Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis

Nadarajah, Ramesh; Al-Saeed, Eman; Hurdus, Ben; Aktaa, Suleman; Hogg, David C.; Bates, Matthew; Cowan, Campbel; Wu, Jianhua; Gale, Chris P

doi:10.1136/heartjnl-2021-320036

Cited by 12 publications

(34 citation statements)

References 49 publications

(36 reference statements)

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“…First, many algorithms developed using traditional regression techniques show only moderate discriminative performance. 10 Second, algorithm prediction horizons are often 5 or 10 years, making it difficult to judge the merits of investigating individuals in the short term. 9 11 Third, reports have infrequently investigated for variation in algorithm prediction performance by sex and ethnicity.…”

Section: Arrhythmias and Sudden Deathmentioning

confidence: 99%

“…Our systematic review evidenced strong discriminative performance for AF prediction using RF across different EHR datasets. 10 RF is a machine learning method consisting of many individual decision trees that operate as an ensemble. 15 FIND-AF was trained using 10-fold cross-validation on the full training set (full details available in online supplemental methods).…”

Section: Find-af Algorithm Developmentmentioning

confidence: 99%

“…Ethnicity information is routinely collected in the UK NHS and so has increasingly high completeness, 16 and we included an 'ethnicity unrecorded' category where it was unavailable because missingness was considered to be informative. 17 Predictor variables were selected a priori from systematic review of variables included in previous AF risk prediction algorithms, 10 plus an updated literature review (online supplemental tables 4-6). Diagnostic code lists only included the primary care coding system (Read codes), ensuring that only information readily available within a primary care EHR could be incorporated within the algorithm.…”

Section: Find-af Algorithm Developmentmentioning

confidence: 99%

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Prediction of short-term atrial fibrillation risk using primary care electronic health records

Nadarajah

Hogg

et al. 2023

Heart

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ObjectiveAtrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)).MethodsWe used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA2DS2-VASc (Congestive heart failure, Hypertension, Age >75 (2 points), Stroke/transient ischaemic attack/thromboembolism (2 points), Vascular disease, Age 65–74, Sex category) and C2HEST (Coronary artery disease/Chronic obstructive pulmonary disease (1 point each), Hypertension, Elderly (age ≥75, 2 points), Systolic heart failure, Thyroid disease (hyperthyroidism)) scores. Cox proportional hazard models with competing risk of death were fit for incident longer-term AF between higher and lower FIND-AF-predicted risk.ResultsOf 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA2DS2-VASc (0.784, 0.773 to 0.794) and C2HEST (0.757, 0.744 to 0.770), and robust by sex and ethnic group. The higher predicted risk cohort, compared with lower predicted risk, had a 20-fold higher 6-month incidence rate for AF and higher long-term hazard for AF (HR 8.75, 95% CI 8.44 to 9.06).ConclusionsFIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.

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Section: Arrhythmias and Sudden Deathmentioning

confidence: 99%

Section: Find-af Algorithm Developmentmentioning

confidence: 99%

Section: Find-af Algorithm Developmentmentioning

confidence: 99%

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Prediction of short-term atrial fibrillation risk using primary care electronic health records

Nadarajah

Hogg

et al. 2023

Heart

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“…A prediction model utilizing this information could more accurately discriminate people into a higher risk category than screening based on age alone. Several models have been developed or validated for the prediction of incident AF in community-based EHRs using traditional regression techniques, 23 but provide only moderate discriminative performance, 24–26 commonly use variables that may be missing in routinely collected community-based records (such as measures of height, weight, and blood pressure), 22 or give risk prediction over 5 or 10 years, which is difficult to translate into an investigational priority in the immediate future. 22 , 26…”

Section: Could Prediction Models For Incident Atrial Fibrillation Pro...mentioning

confidence: 99%

“… 27 These ML methods incorporated a large number of variables, of a variety of data types, and demonstrated strong discriminative performance in the derivation data sets, though there has been limited external validation ( Table 1 ). 23 , 33 …”

Section: Could Prediction Models For Incident Atrial Fibrillation Pro...mentioning

confidence: 99%

What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key

Nadarajah

Frangi

et al. 2021

European Heart Journal - Quality of Care and Clinical Outcomes

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Atrial fibrillation is increasingly common though often undiagnosed, leaving many people untreated and at elevated risk of ischaemic stroke. Current European guidelines do not recommend systematic screening for atrial fibrillation even though a number of studies have shown that periods of serial or continuous rhythm monitoring in older people in the general population increases detection of atrial fibrillation and the prescription of oral anticoagulation. This article discusses the conflicting results of two contemporary landmark trials, STROKESTOP and The LOOP, which provided the first evidence on whether screening for AF confers a benefit for people in terms of clinical outcomes. The benefit and efficiency of systematic screening for AF in the general population could be optimised by targeting screening to only those at higher risk of developing AF. For this purpose, evidence is emerging that prediction models developed using artificial intelligence in routinely-collected electronic health records can provide strong discriminative performance for atrial fibrillation and increase detection rates when combined with rhythm monitoring in a clinical study. We consider future directions for investigation in this field and how this could be best aligned to the current evidence base to target screening in people at elevated risk of stroke.

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Prediction models for heart failure in the community: A systematic review and meta‐analysis

Nadarajah,

Younsi,

Romer

et al. 2023

European J of Heart Fail

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AimsMultivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta‐analysis was performed to determine the performance of models.Methods and resultsFrom inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community‐based cohorts. Discrimination measures for models with c‐statistic data from ≥3 cohorts were pooled by Bayesian meta‐analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta‐analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c‐statistic 0.802, 95% confidence interval [CI] 0.707–0.883), GRaph‐based Attention Model (GRAM; 0.791, 95% CI 0.677–0.885), Pooled Cohort equations to Prevent Heart Failure (PCP‐HF) white men model (0.820, 95% CI 0.792–0.843), PCP‐HF white women model (0.852, 95% CI 0.804–0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748–0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP‐HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study.ConclusionsPrediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.

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Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis

Cited by 12 publications

References 49 publications

Prediction of short-term atrial fibrillation risk using primary care electronic health records

Prediction of short-term atrial fibrillation risk using primary care electronic health records

What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key

Prediction models for heart failure in the community: A systematic review and meta‐analysis

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