당뇨 환자에서 기계학습을 이용한 생리학적 지표 및 위험요인이 심혈관계 예후에 미치는 영향에 대한 검증

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의학과, 2020. 8. 구본권.Background and Objectives: Current European Society of Cardiology and European Association for Cardio-Thoracic Surgery guidelines recommend fractional flow reserve (FFR) measurement as a standard invasive method to identify the ischemia-causing coronary lesions. However, patients after therapeutic procedures still suffer adverse cardiovascular events even after deferral of revascularization according to FFR, potentially due to the presence of microvascular dysfunction that may cause ischemia or foster the progression of obstructive disease. Coronary microvascular dysfunction (CMD) is more frequently observed in patients with diabetes mellitus (DM) and is a major determinant of long-term adverse outcome. Since comprehensive physiologic assessment enables the evaluation of microvascular function which could not be fully demonstrated by angiography, we sought to investigate the prognostic implication of invasive physiologic index-defined CMD in patients with DM and coronary artery disease (part I). Increasing evidence showed that machine learning can provide tools to assist physicians during diagnosis and treatment of diverse clinical conditions, including myocardial infarction. Therefore, we sought to study using machine learning algorithms with an expanded sample size, to validate the physiologic indices and find out the valuable risk factors for cardiovascular outcomes in patients with DM and coronary artery disease (part II). Methods: Part 1: Two hundred and eighty-three patients with available FFR and index of microcirculatory resistance (IMR) were selected from the 3V FFR-FRIENDS study. Patients were classified according to the presence of DM and CMD into group A (DM-, CMD-), group B (DM-, CMD+), group C (DM+, CMD-), and group D (DM+, CMD+). Primary outcome was a major adverse cardiac event (MACE, a composite of cardiac death, myocardial infarction and ischemia-driven revascularization) at 2 years. Part 2: Seven hundred and fourteen patients (235 patients with DM) with deferred coronary revascularization according to FFR (>0.80) were included. This registry hitherto is the biggest cohort whose patients were fully assessed by comprehensive physiologic indices. Comprehensive physiologic evaluation, including coronary flow reserve (CFR), IMR and FFR, was performed at the time of revascularization deferral. The median values of CFR (2.88), FFR (0.88) and IMR (17.85) were used to classify high or low CFR, FFR, and IMR groups. Information gains of variables with 5,000-permutation resampling, minimal depth and Boruta algorithms were used for feature selection. Furthermore, prognostic models were compared using c-index. In this part, patient-oriented composite outcome (POCO) at 5 years, including all-cause death, any myocardial infarction, and any revascularization, was the primary outcome. Results: Part 1: DM population showed significantly higher risk of MACE compared with non-DM population (HR 4.88, 95% CI 1.54-15.48, p=0.003). MACE at 2-year among four groups were 2.2%, 2.0%, 7.0%, and 18.5%, respectively. Group D showed significantly higher risk of MACE compared with group A (HR 8.98, 95% CI 2.15-37.41, p=0.003). The multivariable regression analysis showed the presence of DM and CMD was an independent predictor of 2-year MACE (HR 11.24, 95% CI 2.53-49.88, p=0.002) and integrating CMD into a model with DM increased discriminant ability (C-index 0.683 vs. 0.710, p=0.010, integrated discrimination improvement 0.015, p=0.040). Part 2: Compared with non-DM population, DM population showed a higher risk of POCO at 5 years (HR 2.49, 95% CI 1.64-3.78, p<0.001). Low CFR group had a higher risk of POCO than high CFR group (HR 3.22, 95% CI 1.74-5.97, p<0.001) only in DM population. In contrast, CFR values could not differentiate the risk of POCO in non-DM population. There was a significant interaction between CFR and the presence of DM