Intravascular Ultrasound vs. Fractional Flow Reserve for Percutaneous Coronary Intervention Optimization in Long Coronary Artery Lesions.

BACKGROUND: intravascular ultrasound (IVUS) and fractional flow reserve (FFR) have both been shown to be superior to angiography in optimizing percutaneous coronary intervention (PCI). However, there is still a lack of comparative studies between PCI optimization using physiology and intravascular imaging head-to-head. The aim of this study was to compare the effectiveness of FFR and IVUS PCI optimization strategies on the functional PCI result (assessed with FFR) immediately post-PCI and at 9-12 months after the treatment of long coronary lesions. METHODS: This was a single-center study comparing post-PCI FFR between two different PCI optimization strategies (FFR and IVUS). The study included 154 patients who had hemodynamically significant long lesions, necessitating a stent length of 30 mm or more. The procedural outcomes were functional PCI result immediately post-PCI and at 9-12 months after treatment. Clinical outcomes included target vessel failure (TVF) and functional target vessel restenosis rate during follow-up. RESULTS: Baseline clinical characteristics and FFR (0.65 [0.55-0.71]) did not differ significantly between the two groups and the left anterior descending artery was treated in 82% of cases. The FFR optimization strategy resulted in a significantly shorter stented segment (49 mm vs. 63 mm, p = 0.001) compared to the IVUS optimization strategy. Although the rates of optimal functional PCI result (FFR > 0.9) did not significantly differ between the FFR and IVUS optimization strategies, a proportion of patients in the FFR group (12%) experienced poor post-PCI functional outcome with FFR values ≤ 0.8, which was not observed in the IVUS group. At the 9-12 month follow-up, 20% of patients in the FFR group had target-vessel-related myocardial ischemia, compared to 6% in the IVUS group. The rates of TVF and functional target vessel restenosis during follow-up were also numerically higher in the FFR optimization group. CONCLUSIONS: The use of FFR PCI optimization strategy in the treatment of long coronary artery lesions is associated with a higher incidence of poor functional PCI result and larger myocardial ischemia burden at follow-up compared to the IVUS optimization strategy. However, this discrepancy did not translate into a statistically significant difference in clinical outcomes. This study highlights the importance of using IVUS to optimize long lesions functional PCI outcomes.

Effects of ticagrelor and prasugrel on coronary microcirculation in elective percutaneous coronary intervention.

OBJECTIVE: To compare the effects of ticagrelor and prasugrel on absolute coronary blood flow (Q) and microvascular resistance (R) in patients with stable coronary artery disease (CAD) treated with elective percutaneous coronary intervention (PCI) (NCT05643586). Besides being at least as effective as prasugrel in inhibiting platelet aggregation, ticagrelor has been shown to have additional properties potentially affecting coronary microcirculation. METHODS: We randomly assigned 50 patients to ticagrelor (180 mg) or prasugrel (60 mg) at least 12 hours before intervention. Continuous thermodilution was used to measure Q and R before and after PCI. Platelet reactivity was measured before PCI. Troponin I was measured before, 8 and 24 hours after PCI. RESULTS: At baseline, fractional flow reserve, Q and R were similar in two study groups. Patients in the ticagrelor group showed higher post-PCI Q (242±49 vs 205±53 mL/min, p=0.015) and lower R values (311 (263, 366) vs 362 (319, 382) mm Hg/L/min, p=0.032). Platelet reactivity showed a negative correlation with periprocedural variation of Q values (r=-0.582, p<0.001) and a positive correlation with periprocedural variation of R values (r=0.645, p<0.001). The periprocedural increase in high-sensitivity troponin I was significantly lower in the ticagrelor compared with the prasugrel group (5 (4, 9) ng/mL vs 14 (10, 24) ng/mL, p<0.001). CONCLUSIONS: In patients with stable CAD undergoing PCI, pretreatment with a loading dose of ticagrelor compared with prasugrel improves post-procedural coronary flow and microvascular function and seems to reduce the related myocardial injury.

What do we mean by complex percutaneous coronary intervention? An assessment of agreement amongst interventional cardiologists for defining complexity.

