Predicting Patient-level New Onset Atrial Fibrillation

Study Overview
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Brief Summary
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Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial health-care expenditure as a result of stroke, sudden death, heart failure and unplanned hospitalisation. There is a compelling argument for the early diagnosis of AF, before the first complication occurs, but population-based screening is not recommended. Strategies to identify individuals at higher risk of new onset AF are required. previous risk scores have been limited by data and methodology. The investigators will use routinely collected hospital-linked primary care data and focus on the use of artificial intelligence methods to develop and validate a model for the prediction of incident AF. Specifically, the investigators will investigate how population-based data may be used for precision medicine using a deep neural networks learning model. Using clinical factors readily accessible in primary care, the investigators will provide a method for the identification of individuals in the community who are at risk of AF, as well as when incident AF will occur in those at risk, thus accelerating research assessing technologies for the improvement of risk prediction, and the targeting of high-risk individuals for preventive measures and screening.

Detailed Description
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Atrial fibrillation (AF) is a common chronic condition with substantial impact on health outcomes. Many cases of AF are detected too late - as a manifestation of stroke, heart failure, or other complication. Whilst earlier detection of AF offers the potential to prevent premature cardiovascular disease, population screening is not recommended. Atrial fibrillation (AF) is a leading cardiovascular health problem. It is the most common sustained cardiac arrhythmia, affecting 1-2% of the population of Europe and the USA, with a lifetime risk of one in four in the general population. It has an increasing prevalence as the population ages. Consequently, these estimates are likely to increase, and presently are underestimated given that AF may long remain undiagnosed. AF incurs 1-3% of healthcare expenditure as a result of stroke, sudden death, heart failure, unplanned hospitalisation, and associated complications. The resultant emerging AF epidemic and its associated costly complications (including, but not limited to stroke, depression, heart failure, acute coronary syndrome, cognitive decline and unscheduled hospitalisation) has ensured that AF is now a major threat to healthy longevity. The early diagnosis of AF, ideally before manifestation of the first complication, remains a major public health challenge. While for some patients AF may present with symptomatic palpitations, for others the first diagnosis of AF may be when they present to healthcare professionals with stroke, acute cardiac decompensation or co-morbidity exacerbation - a stage that is unnecessarily late in the disease trajectory. This is because many patients with AF may not have AF-associated symptoms. Given that nearly one third of patients admitted to a stroke ward have AF at the time of their admission to hospital and that oral anticoagulants reduce the risk of stroke by up to two thirds in those with AF who are at higher risk of stroke, there is a compelling argument for the earlier detection of AF. To that end, opportunistic screening for AF (pulse palpation followed by ECG in patients with an irregular pulse) in patients aged 65 years and over is now recommended in national and international guidelines. International guidelines also recommend the use of a 12 lead ECG and ambulatory rhythm monitors (within increasing duration according to perceived risk of AF), escalating to implantable leadless AF recorders in patients with suspected but undiagnosed AF - and each with implications for healthcare costs and patient satisfaction. Whilst there are promising results from systematic screening of elderly populations for AF using self-operated devices, presently there is no recommendation in the United Kingdom (UK) for population-wide systematic screening for AF because it is not yet clear if those identified as at risk would benefit from early diagnosis. Indeed, research is needed to understand better the detection rates, diagnostic accuracy, outcomes of such programs, as well as to define in what sub-populations AF screening would offer the greatest patient and public health value. The identification of AF has important patient and clinical ramifications. Those patients at higher risk of stroke (CHADSVASC score ≥ 2) without a contra-indication should be offered stroke prophylaxis with an oral anticoagulant. Moreover, most patients with AF will have stroke risk factors, making them eligible for an oral anticoagulant, and many will have concomitant cardiovascular disease (such as hypertension, valvular heart disease or heart failure) making them eligible for further investigation or treatment. Equally, in those with AF who are low risk for stroke (and therefore do not qualify for oral anticoagulation), surveillance for increasing stroke risk is advisable. Predicting precisely if and when a person will have new onset AF may allow phenotype and temporal-specific (thus more effective) screening, as well as identify putative risk markers for AF aetiology. For example, patients presently in sinus rhythm, but at higher risk of stroke and predicted to develop AF at a specific time-point in the future may benefit from screening for AF nearer the forecasted date. Equally, modifiable risk factors for the development of AF and for risk of stroke may be proactively addressed in light of knowledge of higher risk of new onset AF, and new risk factors studied for causality. Other possible research opportunities may include the study of patients who do not have and are not predicted to have AF, and the evaluation of lifestyle, device technology and pharmacotherapeutic strategies to reduce the risk of AF in patients at high predicted risk of new onset AF. To date, a number of AF risk prediction tools have been developed, including those from the CHARGE-AF consortium, Framingham Heart Study, the CHADS score, the CHADSVASC score and the CHEST score, among others. The CHEST score (structural heart disease, heart failure, age ≥ 75 years, coronary artery disease, hyperthyroidism, Chronic Obstructive Pulmonary Disease (COPD), and hypertension) derived from 471,446 subjects from the Chinese Yunnan Insurance Database and validated in 451,199 subjects from the Korean National Health Insurance Service was found to predict future incident AF. Of the 4764 participants in the Framingham Heart Study, age, sex, body-mass index, systolic blood pressure, treatment for hypertension, the time from the onset of the P wave to the start of the ventricular depolarization (QRS) complex (PR interval), clinically significant cardiac murmur, and heart failure were found using survival modelling to be components of a score predicting incident AF at 10 years. However, each of the studies to date are limited by one or more of, their use of geographically remote data, historical data, small datasets, lack of temporal information, crude risk modelling with consequent suboptimal model performance and/or predictor variables not readily available to the General Practitioner. Understandably, none have reached widespread clinical practice. Artificial intelligence facilitates the use of vast quantities of event data and the associated temporal information (such as that in primary care datasets), handles large numbers of predictors with automatic variable selection techniques, accommodates nonlinearities and interactions among variables, enables a live learning approach (whereby the prediction model is automatically updated), and can use population-wide data to predict if and when there will be new onset AF for an individual. A range of Artificial Intelligence (AI) techniques have been applied to EHR data and have demonstrated better diagnostic and prediction power over traditional statistical approaches in large scale EHR data. Yet, as highlighted recently, it is important to identify models that are clinically useful. For example, a study which developed an AI-enabled ECG algorithm that predicted AF from ECGs with normal sinus rhythm, whilst an important step forward may not be applicable in the community setting where routine ECGs are not always available. Thus, developing a predictive algorithm for new onset AF from routine primary care electronic health records data using AI techniques could offer the opportunity for early translation to clinical practice. The investigators will develop and validate a deep neural networks learning model, utilising large scale linked electronic health records (EHR) from primary care, to predict the risk of new AF. The prediction algorithm will be trained and tested for its accuracy and robustness in predicting future AF events using Clinical Practice Research Datalink (CPRD)-Global initiative for chronic Obstructive Lung Disease (GOLD), and will be externally validated using similar databases CPRD-AURUM but at different geographic locations. The new predictive algorithm will be compared against a range of classic machine learning techniques as well as traditional statistical predictive modelling methods. Pending a successful model of improving the predicting accuracy of at least 5% compared with existing models, the algorithm could be made readily available through free to use software.

