Probability Of Coronary Artery Disease Research Articles

Abstract 4136463: Novel Systemic Inflammatory Biomarkers Can Predict Significant Coronary Disease in High Risk Patients

Background: Coronary artery disease (CAD) is a major cause of morbidity and mortality worldwide. Chronic inflammation is a known risk factor for atherosclerosis, plaque formation, disease progression, and plaque instability. Novel inflammatory biomarkers have been recently linked to excess risk of adverse cardiovascular events, including incident myocardial infarction. Elevated inflammatory biomarkers may help identify patients at higher risk for severe CAD. Aim: This study aimed to evaluate the use of novel systemic inflammatory markers in predicting the pretest probability of severe CAD in patients referred to diagnostic coronary angiography in a single tertiary care center. Methods: This was a prospective observational cohort study enrolling consecutive patients referred to diagnostic coronary angiography in a tertiary care center. Preprocedural laboratory results were obtained from medical charts, including complete blood counts with differentials. Three novel systemic inflammatory markers were computed using existing mathematical formulas: systemic immune-inflammation index (SII = neutrophil count*platelet/lymphocyte); systemic inflammation response index (SIRI = neutrophil*monocyte/lymphocyte); and systemic immune inflammatory response index (SIIRI = neutrophil*monocyte*platelet/lymphocyte). The primary study outcome was severe CAD defined as the presence of ≥70% stenosis in one of three main coronary arteries requiring percutaneous coronary intervention). The outcome was adjudicated by an independent reviewers blinded to computations of biomarkers. Result: The sample included 264 patients (age 59.3±10.8, 47% Females). Overall, 84 patients (32%) met the primary study outcomes. In univariate analysis, those with severe CAD had significantly higher SIRI (2.9±0.7 vs. 2.6±0.6, p = 0.001) and SIIRI (8.4±0.8 vs. 8.1±0.7, p = 0.015) compared to their counterparts. In multivariate analysis adjusted for age and sex, only SIRI was an independent predictor of severe CAD. Using SIRI > 20.8 (upper quartile) was associated with nearly three-fold excess risk of severe CAD (OR = 2.72, 95% CI 1.45-5.09). Conclusion: Elevated SIRI, a marker of ongoing systemic inflammatory response, could be indicative of severe CAD. As an inexpensive biomarker based exclusively on complete blood count, SIRI can be used to prioritize patients referred for diagnostic angiography and potentially expedite treatments in those at higher risk for coronary events.

ClinicAl verSus algorIthmic predictioN of Obstructive Coronary Artery Disease - the CASINO study

Abstract Background The estimation of pretest probability (PTP) is a key step when evaluating patients with suspected coronary artery disease (CAD). Current European guidelines recommend using an algorithm based on age, sex, and symptom typicality. However, many cardiologists do not regularly use this method, relying instead on overall clinical impression. The aim of this study was to compare clinical and algorithmic prediction of obstructive CAD in a set of symptomatic patients with suspected chronic coronary syndrome (CCS). Methods In this survey study, 10 cardiologists from were asked to estimate the probability of obstructive CAD (on a scale of 1-99% and without the aid of any score or algorithm) for 120 anonymized clinical vignettes of outpatients who underwent diagnostic workup for suspected CCS. The provided information included age, sex, symptom description, cardiovascular risk factors, and know comorbidities. Whenever non-invasive tests had been performed, physicians were also asked to estimate the probability of obstructive CAD after knowing that results. Obstructive CAD was defined as diameter stenosis ≥50% confirmed by invasive coronary angiography. Results Among the 120 included patients (62 women, mean age 61±10 years), symptoms consisted of chest pain in 104 and dyspnea in the remainder. The median PTP of obstructive CAD was 15% (IQR 10-25%) according to the ESC algorithm, and 30% (IQR 15-50%) according to cardiologists’ estimates (p<0.001). The observed prevalence of obstructive CAD was 20% (n=24), yielding suitable calibration for the ESC model (predicted vs. observed p=0.631), and significant overestimation for clinicians’ assessments (predicted vs. observed p=0.014) – Figure 1A. Despite using the same data, cardiologists’ PTP estimates varied widely within the same patients (mean inter-observer coefficient of variation 56%±22%). In a subset of 29 patients whose PTP was appraised twice by the same physicians, the mean absolute difference between assessments (intra-observer variability) was 22%±8%. The ESC algorithm showed fair discriminative power to identify patients with obstructive CAD (C-statistic 0.68, 95% CI 0.58-0.79, p=0.005), whereas the C-statistic for clinicians ranged from 0.53 to 0.72 (average 0.62±0.05, p values for comparisons 0.001-0.677) – Fig 1B. In the subset of 70 patients with prior non-invasive tests, clinicians significantly changed their predictions after knowing test results (mean absolute difference 22%±12%, p<0.001), improving their discriminative power significantly (mean change in C-statistic of 0.08±0.09, p=0.020). Conclusion Clinicians’ assessments of PTP in symptomatic patients display high inter- and intra-observer variability, and tend to overestimate the likelihood of obstructive CAD. Despite using more clinical data, cardiologists fail to outperform the ESC algorithm. The systematic use of this tool should be promoted in order to improve disease prediction and guide non-invasive testing.

Accuracy of machine learning techniques to predict stress echocardiography results using clinical variables

Abstract Background Stress echocardiography (SE) is a diagnostic prognostic imaging modality for suspected coronary artery disease (CAD). Cardiovascular risk factors (CVRF) are used in the assessment of the probability of CAD. The link between the outcome of SE and patients’ variables including CVRF, current medication, and anthropometric variables has not been widely investigated.Objective: The aim of this study is to understand whether a machine learned algorithm can be used to predict significant CAD defined by inducible ischemia on SE in patients with suspected CAD based on anthropometrics, CVRF, and medication as variables. Methods A machine learning (ML) framework was generated to automate the prediction of inducible ischemia on stress echocardiography results. A mutual information-based feature selection methods (support vector machine (SVM) and random forest (RF) classifiers) were used to investigate the amount of information that each feature carried to define the positive outcome of SE.Data from 2201 patients were used to train and validate the framework. Patients mean age was 62 (SD 11) years. The data consists of anthropological data and classical CVRF, atrial fibrillation, prior diagnosis of CAD, stroke, obstructive airways disease, chronic inflammatory disease, chronic kidney disease (CKD>3) and prescribed medications at the time of the test. There were 394 positive and 1807 negative SE cases. Five-fold cross-validation was used to validate the performance of the framework. Results The feature selection methods showed that prescribed medications such as Weight, Diabetes, Antiplatelet drugs and ACE- inhibitor/ARB, were the features that shared the most information about the outcome of SE (sensitivity and specificity of 81% and 51% and 81% and 61% for SVM and RF, respectively) with the accuracy of 82%. Conclusion This study shows that ML can predict the outcome of SE based on only 4 clinical features. Further research correlating the clinical variable and SE result to cardiovascular outcome will improve the model’s clinical utility and facilitate patient selection for early treatment avoiding unnecessary downstream testing.

Computed tomography coronary angiography : What does the nonradiologist expect from the radiologist?

Coronary computed tomography angiography (CCTA) has become acentral tool for the primary diagnosis of stable coronary artery disease (CAD). Its integration into the service catalog of the German statutory health insurance will not only transform the way patients are examined and treated but also enhance the collaboration between nonradiologists and radiologists. This article explores the requirements nonradiologists have for CCTA and identifies ways to promote successful interdisciplinary communication. The study addresses criteria for proper patient selection and preparation for CCTA. It considers the perspectives and needs of patients and various medical specialties, highlighting essential aspects of interdisciplinary communication. CCTA enables precise clarification of CAD and should be used for patients with apretest probability of chronic CAD between 15and 50%. Clear action plans in the diagnostic report are crucial to assist general practitioners and cardiologists in treatment planning. Patients expect clear information about the procedure, possible risks, and results. Close collaboration between various medical disciplines is essential for the successful implementation of CCTA. Clear, structured diagnostic reports with annotated images, along with regular case discussions and feedback loops, can improve report interpretation and interdisciplinary communication. Patient-friendly reports can make diagnostic results more understandable and enhance patient adherence.

Association between smoking status and subclinical coronary atherosclerosis in asymptomatic Korean individuals.

