Construction of a clinical prediction model for the diagnosis of immune thrombocytopenia based on clinical laboratory parameters

• Published in: Journal of cancer research and clinical oncology Vol. 150; no. 9; p. 412
• Main Authors: Zhong, Kangying, Pei, Yuqing, Yang, Ziyan, Zheng, Qin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 06.09.2024; Springer Nature B.V
• Subjects: Adult; adults; Aged; Blood levels; Cancer Research; China; China - epidemiology; Correlation analysis; Diagnosis; Erythrocytes; Female; Hematology; Hemoglobin; hospitals; Humans; Idiopathic thrombocytopenic purpura; Internal Medicine; Lymphocytes; Male; Medicine; Medicine & Public Health; Middle Aged; Oncology; Patients; Platelet Count; Platelets; Prediction models; probability; Purpura, Thrombocytopenic, Idiopathic - blood; Purpura, Thrombocytopenic, Idiopathic - diagnosis; regression analysis; Retrospective Studies; Thrombocytopenia; Young Adult; China; Platelet count variability; Primary immune thrombocytopenia; Immature platelet fraction; Clinical prediction model
• ISSN: 1432-1335; 0171-5216; 1432-1335
• DOI: 10.1007/s00432-024-05914-z

Purpose Primary immune thrombocytopenia (ITP) is an autoimmune bleeding disorder characterized by isolated thrombocytopenia that is often misdiagnosed due to the lack of a gold standard for diagnosis and currently relies on exclusionary approaches. This project combines several laboratory parameters to construct a clinical prediction model for adult ITP patients. Methods A total of 428 patients with thrombocytopenia who visited the West China Hospital of Sichuan University between January 2021 and March 2023 were enrolled. Based on the diagnostic criteria, we divided those patients into an ITP group and a non-ITP group. A total of 34 laboratory parameters were analyzed via univariate analysis and correlation analysis, and the least absolute shrinkage and selection operator regression analysis was used to establish the model. The training and validation sets were divided at a ratio of 7:3, and we used a fivefold cross-validation method to construct the model. Results The model included the following variables: red blood cell, mean corpuscular hemoglobin concentration, red blood cell distribution width-standard deviation, platelet variability index score, immature platelet fraction, lymphocyte absolute value. The prediction model exhibited good performance, with a sensitivity of 0.89 and a specificity of 0.83 in the training set and a sensitivity of 0.90 and a specificity of 0.87 in the validation set. Conclusion The clinical prediction model can assess the probability of ITP in thrombocytopenic patients and has good predictive accuracy for the diagnosis of ITP.