ORIGINAL RESEARCH article
Front. Med.
Sec. Hematology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1599999
Systemic Immune Inflammation Index (SII) Guides Machine Learning for Rapid TTP Diagnosis: A Retrospective Cohort Study
Provisionally accepted- Guangzhou Medical University Second Affiliated Hospital, Guangzhou, China
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Thrombotic thrombocytopenic purpura (TTP) is a rare, life threatening thrombotic microangiopathy that requires prompt diagnosis to reduce mortality. However, its early identification is often hindered by delayed ADAMTS13 testing, particularly in low resource settings. In this study, we developed a machine learning–based model using readily available inflammatory markers, including systemic immune inflammation index (SII), platelet to lymphocyte ratio (PLR), and platelet neutrophil product (PPN), to distinguish TTP from immune thrombocytopenia (ITP). A retrospective analysis of 196 hospitalized patients was conducted, and eight machine learning models were trained and compared. Logistic regression achieved the best performance (AUC = 0.78), with SII identified as the most influential predictor. While the PLASMIC score remains a widely used tool with higher diagnostic accuracy (AUC = 0.92), our model relies only on routine blood tests and offers a fast, accessible alternative for early risk stratification. These findings suggest that composite inflammatory markers combined with machine learning can assist in the rapid triage of suspected TTP cases, especially in emergency or resource-limited environments.
Keywords: thrombotic thrombocytopenic purpura, systemic immune-inflammation index, Inflammatory biomarkers, machine learning, early diagnosis, Logistic regression
Received: 25 Mar 2025; Accepted: 02 Oct 2025.
Copyright: © 2025 Liu and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xu Ye, 15011800057@163.com
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