Inter-observer Variability of Expert-derived Morphologic Risk Predictors in Aortic Dissection

OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. KEY POINTS: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models

Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) in Uncomplicated Type B Aortic Dissection: Study Design and Rationale

PURPOSE To describe the design and methodological approach of a multicenter, retrospective study to externally validate a clinical and imaging-based model for predicting the risk of late adverse events in patients with initially uncomplicated type B aortic dissection (uTBAD). MATERIALS AND METHODS The Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) is a collaboration between 10 academic aortic centers in North America and Europe. Two centers have previously developed and internally validated a recently developed risk prediction model. Clinical and imaging data from eight ROADMAP centers will be used for external validation. Patients with uTBAD who survived the initial hospitalization between January 1, 2001, and December 31, 2013, with follow-up until 2020, will be retrospectively identified. Clinical and imaging data from the index hospitalization and all follow-up encounters will be collected at each center and transferred to the coordinating center for analysis. Baseline and follow-up CT scans will be evaluated by cardiovascular imaging experts using a standardized technique. RESULTS The primary end point is the occurrence of late adverse events, defined as aneurysm formation (≥6 cm), rapid expansion of the aorta (≥1 cm/y), fatal or nonfatal aortic rupture, new refractory pain, uncontrollable hypertension, and organ or limb malperfusion. The previously derived multivariable model will be externally validated by using Cox proportional hazards regression modeling. CONCLUSION This study will show whether a recent clinical and imaging-based risk prediction model for patients with uTBAD can be generalized to a larger population, which is an important step toward individualized risk stratification and therapy.Keywords: CT Angiography, Vascular, Aorta, Dissection, Outcomes Analysis, Aortic Dissection, MRI, TEVAR© RSNA, 2022See also the commentary by Rajiah in this issue

Effectiveness of late gadolinium enhancement to improve outcomes prediction in patients referred for cardiovascular magnetic resonance after echocardiography.

BackgroundEchocardiography (echo) is a first line test to assess cardiac structure and function. It is not known if cardiovascular magnetic resonance (CMR) with late gadolinium enhancement (LGE) ordered during routine clinical practice in selected patients can add additional prognostic information after routine echo. We assessed whether CMR improves outcomes prediction after contemporaneous echo, which may have implications for efforts to optimize processes of care, assess effectiveness, and allocate limited health care resources.Methods and resultsWe prospectively enrolled 1044 consecutive patients referred for CMR. There were 38 deaths and 3 cardiac transplants over a median follow-up of 1.0 years (IQR 0.4-1.5). We first reproduced previous survival curve strata (presence of LGE and ejection fraction (EF) < 50%) for transplant free survival, to support generalizability of any findings. Then, in a subset (n = 444) with contemporaneous echo (median 3 days apart, IQR 1-9), EF by echo (assessed visually) or CMR were modestly correlated (R(2) = 0.66, p < 0.001), and 30 deaths and 3 transplants occurred over a median follow-up of 0.83 years (IQR 0.29-1.40). CMR EF predicted mortality better than echo EF in univariable Cox models (Integrated Discrimination Improvement (IDI) 0.018, 95% CI 0.008-0.034; Net Reclassification Improvement (NRI) 0.51, 95% CI 0.11-0.85). Finally, LGE further improved prediction beyond EF as determined by hazard ratios, NRI, and IDI in all Cox models predicting mortality or transplant free survival, adjusting for age, gender, wall motion, and EF.ConclusionsAmong those referred for CMR after echocardiography, CMR with LGE further improves risk stratification of individuals at risk for death or death/cardiac transplant

Effectiveness of late gadolinium enhancement to improve outcomes prediction in patients referred for cardiovascular magnetic resonance after echocardiography

