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Identifying cardiac syncope based on clinical history: a literature-based model tested in four independent datasets.

Berecki-Gisolf J, Sheldon A, Wieling W, van Dijk N, Costantino G, Furlan R, Shen WK, Sheldon R - PLoS ONE (2013)

Bottom Line: Fitting the test datasets to the full model gave C-statistics of 0.87 (Calgary), 0.84 (Amsterdam), 0.72 (Milan) and 0.71 (Rochester).Model sensitivity and specificity were 92% and 68% for Calgary, 86% and 67% for Amsterdam, 76% and 59% for Milan, and 73% and 52% for Rochester.A model with 5 variables (age, gender, structural heart disease, low number of spells, and lack of prodromal symptoms) was as accurate as the total set.

View Article: PubMed Central - PubMed

Affiliation: Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, Alberta, Canada.

ABSTRACT

Background: We aimed to develop and test a literature-based model for symptoms that associate with cardiac causes of syncope.

Methods and results: Seven studies (the derivation sample) reporting ≥2 predictors of cardiac syncope were identified (4 Italian, 1 Swiss, 1 Canadian, and 1 from the United States). From these, 10 criteria were identified as diagnostic predictors. The conditional probability of each predictor was calculated by summation of the reported frequencies. A model of conditional probabilities and a priori probabilities of cardiac syncope was constructed. The model was tested in four datasets of patients with syncope (the test sample) from Calgary (n=670; 21% had cardiac syncope), Amsterdam (n=503; 9%), Milan (n=689; 5%) and Rochester (3877; 11%). In the derivation sample ten variables were significantly associated with cardiac syncope: age, gender, structural heart disease, low number of spells, brief or absent prodrome, supine syncope, effort syncope, and absence of nausea, diaphoresis and blurred vision. Fitting the test datasets to the full model gave C-statistics of 0.87 (Calgary), 0.84 (Amsterdam), 0.72 (Milan) and 0.71 (Rochester). Model sensitivity and specificity were 92% and 68% for Calgary, 86% and 67% for Amsterdam, 76% and 59% for Milan, and 73% and 52% for Rochester. A model with 5 variables (age, gender, structural heart disease, low number of spells, and lack of prodromal symptoms) was as accurate as the total set.

Conclusion: A simple literature-based Bayesian model of historical criteria can distinguish patients with cardiac syncope from other patients with syncope with moderate accuracy.

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Related in: MedlinePlus

Predicted probability of cardiac syncope (median, inter-quartile range [box], 10-90th percentile [whiskers] and outliers [crosses]) using three sampling methods.The full model includes all patients with a final diagnosis of cardiac syncope (C) and non-cardiac syncope (NC) using the full model; the parsimonious model uses only the 5 variables common to all datasets; and the resampled model uses resampled data with standardized distributions of age and number of spells.
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pone-0075255-g004: Predicted probability of cardiac syncope (median, inter-quartile range [box], 10-90th percentile [whiskers] and outliers [crosses]) using three sampling methods.The full model includes all patients with a final diagnosis of cardiac syncope (C) and non-cardiac syncope (NC) using the full model; the parsimonious model uses only the 5 variables common to all datasets; and the resampled model uses resampled data with standardized distributions of age and number of spells.

Mentions: In an effort to identify the source of discrepancy in model accuracy for the 4 test datasets, we sought to reduce accrual bias by standardizing the distribution of age and number of spells. The conditional probabilities model on the resampled data resulted in c-statistics of 0.80 for Calgary, 0.78 for Amsterdam, 0.73 for Milan and 0.69 for Rochester (Figure 3). There were great differences among centres in the mean predicted probability of cardiac syncope among cardiac syncope patients (Figure 4). However, for all locations, the predicted probability of cardiac syncope was greater in cardiac syncope patients than in non-cardiac syncope patients (Mann-Whitney rank sum test; p<0.001 for all comparisons).


