Heart aging measured with coronary artery calcium scoring and cardiovascular risk assessment algorithms in HIV infected patients.
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ABSTRACT:
SUBMITTER: Raggi P
PROVIDER: S-EPMC5538336 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:BackgroundPrecision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD.MethodsWe analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (Str) and internal validation (Sv) sets [Str (1): Sv (1): 50:50; Str (2): Sv (2): 60:40; Str (3): Sv (3): 70:30; Str (4): Sv (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from Str to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (Mecg), CAC alone (Mcac) and a combination of eRiS and CAC (Mecg+cac) for MACE prediction. A nomogram (Mnom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of Mecg, Mcac, Mecg+cac and Mnom in predicting CV disease-free survival in ASCVD.FindingsOver a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (Str). The Mecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all Sv). The Mecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that Mecg+cac was superior to Mecg (p = 1.8e-10) or Mcac (p < 2.2e-16) alone. The Mnom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, Mnom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables.ConclusionThe use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.
| S-EPMC9580025 | biostudies-literature
Project description:AimsCoronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms.Methods and resultsIn this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years.ConclusionML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease.
| S-EPMC10087019 | biostudies-literature
Project description:BackgroundWe aimed to compare coronary artery calcium scoring (CACS) with computed tomography (CT) with 80 and 120 kVp in a large patient population and to establish whether there is a difference in risk classification between the two scores.MethodsPatients with suspected CAD undergoing MPS were included. All underwent standard CACS assessment with 120-kVp tube voltage and with 80 kVp. Two datasets (low-dose and standard) were generated and compared. Risk classes (0 to 25, 25 to 50, 50 to 75, 75 to 90, and > 90%) were recorded.Results1511 patients were included (793 males, age 69 ± 9.1 years). There was a very good correlation between scores calculated with 120 and 80 kVp (R = 0.94, R2 = 0.88, P < .001), with Bland-Altman limits of agreement of - 563.5 to 871.9 and a bias of - 154.2. The proportion of patients assigned to the < 25% percentile class (P = .03) and with CACS = 0 differed between the two protocols (n = 264 vs 437, P < .001).ConclusionIn a large patient population, despite a good correlation between CACS calculated with standard and low-dose CT, there is a systematic underestimation of CACS with the low-dose protocol. This may have an impact especially on the prognostic value of the calcium score, and the established "power of zero" may no longer be warranted if CACS is assessed with low-dose CT.
| S-EPMC10261226 | biostudies-literature
Project description:BackgroundIn patients with suspected obstructive coronary artery disease (CAD), the risk factor-weighted clinical likelihood (RF-CL) model and the coronary artery calcium score-weighted clinical likelihood (CACS-CL) model improves the identification of obstructive CAD compared with basic pretest probability (PTP) models.ObjectivesThe aim of this study was to assess the prognostic value of the new models.MethodsThe incidences of myocardial infarction and death were stratified according to categories by the RF-CL and CACS-CL and compared with categories by the PTP model. We used cohorts from a Danish register (n = 41,177) and a North American randomized study (n = 3,952). All patients were symptomatic and were referred for diagnostic testing because of clinical indications.ResultsDespite substantial down-reclassification of patients to a likelihood ≤5% of CAD with either the RF-CL (45%) or CACS-CL (60%) models compared with the PTP (18%), the annualized event rates of myocardial infarction and death were low using all 3 models; RF-CL 0.51% (95% CI: 0.46-0.56), CACS-CL 0.48% (95% CI: 0.44-0.56), and PTP 0.37% (95% CI: 0.31-0.44), respectively. Overall, comparison of the predictive power of the 3 models using Harrell's C-statistics demonstrated superiority of the RF-CL (0.64 [95% CI: 0.63-0.65]) and CACS-CL (0.69 [95% CI: 0.67-0.70]) compared with the PTP model (0.61 [95% CI: 0.60-0.62]).ConclusionsThe simple clinical likelihood models that include classical risk factors or risk factors combined with CACS provide improved risk stratification for myocardial infarction and death compared with the standard PTP model. Hence, the optimized RF-CL and CACS-CL models identify 2.5 and 3.3 times more patients, respectively, who may not benefit from further diagnostic testing.
