Non-gated Chest Computed Tomography Research Articles

Coronary calcifications as assessed on routine non-gated chest CT; a gatekeeper to tailor downstream additional imaging in patients with stable chest pain

Background and aimsCurrently applied methods for risk-assessment in coronary artery disease (CAD) often overestimate patients’ risk for obstructive CAD. To enhance risk estimation, assessment of coronary artery calcium (CAC) can be applied. In 10 % of patients presenting with stable chest pain a previous non-gated computed tomography (CT) has been performed, suitable for CAC-assessment. This study is the first to investigate the clinical utility of CAC-assessment on non-gated CT for risk-assessment of obstructive CAD in symptomatic patients. MethodsFor this analysis, all patients referred for coronary computed tomography angiography (CCTA), in whom a previous non-gated chest CT was performed were included. The extent of CAC was assessed on chest CT and ordinally scored. CAD was assessed on CCTA and obstructive CAD defined as stenosis of ≥70 %. Patients were stratified according to CAC-severity and percentages of patients with obstructive CAD were compared between the CAC groups. ResultsIn total, 170 patients of 32–88 years were included and 35 % were male. The percentage of obstructive CAD between the CAC groups differed significantly (p < 0.01). A calcium score of 0 ruled out obstructive CAD irrespective of sex, pre-test probability, type of complaints and number of risk factors with a 100 % certainty. Furthermore, a mild CAC score ruled out obstructive CAD in patients with low – intermediate PTP or non-anginal complaints with 100 % certainty. ConclusionWhen available, CAC on non-gated chest CT can accurately rule out obstructive CAD and can therefore function as a radiation-free and cost-free gatekeeper for additional imaging in patients presenting with stable chest pain.

Epicardial adipose tissue (EAT) thickness on non-gated chest CT as an alternative to EAT volume on cardiac CT.

Epicardial adipose tissue (EAT) volume is usually measured with ECG-gated computed tomography (CT). Measurement of EAT thickness is a more convenient method; however, it is not clear whether EAT thickness measured with non-gated CT is reliable and at which localization it agrees best with the EAT volume. To examine the agreement between ECG-gated EAT volume and non-gated EAT thickness measured from various localizations and to assess the predictive role of EAT thickness for high EAT volume. EAT thickness was measured at six locations using non-contrast thorax CT and EAT volume was measured using ECG-gated cardiac CT (n = 68). The correlation and agreement (Bland-Altman plots) between the thicknesses and EAT volume were assessed. EAT thicknesses were significantly correlated with EAT volume (P < 0.001). The highest correlation (r = 0.860) and agreement were observed for the thickness adjacent to the right ventricular free wall. Also, EAT thickness at this location has a strong potential for discriminating high (>125 cm3) EAT volume (area under the ROC curve=0.889, 95% CI=0.801-0.977; P < 0.001). The sensitivity, specificity, and positive and negative predictive values of EAT thickness for high EAT volume were 76.5%, 88.2%, 68.4%, and 91.8%, respectively, for the cutoff value of 5.75 cm; and 47.1%, 100%, 100%, and 85%, respectively, for the cutoff value of 8.10 cm. EAT thickness measured on non-gated chest CT adjacent to the right ventricular free wall is a reliable and easy-to-use alternative to the volumetric quantification and has a strong potential to predict high EAT volume.

Coronary artery calcium assessment on non-gated chest CT to optimize pre-operative cardiac screening in liver transplantation

BackgroundGuidelines recommend standard pre-operative cardiac screening in all liver transplantation (LT) recipients, despite the relatively low prevalence of obstructive coronary artery disease. Most LT recipients often have non-gated computed tomography (CT) performed of the chest and abdomen. This study evaluated the ability of coronary artery calcification (CAC) assessment on consecutively available scans, to identify a selection of low-risk patients, in whom further cardiac imaging can be safely withheld. MethodsLT recipients with prior non-gated CT chest-abdomen were included. CAC was visually scored on a semi-quantitative ordinal scale. Stress myocardial perfusion, coronary CT angiography (CCTA) and invasive coronary angiography (ICA) were used as golden standard. The sensitivity and specificity of CAC to exclude and predict obstructive CAD were assessed. In addition, peri- and postoperative mortality and cardiac events were analyzed. Results149 LT recipients (ranged 31–71 years) were included. In 75% of patients, no CAC and mild CAC could rule out obstructive CAD on CCTA and ICA with 100% certainty. The threshold of mild CAC had a sensitivity of 100% for both CCTA and ICA and a specificity of 91% and 68%, respectively. None of the patients with no or mild calcifications experienced peri- and post-operative cardiac events or died of cardiac causes. ConclusionVisual evaluation of CAC on prior non-gated CT can accurately and safely exclude obstructive CAD in LT recipients. Incorporation of these already available data can optimize cardiac screening, by safely withholding or correctly allocating dedicated cardiac imaging in LT recipients. Thereby, reducing patients' test burden and save health care expenses.

