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

Authors
  • Nadarajah, Ramesh1, 2
  • Alsaeed, Eman3
  • Hurdus, Ben2
  • Aktaa, Suleman3, 2
  • Hogg, David4
  • Bates, Matthew G D5
  • Cowan, Campbel2
  • Wu, Jianhua3, 6
  • Gale, Chris P3, 2
  • 1 Leeds Institute of Data Analytics, University of Leeds, Leeds, UK [email protected]
  • 2 Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
  • 3 Leeds Institute of Data Analytics, University of Leeds, Leeds, UK.
  • 4 School of Computing, University of Leeds, Leeds, UK.
  • 5 Department of Cardiology, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK.
  • 6 School of Dentistry, University of Leeds, Leeds, Leeds, UK.
Type
Published Article
Journal
Heart
Publisher
BMJ
Publication Date
Jun 10, 2022
Volume
108
Issue
13
Pages
1020–1029
Identifiers
DOI: 10.1136/heartjnl-2021-320036
PMID: 34607811
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Atrial fibrillation (AF) is common and is associated with an increased risk of stroke. We aimed to systematically review and meta-analyse multivariable prediction models derived and/or validated in electronic health records (EHRs) and/or administrative claims databases for the prediction of incident AF in the community. Ovid Medline and Ovid Embase were searched for records from inception to 23 March 2021. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation. Eleven studies met inclusion criteria, describing nine prediction models, with four eligible for meta-analysis including 9 289 959 patients. The CHADS (Congestive heart failure, Hypertension, Age>75, Diabetes mellitus, prior Stroke or transient ischemic attack) (summary c-statistic 0.674; 95% CI 0.610 to 0.732; 95% PI 0.526-0.815), CHA2DS2-VASc (Congestive heart failure, Hypertension, Age>75 (2 points), Stroke/transient ischemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) (summary c-statistic 0.679; 95% CI 0.620 to 0.736; 95% PI 0.531-0.811) and HATCH (Hypertension, Age, stroke or Transient ischemic attack, Chronic obstructive pulmonary disease, Heart failure) (summary c-statistic 0.669; 95% CI 0.600 to 0.732; 95% PI 0.513-0.803) models resulted in a c-statistic with a statistically significant 95% PI and moderate discriminative performance. No model met eligibility for inclusion in meta-analysis if studies at high risk of bias were excluded and certainty of effect estimates was 'low'. Models derived by machine learning demonstrated strong discriminative performance, but lacked rigorous external validation. Models externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance. PROSPERO CRD42021245093. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.

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