Dataset Information

Coronary calcium score and prediction of all-cause mortality in diabetes: the diabetes heart study.

ABSTRACT:

Objective

In diabetes, it remains unclear whether the coronary artery calcium (CAC) score provides additional information about total mortality risk beyond traditional risk factors.

Research design and methods

A total of 1,051 participants, aged 34-86 years, in the Diabetes Heart Study (DHS) were followed for 7.4 years. Subjects were separated into five groups using baseline computed tomography scans and CAC scores (0-9, 10-99, 100-299, 300-999, and ?1,000). Logistic regression was performed adjusting for age, sex, race, smoking, and LDL cholesterol to examine the association between CAC and all-cause mortality. Areas under the curve with and without CAC were compared. Natural splines using continuous measures of CAC were fitted to estimate the relationship between observed CAC and mortality risk.

Results

A total of 17% (178 of 1,051) of participants died during the follow-up. In multivariate analysis, the odds ratios (95% CIs) for all-cause mortality, using CAC 0-9 as the reference group, were CAC 10-99: 1.40 (0.57-3.74); CAC 100-299: 2.87 (1.17-7.77); CAC 300-999: 3.04 (1.32-7.90); and CAC ? 1,000: 6.71 (3.09-16.87). The area under the curve without CAC was 0.68 (95% CI 0.66-0.70), and the area under the curve with CAC was 0.72 (0.70-0.74) (P = 0.0001). Using splines, the estimated risk (95% CI) of mortality for a CAC of 0 was 6.7% (4.6-9.7), and the risk increased nearly linearly, plateauing at CAC ? 1,000 (20.0% [15.7-25.2]).

Conclusions

In diabetes, CAC was shown to be an independent predictor of mortality. Participants with CAC (0-9) were at lower risk (0.9% annual mortality). The risk of mortality increased with increasing levels of CAC, plateauing at approximately CAC ? 1,000 (2.7% annual mortality). More research is warranted to determine the potential utility of CAC scans in diabetes.

SUBMITTER: Agarwal S

PROVIDER: S-EPMC3114476 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Coronary calcium score and prediction of all-cause mortality in diabetes: the diabetes heart study.

Agarwal Subhashish S Morgan Timothy T Herrington David M DM Xu Jianzhao J Cox Amanda J AJ Freedman Barry I BI Carr J Jeffrey JJ Bowden Donald W DW

Diabetes care 20110311 5

<h4>Objective</h4>In diabetes, it remains unclear whether the coronary artery calcium (CAC) score provides additional information about total mortality risk beyond traditional risk factors.<h4>Research design and methods</h4>A total of 1,051 participants, aged 34-86 years, in the Diabetes Heart Study (DHS) were followed for 7.4 years. Subjects were separated into five groups using baseline computed tomography scans and CAC scores (0-9, 10-99, 100-299, 300-999, and ≥1,000). Logistic regression wa ...[more]

PMID: 21398528

Similar Datasets

Project description:Purpose: Many patients with ischemic stroke have concomitant coronary artery disease (CAD). However, it remains unclear which stroke patients should undergo evaluation for asymptomatic CAD, and which screening tools are appropriate. We investigated the role of coronary artery calcium (CAC) score as a screening tool for asymptomatic but severe CAD in acute stroke patients. We determined the selection criteria for CAC screening based on risk factors and cerebral atherosclerosis. Materials and Methods: The present study included consecutive patients with acute stroke who had undergone cerebral angiography and multi-detector computed tomography coronary angiography. Severe CAD was defined as left main artery disease or three-vessel disease. Enrolled patients were randomly assigned to two sets; a set for developing selection criteria and a set for validation. To develop selection criteria, we identified associated factors with severe CAD regarding clinical factors and cerebral atherosclerosis. CAD predictability of selection criteria with the CAC score was calculated. Results: Overall, 2,658 patients were included. Severe CAD was present in 360 patients (13.5%). CAC score was associated with CAD severity (P < 0.001). In the development set (N = 1,860), severe CAD was associated with age >65 years [odds ratio (95% confidence interval), 2.62 (1.93-3.55)], male sex (1.81 [1.33-2.46]), dyslipidemia (1.77 [1.25-2.61]), peripheral artery disease (2.64 [1.37-5.06]) and stenosis in the cervicocephalic branches, including the internal carotid (2.79 [2.06-3.78]) and vertebrobasilar arteries (2.08 [1.57-2.76]). We determined the combination of clinical and arterial factors as the selection criteria for CAC evaluation. The cut-off criterion was two or more elements of the selection criteria. The area under the curve (AUC) of the selection criteria was 0.701. The AUC significantly improved to 0.836 when the CAC score was added (P < 0.001). In the validation set (N = 798), the AUC of the selection criteria only was 0.661, and that of the CAC score was 0.833. The AUC of the selection criteria + CAC score significantly improved to 0.861(P < 0.001). Conclusion: The necessity for CAC evaluation could be determined based on the presence of risk factors and significant stenosis of the cervicocephalic arteries. CAC evaluation may be useful for screening for severe CAD in stroke patients.

Project description:Coronary artery calcium score (CACS) is a reliable predictor for future cardiovascular disease risk. Although deep learning studies using computed tomography (CT) images to predict CACS have been reported, no study has assessed the feasibility of machine learning (ML) algorithms to predict the CACS using clinical variables in a healthy general population. Therefore, we aimed to assess whether ML algorithms other than binary logistic regression (BLR) could predict high CACS in a healthy population with general health examination data. This retrospective observational study included participants who had regular health screening including coronary CT angiography. High CACS was defined by the Agatston score ≥ 100. Univariable and multivariable BLR was performed to assess predictors for high CACS in the entire dataset. When performing ML prediction for high CACS, the dataset was randomly divided into a training and test dataset with a 7:3 ratio. BLR, catboost, and xgboost algorithms with 5-fold cross-validation and grid search technique were used to find the best performing classifier. Performance comparison of each ML algorithm was evaluated with the area under the receiver operating characteristic (AUROC) curve. A total of 2133 participants were included in the final analysis. Mean age and proportion of male sex were 55.4 ± 11.3 years and 1483 (69.5%), respectively. In multivariable BLR analysis, age (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.10-1.15, p < 0.001), male sex (OR, 2.91; 95% CI, 1.57-5.38, p < 0.001), systolic blood pressure (OR, 1.02; 95% CI, 1.00-1.03, p = 0.019), and low-density lipoprotein cholesterol (OR, 1.00; 95% CI, 0.99-1.00, p = 0.047) were significant predictors for high CACS. Performance in predicting high CACS of xgboost was AUROC of 0.823, followed by catboost (0.750) and BLR (0.585). The comparison of AUROC between xgboost and BLR was significant (p for AUROC comparison < 0.001). Xgboost ML algorithm was found to be a more reliable predictor of CACS in healthy participants compared to the BLR algorithm. ML algorithms may be useful for predicting CACS with only laboratory data in healthy participants.

Dataset Information

Coronary calcium score and prediction of all-cause mortality in diabetes: the diabetes heart study.

Objective

Research design and methods

Results

Conclusions

Publications

Coronary calcium score and prediction of all-cause mortality in diabetes: the diabetes heart study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets