Simple risk model and score for predicting of incident atrial fibrillation in Japanese

Hamada, Richiro; Muto, Shigeki

doi:10.1016/j.jjcc.2018.06.005

Cited by 28 publications

(33 citation statements)

References 46 publications

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“…All studies used the C-statistic to assess model discrimination for incident AF within their cohorts. Nine studies assessed calibration by providing both a P -value for a goodness-of-fit test and a calibration plot in at least one of the risk models that these studies reported on refs, 20 , 21 , 24 , 27 , 29 , 30 , 32 , 36 , 40 seven studies assessed calibration only by a P -value for goodness-of-fit test, 22 , 26 , 35 , 39 , 41 , 43 , 44 two studies assessed calibration only by a calibration plot, 25 , 33 and nine studies reported neither of these calibration parameters. 23 , 28 , 31 , 34 , 37 , 38 , 42 , 45 , 46 …”

Section: Resultsmentioning

confidence: 99%

“…Significant improvement was demonstrated in CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) with addition of the P-wave axis 35 and brain natriuretic peptide (BNP) and/or C-reactive protein (CRP), 41 in the FHS-AF 10-year model with addition of BNP and CRP, 39 and in the Seirei model with addition of ECG parameters to the model. 27 …”

Section: Resultsmentioning

confidence: 99%

“…For this domain, we assessed eight cohorts employed by 10 studies as having a high risk of bias for excluding patients for reasons with a known association with risk of future AF. 20 , 26 , 27 , 29 , 33 , 40 , 41 , 43 , 45 , 46 …”

Section: Resultsmentioning

confidence: 99%

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Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis

et al. 2020

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Aims Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing efficiency of future primary AF screening efforts. Methods and results We performed a systematic review on multivariable models derived, validated, and/or augmented for AF prediction in community cohorts using Pubmed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) through 1 August 2019. We performed meta-analysis of model discrimination with the summary C-statistic as the primary expression of associations using a random effects model. In case of high heterogeneity, we calculated a 95% prediction interval. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist for risk of bias assessment. We included 27 studies with a total of 2 978 659 unique participants among 20 cohorts with mean age ranging from 42 to 76 years. We identified 21 risk models used for incident AF risk in community cohorts. Three models showed significant summary discrimination despite high heterogeneity: CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [summary C-statistic 0.71; 95% confidence interval (95% CI) 0.66–0.76], FHS-AF (Framingham Heart Study risk score for AF) (summary C-statistic 0.70; 95% CI 0.64–0.76), and CHA2DS2-VASc (summary C-statistic 0.69; 95% CI 0.64–0.74). Of these, CHARGE-AF and FHS-AF had originally been derived for AF incidence prediction. Only CHARGE-AF, which comprises easily obtainable measurements and medical history elements, showed significant summary discrimination among cohorts that had applied a uniform (5-year) risk prediction window. Conclusion CHARGE-AF appeared most suitable for primary screening purposes in terms of performance and applicability in older community cohorts of predominantly European descent.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis

et al. 2020

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“…Summary measures of association were reported as RRs with 95% CIs. Except for one study [24], all studies categorised physical activity exposure (e.g., leisure-time physical activity, intensity of physical activity, or total or any physical activity) into two or more groups. Risk estimates could not be transformed to consistent comparisons (e.g., top versus bottom thirds of the distribution of physical activity) using standard statistical methods previously described [25,26] because of the varying reporting of outcomes.…”

Section: Data Synthesis and Analysismentioning

confidence: 99%

Physical activity and risk of atrial fibrillation in the general population: meta-analysis of 23 cohort studies involving about 2 million participants

et al. 2021

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Regular physical activity is well established to be associated with reduced risk of cardiovascular disease outcomes. Whether physical activity is associated with the future risk of atrial fibrillation (AF) remains a controversy. Using a systematic review and meta-analysis of published observational cohort studies in general populations with at least one-year of follow-up, we aimed to evaluate the association between regular physical activity and the risk of AF. Relevant studies were sought from inception until October 2020 in MEDLINE, Embase, Web of Science, and manual search of relevant articles. Extracted relative risks (RRs) with 95% confidence intervals (CIs) for the maximum versus the minimal amount of physical activity groups were pooled using random-effects meta-analysis. Quality of the evidence was assessed by GRADE. A total of 23 unique observational cohort studies comprising of 1,930,725 participants and 45,839 AF cases were eligible. The pooled multivariable-adjusted RR (95% CI) for AF comparing the most physically active versus the least physically active groups was 0.99 (0.93–1.05). This association was modified by sex: an increased risk was observed in men: 1.20 (1.02–1.42), with a decreased risk in women: 0.91 (0.84–0.99). The quality of the evidence ranged from low to moderate. Pooled observational cohort studies suggest that the absence of associations reported between regular physical activity and AF risk in previous general population studies and their aggregate analyses could be driven by a sex-specific difference in the associations – an increased risk in men and a decreased risk in women.Systematic review registration: PROSPERO 2020: CRD42020172814

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“…he analysis of previously examined TAF risk factors [4,5,[7][8][9][10][11][12][13][14][15][16][17][18][19] and non-thyrotoxic AF rediction tools [28, [31][32][33][34] helped to define input variables for our models. First, we built everal intermediate models, including more than 30 variables.…”

Section: Input Variablesmentioning

confidence: 99%

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

Ponomartseva

Derevitskii

Kovalchuk

et al. 2021

Preprint

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Background: Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients’ management. However, to our knowledge, an instrument that establishes an individual risk of the condition is unavailable. Therefore, the aim of this study is to build a TAF prediction model and rank TAF predictors in order of importance. Methods: In this retrospective study, we have investigated 36 demographic and clinical features for 420 patients with overt hyperthyroidism, 30% of which had TAF. At first, the association of these features with TAF was evaluated by classical statistical methods. Then, we developed several TAF prediction models with eight different machine learning classifiers and compared them by performance metrics. The models included ten features that were selected based on their clinical effectuality and importance for model output. Finally, we ranked TAF predictors, elicited from the optimal final model, by the machine learning tehniques. Results: The best performance metrics prediction model was built with the extreme gradient boosting classifier. It had the reasonable accuracy of 84% and AUROC of 0.89 on the test set. The model confirmed such well-known TAF risk factors as age, sex, hyperthyroidism duration, heart rate and some concomitant cardiovascular diseases (arterial hypertension and conjestive heart rate). We also identified premature atrial contraction and premature ventricular contraction as new TAF predictors. The top five TAF predictors, elicited from the model, included (in order of importance) PAC, PVC, hyperthyroidism duration, heart rate during hyperthyroidism and age. Conclusions: We developed a machine learning model for TAF prediction. It seems to be the first available analytical tool for TAF risk assessment. In addition, we defined five most important TAF predictors, including premature atrial contraction and premature ventricular contraction as the new ones. These results have contributed to TAF prediction investigation and may serve as a basis for further research focused on TAF prediction improvement and facilitation of thyrotoxic patients’ management.

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Simple risk model and score for predicting of incident atrial fibrillation in Japanese

Abstract: We developed 7-year risk scores for incident AF using usually available clinical factors including ECGs in primary care. These risk scores could identify individuals with high risk of incident AF at health check-up and outpatient clinics.

Cited by 28 publications

References 46 publications

Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis

Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis

Physical activity and risk of atrial fibrillation in the general population: meta-analysis of 23 cohort studies involving about 2 million participants

Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

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