Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm

Hill, Nathan R.; Sandler, Belinda; Mokgokong, Ruth; Lister, Steven; Ward, Thomas; Boyce, Rebecca; Farooqui, Usman; Gordon, Jason

doi:10.1080/13696998.2019.1706543

Cited by 21 publications

(36 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, up to two thirds of AFrelated strokes can be prevented with anticoagulation therapy. 34 Interventions such as the AF risk prediction algorithm evaluated in this study can narrow the population that should be considered for screening in a cost-effective manner, 35 and may potentially enable earlier detection of the condition. There is evidence that some limitations of ECG-based screening may be overcome by the use of portable, hand-held ECG machines by patients at home.…”

Section: Discussionmentioning

confidence: 99%

Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study

Sekelj

Sandler

Johnston

et al. 2020

European Journal of Preventive Cardiology

Self Cite

View full text Add to dashboard Cite

Aims To evaluate the ability of a machine learning algorithm to identify patients at high risk of atrial fibrillation in primary care. Methods A retrospective cohort study was undertaken using the DISCOVER registry to validate an algorithm developed using a Clinical Practice Research Datalink (CPRD) dataset. The validation dataset included primary care patients in London, England aged ≥30 years from 1 January 2006 to 31 December 2013, without a diagnosis of atrial fibrillation in the prior 5 years. Algorithm performance metrics were sensitivity, specificity, positive predictive value, negative predictive value (NPV) and number needed to screen (NNS). Subgroup analysis of patients aged ≥65 years was also performed. Results Of 2,542,732 patients in DISCOVER, the algorithm identified 604,135 patients suitable for risk assessment. Of these, 3.0% (17,880 patients) had a diagnosis of atrial fibrillation recorded before study end. The area under the curve of the receiver operating characteristic was 0.87, compared with 0.83 in algorithm development. The NNS was nine patients, matching the CPRD cohort. In patients aged ≥30 years, the algorithm correctly identified 99.1% of patients who did not have atrial fibrillation (NPV) and 75.0% of true atrial fibrillation cases (sensitivity). Among patients aged ≥65 years ( n = 117,965), the NPV was 96.7% with 91.8% sensitivity. Conclusions This atrial fibrillation risk prediction algorithm, based on machine learning methods, identified patients at highest risk of atrial fibrillation. It performed comparably in a large, real-world population-based cohort and the developmental registry cohort. If implemented in primary care, the algorithm could be an effective tool for narrowing the population who would benefit from atrial fibrillation screening in the United Kingdom.

show abstract

Section: Discussionmentioning

confidence: 99%

Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study

Sekelj

Sandler

Johnston

et al. 2020

European Journal of Preventive Cardiology

Self Cite

View full text Add to dashboard Cite

show abstract

“… 54 Finally, some approaches fall somewhere in between mechanistic and data-driven models. For example, health-technology assessment models used in cost-effectiveness assessment of medical therapies, 55 , 56 are dynamic and causal, but do not include fundamental biophysical laws and are primarily based on data from clinical trials. The following three subsections provide key examples of these computational approaches that are relevant for AF management (as summarized in Table 1 and Figure 1 , blue and yellow boxes).…”

Section: Key Achievements Of Computational Modelling In Afmentioning

confidence: 99%

“…Besides mechanistic and data-driven modelling, health-technology assessment models also play an important role in AF management, particularly for cost-effectiveness analyses of AF screening 56 , 108 and AF therapies (e.g., AADs, anticoagulants and ablation). 55 , 109–111 Health-technology assessment models are typically implemented using Markov models that simulate the transition of virtual populations between different clinical states, each of which have a specific value (e.g.…”

Section: Health-technology Assessment Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Heijman

Sutanto

Crijns

et al. 2021

Cardiovascular Research

View full text Add to dashboard Cite

Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodeling processes. Atrial remodeling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modeling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modeling in AF management. We summarize clinical challenges that may benefit from computational modeling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modeling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.

show abstract

“…In recent years, machine/deep learning and big medical data have been shown to possess immense potential to offer personalized healthcare by risk prediction to increase prevention efficacy and cost effectiveness. [12][13][14][15][16] Machine/ deep learning is an extension of classic statistical methodology that manages high-dimensional data such as images and large-scale electronic medical records (EMRs). The convolutional neural network (CNN), a type of deep learning method, can analyze general and highly variable tasks represented in imaging data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record

Ningrum¹,

Kung²,

Tzeng³

et al. 2021

JMDH

View full text Add to dashboard Cite

Purpose: To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data. Patients and Methods: We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection. Results: This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (−0.76% loss) and 0.9644 (−0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features. Conclusion: Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.

show abstract

Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm

Abstract: Gordon (2020) Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm,

Cited by 21 publications

References 30 publications

Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study

Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record

Contact Info

Product

Resources

About