The Clear Value of Coronary Artery Calcification Evaluation on Non-Gated Chest Computed Tomography for Cardiac Risk Stratification.

To enhance risk stratification in patients suspected of coronary artery disease, the assessment of coronary artery calcium (CAC) could be incorporated, especially when CAC can be readily assessed on previously performed non-gated chest computed tomography (CT). Guidelines recommend reporting on patients' extent of CAC on these non-cardiac directed exams and various studies have shown the diagnostic and prognostic value. However, this method is still little applied, and no current consensus exists in clinical practice. This review aims to point out the clinical utility of different kinds of CAC assessment on non-gated CTs. It demonstrates that these scans indeed represent a merely untapped and underestimated resource for risk stratification in patients with stable chest pain or an increased risk of cardiovascular events. To our knowledge, this is the first review to describe the clinical utility of different kinds of visual CAC evaluation on non-gated unenhanced chest CT. Various methods of CAC assessment on non-gated CT are discussed and compared in terms of diagnostic and prognostic value. Furthermore, the application of these non-gated CT scans in the general practice of cardiology is discussed. The clinical utility of coronary calcium assessed on non-gated chest CT, according to the current literature, is evident. This resource of information for cardiac risk stratification needs no specific requirements for scan protocol, and is radiation-free and cost-free. However, some gaps in research remain. In conclusion, the integration of CAC on non-gated chest CT in general cardiology should be promoted and research on this method should be encouraged.

Chest Computed Tomography for Lymphoma Staging: A Wasted Opportunity for Cardiovascular Risk Stratification?

Patients with a lymphoma diagnosis undergo non-gated chest computed tomography (CT) scans as part of cancer diagnosis or staging. Although coronary artery calcification (CAC) is traditionally evaluated on dedicated cardiac CT, CAC can also be detected on standard chest CT. This exploratory study aimed to determine the prognostic value of CAC detected on non-gated chest CT and to report its use on clinical practice. Consecutive patients with a lymphoma diagnosis who performed non-contrasted non-gated chest CT for cancer diagnosis or staging were included and retrospectively evaluated. Coronary artery calcification was evaluated by quantitative (Agatston score) and qualitative (visual) assessment. Fifty-seven patients were included in this study (mean age 61±15 years; 58% male). Coronary artery calcification was identified in 22 patients (39%), most of them with multi-vessel involvement. Coronary artery calcification was qualitatively classified as mild, moderate and severe in 11%, 19% and 9% patients, respectively. This study suggested that moderate or severe CAC was an independent predictor of all-cause mortality (odds ratio 3, 95% confidence interval 2-11; p=0.04) after adjusting for cardiovascular risk factors and lymphoma staging. Regarding quantitative evaluation, a higher CAC score was also associated with higher mortality. While significant CAC was identified in 22 patients, it was only reported in four patients. The preliminary findings of this hypothesis-generating study support the investigation of CAC identified by chest CT for diagnosis/staging of cancer as a risk modifier in the global risk assessment of patients with lymphoma. The unrecognition and underreporting of this finding may represent a wasted opportunity to detect subclinical coronary atherosclerosis in these patients and may help in guiding preventive cardiology care.

Coronary artery calcium assessment on routine non-gated chest CT as a gatekeeper for additional imaging in patients with stable chest pain

Abstract Aims In Coronary Artery Disease (CAD), currently applied risk assessment methods often overestimate patients’ risk of obstructive CAD. To enhance and patient-tailor risk estimation, assessment of Coronary Artery Calcium (CAC) can be applied. In ∼10% of patients presenting with stable chest pain a non-gated chest computed tomography (CT) has been previously performed, suitable for CAC assessment. This study is the first to investigate the clinical value of CAC assessment on non-gated chest CT for risk assessment of obstructive CAD. Purpose This study aims to point out the clinical utility of visual CAC assessment for enhancing risk assessment and avoid unnecessary additional imaging in patients presenting with stable chest pain. Methods For this study, all patients referred for coronary CT angiography (CCTA), in whom a prior non-gated chest CT was performed, were included. To assess the extent of patients’ CAC, an ordinal score was applied. CAD was assessed on CCTA and obstructive CAD was defined as stenosis of ≥70%. Patients were stratified in to groups according to CAC severity, pre-test probability and number of risk factors. Percentages of patients with obstructive CAD were compared between the groups. Results A total of 181 patients of 30-88 years old were included. The percentage of obstructive CAD was significantly different between de CAC groups (p&amp;lt;0.01). The absence of CAC ruled out obstructive CAD with 100% certainty, irrespective of sex, pre-test probability, type of complaint and number of risk factors. Additionally, a mild CAC score ruled out obstructive CAD in patients with low – intermediate PTP or non-anginal complaints, with 100% certainty. Conclusion CAC assessment on non-gated chest CT can safely rule out obstructive CAD in patients presenting with stable chest pain and can therefore function as a radiation-free and cost-free gatekeeper for additional imaging.CCTA results for CAC severityDecisiontree patients with chest pain