Identifying cardiac syncope based on clinical history: a literature-based model tested in four independent datasets.

Berecki-Gisolf J, Sheldon A, Wieling W, van Dijk N, Costantino G, Furlan R, Shen WK, Sheldon R - PLoS ONE (2013)

Predicted probability of cardiac syncope (median, inter-quartile range [box], 10-90th percentile [whiskers] and outliers [crosses]) using three sampling methods.The full model includes all patients with a final diagnosis of cardiac syncope (C) and non-cardiac syncope (NC) using the full model; the parsimonious model uses only the 5 variables common to all datasets; and the resampled model uses resampled data with standardized distributions of age and number of spells.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3815402&req=5

pone-0075255-g004: Predicted probability of cardiac syncope (median, inter-quartile range [box], 10-90th percentile [whiskers] and outliers [crosses]) using three sampling methods.The full model includes all patients with a final diagnosis of cardiac syncope (C) and non-cardiac syncope (NC) using the full model; the parsimonious model uses only the 5 variables common to all datasets; and the resampled model uses resampled data with standardized distributions of age and number of spells.
Mentions: In an effort to identify the source of discrepancy in model accuracy for the 4 test datasets, we sought to reduce accrual bias by standardizing the distribution of age and number of spells. The conditional probabilities model on the resampled data resulted in c-statistics of 0.80 for Calgary, 0.78 for Amsterdam, 0.73 for Milan and 0.69 for Rochester (Figure 3). There were great differences among centres in the mean predicted probability of cardiac syncope among cardiac syncope patients (Figure 4). However, for all locations, the predicted probability of cardiac syncope was greater in cardiac syncope patients than in non-cardiac syncope patients (Mann-Whitney rank sum test; p<0.001 for all comparisons).

Bottom Line: Fitting the test datasets to the full model gave C-statistics of 0.87 (Calgary), 0.84 (Amsterdam), 0.72 (Milan) and 0.71 (Rochester).Model sensitivity and specificity were 92% and 68% for Calgary, 86% and 67% for Amsterdam, 76% and 59% for Milan, and 73% and 52% for Rochester.A model with 5 variables (age, gender, structural heart disease, low number of spells, and lack of prodromal symptoms) was as accurate as the total set.

View Article: PubMed Central - PubMed

Affiliation: Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, Alberta, Canada.

ABSTRACT

Background: We aimed to develop and test a literature-based model for symptoms that associate with cardiac causes of syncope.

Methods and results: Seven studies (the derivation sample) reporting ≥2 predictors of cardiac syncope were identified (4 Italian, 1 Swiss, 1 Canadian, and 1 from the United States). From these, 10 criteria were identified as diagnostic predictors. The conditional probability of each predictor was calculated by summation of the reported frequencies. A model of conditional probabilities and a priori probabilities of cardiac syncope was constructed. The model was tested in four datasets of patients with syncope (the test sample) from Calgary (n=670; 21% had cardiac syncope), Amsterdam (n=503; 9%), Milan (n=689; 5%) and Rochester (3877; 11%). In the derivation sample ten variables were significantly associated with cardiac syncope: age, gender, structural heart disease, low number of spells, brief or absent prodrome, supine syncope, effort syncope, and absence of nausea, diaphoresis and blurred vision. Fitting the test datasets to the full model gave C-statistics of 0.87 (Calgary), 0.84 (Amsterdam), 0.72 (Milan) and 0.71 (Rochester). Model sensitivity and specificity were 92% and 68% for Calgary, 86% and 67% for Amsterdam, 76% and 59% for Milan, and 73% and 52% for Rochester. A model with 5 variables (age, gender, structural heart disease, low number of spells, and lack of prodromal symptoms) was as accurate as the total set.

Conclusion: A simple literature-based Bayesian model of historical criteria can distinguish patients with cardiac syncope from other patients with syncope with moderate accuracy.

Show MeSH
Related in: MedlinePlus