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Updated: May 28, 2025

Microfluidics in Assessing Platelet Function
Published on: November 8, 2024
Impact of platelet transfusion and bleeding risk stratification in patients with immune thrombocytopenia before
Ka-Won Kang1, Yumin Choi2, Hyung-Jun Lim3
1Division of Hematology-Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
The main treatment goal for immune thrombocytopenia (ITP) is bleeding risk reduction, particularly during procedures. While adjusting platelet thresholds with ITP treatments is recommended, platelet transfusions are commonly used despite controversial benefits. We evaluated the effectiveness of platelet transfusion in reducing post-procedure bleeding risk and identified predictive indicators of bleeding. A nationally representative database was used to develop a model predicting post-procedure bleeding risk in patients with ITP. Machine learning analyses, including random forest feature importance and Shapley additive explanations (SHAP) values, assessed 34 risk factors, including the platelet transfusion amount. The random forest model had an area under the receiver-operating characteristic curve of 93.6%. Key variables influencing bleeding risk included platelet transfusion amount, high-risk procedure, anticoagulant use, anemia, age, ITP treatment, and newly diagnosed ITP, all positively correlated with bleeding risk. Conversely, no antiplatelet or anticoagulant use and moderate- or low-risk procedures were negatively associated with bleeding risk. SHAP plots showed that platelet transfusion amount correlated with high-risk procedures, and bleeding risk increased with age in high-risk procedures. Bleeding risk in patients with ITP is primarily determined by procedural risk and patient condition, rather than platelet transfusion. Minimizing unnecessary platelet transfusions and addressing bleeding risk factors pre-procedure is crucial.
The main treatment goal for immune thrombocytopenia (ITP) is bleeding risk reduction, particularly during procedures. While adjusting platelet thresholds with ITP treatments is recommended, platelet transfusions are commonly used despite controversial benefits. We evaluated the effectiveness of platelet transfusion in reducing post-procedure bleeding risk and identified predictive indicators of bleeding. A nationally representative database was used to develop a model predicting post-procedure bleeding risk in patients with ITP. Machine learning analyses, including random forest feature importance and Shapley additive explanations (SHAP) values, assessed 34 risk factors, including the platelet transfusion amount. The random forest model had an area under the receiver-operating characteristic curve of 93.6%. Key variables influencing bleeding risk included platelet transfusion amount, high-risk procedure, anticoagulant use, anemia, age, ITP treatment, and newly diagnosed ITP, all positively correlated with bleeding risk. Conversely, no antiplatelet or anticoagulant use and moderate- or low-risk procedures were negatively associated with bleeding risk. SHAP plots showed that platelet transfusion amount correlated with high-risk procedures, and bleeding risk increased with age in high-risk procedures. Bleeding risk in patients with ITP is primarily determined by procedural risk and patient condition, rather than platelet transfusion. Minimizing unnecessary platelet transfusions and addressing bleeding risk factors pre-procedure is crucial.
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