Official Title
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Predicting Patient-level New Onset Atrial Fibrillation From Population-based Nationwide Electronic Health Records: A Precision Medicine Investigation Using Artificial Intelligence

Conditions
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Atrial Fibrillation

Intervention / Treatment
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* Other: Observational


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Diagnosed AF after 1 January 2009 (Identified using Read codes (for the CPRD patient profile) and ICD-10 codes (for HES events) In Clinical Practice Research Datalink -Global initiative for chronic Obstructive Lung Disease (CPRD-GOLD) and eligible for data linkage. Have at least 1-year follow-up in the period between 1st Jan 1998 and 31st December 2018. Exclusion Criteria: Under 18 at date of the first registration in CPRD Diagnosed with AF before 1st Jan 1998 In CPRD-GOLD and not eligible for data linkage Has less than one year follow up in CPRD

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| All eligible patients<br>Observational cohort using anonymized patient-level primary care data linked to secondary administrative data; CPRD-GOLD and CPRD-AURUM. | Other: Observational<br>* Observational - no intervention given<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| To develop and validate a deep learning hierarchical model for predicting the risk, and where appropriate period, of new onset AF | Predictive factors will be identified using Read codes (diagnoses), measurements and Prod codes (medications) in CPRD; ICD10 codes and statistical classification (OPCS) codes in Hospital Episode Statistics (HES); and ICD 10 codes (ICD9 codes for the period before 2001) in Office of National Statistics (ONS) data. All variables will be considered as potential predictors, and may include: sociodemographic variables: age, sex, ethnicity, index of multiple deprivation; all (repeated) hospitalised disease conditions during follow-up clinical assessments, such as ECG, heart rate, height, weight, medications prescribed, lifestyle factors (e.g. smoking status, alcohol consumption); all biomarkers collected during follow-up The temporal information of all clinical assessments, hospitalised events, medications will be included. | Between 1st Jan 1998 and 31st December 2018 | 
| To identify and quantify the magnitude of predictors of new onset AF | The proposed deep learning model can extract informative risk factors from EHR data. Specifically, a risk factor selection strategy proposed in Huang et al will be adapted to identify informative risk factors. The model will provide weights of the identified risk factors to help understand the significance of risk factors at different risk levels. The impact of the number of risk factors on the performance of AF risk prediction will be assessed through the curves of both area under curve (AUC) and prediction accuracy plotted against the number of risk factors. Some predictors, such as BMI, blood pressure, frequency of General Practitioner (GP) visits, strength of prescribed medication, may change over time. The incremental prognostic values of including these variable trajectories will be explored and the impact on predictive accuracy will be assessed. | Between 1st Jan 1998 and 31st December 2018 |