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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Article
  • Peer Reviewed
UCLA Previously Published Works (2018)
Background Studies have demonstrated that the current US guidelines based on American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations Risk Calculator may underestimate risk of atherosclerotic cardiovascular disease ( CVD ) in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events. Similarly, the guidelines may overestimate risk in low risk populations resulting in unnecessary statin therapy. We used Machine Learning ( ML ) to tackle this problem. Methods and Results We developed a ML Risk Calculator based on Support Vector Machines ( SVM s) using a 13-year follow up data set from MESA (the Multi-Ethnic Study of Atherosclerosis) of 6459 participants who were atherosclerotic CVD-free at baseline. We provided identical input to both risk calculators and compared their performance. We then used the FLEMENGHO study (the Flemish Study of Environment, Genes and Health Outcomes) to validate the model in an external cohort. ACC / AHA Risk Calculator, based on 7.5% 10-year risk threshold, recommended statin to 46.0%. Despite this high proportion, 23.8% of the 480 "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.76, specificity 0.56, and AUC 0.71. In contrast, ML Risk Calculator recommended only 11.4% to take statin, and only 14.4% of "Hard CVD " events occurred in those not recommended statin, resulting in sensitivity 0.86, specificity 0.95, and AUC 0.92. Similar results were found for prediction of "All CVD " events. Conclusions The ML Risk Calculator outperformed the ACC/AHA Risk Calculator by recommending less drug therapy, yet missing fewer events. Additional studies are underway to validate the ML model in other cohorts and to explore its ability in short-term CVD risk prediction.
    Cover page: Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2008)

    This study investigated whether cardiac computed tomography angiography (CTA) can predict all-cause mortality in symptomatic patients.

    Noninvasive coronary angiography is being increasingly performed by CTA to assess for obstructive coronary artery disease (CAD), and minimal outcome data exist for coronary CTA. We have utilized a cohort of symptomatic patients who underwent electron beam tomography to allow for longer follow-up (up to 12 years) than currently available with newer 64-slice multidetector-row computed tomography studies.

    In all, 2,538 consecutive patients who underwent CTA by electron beam tomography (age 59 +/- 14 years, 70% males) without known CAD were studied. Computed tomographic angiography results were categorized as significant CAD (> or =50% luminal narrowing), mild CAD (<50% stenosis), and normal coronary arteries. Multivariable Cox proportional hazards models were developed to predict all-cause mortality. Risk-adjusted models incorporated traditional risk factors for coronary disease and coronary artery calcification (CAC).

    During a mean follow-up of 78 +/- 12 months, the death rate was 3.4% (86 deaths). The CTA-diagnosed CAD was an independent predictor of mortality in a multivariable model adjusted for age, gender, cardiac risk factors, and CAC (p < 0.0001). The addition of CAC to CTA-diagnosed CAD increased the concordance index significantly (0.69 for risk factors, 0.83 for the CTA-diagnosed CAD, and 0.89 for the addition of CAC to CAD, p < 0.0001). Risk-adjusted hazard ratios for CTA-diagnosed CAD were 1.7-, 1.8-, 2.3-, and 2.6-fold for 3-vessel nonobstructive, 1-vessel obstructive, 2-vessel obstructive, and 3-vessel obstructive CAD, respectively (p < 0.0001), when compared with the group who did not have CAD.

    The primary results of our study reveal that the burden of angiographic disease detected by CTA provides both independent and incremental value in predicting all-cause mortality in symptomatic patients independent of age, gender, conventional risk factors, and CAC.

      Cover page: Mortality incidence and the severity of coronary atherosclerosis assessed by computed tomography angiography.
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2024)
      Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) applied to CAC scans can predict non-CHD events, including heart failure, atrial fibrillation, and stroke. We applied AI-enabled automated cardiac chambers volumetry and calcified plaque characterization to CAC scans (AI-CAC) of 5830 asymptomatic individuals (52.2% women, age 61.7 ± 10.2 years) in the multi-ethnic study of atherosclerosis during 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow-up for AI-CAC vs. Agatston score was (0.784 vs. 0.701), (0.771 vs. 0.709), (0.789 vs. 0.712) and (0.816 vs. 0.729) (p < 0.0001 for all), respectively. AI-CAC plaque characteristics, including number, location, density, plus number of vessels, significantly improved CHD prediction in the CAC 1-100 cohort vs. Agatston Score. AI-CAC significantly improved the Agatston score for predicting all CVD events.
        Cover page: Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction
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