Using machine learning to predict anticoagulation control in atrial fibrillation: A UK Clinical Practice Research Datalink study

Gordon, Jason; Norman, Max; Hurst, Michael; Mason, Thomas J.; Dickerson, Carissa; Sandler, Belinda; Pollock, Kevin G; Farooqui, Usman; Groves, Lara; Tsang, Carmen; Clifton, David A.; Bakhai, Ameet; Hill, Nathan R.

doi:10.1016/j.imu.2021.100688

Cited by 9 publications

(9 citation statements)

References 43 publications

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“…The most common reason for exclusion on the second screen was that the article did not evaluate human subjects (37.8%), followed by not including medication use (37.1%). Of these, a total of 22 articles were selected to be discussed in our review 11–32 . Of the included studies, data were primarily derived from an academic hospital (43.5%), were located within the United States (43.5%), were retrospective cohort studies (91.3%), and were published in 2020 or later (65.2%).…”

Section: Resultsmentioning

confidence: 99%

“…This study performed external validation using data from the International Warfarin Pharmacogenetics Consortium. A different study evaluated predictors for time at goal, finding depression to be a consistent predictor for inadequate control 30 …”

Section: Resultsmentioning

confidence: 99%

“…A different study evaluated predictors for time at goal, finding depression to be a consistent predictor for inadequate control. 30…”

Section: Improvement In Warfarin Dosingmentioning

confidence: 99%

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Role of artificial intelligence in pharmacy practice: A narrative review

Wong,

Wentz,

Palisano

et al. 2023

J Am Coll Clin Pharm

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Artificial intelligence (AI) has emerged as a potentially useful tool in transforming healthcare. Roles for AI include drug safety, operations, as well as precision medicine to improve patient outcomes. Within pharmacy practice, limited guidance is available describing the purpose of AI. This gap is important to address given the current growth of AI in medicine. The purpose of this narrative review was to review the literature evaluating the potential role of AI in three relevant areas of pharmacy practice. A review of Medline and Embase from January 2004 to June 2022 evaluating search terms involving AI and drug safety, pharmacy operations, or precision medicine. Articles had to be focused on human subjects, be either an observational cohort study or randomized control trial, and involve medication use to be included in this review. Data collection included the study design, AI methodology, and primary outcome and associated AI model performance measures. A total of 24 232 articles were identified from the search strategy, with 403 articles meeting inclusion criteria (1.7%). A total of 22 articles were included in this review. Articles focused on drug safety included safe use of opioids and the role of social media in identifying adverse drug reactions. In terms of pharmacy operations, articles included the identification of medications at risk for shortage and visual identification of medications. Finally, for precision medicine, articles included determining likelihood of beneficial effects of medications for new diagnoses and dosing of warfarin. The role of AI in improving patient outcomes is numerous. However, challenges that remain include transparency of generated AI models, generalizability, and lack of external validation. Future areas of focus include education of pharmacy clinicians on the role of AI and the increased use of open‐source models/data to allow for external validation of AI.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Role of artificial intelligence in pharmacy practice: A narrative review

Wong,

Wentz,

Palisano

et al. 2023

J Am Coll Clin Pharm

View full text Add to dashboard Cite

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“…Theoretically, ML algorithms could be applied to develop predictive models to optimize warfarin dosing, using either regression or multi classification models. Nine studies were included [96][97][98][99][100][101][102][103][104] . Most of them derive from a multiethnic International Warfarin Pharmacogenetics Consortium (IWPC) dataset 105 using several ML regression models (Supplementary Table 5).…”

Section: Prediction Of a Personalized Anticoagulant Managementmentioning

confidence: 99%

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

Danilatou,

Dimopoulos,

Kostoulas

et al. 2024

Thromb Haemost

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Background: Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific Clinical Prediction Models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records (EHRs). We aimed to explore ML-CPMs applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. Methods: Three databases were searched, PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. Results: Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. Conclusion: ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of AI in VTE.

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“…Some of these limitations can be overcome by incorporation of machine/deep learning methods to infer prothrombotic biomarkers using image-based biophysical models to accelerate aspects of patient-specific functional assessment (Figure 7) (9,(104)(105)(106)(107)(108)(109)(110)(111)(112). An example of this approach is the advent of physics informed neural networks (PINNs) which integrate the PDEs discussed in Section 3.2 as part of the loss function to enable more correct approximations of the solution than earlier forms of machine learning, even with limited data availability (104,(113)(114)(115).…”

Section: Application Of Artificial Intelligencementioning

confidence: 99%

Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke

Qureshi

Lip

Nordsletten

et al. 2023

Front. Cardiovasc. Med.

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Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction—known as Virchow’s triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools—such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage—have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields.

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Using machine learning to predict anticoagulation control in atrial fibrillation: A UK Clinical Practice Research Datalink study

Cited by 9 publications

References 43 publications

Role of artificial intelligence in pharmacy practice: A narrative review

Role of artificial intelligence in pharmacy practice: A narrative review

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke

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