regarding the risk of POCO (interaction p=0.025). Independent predictors of POCO at 5 years were low CFR and family history of coronary artery disease in DM population, and percent diameter stenosis and multi-vessel disease in non-DM population. Among all angiographic and physiologic parameters, CFR showed the highest information gain. In DM population, CFR, consistently, was the most important feature followed by Age and FFR using Minimal Depth algorithm. Moreover, CFR was the valuable features to predict POCO using Boruta algorithm in DM population. In DM population, adding clinical risk factors (c-index 0.75 0.65-0.85, p=0.500) or clinical risk factors and invasive parameters together (c-index 0.75, 95%CI 0.65-0.85, p=0.535) into features from Boruta (c-index 0.73, 95% CI 0.63-0.83) did not show a better discriminant ability. Conclusions: The patients with DM and CMD were associated with increased risk of cardiovascular events. Integration of CMD improved risk stratification to predict the occurrence of MACE. The importance of risk factors for cardiovascular outcomes is different according to the presence of DM. CFR consistently was the important prognostic factor in patients with DM regardless of methods. Machine learning could help find out the most effective combination with acceptable numbers of features for better outcome prediction.배경 및 목적: 유럽심장학회 (European Society of Cardiology) 및 유럽심장외과협회 (European Society of Cardio-Thoracific Academy) 지침에서 관상동맥 허혈 진단을 위한 침습적 표준 방법으로 분획혈류예비력 (FFR, Fractal Flow Reserve) 측정을 권고하고 있음. 그러나 표준지침으로 치료받은 일부 환자들은 여전히 심혈관 사건을 겪음. 이는 잠재적으로 허혈을 유발하거나 폐쇄성 질환의 진행을 촉진할 수 있는 미세혈관 기능장애 때문임. 이런 미세기능장애는 관상동맥 조영술로 평가할 수 없음. 관상동맥미세혈관기능장애는 당뇨환자에서 더 자주 생기고 장기 예후의 주요 위험요인임. 종합적인 생리학적 평가로 미세혈관 기능의 평가가 가능하기 때문에 당뇨 및 관상동맥질환이 있는 환자에서 대한 침습적 생리지표로 정의한 미세혈관 장애가 예후에 미치는 영향을 조사하고자 본 연구의 Part 1을 시행하였음. 또 샘플 크기가 확장된 본 연구의 Part 2에서는 기계학습을 이용하여 당뇨 환자에서 생리학적 지표 및 위험요인이 심혈관계 예후에 미치는 영향을 검증하고자 시행하였음. 방법: 본 연구의 첫번째 부분은 3V FFR-FRIENS study에서 사용 가능한 FFR 및 index of microcirculatory resistance (IMR)가 있는 환자 283명이 선택됨. CMD (coronary microvascular dysfunction)는 IMR≥25U로 정의함. 환자는 DM과 CMD에 따라 그룹 A(DM-, CMD-), 그룹 B(DM-, CMD+), 그룹 C(DM+, CMD-), 그룹 D(DM+, CMD+)로 분류됨. 이 선행 연구에서 1차 평가변수는 2년의 major adverse cardiac event (MACE, 심장사, 심근경색 및 허혈성 기반 혈관재개통술)로 정의함. 두번째 부분은 Korea-Japan-Spain registry에서 FFR (>0.80)에 따라 관상동맥 재개통술이 지연되고 관상동맥혈류예비력(CFR, coronary flow reserve), IMR을 포함한 종합적인 생리학적 평가가 이루어진 환자 714명(DM을 가진 환자 235명)이 선택됨. CFR, IMR, FFR의 높은 그룹 또는 낮은 그룹을 분류하는데 중간값 CFR(2.88), FFR(0.88), IMR(17.85)이 사용됨. 이 부분의 1차 평가변수는 POCO (patient-oriented composite outcome) 5년 내의 모든 원인 사망, 심근경색, 모든 혈관재개통술로 정의함. 결과: 첫 부분에서 당뇨 환자들은 비당뇨환자에 비해 MACE의 위험성이 높음(HR 4.88, 95% CI 1.54-15.48, p=0.003). 4개 그룹의 2년 MACE는 각각 2.2%, 2.0%, 7.0%, 18.5%. 그룹 D는 그룹 A에 비해 MACE의 위험도가 현저히 높음(HR 8.98, 95% CI 2.15-37.41, p=0.003). 다변량 회귀 분석에서 2년 MACE의 독립적인 예측인자는 CMD를 동반한 당뇨환자 (HR 11.24, 95% CI 2.53-49.88, p=0.002). CMD를 당뇨에 추가했을때 예측 능력이 향상됨(C-index 0.683 vs 0.710, p=0.010). 두번째 부분에서, 비당뇨군과 비교했을 때, 당뇨군은 5년 POCO의 위험성이 더 높음(HR 2.49, 95% CI 1.64-3.78, p<0.001). 당뇨군에서 낮은 CFR 그룹은 높은 CFR 그룹보다 POCO의 위험이 높음(HR 3.22, 95% CI 1.74-5.97, p<0.001). CFR 값은 비당뇨군에서 POCO의 위험을 구별할 수 없음. POCO의 위험성을 예측함에 있어서 CFR과 당뇨 사이에 유의한 상호작용이 있었다(interaction p=0.025). 5년 POCO에 대한 독립적인 예측 인자는 당뇨군에서 낮은 CFR과 관상동맥 가족력, 비당뇨군에서 관상동맥 질환의 percent diameter stenosis와 다혈관 질환임. 당뇨군에서 POCO를 예측함에 있어서 다른 요인에 비해 CFR은 가장 많은 정보를 가지고 있었음. "Minimum Depth" 알고리즘을 사용했을 때 CFR은 가장 중요한 예측요인이고 "Boruta" 알고리즘을 사용했을 때 의미 있는 요인으로 나타남. 당뇨군에서 임상적 위험 인자(c-index 0.75 0.65-0.85, p=0.500) 혹은 임상적 시술적 위험인자(c-index 0.75, 95%CI 0.65-0.85, p=0.535)를 동시에 Boruta 알고리즘에서 선택되어진 위험인자로 구성된 모델(c-index 0.73, 95% CI 0.63-0.83)에 추가하였을 때 모델의 예측력은 유의하게 높아지지 않았음. 결론: CMD를 동반한 당뇨는 심혈관 질환 위험의 증가와 관련이 있음. 당뇨환자에서 CMD의 추가는 MACE발생의 예측력을 높임. 관상동맥 생리학적 지표와 위험 인자들이 예후에 미치는 역할은 당뇨여부에 따라 다름. 어떤 방법을 사용하는지 불구하고 CFR은 예후를 예측하는 중요한 지표임. 기계학습은 가장 효과적이고 효율적인 변수조합을 찾아 예후를 더 잘 예측할 수 있음.Introduction 2 First Part 4 Methods 4 Results 7 Second Part 10 Methods 10 Results 16 Discussion 19 Conclusions 26 References 28 국문초록 58Docto

