Early Diagnosis Of Myocardial Infarction Research Articles

The Biomarkers Associated with Unstable Angina Pectoris (UAP)

Objectives: Coronary artery disease (CAD) is the most common form of CVD that begins as stable angina pectoris (SAP) followed by unstable angina pectoris (UAP) and finally critical stage myocardial infarction (MI), which results from rupture of atherosclerotic plaque and a thrombus that blocks the coronary artery supplying the heart, resulted from the death of part of the heart muscle. The underlying cause of coronary artery disease (CAD) is atherosclerosis, which results from the formation of layers of fatty deposits on the inner walls of the arteries. Early diagnosis of MI has an important role in assisting clinicians in prompt planning for its treatment and thus reducing the number of its deaths. Methods: The present study included 20 UAP patients and 20 healthy individuals (controls). In all the cases and controls, levels of cardiac and inflammatory biomarkers were measured by special kits from Hotgen Company via phosphorous technology using UPT-3A converting phosphor immunological analyzer. Results: The levels of both cardiac and inflammatory biomarkers were significantly increased in UAP cases as compared to controls. Also, there was significant positive correlation between cardiac and inflammatory biomarkers in UAP cases. Conclusions: The positive correlation between most cardiac and inflammatory biomarkers in UAP indicates that inflammation occurs in UAP. Hence it is concluded that inflammatory biomarkers (CRP, Hs-CRP and PCT) and cardiac biomarker (cTnI, H-FABP, CK-MB, NT-proBNP and D-dimer) can be markers of UAP and can be used for diagnostic purposes in patients.

Diagnostic importance of H-FABP in the early diagnosis of myocardial infarction

Diagnostic importance of H-FABP in the early diagnosis of myocardial infarction

Metabolomics Analysis Identifies Differential Metabolites as Biomarkers for Acute Myocardial Infarction.

Myocardial infarction (MI), including ST-segment elevation MI (STEMI) and non-ST-segment elevation MI (NSTEMI), is still a leading cause of death worldwide. Metabolomics technology was used to explore differential metabolites (DMs) as potential biomarkers for early diagnosis of STEMI and NSTEMI. In the study, 2531 metabolites, including 1925 DMs, were discovered. In the selected 27 DMs, 14 were successfully verified in a new cohort, and the AUC values were all above 0.8. There were 10 in STEMI group, namely L-aspartic acid, L-acetylcarnitine, acetylglycine, decanoylcarnitine, hydroxyphenyllactic acid, ferulic acid, itaconic acid, lauroylcarnitine, myristoylcarnitine, and cis-4-hydroxy-D-proline, and 5 in NSTEMI group, namely L-aspartic acid, arachidonic acid, palmitoleic acid, D-aspartic acid, and palmitelaidic acid. These 14 DMs may be developed as biomarkers for the early diagnosis of MI with high sensitivity and specificity. These findings have particularly important clinical significance for NSTEMI patients because these patients have no typical ECG changes.

Machine Learning for Myocardial Infarction Compared With Guideline-Recommended Diagnostic Pathways.

Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) is a validated clinical decision support tool that uses machine learning with or without serial cardiac troponin measurements at a flexible time point to calculate the probability of myocardial infarction (MI). How CoDE-ACS performs at different time points for serial measurement and compares with guideline-recommended diagnostic pathways that rely on fixed thresholds and time points is uncertain. Patients with possible MI without ST-segment-elevation were enrolled at 12 sites in 5 countries and underwent serial high-sensitivity cardiac troponin I concentration measurement at 0, 1, and 2 hours. Diagnostic performance of the CoDE-ACS model at each time point was determined for index type 1 MI and the effectiveness of previously validated low- and high-probability scores compared with guideline-recommended European Society of Cardiology (ESC) 0/1-hour, ESC 0/2-hour, and High-STEACS (High-Sensitivity Troponin in the Evaluation of Patients With Suspected Acute Coronary Syndrome) pathways. In total, 4105 patients (mean age, 61 years [interquartile range, 50-74]; 32% women) were included, among whom 575 (14%) had type 1 MI. At presentation, CoDE-ACS identified 56% of patients as low probability, with a negative predictive value and sensitivity of 99.7% (95% CI, 99.5%-99.9%) and 99.0% (98.6%-99.2%), ruling out more patients than the ESC 0-hour and High-STEACS (25% and 35%) pathways. Incorporating a second cardiac troponin measurement, CoDE-ACS identified 65% or 68% of patients as low probability at 1 or 2 hours, for an identical negative predictive value of 99.7% (99.5%-99.9%); 19% or 18% as high probability, with a positive predictive value of 64.9% (63.5%-66.4%) and 68.8% (67.3%-70.1%); and 16% or 14% as intermediate probability. In comparison, after serial measurements, the ESC 0/1-hour, ESC 0/2-hour, and High-STEACS pathways identified 49%, 53%, and 71% of patients as low risk, with a negative predictive value of 100% (99.9%-100%), 100% (99.9%-100%), and 99.7% (99.5%-99.8%); and 20%, 19%, or 29% as high risk, with a positive predictive value of 61.5% (60.0%-63.0%), 65.8% (64.3%-67.2%), and 48.3% (46.8%-49.8%), resulting in 31%, 28%, or 0%, who require further observation in the emergency department, respectively. CoDE-ACS performs consistently irrespective of the timing of serial cardiac troponin measurement, identifying more patients as low probability with comparable performance to guideline-recommended pathways for MI. Whether care guided by probabilities can improve the early diagnosis of MI requires prospective evaluation. URL: https://www.clinicaltrials.gov; Unique identifier: NCT00470587.

