Validation and comparison of four models to calculate pretest probability of obstructive coronary artery disease in a Chinese population: A coronary computed tomographic angiography study

Zhou, Jia; Liu, Yujie; Huang, Lihong; Tan, Yahang; Li, Xingchen; Zhang, Hong; Ma, Yanhe; Zhang, Ying

doi:10.1016/j.jcct.2017.05.004

Cited by 16 publications

(28 citation statements)

References 36 publications

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“…5,6 However, multiple investigations have shown that such risk assessment models have suboptimal performance in certain cohorts. [23][24][25] Additionally, several models have been validated in more than one external population, with a trend towards lower discriminative ability over the past few years. 26 Indeed, differences in derivation (utilization of various imaging modalities as well as different cut-off values for the definition of obstructive CAD, utilization of imputation methodologies for missing values), model complexity as well as inconsistent external validation often exist, which limit their utilization in routine practice.…”

Section: Discussionmentioning

confidence: 99%

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Al’Aref

Maliakal

Singh

et al. 2019

European Heart Journal

158

101

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Aims Symptom-based pretest probability scores that estimate the likelihood of obstructive coronary artery disease (CAD) in stable chest pain have moderate accuracy. We sought to develop a machine learning (ML) model, utilizing clinical factors and the coronary artery calcium score (CACS), to predict the presence of obstructive CAD on coronary computed tomography angiography (CCTA). Methods and results The study screened 35 281 participants enrolled in the CONFIRM registry, who underwent ≥64 detector row CCTA evaluation because of either suspected or previously established CAD. A boosted ensemble algorithm (XGBoost) was used, with data split into a training set (80%) on which 10-fold cross-validation was done and a test set (20%). Performance was assessed of the (1) ML model (using 25 clinical and demographic features), (2) ML + CACS, (3) CAD consortium clinical score, (4) CAD consortium clinical score + CACS, and (5) updated Diamond-Forrester (UDF) score. The study population comprised of 13 054 patients, of whom 2380 (18.2%) had obstructive CAD (≥50% stenosis). Machine learning with CACS produced the best performance [area under the curve (AUC) of 0.881] compared with ML alone (AUC of 0.773), CAD consortium clinical score (AUC of 0.734), and with CACS (AUC of 0.866) and UDF (AUC of 0.682), P < 0.05 for all comparisons. CACS, age, and gender were the highest ranking features. Conclusion A ML model incorporating clinical features in addition to CACS can accurately estimate the pretest likelihood of obstructive CAD on CCTA. In clinical practice, the utilization of such an approach could improve risk stratification and help guide downstream management.

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Section: Discussionmentioning

confidence: 99%

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Al’Aref

Maliakal

Singh

et al. 2019

European Heart Journal

158

101

View full text Add to dashboard Cite

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“…Full details for the study cohort have been published previously [ 7 ]. This is a retrospective and observational cohort of 5743 patients who underwent CCTA for stable chest pain.…”

Section: Methodsmentioning

confidence: 99%

“…Details of CCS and CCTA scan have been previously described [ 7 ]. CCS was determined using the Agatston method [ 14 ].…”

Section: Methodsmentioning

confidence: 99%

“…Updated Diamond-Forrester method (UDFM), a traditional age, sex and chest pain typicality-based approach to the PTP of obstructive CAD on invasive coronary angiography [ 6 ], is currently recommended by the European Society of Cardiology (ESC) [ 4 ]. However, several studies determined that UDFM seemed to overestimate the PTP of obstructive CAD, especially in low risk populations [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…With modern statistical methods and multicenter data from populations who underwent coronary computed tomographic angiography (CCTA), new models, e.g. CONFIRM score [ 10 ] and Genders extended model (GEM) [ 11 ], were developed and what’s more, the addition of coronary calcium score (CCS) in GEM dramatically improved the estimation of PTP [ 7 , 8 ]. However, neither CONFIRM score nor GEM has been systematacially validated in symptomatic individuals at low extreme of traditional risk factor (RF) burden, for whom the selection of an appropriate diagnostic strategy is important but difficult [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Coronary calcium score improves the estimation for pretest probability of obstructive coronary artery disease and avoids unnecessary testing in individuals at low extreme of traditional risk factor burden: validation and comparison of CONFIRM score and genders extended model