In this study, we sought to evaluate the association between smoking status and subclinical coronary atherosclerosis, as detected by coronary computed tomography angiography (CCTA), in asymptomatic individuals. We retrospectively analyzed 9,285 asymptomatic participants (mean age, 53.7±8.0 years; n=6,017, 64.8% male) with no history of coronary artery disease (CAD) who had undergone self-referred CCTA. Of these participants, 4,333 (46.7%) were considered never smokers, 2,885 (31.1%) former smokers, and 2,067 (22.3%) current smokers. We assessed the degree and characteristics of subclinical coronary atherosclerosis using CCTA, with obstructive CAD defined as a diameter stenosis of at least 50%. Compared with never-smokers, former smokers exhibited no significant differences in the probabilities of obstructive CAD, any coronary plaque, calcified plaque, or mixed plaque, as determined using adjusted odds ratios (aORs; p>0.05 for all). However, the risk of non-calcified plaque was significantly higher in former smokers (aOR, 1.34; 95% confidence interval [CI], 1.00 to 1.78; p=0.048). Current smokers had significantly higher rates of obstructive CAD (aOR, 1.46; 95% CI, 1.10 to 1.96; p=0.010), any coronary plaque (aOR, 1.41; 95% CI, 1.20 to 1.65; p<0.001), calcified plaque (aOR, 1.32; 95% CI, 1.13 to 1.55; p=0.001), non-calcified plaque (aOR, 1.72; 95% CI, 1.28 to 2.32; p<0.001), and mixed plaque (aOR, 2.00; 95% CI, 1.39 to 2.86; p<0.001) compared to never smokers. This cross-sectional study revealed a significant association between current smoking and subclinical coronary atherosclerosis, as detected on CCTA. Additionally, former smoking demonstrated an association with non-calcified plaque, indicating elevated cardiovascular risk.

Diagnostic accuracy of non-invasive investigations for coronary artery disease in hypertensive patients: A meta-analysis

Diagnostic accuracy of non-invasive investigations for coronary artery disease in hypertensive patients: A meta-analysis

Associations of circulating adipokines and coronary artery disease in young adults

Aim. To evaluate the associations of abdominal obesity (AO), adipokines and premature coronary artery disease (CAD) in young people for a deeper understanding of the pathogenesis of atherosclerotic diseases.Material and methods. A total of 1457 people were examined, including 653 (44,8%) men. Mean age was 36,7±6,0 years. Premature CAD was detected in 46 people, while 4 following subgroups were formed (138 people in total): with CAD and AO; with CAD, without AO; control groups by age and sex: without CAD, with AO; without CAD, without AO.Results. Premature CAD in young people is associated with an increase in non-high-density lipoprotein cholesterol (non-HDL-C). Univariate analysis found that the probability of CAD increased by 1% with an increase in adiponectin level, and by 0,2% with an increase of 1 ng/ml in resistin level. Multivariate regression analysis, including sex, age, waist circumference, non-HD-C, adiponectin, and resistin, significant associations were obtained for non-HDL-C and adiponectin. With AO, the CAD probability increased by 0,6% with an increase in plasminogen activator inhibitor-1 level (PAI-1). However, when non-HDL-C was included in the model, significance for this biomarker was not achieved.Conclusion. In young people, CAD is associated with increased levels of non-HDL cholesterol, adiponectin, resistin and PAI-1. Independent associations with premature CAD were obtained only for adiponectin, which makes this marker promising for study in young people.

Performance of the 2019 ESC pre-test probability model in predicting obstructive coronary artery disease in a Chinese population using coronary computed tomography angiography outcomes

BackgroundThe 2019 European Society of Cardiology (ESC) guidelines proposed a pre-test probability (PTP) model to determine the likelihood of coronary artery disease (CAD). However, the prediction accuracy of this model has not yet been evaluated in Chinese populations. This study aimed to validate the 2019 ESC-PTP model in predicting CAD using coronary computed tomography angiography (CCTA) outcomes in a Chinese population. MethodsA total of 26,346 consecutive patients with suspected CAD who underwent CCTA were included. The 2019 ESC-PTP model and 2013 ESC-PTP model were calculated for each patient, considering age, sex, and the symptom of chest pain, and the patients were categorized into low-, intermediate-, and high-risk groups. The predictive performance of the 2019 ESC-PTP model was evaluated by comparing it with the 2013 ESC-PTP model and the observed prevalence of CAD from CCTA. ResultsAmong the 11,234 patients analyzed in the study, 1896 (16.9%) patients were found to have obstructive CAD from CCTA. The 2019 ESC-PTP model had better calibration compared to the 2013 ESC-PTP model. After categorization, 80.9% of patients (67.9% in men and 94.4% in women) were in the same risk category as in the 2019 ESC-PTP model, but the risks of younger patients (7.5% versus 2.5%; P ​< ​0.001) and patients with non-anginal chest pain (13.7% versus 8.2%; P ​< ​0.001) were underestimated in the 2019 ESC-PTP model. ConclusionThe 2019 ESC-PTP model demonstrated a good calibration in predicting CAD in a Chinese population who underwent CCTA, but it exhibited an underestimation of CAD probability in younger patients and patients with non-anginal chest pain.

