Detection of Proximal Coronary Artery Disease in the Work-up for Transcatheter Aortic Valve Implantation Using CTA (From the DEPICT CTA Database)

Methods Objectives To assess the diagnostic accuracy and yield of pre-TAVI CTA to detect coronary lesions (≥50% DS and ≥70% DS) in the proximal coronary segments on a per-patient and a per-segment level. The secondary objective was to perform a subgroup analysis of the individual included studies and to assess the influence of atrial fibrillation and heart rate on the diagnostic accuracy.

Literature search and study selection Studies were selected from our previous systematic review and meta-analysis on the diagnostic accuracy of CTA for the detection of significant CAD in TAVI work-up [4]. These studies were supplemented with studies that were retrieved by an updated literature search in OVID MEDLINE (including Epub Ahead of Print, In-Process \& Other Non-Indexed Citations) and OVID EMBASE from January 1, 1946 to October 01, 2019. Studies were excluded if they were not reporting original data of patients who received both pre-procedural multi-detector computed tomography (MDCT) and CAG for the evaluation of CAD in the work-up of TAVI. The authors of 8 studies were approached to jointly perform a meta-analysis of individual patient data. The authors of 4 studies could accommodate the data for a per segment analysis [5-8]. The methodological quality of the included studies was assessed using the modified Quality Assessment of Studies of Diagnostic Accuracy Included in Systematic Reviews-2 criteria (QUADAS-2) [9].

Data collection The data sets included patient characteristics regarding age, sex, body mass index (BMI) and the heart rate during CT-scan. Information about co-morbidities included the presence of diabetes mellitus, atrial fibrillation, hypercholesterolemia, peripheral atrial disease, hypertension, a history of smoking, presence of CAD and a history of PCI or coronary artery bypass grafting. Technical CT scanner information included scanner type, number of detector rows, number of slices, detector width, CT scanner rotation time, scan protocol and settings (tube voltage and tube current), contrast agent type and volume, dose length product, and nitroglycerine use.

The coronary segments were categorized in three categories: lesions <50% DS, lesion ≥50-70% DS or lesions ≥70% DS), according to the 18-segment coronary model of the Society of Cardiovascular Computed Tomography or the American Heart Association modified 15-segment model [10,11]. All studies used a cut-off of ≥50% diameter stenosis to determine the presence of obstructive CAD. Three out of four studies also used a cut-off value of ≥70% DS [5,7,8], which were used for an additional analysis of the diagnostic accuracy to detect ≥70% DS proximal obstructive coronary lesions. If a segment had poor image quality or severe motion artefacts, it was labelled as non-diagnostic.

Statistical analysis Data analysis was performed using the statistical software R version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were presented as means with standard deviations (SD). The distribution of continuous variables was tested with the Shapiro-Wilk test. Categorical variables were presented as frequencies and percentages. The prevalence of proximal lesions of ≥50% DS and of ≥70% DS was computed from the measurements with invasive CAG, because revascularization with PCI is only considered in case of a proximal ≥70% DS lesion.(ref ESC guideline) Diagnostic accuracy of pre-TAVI CTA, as compared to pre-TAVI CAG, was defined as the sensitivity, specificity, positive - (PPV) and negative predictive value (NPV) and was derived from the number of true positives (TP), true negatives (TN), false positives (TP) and false negatives (FN). The analysis of the primary endpoint was performed on a per-patient and per-segment level. Diagnostic accuracy was computed for CTA to detect obstructive proximal coronary lesions of more than 50% DS and also of ≥70% DS lesions in a subgroup of patients were the data was available. All non-diagnostic segments were considered as abnormal tests and were labelled as if there was a lesion. For the per-patient analysis of diagnostic accuracy, patients were regarded as having proximal obstructive lesions if there was a minimum of one non-diagnostic segment. Diagnostic yield was defined as the number of patients that would not need an invasive CAG if pre-TAVI CTA was used as a gatekeeper to rule out obstructive proximal lesions and was derived from the sum of the negatives (TN + FN). For the subgroup analysis, we analysed the diagnostic accuracy in the individual included studies and assessed the diagnostic accuracy in the subgroups with atrial fibrillation and a heart rate <70 and ≥70 b/min. The diagnostic accuracy in the individual studies and the subgroups was assessed on a per-patient level for lesions ≥50% DS.