Machine learning–based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography
Objectives Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance. Methods This retrospective study enrolled patients who underwent coronary CT ang...
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| Published in | European radiology Vol. 34; no. 9; pp. 5633 - 5643 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1432-1084 0938-7994 1432-1084 |
| DOI | 10.1007/s00330-024-10629-3 |
Cover
| Abstract | Objectives
Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.
Methods
This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke.
Results
Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (
n
= 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years’ follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35–3.79,
p
= 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (
C
-index, 0.67 vs. 0.69;
p
= 0.618).
Conclusion
ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography.
Clinical relevance statement
In patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning–based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis.
Key Points
• The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods.
• We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification.
• The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography. |
|---|---|
| AbstractList | ObjectivesUtilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.MethodsThis retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke.ResultsOverall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years’ follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35–3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618).ConclusionML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography.Clinical relevance statementIn patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning–based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis.Key Points• The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods.• We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification.• The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography. Objectives Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance. Methods This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke. Results Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort ( n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years’ follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35–3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems ( C -index, 0.67 vs. 0.69; p = 0.618). Conclusion ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography. Clinical relevance statement In patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning–based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis. Key Points • The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods. • We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification. • The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography. Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance. This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke. Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years' follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35-3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618). ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography. In patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning-based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis. • The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods. • We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification. • The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography. Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.OBJECTIVESUtilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance.This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke.METHODSThis retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography. Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score ≥ 400). The ML-based CAC (ML-CAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke.Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years' follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35-3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618).RESULTSOverall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years' follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35-3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618).ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography.CONCLUSIONML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography.In patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning-based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis.CLINICAL RELEVANCE STATEMENTIn patients referred for invasive coronary angiography who have not undergone preoperative CT-measured coronary artery calcium scoring, machine learning-based coronary artery calcium score assessment can serve as an alternative for predicting the prognosis.• The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods. • We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification. • The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography.KEY POINTS• The coronary artery calcium (CAC) score, a solid prognostic indicator, can be predicted using non-CT methods. • We developed a machine learning (ML)-CAC model utilising nine clinical variables to predict severe coronary calcification. • The ML-CAC system offers significant prognostic value in patients referred for invasive coronary angiography. |
| Author | Jian, Wen Han, Yingchun Wu, Zheng Dong, Zhujun Shi, Yuchen Du, Jie Shen, Xueqian Zheng, Ze Liu, Jinghua |
| Author_xml | – sequence: 1 givenname: Wen surname: Jian fullname: Jian, Wen organization: Center for Coronary Artery Disease, Beijing Anzhen Hospital of Capital Medical University – sequence: 2 givenname: Zhujun surname: Dong fullname: Dong, Zhujun organization: Beijing Anzhen Hospital of Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases – sequence: 3 givenname: Xueqian surname: Shen fullname: Shen, Xueqian organization: Center for Coronary Artery Disease, Beijing Anzhen Hospital of Capital Medical University – sequence: 4 givenname: Ze surname: Zheng fullname: Zheng, Ze organization: Center for Coronary Artery Disease, Beijing Anzhen Hospital of Capital Medical University – sequence: 5 givenname: Zheng surname: Wu fullname: Wu, Zheng organization: Center for Coronary Artery Disease, Beijing Anzhen Hospital of Capital Medical University – sequence: 6 givenname: Yuchen surname: Shi fullname: Shi, Yuchen organization: Center for Coronary Artery Disease, Beijing Anzhen Hospital of Capital Medical University – sequence: 7 givenname: Yingchun surname: Han fullname: Han, Yingchun organization: Beijing Anzhen Hospital of Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases – sequence: 8 givenname: Jie surname: Du fullname: Du, Jie organization: Beijing Anzhen Hospital of Capital Medical University and Beijing Institute of Heart Lung and Blood Vessel Diseases – sequence: 9 givenname: Jinghua orcidid: 0000-0002-4351-0823 surname: Liu fullname: Liu, Jinghua email: liujinghua@vip.sina.com organization: Center for Coronary Artery Disease, Beijing Anzhen Hospital of Capital Medical University |
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| Keywords | Vascular calcification Coronary angiography Prognosis X-ray computed tomography Machine learning |
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Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary... Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification,... ObjectivesUtilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary... |
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| SubjectTerms | Aged Algorithms Angiography Arteriosclerosis Calcification Calcification (ectopic) Calcium Cardiac Cerebral infarction Computed Tomography Angiography - methods Coronary Angiography - methods Coronary artery Coronary Artery Disease - diagnostic imaging Coronary vessels Diagnostic Radiology Female Humans Imaging Internal Medicine Interventional Radiology Learning algorithms Machine Learning Male Medical imaging Medical prognosis Medicine Medicine & Public Health Middle Aged Myocardial infarction Neuroradiology Predictive Value of Tests Prognosis Radiology Retrospective Studies Support vector machines Test sets Ultrasound Vascular Calcification - diagnostic imaging |
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| Title | Machine learning–based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive coronary angiography |
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