Robust evaluation of risk factors and development of clinically useful risk scores is still needed.Ītrial fibrillation (AF) is the most common arrhythmia diagnosed in clinical practice, and worldwide incidence and prevalence is increasing. Risks of bias were substantial and included a lack of internal validation in model development studies, flawed variable selection and weighting, low event rates and poor reporting of missing data. Thus overall assessment of model performance remains incomplete. how well predicted risk agrees with observed risk) was rarely reported. Model discriminatory ability based on the c-statistic was highly variable no model had consistently poor or good discriminatory ability. To the best of our knowledge this is the first comprehensive systematic review of such models to (i) include detailed risk of bias assessment of model development and validation studies and (ii) provide a descriptive summary of measures of model performance in forest plots. Several prognostic models have been developed to predict individual risk of recurrence of atrial fibrillation (AF) after catheter ablation. The main risk of bias concern was the lack of internal validation which may have resulted in overly optimistic and/or biased model performance estimates. Most studies did not assess model calibration. Model discriminatory ability was highly variable and no model had consistently poor or good performance. Common model variables were left atrial parameters, type of AF, and age. Thirty-three studies developing or validating 13 models were included eight studies compared two or more models. Meta-analysis was not feasible due to clinical and methodological differences between studies, but c-statistics were presented in forest plots. The Prediction Study Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias. Model performance (discrimination and calibration) measures were extracted. Studies were eligible for inclusion if they reported the development, validation, or impact assessment of a model for predicting AF recurrence after ablation. Systematic searches of bibliographic databases were conducted (November 2018).
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