors.
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Abstract
aimsTo explore the clinico-sero-pathological characteristics and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different muscular perifascicular (PF) changes.
methodsIIM patients in our center were enrolled and the clinico-sero-pathological data were retrospectively analyzed. A decision tree model was established through machine learning.
resultsThere were 231 IIM patients enrolled, including 53 with perifascicular atrophy (PFA), 39 with perifascicular necrosis (PFN), and 26 with isolated perifascicular enhancement of MHC-I/MHC-II (PF-MHCn). Clinically, PFA patients exhibited skin rashes and dermatomyositis-specific antibodies (DM-MSAs, 74.5%) except for anti-Mi2. PFN patients showed the most severe muscle weakness, highest creatine kinase (CK), anti-Mi2 (56.8%), and anti-Jo-1 (24.3%) antibodies. PF-MHCn patients demonstrated negative MSAs (48.0%) and elevated CK. Histopathologically, MAC predominantly deposited on PF capillaries in PFA but on non-necrotic myofiber in PFN (43.4% and 36.8%, p < 0.001). MxA expression was least in PF-MHCn (36.0% vs. 83.0% vs. 63.2%, p < 0.001). The decision tree model could effectively predict different subgroups, especially PFA and PFN.
conclusionsThree types of PF change of IIMs representing distinct clinico-serological characteristics and pathomechanism. Undiscovered MSAs should be explored especially in PF-MHCn patients. The three pathological features could be accurately predicted through the decision tree model.
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