Systematic review and meta-analysis on coronary calcifications in COVID-19

CT coronary artery calcification score as a prognostic marker in COVID-19

Background

Coronary artery calcification (CA) score has been established as a quantitative imaging biomarker to reflect arteriosclerosis and general vessel status. It is established as an important prognostic factor for coronary heart disease but also for other disease entities. Our aim was to use this imaging marker derived from computed tomography (CT) images to elucidate the prognostic relevance in patients with coronavirus disease 2019 (COVID-19).

Methods

The clinical database was retrospectively screened for patients with COVID-19 between 2020 and 2022. A total of 241 patients (85 female patients, 35.3%) were included into the analysis. CA scoring was performed semiquantitatively on thoracic CT images with the established Weston score.

Results

Overall, 61 patients (25.3%) of the investigated patient sample died. In survivors, the mean CA score was 2.3±3.0 and in non-survivors, it was 4.2±4.1 (P=0.002). In univariable regression analysis, CA was associated with 30-day mortality [odds ratio (OR) =1.15; 95% confidence interval (CI): 1.06-1.25, P<0.001]. These results were confirmed by the multivariable regression analysis adjusted for age and sex, the CA score predicted 30-day mortality (OR =1.28; 95% CI: 1.08-1.4, P=0.002).

Conclusions

CA score is an independent risk factor in COVID-19. As CA scoring can easily be performed by the radiologist, it should be further investigated as an imaging marker in patients with COVID-19 and potentially be translated into clinical routine.

Cardiac Injury in COVID-19: A Systematic Review of Relevant Meta-Analyses

Background

Cardiac injury (CI) is not a rare condition among hospitalized patients with coronavirus disease 2019 (COVID-19). Its prognostic value has been extensively reported through the literature, mainly in the context of observational studies. An impressive number of relevant meta-analyses has been conducted. These meta-analyses present similar and consistent results; yet interesting methodological issues emerge.

Methods

A systematic literature search was conducted aiming to identify all relevant meta-analyses on (i) the incidence, and (ii) the prognostic value of CI among hospitalized patients with COVID-19.

Results

Among 118 articles initially retrieved, 73 fulfilled the inclusion criteria and were included in the systematic review. Various criteria were used for CI definition mainly based on elevated cardiac biomarkers levels. The most frequently used biomarker was troponin. 30 meta-analyses reported the pooled incidence of CI in hospitalized patients with COVID-19 that varies from 5% to 37%. 32 meta-analyses reported on the association of CI with COVID-19 infection severity, with only 6 of them failing to show a statistically significant association. Finally, 46 meta-analyses investigated the association of CI with mortality and showed that patients with COVID-19 with CI had increased risk for worse prognosis. Four meta-analyses reported pooled adjusted hazard ratios for death in patients with COVID-19 and CI vs those without CI ranging from 1.5 to 3.

Conclusions

The impact of CI on the prognosis of hospitalized patients with COVID-19 has gained great interest during the pandemic. Methodological issues such as the inclusion of not peer-reviewed studies, the inclusion of potentially overlapping populations or the inclusion of studies with unadjusted analyses for confounders should be taken into consideration. Despite these limitations, the adverse prognosis of patients with COVID-19 and CI has been consistently demonstrated.

AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients

Purpose

To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification.

Material and Methods

In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled.

In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival.

A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020).

Results

The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766).

Conclusions

AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11547-022-01518-0.

Coronary artery calcification-does it predict the CAD-RADS category?

Purpose

Coronary calcium scores (CCSs) in cardiac-gated computed tomography (CCT) are diagnostic for coronary artery disease (CAD). This study aims to investigate if CCSs can foretell CAD-reporting and data system (CAD-RADS) without performing computed tomography angiography (CTA).

Methods

Profiles of 544 patients were studied who had gone through CCT and CTA; the number of calcified regions of interest (ROIs), the Agatston, area, volume, and mass CCSs were calculated. Among the CAD-RADS categories (1 to 5), the mean values were compared for each CCS separately. A cut-offfor each CCS was declared using ROC curve analysis, more than which could predict significant CAD (CAD-RADS 3 to 5). Also, logistic regression models indicated the most probable CAD-RADS category based on the CCSs. P < 0.05 was considered significant.

Results

Among 53% male and 47% female participants with a mean (SD) age of 62.57 (0.84) years, numbers of calcified ROIs were significantly different between each pair of CAD-RADS categories. While other CCSs did not show a significant difference between CAD-RADS 1 and 2 or 2 and 3. All CCSs were significantly different between the non-significant and significant CAD groups; cut-offs for the number of calcified ROIs, the Agatston, area, volume, and mass scores were 9, 128, 44mm², 111mm³, and 22 mg, respectively. Formulae A and B predicted the most probable CAD-RADS category (accuracy: 79%) and the probability of significant/non-significant CAD (accuracy: 81%), respectively.

Conclusion

CCSs could predict CAD-RADS with an accuracy of 80%. Further studies are needed to introduce more predictive calcium indices.