Impact of Platelet Transfusion and Bleeding Risk Stratification in Patients with Immune Thrombocytopenia before Procedures: A Nationwide Population-Based Cohort Study

Background: The main treatment goal for immune thrombocytopenia (ITP) is to reduce bleeding risk, requiring extra precautions during procedures. Adjusting platelet thresholds with ITP treatments is usually recommended, but platelet transfusions are commonly used despite their controversial benefits. This study aims to evaluate the effectiveness of platelet transfusion in reducing post-procedure bleeding risk and to identify predictive indicators of post-procedure bleeding. Methods: This study used a nationally representative database to develop a model to predict the risk of post-procedure bleeding in patients with ITP. Machine learning analyses, including random forest feature importance and Shapley additive explanations (SHAP) values, were employed. This contained a total of 34 post-procedural bleeding risk factors, including the amount of platelet transfusion. Results: The random forest model had an area under the receiver-operating characteristic curve of 93.6%. The analysis identified the following variables as most important: amount of platelet transfusion, high-risk procedure, use of anticoagulant drugs, no use of antiplatelet or anticoagulant drugs, anemia, age, low-risk procedure, moderate-risk procedure, ITP treatment, and newly diagnosed ITP. Amount of platelet transfusion, high-risk procedures, use of anticoagulant drugs, anemia, ITP treatment, and newly diagnosed ITP positively correlated with post-procedure bleeding risk. In contrast, no use of antiplatelet or anticoagulant drugs and moderate- or low-risk procedures were negatively associated with post-procedure bleeding risk. In the SHAP dependence plot, the amount of platelet transfusion was associated with high-risk procedures. Additionally, among patients undergoing high-risk procedures, the likelihood of post-procedure bleeding increased with age. Conclusions: Platelet transfusion does not significantly reduce the risk of post-procedural bleeding in patients with ITP. The risk of post-procedural bleeding is more closely related to the bleeding risk of the procedure and the patient's medical condition. Minimizing inappropriate platelet transfusions and addressing factors that can increase bleeding risk before procedures are crucial. Keywords: Immune thrombocytopenia, Procedure, Platelet transfusion, Bleeding risk, Machine learning analysis

Authors

Institutions

Publication Details

Journal
Blood
Published
2024-11-05
DOI
https://doi.org/10.1182/blood-2024-194107
Primary Topic
Platelet Disorders and Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Impact of Platelet Transfusion and Bleeding Risk Stratification in Patients with Immune Thrombocytopenia before Procedures: A Nationwide Population-Based Cohort Study

Ka‐Won Kang, Kwang‐Sig Lee, Yong Park, Yumin Choi et al.
Blood
Platelet Disorders and Treatments
article

Impact of Platelet Transfusion and Bleeding Risk Stratification in Patients with Immune Thrombocytopenia before Procedures: A Nationwide Population-Based Cohort Study

Ka‐Won Kang, Kwang‐Sig Lee, Yong Park, Yumin Choi, Hyungjun Lim, Ki Hoon Ahn, Kunye Kwak, Yoon Seok Choi, Byung‐Soo Kim
article en

Abstract

Background: The main treatment goal for immune thrombocytopenia (ITP) is to reduce bleeding risk, requiring extra precautions during procedures. Adjusting platelet thresholds with ITP treatments is usually recommended, but platelet transfusions are commonly used despite their controversial benefits. This study aims to evaluate the effectiveness of platelet transfusion in reducing post-procedure bleeding risk and to identify predictive indicators of post-procedure bleeding. Methods: This study used a nationally representative database to develop a model to predict the risk of post-procedure bleeding in patients with ITP. Machine learning analyses, including random forest feature importance and Shapley additive explanations (SHAP) values, were employed. This contained a total of 34 post-procedural bleeding risk factors, including the amount of platelet transfusion. Results: The random forest model had an area under the receiver-operating characteristic curve of 93.6%. The analysis identified the following variables as most important: amount of platelet transfusion, high-risk procedure, use of anticoagulant drugs, no use of antiplatelet or anticoagulant drugs, anemia, age, low-risk procedure, moderate-risk procedure, ITP treatment, and newly diagnosed ITP. Amount of platelet transfusion, high-risk procedures, use of anticoagulant drugs, anemia, ITP treatment, and newly diagnosed ITP positively correlated with post-procedure bleeding risk. In contrast, no use of antiplatelet or anticoagulant drugs and moderate- or low-risk procedures were negatively associated with post-procedure bleeding risk. In the SHAP dependence plot, the amount of platelet transfusion was associated with high-risk procedures. Additionally, among patients undergoing high-risk procedures, the likelihood of post-procedure bleeding increased with age. Conclusions: Platelet transfusion does not significantly reduce the risk of post-procedural bleeding in patients with ITP. The risk of post-procedural bleeding is more closely related to the bleeding risk of the procedure and the patient's medical condition. Minimizing inappropriate platelet transfusions and addressing factors that can increase bleeding risk before procedures are crucial. Keywords: Immune thrombocytopenia, Procedure, Platelet transfusion, Bleeding risk, Machine learning analysis

BloodVol. 144(Supplement 1)
Korea University (KR)
Openalex Percentile: Top 64%
Platelet Disorders and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.