⚡ Quick Summary
This study explored the use of CURATE.AI, an AI-assisted platform, for optimizing warfarin dosing in a cohort of 127 patients. The findings indicate that CURATE.AI outperformed traditional methods in terms of prediction accuracy and may reduce the risk of bleeding complications.
🔍 Key Details
- 📊 Dataset: 127 patients
- ⚙️ Technology: CURATE.AI for warfarin dose optimization
- 📈 Metrics: Percentage Absolute Prediction Error and Percentage Prediction Error of 20%
- 🩸 Safety: Negligible underprediction bias
🔑 Key Takeaways
- 💡 CURATE.AI provides a systematic approach to warfarin dosing.
- 📉 Improved accuracy in predicting warfarin doses compared to traditional methods.
- 🩺 Potentially lower bleeding risk due to reduced underprediction bias.
- 🌐 On-par with physician-guided dosing in terms of therapeutic range.
- 🔍 Lays groundwork for future prospective studies on CURATE.AI.
- 💰 Cost-effective alternative to new oral anticoagulants.
- 🌍 Significant implications for public health and individual patient care.
📚 Background
Warfarin is a widely used anticoagulant, but its dosing can be challenging due to inter- and intra-patient variability. Traditional methods often rely on physician judgment, which can lead to frequent deviations from the target international normalized ratio (INR). This variability can result in serious adverse events, including thromboembolism and hemorrhage. The integration of artificial intelligence, such as CURATE.AI, aims to enhance the precision of warfarin dosing.
🗒️ Study
The study utilized a retrospective analysis of data from 127 patients to evaluate the effectiveness of CURATE.AI in optimizing warfarin dosing. By generating a personalized response profile based on warfarin doses and corresponding INR changes, CURATE.AI was able to recommend optimal dosing strategies tailored to individual patient needs.
📈 Results
CURATE.AI demonstrated superior performance with a lower Percentage Absolute Prediction Error and a reduced Percentage Prediction Error of 20% compared to existing models. The study also noted a negligible underprediction bias, which is crucial for minimizing the risk of bleeding complications associated with anticoagulant therapy.
🌍 Impact and Implications
The implications of this study are profound. By providing a reliable and systematic approach to warfarin dosing, CURATE.AI could significantly improve patient outcomes while reducing the mental burden on healthcare providers. This technology not only enhances the safety and efficacy of warfarin therapy but also offers a cost-effective alternative to newer, more expensive anticoagulants, potentially benefiting both individual patients and public health systems.
🔮 Conclusion
This study highlights the transformative potential of AI in clinical settings, particularly in the realm of anticoagulation management. CURATE.AI stands out as a promising tool for optimizing warfarin dosing, paving the way for future research and clinical applications. As we continue to explore the integration of AI in healthcare, the prospects for improved patient care and safety are indeed exciting!
💬 Your comments
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AI-assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis.
Abstract
BACKGROUND: 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.
OBJECTIVES: 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.
RESULTS AND CONCLUSIONS: 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.
Author: [‘Gan TRX’, ‘Tan LWJ’, ‘Egermark M’, ‘Truong ATL’, ‘Kumar K’, ‘Tan SB’, ‘Tang S’, ‘Blasiak A’, ‘Goh BC’, ‘Ngiam KY’, ‘Ho D’]
Journal: Bioeng Transl Med
Citation: Gan TRX, et al. AI-assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis. AI-assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis. 2025; 10:e10757. doi: 10.1002/btm2.10757