BACKGROUND: Echocardiography (echo) is a first line test to assess cardiac structure and function. It is not known if cardiovascular magnetic resonance (CMR) with late gadolinium enhancement (LGE) ordered during routine clinical practice in selected patients can add additional prognostic information after routine echo. We assessed whether CMR improves outcomes prediction after contemporaneous echo, which may have implications for efforts to optimize processes of care, assess effectiveness, and allocate limited health care resources. METHODS AND RESULTS: We prospectively enrolled 1044 consecutive patients referred for CMR. There were 38 deaths and 3 cardiac transplants over a median follow-up of 1.0 years (IQR 0.4-1.5). We first reproduced previous survival curve strata (presence of LGE and ejection fraction (EF) < 50%) for transplant free survival, to support generalizability of any findings. Then, in a subset (n = 444) with contemporaneous echo (median 3 days apart, IQR 1–9), EF by echo (assessed visually) or CMR were modestly correlated (R(2) = 0.66, p < 0.001), and 30 deaths and 3 transplants occurred over a median follow-up of 0.83 years (IQR 0.29-1.40). CMR EF predicted mortality better than echo EF in univariable Cox models (Integrated Discrimination Improvement (IDI) 0.018, 95% CI 0.008-0.034; Net Reclassification Improvement (NRI) 0.51, 95% CI 0.11-0.85). Finally, LGE further improved prediction beyond EF as determined by hazard ratios, NRI, and IDI in all Cox models predicting mortality or transplant free survival, adjusting for age, gender, wall motion, and EF. CONCLUSIONS: Among those referred for CMR after echocardiography, CMR with LGE further improves risk stratification of individuals at risk for death or death/cardiac transplant

Computed tomography aortic valve calcium scoring in patients with aortic stenosis

Background—Computed tomography aortic valve calcium scoring (CT-AVC) holds promise for the assessment of patients with aortic stenosis (AS). We sought to establish the clinical utility of CT-AVC in an international multicenter cohort of patients. Methods and Results—Patients with AS who underwent ECG-gated CT-AVC within 3 months of echocardiography were entered into an international, multicenter, observational registry. Optimal CT-AVC thresholds for diagnosing severe AS were determined in patients with concordant echocardiographic assessments, before being used to arbitrate disease severity in those with discordant measurements. In patients with long-term follow-up, we assessed whether CT-AVC thresholds predicted aortic valve replacement and death. In 918 patients from 8 centers (age, 77±10 years; 60% men; peak velocity, 3.88±0.90 m/s), 708 (77%) patients had concordant echocardiographic assessments, in whom CT-AVC provided excellent discrimination for severe AS (C statistic: women 0.92, men 0.89). Our optimal sex-specific CT-AVC thresholds (women 1377 Agatston unit and men 2062 Agatston unit) were nearly identical to those previously reported (women 1274 Agatston unit and men 2065 Agatston unit). Clinical outcomes were available in 215 patients (follow-up 1029 [126–2251] days). Sexspecific CT-AVC thresholds independently predicted aortic valve replacement and death (hazard ratio, 3.90 [95% confidence interval, 2.19–6.78]; P<0.001) after adjustment for age, sex, peak velocity, and aortic valve area. Among 210 (23%) patients with discordant echocardiographic assessments, there was considerable heterogeneity in CT-AVC scores, which again were an independent predictor of clinical outcomes (hazard ratio, 3.67 [95% confidence interval, 1.39–9.73]; P=0.010). Conclusions—Sex-specific CT-AVC thresholds accurately identify severe AS and provide powerful prognostic information. These findings support their integration into routine clinical practic

Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) in Uncomplicated Type B Aortic Dissection:Study Design and Rationale

PURPOSE: To describe the design and methodological approach of a multicenter, retrospective study to externally validate a clinical and imaging-based model for predicting the risk of late adverse events in patients with initially uncomplicated type B aortic dissection (uTBAD). MATERIALS AND METHODS: The Registry of Aortic Diseases to Model Adverse Events and Progression (ROADMAP) is a collaboration between 10 academic aortic centers in North America and Europe. Two centers have previously developed and internally validated a recently developed risk prediction model. Clinical and imaging data from eight ROADMAP centers will be used for external validation. Patients with uTBAD who survived the initial hospitalization between January 1, 2001, and December 31, 2013, with follow-up until 2020, will be retrospectively identified. Clinical and imaging data from the index hospitalization and all follow-up encounters will be collected at each center and transferred to the coordinating center for analysis. Baseline and follow-up CT scans will be evaluated by cardiovascular imaging experts using a standardized technique. RESULTS: The primary end point is the occurrence of late adverse events, defined as aneurysm formation (≥6 cm), rapid expansion of the aorta (≥1 cm/y), fatal or nonfatal aortic rupture, new refractory pain, uncontrollable hypertension, and organ or limb malperfusion. The previously derived multivariable model will be externally validated by using Cox proportional hazards regression modeling. CONCLUSION: This study will show whether a recent clinical and imaging-based risk prediction model for patients with uTBAD can be generalized to a larger population, which is an important step toward individualized risk stratification and therapy.Keywords: CT Angiography, Vascular, Aorta, Dissection, Outcomes Analysis, Aortic Dissection, MRI, TEVAR© RSNA, 2022See also the commentary by Rajiah in this issue