Imperial College London

Dr Fu Siong Ng

Faculty of MedicineNational Heart & Lung Institute

Clinical Senior Lecturer in Cardiac Electrophysiology



+44 (0)20 7594 Website




ICTEM buildingHammersmith Campus






BibTex format

author = {Sekelj, S and Sandler, B and Johnston, E and Pollock, KG and Hill, NR and Gordon, J and Tsang, C and Khan, S and Ng, FS and Farooqui, U},
doi = {10.1177/2047487320942338},
journal = {European Journal of Preventive Cardiology},
pages = {1--9},
title = {Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study},
url = {},
year = {2020}

RIS format (EndNote, RefMan)

AB - AimsTo evaluate the ability of a machine learning algorithm to identify patients at high risk of atrial fibrillation in primary care.MethodsA 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.ResultsOf 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.ConclusionsThis 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.
AU - Sekelj,S
AU - Sandler,B
AU - Johnston,E
AU - Pollock,KG
AU - Hill,NR
AU - Gordon,J
AU - Tsang,C
AU - Khan,S
AU - Ng,FS
AU - Farooqui,U
DO - 10.1177/2047487320942338
EP - 9
PY - 2020///
SN - 2047-4873
SP - 1
TI - Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study
T2 - European Journal of Preventive Cardiology
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