Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry

Rosendael, Alexander R. van; Maliakal, Gabriel; Kolli, Kranthi K.; Beecy, Ashley; Al’Aref, Subhi J.; Dwivedi, Aeshita; Singh, Gurpreet; Panday, Mohit; Kumar, Amit; Ma, Xiaoyue; Achenbach, Stephan; Al‐Mallah, Mouaz H.; Andreini, Daniele; Bax, Jeroen J.; Berman, Daniel S.; Budoff, Matthew J.; Cademartiri, Filippo; Callister, Tracy Q.; Chang, Hyuk Jae; Chinnaiyan, Kavitha M.; Chow, Benjamin J.W.; Cury, Ricardo C.; Delago, Augustin; Feuchtner, Gudrun; Hadamitzky, Martin; Hausleiter, Jöerg; Kaufmann, Philipp A.; Kim, Yong Jin; Leipsic, Jonathon; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert; Rubinshtein, Ronen; Shaw, Leslee J.; Villines, Todd C.; Gransar, Heidi; Lu, Yao; Jones, Edward; Peña, Jessica M.; Lin, Fay Y.; Min, James K.

doi:10.1016/j.jcct.2018.04.011

Cited by 151 publications

(73 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From adolescence to adulthood, primary prevention aims to control the magnitude of risk factors, which is also supported by imaging screening, such as carotid ultrasound. Secondary prevention and tertiary prevention plans to perform appropriate preventive interventions aiming to reduce the onset of obstructive CHD and the incidence or recurrence of adverse events are also supported by imaging techniques such as: carotid ultrasound A. assessment of subclinical carotid atherosclerosis of left internal carotid artery (ICA); B. spectral doppler waveforms of ICA; C. FFR-CT; D. CCTA (curved multiplanar reconstruction of a stenosis of 50% characterized by a mixed plaque on left marginal artery); E. CMR first pass perfusion acquisition; F. CMR late gadolinium enhancement acquisition; G. 82 Rb PET/CT Preserved MBF-MFR ( 2); H. Low Dose Gated-CT showing calcified plaque on circumflex coronary artery; I. Off-line Reconstruction. 82 Rb PET: Preserved Perfusion on LDA territory/CCTA: Normal LDA vessel; J. Co-registrated studies.…”

Section: Cardiac Imaging For Chd Diagnosis and Risk Predictionmentioning

confidence: 99%

“…Secondary prevention and tertiary prevention plans to perform appropriate preventive interventions aiming to reduce the onset of obstructive CHD and the incidence or recurrence of adverse events are also supported by imaging techniques such as: carotid ultrasound A. assessment of subclinical carotid atherosclerosis of left internal carotid artery (ICA); B. spectral doppler waveforms of ICA; C. FFR-CT; D. CCTA (curved multiplanar reconstruction of a stenosis of 50% characterized by a mixed plaque on left marginal artery); E. CMR first pass perfusion acquisition; F. CMR late gadolinium enhancement acquisition; G. 82 Rb PET/CT Preserved MBF-MFR ( 2); H. Low Dose Gated-CT showing calcified plaque on circumflex coronary artery; I. Off-line Reconstruction. 82 Rb PET: Preserved Perfusion on LDA territory/CCTA: Normal LDA vessel; J. Co-registrated studies. 82 Rb PET: Transient and chronic ischemia on LDA territory/CCTA: Occlusive stenosis of LDA; K. 82 Rb PET MPI Stressor Dypiridamole: Transient Perfusion Defect on antero-apical myocardial segments; L. Multimodality MPI 82 Rb PET/CT-CAC score showing extensive calcified plaque on LDA; M. Multimodality MPI 82 Rb PET/CT-CAC score showing a CAC score ≥ 400, consistent with ischemia with high risk of future coronary events.…”