Diagnostic performance of 3.0 T unenhanced Dixon water-fat separation coronary MR angiography in patients with low-to-intermediate risk of coronary artery disease

PurposeTo evaluate the diagnostic performance of 3.0 T unenhanced compressed-sensing sensitivity encoding (CS-SENSE) Dixon water-fat separation coronary MR angiography (CMRA) in patients with low-to-intermediate risk of coronary artery disease (CAD) and its ability to grade the severity of CAD based on Coronary Artery Disease Reporting and Data System (CAD-RADS). MethodsA total of 55 patients who was clinically evaluated as low-to-intermediate risk of CAD were finally included to undergo both 3.0 T CS-SENSE water-fat separation CMRA and coronary computed tomography angiography (CCTA), and 11 of them also underwent X-ray coronary angiography (CAG). The severity of coronary artery disease was graded in patients who had completed both CCTA and CMRA examinations by the use of CAD-RADS reports for the patients with stable chest pain, and the diagnostic consistency between the two approaches was evaluated. Diagnostic performance of CMRA was assessed using the combination of CCTA and CAG as the reference standard for excluding or confirming CAD respectively. ResultsThe sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 3.0 T unenhanced water-fat separation coronary MRA were 90.0%, 95.0%, 81.8%, 97.4% and 94.0% for a patient-based analysis respectively. In comparison with CCTA, 3.0 T Dixon water-fat separation CMRA demonstrated excellent consistency in grading the severity of coronary heart disease according to CAD-RADS (0.77 for kappa value). ConclusionIn the group of low-to-intermediate probability for CAD, 3.0 T unenhanced CS-SENSE Dixon water-fat separation CMRA can present satisfactory diagnostic performance for the exclusion of CAD with high sensitivity and negative predictive value as well as the evaluation of grading the severity of coronary artery disease.

Efficacy of Coronary Calcium Score in Predicting Coronary Artery Morphology in Patients With Obstructive Coronary Artery Disease

BackgroundCoronary artery calcium score (CACS) is an established marker of coronary artery disease (CAD) and has been extensively used to stratify risk in asymptomatic individuals. However, the value of CACS in predicting plaque morphology in patients with advanced CAD is less established. The present analysis aims to assess the association between CACS and plaque characteristics detected by near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) imaging in patients with obstructive CAD. MethodsSeventy patients with obstructive CAD underwent coronary computed tomography angiography (CTA) and 3-vessel NIRS-IVUS imaging were included in the present analysis. The CTA data were used to measure the CACS in the entire coronary tree and the segments assessed by NIRS-IVUS, and these estimations were associated with the NIRS-IVUS measurements at a patient and segment level. ResultsIn total, 65 patients (188 segments) completed the study protocol and were included in the analysis. A weak correlation was noted between the CACS, percent atheroma volume (r = 0.271, P = .002), and the calcific burden measured by NIRS-IVUS (r = 0.648, P < .001) at patient-level analysis. Conversely, there was no association between the CACS and the lipid content, or the incidence of high-risk plaques detected by NIRS. Linear regression analysis at the segment level demonstrated an association between the CACS and the total atheroma volume (coefficient, 0.087; 95% CI, 0.024-0.149; P = .008) and the calcific burden (coefficient, 0.117; 95% CI, 0.048-0.186; P = .001), but there was no association between the lipid content or the incidence of high-risk lesions. ConclusionsIn patients with obstructive CAD, the CACS is not associated with the lipid content or plaque phenotypes. These findings indicate that the CACS may have a limited value for screening or stratifying cardiovascular risk in symptomatic patients with a high probability of CAD.

Сравнительная характеристика больных с ишемической болезнью сердца и стенозирующим атеросклерозом коронарных артерий в зависимости от показателей моментального резерва кровотока

The paper presents a comparative analysis of 56 patients suffering from stable ischemic heart disease and coronary artery stenosis in the range of 65–90% depending on the instantaneous wave-free ratio (iFR). Two groups of 28 patients took part in the research. The index iFR was less than 0,80 in the first group, whereas it was more than 0,94 in the second. It reveals that the indexes of the pre-test probability of coronary artery disease (CAD) did not significantly differ among the groups of patients irrespective of the different iFR values. Patients with low iFR indexes (the first group) tend to have diabetes mellitus as the result of overweight, predominant systolic hypertension, diastolic myocardial dysfunction, atherogenic dyslipidemia, and visible signs of renal dysfunction. According to the exercise ECG Stress test results, the patients of the first group were more frequently diagnosed with symptoms of the myocardial ischemia and its electrical instability in the form of high gradations of ventricular extrasystole. The paper outlines that the previously described factors increase the probability of low iFR with the same initial pre-test probability of CAD, so they can be used as indicators of the advisability of performing endovascular interventions in patients with chronic coronary syndrome.

