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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

Receiver-operator curves (ROC) of the predicted probabilities against the actual outcome, for each dataset.Model results are shown using all available variables (closed triangles, solid line), for a parsimonious model using only variables common to all datasets (age, gender, structural heart disease, number of spells, and prodromal symptoms; open circles, short dashed line), and for a model using data resampled to create standard distributions of number of spells and age (categorical; open inverted triangles).
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pone-0075255-g003: Receiver-operator curves (ROC) of the predicted probabilities against the actual outcome, for each dataset.Model results are shown using all available variables (closed triangles, solid line), for a parsimonious model using only variables common to all datasets (age, gender, structural heart disease, number of spells, and prodromal symptoms; open circles, short dashed line), and for a model using data resampled to create standard distributions of number of spells and age (categorical; open inverted triangles).

Mentions: The conditional probabilities model, using all available information for each dataset, resulted in c-statistics of 0.87 for the Calgary data [7,8,20], 0.84 for the Amsterdam data [21], 0.72 for the Milan data [9] and 0.71 for the Rochester data [2] (Figure 3). With a cut-off value of the probability of cardiac syncope of 0.02 (selected to favour sensitivity over specificity), the sensitivity and specificity of the full model were 92% and 68% for Calgary, 86% and 67% for Amsterdam, 76% and 59% for Milan, and 73% and 52% for Rochester. Using only variables common to all datasets (age, gender, structural heart disease, number of spells, and prodromal symptoms) a more parsimonious model resulted in very similar c-statistics of 0.88, 0.83, 0.72 and 0.69, respectively.


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)

Receiver-operator curves (ROC) of the predicted probabilities against the actual outcome, for each dataset.Model results are shown using all available variables (closed triangles, solid line), for a parsimonious model using only variables common to all datasets (age, gender, structural heart disease, number of spells, and prodromal symptoms; open circles, short dashed line), and for a model using data resampled to create standard distributions of number of spells and age (categorical; open inverted triangles).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0075255-g003: Receiver-operator curves (ROC) of the predicted probabilities against the actual outcome, for each dataset.Model results are shown using all available variables (closed triangles, solid line), for a parsimonious model using only variables common to all datasets (age, gender, structural heart disease, number of spells, and prodromal symptoms; open circles, short dashed line), and for a model using data resampled to create standard distributions of number of spells and age (categorical; open inverted triangles).
Mentions: The conditional probabilities model, using all available information for each dataset, resulted in c-statistics of 0.87 for the Calgary data [7,8,20], 0.84 for the Amsterdam data [21], 0.72 for the Milan data [9] and 0.71 for the Rochester data [2] (Figure 3). With a cut-off value of the probability of cardiac syncope of 0.02 (selected to favour sensitivity over specificity), the sensitivity and specificity of the full model were 92% and 68% for Calgary, 86% and 67% for Amsterdam, 76% and 59% for Milan, and 73% and 52% for Rochester. Using only variables common to all datasets (age, gender, structural heart disease, number of spells, and prodromal symptoms) a more parsimonious model resulted in very similar c-statistics of 0.88, 0.83, 0.72 and 0.69, respectively.

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