SleepMI: An AI-based screening algorithm for myocardial infarction using nocturnal electrocardiography

Myocardial infarction (MI) is a common cardiovascular disease, the early diagnosis of which is essential for effective treatment and reduced mortality. Therefore, novel methods are required for automatic screening or early diagnosis of MI, and many studies have proposed diverse conventional methods for its detection. In this study, we aimed to develop a sleep-myocardial infarction (sleepMI) algorithm for automatic screening of MI based on nocturnal electrocardiography (ECG) findings from diagnostic polysomnography (PSG) data using artificial intelligence (AI) models. The proposed sleepMI algorithm was designed using representation and ensemble learning methods and optimized via dropout and batch normalization. In the sleepMI algorithm, a deep convolutional neural network and light gradient boost machine (LightGBM) models were mixed to obtain robust and stable performance for screening MI from nocturnal ECG findings. The nocturnal ECG signal was extracted from 2,691 participants (2,331 healthy individuals and 360 patients with MI) from the PSG data of the second follow-up stage of the Sleep Heart Health Study. The nocturnal ECG signal was extracted 3 h after sleep onset and segmented at 30-s intervals for each participant. All ECG datasets were divided into training, validation, and test sets consisting of 574,729, 143,683, and 718,412 segments, respectively. The proposed sleepMI model exhibited very high performance with precision, recall, and F1-score of 99.38%, 99.38%, and 99.38%, respectively. The total mean accuracy for automatic screening of MI using a nocturnal single-lead ECG was 99.387%. MI events can be detected using conventional 12-lead ECG signals and polysomnographic ECG recordings using our model.

Plasma metabolites and risk of myocardial infarction: a bidirectional Mendelian randomization study.

Myocardial infarction (MI) is a critical cardiovascular event with multifaceted etiology, involving several genetic and environmental factors. It is essential to understand the function of plasma metabolites in the development of MI and unravel its complex pathogenesis. This study employed a bidirectional Mendelian randomization (MR) approach to investigate the causal relationships between plasma metabolites and MI risk. We used genetic instruments as proxies for plasma metabolites and MI and conducted MR analyses in both directions to assess the impact of metabolites on MI risk and vice versa. In addition, the large-scale genome-wide association studies datasets was used to identify genetic variants associated with plasma metabolite (1400 metabolites) and MI (20,917 individuals with MI and 440,906 individuals without MI) susceptibility. Inverse variance weighted was the primary method for estimating causal effects. MR estimates are expressed as beta coefficients or odds ratio (OR) with 95% CI. We identified 14 plasma metabolites associated with the occurrence of MI (P < 0.05), among which 8 plasma metabolites [propionylglycine levels (OR = 0.922, 95% CI: 0.881-0.965, P < 0.001), gamma-glutamylglycine levels (OR = 0.903, 95% CI: 0.861-0.948, P < 0.001), hexadecanedioate (C16-DC) levels (OR = 0.941, 95% CI: 0.911-0.973, P < 0.001), pentose acid levels (OR = 0.923, 95% CI: 0.877-0.972, P = 0.002), X-24546 levels (OR = 0.936, 95% CI: 0.902-0.971, P < 0.001), glycine levels (OR = 0.936, 95% CI: 0.909-0.964, P < 0.001), glycine to serine ratio (OR = 0.930, 95% CI: 0.888-0.974, P = 0.002), and mannose to trans-4-hydroxyproline ratio (OR = 0.912, 95% CI: 0.869-0.958, P < 0.001)] were correlated with a decreased risk of MI, whereas the remaining 6 plasma metabolites [1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) levels (OR = 1.051, 95% CI: 1.018-1.084, P = 0.002), behenoyl dihydrosphingomyelin (d18:0/22:0) levels (OR = 1.076, 95% CI: 1.027-1.128, P = 0.002), 1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) levels (OR = 1.067, 95% CI: 1.027-1.109, P = 0.001), alpha-ketobutyrate levels (OR = 1.108, 95% CI: 1.041-1.180, P = 0.001), 5-acetylamino-6-formylamino-3-methyluracil levels (OR = 1.047, 95% CI: 1.019-1.076, P < 0.001), and N-acetylputrescine to (N (1) + N (8))-acetylspermidine ratio (OR = 1.045, 95% CI: 1.018-1.073, P < 0.001)] were associated with an increased risk of MI. Furthermore, we also observed that the mentioned relationships were unaffected by horizontal pleiotropy (P > 0.05). On the contrary, MI did not lead to significant alterations in the levels of the aforementioned 14 plasma metabolites (P > 0.05 for each comparison). Our bidirectional MR study identified 14 plasma metabolites associated with the occurrence of MI, among which 13 plasma metabolites have not been reported previously. These findings provide valuable insights for the early diagnosis of MI and potential therapeutic targets.