Efficacy of Intensive Lifestyle Education and Forest-Based Exercise for the Primary Prevention of the Risk of Coronary Artery Disease

Introduction: Despite its efficacy, conventional center-based cardiac rehabilitation has several limitations which have led to the emergence of home-based programs and intensive cardiac rehabilitation as alternative methods for overcoming these limitations. Alternative methods for primary prevention have been recommended for similar reasons. Lifestyle modification is considered key to success in both primary and secondary prevention. Therefore, this primary prevention study aimed to investigate the efficiency of intensive lifestyle education and home-based programs involving unstructured exercise in urban forests to prevent coronary artery disease (CAD). The availability of urban forests as preventive exercise environments was also examined. Methods: Patients with risk factors for CAD participated in primary prevention using either FBEG (forest-based exercise group (FBEG, n = 11) or CBEG (center-based exercise group (CBEG, n = 17) for 12 weeks. The FBEG was provided with intensive residential lifestyle education and followed a home program that included performing exercise in an urban forest. The CBEG followed a conventional supervised exercise program at a fitness facility. Changes in body composition, cardiometabolic variables, and functional capacity were tested using a 2-way repeated ANOVA measurement. An independent t-test was used to examine the differences in weekly energy expenditure between the two groups. Results: Significant within-group differences were identified in body composition, cardiometabolic variables, and the 10-yr probability of CAD in both groups. However, the functional capacity, weekly energy expenditure, and attendance rate showed between-group differences, with superiority in the FBEG. Conclusion: Intensive lifestyle education and subsequent home-based programs with unstructured exercise in the forest were as effective as a conventional center-based program, with superiority in terms of the change of some variables. Intensive education on experiencing and habituating a healthy lifestyle seemed to play an important role in improving motivation.

CLINICAL, ANGIOGRAPHIC AND FUNCTIONAL PARAMETERS DETERMINING DECISION TO PERFORM REVASCULARIZATION IN STABLE CORONARY ARTERY DISEASE PATIENTS WITH INTERMEDIATE CORONARY LESIONS

The aim: to study the clinical, angiographic and functional parameters, and their relation to the decision on percutaneous coronary intervention (PCI) in stable coronary artery disease (CAD) patients with intermediate coronary stenoses (ІСS) at patient and lesion levels.&#x0D; Materials and methods. The cross-sectional study enrolled 123 patients (62±9 years; 73,2 % males) with stable CAD and angiographically ICS (50-90 %). Stable angina CCS class II (CCSII) was diagnosed in 70 (56,9 %) patients, class III (CCSIII) – 29 (23,6 %); 24 (19,5 %) patients were free from angina (Afree). Fractional flow reserve (FFR) was assessed in 74 (60,2 %) patients (min per patient). The hemodynamically significant lesion was considered if FFR ≤0,80 a.u. The decision to perform PCI was undertaken in 93 (76 %) patients. In addition, the functional data from 128 lesions were analyzed.&#x0D; Results. The CCSIII group was characterized by less frequent previous PCI (21 % vs. 46 % in the pooled group [CCSII + Afree]; р=0,018); the predominance of cases with (max) severe [70-90 %] coronary stenosis (96 % vs. 78 % in CCSII and 54 % in Afree; ptrend&lt;0,001); the lower (min) FFR ([median, quartiles] 0,70 (0,64-0,74) vs. 0,87 (0,81-0,90) in Afree; p˂0,002); and the decision to perform PCI in the vast majority of cases (93 % vs. 79 % in CCSII and 46 % in Afree; ptrend&lt;0,001). The Afree/CCSII/CCSIII groups demonstrated a trend towards a decrease in frequency of cases with (max) [60-69 %] stenosis (38 %, 13 % and none, respectively; ptrend&lt;0,001), and a tendency towards the cases with (max) [80-90 %] stenosis to be more prevalent (29 %, 39 % and 55 %, respectively; ptrend=0,051). The frequency of cases with (max) stenosis [70-79 %] was comparable in the studied groups (Afree/CCSII/CCSIII: 25 %/39 %/41 %, respectively [ptrend=0,240]). At lesion-level, all [80-90 %] lesions (n=28) appeared to be hemodynamically significant. In turn, the [60-69 %] lesions (n=24) included 5 (21 %) significant ones. Finally, the [70-79 %] lesions (n=44) included 28 (64 %) significant and 16 (36 %) non-significant ones.&#x0D; Conclusions. Among the stable CAD patients with angiographically ICS, those with CCSIII were characterized by less frequent previous PCI, significant decrease in FFR and the decision to perform index PCI in the vast majority of cases. There is a need for more precise diagnosis and assessment of myocardial ischemia in patients with an intermediate pre-test obstructive CAD probability, including the cases of previously performed PCI.

