Ultra-High-Resolution Coronary CT Angiography for Assessment of Patients with Severe Coronary Artery Calcification: Initial Experience

PURPOSE

Conventional CT technology yields only modest accuracy of coronary artery stenosis assessment in severely calcified lesions. Reported herein are this study's initial observations on the potential of ultra-high-resolution CT (UHR-CT) for evaluating severely calcified coronary arterial lesions.

MATERIALS AND METHODS

Fifteen patients 45 years of age or older, with history of coronary artery disease, referred for invasive coronary angiography, were prospectively enrolled. Patients underwent UHR-CT within 30 days prior to cardiac catheterization. Image noise levels and diagnostic confidence (level 1-5) using UHR-CT were compared with reconstructed images simulating conventional CT technology. Stenosis assessment for the major coronary arteries and the left main coronary artery with UHR-CT and invasive angiography were compared. Results from clinically driven coronary CT using conventional technology were considered for comparison when available.

RESULTS

Mean patient age was 67 years (range, 53-79 years). Thirteen patients were men, nine had obesity. Radiation dose was 9.3 mSv owing to expanded x-ray exposure to accommodate research software application (70%-99% of R-R cycle). Overall image noise was considerably greater for UHR-CT (50.9 ± 7.8 [standard deviation]) versus conventional CT image reconstruction (19.5 ± 8.3, P < .01), yet diagnostic confidence scores for UHR-CT were high (4.3 ± 0.9). Average calcium score in patients without stents (n = 6) was 1205, and of 86 vessels evaluated, 22 had 70% or greater stenosis depicted with invasive angiography (26%). Stenosis comparison with invasive angiography yielded 86% (19 of 22) sensitivity and 88% (56 of 64) specificity (95% CI: 65%, 97%; and 77%, 95%, respectively).

CONCLUSION

Initial observations suggest UHR-CT may be effective in overcoming the limitation of conventional CT for accurately evaluating coronary artery stenoses in severely calcified vessels.Keywords: CT-Angiography, Coronary Arteries, ArteriosclerosisClinical trial registration no. NCT04272060See also commentary by Shanbhag and Chen in this issue.© RSNA, 2021.

Prediction of Mortality in hospitalized COVID-19 patients in a statewide health network

Importance

A predictive model to automatically identify the earliest determinants of both hospital discharge and mortality in hospitalized COVID-19 patients could be of great assistance to caregivers if the predictive information is generated and made available in the immediate hours following admission.

Objective

To identify the most important predictors of hospital discharge and mortality from measurements at admission for hospitalized COVID-19 patients.

Design

Observational cohort study.

Setting

Electronic records from hospitalized patients.

Participants

Patients admitted between March 3rd and August 24th with COVID-19 in Johns Hopkins Health System hospitals.

Exposures

216 phenotypic variables collected within 48 hours of admission.

Main Outcomes

We used age-stratified (<60 and >=60 years) random survival forests with competing risks to identify the most important predictors of death and discharge. Fine-Gray competing risk regression (FGR) models were then constructed based on the most important RSF-derived covariates.

Results

Of 2212 patients, 1913 were discharged (age 57±19, time-to-discharge 9±11 days) while 279 died (age 75±14, time to death 14±15 days). Patients >= 60 years were nearly 10 times as likely to die within 60 days of admission as those <60. As the pandemic evolved, the rate of hospital discharge increased in both older and younger patients. Incident death and hospital discharge were accurately predicted by measures of respiratory distress, inflammation, infection, renal function, red cell turn over and cardiac stress. FGR models for each of hospital discharge and mortality as outcomes based on these variables performed well in the older (AUC 0·80-0·85 at 60-days) and younger populations (AUC >0·90 at 60-days).

Conclusions and Relevance

We identified markers collected within 2 days of admission that predict hospital discharge and mortality in COVID-19 patients and provide prediction models that may be used to guide patient care. Our proposed model suggests that hospital discharge and mortality can be forecasted with high accuracy based on 8-10 variables at this stage of the COVID-19 pandemic. Our findings also point to several specific pathways that could be the focus of future investigations directed at reducing mortality and expediting hospital discharge among COVID-19 patients. Probability of hospital discharge increased over the course of the pandemic.