Decoding calcific mitral valve disease: a novel deep learning model uncovers the role of calcium burden

Abstract Background Mitral annular calcification (MAC) is a common degenerative disease that affects older adults and causes calcium deposition in the mitral annulus, increasing the risk of mitral valve (MV) dysfunction, cardiovascular events, conduction abnormalities, and mortality. While the primary imaging modality recommended for the clinical evaluation of MAC is echocardiography, it has been reported to be found incidentally in approximately 8% of routine thoracic computed tomography (CT) scans. CT scan is a sensitive imaging modality for assessing MAC, providing the resolution necessary to understand the anatomical involvement of calcification. Research has shown that the MAC burden is associated with disease activity and progression. Still, the exact correlation between the quantitative calcium burden and the degree of severity of mitral valve disease is unclear. Purpose Our study aimed to determine the association between MAC, as assessed on CT, and significant mitral stenosis (MS) or mitral regurgitation (MR), as visualised on echocardiography. In addition, we sought to determine the quantitative MAC cutoffs that would optimally predict significant MS or MR on echocardiography. Methods A retrospective cohort study was conducted at our academic centre. Inclusion criteria were: age≥60 years, resting transthoracic echocardiogram performed between 2013-2022, non-gated chest CT performed within one year before echocardiography, and any degree of MAC documented on the echocardiography report. CT scans were requested for clinical indications unrelated to mitral valve disease. Exclusion criteria were: prior mitral valve intervention, endocarditis, and congenital mitral valve disease. Significant calcific mitral valve disease was defined as ≥moderate MR and ≥mild MS on the echocardiography report, ascertained by expert readers based on multiparametric ASE criteria. MAC volume was quantified on the multi-slice CT DICOM images using a 3D U-Net deep neural network previously trained by our group on an independent cohort. Results The cohort consisted of 1,560 unique patients with a mean age of 79 years and 61% females. The echocardiographic prevalence of significant MR and MS was 10% and 4%, respectively, for 211 affected patients. The CT-based mean MAC volume was 949 mm³ in patients with significant MR or MS, as opposed to 334 mm³ in those without (P&amp;lt;0.001). MAC volume &amp;gt;1000 mm³ was the optimal cutoff to predict significant MS (specificity 90%, sensitivity 51%, area under ROC curve 0.79) and significant MR (specificity 89%, sensitivity 21%, area under ROC curve 0.60). Adjusting for age, sex, and comorbid conditions did not affect the observed association between MAC volume and mitral valvulopathy. Conclusion A novel deep learning model for quantifying MAC volume from non-cardiac clinical CT scans was efficient in screening for calcific mitral valve disease, particularly MS, and identifying patients who may benefit from further evaluation.

Accuracy of incidental visual coronary artery calcium assessment compared with dedicated coronary artery calcium scoring

IntroductionCAC can be detected on routine chest computed tomography (CT) scans and may contribute to CVD risk estimation, but the accuracy of visual CAC scoring may be affected by the specialty of the interpreting radiologist and/or the use of contrast. MethodsThe accuracy of visual CAC estimation on non-gated CT scans was evaluated at UT Southwestern Medical Center (UTSW) and Parkland Health and Hospital System (PHHS). All adults who underwent CAC scanning and a non-gated CT scan within 6 months were identified and the scores from the two CTs were compared overall and stratified by type of reader and whether contrast was used. Visual CAC categories of none, small, moderate, and large were compared to CAC ​= ​0, 1–99, 100–399, and ≥400, respectively. ResultsFrom 2016 to 2021, 934 patients (mean age 60 ​± ​12 ​y, 43% male, 61% White, 34% Black, 24% Hispanic, 54% from PHHS) had both CT scans. Of these, 441 (47%) had no CAC, 278 (30%) small, 147 (16%) moderate, and 66 (7%) large CAC on non-gated CT. Visual CAC estimates were highly correlated with CAC scores (Kendalls tau-b ​= ​0.76, p ​< ​0.0001). Among those with no visual CAC, 76% had CAC ​= ​0 (72% of contrast-enhanced vs 85% of non-contrast scans, 88% of scans interpreted by CT radiologist vs 78% of those interpreted by other radiologist). In those with moderate-to-large visual CAC, 99% had CAC >0 and 88% had CAC ≥100, including 89% of those with contrast, 90% of those without contrast, 80% of those read by a CT radiologist, and 88% of those read by a non-CT radiologist. DiscussionVisual CAC estimates on non-gated CT scans are concordant with Agatston score categories from cardiac CT scans. A lack of visual CAC on non-gated CT scans may not be sufficient to “de-risk” patients, particularly for contrast-enhanced scans and those read by non-CT radiologists. However, the presence of moderate-to-large CAC, including on contrasted scans and regardless of radiologist type, is highly predictive of CAC and may be used to identify high-risk patients for prevention interventions.