BACKGROUND: In the last decade, percutaneous coronary intervention (PCI) has evolved toward the treatment of complex disease in patients with multiple comorbidities. Whilst there are several definitions of complexity, it is unclear whether there is agreement between cardiologists in classifying complexity of cases. Inconsistent identification of complex PCI can lead to significant variation in clinical decision-making. AIM: This study aimed to determine the inter-rater agreement in rating the complexity and risk of PCI procedures. METHOD: An online survey was designed and disseminated amongst interventional cardiologists by the European Association of Percutaneous Cardiovascular Intervention (EAPCI) board. The survey presented four patient vignettes, with study participants assessing these cases to classify their complexity. RESULTS: From 215 respondents, there was poor inter-rater agreement in classifying the complexity level (k = 0.1) and a fair agreement (k = 0.31) in classifying the risk level. The experience level of participants did not show any significant impact on the inter-rater agreement of rating the complexity level and the risk level. There was good level of agreement between participants in terms of rating 26 factors for classifying complex PCI. The top five factors were (1) impaired left ventricular function, (2) concomitant severe aortic stenosis, (3) last remaining vessel PCI, (4) requirement fort calcium modification and (5) significant renal impairment. CONCLUSION: Agreement among cardiologists in classifying complexity of PCI is poor, which may lead to suboptimal clinical decision-making, procedural planning as well as long-term management. Consensus is needed to define complex PCI, and this requires clear criteria incorporating both lesion and patient characteristics.

Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications.

BACKGROUND: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over recent decades, there has been a steady increase in the number of research studies that are using machine learning (ML) to read or interrogate ECG data. OBJECTIVE: The aim of this study is to review the use of ML with ECG data using a time series approach. METHODS: Papers that address the subject of ML and the ECG were identified by systematically searching databases that archive papers from January 1995 to October 2019. Time series analysis was used to study the changing popularity of the different types of ML algorithms that have been used with ECG data over the past two decades. Finally, a meta-analysis of how various ML techniques performed for various diagnostic classifications was also undertaken. RESULTS: A total of 757 papers was identified. Based on results, the use of ML with ECG data started to increase sharply (p < 0.001) from 2012. Healthcare applications, especially in heart abnormality classification, were the most common application of ML when using ECG data (p < 0.001). However, many new emerging applications include using ML and the ECG for biometrics and driver drowsiness. The support vector machine was the technique of choice for a decade. However, since 2018, deep learning has been trending upwards and is likely to be the leading technique in the coming few years. Despite the accuracy paradox, accuracy was the most frequently used metric in the studies reviewed, followed by sensitivity, specificity, F1 score and then AUC. CONCLUSION: Applying ML using ECG data has shown promise. Data scientists and physicians should collaborate to ensure that clinical knowledge is being applied appropriately and is informing the design of ML algorithms. Data scientists also need to consider knowledge guided feature engineering and the explicability of the ML algorithm as well as being transparent in the algorithm's performance to appropriately calibrate human-AI trust. Future work is required to enhance ML performance in ECG classification.

Exploring decision making 'noise' when interpreting the electrocardiogram in the context of cardiac cath lab activation.

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this 'noise' and improve the quality of ECG based decision making.

Bioabsorbable polymer drug-eluting stents with 4-month dual antiplatelet therapy versus durable polymer drug-eluting stents with 12-month dual antiplatelet therapy in patients with left main coronary artery disease: the IDEAL-LM randomised trial.

BACKGROUND: Improvements in drug-eluting stent design have led to a reduced frequency of repeat revascularisation and new biodegradable polymer coatings may allow a shorter duration of dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI). AIMS: The Improved Drug-Eluting stent for All-comers Left Main (IDEAL-LM) study aims to investigate long-term clinical outcomes after implantation of a biodegradable polymer platinum-chromium everolimus-eluting stent (BP-PtCr-EES) followed by 4 months DAPT compared to a durable polymer cobalt-chromium everolimus-eluting stent (DP-CoCr-EES) followed by 12 months DAPT in patients undergoing PCI of unprotected left main coronary artery (LMCA) disease. METHODS: This is a multicentre randomised clinical trial study in patients with an indication for coronary artery revascularisation who have been accepted for PCI for LMCA disease after Heart Team consultation. Patients were randomly assigned in a 1:1 ratio to receive either the BP-PtCr-EES or the DP-CoCr-EES. The primary endpoint was a non-inferiority comparison of the rate of major adverse cardiovascular events (MACE), defined as all-cause death, myocardial infarction, or ischaemia-driven target vessel revascularisation at 2 years. RESULTS: Between December 2014 and October 2016, 818 patients (410 BP-PtCr-EES and 408 DP-CoCr-EES) were enrolled at 29 centres in Europe. At 2 years, the primary endpoint of MACE occurred in 59 patients (14.6%) in the BP-PtCr-EES group and 45 patients (11.4%) in the DP-CoCr-EES group; 1-sided upper 95% confidence interval (CI) 7.18%; p=0.04 for non-inferiority; p=0.17 for superiority. The secondary endpoint event of BARC 3 or 5 bleeding occurred in 11 patients (2.7%) in the BP-PtCr-EES group and 2 patients (0.5%) in the DP-CoCr-EES group (p=0.02). CONCLUSIONS: In patients undergoing PCI of LMCA disease, after two years of follow-up, the use of a BP-PtCr-EES with 4 months of DAPT was non-inferior to a DP-CoCr-EES with 12 months of DAPT with respect to the composite endpoint of all-cause death, myocardial infarction or ischaemia-driven target vessel revascularisation.

Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction: Preangiogram prediction remains difficult.

BACKGROUND: Treatment decisions in myocardial infarction (MI) are currently stratified by ST elevation (ST-elevation myocardial infarction [STEMI]) or lack of ST elevation (non-ST elevation myocardial infarction [NSTEMI]) on the electrocardiogram. This arose from the assumption that ST elevation indicated acute coronary artery occlusion (OMI). However, one-quarter of all NSTEMI cases are an OMI, and have a higher mortality. The purpose of this study was to identify features that could help identify OMI. METHODS: Prospectively collected data from patients undergoing percutaneous coronary intervention (PCI) was analyzed. Data included presentation characteristics, comorbidities, treatments, and outcomes. Latent class analysis was undertaken, to determine patterns of presentation and history associated with OMI. RESULTS: A total of 1412 patients underwent PCI for acute MI, and 263 were diagnosed as OMI. Compared to nonocclusive MI, OMI patients are more likely to have fewer comorbidities but no difference in cerebrovascular disease and increased acute mortality (4.2% vs. 1.1%; p < .001). Of OMI, 29.5% had delays to their treatment such as immediate reperfusion therapy. With latent class analysis, while clusters of similar patients are observed in the data set, the data available did not usefully identify patients with OMI compared to non-OMI. CONCLUSION: Features between OMI and STEMI are broadly very similar. However, there was no difference in age and risk of cerebrovascular disease in the OMI/non-OMI group. There are no reliable characteristics therefore for identifying OMI versus non-OMI. Delays to treatment also suggest that OMI patients are still missing out on optimal treatment. An alternative strategy is required to improve the identification of OMI patients.

A clinician's guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML).

Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside.

TACE/ADAM17 substrates associate with ACS (Ep-CAM, HB-EGF) and follow-up MACE (TNFR1 and TNFR2).

Background and aims: TACE/ADAM17 is a membrane bound metalloprotease, which cleaves substrates involved in immune and inflammatory responses and plays a role in coronary artery disease (CAD). We measured TACE and its substrates in CAD patients to identify potential biomarkers within this molecular pathway with potential for acute coronary syndrome (ACS) and major adverse cardiovascular events (MACE) prediction. Methods: Blood samples were obtained from consecutive patients (n = 229) with coronary angiographic evidence of CAD admitted with ACS or electively. MACE were recorded after a median 3-year follow-up. Controls (n = 115) had a <10% CAD risk as per the HeartSCORE. TACE and TIMP3 protein and mRNA levels were measured by ELISA and RT-qPCR respectively. TACE substrates were measured using a multiplex proximity extension assay. Results: TACE mRNA and cell protein levels (p < 0.01) and TACE substrates LDLR (p = 0.006), TRANCE (p = 0.045), LAG-3 (p < 0.001) and ACE2 (p < 0.001) plasma levels were significantly higher in CAD patients versus controls. TACE inhibitor TIMP3 mRNA levels were significantly lower in CAD patients and tended to be lower in the ACS population (p < 0.05). TACE substrates TNFR1 (OR:3.237,CI:1.514-6.923,p = 0.002), HB-EGF (OR:0.484,CI:0.288-0.813,p = 0.006) and Ep-CAM (OR:0.555,CI:0.327-0.829,p = 0.004) accurately classified ACS patients with HB-EGF and Ep-CAM levels being lower compared to electively admitted patients. TNFR1 (OR:2.317,CI:1.377-3.898,p = 0.002) and TNFR2 (OR:1.902,CI:1.072-3.373,p = 0.028) were significantly higher on admission in those patients who developed MACE within 3 years. Conclusions: We demonstrate a possible role of TACE substrates LAG-3, HB-EGF and Ep-CAM in atherosclerotic plaque development and stability. We also underline the importance of measuring TNFR1 and TNFR2 earlier than previously appreciated for MACE prediction. We report an important role of TIMP3 in regulating TACE levels.

The Interdependency and Co-Regulation of the Vitamin D and Cholesterol Metabolism.

Vitamin D and cholesterol metabolism overlap significantly in the pathways that contribute to their biosynthesis. However, our understanding of their independent and co-regulation is limited. Cardiovascular disease is the leading cause of death globally and atherosclerosis, the pathology associated with elevated cholesterol, is the leading cause of cardiovascular disease. It is therefore important to understand vitamin D metabolism as a contributory factor. From the literature, we compile evidence of how these systems interact, relating the understanding of the molecular mechanisms involved to the results from observational studies. We also present the first systems biology pathway map of the joint cholesterol and vitamin D metabolisms made available using the Systems Biology Graphical Notation (SBGN) Markup Language (SBGNML). It is shown that the relationship between vitamin D supplementation, total cholesterol, and LDL-C status, and between latitude, vitamin D, and cholesterol status are consistent with our knowledge of molecular mechanisms. We also highlight the results that cannot be explained with our current knowledge of molecular mechanisms: (i) vitamin D supplementation mitigates the side-effects of statin therapy; (ii) statin therapy does not impact upon vitamin D status; and critically (iii) vitamin D supplementation does not improve cardiovascular outcomes, despite improving cardiovascular risk factors. For (iii), we present a hypothesis, based on observations in the literature, that describes how vitamin D regulates the balance between cellular and plasma cholesterol. Answering these questions will create significant opportunities for advancement in our understanding of cardiovascular health.

Comparing Single-Page, Multipage, and Conversational Digital Forms in Health Care: Usability Study.

BACKGROUND: Even in the era of digital technology, several hospitals still rely on paper-based forms for data entry for patient admission, triage, drug prescriptions, and procedures. Paper-based forms can be quick and convenient to complete but often at the expense of data quality, completeness, sustainability, and automated data analytics. Digital forms can improve data quality by assisting the user when deciding on the appropriate response to certain data inputs (eg, classifying symptoms). Greater data quality via digital form completion not only helps with auditing, service improvement, and patient record keeping but also helps with novel data science and machine learning research. Although digital forms are becoming more prevalent in health care, there is a lack of empirical best practices and guidelines for their design. The study-based hospital had a definite plan to abolish the paper form; hence, it was not necessary to compare the digital forms with the paper form. OBJECTIVE: This study aims to assess the usability of three different interactive forms: a single-page digital form (in which all data input is required on one web page), a multipage digital form, and a conversational digital form (a chatbot). METHODS: The three digital forms were developed as candidates to replace the current paper-based form used to record patient referrals to an interventional cardiology department (Cath-Lab) at Altnagelvin Hospital. We recorded usability data in a counterbalanced usability test (60 usability tests: 20 subjects×3 form usability tests). The usability data included task completion times, System Usability Scale (SUS) scores, User Experience Questionnaire data, and data from a postexperiment questionnaire. RESULTS: We found that the single-page form outperformed the other two digital forms in almost all usability metrics. The mean SUS score for the single-page form was 76 (SD 15.8; P=.01) when compared with the multipage form, which had a mean score of 67 (SD 17), and the conversational form attained the lowest scores in usability testing and was the least preferred choice of users, with a mean score of 57 (SD 24). An SUS score of >68 was considered above average. The single-page form achieved the least task completion time compared with the other two digital form styles. CONCLUSIONS: In conclusion, the digital single-page form outperformed the other two forms in almost all usability metrics; it had the least task completion time compared with those of the other two digital forms. Moreover, on answering the open-ended question from the final customized postexperiment questionnaire, the single-page form was the preferred choice.