| S-EPMC10466200 | biostudies-literature
Project description:Background and aimsLeft main (LM) coronary artery disease is associated with greater myocardial infarction-related mortality, however, coronary artery calcium (CAC) scoring does not account for disease location. We explored whether LM CAC predicts excess mortality in asymptomatic adults.MethodsCause-specific cardiovascular and all-cause mortality was studied in 28,147 asymptomatic patients with non-zero CAC scores in the CAC Consortium. Multivariate regression was performed to evaluate if the presence and burden of LM CAC predict mortality after adjustment for clinical risk factors and the Agatston CAC score. We further analyzed the per-unit hazard associated with LM CAC in comparison to CAC in other arteries.ResultsThe study population had mean age of 58.3 ± 10 years and CAC score of 301 ± 631. LM CAC was present in 21.7% of the cases. During 312,398 patient-years of follow-up, 1,907 deaths were observed. LM CAC was associated with an increased burden of clinical risk factors and total CAC, and was independently predictive of increased hazard for all-cause (HR 1.2 [1.1, 1.3]) and cardiovascular disease death (HR 1.3 [1.1, 1.5]). The hazard for death increased proportionate to the percentage of CAC localized to the LM. On a per-100 Agatston unit basis, LM CAC was associated with a 6-9% incremental hazard for death beyond knowledge of CAC in other arteries.ConclusionsThe presence and high burden of left main CAC are independently associated with a 20-30% greater hazard for cardiovascular and total mortality in asymptomatic adults, arguing that LM CAC should be routinely noted in CAC score reports when present.
| S-EPMC6599487 | biostudies-literature
Project description:Deep learning methods have demonstrated the ability to perform accurate coronary artery calcium (CAC) scoring. However, these methods require large and representative training data hampering applicability to diverse CT scans showing the heart and the coronary arteries. Training methods that accurately score CAC in cross-domain settings remains challenging. To address this, we present an unsupervised domain adaptation method that learns to perform CAC scoring in coronary CT angiography (CCTA) from non-contrast CT (NCCT). To address the domain shift between NCCT (source) domain and CCTA (target) domain, feature distributions are aligned between two domains using adversarial learning. A CAC scoring convolutional neural network is divided into a feature generator that maps input images to features in the latent space and a classifier that estimates predictions from the extracted features. For adversarial learning, a discriminator is used to distinguish the features between source and target domains. Hence, the feature generator aims to extract features with aligned distributions to fool the discriminator. The network is trained with adversarial loss as the objective function and a classification loss on the source domain as a constraint for adversarial learning. In the experiments, three data sets were used. The network is trained with 1,687 labeled chest NCCT scans from the National Lung Screening Trial. Furthermore, 200 labeled cardiac NCCT scans and 200 unlabeled CCTA scans were used to train the generator and the discriminator for unsupervised domain adaptation. Finally, a data set containing 313 manually labeled CCTA scans was used for testing. Directly applying the CAC scoring network trained on NCCT to CCTA led to a sensitivity of 0.41 and an average false positive volume 140 mm3/scan. The proposed method improved the sensitivity to 0.80 and reduced average false positive volume of 20 mm3/scan. The results indicate that the unsupervised domain adaptation approach enables automatic CAC scoring in contrast enhanced CT while learning from a large and diverse set of CT scans without contrast. This may allow for better utilization of existing annotated data sets and extend the applicability of automatic CAC scoring to contrast-enhanced CT scans without the need for additional manual annotations. The code is publicly available at https://github.com/qurAI-amsterdam/CACscoringUsingDomainAdaptation.
| S-EPMC9510682 | biostudies-literature
Project description:We aimed to determine the proper modified thresholds for detecting and weighting CAC scores at 100 kV through histogram matching in comparison with 120 kV as a standard reference. From the training set (680 participants), modified thresholds at 100 kV were obtained through histogram matching of calcium pixels to 120 kV. From the validation set (213 participants), a standard CAC score at 120 kV, and modified CAC score at 100 kV using modified thresholds were compare through the paired t test and the Bland-Altman plot. Agreement for risk categories (no, minimal, mild, moderate, and severe) was evaluated using kappa statistics. Radiation doses were also compared. For the validation set, there was no significant difference between standard (median, 18.7; IQR, 0.0-207.0) and modified (median, 17.3; IQR, 0.0-220.9) CAC scores (P = 0.689). A small bias was achieved (0.74) with 95% limits of agreement from - 52.35 to 53.83. Agreements for risk categories were excellent (κ = 0.994). The mean dose-length-product of 100-kV scanning (30.1 ± 0.8 mGy * cm) was significantly decreased compared to 120-kV scanning (42.9 ± 0.6 mGy * cm) (P < 0.001). Histogram-derived modified thresholds at 100 kV can enable accurate CAC scoring while reducing radiation exposure.
