Kamio Tadashi, Ikegami Masaru, Mizuno Megumi, Ishii Seiichiro, Tajima Hayato, Machida Yoshihito, Fukaguchi Kiyomitsu
Department of Anesthesiology and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan.
Department of Critical Care Medicine, Shonan Kamakura General Hospital, Kamakura, Kanagawa, Japan.
PLoS One. 2025 Jul 21;20(7):e0328709. doi: 10.1371/journal.pone.0328709. eCollection 2025.
Predicting optimal coagulation control using heparin in intensive care units (ICUs) remains a significant challenge. This study aimed to develop a machine learning (ML) model to predict activated partial thromboplastin time (aPTT) in ICU patients receiving unfractionated heparin for anticoagulation and to identify key predictive factors.
Data were obtained from the Tokushukai Medical Database, covering six hospitals with ICUs in Japan, collected between 2018 and 2022. The study included 945 ICU patients who received unfractionated heparin. The dataset comprised both static and dynamic features, which were used to construct and train ML models. Models were developed to predict aPTT following initial and multiple heparin doses. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC AUC), area under the precision-recall curve (PR AUC), precision, recall, F1 score, and accuracy. SHAP analysis was conducted to determine key predictive factors.
The random forest model demonstrated the highest predictive performance, with ROC AUC values of 0.707 for the first infusion and 0.732 for multiple infusions. Corresponding PR AUC values were 0.539 and 0.551. Despite moderate overall predictive performance, the model exhibited high precision (0.585 for the first infusion and 0.589 for multiple infusions), indicating effectiveness in correctly identifying true positive cases. However, recall and F1 scores were lower, suggesting that some cases, particularly in sub-therapeutic and supra-therapeutic ranges, may have been missed. Incorporating time-series data, such as vital signs, provided only marginal improvements in performance.
ML models demonstrated moderate performance in predicting aPTT following heparin infusion in ICU patients, with the random forest model achieving the highest classification accuracy. Although the models effectively identified true positive cases, their overall predictive performance remained limited, necessitating further refinement. The inclusion of static and dynamic features did not significantly enhance model accuracy. Future studies should explore additional factors to improve predictive models for optimizing individualized anticoagulation management in ICUs.
在重症监护病房（ICU）中使用肝素预测最佳凝血控制仍然是一项重大挑战。本研究旨在开发一种机器学习（ML）模型，以预测接受普通肝素抗凝治疗的ICU患者的活化部分凝血活酶时间（aPTT），并确定关键预测因素。
数据来自德洲会医疗数据库，涵盖日本六家设有ICU的医院，收集时间为2018年至2022年。该研究纳入了945例接受普通肝素治疗的ICU患者。数据集包括静态和动态特征，用于构建和训练ML模型。开发模型以预测首次和多次肝素剂量后的aPTT。使用受试者工作特征曲线下面积（ROC AUC）、精确召回曲线下面积（PR AUC）、精确率、召回率、F1分数和准确率评估模型性能。进行SHAP分析以确定关键预测因素。
随机森林模型表现出最高的预测性能，首次输注时的ROC AUC值为0.707，多次输注时为0.732。相应的PR AUC值分别为0.539和0.551。尽管总体预测性能中等，但该模型具有较高的精确率（首次输注时为0.585，多次输注时为0.589），表明在正确识别真阳性病例方面有效。然而，召回率和F1分数较低，这表明一些病例，特别是在治疗不足和治疗过度范围内的病例，可能被遗漏了。纳入生命体征等时间序列数据仅在性能上带来了边际改善。
ML模型在预测ICU患者肝素输注后的aPTT方面表现中等，随机森林模型实现了最高的分类准确率。虽然这些模型有效地识别了真阳性病例，但其总体预测性能仍然有限，需要进一步改进。纳入静态和动态特征并未显著提高模型准确性。未来的研究应探索其他因素，以改进预测模型，优化ICU中的个体化抗凝管理。
