Validity of Coronary Artery Disease Consortium Models for Predicting Obstructive Coronary Artery Disease &amp; Cardiovascular Events in Patients with Acute Chest Pain Considered for Coronary Computed Tomographic Angiography

Teressa, Getu; Zhang, Mengru; Lavenburg, Philip; Cantor, Gregg; Noack, Pamela; Yang, Jie; Goyfman, Michael; Feldmann, Eric J.; Butler, Javed; Poon, Michael

doi:10.1016/j.amjcard.2018.07.005

Cited by 4 publications

(10 citation statements)

References 27 publications

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“…Moreover, miscalibration results from a reduced effect of sex and chest pain typicality and an increased effect of diabetes and dyslipidaemia. 39 The latter has been verified by external validation 32,40,47,52 Results on miscalibration analysis could be considered quite consistent across papers. This finding indicates smaller than expected effects of the diagnostic characteristics, chest pain typicality in particular.…”

Section: Predictive Variables and Discrimination Capabilitymentioning

confidence: 54%

“…The CADC-Clin performances significantly improve with respect to the related CADC-basic. 30,39,40,47 Comparisons of either uDFM or CADC-Clin with the PROMISE history-based score do not lead to a clear evaluation of the advantages of one over the other in terms of AUC, 29,33 while the CONFIRM score proves to be better than the DFM. 42 The substantially steady results of the CONFIRM score on several data-sets are also confirmed on a validation data-set consisting of subjects at the low extreme of traditional cardiovascular risk factor burden.…”

Section: Predictive Variables and Discrimination Capabilitymentioning

confidence: 98%

“…Studies are mainly conducted in North America 30,31,34,40,44,[46][47][48][49][50] or Europe 26,28,29,32,33,37,41,45,52 .…”

Section: Data Synthesis and Presentationmentioning

confidence: 99%

“…The Updated Diamond-Forrester model (uDFM), 25,26,[53][54][55][28][29][30]33,37,41,45,52 and the CAD consortium clinical model (CADC-Clin) [28][29][30]32,33,39,40,47,52,55 are the most assessed models.…”

Section: Data Synthesis and Presentationmentioning

confidence: 99%

“…This finding indicates smaller than expected effects of the diagnostic characteristics, chest pain typicality in particular. 32,40,47 Model calibration can be worse in women compared to men, a situation that also arises from the validation of other models (e.g., DFM 37 ). The CADC-Clin performances significantly improve with respect to the related CADC-basic.…”

Section: Predictive Variables and Discrimination Capabilitymentioning

confidence: 99%

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Validated models for pre-test probability of stable coronary artery disease: a systematic review suggesting how to improve validation procedures

Mincarone

Bodini

Tumolo

et al. 2020

Preprint

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An overuse of invasive and non-invasive anatomical testing for the diagnosis of coronary artery disease (CAD) affects patients’ and healthcare professionals’ safety, and the sustainability of Healthcare Systems. Pre-test probability (PTP) models can be routinely used as gatekeeper for initial patient management. Although with different positions, international organizations clearly underline the need for more information on the various risk factors acting as modifier of the PTP.This systematic review addresses validation of PTP models adopting variables available at the first-line assessment of a suspected stable CAD. A comprehensive search has been done in MEDLINE®, HealthSTAR, and Global Health databases.Nearly all the models considered in the 27 analysed papers include age, sex, and chest pain symptoms. Other common risk factors are smoking, hypertension, diabetes mellitus and dyslipidaemia. Only one model considers genetic profile. Reported AUCs range from 0.51 to 0.81. Relevant heterogeneity sources have been highlighted, such as the sample size, the presence of a PTP cut-off and the adoption of different definitions of CAD which can prevent comparisons of results. Very few papers address a complete validation, making then impossible to understand the reasons why the model does not show a good discrimination capability on a different data set.We consequently recommend a more clear statement of endpoints, their consistent measurement both in the derivation and validation phases, more comprehensive validation analyses and the enhancement of threshold validations of PTP to assess the effects of PTP on clinical management.

