Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura
Qin Guo,1,* Shengying Xia,2,* Yan Ding,3 Fan Liu3 1Department of General Surgery, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, 430016, People’s Republic of China; 2Department of Emergency and Critical Care Center, Wuhan Ch...
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Dove Medical Press
2025-01-01
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| author | Guo Q Xia S Ding Y Liu F |
| author_facet | Guo Q Xia S Ding Y Liu F |
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| description | Qin Guo,1,* Shengying Xia,2,* Yan Ding,3 Fan Liu3 1Department of General Surgery, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, 430016, People’s Republic of China; 2Department of Emergency and Critical Care Center, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science &Technology, Wuhan, 430016, People’s Republic of China; 3Department of Rheumatology and Immunology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science &Technology, Wuhan, 430016, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fan Liu; Yan Ding, Department of Rheumatology and Immunology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science &Technology, No. 100 hong Kong Road, Jiang’an District, Wuhan, 430016, People’s Republic of China, Tel +86 02782824022, Email LFwhetyy@outlook.com; DYwhetyy@outlook.comBackground: Henoch-Schönlein Purpura (HSP) is a common systemic vasculitis in children that often involves the gastrointestinal system (GIS). Identifying reliable predictive markers for GIS complications is crucial for early intervention and improved patient outcomes.Objective: This study aims to identify laboratory markers predictive of GIS complications in children with HSP using a machine learning approach.Methods: This retrospective study included children diagnosed with HSP and a control group from May 2020 to January 2024. Detailed demographic and laboratory data, including WBC count, lymphocyte count, neutrophil count, platelet count, hemoglobin, NLR, PLR, MPV, MPR, C-reactive protein, ESR, albumin, BUN, creatinine, sodium, potassium, calcium, IgA, PT, aPTT, and INR, were collected. GIS complications was classified based on clinical symptoms and diagnostic findings. Patients were categorized into groups without GIS complications, with mild GIS complications, and with severe GIS complications. We compared laboratory parameters across these groups to identify significant differences associated with GIS complications. Furthermore, a predictive model was developed by a Random Forest classifier to identify key markers and assess their ability to distinguish between patients with and without GIS complications.Results: Significant differences were observed in several laboratory parameters between HSP patients and the control group, and between patients with and without GIS complications. Key predictive markers identified included neutrophil count, NLR, WBC count, PLR, and platelet count. The RandomForest model achieved an accuracy of 91% and an AUC of 0.90.Conclusion: Our findings highlight the importance of specific laboratory markers in predicting GIS complications in HSP. The use of machine learning models can enhance the early identification and management of high-risk patients, potentially improving clinical outcomes.Keywords: Henoch-Schönlein Purpura, gastrointestinal complications, laboratory markers, machine learning, random forest classifier |
| format | Article |
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| institution | Kabale University |
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| series | Journal of Multidisciplinary Healthcare |
| spelling | doaj-art-0df67b4afdea46858ace0beb54d7141f2025-01-21T16:58:07ZengDove Medical PressJournal of Multidisciplinary Healthcare1178-23902025-01-01Volume 1827928899425Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein PurpuraGuo QXia SDing YLiu FQin Guo,1,* Shengying Xia,2,* Yan Ding,3 Fan Liu3 1Department of General Surgery, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, 430016, People’s Republic of China; 2Department of Emergency and Critical Care Center, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science &Technology, Wuhan, 430016, People’s Republic of China; 3Department of Rheumatology and Immunology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science &Technology, Wuhan, 430016, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fan Liu; Yan Ding, Department of Rheumatology and Immunology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science &Technology, No. 100 hong Kong Road, Jiang’an District, Wuhan, 430016, People’s Republic of China, Tel +86 02782824022, Email LFwhetyy@outlook.com; DYwhetyy@outlook.comBackground: Henoch-Schönlein Purpura (HSP) is a common systemic vasculitis in children that often involves the gastrointestinal system (GIS). Identifying reliable predictive markers for GIS complications is crucial for early intervention and improved patient outcomes.Objective: This study aims to identify laboratory markers predictive of GIS complications in children with HSP using a machine learning approach.Methods: This retrospective study included children diagnosed with HSP and a control group from May 2020 to January 2024. Detailed demographic and laboratory data, including WBC count, lymphocyte count, neutrophil count, platelet count, hemoglobin, NLR, PLR, MPV, MPR, C-reactive protein, ESR, albumin, BUN, creatinine, sodium, potassium, calcium, IgA, PT, aPTT, and INR, were collected. GIS complications was classified based on clinical symptoms and diagnostic findings. Patients were categorized into groups without GIS complications, with mild GIS complications, and with severe GIS complications. We compared laboratory parameters across these groups to identify significant differences associated with GIS complications. Furthermore, a predictive model was developed by a Random Forest classifier to identify key markers and assess their ability to distinguish between patients with and without GIS complications.Results: Significant differences were observed in several laboratory parameters between HSP patients and the control group, and between patients with and without GIS complications. Key predictive markers identified included neutrophil count, NLR, WBC count, PLR, and platelet count. The RandomForest model achieved an accuracy of 91% and an AUC of 0.90.Conclusion: Our findings highlight the importance of specific laboratory markers in predicting GIS complications in HSP. The use of machine learning models can enhance the early identification and management of high-risk patients, potentially improving clinical outcomes.Keywords: Henoch-Schönlein Purpura, gastrointestinal complications, laboratory markers, machine learning, random forest classifierhttps://www.dovepress.com/predictive-laboratory-markers-for-gastrointestinal-complications-in-ch-peer-reviewed-fulltext-article-JMDHhenoch-schönlein purpuragastrointestinal complicationslaboratory markersmachine learningrandomforest classifier |
| spellingShingle | Guo Q Xia S Ding Y Liu F Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura Journal of Multidisciplinary Healthcare henoch-schönlein purpura gastrointestinal complications laboratory markers machine learning randomforest classifier |
| title | Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura |
| title_full | Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura |
| title_fullStr | Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura |
| title_full_unstemmed | Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura |
| title_short | Predictive Laboratory Markers for Gastrointestinal Complications in Children with Henoch-Schönlein Purpura |
| title_sort | predictive laboratory markers for gastrointestinal complications in children with henoch sch ouml nlein purpura |
| topic | henoch-schönlein purpura gastrointestinal complications laboratory markers machine learning randomforest classifier |
| url | https://www.dovepress.com/predictive-laboratory-markers-for-gastrointestinal-complications-in-ch-peer-reviewed-fulltext-article-JMDH |
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