Martijn J. A. Gondrie

Comparing coronary artery calcium and thoracic aorta calcium for prediction of all-cause mortality and cardiovascular events on low-dose non-gated computed tomography in a high-risk population of heavy smokers.

BACKGROUND Coronary artery calcium (CAC) and thoracic aorta calcium (TAC) can be detected simultaneously on low-dose, non-gated computed tomography (CT) scans. CAC has been shown to predict cardiovascular (CVD) and coronary (CHD) events. A comparable association between TAC and CVD events has yet to be established, but TAC could be a more reproducible alternative to CAC in low-dose, non-gated CT. This study compared CAC and TAC as independent predictors of all-cause mortality and cardiovascular events in a population of heavy smokers using low-dose, non-gated CT. METHODS Within the NELSON study, a population-based lung cancer screening trial, the CT screen group consisted of 7557 heavy smokers aged 50-75 years. Using a case-cohort study design, CAC and TAC scores were calculated in a total of 958 asymptomatic subjects who were followed up for all-cause death, and CVD, CHD and non-cardiac events (stroke, aortic aneurysm, peripheral arterial occlusive disease). We used Cox proportional-hazard regression to compute hazard ratios (HRs) with adjustment for traditional cardiovascular risk factors. RESULTS A close association between the prevalence of TAC and increasing levels of CAC was established (p<0.001). Increasing CAC and TAC risk categories were associated with all-cause mortality (p for trend=0.01 and 0.001, respectively) and CVD events (p for trend <0.001 and 0.03, respectively). Compared with the lowest quartile (reference category), multivariate-adjusted HRs across categories of CAC were higher (all-cause mortality, HR: 9.13 for highest quartile; CVD events, HR: 4.46 for highest quartile) than of TAC scores (HR: 5.45 and HR: 2.25, respectively). However, TAC is associated with non-coronary events (HR: 4.69 for highest quartile, p for trend=0.01) and CAC was not (HR: 3.06 for highest quartile, p for trend=0.40). CONCLUSIONS CAC was found to be a stronger predictor than TAC of all-cause mortality and CVD events in a high-risk population of heavy smokers scored on low-dose, non-gated CT. TAC, however, is stronger associated with non-cardiac events than CAC and could prove to be a preferred marker for these events.

Coronary artery calcium can predict all-cause mortality and cardiovascular events on low-dose ct screening for lung cancer

OBJECTIVE Performing coronary artery calcium (CAC) screening as part of low-dose CT lung cancer screening has been proposed as an efficient strategy to detect people with high cardiovascular risk and improve outcomes of primary prevention. This study aims to investigate whether CAC measured on low-dose CT in a population of former and current heavy smokers is an independent predictor of all-cause mortality and cardiac events. SUBJECTS AND METHODS We used a case-cohort study and included 958 subjects 50 years old or older within the screen group of a randomized controlled lung cancer screening trial. We used Cox proportional-hazard models to compute hazard ratios (HRs) adjusted for traditional cardiovascular risk factors to predict all-cause mortality and cardiovascular events. RESULTS During a median follow-up of 21.5 months, 56 deaths and 127 cardiovascular events occurred. Compared with a CAC score of 0, multivariate-adjusted HRs for all-cause mortality for CAC scores of 1-100, 101-1000, and more than 1000 were 3.00 (95% CI, 0.61-14.93), 6.13 (95% CI, 1.35-27.77), and 10.93 (95% CI, 2.36-50.60), respectively. Multivariate-adjusted HRs for coronary events were 1.38 (95% CI, 0.39-4.90), 3.04 (95% CI, 0.95-9.73), and 7.77 (95% CI, 2.44-24.75), respectively. CONCLUSION This study shows that CAC scoring as part of low-dose CT lung cancer screening can be used as an independent predictor of all-cause mortality and cardiovascular events.