Computed tomography perfusion or fractional flow reserve–computed tomography to assess the significance of stenosis: Literature review

Abstract Detection of myocardial ischemia based on detection and grading of myocardial perfusion is a cornerstone of coronary artery disease (CAD) diagnosis. Coronary CT angiography (CCTA) is a recognized imaging test for exclusion of anatomically significant coronary stenosis in patients with low or moderate probability of CAD, but gives no information on its physiological significance. The two major approaches to evaluate the presence of induced myocardial ischemia using CT, static or dynamic perfusion CT (CTP) or non-invasive fractional flow reserve analysis (FFRCT), have been validated in multiple single- and multi-center studies with comparable results of high sensitivity, specificity and accuracy. Several studies with head-to-head comparison of CTP and FFRCT using invasive FFR measurement as a reference standard showed a slight superiority of CTP, especially in groups of patients with the borderline values of FFR-CT. The principle of myocardial perfusion assessment with CTP looks more physiological than computation models used in FFR-CT. However, the best results were shown by combined use of CCTA, CTP and FFRCT, providing the integrated anatomical and physiological information. Technical advances in CT technique, artificial intelligence and machine learning, together with accumulation of new clinical evidence, will result in development of the best strategy for comprehensive assessment of both coronary anatomy and myocardial perfusion with help of cardiac CT.

Investigation of Coronary Artery Disease by Coronary Computed Tomography Angiography and the Diagnostic Value of First-pass Myocardial Perfusion Imaging without Stress

Investigation of Coronary Artery Disease by Coronary Computed Tomography Angiography and the Diagnostic Value of First-pass Myocardial Perfusion Imaging without Stress

FFRct use for acute chest pain triage in the emergency department: a cost-effectiveness analysis

Aims To model and assess the cost-effectiveness of CT-based fractional flow reserve (FFRct) for a population of low to intermediate risk patients for coronary artery disease (CAD) presenting to the emergency department (ED) with acute chest pain. Methods and results Using a decision tree model with a 1 year time horizon and from a health care perspective, two diagnostic pathways using FFRct are compared to current clinical routine combining coronary computed tomography angiography (CCTA) with an exercise test. Model data are drawn from the literature and nationally reported data. Outcomes are assessed as the number of avoided invasive coronary angiographies (ICAs) showing no obstructive CAD and quality of life (QoL) in a theoretical cohort of 1000 patients. Sensitivity analyses are performed to test the robustness of the results. Determining FFRct when CCTA is inconclusive is a cost-effective and dominant strategy with a potential saving of 198€/patient, 154 avoided unnecessary ICA showing no obstructive CAD (uICA)/1000 patients and an average improvement in QoL of 0.008 QALY/patient. With an additional 574€/patient, 8 avoided uICA/1000 patients and an improvement in QoL of 0.001 QALY/patient, a strategy where FFRct is always performed is cost-effective only when considering high cost-effectiveness thresholds. Conclusions For patients presenting to the ED with acute chest pain and a low to intermediate pre-test probability of CAD, a diagnostic strategy where FFRct is determined after an inconclusive CCTA is cost-effective. Clinical trials investigating both sensitivity and specificity of FFRct, as well as QoL associated with the use of this technology in this setting are warranted.

The incremental value of coronary artery calcium in predicting obstructive coronary artery disease in a symptomatic mixed Asian cohort: external validation of RF-CL and CACS-CL

