Key result
Externally validated EHR models predicting incident AF show moderate predictive ability and high bias risk.
Why the study?
Atrial fibrillation is common and associated with an increased stroke risk, prompting a systematic evaluation of multivariable prediction models derived or validated in EHRs and claims databases for incident AF.
What is the discriminative performance of multivariable prediction models for incident atrial fibrillation in community-based electronic health records?
Systematic Review
What is the discriminative performance of multivariable prediction models for incident atrial fibrillation in community-based electronic health records?
Existing prediction models for incident atrial fibrillation in EHRs, such as CHA2DS2-VASc and HATCH, show only moderate discriminative ability and are limited by high risk of bias, highlighting the need for better validated novel methods.
OBJECTIVE: 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. METHODS: 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. 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. CONCLUSIONS: 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. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021245093.
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Nadarajah et al. (2021) conducted a systematic review in Incident atrial fibrillation. Multivariable prediction models (CHA2DS2-VASc and HATCH) was evaluated on Prediction of incident AF (discrimination measured by c-statistic) (summary c-statistic 0.679 (CHA2DS2-VASc), 95% CI 0.620 to 0.736). Externally validated models for predicting incident AF in EHRs, such as CHA2DS2-VASc (c-statistic 0.679; 95% CI 0.620-0.736), demonstrate moderate predictive ability and high risk of bias.
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