Gan Tiffany Rui Xuan, Tan Lester W J, Egermark Mathias, Truong Anh T L, Kumar Kirthika, Tan Shi-Bei, Tang Sarah, Blasiak Agata, Goh Boon Cher, Ngiam Kee Yuan, Ho Dean
Division of Surgery Ng Teng Fong General Hospital Singapore Singapore.
The N.1 Institute for Health (N.1), National University of Singapore Singapore Singapore.
Bioeng Transl Med. 2025 Feb 3;10(3):e10757. doi: 10.1002/btm2.10757. eCollection 2025 May.
Standard-of-care for warfarin dose titration is conventionally based on physician-guided drug dosing. This may lead to frequent deviations from target international normalized ratio (INR) due to inter- and intra-patient variability and may potentially result in adverse events including recurrent thromboembolism and life-threatening hemorrhage.
We aim to employ CURATE.AI, a small-data, artificial intelligence-derived platform that has been clinically validated in a range of indications, to optimize and guide warfarin dosing.
PATIENTS/METHODS: A personalized CURATE.AI response profile was generated using warfarin dose (inputs) and corresponding change in INR between two consecutive days (phenotypic outputs) and used to identify and recommend an optimal dose to achieve target treatment outcomes. CURATE.AI's predictive performance was then evaluated with a set of metrics that assessed both technical performance and clinical relevance.
In this retrospective study of 127 patients, CURATE.AI fared better in terms of Percentage Absolute Prediction Error and Percentage Prediction Error of 20% compared to other models in the literature. It also had negligible underprediction bias, potentially translating into lower bleeding risk. Modeled potential time in therapeutic range with CURATE.AI was not significantly different from physician-guided dosing, so it is on-par yet provides a systematic approach to warfarin dosing, easing the mental-burden on guesswork by physicians.This study lays the groundwork for the prospective study of CURATE.AI as a clinical decision support system. CURATE.AI may facilitate the effective use of affordable warfarin with a well-established safety profile, without the need for costly, new oral anticoagulants. This can have significant impact both on the individual and public health.
华法林剂量滴定的标准治疗方法传统上基于医生指导的药物给药。由于患者间和患者内的变异性，这可能导致频繁偏离目标国际标准化比值（INR），并可能潜在地导致不良事件，包括复发性血栓栓塞和危及生命的出血。
我们旨在采用CURATE.AI，这是一个基于小数据的人工智能衍生平台，已在一系列适应症中得到临床验证，以优化和指导华法林给药。
患者/方法：使用华法林剂量（输入）和连续两天之间INR的相应变化（表型输出）生成个性化的CURATE.AI反应曲线，并用于识别和推荐最佳剂量以实现目标治疗结果。然后使用一组评估技术性能和临床相关性的指标来评估CURATE.AI的预测性能。
在这项对127名患者的回顾性研究中，与文献中的其他模型相比，CURATE.AI在绝对预测误差百分比和20%的预测误差百分比方面表现更好。它的预测不足偏差也可以忽略不计，这可能转化为较低的出血风险。使用CURATE.AI模拟的治疗范围内的潜在时间与医生指导给药没有显著差异，因此它与之相当，但提供了一种系统的华法林给药方法，减轻了医生猜测的心理负担。本研究为将CURATE.AI作为临床决策支持系统的前瞻性研究奠定了基础。CURATE.AI可能有助于有效使用具有良好安全记录的经济实惠的华法林，而无需昂贵的新型口服抗凝剂。这对个人和公共健康都可能产生重大影响。