Abstract Background Estimating the pre-test probability (PTP) of coronary artery disease (CAD) is an essential step in the evaluation of patients with stable chest pain. Risk factor-weighted clinical likelihood (RF-CL) and coronary artery calcium score-weighted clinical likelihood (CACS-CL) are two simple models recently proposed to estimate the probability of obstructive CAD. However, these tools were predominantly developed and validated on Western populations. There is limited evidence of their predictive value in Asian populations. Purpose We sought to evaluate the performance of RF-CL and CACS-CL in a symptomatic mixed Asian cohort. Methods The study cohort included symptomatic patients referred for coronary computed tomography angiography (CTA) at a tertiary cardiac centre in Singapore. Patients with known CAD were excluded. Obstructive CAD was defined as ≥ 50% stenosis on coronary CTA. RF-CL was calculated with age, sex, symptom characteristics, and the number of risk factors. CACS-CL was calculated from RF-CL by further incorporating the CACS. Discriminative performance was evaluated with the area under the receiver-operating characteristic curves (AUC). Categorical net reclassification index (NRI) was calculated to assess the reclassification improvement of CACS-CL as compared with RF-CL. Results A total of 1293 patients were analysed (mean age 55.8±11.3 years, 45.6% female). The majority of the patients were of Asian descent (73.9% Chinese, 10.6% Indian, 6.4% Malay). The overall prevalence of obstructive CAD was 26.1% (n=338). The RF-CL achieved an AUC of 0.72 (95% CI, 0.69-0.75). The incorporation of CACS in CACS-CL further improved the AUC to 0.89 (95% CI, 0.87-0.91; p&amp;lt;0.001) (Figure 1). Compared to RF-CL, CACS-CL demonstrated an improved reclassification, resulting in a categorical NRI of 0.28 (95% CI, 0.23-0.33) at a 5% cutoff. Among 955 patients without obstructive CAD, the CACS-CL model reclassified a significant proportion (21.4%, n=204) into the low-risk group, in which further testing is not recommended. (Table 1) Conclusions In a symptomatic mixed Asian cohort, the RF-CL model had good discriminative performance in predicting obstructive CAD. Incorporating CACS in the CACS-CL model improved both discrimination and reclassification substantially. These results indicate that the RF-CL and CACS-CL perform similarly in different ethnicities and support their use in optimising the diagnostic workflow in Asian patients with stable chest pain.

Pre-test probability for coronary artery disease in patients with chest pain based on machine learning techniques

Abstract Background A correct and prompt diagnosis of coronary artery disease (CAD) is a crucial component of disease management to reduce the risk of death and improve the quality of life in patients with CAD. Currently, the American College of Cardiology (ACC)/American Heart Association (AHA) and the European Society of Cardiology (ESC) guidelines recommend selecting an appropriate pre-diagnosis test for an individual patient according to the CAD probability. The purpose of this study was to develop a practical pre-test probability (PTP) for obstructive CAD in patients with chest pain using machine learning (ML); also, the performance of ML-PTP for CAD is compared to the final result of coronary angiography (CAG). Methods We used a database from a single-center, prospective, all-comer registry designed to reflect real-world practice since 2004. All subjects underwent invasive CAG at our hospital in South Korea. We used logistic regression algorithms, random forest (RF), supporting vector machine, and K-nearest neighbor classification for the ML models. The dataset was divided into two consecutive sets according to the registration period to validate the ML models. ML training for PTP and internal validation used the first dataset registered between 2004 and 2012 (8,631 patients). The second dataset registered between 2013 and 2014 (1,546 patients) was used for external validation. The primary endpoint was obstructive CAD. Obstructive CAD was defined as having a stenosis diameter of greater than 70% on the quantitative CAG of the main epicardial coronary artery. Results We derived an ML-based model consisting of three different models according to the subject used to obtain the information, such as the patient himself (dataset 1), the community's first medical center (dataset 2), and doctors (dataset 3). The performance range of the ML-PTP models as the non-invasive test had C-statistics of 0.795 to 0.984 compared to the result of invasive testing via CAG in patients with chest pain. The training ML-PTP models were adjusted to have 99% sensitivity for CAD so as not to miss actual CAD patients. In the testing dataset, the best accuracy of the ML-PTP model was 45.7% using dataset 1, 47.2% using dataset 2, and 92.8% using dataset 3 and the RF algorithm. The CAD prediction sensitivity was 99.0%, 99.0%, and 98.0%, respectively. Conclusion We successfully developed a high-performance model of ML-PTP for CAD which is expected to reduce the need for non-invasive tests in chest pain. However, since this PTP model is derived from data of a single medical center, multicenter verification is required to use it as a PTP recommended by the major American societies and the ESC.