Existing Nongated CT Coronary Calcium Predicts Operative Risk in Patients Undergoing Noncardiac Surgeries (ENCORES).

Preoperative cardiovascular risk stratification before noncardiac surgery is a common clinical challenge. Coronary artery calcium scores from ECG-gated chest computed tomography (CT) imaging are associated with perioperative events. At the time of preoperative evaluation, many patients will not have had ECG-gated CT imaging, but will have had nongated chest CT studies performed for a variety of noncardiac indications. We evaluated relationships between coronary calcium severity estimated from previous nongated chest CT imaging and perioperative major clinical events (MCE) after noncardiac surgery. We retrospectively identified consecutive adults age ≥45 years who underwent in-hospital, major noncardiac surgery from 2016 to 2020 at a large academic health system composed of 4 acute care centers. All patients had nongated (contrast or noncontrast) chest CT imaging performed within 1 year before surgery. Coronary calcium in each vessel was retrospectively graded from absent to severe using a 0 to 3 scale (absent, mild, moderate, severe) by physicians blinded to clinical data. The estimated coronary calcium burden (ECCB) was computed as the sum of scores for each coronary artery (0 to 9 scale). A Revised Cardiac Risk Index was calculated for each patient. Perioperative MCE was defined as all-cause death or myocardial infarction within 30 days of surgery. A total of 2554 patients (median age, 68 years; 49.7% women; median Revised Cardiac Risk Index, 1) were included. The median time interval from nongated chest CT imaging to noncardiac surgery was 15 days (interquartile range, 3-106 days). The median ECCB was 1 (interquartile range, 0-3). Perioperative MCE occurred in 136 (5.2%) patients. Higher ECCB values were associated with stepwise increases in perioperative MCE (0: 2.9%, 1-2: 3.7%, 3-5: 8.0%; 6-9: 12.6%, P<0.001). Addition of ECCB to a model with the Revised Cardiac Risk Index improved the C-statistic for MCE (from 0.675 to 0.712, P=0.018), with a net reclassification improvement of 0.428 (95% CI, 0.254-0.601, P<0.0001). An ECCB ≥3 was associated with 2-fold higher adjusted odds of MCE versus an ECCB <3 (adjusted odds ratio, 2.11 [95% CI, 1.42-3.12]). Prevalence and severity of coronary calcium obtained from existing nongated chest CT imaging improve preoperative clinical risk stratification before noncardiac surgery.

Coronary artery calcium identified on non-gated chest CT scans - A wasted opportunity to avoid the tragedy

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Coronary artery calcium (CAC) is an independent predictor of cardiovascular events. While it is traditionally performed utilizing gating with specific acquisition parameters, CAC can be identified in non-gated standard chest computed tomography (CT). This study aimed to assess CAC on chest CTs, evaluating its correlation with coronary lesions on coronary angiography (CAG) and prognosis. Methods We retrospectively reviewed patients (pts) who underwent CAG due to acute coronary syndrome (ACS) who had undergone a prior non-gated non-contrast chest CT. CAC was qualitatively evaluated by visual assessment (mild/moderate/severe) and quantitatively assessed using Agatson score and stratified by terciles. Evaluation was performed by an investigator blinded to CAG report. Results We included 114 pts after reviewing 1000 CAGs: 67% male, mean age 68 years, 78% hypertension, 62% dyslipidemia, 38% chronic kidney disease, 38% diabetes. The mean time difference between CT and CAG was 23 months. CAG was performed due to unstable angina in 33% of pts, NSTEMI in 52% and STEMI in 16%. Significant lesions were found in 57% (69% performed PCI and 17% surgical revascularization). CAC was visual classified as mild, moderate and severe in 31%, 33% and 16% of pts, respectively. Moderate or severe CAC was an independent predictor of significant lesions on CAG [OR 22, 95%CI 8–61, p&amp;lt;0.001] and all-cause mortality [OR 4, 95%CI 2–9, p = 0.001]. Pts with severe CAC had higher peak troponin than those with mild/moderate CAC (1780 vs 315 ng/L, p = 0.024). Quantitative CAC score accurately predicted significant lesions (AUC 0.81, p&amp;lt;0.001; figure 1A), with higher scores in this subgroup (1308 vs 120, p&amp;lt;0.001) and strongly correlated with SYNTAX score (p&amp;lt;0.001). Survival analysis stratified by severity of CAC assessment is shown in figure 1B and 1C. The most severely calcified artery in the CT often matched the culprit vessel of future ACS, with 79%, 60% and 50% concordance for left anterior descending, circumflex, and right coronary artery, respectively. While significant CAC was identified in 80% of CTs, formal reporting was as low as 25%, even with severe CAC, where only 2/18 reports mentioned it. Furthermore, only 62% pts were on statin therapy at the time of CAG. Conclusion CAC evaluation in chest CTs was feasible and strongly associated with the extent/severity of coronary artery disease on CAG, as well as mortality. Notwithstanding, CAC underreporting was frequent and statin therapy underused, suggesting a simple and common opportunity for preventive care.