Detection of myocardial infarction using analysis of vectorcardiographic loops

Vectorcardiography is an alternative form of ECG for measuring electrical activity of the heart. It achieves higher sensitivity and provides the cardiologist additional information that can contribute to early diagnosis. This study is focused on proposal of a methodology for the processing of directly measured and transformed VCG records by using Kors regression transformation. A total 16 VCG features were extracted, while 12 features showed relevant information based on the statistical analysis and the method of maximum relevance minimum redundancy. These features served as input to the LDA and decision trees classifiers, while LDA achieved the most accurate results with accuracy 91.5%, specificity 76.3% and sensitivity 94.8% for directly measured VCG and accuracy 90.9%, specificity 76.3% and sensitivity 94.0% for transformed VCG. We conclude that this proposed methodology and the results obtained from it can be beneficial for the early diagnosis of myocardial infarction within the framework of automated detection.

Machine learning versus guideline recommended pathways for the diagnosis of myocardial infarction

Abstract Background CoDE-ACS (Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome) is a validated clinical decision-support tool that uses machine learning to calculate the probability of myocardial infarction (MI) for an individual patient. Current European guidelines recommend the use of 0/1h- and 0/2h-algorithms optimised to rule-out or rule-in MI and the High-STEACS pathway, which enables rapid discharge or admission in patients with suspected MI. However, it is unknown how CoDE-ACS performs against these guideline recommended pathways. Methods We enrolled adult patients presenting with suspected acute coronary syndromes at 12 sites in 5 European countries. Patients with ST-elevation MI and those with missing high-sensitivity (hs)-cTnI concentrations at 0h, 1h or 2h were excluded. Final diagnoses were adjudicated by two independent cardiologists according to current guidelines and the 4th universal definition of MI using all information including serial hs-cTnI measurements and imaging. We compare the diagnostic performance and effectiveness of CoDE-ACS with the 0/1h- and 0/2h-algorithms and the High-STEACS pathway. Results In 4,105 patients (median age 61 [IQR 50-74], 32% women), the prevalence of type 1 MI was 14%. At presentation, CoDE-ACS identified 56% (2,280/4,105) of patients as low-probability, with a 99.7% (99.5-99.9%) negative predictive value (NPV) and 99.0% (98.6-99.2%) sensitivity, which ruled out twice as many patients as other pathways. CoDE-ACS incorporating 1h hs-cTnI measurements identified 65% (2,671/4,105) patients as low-probability with a 99.7% (99.5-99.8%) NPV and 98.6% (98.2-98.9%) sensitivity and 19% (770/4,105) patients as high-probability with a 64.9% (63.5-66.4%) PPV and 92.4% (91.5-93.1%) specificity, with 16% classified as intermediate probability. In comparison, the 0/1h-algorithm identified 49% (2,014/4,105) patients as low-risk with a 100% (99.9-100%) NPV and 100% (99.9-100%) sensitivity and 20% (829/4,105) patients as high-risk with a 61.5% (60.0-63.0%) PPV and 91.0% (90.0-91.8%) specificity, with 31% triaged to the observe zone. Findings for CoDE-ACS incorporating hs-cTnI measurements at 2h, the 0/2h-algorithm and the High-STEACS pathway were comparable (Table). Conclusion All guideline recommended clinical pathways provide excellent performance for the diagnosis of MI. CoDE-ACS provides comparable diagnostic performance, but identifies twice as many patients as low-probability at presentation and fewer patients require further observation following serial hs-cTnI measurements. Implementation studies are warranted to determine whether CoDE-ACS can further improve the early diagnosis of MI.