| S-EPMC8408203 | biostudies-literature
Project description:ObjectivesCoronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.MethodsFully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).ResultsOf 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.ConclusionsThis artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation.Key points• Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.
| S-EPMC9755106 | biostudies-literature
Project description:ASCVD are the leading causes of mortality and morbidity among Globe. Evaluation of patients' comprehensive and personalized risk provides risk management strategies and preventive interventions to achieve gain for patients. Framingham Risk Score (FRS) and Systemic Coronary Risk Evaluation Score (SCORE) are two well studied risk scoring models, however, can miss some (20-35%) of future cardiovascular events. To obtain more accurate risk assessment recalibrating risk models through utilizing novel risk markers have been studied in last 3 decades and both ESC and AHA recommends assessing Family History, hs-CRP, CACS, ABI, and CIMT. Subclinical Cardiovascular Disease (SCVD) has been conceptually developed for investigating gradually progressing asymptomatic development of atherosclerosis and among these novel risk markers it has been well established by literature that CACS having highest improvement in risk assessment. This review study mainly selectively discussing studies with CACS measurement. A CACS = 0 can down-stratify risk of patients otherwise treated or treatment eligible before test and can reduce unnecessary interventions and cost, whereas CACS ≥ 100 is equivalent to statin treatment threshold of ≥ 7.5% risk level otherwise statin ineligible before test. Since inflammation, insulin resistance, oxidative stress, dyslipidemia and ongoing endothelial damage due to hypertension could lead to CAC, ASCVD linked with comorbidities. Recent cohort studies have shown a CACS 100-300 as a sign of increased cancer risk. Physical activity, dietary factors, cigarette use, alcohol consumption, metabolic health, family history of CHD, aging, exposures of neighborhood environment and non-cardiovascular comorbidities can determine CACs changes.
| S-EPMC8604741 | biostudies-literature
Project description:IntroductionCardiovascular disease (CVD) is the leading cause of death in women around the world. Aboriginal and Torres Strait Islander women (Australian Indigenous women) have a high burden of CVD, occurring on average 10-20 years earlier than non-Indigenous women. Traditional risk prediction tools (eg, Framingham) underpredict CVD risk in women and Indigenous people and do not consider female-specific 'risk-enhancers' such as hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM) and premature menopause. A CT coronary artery calcium score ('CT-calcium score') can detect calcified atherosclerotic plaque well before the onset of symptoms, being the single best predictor for future cardiac events. A CT-calcium score may therefore help physicians intensify medical therapy in women with risk-enhancing factors.Methods and analysisThis multisite, single-blind randomised (1:1) controlled trial of 700 women will assess the effectiveness of a CT-calcium score-guided approach on cardiovascular risk factor control and healthy lifestyle adherence, compared with standard care. Women without CVD aged 40-65 (35-65 for Aboriginal and Torres Strait Islander women) at low-intermediate risk on standard risk calculators and with at least one risk-enhancing factor (eg, HDP, GDM, premature menopause) will be recruited. Aboriginal and Torres Strait Islander women will be actively recruited, aiming for ~10% of the sample size. The 6-month coprimary outcomes will be low-density lipoprotein cholesterol and systolic blood pressure. Barriers and enablers will be assessed, and a health economic analysis performed.Ethics and disseminationWestern Sydney Local Health District Research Ethics Committee (HREC 2021/ETH11250) provided ethics approval. Written informed consent will be obtained before randomisation. Consent will be sought for access to individual participant Medicare Benefits Schedule, Pharmaceutical Benefits Scheme claims usage through Medicare Australia and linked Admitted Patient Data Collection. Study results will be disseminated via peer-reviewed publications and presentations at national and international conferences.Trial registration numberACTRN12621001738819p.
| S-EPMC9772643 | biostudies-literature