Evaluation of pericoronary adipose tissue attenuation on CT

Pericoronary adipose tissue (PCAT) is the fat deposit surrounding coronary arteries. Although PCAT is part of the larger epicardial adipose tissue (EAT) depot, it has different pathophysiological features and roles in the atherosclerosis process. While EAT evaluation has been studied for years, PCAT evaluation is a relatively new concept. PCAT, especially the mean attenuation derived from CT images may be used to evaluate the inflammatory status of coronary arteries non-invasively. The most commonly used measure, PCATMA, is the mean attenuation of adipose tissue of 3 mm thickness around the proximal right coronary artery with a length of 40 mm. PCATMA can be analyzed on a per-lesion, per-vessel or per-patient basis. Apart from PCATMA, other measures for PCAT have been studied, such as thickness, and volume. Studies have shown associations between PCATMA and anatomical and functional severity of coronary artery disease. PCATMA is associated with plaque components and high-risk plaque features, and can discriminate patients with flow obstructing stenosis and myocardial infarction. Whether PCATMA has value on an individual patient basis remains to be determined. Furthermore, CT imaging settings, such as kV levels and clinical factors such as age and sex affect PCATMA measurements, which complicate implementation in clinical practice. For PCATMA to be widely implemented, a standardized methodology is needed. This review gives an overview of reported PCAT methodologies used in current literature and the potential use cases in clinical practice.</p

