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 inc...
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| Format: | Article |
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Wiley
2025-05-01
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| Series: | Bioengineering & Translational Medicine |
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| Online Access: | https://doi.org/10.1002/btm2.10757 |
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| author | Tiffany Rui Xuan Gan Lester W. J. Tan Mathias Egermark Anh T. L. Truong Kirthika Kumar Shi‐Bei Tan Sarah Tang Agata Blasiak Boon Cher Goh Kee Yuan Ngiam Dean Ho |
| author_facet | Tiffany Rui Xuan Gan Lester W. J. Tan Mathias Egermark Anh T. L. Truong Kirthika Kumar Shi‐Bei Tan Sarah Tang Agata Blasiak Boon Cher Goh Kee Yuan Ngiam Dean Ho |
| author_sort | Tiffany Rui Xuan Gan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-40c25dc7f58047bf8bcaef29701b84fe |
| institution | Kabale University |
| issn | 2380-6761 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Bioengineering & Translational Medicine |
| spelling | doaj-art-40c25dc7f58047bf8bcaef29701b84fe2025-08-20T03:49:41ZengWileyBioengineering & Translational Medicine2380-67612025-05-01103n/an/a10.1002/btm2.10757AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysisTiffany Rui Xuan Gan0Lester W. J. Tan1Mathias Egermark2Anh T. L. Truong3Kirthika Kumar4Shi‐Bei Tan5Sarah Tang6Agata Blasiak7Boon Cher Goh8Kee Yuan Ngiam9Dean Ho10Division of Surgery Ng Teng Fong General Hospital Singapore SingaporeThe N.1 Institute for Health (N.1), National University of Singapore Singapore SingaporeThe N.1 Institute for Health (N.1), National University of Singapore Singapore SingaporeThe N.1 Institute for Health (N.1), National University of Singapore Singapore SingaporeThe N.1 Institute for Health (N.1), National University of Singapore Singapore SingaporeThe N.1 Institute for Health (N.1), National University of Singapore Singapore SingaporeYong Loo Lin School of Medicine National University of SingaporeThe N.1 Institute for Health (N.1), National University of Singapore Singapore SingaporeDivision of Haematology‐Oncology National University Hospital Singapore SingaporeDivision of Surgery National University Hospital Singapore SingaporeThe N.1 Institute for Health (N.1), National University of Singapore Singapore SingaporeAbstract 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.https://doi.org/10.1002/btm2.10757artificial intelligenceblood coagulationclinicaldecision support systemsprecision medicinewarfarin |
| spellingShingle | Tiffany Rui Xuan Gan Lester W. J. Tan Mathias Egermark Anh T. L. Truong Kirthika Kumar Shi‐Bei Tan Sarah Tang Agata Blasiak Boon Cher Goh Kee Yuan Ngiam Dean Ho AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis Bioengineering & Translational Medicine artificial intelligence blood coagulation clinical decision support systems precision medicine warfarin |
| title | AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis |
| title_full | AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis |
| title_fullStr | AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis |
| title_full_unstemmed | AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis |
| title_short | AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis |
| title_sort | ai assisted warfarin dose optimisation with curate ai for clinical impact retrospective data analysis |
| topic | artificial intelligence blood coagulation clinical decision support systems precision medicine warfarin |
| url | https://doi.org/10.1002/btm2.10757 |
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