Wang

Liu

Zhou

et al. 2018

BMC Cardiovasc Disord

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BackgroundReliability of models for estimating pretest probability (PTP) of obstructive coronary artery disease (CAD) has not been investigated in individuals at low extreme of traditional risk factor (RF) burden. Thus, we sought to validate and compare CONFIRM score and Genders extended model (GEM) among these individuals.MethodsWe identified symptomatic individuals with 0 or 1 RF who underwent coronary calcium scan and coronary computed tomographic angiography (CCTA). Follow-up clinical data were also recorded. PTP of obstructive CAD for every individual was estimated according to CONFIRM score and GEM, respectively. Area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI) and Hosmer–Lemeshow (H-L) test were used to assess the performance of models.ResultsThere were 1201 individuals with 0 RF and 2415 with 1 RF. The AUC for GEM was significantly larger than that for CONFIRM score, no matter in individuals with 0 (0.843 v.s. 0.762, p < 0.0001) or 1 (0.823 v.s. 0.752, p < 0.0001) RF. Compared to CONFIRM score, GEM demonstrated positive IDI (5% in individuals with 0 RF and 8% in individuals with 1 RF), positive NRI (41.50% in individuals with 0 RF and 40.19% in individuals with 1 RF), better prediction of clinical events and less discrepancy between observed and predicted probabilities, resulting in a significant decrease of unnecessary testing, especially in negative individuals.ConclusionIn individuals at low extreme of traditional RF burden of CAD, the addition of coronary calcium score provided a more accurate estimation for PTP and application of GEM instead of CONFIRM score could avoid unnecessary testing.

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Clinical pre-test probability for obstructive coronary artery disease: insights from the European DISCHARGE pilot study

et al. 2020

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Objectives To test the accuracy of clinical pre-test probability (PTP) for prediction of obstructive coronary artery disease (CAD) in a pan-European setting. Methods Patients with suspected CAD and stable chest pain who were clinically referred for invasive coronary angiography (ICA) or computed tomography (CT) were included by clinical sites participating in the pilot study of the European multi-centre DISCHARGE trial. PTP of CAD was determined using the Diamond-Forrester (D+F) prediction model initially introduced in 1979 and the updated D+F model from 2011. Obstructive coronary artery disease (CAD) was defined by one at least 50% diameter coronary stenosis by both CT and ICA. Results In total, 1440 patients (654 female, 786 male) were included at 25 clinical sites from May 2014 until July 2017. Of these patients, 725 underwent CT, while 715 underwent ICA. Both prediction models overestimated the prevalence of obstructive CAD (31.7%, 456 of 1440 patients, PTP: initial D+F 58.9% (28.1–90.6%), updated D+F 47.3% (34.2–59.9%), both p < 0.001), but overestimation of disease prevalence was higher for the initial D+F (p < 0.001). The discriminative ability was higher for the updated D+F 2011 (AUC of 0.73 95% confidence interval [CI] 0.70–0.76 versus AUC of 0.70 CI 0.67–0.73 for the initial D+F; p < 0.001; odds ratio (or) 1.55 CI 1.29–1.86, net reclassification index 0.11 CI 0.05–0.16, p < 0.001). Conclusions Clinical PTP calculation using the initial and updated D+F prediction models relevantly overestimates the actual prevalence of obstructive CAD in patients with stable chest pain clinically referred for ICA and CT suggesting that further refinements to improve clinical decision-making are needed. Trial registration https://www.clinicaltrials.gov/ct2/show/NCT02400229 Key Points • Clinical pre-test probability calculation using the initial and updated D+F model overestimates the prevalence of obstructive CAD identified by ICA and CT. • Overestimation of disease prevalence is higher for the initial D+F compared with the updated D+F. • Diagnostic accuracy of PTP assessment varies strongly between different clinical sites throughout Europe.

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Validation and comparison of four models to calculate pretest probability of obstructive coronary artery disease in a Chinese population: A coronary computed tomographic angiography study

Cited by 16 publications

References 36 publications

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry

Coronary calcium score improves the estimation for pretest probability of obstructive coronary artery disease and avoids unnecessary testing in individuals at low extreme of traditional risk factor burden: validation and comparison of CONFIRM score and genders extended model

Clinical pre-test probability for obstructive coronary artery disease: insights from the European DISCHARGE pilot study

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