THE USE OF BASEL SCORE IN EARLY DETECTION OF CORONARY LESION SEVERITY IN NON-ST SEGMENT ELEVATION MYOCARDIAL INFARCTION AND UNSTABLE ANGINA PECTORIS

Early diagnosis of cardiac ischemia is crucial for effective management of acute myocardial infarction. The BASEL (Better Analysis of ST-segment Elevations and Depressions in a 12-leads-ECG) score has been shown to provide additional diagnostic value to the established electrocardiographic (ECG) criteria. This study aimed to evaluate the use of BASEL score to determine the severity of coronary lesions in patients with non-ST segment elevation acute coronary syndrome. This study used a cross-sectional approach and was conducted from January 2021 to January 2022. From a total of 90 subjects, more than three-quarters were male, while the mean age was 60.3 years. The median BASEL score was 2.3 (1–4.2). GRACE 2.0 score had a mean of 97.3±26. The SYNTAX I score had a mean of 25±15.6, the SYNTAX II – PCI median score was 34.5 (25.9-42.4), and the SYNTAX II-CABG mean score was 23.4±11.9. The BASEL score showed a significant association with the SYNTAX I score both in univariate 2.60 (2.60-3.59),

Intracoronary electrocardiogram as a direct measure of myocardial ischemia

The electrocardiogram is a valuable diagnostic method providing insight into pathologies of the heart, especially rhythm disorders or insufficient myocardial blood supply (myocardial ischemia). The commonly used surface ECG is, however, limited in detecting short-lasting myocardial ischemia, in particular in the territory of the left circumflex coronary artery supplying the postero-lateral wall of the left ventricle. Conversely, an ECG recorded in close vicinity to the myocardium, i.e., within a coronary artery (intracoronary ECG, icECG) has been thought to overcome these limitations. Since its first implementation during cardiac catheterization in 1985, icECG has shown ample evidence for its diagnostic value given the higher sensitivity for myocardial ischemia detection when compared to the surface ECG. In addition, icECG has been demonstrated to be a direct measure of myocardial ischemia in real-time, thus, providing valuable information during percutaneous coronary diagnostics and interventions. However, a lack of analysing systems to obtain and quantify icECG in real-time discourages routine use. The goals of this MD-PhD thesis are two-fold: First, to determine the diagnostic accuracy of icECG ST-segment shift during pharmacologic inotropic stress in comparison to established indices for coronary lesion severity assessment using quantitative angiographic percent diameter stenosis as reference (Project I). Second, to determine the optimal icECG parameter for myocardial ischemia detection and quantification (Project II and III). In essence, this thesis demonstrates that the icECG is an easy available diagnostic method providing highly accurate information on the amount of myocardial ischemia in real-time. Quantitative assessment of acute, transmural myocardial ischemia by icECG is most accurately performed by measuring ST-segment shift at the J-point, while the quantitative assessment during physical exercise, respectively its pharmacologic simulation, is most accurately performed by measuring ST-segment shift 60ms after the J-point

Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information

Cardiac computed tomography radiomics: an emerging tool for the non-invasive assessment of coronary atherosclerosis

