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Published on: July 20, 2022
AI-enabled cardiac volumetry on non-contrast calcium scoring CT for predicting atrial fibrillation and mortality
Jong Eun Lee1, Ji-Hoon Jung2, Hongmin Oh3
1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.
Insights
Artificial intelligence (AI)-enabled cardiac volumetry from coronary calcium scoring CT (CSCT) significantly improves prediction of new atrial fibrillation (AF). However, its added value for predicting all-cause mortality beyond coronary artery calcium (CAC) is limited.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- The prognostic significance of cardiac volumetry derived from non-contrast coronary calcium scoring CT (CSCT) is not well-established.
- This study investigates the utility of AI-enabled cardiac volumetry from CSCT for predicting incident atrial fibrillation (AF) and all-cause mortality.
Purpose of the Study:
- To evaluate if AI-enabled cardiac volumetry from CSCT can improve the prediction of incident AF and all-cause mortality.
- To validate AI-derived volumetric measurements against manual measurements.
Main Methods:
- Analysis of 4402 adults who underwent CSCT.
- A deep-learning model quantified cardiac chamber volumes, LV mass, and CAC.
- Validation of AI measurements against manual measurements using CCC and Spearman correlation.
- Cox regression models assessed associations with incident AF and all-cause mortality, with incremental predictive value evaluated using C-index, IDI, and NRI.
Main Results:
- AI-enabled cardiac volumetry demonstrated excellent agreement with manual CSCT measurements (CCC range, 0.80-0.98).
- Enlarged left atrial (LA) and right atrial (RA) volumes independently predicted incident AF (HRs, 7.77 and 9.61; p < 0.001).
- Enlarged LA volume and increased LV mass were associated with all-cause mortality (HRs, 1.61 and 1.73; p=0.012 and p=0.032, respectively).
- AI volumetry significantly improved AF prediction discrimination (C-index, 0.74 to 0.83; p < 0.001).
- Incremental prognostic value for all-cause mortality beyond CAC and clinical variables was modest and not significant.
Conclusions:
- AI-enabled cardiac volumetry from CSCT substantially enhances the prediction of incident AF.
- The additional prognostic value of AI volumetry for mortality prediction, beyond CAC, is limited.
Background:
The prognostic value of cardiac volumetry derived from non-contrast coronary calcium scoring CT (CSCT) remains uncertain. This study evaluated whether artificial intelligence (AI)-enabled cardiac volumetry from CSCT improves prediction of incident atrial fibrillation (AF) and all-cause mortality.
Methods:
We analyzed 4402 adults (median age, 55.8 years; 68.6% men) who underwent CSCT at two centers between 2007 and 2014. A deep-learning model automatically quantified four cardiac chamber volumes, left ventricular (LV) mass, and CAC. AI-enabled volumetric measurements were validated against human expert-validated manual measurements using concordance correlation coefficients (CCC) and Spearman correlation. Associations with incident AF and all-cause mortality were evaluated using multivariable Cox regression, and incremental predictive value was assessed using Harrell's C-index, integrated discrimination improvement, and net reclassification improvement.
Results:
AI-enabled cardiac volumetry showed excellent agreement with manual CSCT measurements (CCC range, 0.80-0.98). During a median follow-up of 14 years, AF occurred in 102 individuals (2.3%), and all-cause mortality occurred in 299 individuals (6.8%). Enlarged left atrial (LA) and right atrial (RA) volumes independently predicted incident AF (hazard ratios [HRs], 7.77 and 9.61; both p < 0.001). Enlarged LA volume and increased LV mass were independently associated with all-cause mortality (HR, 1.61; p = 0.012 and HR, 1.73; p = 0.032, respectively). AI-enabled cardiac volumetry significantly improved discrimination for AF prediction (C-index, 0.74 to 0.83; p < 0.001), whereas its incremental prognostic value for all-cause mortality beyond CAC and clinical variables was modest and not statistically significant.
Conclusions:
AI-enabled cardiac volumetry from CSCT significantly enhances prediction of incident AF, while its additional value for mortality prediction beyond CAC remains limited.
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