The role of senescence in the pathogenesis of atrial fibrillation: A target process for health improvement and drug development.

Cellular senescence is a state of growth arrest that occurs after cells encounter various stresses. Senescence contributes to tumour suppression, embryonic development, and wound healing. It impacts on the pathology of various diseases by secreting inflammatory chemokines, immune modulators and other bioactive factors. These secretory biosignatures ultimately cause inflammation, tissue fibrosis, immunosenescence and many ageing-related diseases such as atrial fibrillation (AF). Because the molecular mechanisms underpinning AF development remain unclear, current treatments are suboptimal and have serious side effects. In this review, we summarize recent results describing the role of senescence in AF. We propose that senescence factors induce AF and have a causative role. Hence, targeting senescence and its secretory phenotype may attenuate AF.

Reliable Deep Learning-Based Detection of Misplaced Chest Electrodes During Electrocardiogram Recording: Algorithm Development and Validation.

BACKGROUND: A 12-lead electrocardiogram (ECG) is the most commonly used method to diagnose patients with cardiovascular diseases. However, there are a number of possible misinterpretations of the ECG that can be caused by several different factors, such as the misplacement of chest electrodes. OBJECTIVE: The aim of this study is to build advanced algorithms to detect precordial (chest) electrode misplacement. METHODS: In this study, we used traditional machine learning (ML) and deep learning (DL) to autodetect the misplacement of electrodes V1 and V2 using features from the resultant ECG. The algorithms were trained using data extracted from high-resolution body surface potential maps of patients who were diagnosed with myocardial infarction, diagnosed with left ventricular hypertrophy, or a normal ECG. RESULTS: DL achieved the highest accuracy in this study for detecting V1 and V2 electrode misplacement, with an accuracy of 93.0% (95% CI 91.46-94.53) for misplacement in the second intercostal space. The performance of DL in the second intercostal space was benchmarked with physicians (n=11 and age 47.3 years, SD 15.5) who were experienced in reading ECGs (mean number of ECGs read in the past year 436.54, SD 397.9). Physicians were poor at recognizing chest electrode misplacement on the ECG and achieved a mean accuracy of 60% (95% CI 56.09-63.90), which was significantly poorer than that of DL (P<.001). CONCLUSIONS: DL provides the best performance for detecting chest electrode misplacement when compared with the ability of experienced physicians. DL and ML could be used to help flag ECGs that have been incorrectly recorded and flag that the data may be flawed, which could reduce the number of erroneous diagnoses.

Human-Computer Agreement of Electrocardiogram Interpretation for Patients Referred to and Declined for Primary Percutaneous Coronary Intervention: Retrospective Data Analysis Study.