In the last decades, significant advances have been made in the preventive approaches to cardiovascular disease. Even so, coronary artery disease remains one of the main causes of morbidity and mortality worldwide. Invasive imaging modalities, such as intravascular ultrasound or optical coherence tomography, have played a key role in the comprehension of the pathological processes underlying myocardial infarction and cerebrovascular disease. These imaging techniques have contributed greatly to the identification and phenotyping of the culprit lesion, the so-called vulnerable plaque. Coronary computed tomographic angiography (CCTA) has emerged in more recent years as the non-invasive modality of choice in the study of coronary atherosclerosis, showing in many studies a diagnostic yield comparable to invasive approaches. Moreover, being able to describe extra-luminal characteristics of the affected vessel, CCTA has greatly contributed towards shifting the attention of researchers from the mere quantification of luminal stenosis to the identification of adverse plaque features, which appear to have a stronger prognostic value. However, the identification of some of the hallmarks of vulnerable plaques is qualitative in nature and, therefore, subject to some degree of inter-reader variability. Moreover, CCTA is still unable to identify some fine markers of plaque vulnerability which can be detected by invasive techniques, such as neovascularization and plaque erosion, among others. Nonetheless, radiological images can be viewed as vast 3-D datasets which, via the use of recent technology, allow for the extraction of numerous quantitative features that may be used to accurately phenotype a given lesion. Radiomics is the process of extrapolating innumerable parameters from a given region of interest, with the goal of establishing correlations between quantitative variables and clinical data. These datasets can then be manipulated to create predictive models via the use of automated algorithms in a process called machine learning. As a result of these approaches, radiological images may offer information regarding the characterization of a plaque which can go much beyond the boundaries of what can be qualitatively asserted by the human eye, contributing to expanding the knowledge of the disease and ultimately assist clinical decisions. Thus far, radiomics has found its more consistent area of application in the field of oncology; to present date, the amount of clinical data regarding coronary artery disease is still relatively small, partly due to the technical difficulties associated with the implementation of such techniques to the study of a small and geometrically complex lesion such as the coronary plaque. The present review, after a summary of the imaging modalities most commonly used nowadays in the study of coronary plaques, will provide a perspective on the application of radiomic analysis to coronary artery disease

CT-based fractional flow reserve: development and expanded application

Computations of fractional flow reserve, based on CT coronary angiography and computational fluid dynamics (CT-based FFR) to assess the severity of coronary artery stenosis, was introduced around a decade ago and is now one of the most successful applications of computational fluid dynamic modelling in clinical practice. Although the mathematical modelling framework behind this approach and the clinical operational model vary, its clinical efficacy has been demonstrated well in general. In this review, technical elements behind CT-based FFR computation are summarised with some key assumptions and challenges. Examples of these challenges include the complexity of the model (such as blood viscosity and vessel wall compliance modelling), whose impact has been debated in the research. Efforts made to address the practical challenge of processing time are also reviewed. Then, further application areas – myocardial bridge, renal stenosis and lower limb stenosis – are discussed along with specific challenges expected in these areas

Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials

Objective: To determine the potential impact of on-site CT-derived fractional flow reserve (CT-FFR) on the diagnostic efficiency and effectiveness of coronary CT angiography (CCTA) in patients with obstructive coronary artery disease (CAD) on CCTA. Methods: This observational cohort study included patients with suspected CAD who had been randomized to cardiac CT in the CRESCENT I and II trials. On-site CT-FFR was blindly performed in all patients with at least one ≥ 50% stenosis on CCTA and no exclusion criteria for CT-FFR. We retrospectively assessed the effect of adding CT-FFR to the CT protocol in patients with a stenosis ≥ 50% on CCTA in terms of diagnostic effectiveness, i.e., the number of additional tests required to determine the final diagnosis, reclassification of the initial management strategy, and invasive coronary angiography (ICA) efficiency, i.e., ICA rate without ≥ 50% CAD. Results: Fifty-three patients out of the 372 patients (14%) had at least one ≥ 50% stenosis on CCTA of whom 42/53 patients (79%) had no exclusion criteria for CT-FFR. CT-FFR showed a hemodynamically significant stenosis (≤ 0.80) in 27/53 patients (51%). The availability of CT-FFR would have reduced the number of patients requiring additional testing by 57%-points compared with CCTA alone (37/53 vs. 7/53, p < 0.001). The initial management strategy would have changed for 30 patients (57%, p < 0.001). Reserving ICA for patients with a CT-FFR ≤ 0.80 would have reduced the number of ICA following CCTA by 13%-points (p = 0.016). Conclusion: Implementation of on-site CT-FFR may change management and improve diagnostic efficiency and effectiveness in patients with obstructive CAD on CCTA. Key Points: • The availability of on-site CT-FFR in the diagnostic evaluation of patients with obstructive CAD on CCTA would have significantly reduced the number of patients requiring additional testing compared with CCTA alone. • The implementation of on-site CT-FFR would have changed the initial management strategy significantly in the patients with obstructive CAD on CCTA. • Restricting ICA to patients with a positive CT-FFR would have significantly reduced the ICA rate in patients with obstructive CAD on CCTA