Rethinking False Positive Exercise Electrocardiographic Stress Tests by Assessing Coronary Microvascular Function.

BACKGROUND: Exercise electrocardiographic stress testing (EST) has historically been validated against the demonstration of obstructive coronary artery disease. However, myocardial ischemia can occur because of coronary microvascular dysfunction (CMD) in the absence of obstructive coronary artery disease. OBJECTIVES: The aim of this study was to assess the specificity of EST to detect an ischemic substrate against the reference standard of coronary endothelium-independent and endothelium-dependent microvascular function in patients with angina with nonobstructive coronary arteries (ANOCA). METHODS: Patients with ANOCA underwent invasive coronary physiological assessment using adenosine and acetylcholine. CMD was defined as impaired endothelium-independent and/or endothelium-dependent function. EST was performed using a standard Bruce treadmill protocol, with ischemia defined as the appearance of ≥0.1-mV ST-segment depression 80 ms from the J-point on electrocardiography. The study was powered to detect specificity of ≥91%. RESULTS: A total of 102 patients were enrolled (65% women, mean age 60 ± 8 years). Thirty-two patients developed ischemia (ischemic group) during EST, whereas 70 patients did not (nonischemic group); both groups were phenotypically similar. Ischemia during EST was 100% specific for CMD. Acetylcholine flow reserve was the strongest predictor of ischemia during exercise. Using endothelium-independent and endothelium-dependent microvascular dysfunction as the reference standard, the false positive rate of EST dropped to 0%. CONCLUSIONS: In patients with ANOCA, ischemia on EST was highly specific of an underlying ischemic substrate. These findings challenge the traditional belief that EST has a high false positive rate.

ChaMP-CMD: A Phenotype-Blinded, Randomized Controlled, Cross-Over Trial.

BACKGROUND: Angina with nonobstructive coronary arteries is a common condition for which no effective treatment has been established. We hypothesized that the measurement of coronary flow reserve (CFR) allows identification of patients with angina with nonobstructive coronary arteries who would benefit from anti-ischemic therapy. METHODS: Patients with angina with nonobstructive coronary arteries underwent blinded invasive CFR measurement and were randomly assigned to receive 4 weeks of amlodipine or ranolazine. After a 1-week washout, they crossed over to the other drug for 4 weeks; final assessment was after the cessation of study medication for another 4 weeks. The primary outcome was change in treadmill exercise time, and the secondary outcome was change in Seattle Angina Questionnaire summary score in response to anti-ischemic therapy. Analysis was on a per protocol basis according to the following classification: coronary microvascular disease (CMD group) if CFR<2.5 and reference group if CFR≥2.5. The study protocol was registered before the first patient was enrolled (International Standard Randomised Controlled Trial Number: ISRCTN94728379). RESULTS: Eighty-seven patients (61±8 years of age; 62% women) underwent random assignment (57 CMD group and 30 reference group). Baseline exercise time and Seattle Angina Questionnaire summary scores were similar between groups. The CMD group had a greater increment (delta) in exercise time than the reference group in response to both amlodipine (difference in delta, 82 s [95% CI, 37-126 s]; P<0.001) and ranolazine (difference in delta, 68 s [95% CI, 21-115 s]; P=0.005). The CMD group reported a greater increment (delta) in Seattle Angina Questionnaire summary score than the reference group in response to ranolazine (difference in delta, 7 points [95% CI, 0-15]; P=0.048), but not to amlodipine (difference in delta, 2 points [95% CI, -5 to 8]; P=0.549). CONCLUSIONS: Among phenotypically similar patients with angina with nonobstructive coronary arteries, only those with an impaired CFR derive benefit from anti-ischemic therapy. These findings support measurement of CFR to diagnose and guide management of this otherwise heterogeneous patient group.

Coronary Slow Flow Is Not Diagnostic of Microvascular Dysfunction in Patients With Angina and Unobstructed Coronary Arteries.

Background Guidelines recommend that coronary slow flow phenomenon (CSFP), defined as corrected thrombolysis in myocardial infarction frame count (CTFC) >$$ > $$27, can diagnose coronary microvascular dysfunction (CMD) in patients with angina and nonobstructed coronary arteries. CSFP has also historically been regarded as a sign of coronary endothelial dysfunction (CED). We sought to validate the utility of CTFC, as a binary classifier of CSFP and as a continuous variable, to diagnose CMD and CED. Methods and Results Patients with angina and nonobstructed coronary arteries had simultaneous coronary pressure and flow velocity measured using a dual sensor-tipped guidewire during rest, adenosine-mediated hyperemia, and intracoronary acetylcholine infusion. CMD was defined as the inability to augment coronary blood flow in response to adenosine (coronary flow reserve <2.5) and CED in response to acetylcholine (acetylcholine flow reserve ≤1.5); 152 patients underwent assessment using adenosine, of whom 82 underwent further acetylcholine testing. Forty-six patients (30%) had CSFP, associated with lower flow velocity and higher microvascular resistance as compared with controls (16.5±$$ \pm $$6.9 versus 20.2±$$ \pm $$6.9 cm/s; P=0.001 and 6.26±$$ \pm $$1.83 versus 5.36±$$ \pm $$1.83 mm Hg/cm/s; P=0.009, respectively). However, as a diagnostic test, CSFP had poor sensitivity and specificity for both CMD (26.7% and 65.2%) and CED (21.1% and 56.0%). Furthermore, on receiver operating characteristics analyses, CTFC could not predict CMD or CED (area under the curve, 0.41 [95% CI, 0.32%-0.50%] and 0.36 [95% CI, 0.23%-0.49%], respectively). Conclusions In patients with angina and nonobstructed coronary arteries, CSFP and CTFC are not diagnostic of CMD or CED. Guidelines supporting the use of CTFC in the diagnosis of CMD should be revisited.

Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis.

Background: Coronary artery disease (CAD) is a leading cause of death worldwide, and the diagnostic process comprises of invasive testing with coronary angiography and non-invasive imaging, in addition to history, clinical examination, and electrocardiography (ECG). A highly accurate assessment of CAD lies in perfusion imaging which is performed by myocardial perfusion scintigraphy (MPS) and magnetic resonance imaging (stress CMR). Recently deep learning has been increasingly applied on perfusion imaging for better understanding of the diagnosis, safety, and outcome of CAD.The aim of this review is to summarise the evidence behind deep learning applications in myocardial perfusion imaging. Methods: A systematic search was performed on MEDLINE and EMBASE databases, from database inception until September 29, 2020. This included all clinical studies focusing on deep learning applications and myocardial perfusion imaging, and excluded competition conference papers, simulation and animal studies, and studies which used perfusion imaging as a variable with different focus. This was followed by review of abstracts and full texts. A meta-analysis was performed on a subgroup of studies which looked at perfusion images classification. A summary receiver-operating curve (SROC) was used to compare the performance of different models, and area under the curve (AUC) was reported. Effect size, risk of bias and heterogeneity were tested. Results: 46 studies in total were identified, the majority were MPS studies (76%). The most common neural network was convolutional neural network (CNN) (41%). 13 studies (28%) looked at perfusion imaging classification using MPS, the pooled diagnostic accuracy showed AUC = 0.859. The summary receiver operating curve (SROC) comparison showed superior performance of CNN (AUC = 0.894) compared to MLP (AUC = 0.848). The funnel plot was asymmetrical, and the effect size was significantly different with p value < 0.001, indicating small studies effect and possible publication bias. There was no significant heterogeneity amongst studies according to Q test (p = 0.2184). Conclusion: Deep learning has shown promise to improve myocardial perfusion imaging diagnostic accuracy, prediction of patients' events and safety. More research is required in clinical applications, to achieve better care for patients with known or suspected CAD.

Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.

Background: The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging. Methods: The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach. Results: A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496). Conclusions: Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.

Coagulation derangement and risk factors for valve thrombosis following transcatheter aortic valve implantation.

AIMS: Durability of transcatheter aortic valve implantation (TAVI) is key to its expansion. We sought to identify incidence of valve thrombosis and predictors of valve thrombosis in our single centre with associated coagulation testing pre-TAVI and post-TAVI. METHODS AND RESULTS: This single-centre observational study comprised patients undergoing transfemoral TAVI discussed in the Heart Team meeting . Patients were followed up with echocardiography at 120 days to identify incidence of elevated transvalvular gradient and multivariable analysis was performed to identify factors associated with an increased odds of developing valve thrombosis. In addition, 11 patients underwent baseline, day 1 and day 120 post-TAVI coagulation testing. Between August 2017 and August 2019, 437 consecutive patients underwent transfemoral TAVI. Of these patients, 207/437 (47.4%) had 3-month follow-up echo data available and were analysed. Of these patients, 26/207 (12.6%) had elevated transvalvular gradients. These patients tended to be younger (80±14 vs 83±6 years; p=0.047) with a lower ejection fraction (49±13 vs 54%±11%; p=0.021), with a greater proportion of the population experiencing atrial fibrillation (14/21, 54% vs 68/181, 38%; p=0.067). Following multivariable analysis, there remained a trend towards higher eccentricity index associated with elevated gradients. Baseline (pre-TAVI) elevation of thrombin antithrombin levels (56±63; reference range 1.0-4.1 ng/L) and PF 1+2 (791±632; reference range 69-229 ng/mL) normalised at 120 days post-TAVI CONCLUSION: This study demonstrated that in the cohort of patients undergoing transfemoral TAVI in our centre: younger age, poor ejection fraction, atrial fibrillation and increased baseline eccentricity of the aortic valve annulus were present to a greater extent in patients exhibiting elevated transvalvular gradients at 3-month follow-up. Further work is required to delineate the extent of coagulation derangement and confirm predictors of thrombosis.

Donor-Transmitted Cancer in Orthotopic Solid Organ Transplant Recipients: A Systematic Review.

Donor-transmitted cancer (DTC) has major implications for the affected patient as well as other recipients of organs from the same donor. Unlike heterotopic transplant recipients, there may be limited treatment options for orthotopic transplant recipients with DTC. We systematically reviewed the evidence on DTC in orthotopic solid organ transplant recipients (SOTRs). We searched MEDLINE, EMBASE, PubMed, Scopus, and Web of Science in January 2020. We included cases where the outcome was reported and excluded donor-derived cancers. We assessed study quality using published checklists. Our domains of interest were presentation, time to diagnosis, cancer extent, management, and survival. There were 73 DTC cases in liver (n = 51), heart (n = 10), lung (n = 10) and multi-organ (n = 2) recipients from 58 publications. Study quality was variable. Median time to diagnosis was 8 months; 42% were widespread at diagnosis. Of 13 cases that underwent re-transplantation, three tumours recurred. Mortality was 75%; median survival 7 months. Survival was worst in transmitted melanoma and central nervous system tumours. The prognosis of DTC in orthotopic SOTRs is poor. Although re-transplantation offers the best chance of cure, some tumours still recur. Publication bias and clinical heterogeneity limit the available evidence. From our findings, we suggest refinements to clinical practice. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020165001, Prospero Registration Number: CRD42020165001.