External validation of the myocardial-ischemic-injury-index: a machine learning algorithm for the early diagnosis of myocardial infarction

Abstract Introduction The myocardial-ischemic-injury-index (MI3) is a machine learning algorithm derived in 2019 for the early diagnosis of type 1 non-ST-elevation myocardial infarction (NSTEMI). MI3 incorporates age, sex, and two high-sensitivity cardiac troponin I (hs-cTnI) Architecht measurements. The performance of the MI3 algorithm is unknown in a setting where the time difference between the first and second troponin measurement is less than 3 hours. Thus, external validation seems mandatory before this algorithm could be considered for clinical use. Purpose To externally validate the performance of the MI3 algorithm and compare its performance with the ESC 0/1h-algorithm, the recommended algorithm by the European Society of Cardiology NSTEMI guidelines. Methods In a multicentre international diagnostic study, we prospectively enrolled unselected patients presenting to the emergency department with symptoms suggestive of MI. Final diagnoses were centrally adjudicated by two independent cardiologists using all available medical records, including cardiac imaging, serial hs-cTn and 90-days follow-up. Hs-cTnI (Architect) concentrations were measured in a blinded fashion at presentation and serially thereafter. The primary endpoint was type 1 NSTEMI during the index visit. Results Among 6487 patients with two available hs-cTnI concentrations, type 1 NSTEMI was the adjudicated final diagnosis in 882 (13.6%) patients. The median time difference between blood draws was 60 minutes (IQR: 57-70). Model performance was good, with a very high area under the receiver-operating-characteristic curve (0.961 [95% CI: 0.957-0.965]) and an optimal calibration overall (intercept -0.09 [-0.2-0.02]; slope 1.02 [0.97-1.08]), Figure 1. The originally defined low-probability MI3 score&amp;lt;1.6 and high-probability MI3 score≥49.7 triaged 4186 (64.5%) patients towards rule-out (sensitivity 99.1% [98.2-99.5]; negative predictive value [NPV] 99.8% [99.6-99.9]) and 915 (14.1%) patients towards rule-in (specificity 95.0% [94.3-95.5]; positive predictive value [PPV] 69.1% [66.0-72.0]), respectively (Figure 2). Both the sensitivity and the NPV of the ESC 0/1h-algorithm were greater than of the MI3 algorithm (difference for sensitivity: 0.84% [0.18-1.51], P=0.008; difference for NPV: 0.18% [0.05-0.33], P=0.016). In contrast, the specificity and PPV for the MI3 algorithm were higher (difference for specificity: 3.85% [3.29-4.40], P&amp;lt;0.001; difference for PPV: 7.82% [5.9-9.74], P&amp;lt;0.001). Conclusion In patients with two hs-cTnI (Architect) measurements with a median time difference of 60 minutes, the MI3 algorithm showed a very high discrimination and good calibration for type 1 NSTEMI.Diagnostic performance MI3 algorithmCutoff performance of the MI3 algorithm

Comprehensive validation of early diagnostic algorithms for myocardial infarction in the emergency department.