Section: Cardiac Imaging For Chd Diagnosis and Risk Predictionmentioning

confidence: 99%

“…82 Rb PET: Preserved Perfusion on LDA territory/CCTA: Normal LDA vessel; J. Co-registrated studies. 82 Rb PET: Transient and chronic ischemia on LDA territory/CCTA: Occlusive stenosis of LDA; K. 82 Rb PET MPI Stressor Dypiridamole: Transient Perfusion Defect on antero-apical myocardial segments; L. Multimodality MPI 82 Rb PET/CT-CAC score showing extensive calcified plaque on LDA; M. Multimodality MPI 82 Rb PET/CT-CAC score showing a CAC score ≥ 400, consistent with ischemia with high risk of future coronary events.…”

Section: Cardiac Imaging For Chd Diagnosis and Risk Predictionmentioning

confidence: 99%

“…Machine and deep learning approaches involve using raw imaging data to extrapolate imaging features and, together with clinical information and big data, are processed into algorithms to build disease models and neural networks in order to improve diagnosis, make clinical decisions, and predict outcomes towards a personalized medical approach 81) . Recent studies on machine learning approaches exploiting CTCA showed high prognostic accuracy in risk stratification and prediction of mortality in CHD patients [82][83][84] . Furthermore, in a recent study, a novel algorithm for T1 mapping and extracellular volume fraction in CMR was tested, providing a better computational efficiency than other well-consolidated methods 85) .…”

Section: Personalized Therapy For Primary Prevention and Secondary Prmentioning

confidence: 99%

See 3 more Smart Citations

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Infante

Viscovo

Rimini

et al. 2020

JAT

View full text Add to dashboard Cite

The official journal of the Japan Atherosclerosis Society and the Asian Pacific Society of Atherosclerosis and Vascular Diseases Review Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies. events 10, 11). In the era of next generation sequencing, the development of a plethora of high-throughput platforms has provided significant impact concerning the knowledge of CHD molecular characterization generating big "omics" data sets 12-14). Furthermore, several non-invasive imaging techniques have been developed over the last few years to detect CHD with the aim to guide optimal patient management, such as coronary computed tomography angiography (CCTA), cardiac magnetic resonance (CMR), and nuclear medicine technique positron emission tomography (PET) 15). These advances have raised challenges Copyright©2020 Japan Atherosclerosis Society This article is distributed under the terms of the latest version of CC BY-NC-SA defined by the Creative Commons Attribution License.

show abstract

Section: Cardiac Imaging For Chd Diagnosis and Risk Predictionmentioning

confidence: 99%

Section: Cardiac Imaging For Chd Diagnosis and Risk Predictionmentioning

confidence: 99%

Section: Cardiac Imaging For Chd Diagnosis and Risk Predictionmentioning

confidence: 99%

Section: Personalized Therapy For Primary Prevention and Secondary Prmentioning

confidence: 99%

See 2 more Smart Citations

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Infante

Viscovo

Rimini

et al. 2020

JAT

View full text Add to dashboard Cite

show abstract

“…In recent years, with the development of machine learning, many studies have deployed multivariate prediction models such as eXtreme Gradient Boosting (XGBoost) models, random forest models and logistic regression models for the research of healthcare problems. 5,6 This study aimed to develop a new risk prediction model based on a machine learning method to predict the mortality of patients on their rst day in the ICU with easy-to-obtain variables.…”

