Limits...
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.

Show MeSH

Related in: MedlinePlus

Predictors of cardiac syncope in the derivation populations.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3815402&req=5

pone-0075255-g002: Predictors of cardiac syncope in the derivation populations.

Mentions: Eleven variables were statistically significantly associated with the diagnosis in 3 or more derivation studies listed in Table 1. The resulting conditional probability tables for these variables are given in Table 2. Palpitations were more common among cardiac syncope patients in some studies [5,6], and more common among vasovagal patients in others [15,18]. Overall the association of palpitations with cardiac vs. non-cardiac syncope was not statistically significant; therefore, this variable was excluded. The resulting 10 variables associated with cardiac syncope were age >60 years, male sex, structural heart disease, <3 spells, and supine syncope and effort syncope. Factors associated with non-cardiac syncope included age < 40 years, a long prodrome, nausea, diaphoresis, and blurred vision. These had moderate accuracy in identifying patients with cardiac syncope (Figure 2).


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)

Predictors of cardiac syncope in the derivation populations.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3815402&req=5

pone-0075255-g002: Predictors of cardiac syncope in the derivation populations.
Mentions: Eleven variables were statistically significantly associated with the diagnosis in 3 or more derivation studies listed in Table 1. The resulting conditional probability tables for these variables are given in Table 2. Palpitations were more common among cardiac syncope patients in some studies [5,6], and more common among vasovagal patients in others [15,18]. Overall the association of palpitations with cardiac vs. non-cardiac syncope was not statistically significant; therefore, this variable was excluded. The resulting 10 variables associated with cardiac syncope were age >60 years, male sex, structural heart disease, <3 spells, and supine syncope and effort syncope. Factors associated with non-cardiac syncope included age < 40 years, a long prodrome, nausea, diaphoresis, and blurred vision. These had moderate accuracy in identifying patients with cardiac syncope (Figure 2).

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