To comprehensively evaluate diagnostic algorithms for myocardial infarction using a high-sensitivity cardiac troponin I (hs-cTnI) assay. We prospectively enrolled patients with suspected myocardial infarction without ST-segment elevation from nine emergency departments in Japan. The diagnostic algorithms evaluated: (i) based on hs-cTnI alone, such as the European Society of Cardiology (ESC) 0/1-h or 0/2-h and High-STEACS pathways; or (ii) used medical history and physical findings, such as the ADAPT, EDACS, HEART, and GRACE pathways. We evaluated the negative predictive value (NPV), sensitivity as safety measures, and proportion of patients classified as low or high-risk as an efficiency measure for a primary outcome of type 1 myocardial infarction or cardiac death within 30 days. We included 437 patients, and the hs-cTnI was collected at 0 and 1 hours in 407 patients and at 0 and 2 hours in 394. The primary outcome occurred in 8.1% (33/407) and 6.9% (27/394) of patients, respectively. All the algorithms classified low-risk patients without missing those with the primary outcome, except for the GRACE pathway. The hs-cTnI-based algorithms classified more patients as low-risk: the ESC 0/1-h 45.7%; the ESC 0/2-h 50.5%; the High-STEACS pathway 68.5%, than those using history and physical findings (15-30%). The High-STEACS pathway ruled out more patients (20.5%) by hs-cTnI measurement at 0 hours than the ESC 0/1-h and 0/2-h algorithms (7.4%). The hs-cTnI algorithms, especially the High-STEACS pathway, had excellent safety performance for the early diagnosis of myocardial infarction and offered the greatest improvement in efficiency.

Segmental and transmural motion of the rat myocardium estimated using quantitative ultrasound with new strategies for infarct detection.

Introduction: The estimation of myocardial motion abnormalities has great potential for the early diagnosis of myocardial infarction (MI). This study aims to quantitatively analyze the segmental and transmural myocardial motion in MI rats by incorporating two novel strategies of algorithm parameter optimization and transmural motion index (TMI) calculation. Methods: Twenty-one rats were randomly divided into three groups (n = 7 per group): sham, MI, and ischemia-reperfusion (IR) groups. Ultrasound radio-frequency (RF) signals were acquired from each rat heart at 1 day and 28 days after animal model establishment; thus, a total of six datasets were represented as Sham1, Sham28, MI1, MI28, IR1, and IR28. The systolic cumulative displacement was calculated using our previously proposed vectorized normalized cross-correlation (VNCC) method. A semiautomatic regional and layer-specific myocardium segmentation framework was proposed for transmural and segmental myocardial motion estimation. Two novel strategies were proposed: the displacement-compensated cross-correlation coefficient (DCCCC) for algorithm parameter optimization and the transmural motion index (TMI) for quantitative estimation of the cross-wall transmural motion gradient. Results: The results showed that an overlap value of 80% used in VNCC guaranteed a more accurate displacement calculation. Compared to the Sham1 group, the systolic myocardial motion reductions were significantly detected (p < 0.05) in the middle anteroseptal (M-ANT-SEP), basal anteroseptal (B-ANT-SEP), apical lateral (A-LAT), middle inferolateral (M-INF-LAT), and basal inferolateral (B-INF-LAT) walls as well as a significant TMI drop (p < 0.05) in the M-ANT-SEP wall in the MI1 rats; significant motion reductions (p < 0.05) were also detected in the B-ANT-SEP and A-LAT walls in the IR1 group. The motion improvements (p < 0.05) were detected in the M-INF-LAT wall in the MI28 group and the apical septal (A-SEP) wall in the IR28 group compared to the MI1 and IR1 groups, respectively. Discussion: Our results show that the MI-induced reductions and reperfusion-induced recovery in systolic myocardial contractility could be successfully evaluated using our method, and most post-MI myocardial segments could recover systolic function to various extents in the remodeling phase. In conclusion, the ultrasound-based quantitative estimation framework for estimating segmental and transmural motion of the myocardium proposed in our study has great potential for non-invasive, novel, and early MI detection.

Cardiac biomarkers and their clinical relevance

Cardiac biomarkers are an integral, guideline-recommended part of the diagnosis and follow-up of heart diseases. High sensitivity tests for troponin I or T allow for the early diagnosis of myocardial infarction. Rule-in and rule-out algorithms based on the dynamic of plasma concentrations in the first hour after admission improve safe, evidence-based decision making for patients with acute chest pain. Low concentrations of brain natriuretic peptides (BNP or NT-proBNP) reliably exclude heart failure. Elevated BNP/NT-proBNP concentrations are part of the definition of all types of heart failure but require additional tests to diagnose heart failure. Chronic elevations of troponins and BNP/NT-proBNP identify subpopulations at increased risk of cardiovascular events even in the absence of manifest cardiac disease. Whether and how this risk can be reduced requires further evaluation. Several novel biomarkers were recently discovered and characterised. Their place in cardiovascular medicine has yet to be defined.