Section: Introductionmentioning

confidence: 99%

A New Risk Model Based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit

Peng

Huang²,

Chen³

et al. 2019

Preprint

View full text Add to dashboard Cite

Background and objectives : Intensive care unit (ICU) resources are inadequate for the large population in China, so it is essential for physicians to evaluate the condition of patients at admission. In this study, our objective was to construct a machine learning risk prediction model for mortality in respiratory intensive care units (RICUs). Methods: This study involved 817 patients who made 1,063 visits and who were admitted to the RICU from January 1, 2012, to December 31, 2017. Potential predictors such as demographic information, laboratory results, vital signs and clinical characteristics were considered. Among the 1,063 visits, the RICU mortality rate was 13.5%. We constructed eXtreme Gradient Boosting (XGBoost) models and compared predictive performances with random forest models, logistic regression models and clinical scores such as Acute Physiology and Chronic Health Evaluation II (APACHE II) and the sequential organ failure assessment (SOFA) system. Results : For this dataset, XGBoost models achieved the best performance with the area under the receiver operating characteristics curve (AUROC) of 0.923 (95% CI: 0.889 – 0.957) in the test set, which was significantly greater than APACHE II (0.811, 95% CI: 0.778 – 0.844) and SOFA (0.805, 95% CI: 0.770 – 0.840). The Hosmer-Lemeshow statistic was 12.667 with a P-value of 0.124, which indicated a good calibration of our predictive model in the test set. The final model contained variables that were previously known to be associated with mortality, but it also included some features absent from the clinical scores. The mean N-terminal pro-B-type natriuretic peptide (NT-proBNP) of survivors was significantly lower than that of the non-survival group (2066.43 pg/mL vs. 8232.81 pg/mL; p<0.001). Conclusions : Our results showed that the XGBoost model could be a suitable model for predicting RICU mortality with easy-to-collect variables at admission and help intensivists improve clinical decision-making for RICU patients. We found that NT-proBNP can be a good indicator of poor prognosis.

show abstract

Radiomic Analysis of Native T₁ Mapping Images Discriminates Between MYH7 and MYBPC3‐Related Hypertrophic Cardiomyopathy

Wang

Yang

et al. 2020

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background: The phenotype via conventional cardiac MRI analysis of MYH7 (β-myosin heavy chain)-and MYBPC3 (β-myosin-binding protein C)-associated hypertrophic cardiomyopathy (HCM) groups is similar. Few studies exist on the genotypic-phenotypic association as assessed by machine learning in HCM patients. Purpose: To explore the phenotypic differences based on radiomics analysis of T 1 mapping images between MYH7 and MYBPC3-associated HCM subgroups. Study Type: Prospective observational study. Subjects: In all, 102 HCM patients with pathogenic, or likely pathogenic mutation, in MYH7 (n = 68) or MYBPC3 (n = 34) genes. Field Strength/Sequence: Cardiac MRI was performed at 3.0T with balanced steady-state free precession (bSSFP), phasesensitive inversion recovery (PSIR) late gadolinium enhancement (LGE), and modified Look-Locker inversion recovery (MOLLI) T 1 mapping sequences. Assessment: All patients underwent next-generation sequencing and Sanger genetic sequencing. Left ventricular native T 1 and LGE were analyzed. One hundred and fifty-seven radiomic features were extracted and modeled using a support vector machine (SVM) combined with principal component analysis (PCA). Each subgroup was randomly split 4:1 (feature selection / test validation). Statistical Tests: Mann-Whitney U-tests and Student's t-tests were performed to assess differences between subgroups. A receiver operating characteristic (ROC) curve was used to assess the model's ability to stratify patients based on radiomic features. Results: There were no significant differences between MYH7and MYBPC3-associated HCM subgroups based on traditional native T 1 values (global, basal, and middle short-axis slice native T 1 ; P = 0.760, 0.914, and 0.178, respectively). However, the SVM model combined with PCA achieved an accuracy and area under the curve (AUC) of 92.0% and 0.968 (95% confidence interval [CI]: 0.968-0.971), respectively. For the test validation dataset, the accuracy and AUC were 85.5% and 0.886 (95% CI: 0.881-0.901), respectively. Data Conclusion: Radiomic analysis of native T 1 mapping images may be able to discriminate between MYH7and MYBPC3-associated HCM patients, exceeding the performance of conventional native T 1 values. Level of Evidence: 3 Technical Efficacy Stage: 2

show abstract

Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry

Cited by 151 publications

References 28 publications

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

A New Risk Model Based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit

Radiomic Analysis of Native T₁ Mapping Images Discriminates Between MYH7 and MYBPC3‐Related Hypertrophic Cardiomyopathy

Contact Info

Product

Resources

About

Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry

Cited by 151 publications

References 28 publications

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease

A New Risk Model Based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit

Radiomic Analysis of Native T1 Mapping Images Discriminates Between MYH7 and MYBPC3‐Related Hypertrophic Cardiomyopathy

Contact Info

Product

Resources

About

Radiomic Analysis of Native T₁ Mapping Images Discriminates Between MYH7 and MYBPC3‐Related Hypertrophic Cardiomyopathy