{"doi":"10.1111/cns.14882","title":"Clinico‐sero‐pathological profiles and risk prediction model of idiopathic inflammatory myopathy <scp>(IIM)</scp> patients with different perifascicular changes","abstract":"<jats:title>Abstract</jats:title><jats:sec><jats:title>Aims</jats:title><jats:p>To explore the clinico‐sero‐pathological characteristics and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different muscular perifascicular (PF) changes.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>IIM patients in our center were enrolled and the clinico‐sero‐pathological data were retrospectively analyzed. A decision tree model was established through machine learning.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>There 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%, <jats:italic>p</jats:italic> &lt; 0.001). MxA expression was least in PF‐MHCn (36.0% vs. 83.0% vs. 63.2%, <jats:italic>p</jats:italic> &lt; 0.001). The decision tree model could effectively predict different subgroups, especially PFA and PFN.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Three 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.</jats:p></jats:sec>","journal":"CNS Neuroscience &amp; Therapeutics","year":2024,"id":645746,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":749769,"name":"Lijun Fu","orcid":"0000-0003-2501-3740","position":1,"is_corresponding":false},{"id":1681588,"name":"Guoyong Zhang","orcid":null,"position":2,"is_corresponding":false},{"id":1666318,"name":"Ying Hou","orcid":"0000-0001-8116-4928","position":3,"is_corresponding":false},{"id":859136,"name":"Xiaotian Ma","orcid":"0009-0005-9462-2170","position":4,"is_corresponding":false},{"id":659036,"name":"Dandan Zhao","orcid":"0000-0001-5194-750X","position":5,"is_corresponding":false},{"id":717111,"name":"Wei Li","orcid":"0000-0002-7530-4872","position":6,"is_corresponding":false},{"id":1666319,"name":"Tingjun Dai","orcid":"0000-0003-4928-0421","position":7,"is_corresponding":false},{"id":367444,"name":"Qiang Shu","orcid":"0000-0002-4106-6255","position":8,"is_corresponding":false},{"id":1603759,"name":"Chuanzhu Yan","orcid":"0000-0002-2191-5184","position":9,"is_corresponding":false},{"id":90231,"name":"Bing Zhao","orcid":"0000-0001-9891-3569","position":10,"is_corresponding":false},{"id":349327,"name":"Lining Zhang","orcid":"0000-0002-7563-6957","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Clinico‐sero‐pathological profiles and risk prediction model of idiopathic inflammatory myopathy <scp>(IIM)</scp> patients with different perifascicular changes","abstract":"<jats:title>Abstract</jats:title><jats:sec><jats:title>Aims</jats:title><jats:p>To explore the clinico‐sero‐pathological characteristics and risk prediction model of idiopathic inflammatory myopathy (IIM) patients with different muscular perifascicular (PF) changes.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>IIM patients in our center were enrolled and the clinico‐sero‐pathological data were retrospectively analyzed. A decision tree model was established through machine learning.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>There 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%, <jats:italic>p</jats:italic> &lt; 0.001). MxA expression was least in PF‐MHCn (36.0% vs. 83.0% vs. 63.2%, <jats:italic>p</jats:italic> &lt; 0.001). The decision tree model could effectively predict different subgroups, especially PFA and PFN.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Three 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.</jats:p></jats:sec>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39097917","pmcid":"PMC11298199","openalex_id":null,"authors":[],"funders":[{"funder_name":"Natural Science Foundation of Shandong Province","grant_id":"ZR2023QH364","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"82071412","title":null},{"funder_name":"Qingdao Key health Discipline Development Fund and Qingdao Clinical Research Center for Rare Diseases of Nervous System","grant_id":"22-3-7-lczx-3-nsh","title":null}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/cns.14882","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/cns.14882","host_type":"publisher"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11298199/pdf/CNS-30-e14882.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11298199","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11298199?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":[],"mesh_terms":["Muscle, Skeletal","Humans","Myositis","Necrosis","Autoantibodies","Retrospective Studies","Decision Trees","Adult","Aged","Middle Aged","Female","Male","Machine Learning"],"keywords":["Idiopathic Inflammatory Myopathy","Perifascicular Atrophy","Perifascicular Necrosis","Perifascicular Enhancement Of Mhc‐i And/or Mhc‐ii"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T10:01:06.487033Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}