Nursing care for patients diagnosed with acute myocardial in-farction in the intensive care unit: a narrative review

Cardiovascular diseases are among the main pathologies that cause deaths in Brazil, and directly contribute to the increase in the number of intensive care unit hospitalizations. These cardiological dysfunctions are caused by several factors that must be treated immediately after the appearance of the first signs and symptoms. The care environment in the Intensive Care Unit (ICU), the focus of the present study, is an environment intended to assist seriously ill and unstable patients that are usually in a hospital setting and is considered high complexity due to its cutting-edge technological and computerized apparatus. The role of the nurse begins as soon as the patient is admitted upon arriving at healthcare facilities in general, with the main function of early diagnosis and immediate initiation of emergency care, increasing the patient's chances of survival. The early diagnosis of myocardial infarction (MI) and therapeutic interventions directly affect the morbidity and mortality of patients. Therefore, this study aimed, through a narrative review, to highlight the main risk factors and understand the importance of nursing care in the assistance of patients with Acute Myocardial Infarction in the Intensive Care Unit.

Modern View on the Role of Sex-Specific Levels of High-Sensitive Cardiospecific Troponins T and I in the Diagnosis of Myocardial Infarction.

It is well known that the molecules of cardiospecific troponins T and I are localized in the troponin-tropomyosin complex of the cytoplasm of cardiac myocytes and, due to the specific localization, these cardiospecific troponins are widely used as diagnostic biomarkers of myocardial infarction. Cardiospecific troponins are released from the cytoplasm of cardiac myocytes as a result of irreversible cell damage (for example, ischemic necrosis of cardiomyocytes in myocardial infarction or apoptosis of cardiac myocytes in cardiomyopathies and heart failure) or reversible damage (for example, intense physical exertion, hypertension, the influence of stress factors, etc.). Current immunochemical methods for determining cardiospecific troponins T and I have extremely high sensitivity to subclinical (minor) damage to myocardial cells and, thanks to modern high-sensitive methods, it is possible to detect damage to cardiac myocytes in the early (subclinical) stages of a number of cardiovascular pathologies, including myocardial infarction. So, recently, leading cardiological communities (the European Society of Cardiology, the American Heart Association, the American College of Cardiology, etc.) have approved algorithms for early diagnosis of myocardial infarction based on the assessment of serum levels of cardiospecific troponins in the first 1 - 3 h after the onset of pain syndrome. An important factor that may affect early diagnostic algorithms of myocardial infarction are sex-specific features of serum levels of cardiospecific troponins T and I. This manuscript presents a modern view on the role of sex-specific serum levels of cardiospecific troponins T and I in the diagnosis of myocardial infarction and the mechanisms of formation of sex-specific serum levels of troponins.

Epigenetic regulation in myocardial infarction: Non-coding RNAs and exosomal non-coding RNAs.

Myocardial infarction (MI) is one of the leading causes of deaths globally. The early diagnosis of MI lowers the rate of subsequent complications and maximizes the benefits of cardiovascular interventions. Many efforts have been made to explore new therapeutic targets for MI, and the therapeutic potential of non-coding RNAs (ncRNAs) is one good example. NcRNAs are a group of RNAs with many different subgroups, but they are not translated into proteins. MicroRNAs (miRNAs) are the most studied type of ncRNAs, and have been found to regulate several pathological processes in MI, including cardiomyocyte inflammation, apoptosis, angiogenesis, and fibrosis. These processes can also be modulated by circular RNAs and long ncRNAs via different mechanisms. However, the regulatory role of ncRNAs and their underlying mechanisms in MI are underexplored. Exosomes play a crucial role in communication between cells, and can affect both homeostasis and disease conditions. Exosomal ncRNAs have been shown to affect many biological functions. Tissue-specific changes in exosomal ncRNAs contribute to aging, tissue dysfunction, and human diseases. Here we provide a comprehensive review of recent findings on epigenetic changes in cardiovascular diseases as well as the role of ncRNAs and exosomal ncRNAs in MI, focusing on their function, diagnostic and prognostic significance.