Opportunistic Screening for Atrial Fibrillation on Routine Chest Computed Tomography.

Quantitative biomarkers from chest computed tomography (CT) can facilitate the incidental detection of important diseases. Atrial fibrillation (AFib) substantially increases the risk for comorbid conditions including stroke. This study investigated the relationship between AFib status and left atrial enlargement (LAE) on CT. A total of 500 consecutive patients who had undergone nongated chest CTs were included, and left atrium maximal axial cross-sectional area (LA-MACSA), left atrium anterior-posterior dimension (LA-AP), and vertebral body cross-sectional area (VB-Area) were measured. Height, weight, age, sex, and diagnosis of AFib were obtained from the medical record. Parametric statistical analyses and receiver operating characteristic curves were performed. Machine learning classifiers were run with clinical risk factors and LA measurements to predict patients with AFib. Eighty-five patients with a diagnosis of AFib were identified. Mean LA-MACSA and LA-AP were significantly larger in patients with AFib than in patients without AFib (28.63 vs. 20.53cm 2 , P <0.000001; 4.34 vs. 3.5cm, P <0.000001, respectively), both with area under the curves (AUCs) of 0.73. Multivariable logistic regression analysis including age, sex, and VB-Area with LA-MACSA improved the AUC for predicting AFib (AUC=0.77). An LA-MACSA threshold of 30cm 2 demonstrated high specificity for AFib diagnosis at 92% and sensitivity of 48%, and LA-AP threshold at 4.5cm demonstrated 90% specificity and 42% sensitivity. A Bayesian machine learning model using age, sex, height, body surface area, and LA-MACSA predicted AFib with an AUC of 0.743. LA-MACSA or LA-AP can be rapidly measured from routine chest CT, and when >30 cm 2 and >4.5cm, respectively, are specific indicators to predict patients at increased risk for AFib.

Abstract 12093: Inter-Reader Reliability of Coronary Calcium Estimation From Non-Gated Chest Computed Tomography Scans Using a Semi-Quantitative Scoring Method

Introduction: Coronary artery calcium (CAC) is strongly associated with cardiovascular risk. CAC burden can be qualitatively estimated from non-gated chest computed tomography (CT) imaging and may be used to predict future cardiovascular outcomes. We evaluated whether resident physician trainees can reliably estimate CAC burden from existing non-gated chest CTs using a semi-quantitative scoring method. Methods: A group of 4 resident physicians without formal training in CT image interpretation underwent a training session to qualitatively estimate CAC burden from non-gated, contrast and non-contrast CT scans. CAC was estimated for each coronary artery as absent, mild (1-24% vessel length), moderate (25-49% vessel length) and severe (&gt;50% vessel length). All Chest CTs were reviewed by two physicians, each blinded to the interpretations of the other reviewer, to assess (a) the presence or absence of CAC, (b) the presence or absence of moderate-severe CAC, and (c) a semi-quantitative CAC index, at a per-patient level and by vessel. Inter-reader reliability (IRR) was assessed by percent agreement, Cohen's kappa coefficient, and the intraclass correlation coefficient (ICC). Results: Using a semi-quantitative scoring system to estimate an index of overall CAC burden, the ICC was 0.96 (CI 0.94-0.98). The percent agreement for the presence of any CAC was 74% (kappa 0.82) and 89% for moderate-to-severe CAC (kappa 0.85). The IRR stratified for each coronary vessel is shown in Table 1. CAC estimated from both contrast and non-contrast scans was associated with substantial agreement between readers ( Table 1 ). Conclusions: With a single training session, resident physicians can quickly and reliably estimate CAC burden from non-gated chest CT imaging. This may facilitate cardiovascular risk stratification in patients without dedicated cardiac testing. 1b

Abstract 11331: Correlations Between Coronary Artery Calcium From Non-Gated Computed Tomography and Conventional Estimates of Peri-Operative Risk by Age and Sex