BACKGROUND: When a patient is suspected of having an acute myocardial infarction, they are accepted or declined for primary percutaneous coronary intervention partly based on clinical assessment of their 12-lead electrocardiogram (ECG) and ST-elevation myocardial infarction criteria. OBJECTIVE: We retrospectively determined the agreement rate between human (specialists called activator nurses) and computer interpretations of ECGs of patients who were declined for primary percutaneous coronary intervention. METHODS: Various features of patients who were referred for primary percutaneous coronary intervention were analyzed. Both the human and computer ECG interpretations were simplified to either "suggesting" or "not suggesting" acute myocardial infarction to avoid analysis of complex heterogeneous and synonymous diagnostic terms. Analyses, to measure agreement, and logistic regression, to determine if these ECG interpretations (and other variables such as patient age, chest pain) could predict patient mortality, were carried out. RESULTS: Of a total of 1464 patients referred to and declined for primary percutaneous coronary intervention, 722 (49.3%) computer diagnoses suggested acute myocardial infarction, whereas 634 (43.3%) of the human interpretations suggested acute myocardial infarction (P<.001). The human and computer agreed that there was a possible acute myocardial infarction for 342 out of 1464 (23.3%) patients. However, there was a higher rate of human-computer agreement for patients not having acute myocardial infarctions (450/1464, 30.7%). The overall agreement rate was 54.1% (792/1464). Cohen κ showed poor agreement (κ=0.08, P=.001). Only the age (odds ratio [OR] 1.07, 95% CI 1.05-1.09) and chest pain (OR 0.59, 95% CI 0.39-0.89) independent variables were statistically significant (P=.008) in predicting mortality after 30 days and 1 year. The odds for mortality within 1 year of referral were lower in patients with chest pain compared to those patients without chest pain. A referral being out of hours was a trending variable (OR 1.41, 95% CI 0.95-2.11, P=.09) for predicting the odds of 1-year mortality. CONCLUSIONS: Mortality in patients who were declined for primary percutaneous coronary intervention was higher than the reported mortality for ST-elevation myocardial infarction patients at 1 year. Agreement between computerized and human ECG interpretation is poor, perhaps leading to a high rate of inappropriate referrals. Work is needed to improve computer and human decision making when reading ECGs to ensure that patients are referred to the correct treatment facility for time-critical therapy.

Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis.

INTRODUCTION: Electrode misplacement and interchange errors are known problems when recording the 12­lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in cardiac care. The objectives of this systematic review and meta-analysis is to 1) study the impact of electrode misplacement on ECG signals and ECG interpretation, 2) to determine the most challenging electrode misplacements to detect using machine learning (ML), 3) to analyse the ML performance of algorithms that detect electrode misplacement or interchange according to sensitivity and specificity and 4) to identify the most commonly used ML technique for detecting electrode misplacement/interchange. This review analysed the current literature regarding electrode misplacement/interchange recognition accuracy using machine learning techniques. METHOD: A search of three online databases including IEEE, PubMed and ScienceDirect identified 228 articles, while 3 articles were included from additional sources from co-authors. According to the eligibility criteria, 14 articles were selected. The selected articles were considered for qualitative analysis and meta-analysis. RESULTS: The articles showed the effect of lead interchange on ECG morphology and as a consequence on patient diagnoses. Statistical analysis of the included articles found that machine learning performance is high in detecting electrode misplacement/interchange except left arm/left leg interchange. CONCLUSION: This review emphasises the importance of detecting electrode misplacement detection in ECG diagnosis and the effects on decision making. Machine learning shows promise in detecting lead misplacement/interchange and highlights an opportunity for developing and operationalising deep learning algorithms such as convolutional neural network (CNN) to detect electrode misplacement/interchange.

Spontaneous regression of ALK fusion protein-positive non-small cell lung carcinoma: a case report and review of the literature.

BACKGROUND: ALK-rearrangement is observed in < 5% non-small cell lung cancer (NSCLC) cases and prior to the advent of oral tyrosine kinase inhibitors, the natural history of oncogenic NSCLC was typically poor. Literature relating to regression of treatment-naïve NSCLC is limited, and regression without treatment has not been noted in the ALK-rearranged sub-population. CASE PRESENTATION: A 76 year old 'never smoker' female with an ALK-rearranged left upper lobe T2 N0 NSCLC experienced a stroke following elective DC cardioversion for new atrial fibrillation. Following a good recovery, updated imaging demonstrated complete regression of the left upper lobe lesion and a reduction of the previously documented mediastinal lymph node. Remaining atelectasis was non-avid on repeat PET-CT imaging, 8 months from the baseline PET-CT. When the patient developed new symptoms 6 months later a further PET-CT demonstrated FDG-avid local recurrence. She completed 55 Gy in 20 fractions but at 18 months post-radiotherapy there was radiological progression in the lungs with new pulmonary metastases and effusion and new bone metastases. Owing to poor performance status, she was not considered fit for targeted therapy and died 5 months later. CONCLUSION: All reported cases of spontaneous regression in lung cancer have been collated within. Documented precipitants of spontaneous regression across tumour types include biopsy and immune reconstitution; stroke has not been reported previously. The favourable response achieved with radical radiotherapy alone in this unusual case of indolent oncogenic NSCLC reinforces the applicability of radiotherapy in locally advanced ALK-rearranged tumours, in cases not behaving aggressively. As a common embolic event affecting the neurological and pulmonary vasculature is less likely, an immune-mediated mechanism may underpin the phenomenon described in this patient, implying that hitherto unharnessed principles of immuno-oncology may have relevance in oncogenic NSCLC. Alternatively, high electrical voltage applied percutaneously adjacent to the tumour during cardioversion in this patient may have induced local tumour cell lethality.

Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification.

OBJECTIVES: Timely prehospital diagnosis and treatment of acute coronary syndrome (ACS) are required to achieve optimal outcomes. Clinical decision support systems (CDSS) are platforms designed to integrate multiple data and can aid with management decisions in the prehospital environment. The review aim was to describe the accuracy of CDSS and individual components in the prehospital ACS management. METHODS: This systematic review examined the current literature regarding the accuracy of CDSS for ACS in the prehospital setting, the influence of computer-aided decision-making and of 4 components: electrocardiogram, biomarkers, patient history, and examination findings. The impact of these components on sensitivity, specificity, and positive and negative predictive values was assessed. RESULTS: A total of 11,439 articles were identified from a search of databases, of which 199 were screened against the eligibility criteria. Eight studies were found to meet the eligibility and quality criteria. There was marked heterogeneity between studies which precluded formal meta-analysis. However, individual components analysis found that patient history led to significant improvement in the sensitivity and negative predictive values. CDSS which incorporated all 4 components tended to show higher sensitivities and negative predictive values. CDSS incorporating computer-aided electrocardiogram diagnosis showed higher specificities and positive predictive values. CONCLUSIONS: Although heterogeneity precluded meta-analysis, this review emphasizes the potential of ACS CDSS in prehospital environments that incorporate patient history in addition to integration of multiple components. The higher sensitivity of certain components, along with higher specificity of computer-aided decision-making, highlights the opportunity for developing an integrated algorithm with computer-aided decision support.

Coronary microcirculation and peri-procedural myocardial injury during elective percutaneous coronary intervention.

BACKGROUND: Coronary microvascular dysfunction before percutaneous coronary intervention (PCI) predicts PCI-related myocardial injury in patients with stable coronary artery disease (CAD). Whether the dynamic changes of the microcirculation during PCI might be associated with the occurrence of procedure-related myocardial injury and infarction is still unclear. We aimed to investigate the impact of pre- and post-PCI microvascular function, evaluated with the index of microvascular resistance (IMR) on the occurrence of PCI-related myocardial injury and infarction. METHODS: In consecutive patients with stable CAD referred for elective PCI, coronary physiological indexes, including IMR, were measured before and after revascularization. High sensitivity Troponin T (hs-TnT) was assessed up to 24 h after PCI, and PCI-related myocardial injury and type 4a myocardial infarction (MI) were defined according to the fourth universal definition of myocardial infarction. RESULTS: In the 50 patients enrolled, a significant correlation was found between maximum post-PCI hs-Tn and IMR, both at baseline (rho = 0.309, p=0.029) and post-PCI (rho = 0.378, p=0.007). Patients who developed type 4a MI, compared with patients who did not, presented significantly higher IMR levels, both at baseline (28.3 ± 12.2 vs. 19.6 ± 8.8, p=0.020) and post-PCI (45.4 ± 21.3 vs. 21.6 ± 11.2, p<0.0001). Patients with post-PCI IMR > 38 showed significantly higher maximum post-PCI hs-Tn levels (105.4 [49.4-126.9] vs. 22.4 [11.7-38.6] ng/ml, p<0.0001), and developed type 4a MI more frequently (66.8% vs. 4.9%, p<0.0001). CONCLUSIONS: Dynamic changes of microvascular resistance post-PCI are strongly correlated with PCI-related myocardial injury and post-PCI IMR is a strong predictor of type 4a MI in patients with stable CAD undergoing elective PCI.