MCA-net: A multi-task channel attention network for Myocardial infarction detection and location using 12-lead ECGs

Problem:Myocardial infarction (MI) is a classic cardiovascular disease (CVD) that requires prompt diagnosis. However, due to the complexity of its pathology, it is difficult for cardiologists to make an accurate diagnosis in a short period. Aim:In the clinical, MI can be detected and located by the morphological changes on a 12-lead electrocardiogram (ECG). Therefore, we need to develop an automatic, high-performance, and easily scalable algorithm for MI detection and location using 12-lead ECGs to effectively reduce the burden on cardiologists. Methods:This paper proposes a multi-task channel attention network (MCA-net) for MI detection and location using 12-lead ECGs. It employs a channel attention network based on a residual structure to efficiently capture and integrate features from different leads. On top of this, a multi-task framework is used to additionally introduce the shared and complementary information between MI detection and location tasks to further enhance the model performance. Results:Our method is evaluated on two datasets (The PTB and PTBXL datasets). It achieved more than 90% accuracy for MI detection task on both datasets. For MI location tasks, we achieved 68.90% and 49.18% accuracy on the PTB dataset, respectively. And on the PTBXL dataset, we achieved more than 80% accuracy. Conclusion:Numerous comparison experiments demonstrate that MCA-net outperforms the state-of-the-art methods and has a better generalization. Therefore, it can effectively assist cardiologists to detect and locate MI and has important implications for the early diagnosis of MI and patient prognosis.

Performance of high-sensitivity cardiac troponin T versus I for the early diagnosis of myocardial infarction

Abstract Background Clinical practice guidelines assume that both cardiac troponin (cTn) T and cTnI concentrations reflect identical pathophysiological processes and are equally effective in the detection of myocardial injury. However, there are differences between cTnT and cTnI that have been reported. Purpose The aim of this study was to directly compare the diagnostic performance of high-sensitivity cardiac troponin (hs-cTn) T versus hs-cTnI for the early diagnosis of acute myocardial infarction (MI). Methods In a prospective multicentre study, diagnostic and prognostic accuracies of hs-cTnT and I were analyzed in consecutive patients presenting to the emergency department with acute chest pain. The final diagnosis was adjudicated by two independent cardiologists using all information pertaining to the individual patient according to the fourth universal definition of MI. Adjudication of the final diagnoses was performed twice: once using serial measurements of hs-cTnT and once using hs-cTnI. Furthermore, the clinical performance of hs-cTnT/I when embedded in the European Society of Cardiology (ESC) 0/1h-algorithm was assessed. Results Among 5087 consecutive patients (median [Interquartile range, IQR] age 61 [49.0, 74.0] years, 33.2% female), 951 (18.7%) and 901 patients (17.7%) had an adjudicated final diagnosis of non-ST-segment elevation myocardial infarction (NSTEMI) when using serial measurements of hs-cTnT and hs-cTnI for adjudication, respectively. Diagnostic accuracy was very high for both hs-cTnT and hs-cTnI and comparable when using hs-cTnT for adjudication (hs-cTnT: area under the curve [AUC] 0.93 [95% CI 0.92–0.94] versus hs-cTnI AUC 0.93 [95% CI 0.92–0.94]; p=0.891). However, when using serial measurements of hs-cTnI for adjudication, diagnostic accuracy was significantly higher for hs-cTnI (AUC 0.93 [95% CI 0.92–0.94] versus AUC 0.94 [95% CI 0.94–0.95], p&amp;lt;0.001; Figure 1). This was confirmed in subgroup analyses including early presenter (≤3h), patients with renal failure, known coronary artery disease and elderly (≥70 years). However, both assays performed excellent with very high safety for rule-out and high accuracy for rule-in MI when embedded in the ESC 0/1h-algorithm. Prognostic accuracies for 730-day all-cause mortality and cardiovascular death were significantly higher for hs-cTnT compared to hs-cTnI (Figure 2). Conclusions While there seem to be differences between hs-cTnT and hs-cTnI in their diagnostic and prognostic performance, clinical relevance needs to be further evaluated since both assays performed excellent when embedded in their respective early triage algorithms. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Swiss National Foundation, Swiss Heart Foundation

0/1h-algorithm using a new high-sensitivity cardiac troponin I assay for early diagnosis of myocardial infarction