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Section: Predictive Variables and Discrimination Capabilitymentioning

confidence: 54%

Section: Predictive Variables and Discrimination Capabilitymentioning

confidence: 98%

“…Studies are mainly conducted in North America 30,31,34,40,44,[46][47][48][49][50] or Europe 26,28,29,32,33,37,41,45,52 .…”

Section: Data Synthesis and Presentationmentioning

confidence: 99%

Section: Data Synthesis and Presentationmentioning

confidence: 99%

Section: Predictive Variables and Discrimination Capabilitymentioning

confidence: 99%

See 3 more Smart Citations

Validated models for pre-test probability of stable coronary artery disease: a systematic review suggesting how to improve validation procedures

Mincarone

Bodini

Tumolo

et al. 2020

Preprint

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Comparing the Modified History, Electrocardiogram, Age, Risk Factors, and Troponin Score and Coronary Artery Disease Consortium Model for Predicting Obstructive Coronary Artery Disease and Cardiovascular Events in Patients With Acute Chest Pain

Teressa

Bhasin

Noack

et al. 2019

Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine

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The objective of this study was to compare the History, Electrocardiogram, Age, Risk factors, and Troponin (HEART) score and clinical coronary artery disease (CAD) consortium (CADC) model for predicting obstructive CAD (≥50% stenosis on coronary computed tomographic angiography) and 30-day major adverse cardiovascular events (MACE, composite of acute myocardial infarction, revascularization, and mortality). We studied 1981 patients with no known CAD who presented with acute chest pain and had negative initial troponin and electrocardiogram. Chest pain was classified as typical, atypical, and nonanginal and used to score the history component of the modified HEART score. The C-statistic for predicting obstructive CAD was 0.747 [95% confidence interval (CI), 0.712–0.783] for the HEART score and 0.792 (95% CI, 0.762–0.823) for the CADC model (P = 0.0005). The C-statistic for predicting 30-day MACE was 0.820 (95% CI, 0.774–0.864) for the HEART score and 0.850 (95% CI, 0.800–0.891) for the CADC model (P = 0.11). Among the 48.3% of patients for whom the CADC model predicted ≤5% probability of obstructive CAD, the observed 30-day MACE was 0.6%; among the 48.9% of patients for whom the HEART score was ≤2, the 30-day MACE was 0.6%. In conclusion, the CADC model was more effective at predicting obstructive CAD compared to the HEART score. The HEART score and CADC model were equally effective to safely identify low-risk patients by achieving <1% missed 30-day MACE.

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Discrimination capability of pretest probability of stable coronary artery disease: a systematic review and meta-analysis suggesting how to improve validation procedures

et al. 2021

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ObjectiveExternally validated pretest probability models for risk stratification of subjects with chest pain and suspected stable coronary artery disease (CAD), determined through invasive coronary angiography or coronary CT angiography, are analysed to characterise the best validation procedures in terms of discriminatory ability, predictive variables and method completeness.DesignSystematic review and meta-analysis.Data sourcesGlobal Health (Ovid), Healthstar (Ovid) and MEDLINE (Ovid) searched on 22 April 2020.Eligibility criteriaWe included studies validating pretest models for the first-line assessment of patients with chest pain and suspected stable CAD. Reasons for exclusion: acute coronary syndrome, unstable chest pain, a history of myocardial infarction or previous revascularisation; models referring to diagnostic procedures different from the usual practices of the first-line assessment; univariable models; lack of quantitative discrimination capability.MethodsEligibility screening and review were performed independently by all the authors. Disagreements were resolved by consensus among all the authors. The quality assessment of studies conforms to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A random effects meta-analysis of area under the receiver operating characteristic curve (AUC) values for each validated model was performed.Results27 studies were included for a total of 15 models. Besides age, sex and symptom typicality, other risk factors are smoking, hypertension, diabetes mellitus and dyslipidaemia. Only one model considers genetic profile. AUC values range from 0.51 to 0.81. Significant heterogeneity (p<0.003) was found in all but two cases (p>0.12). Values of I2 >90% for most analyses and not significant meta-regression results undermined relevant interpretations. A detailed discussion of individual results was then carried out.ConclusionsWe recommend a clearer statement of endpoints, their consistent measurement both in the derivation and validation phases, more comprehensive validation analyses and the enhancement of threshold validations to assess the effects of pretest models on clinical management.PROSPERO registration numberCRD42019139388.

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Validity of Coronary Artery Disease Consortium Models for Predicting Obstructive Coronary Artery Disease & Cardiovascular Events in Patients with Acute Chest Pain Considered for Coronary Computed Tomographic Angiography

Cited by 4 publications

References 27 publications

Validated models for pre-test probability of stable coronary artery disease: a systematic review suggesting how to improve validation procedures

Validated models for pre-test probability of stable coronary artery disease: a systematic review suggesting how to improve validation procedures

Comparing the Modified History, Electrocardiogram, Age, Risk Factors, and Troponin Score and Coronary Artery Disease Consortium Model for Predicting Obstructive Coronary Artery Disease and Cardiovascular Events in Patients With Acute Chest Pain

Discrimination capability of pretest probability of stable coronary artery disease: a systematic review and meta-analysis suggesting how to improve validation procedures

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