Dai Meng-Fei, Li Shu-Yue, Zhang Ji-Fan, Wang Bao-Yan, Zhou Lin, Yu Feng, Xu Hang, Ge Wei-Hong
Department of Pharmacy, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Front Pharmacol. 2022 Sep 26;13:933156. doi: 10.3389/fphar.2022.933156. eCollection 2022.
Patients who received warfarin require constant monitoring by hospital staff. However, social distancing and stay-at-home orders, which were universally adopted strategies to avoid the spread of COVID-19, led to unprecedented challenges. This study aimed to optimize warfarin treatment during the COVID-19 pandemic by determining the role of the Internet clinic and developing a machine learning (ML) model to predict anticoagulation quality. This retrospective study enrolled patients who received warfarin treatment in the hospital anticoagulation clinic (HAC) and "Internet + Anticoagulation clinic" (IAC) of the Nanjing Drum Tower Hospital between January 2020 and September 2021. The primary outcome was the anticoagulation quality of patients, which was evaluated by both the time in therapeutic range (TTR) and international normalized ratio (INR) variability. Anticoagulation quality and incidence of adverse events were compared between HAC and IAC. Furthermore, five ML algorithms were used to develop the anticoagulation quality prediction model, and the SHAP method was introduced to rank the feature importance. Totally, 241 patients were included, comprising 145 patients in the HAC group and 96 patients in the IAC group. In the HAC group and IAC group, 73.1 and 69.8% ( = 0.576) of patients achieved good anticoagulation quality, with the average TTR being 79.9 ± 20.0% and 80.6 ± 21.1%, respectively. There was no significant difference in the incidence of adverse events between the two groups. Evaluating the five ML models using the test set, the accuracy of the XGBoost model was 0.767, and the area under the receiver operating characteristic curve was 0.808, which showed the best performance. The results of the SHAP method revealed that age, education, hypertension, aspirin, and amiodarone were the top five important features associated with poor anticoagulation quality. The IAC contributed to a novel management method for patients who received warfarin during the COVID-19 pandemic, as effective as HAC and with a low risk of virus transmission. The XGBoost model could accurately select patients at a high risk of poor anticoagulation quality, who could benefit from active intervention.
接受华法林治疗的患者需要医院工作人员持续监测。然而，社交 distancing 和居家令是普遍采用的避免 COVID-19 传播的策略，这带来了前所未有的挑战。本研究旨在通过确定互联网诊所的作用并开发机器学习（ML）模型来预测抗凝质量，从而在 COVID-19 大流行期间优化华法林治疗。这项回顾性研究纳入了 2020 年 1 月至 2021 年 9 月期间在南京鼓楼医院医院抗凝诊所（HAC）和“互联网 + 抗凝诊所”（IAC）接受华法林治疗的患者。主要结局是患者的抗凝质量，通过治疗范围内时间（TTR）和国际标准化比值（INR）变异性进行评估。比较了 HAC 和 IAC 之间的抗凝质量和不良事件发生率。此外，使用五种 ML 算法开发抗凝质量预测模型，并引入 SHAP 方法对特征重要性进行排名。总共纳入 241 例患者，其中 HAC 组 145 例，IAC 组 96 例。在 HAC 组和 IAC 组中，分别有 73.1% 和 69.8%（P = 0.576）的患者达到了良好的抗凝质量，平均 TTR 分别为 79.9±20.0% 和 80.6±21.1%。两组之间不良事件发生率无显著差异。使用测试集评估五个 ML 模型，XGBoost 模型的准确率为 0.767，受试者工作特征曲线下面积为 0.808，表现最佳。SHAP 方法的结果显示，年龄、教育程度、高血压、阿司匹林和胺碘酮是与抗凝质量差相关的前五个重要特征。IAC 为 COVID-19 大流行期间接受华法林治疗的患者提供了一种新的管理方法，与 HAC 一样有效，且病毒传播风险低。XGBoost 模型可以准确选择抗凝质量差风险高的患者，这些患者可以从积极干预中受益。
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