Abstract Background The clinical performance of the novel high-sensitivity cardiac troponin I EXL (hs-cTnI-EXL) assay is unknown so far. Purpose We aimed to validate the clinical performance of the hs-cTnI-EXL assay and to derive and validate an hs-cTnI-EXL-specific 0/1h-algorithm for the early diagnosis of myocardial infarction (MI). Methods This multicenter study included patients presenting to the emergency department with symptoms suggestive of myocardial infarction. Central adjudication of final diagnoses was performed by two independent cardiologists using all clinical information including cardiac imaging twice: first, using serial hs-cTnI-Architect (primary analysis) and second, using serial hs-cTnT-Elecsys (secondary analysis) concentrations in addition to those clinically used (hs)-cTn. Hs-cTnI-EXL was measured at presentation and at 1h. The primary objective was to directly compare diagnostic accuracy quantified by the area under the receiver-operating-characteristic curve (AUC) of hs-cTnI-EXL, hs-cTnI-Architect and hs-cTnT-Elecsys. Secondary objectives included the derivation and validation of an hs-cTnI- EXL-specific 0/1h-algorithm. Results MI was the adjudicated final diagnosis in 204/1454 (14%) patients. At presentation, the AUC for hs-cTnI-EXL was 0.94 (95% CI, 0.93–0.96), being comparable to hs-cTnI-Architect (0.95; 95% CI, 0.93–0.96) and hs-cTnT-Elecsys (0.93; 95% CI, 0.91–0.95; Figure 1). In the derivation cohort (n=813), an optimal hs-cTnI-EXL-0/1h-algorithm was rule-out of MI with &amp;lt;9ng/L if onset of chest pain &amp;gt;3h or &amp;lt;9ng/L &amp; 0h-1h-change &amp;lt;5ng/L, and rule-in with ≥160ng/L or 0h-1h-change ≥100ng/L. In the validation cohort (n=345), this hs-cTnI-EXL-0/1h-algorithm also performed well: rule-out in 56% of patients, negative predictive value 99.5% (95% CI, 97.1–99.9), sensitivity 97.8% (95% CI, 88.7–99.6), rule-in in 9% of patients, positive predictive value 83.3% (95% CI, 66.4–92.7), specificity 98.3% (95% CI, 96.1–99.3; Figure 2). Secondary analyses confirmed the findings using adjudication including serial measurements of hs-cTnT-Elecsys. Conclusions Hs-cTnI-EXL has comparable diagnostic performance to the currently best-validated hs-cTnT/I assays. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Swiss National Foundation, Swiss Heart Foundation

High-sensitive and disposable myocardial infarction biomarker immunosensor with optofluidic microtubule lasing

Abstract The early diagnosis of myocardial infarction can significantly improve the survival rate in emergency treatment, which is mainly implemented by the immunoassay for myocardial infarction biomarkers such as cardiac troponins in blood. In this work, a disposable optofluidic microtubule whispering gallery mode (WGM) immunosensor for label-free cardiac troponin I-C (cTnI-C) complex detection has been proposed and demonstrated with active interrogation enhancement. The disposable microtubule is simply fabricated by a silica capillary with pressurized tapering technology for thin-wall, and the cTnI antibodies are immobilized on the inner wall surface of the microtubule through the self-adherent polydopamine substrate. By configuring the two coupling microfibers, the double-fiber-coupled microtubule cavity can serve as a tunable filter for the mutual-coupled polarimetric fiber ring laser (FRL), whose output laser wavelength is determined by the cTnI-C concentration in the optofluidic microtubule with inherent microfluidic channel. Due to the cyclic-cumulative gain of the FRL, the characteristic resonant peak of optical sensing signal is enhanced in the spectral width compression and the optical signal-to-noise ratio improvement, and therefore the optical immunosensor for cTnI-C can be achieved by tracking the output laser wavelength of the FRL conveniently. The dynamic binding and unbinding process of cTnI-C antigen–antibody is illustrated by monitoring the lasing peak wavelength continuously. Our all-fiber immunosensor demonstrated here has the advantages of fast label-free detection, real-time monitor, high sensitivity and disposable sensing element, which can be an innovative detecting tool in early diagnosis of myocardial infarction.

Detection of myocardial infarction on recent dataset using machine learning

&lt;span&gt;In developing countries such as India, with a large aging population and limited access to medical facilities, remote and timely diagnosis of myocardial infarction (MI) has the potential to save the life of many. An electrocardiogram is the primary clinical tool utilized in the onset or detection of a previous MI incident. Artificial intelligence has made a great impact on every area of research as well as in medical diagnosis. In medical diagnosis, the hypothesis might be doctors' experience which would be used as input to predict a disease that saves the life of mankind. It is been observed that a properly cleaned and pruned dataset provides far better accuracy than an unclean one with missing values. Selection of suitable techniques for data cleaning alongside proper classification algorithms will cause the event of prediction systems that give enhanced accuracy. In this proposal detection of myocardial infarction using new parameters is proposed with increased accuracy and efficiency of the existing model. Additional parameters are used to predict MI with more accuracy. The proposed model is used to predict an early diagnosis of MI with the help of expertise experiences and data gathered from hospitals.&lt;/span&gt;