Lung Cancer Screening CT-Based Prediction of Cardiovascular Events

OBJECTIVES The aim of this study was to derivate and validate a prediction model for cardiovascular events based on quantification of coronary and aortic calcium volume in lung cancer screening chest computed tomography (CT). BACKGROUND CT-based lung cancer screening in heavy smokers is a very timely topic. Given that the heavily smoking screening population is also at risk for cardiovascular disease, CT-based screening may provide the opportunity to additionally identify participants at high cardiovascular risk. METHODS Inspiratory screening CT of the chest was obtained in 3,648 screening participants. Next, smoking characteristics, patient demographics, and physician-diagnosed cardiovascular events were collected from 10 years before the screening CT (i.e., cardiovascular history) until 3 years after the screening CT (i.e., follow-up time). Cox proportional hazards analysis was used to derivate and validate a prediction model for cardiovascular risk. Age, smoking status, smoking history, and cardiovascular history, together with automatically quantified coronary and aortic calcium volume from the screening CT, were included as independent predictors. The primary outcome measure was the discriminatory value of the model. RESULTS Incident cardiovascular events occurred in 145 of 1,834 males (derivation cohort) and 118 of 1,725 males and 2 of 89 females (validation cohort). The model showed good discrimination in the validation cohort with a C-statistic of 0.71 (95% confidence interval: 0.67 to 0.76). When high risk was defined as a 3-year risk of 6% and higher, 589 of 1,725 males were regarded as high risk and 72 of 118 of all events were correctly predicted by the model. CONCLUSIONS Quantification of coronary and aortic calcium volumes in lung cancer screening CT images-information that is readily available-can be used to predict cardiovascular risk. Such an approach might prove useful in the reduction of cardiovascular morbidity and mortality and may enhance the cost-effectiveness of CT-based screening in heavy smokers.

Development and validation of a prediction rule for recurrent vascular events based on a cohort study of patients with arterial disease: the SMART risk score

Objectives To enable risk stratification of patients with various types of arterial disease by the development and validation of models for prediction of recurrent vascular event risk based on vascular risk factors, imaging or both. Design Prospective cohort study. Setting University Medical Centre. Patients 5788 patients referred with various clinical manifestations of arterial disease between January 1996 and February 2010. Main outcome measures 788 recurrent vascular events (ie, myocardial infarction, stroke or vascular death) that were observed during 4.7 (IQR 2.3 to 7.7) years’ follow-up. Results Three Cox proportional hazards models for prediction of 10-year recurrent vascular event risk were developed based on age and sex in addition to clinical parameters (model A), carotid ultrasound findings (model B) or both (model C). Clinical parameters were medical history, current smoking, systolic blood pressure and laboratory biomarkers. In a separate part of the dataset, the concordance statistic of model A was 0.68 (95% CI 0.64 to 0.71), compared to 0.64 (0.61 to 0.68) for model B and 0.68 (0.65 to 0.72) for model C. Goodness-of-fit and calibration of model A were adequate, also in separate subgroups of patients having coronary, cerebrovascular, peripheral artery or aneurysmal disease. Model A predicted <20% risk in 59% of patients, 20–30% risk in 19% and >30% risk in 23%. Conclusions Patients at high risk for recurrent vascular events can be identified based on readily available clinical characteristics.

Cardiovascular disease:prediction with ancillary aortic findings on chest CT scans in routine practice

PURPOSE To predict cardiovascular disease (CVD) in a clinical care population by using prevalent subclinical ancillary aortic findings detected on chest computed tomographic (CT) images. MATERIALS AND METHODS The study was approved by the medical ethics committee of the primary participating facility and the institutional review boards of all other participating centers. From a total of 6975 patients who underwent diagnostic contrast material-enhanced chest CT for noncardiovascular indications, a representative sample population of 817 patients plus 347 patients who experienced a cardiovascular event during a mean follow-up period of 17 months were assigned visual scores for ancillary aortic abnormalities--on a scale of 0-8 for calcifications, a scale of 0-4 for plaques, a scale of 0-4 for irregularities, and a scale of 0-1 for elongation. Four Cox proportional hazard models incorporating different sum scores for the aortic abnormalities plus age, sex, and chest CT indication were compared for discrimination and calibration. The prediction model that performed best was chosen and externally validated. RESULTS Each aortic abnormality was highly predictive, and all models performed well (c index range, 0.70-0.72; goodness-of-fit P value range, .45-.76). The prediction model incorporating the sum score for aortic calcifications was chosen owing to its good performance (c index, 0.72; goodness-of-fit P = .47) and its applicability to nonenhanced CT scanning. Validation of this model in an external data set also revealed good performance (c index, 0.71; goodness-of-fit P = .25; sensitivity, 46%; specificity, 76%). CONCLUSION A derived prediction model incorporating ancillary aortic findings detected on routine diagnostic CT images complements established risk scores and may help to identify patients at high risk for CVD. Timely application of preventative measures may ultimately reduce the number or severity of CVD events.