Introduction: Perioperative risk estimation prior to non-cardiac surgery is a common clinical challenge. Coronary artery calcification (CAC) is more common in men and older individuals. We sought to determine if an index of coronary calcium derived from pre-operative non-gated chest computed tomography (CT) is associated with conventional estimates of peri-operative risk in subgroups by age and sex. Methods: We retrospectively identified men and women age ≥45 years who had undergone non-cardiac surgery at a large urban health system between 2016 and 2020 and had a non-gated chest CT within 1 year prior to surgery. Coronary calcium in each coronary artery (LAD, LCX, RCA) was graded as absent, mild, moderate, or severe with a corresponding numeric score (range: 0-3). A novel coronary calcium index (CCI) was determined as the sum of these scores. Correlations between the CCI and Revised Cardiac Risk Index (RCRI), a conventional measure of perioperative risk, were assessed in subgroups by age and sex using Spearman's correlation coefficient (𝜌;). Results: Among 2,560 patients, 49.8% (1,273) were female and 60.0% (1,538) were ≥65 years old. The CCI exhibited a correlation with RCRI in men (𝜌;: 0.204, p&lt;0.001) and women (𝜌;: 0.200, p&lt;0.001), with no differences by sex (p=0.95). Correlations between the CCI and RCRI were also observed in younger (age 45-64 years; 𝜌;: 0.180, p&lt;0.001) and older (age ≥65 years; 𝜌;: 0.204, p&lt;0.001) adults, with no differences by age group (p=0.53) Conclusion: An index of coronary calcium derived from non-gated chest CT imaging prior to surgery correlated with RCRI among all subgroups by age and sex. Coronary calcium from non-gated imaging represents a potentially promising approach to pre-operative risk evaluation.

Abstract 10982: A Novel Index of Coronary Artery Calcium Derived From Preoperative Non-Gated Chest Computed Tomography Correlates With Traditional Estimates of Perioperative Risk

Introduction: Cardiovascular events are feared complications of non-cardiac surgery. Prior studies demonstrate associations between coronary artery calcium (CAC) from ECG-gated computed tomography (CT) imaging and perioperative major adverse cardiac events. We sought to evaluate correlations between an index of CAC severity from preoperative non-gated CT imaging and traditional estimates of perioperative risk. Methods We identified consecutive adults age ≥45 years who underwent in-hospital, major non-cardiac surgery across a large urban health system between 2016 and 2020 and had non-gated (contrast or non-contrast) chest CT imaging performed within 1 year prior to surgery. Patients with prior coronary revascularization or heart valve surgery were excluded. CAC severity in each coronary artery was assessed and graded from absent to severe using a 0-3 scale by physicians blinded to clinical data. An index of CAC was computed as the sum of the scores for each coronary artery. The Revised Cardiac Risk Index (RCRI) was determined for each patient. Correlations between the CAC index and RCRI were assessed using Spearman Correlation Coefficient (𝜌;). Results A total of 2,560 patients met inclusion criteria - mean age 68.1 ± 12.3 years, 49.8% female, 60% white (Figure 1A). The median pre-operative RCRI score was 1 [IQR 0-1, range 0-6] (Figure 1B). A majority of patients (64.3%) had non-contrast chest CT imaging. The median CAC index was 1 [IQR 0-3, range 0-9] (Figure 1C). A correlation between the CAC index and RCRI (Coefficient (𝜌;): 0.209, p&lt;0.001) was observed, with stepwise increases in the CAC index when stratified by pre-operative RCRI (Figure 1D). Conclusions A pragmatic, novel index of CAC derived from existing non-gated chest CT imaging correlates with pre-operative RCRI. As the RCRI is a validated preoperative risk stratification tool, this finding demonstrates the potential to leverage pre-existing, non-gated CT imaging to inform preoperative risk assessment.

Incidental coronary calcium in cancer patients treated with anthracycline and/or trastuzumab.

Cancer patients are at increased risk of cardiovascular disease (CVD) after treatment with potentially cardiotoxic treatments. Many cancer patients undergo non-gated chest computed tomography (NCCT) for cancer staging prior to treatment. We aimed to assess whether coronary artery calcification on NCCT predicts CVD risk in cancer patients. Six hundred and three patients (mean age: 61.3 years, 30.8% male) with either breast cancer, lymphoma, or sarcoma were identified retrospectively. Primary endpoint was a major adverse cardiac event (MACE) composite including non-fatal myocardial infarction, new heart failure (HF) diagnosis, HF hospitalization, and cardiac death, with Fine-Gray analysis for non-cardiac death as competing risk. Secondary endpoints included a coronary composite and a HF composite. Coronary artery calcification was present in 194 (32.2%) and clinically reported in 85 (43.8%) patients. At a median follow-up of 5.3 years, 256 (42.5%) patients died of non-cardiac causes. Coronary artery calcification presence or extent was not an independent predictor of MACE [sub-distribution hazards ratio (SHR) 1.28; 0.73-2.27]. Coronary artery calcification extent was a significant predictor of the coronary composite outcome (SHR per two-fold increase 1.14; 1.01-1.28), but not of the HF composite outcome (SHR per two-fold increase 1.04; 0.95-1.14). Coronary artery calcification detected incidentally on NCCT scans in cancer patients is prevalent and often not reported. Coronary artery calcification presence or extent did not independently predict MACE. Coronary artery calcification extent was independently associated with increased risk of CAD events but not HF events.

Automatic coronary artery calcium scoring on routine chest computed tomography (CT): comparison of a deep learning algorithm and a dedicated calcium scoring CT.