Prognostic value of a novel artificial intelligence-based coronary computed tomography angiography-derived ischemia algorithm for patients with suspected coronary artery disease

Abstract Introduction Coronary computed tomography angiography (CCTA) has emerged as the first-line, non-invasive imaging tool to detect coronary artery disease (CAD) in patients with low to intermediate pre-test probability of CAD. However, CCTA cannot directly assess the presence of ischemia, which may lead to further downstream testing. A novel artificial-intelligence (AI)-based algorithm, comprising a machine-learned method that leverages atherosclerosis and vascular morphology features to determine vessel-level coronary ischemia from CCTA images, may be useful in this regard. Purpose We aimed to investigate the prognostic value of the AI-based ischemia algorithm for clinical events among symptomatic patients with suspected CAD referred for CCTA. Methods The AI-based ischemia algorithm was calculated by analysts blinded for patient characteristics and clinical outcomes using CCTA data from patients with suspected CAD who underwent clinically indicated CCTA at a large tertiary care center from February 2007 to December 2016. The primary endpoint was the composite of death, myocardial infarction (MI), and unstable angina pectoris (uAP) throughout a median follow-up of 8.1 years (IQR 6.0-9.6). Secondary endpoints were the association between the AI-based ischemia algorithm and the primary endpoint among patients with no/non-obstructive (i.e. ≤50%) CAD vs. obstructive (i.e. &amp;gt;50% stenosis) CAD in CCTA. Results 1574 patients were evaluated by the AI-based ischemia algorithm and 1540 were analyzable. Patients with positive AI-based ischemia findings (30.4%, n=468) had a significantly higher crude rate of the primary endpoint as compared to patients with negative AI-based ischemia findings (69.6%, n=1072) (HR 2.37, 95% CI 1.80-3.11, p&amp;lt;0.001), driven by higher rates of all endpoint composites (death: HR 1.48, 95% CI 1.13-2.22, p=0.007; MI: HR 5.42, 95% CI 3.10-9.49, p&amp;lt;0.001; uAP: HR 6.76, 95% CI 3.01-15.20, p&amp;lt;0.001). Results were consistent after adjusting for clinical confounders (age, sex, diabetes mellitus, smoking, hypertension, dyslipidemia, BMI&amp;gt;25 kg/m2, CAD family history) (primary endpoint: HR 1.66, 95% CI 1.24-2.22, p=0.001). Positive AI-based ischemia findings were associated with a significantly higher rate of the primary endpoint among patients with no/non-obstructive CAD in CCTA (n=1001/1487) (HR 2.56, 95% CI 1.53-4.27, p&amp;lt;0.001), but not among those with anatomically obstructive CAD in CCTA (n=486/1487) (HR 1.08, 95% CI 0.70-1.66, p=0.715). Conclusions A novel AI-based CCTA-derived ischemia algorithm was associated with a 1.7-fold increased adjusted rate of long-term death, MI, or uAP. This AI-based ischemia algorithm may be helpful to improve risk stratification, especially among patients with normal/non-obstructive CCTA.Kaplan Meier CCTA ≤50% and &amp;gt;50% stenosis

Integrated myocardial flow reserve (iMFR) assessment: optimized PET blood flow quantification for diagnosis of coronary artery disease.

Distinguishing obstructive epicardial coronary artery disease (CAD) from microvascular dysfunction and diffuse atherosclerosis would be of immense benefit clinically. However, quantitative measures of absolute myocardial blood flow (MBF) integrate the effects of focal epicardial stenosis, diffuse atherosclerosis, and microvascular dysfunction. In this study, MFR and relative perfusion quantification were combined to create integrated MFR (iMFR) which was evaluated using data from a large clinical registry and an international multi-center trial and validated against invasive coronary angiography (ICA). This study included 1,044 clinical patients referred for 82Rb rest/stress positron emission tomography myocardial perfusion imaging and ICA, along with 231 patients from the Flurpiridaz 301 trial (clinicaltrials.gov NCT01347710). MFR and relative perfusion quantification were combined to create an iMFR map. The incremental value of iMFR was evaluated for diagnosis of obstructive stenosis, adjusted for patient demographics and pre-test probability of CAD. Models for high-risk anatomy (left main or three-vessel disease) were also constructed. iMFR parameters of focally impaired perfusion resulted in best fitting diagnostic models. Receiver-operating characteristic analysis showed a slight improvement compared to standard quantitative perfusion approaches (AUC 0.824 vs. 0.809). Focally impaired perfusion was also associated with high-risk CAD anatomy (OR 1.40 for extent, and OR 2.40 for decreasing mean MFR). Diffusely impaired perfusion was associated with lower likelihood of obstructive CAD, and, in the absence of transient ischemic dilation (TID), with lower likelihood of high-risk CAD anatomy. Focally impaired perfusion extent derived from iMFR assessment is a powerful incremental predictor of obstructive CAD while diffusely impaired perfusion extent can help rule out obstructive and high-risk CAD in the absence of TID.