Coronary Artery Calcification Scoring in Low-Dose Ungated CT Screening for Lung Cancer: Interscan Agreement

OBJECTIVE In previous studies detection of coronary artery calcification (CAC) with low-dose ungated MDCT performed for lung cancer screening has been compared with detection with cardiac CT. We evaluated the interscan agreement of CAC scores from two consecutive low-dose ungated MDCT examinations. SUBJECTS AND METHODS The subjects were 584 participants in the screening segment of a lung cancer screening trial who underwent two low-dose ungated MDCT examinations within 4 months (mean, 3.1 +/- 0.6 months) of a baseline CT examination. Agatston score, volume score, and calcium mass score were measured by two observers. Interscan agreement of stratification of participants into four Agatston score risk categories (0, 1-100, 101-400, > 400) was assessed with kappa values. Interscan variability and 95% repeatability limits were calculated for all three calcium measures and compared by repeated measures analysis of variance. RESULTS An Agatston score > 0 was detected in 443 baseline CT examinations (75.8%). Interscan agreement of the four risk categories was good (kappa = 0.67). The Agatston scores were in the same risk category in both examinations in 440 cases (75.3%); 578 participants (99.0%) had scores differing a maximum of one category. Furthermore, mean interscan variability ranged from 61% for calcium volume score to 71% for Agatston score (p < 0.01). A limitation of this study was that no comparison of CAC scores between low-dose ungated CT and the reference standard ECG-gated CT was performed. CONCLUSION Cardiovascular disease risk stratification with low-dose ungated MDCT is feasible and has good interscan agreement of stratification of participants into Agatston score risk categories. High mean interscan variability precludes the use of this technique for monitoring CAC scores for individual patients.

Diagnosis of chronic obstructive pulmonary disease in lung cancer screening Computed Tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening

BackgroundBeyond lung cancer, screening CT contains additional information on other smoking related diseases (e.g. chronic obstructive pulmonary disease, COPD). Since pulmonary function testing is not regularly incorporated in lung cancer screening, imaging biomarkers for COPD are likely to provide important surrogate measures for disease evaluation. Therefore, this study aims to determine the independent diagnostic value of CT emphysema, CT air trapping and CT bronchial wall thickness for COPD in low-dose screening CT scans.MethodsPrebronchodilator spirometry and volumetric inspiratory and expiratory chest CT were obtained on the same day in 1140 male lung cancer screening participants. Emphysema, air trapping and bronchial wall thickness were automatically quantified in the CT scans. Logistic regression analysis was performed to derivate a model to diagnose COPD. The model was internally validated using bootstrapping techniques.ResultsEach of the three CT biomarkers independently contributed diagnostic value for COPD, additional to age, body mass index, smoking history and smoking status. The diagnostic model that included all three CT biomarkers had a sensitivity and specificity of 73.2% and 88.%, respectively. The positive and negative predictive value were 80.2% and 84.2%, respectively. Of all participants, 82.8% was assigned the correct status. The C-statistic was 0.87, and the Net Reclassification Index compared to a model without any CT biomarkers was 44.4%. However, the added value of the expiratory CT data was limited, with an increase in Net Reclassification Index of 4.5% compared to a model with only inspiratory CT data.ConclusionQuantitatively assessed CT emphysema, air trapping and bronchial wall thickness each contain independent diagnostic information for COPD, and these imaging biomarkers might prove useful in the absence of lung function testing and may influence lung cancer screening strategy. Inspiratory CT biomarkers alone may be sufficient to identify patients with COPD in lung cancer screening setting.