The aim of this study was to investigate the reliability and accuracy of automatic coronary artery calcium (CAC) scoring and risk classification in non-gated, non-contrast chest computed tomography (CT) of different slice thicknesses using a deep learning algorithm. This retrospective study was performed at 2 tertiary hospitals. Paired, dedicated calcium-scoring CT scans and non-gated, non-contrast chest CT scans taken within a month from the same patients were included. Chest CT images were grouped according to the slice thickness (group A: 1 mm; group B: 3 mm). For internal scans, the CAC score manually measured on dedicated calcium scoring CT images was used as the gold standard. The deep learning algorithm for group A was trained using 150 chest CT scans and tested using 144 scans, and that for group B was trained using 170 chest CT scans and tested using 144 scans. The intraclass correlation coefficient (ICC) was used to evaluate the correlation between the algorithm and the gold standard. Agreement between the deep learning algorithm, the manual results on chest CT, and the gold standard was determined by Bland-Altman analysis. Cardiac risk categories were compared. External validation was performed on 334 paired scans from a different organization. A total of 608 internal paired scans (1 mm: 294; 3 mm: 314) of 406 individuals and 334 external paired scans (1 mm: 117; 3 mm: 117) of 117 individuals were included in the analysis. The ICCs between the deep learning algorithm and the gold standard were excellent in both group A (0.90; 95% CI: 0.85-0.93) and group B (0.94; 95% CI: 0.92-0.96). The Bland-Altman plots showed good agreement in both groups. For the cardiovascular risk category, the deep learning algorithm accurately classified 71% of cases in group A and 81% of cases in group B. The Kappa values for risk classification were 0.72 in group A and 0.82 in group B. External validation yielded equally good results. The automatic calculation of CAC score and cardiovascular risk stratification on non-gated chest CT using a deep learning algorithm was reliable and accurate on both 1 and 3 mm scans. Chest CT with a slice thickness of 3 mm was slightly more accurate in CAC detection and risk classification.

Nongated Computed Tomography Predicts Perioperative Cardiovascular Risk in Lung Cancer Surgery

BackgroundThe coronary artery calcification score (CACS), a strong predictor of cardiovascular events and mortality, can be assessed by nongated chest computed tomography (CT). The study aimed to determine whether CACS based on nongated CT is predictive of perioperative cardiovascular events during intermediate-risk lung cancer surgery. MethodsIn this retrospective, single-center study, we used nongated CT images to evaluate CACS in 4491 patients with lung cancer who underwent intermediate-risk surgeries. Perioperative cardiovascular events were defined as in-hospital cardiac death, nonfatal myocardial infarction, heart failure, atrial and ventricular arrhythmia with hemodynamic compromise, and complete heart block. Risk factors of perioperative cardiovascular events were identified by multivariate logistic regression analysis. ResultsIn total, 110 inpatients (2.5%) experienced perioperative cardiac events. Coronary calcification was observed on nongated CT in 1070 (23.8%) patients. CACS was significantly associated with the incidence of cardiovascular events and longer hospital stays. According to receiver operating characteristic curve analysis, the CACS cutoff value was set to 1. In the multivariate analysis, CACS ≥1 (odds ratio, 1.75; 95% CI, 1.14-2.68; P = .011) or the number of calcified vessels (odds ratio, 1.23; 95% CI, 1.01-1.50; P = .043), age, forced expiratory volume in 1 second/predicted, operation time, and thoracotomy were predictive of cardiovascular complications. ConclusionsCACS is an independent predictor of severe perioperative cardiovascular risk in patients undergoing intermediate-risk lung cancer surgery. CACS may represent a valuable tool for preoperative risk assessment of these patients.

Accuracy of non-gated low-dose non-contrast chest CT with tin filtration for coronary artery calcium scoring

ObjectiveThe study investigated the accuracy of coronary artery calcium scores (CACS) and the potential for reducing radiation dose using non-gated low-dose non-contrast chest computed tomography (CT) scanning with tin filtration for one-stop screening of the lungs and heart. MethodsA prospective study was conducted,193 Patients received two scans for determining CACS, including an ECG-gated CT at 120 kV (ECG-gated CT), followed by a non-gated low-dose chest CT using 100 kV with tin filtration (non-gated Sn100 kV-LDCT). The Agatston score (AS), risk stratification, and radiation dose were compared between the scan types. ResultsThere was good consistency in the AS from both an ECG-gated CT and a non-gated low-dose chest CT scan, which had a high correlation (r = 0.970). The Kappa value of risk stratification of the two scan types was 0.549. The area under the ROC curve (AUC) of the CACS was used to develop a new risk stratification standard for non-gated Sn100 kV-LDCT evaluation of CACS. In comparison to the CACS measured by ECG-gated CT, non-gated Sn100 kV-LDCT had an AUC of 0.951 and an optimal critical value of 4.6 in the low-risk category. The AUC of low-medium risk was 0.966, and the optimal critical value was 41.2. The AUC of the medium-high risk category was 0.968, and the optimal critical value was 230. The consistency in CACS measured by ECG-gated CT and non-gated Sn100 kV-LDCT had a Kappa value of 0.831. The Effective dose (ED) of non-gated Sn100 kV-LDCT and ECG-gated CT was 0.056 ± 0.017 mSv and 0.685 ± 0.455 mSv, respectively (p < 0.05). ConclusionThe Agatston score of CACS using non-gated low-dose chest CT was accurate, but there was an underestimation in risk stratification. This study developed a new risk stratification standard for non-gated Sn100 kV-LDCT evaluation of CACS, which is in closer agreement with CACS derived from ECG-gated CT scans.

