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血栓症および脳卒中リスクの予測モデリングにおけるデジタルツイン：現状のアプローチと将来の方向性

Adelaide de Vecchi¹, Oscar Camara², Riccardo Cavarra¹

¹Department of Biomedical Engineering and Imaging Sciences, King's College London, London, England, United Kingdom of Great Britain and Northern Ireland.

Thrombosis and haemostasis

|February 9, 2026

まとめ

患者固有のデジタルツインは、多様なデータを統合することで血栓症リスクを動的に予測し、静的な臨床スコアを超えたものとなります。このアプローチは、正確で適時な抗凝固療法と患者の転帰の改善を目指しています。

背景:

血栓症は、心房細動、静脈血栓塞栓症、およびアテローム性動脈硬化症などの状態を通じて、世界的にかなりの死亡原因となっています。現在の臨床リスクスコアは静的であり、動的な患者要因を考慮できず、誤分類や予防可能な血栓イベントにつながっています。

結論:

デジタルツイン技術は、集団の経験則から正確で適時なリスク予測へと移行することにより、個別化された抗凝固療法に有望です。デジタルツインを臨床実践に展開するには、データ、モデリング、検証、および規制経路における課題に対処する必要があります。このアプローチは、血栓症駆動型疾患の動的なリスク評価と適応的な治療戦略への移行を促進します。

キーワード:

デジタルツイン血栓症脳卒中リスク予測抗凝固療法個別化医療機械学習計算モデリング

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