Predicting recurrent atrial fibrillation after catheter ablation: a systematic review of prognostic models

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@article{c076a69bda0c4df2b4ae23cec6d06f12,
title = "Predicting recurrent atrial fibrillation after catheter ablation: a systematic review of prognostic models",
abstract = "AimsWe assessed the performance of modelsf (risk scores) for predicting recurrence of atrial fibrillation (AF) in patients who have undergone catheter ablation.Methods and resultsSystematic searches of bibliographic databases were conducted (November 2018). Studies were eligible for inclusion if they reported the development, validation, or impact assessment of a model for predicting AF recurrence after ablation. Model performance (discrimination and calibration) measures were extracted. The Prediction Study Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias. Meta-analysis was not feasible due to clinical and methodological differences between studies, but c-statistics were presented in forest plots. Thirty-three studies developing or validating 13 models were included; eight studies compared two or more models. Common model variables were left atrial parameters, type of AF, and age. Model discriminatory ability was highly variable and no model had consistently poor or good performance. Most studies did not assess model calibration. 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. No model impact studies were identified.ConclusionOur systematic review suggests that clinical risk prediction of AF after ablation has potential, but there remains a need for robust evaluation of risk factors and development of risk scores.",
keywords = "Atrial fibrillation, catheter ablation, recurrence, prognostic model, model performance, systematic review",
author = "Janine Dretzke and Naomi Chuchu and Ridhi Agarwal and Clare Herd and Chua, {Winnie Wei Ling} and Larissa Fabritz and Susan Bayliss and Dipak Kotecha and Jon Deeks and Paulus Kirchhof and Yemisi Takwoingi",
year = "2020",
month = may,
day = "1",
doi = "10.1093/europace/euaa041",
language = "English",
volume = "22",
pages = "748–760",
journal = "Europace",
issn = "1099-5129",
publisher = "Oxford University Press",
number = "5",

}

RIS

TY - JOUR

T1 - Predicting recurrent atrial fibrillation after catheter ablation: a systematic review of prognostic models

AU - Dretzke, Janine

AU - Chuchu, Naomi

AU - Agarwal, Ridhi

AU - Herd, Clare

AU - Chua, Winnie Wei Ling

AU - Fabritz, Larissa

AU - Bayliss, Susan

AU - Kotecha, Dipak

AU - Deeks, Jon

AU - Kirchhof, Paulus

AU - Takwoingi, Yemisi

PY - 2020/5/1

Y1 - 2020/5/1

N2 - AimsWe assessed the performance of modelsf (risk scores) for predicting recurrence of atrial fibrillation (AF) in patients who have undergone catheter ablation.Methods and resultsSystematic searches of bibliographic databases were conducted (November 2018). Studies were eligible for inclusion if they reported the development, validation, or impact assessment of a model for predicting AF recurrence after ablation. Model performance (discrimination and calibration) measures were extracted. The Prediction Study Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias. Meta-analysis was not feasible due to clinical and methodological differences between studies, but c-statistics were presented in forest plots. Thirty-three studies developing or validating 13 models were included; eight studies compared two or more models. Common model variables were left atrial parameters, type of AF, and age. Model discriminatory ability was highly variable and no model had consistently poor or good performance. Most studies did not assess model calibration. 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. No model impact studies were identified.ConclusionOur systematic review suggests that clinical risk prediction of AF after ablation has potential, but there remains a need for robust evaluation of risk factors and development of risk scores.

AB - AimsWe assessed the performance of modelsf (risk scores) for predicting recurrence of atrial fibrillation (AF) in patients who have undergone catheter ablation.Methods and resultsSystematic searches of bibliographic databases were conducted (November 2018). Studies were eligible for inclusion if they reported the development, validation, or impact assessment of a model for predicting AF recurrence after ablation. Model performance (discrimination and calibration) measures were extracted. The Prediction Study Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias. Meta-analysis was not feasible due to clinical and methodological differences between studies, but c-statistics were presented in forest plots. Thirty-three studies developing or validating 13 models were included; eight studies compared two or more models. Common model variables were left atrial parameters, type of AF, and age. Model discriminatory ability was highly variable and no model had consistently poor or good performance. Most studies did not assess model calibration. 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. No model impact studies were identified.ConclusionOur systematic review suggests that clinical risk prediction of AF after ablation has potential, but there remains a need for robust evaluation of risk factors and development of risk scores.

KW - Atrial fibrillation

KW - catheter ablation

KW - recurrence

KW - prognostic model

KW - model performance

KW - systematic review

U2 - 10.1093/europace/euaa041

DO - 10.1093/europace/euaa041

M3 - Article

C2 - 32227238

VL - 22

SP - 748

EP - 760

JO - Europace

JF - Europace

SN - 1099-5129

IS - 5

ER -