Incidental Imaging Findings from Routine Chest CT Used to Identify Subjects at High Risk of Future Cardiovascular Events

PURPOSE To investigate the contribution of incidental findings at chest computed tomography (CT) in the detection of subjects at high risk for cardiovascular disease (CVD) by deriving and validating a CT-based prediction rule. MATERIALS AND METHODS This retrospective study was approved by the ethical review board of the primary participating facility, and informed consent was waived. The derivation cohort comprised 10 410 patients who underwent diagnostic chest CT for noncardiovascular indications. During a mean follow-up of 3.7 years (maximum, 7.0 years), 1148 CVD events (cases) were identified. By using a case-cohort approach, CT scans from the cases and from an approximately 10% random sample of the baseline cohort (n = 1366) were graded visually for several cardiovascular findings. Multivariable Cox proportional hazards analysis with backward elimination technique was used to derive the best-fitting parsimonious prediction model. External validation (discrimination, calibration, and risk stratification) was performed in a separate validation cohort (n = 1653). RESULTS The final model included patient age and sex, CT indication, left anterior descending coronary artery calcifications, mitral valve calcifications, descending aorta calcifications, and cardiac diameter. The model demonstrated good discriminative value, with a C statistic of 0.71 (95% confidence interval: 0.68, 0.74) and a good overall calibration, as assessed in the validation cohort. This imaging-based model allows accurate stratification of individuals into clinically relevant risk categories. CONCLUSION Structured reporting of incidental CT findings can mediate accurate stratification of individuals into clinically relevant risk categories and subsequently allow those at higher risk of future CVD events to be distinguished.

Unrequested Findings on Cardiac Computed Tomography: Looking Beyond the Heart

Objectives To determine the prevalence of clinically relevant unrequested extra-cardiac imaging findings on cardiac Computed Tomography (CT) and explanatory factors thereof. Methods A systematic review of studies drawn from online electronic databases followed by meta-analysis with meta-regression was performed. The prevalence of clinically relevant unrequested findings and potentially explanatory variables were extracted (proportion of smokers, mean age of patients, use of full FOV, proportion of men, years since publication). Results Nineteen radiological studies comprising 12922 patients met the inclusion criteria. The pooled prevalence of clinically relevant unrequested findings was 13% (95% confidence interval 9–18, range: 3–39%). The large differences in prevalence observed were not explained by the predefined (potentially explanatory) variables. Conclusions Clinically relevant extra-cardiac findings are common in patients undergoing routine cardiac CT, and their prevalence differs substantially between studies. These differences may be due to unreported factors such as different definitions of clinical relevance and differences between populations. We present suggestions for basic reporting which may improve the interpretability and comparability of future research.

The PROgnostic Value of unrequested Information in Diagnostic Imaging (PROVIDI) Study: rationale and design

We describe the rationale for a new study examining the prognostic value of unrequested findings in diagnostic imaging. The deployment of more advanced imaging modalities in routine care means that such findings are being detected with increasing frequency. However, as the prognostic significance of many types of unrequested findings is unknown, the optimal response to such findings remains uncertain and in many cases an overly defensive approach is adopted, to the detriment of patient-care. Additionally, novel and promising image findings that are newly available on many routine scans cannot be used to improve patient care until their prognostic value is properly determined. The PROVIDI study seeks to address these issues using an innovative multi-center case-cohort study design. PROVIDI is to consist of a series of studies investigating specific, selected disease entities and clusters. Computed Tomography images from the participating hospitals are reviewed for unrequested findings. Subsequently, this data is pooled with outcome data from a central population registry. Study populations consist of patients with endpoints relevant to the (group of) disease(s) under study along with a random control sample from the cohort. This innovative design allows PROVIDI to evaluate selected unrequested image findings for their true prognostic value in a series of manageable studies. By incorporating unrequested image findings and outcomes data relevant to patients, truly meaningful conclusions about the prognostic value of unrequested and emerging image findings can be reached and used to improve patient-care.