Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study

Abstract

Background: Body CT scans are frequently performed for a wide variety of clinical indications, but potentially valuable biometric information typically goes unused. We investigated the prognostic ability of automated CT-based body composition biomarkers derived from previously-developed deep-learning and feature-based algorithms for predicting major cardiovascular events and overall survival in an adult screening cohort, compared with clinical parameters.

Methods: Mature and fully-automated CT-based algorithms with pre-defined metrics for quantifying aortic calcification, muscle density, visceral/subcutaneous fat, liver fat, and bone mineral density (BMD) were applied to a generally-healthy asymptomatic outpatient cohort of 9223 adults (mean age, 57.1 years; 5152 women) undergoing abdominal CT for routine colorectal cancer screening. Longitudinal clinical follow-up (median, 8.8 years; IQR, 5.1-11.6 years) documented subsequent major cardiovascular events or death in 19.7% (n=1831). Predictive ability of CT-based biomarkers was compared against the Framingham Risk Score (FRS) and body mass index (BMI).

Findings: Significant differences were observed for all five automated CT-based body composition measures according to adverse events (p<0.001). Univariate 5-year AUROC (with 95% CI) for automated CT-based aortic calcification, muscle density, visceral/subcutaneous fat ratio, liver density, and vertebral density for predicting death were 0.743(0.705-0.780)/0.721(0.683-0.759)/0.661(0.625-0.697)/0.619 (0.582-0.656)/0.646(0.603-0.688), respectively, compared with 0.499(0.454-0.544) for BMI and 0.688(0.650-0.727) for FRS (p<0.05 for aortic calcification vs. FRS and BMI); all trends were similar for 2-year and 10-year ROC analyses. Univariate hazard ratios (with 95% CIs) for highest-risk quartile versus others for these same CT measures were 4.53(3.82-5.37) /3.58(3.02-4.23)/2.28(1.92-2.71)/1.82(1.52-2.17)/2.73(2.31-3.23), compared with 1.36(1.13-1.64) and 2.82(2.36-3.37) for BMI and FRS, respectively. Similar significant trends were observed for cardiovascular events. Multivariate combinations of CT biomarkers further improved prediction over clinical parameters (p<0.05 for AUROCs). For example, by combining aortic calcification, muscle density, and liver density, the 2-year AUROC for predicting overall survival was 0.811 (0.761-0.860).

Interpretation: Fully-automated quantitative tissue biomarkers derived from CT scans can outperform established clinical parameters for pre-symptomatic risk stratification for future serious adverse events, and add opportunistic value to CT scans performed for other indications.

Figure 1A.. Depiction of the fully-automated CT biomarkers tools utilized in this study.

Schematic depiction (A) of the automated process for asses sing fat, muscle, liver, aortic calcium, and bone from original abdominal CT scan data. In practice, the visual tool outputs allow for slice-by-slice quality assurance for automated segmentation results in individual patients. CT biomarkers results were then correlated with subsequent adverse clinical outcomes. Importantly, these automated algorithms were pre-selected based on prior work and applied in a static fashion, without additional “learning” or adjustment.

Figures 1B and 1C.. Case example in an asymptomatic 57-year-old man undergoing CT for colorectal cancer screening.

At the time of CT screening, he had a BMI of 27.3 and FRS of 5% (low risk). However, several CT-based metabolic markers were indicative of underlying disease (B), including a visceral-to-subcutaneous fat ratio of 3.1 (99^th percentile), abdominal aortic Agatston score of 5070 (97^th percentile), and steatotic liver density of 28 HU (97^th percentile). Multivariate Cox model prediction based on these three CT-based results put risk of CV event within 2, 5, and 10 years at 19%, 40%, and 67%, respectively, and of death at 4%, 11%, and 27%, respectively. At longitudinal clinical follow-up, the patient suffered an acute MI three years after this initial CT and died 12 years after CT at the age of 64. Contrast-enhanced CT (C) performed seven months before death for minor trauma was interpreted as negative but does show significant progression of vascular calcification, visceral fat, and hepatic steatosis.

Figure 2.. ROC curves for predicting overall survival

A. ROC curves for the clinical parameters of FRS and BMI, as well as univariate CT measures of aortic calcification and muscle density for predicting death over a 5-year time horizon. Without additional demographic or other input data, both CT parameters outperform the multivariate FRS according to area under the curve (AUC). In general, BMI was a poor predictor of outcomes at all time points in this study. The numbers in parentheses represent 95% confidence intervals). B. Multivariate combination of CT-based biomarkers further improved prediction. In this example, CT-based aortic calcification, muscle density, and liver density for predicting overall survival over 2-year time horizon give an AUC or 0.811 (95% CI, 0.761–0.860).

Figure 3.. Kaplan-Meier time-to-death plots by quartile for clinical parameter and univariate CT biomarkers

Good separation between the “worst” versus other quartiles over time was observed for the automated aortic calcium and muscle density values. Quartile separation was less pronounced for the CT-based fat and liver measures, but each is noticeably better than BMI. The L1 BMD results are not shown but separation of the worst quartile was comparable to muscle assessment.