Person:

Cai, Tianrun

Purpose

Coronary Computed Tomography Angiography (CCTA) contrast opacification gradients, or Transluminal Attenuation Gradients (TAG) offer incremental value to predict functionally significant lesions. This study introduces and evaluates an automated gradients software package that can potentially supplant current, labor-intensive manual TAG calculation methods.

Methods

All 60 major coronary arteries in 20 patients who underwent a clinically indicated single heart beat 320×0.5 mm detector row CCTA were retrospectively evaluated by two readers using a previously validated manual measurement approach and two additional readers who used the new automated gradient software. Accuracy of the automated method against the manual measurements, considered the reference standard, was assessed via linear regression and Bland-Altman analyses. Inter- and intra-observer reproducibility and factors that can affect accuracy or reproducibility of both manual and automated TAG measurements, including CAD severity and iterative reconstruction, were also assessed.

Results

Analysis time was reduced by 68% when compared to manual TAG measurement. There was excellent correlation between automated TAG and the reference standard manual TAG. Bland-Altman analyses indicated low mean differences (1 HU/cm) and narrower inter- and intra-observer limits of agreement for automated compared to manual measurements (25% and 36% reduction with automated software, respectively). Among patient and technical factors assessed, none affected agreement of manual and automated TAG measurement.

Conclusion

Automated 320×0.5 mm detector row gradient software reduces computation time by 68% with high accuracy and reproducibility.

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping using EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures reducing the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 days if all data are available; however, the timing is largely dependent on the chart review step which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes/no).