Evaluation of spirometry-gated computed tomography to measure lung volumes in emphysema patients.

IntroductionIn emphysema patient being evaluated for bronchoscopic lung volume reduction (BLVR), accurate measurement of lung volumes is important. Total lung capacity (TLC) and residual volume (RV) are commonly measured by body plethysmography but can also be derived from chest computed tomography (CT). Spirometry-gated CT scanning potentially improves the agreement of CT and body plethysmography. The aim of this study was to compare lung volumes derived from spirometry-gated CT and “breath-hold-coached” CT to the reference standard: body plethysmography.MethodsIn this single-centre retrospective cohort study, emphysema patients being evaluated for BLVR underwent body plethysmography, inspiration (TLC) and expiration (RV) CT scan with spirometer guidance (“gated group”) or with breath-hold-coaching (“non-gated group”). Quantitative analysis was used to calculate lung volumes from the CT.Results200 patients were included in the study (mean±sd age 62±8 years, forced expiratory flow in 1 s 29.2±8.7%, TLC 7.50±1.46 L, RV 4.54±1.07 L). The mean±sd CT-derived TLC was 280±340 mL lower compared to body plethysmography in the gated group (n=100), and 590±430 mL lower for the non-gated group (n=100) (both p<0.001). The mean±sd CT-derived RV was 300±470 mL higher in the gated group and 700±720 mL higher in the non-gated group (both p<0.001). Pearson correlation factors were 0.947 for TLC gated, 0.917 for TLC non-gated, 0.823 for RV gated, 0.693 for RV non-gated, 0.539 for %RV/TLC gated and 0.204 for %RV/TLC non-gated. The differences between the gated and non-gated CT results for TLC and RV were significant for all measurements (p<0.001).ConclusionIn severe COPD patients with emphysema, CT-derived lung volumes are strongly correlated to body plethysmography lung volumes, and especially for RV, more accurate when using spirometry gating.

Performance of artificial intelligence-based coronary artery calcium scoring in non-gated chest CT

ObjectivesTo evaluate the risk category performance of artificial intelligence-based coronary artery calcium score (AI-CACS) software used in non-gated chest computed tomography (CT) on three types of CT machines, considering the manual method as the standard. MethodsA total of 901 patients who underwent both chest CT and electrocardiogram (ECG)-gated non-contrast-enhanced cardiac CT with the same equipment within a 3-month period were enrolled in the study. AI-CACS software was based on a deep learning algorithm and was trained on multi-vendor, multi-scanner, and multi-hospital anonymized data from the chest CT database. The AI-CACS was automatically obtained from chest CT data by the AI-CACS software, while the manual CACS was obtained from cardiac CT data by the manual method. The correlation of the AI-CACS and manual CACS, concordance rate and kappa value of the risk categories determined by the two methods were calculated. The chi-square test was used to evaluate the differences in risk categories among the three types of CT machines from different manufacturers. The risk category performance of the AI-CACS for dichotomous risk categories bounded by 0, 100 and 400 was assessed. ResultsThe correlation of the AI-CACS with the manual CACS was ρ = 0.893 (p < 0.001). The Bland-Altman plot (AI-CACS minus manual CACS) showed a mean difference of −27.2 and 95% limits of agreement of −290.0 to 235.6. The agreement of risk categories for the CACS was kappa (κ) = 0.679 (p < 0.001), and the concordance rate was 80.6%. The risk categories determined by the AI-CACS software on three types of CT machines were not significantly different (p = 0.7543). As dichotomous risk categories bounded by 0, 100 and 400, the accuracy, kappa value, and area under the curve of the AI-CACS were 88.6% vs. 92.9% vs. 97.9%, 0.77 vs. 0.77 vs. 0.83, and 0.885 vs. 0.964 vs. 0.981, respectively. ConclusionsThere was good correlation and agreement between the AI-CACS and manual CACS in terms of the risk category. It is feasible to obtain the CACS using AI software based on non-gated chest CT data in a short time without increasing the radiation dose or economic burden. The AI-CACS software algorithm has good clinical universality and can be applied to CT machines from different manufacturers.