Effect of temporal sampling protocols on myocardial blood flow measurements using Rubidium-82 PET

Patient-tailored risk assessment of obstructive coronary artery disease using Rubidium-82 PET-based myocardial flow quantification with visual interpretation

INTRODUCTION

Our aim was to estimate the probability of obstructive CAD (oCAD) for an individual patient as a function of the myocardial flow reserve (MFR) measured with Rubidium-82 (Rb-82) PET in patients with a visually normal or abnormal scan.

MATERIALS AND METHODS

We included 1519 consecutive patients without a prior history of CAD referred for rest-stress Rb-82 PET/CT. All images were visually assessed by two experts and classified as normal or abnormal. We estimated the probability of oCAD for visually normal scans and scans with small (5%-10%) or larger defects (> 10%) as function of MFR. The primary endpoint was oCAD on invasive coronary angiography, when available.

RESULTS

1259 scans were classified as normal, 136 with a small defect and 136 with a larger defect. For the normal scans, the probability of oCAD increased exponentially from 1% to 10% when segmental MFR decreased from 2.1 to 1.3. For scans with small defects, the probability increased from 13% to 40% and for larger defects from 45% to > 70% when segmental MFR decreased from 2.1 to 0.7.

CONCLUSION

Patients with > 10% risk of oCAD can be distinguished from patients with < 10% risk based on visual PET interpretation only. However, there is a strong dependence of MFR on patient's individual risk of oCAD. Hence, combining both visual interpretation and MFR results in a better individual risk assessment which may impact treatment strategy.

Machine learning based model to diagnose obstructive coronary artery disease using calcium scoring, PET imaging, and clinical data

INTRODUCTION

Accurate risk stratification in patients with suspected stable coronary artery disease is essential for choosing an appropriate treatment strategy. Our aim was to develop and validate a machine learning (ML) based model to diagnose obstructive CAD (oCAD).

METHOD

We retrospectively have included 1007 patients without a prior history of CAD who underwent CT-based calcium scoring (CACS) and a Rubidium-82 PET scan. The entire dataset was split 4:1 into a training and test dataset. An ML model was developed on the training set using fivefold stratified cross-validation. The test dataset was used to compare the performance of expert readers to the model. The primary endpoint was oCAD on invasive coronary angiography (ICA).

RESULTS

ROC curve analysis showed an AUC of 0.92 (95% CI 0.90-0.94) for the training dataset and 0.89 (95% CI 0.84-0.93) for the test dataset. The ML model showed no significant differences as compared to the expert readers (p ≥ 0.03) in accuracy (89% vs. 88%), sensitivity (68% vs. 69%), and specificity (92% vs. 90%).

CONCLUSION

The ML model resulted in a similar diagnostic performance as compared to expert readers, and may be deployed as a risk stratification tool for obstructive CAD. This study showed that utilization of ML is promising in the diagnosis of obstructive CAD.

Diagnostic value of regional myocardial flow reserve measurements using Rubidium-82 PET

Purpose

Visual assessment of Rubidium (Rb-82) PET myocardial perfusion images is usually combined with global myocardial flow reserve (MFR) measurements. However, small regional blood flow deficits may go unnoticed. Our aim was to compare the diagnostic value of regional with global MFR in the detection of obstructive coronary artery disease (oCAD).

Methods

We retrospectively included 1519 patients referred for rest and regadenoson-induced stress Rb-82 PET/CT without prior history of oCAD. MFR was determined globally, per vessel territory and per myocardial segment and compared using receiver-operating characteristic analysis. Vessel MFR was defined as the lowest MFR of the coronary territories and segmental MFR as the lowest MFR of the 17-segments. The primary endpoint was oCAD on invasive coronary angiography.

Results

The 148 patients classified as having oCAD had a lower global MFR (median 1.9, interquartile range [1.5–2.4] vs. 2.4 [2.0–2.9]), lower vessel MFR (1.6 [1.2–2.1] vs. 2.2 [1.9–2.6]) and lower segmental MFR (1.3 [ 0.9–1.6] vs. 1.8 [1.5–2.2]) as compared to the non-oCAD patients (p < 0.001). The area under the curve for segmental MFR (0.81) was larger (p ≤ 0.005) than of global MFR (0.74) and vessel MFR (0.78).

Conclusions

The use of regional MFR instead of global MFR is recommended as it improves the diagnostic value of Rb-82 PET in the detection of oCAD.