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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.
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Cardiologistas , Doença da Artéria Coronariana , Intervenção Coronária Percutânea , Humanos , Intervenção Coronária Percutânea/métodos , Resultado do Tratamento , Inquéritos e Questionários , Consenso , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/terapia , Doença da Artéria Coronariana/etiologiaRESUMO
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.
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Eletrocardiografia , Aprendizado de Máquina , Algoritmos , Eletrodos , Humanos , Redes Neurais de ComputaçãoRESUMO
Saphenous vein graft (SVG) aneurysms are a rare, frequently late presenting, potentially fatal complication of coronary artery bypass graft (CABG) surgery. They are often discovered incidentally during radiological tasks such as chest x-ray or CT but can present clinically with symptoms such as worsening angina and breathlessness as well as complications such as rupture or myocardial infarction. Given the risks if left untreated, consideration should be given to treatment either through percutaneous routes or open surgery. However, because of a lack of strong evidence, there are no definitive guidelines on the treatment of SVG aneurysms. We describe a patient with an extensive cardiac surgical history who presented with angina and breathlessness and was found to have a large SVG aneurysm, subsequently successfully treated with percutaneous coronary intervention with covered stents.
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Aneurisma/terapia , Ponte de Artéria Coronária/efeitos adversos , Intervenção Coronária Percutânea , Veia Safena/transplante , Idoso , Aneurisma/diagnóstico por imagem , Aneurisma/etiologia , Humanos , Masculino , Intervenção Coronária Percutânea/instrumentação , Veia Safena/diagnóstico por imagem , Stents , Resultado do TratamentoRESUMO
BACKGROUND: Acute Coronary Syndrome (ACS) is currently diagnosed using a 12lead Electrocardiogram (ECG). Our recent work however has shown that interpretation of the 12lead ECG is complex and that clinicians can be sub-optimal in their interpretation. Additionally, ECG does not always identify acute total occlusions in certain patients. PURPOSE: The aim of the present study was to undertake an exploratory analysis to compare protein expression profiles of ACS patients that may in the future augment ECG diagnosis. METHODS: Patients were recruited consecutively at the cardiac catheterization laboratory at Altnagelvin Hospital over a period of 6â¯months. A low risk control group was recruited by advertisement. Blood samples were analysed using the multiplex proximity extension assays by OLINK proteomics. Support vector machine (SVM) learning was used as a classifier to distinguish between patient groups on training data. The ST segment elevation level was extracted from each ECG for a subset of patients and combined with the protein markers. Quadratic SVM (QSVM) learning was then used as a classifier to distinguish STEMI from NSTEMI on training and test data. RESULTS: Of the 344 participants recruited, 77 were initially diagnosed with NSTEMI, 7 with STEMI, and 81 were low risk controls. The other participants were those diagnosed with angina (stable and unstable) or non-cardiac patients. Of the 368 proteins analysed, 20 proteins together could significantly differentiate between patients with ACS and patients with stable angina (ROC-AUCâ¯=â¯0.96). Six proteins discriminated significantly between the stable angina and the low risk control groups (ROC-AUCâ¯=â¯1.0). Additionally, 16 proteins together perfectly discriminated between the STEMI and NSTEMI patients (ROC-AUCâ¯=â¯1). ECG comparisons with the protein biomarker data for a subset of patients (STEMI nâ¯=â¯6 and NSTEMI nâ¯=â¯6), demonstrated that 21 features (20 proteins + ST elevation) resulted in the highest classification accuracy 91.7% (ROC-AUCâ¯=â¯0.94). The 20 proteins without the ST elevation feature gave an accuracy of 80.6% (ROC-AUC 0.91), while the ST elevation feature without the protein biomarkers resulted in an accuracy of 69.3% (ROC-AUCâ¯=â¯0.81). CONCLUSIONS: This preliminary study identifies panels of proteins that should be further explored in prospective studies as potential biomarkers that may augment ECG diagnosis and stratification of ACS. This work also highlights the importance for future studies to be designed to allow a comparison of blood biomarkers not only with ECG's but also with cardio angiograms.
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Síndrome Coronariana Aguda , Proteínas Sanguíneas , Infarto do Miocárdio , Síndrome Coronariana Aguda/diagnóstico , Biomarcadores , Proteínas Sanguíneas/análise , Eletrocardiografia , Humanos , Estudos ProspectivosRESUMO
BACKGROUND: Electrocardiogram (ECG) lead misplacement can adversely affect ECG diagnosis and subsequent clinical decisions. V1 and V2 are commonly placed superior of their correct position. The aim of the current study was to use machine learning approaches to detect V1 and V2 lead misplacement to enhance ECG data quality. METHOD: ECGs for 453 patients, (normal nâ¯=â¯151, Left Ventricular Hypertrophy (LVH) nâ¯=â¯151, Myocardial Infarction nâ¯=â¯151) were extracted from body surface potential maps. These were used to extract both the correct and incorrectly placed V1 and V2 leads. The prevalence for correct and incorrect leads were 50%. Sixteen features were extracted in three different domains: time-based, statistical and time-frequency features using a wavelet transform. A hybrid feature selection approach was applied to select an optimal set of features. To ensure optimal model selection, five classifiers were used and compared. The aforementioned feature selection approach and classifiers were applied for V1 and V2 misplacement in three different positions: first, second and third intercostal spaces (ICS). RESULTS: The accuracy for V1 misplacement detection was 93.9%, 89.3%, 72.8% in the first, second and third ICS respectively. In V2, the accuracy was 93.6%, 86.6% and 68.1% in the first, second and third ICS respectively. There is a noticeable decline in accuracy when detecting misplacement in the third ICS which is expected.
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Eletrocardiografia , Infarto do Miocárdio , Eletrodos , Humanos , Aprendizado de Máquina , TóraxRESUMO
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.
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Inteligência Artificial , Benchmarking , Algoritmos , Eletrocardiografia , Humanos , Aprendizado de Máquina , Fatores de TempoRESUMO
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.
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Infarto do Miocárdio , Infarto do Miocárdio sem Supradesnível do Segmento ST , Intervenção Coronária Percutânea , Infarto do Miocárdio com Supradesnível do Segmento ST , Humanos , Análise de Classes Latentes , Infarto do Miocárdio/diagnóstico , Infarto do Miocárdio/terapia , Infarto do Miocárdio sem Supradesnível do Segmento ST/diagnóstico , Intervenção Coronária Percutânea/efeitos adversos , Sistema de Registros , Infarto do Miocárdio com Supradesnível do Segmento ST/diagnóstico por imagem , Infarto do Miocárdio com Supradesnível do Segmento ST/cirurgia , Resultado do TratamentoRESUMO
BACKGROUND: Shock index (SI - heart rate/systolic blood pressure) has been studied as a measure of haemodynamic status. We aimed to determine whether SI measures within 72 h of admission were associated with adverse outcomes in intracerebral haemorrhage (ICH). METHODS: Patients were drawn from the Virtual International Stroke Trials Archive-Intracerebral Haemorrhage (VISTA-ICH). Multivariable Cox regressions modelled the relationship between SI (on admission, 24, 48, 72 h) and mortality (at 3-, 7-, and 90-days), 90-day incident pneumonia and cardiovascular events (MACE). Ordinal logistic regressions modelled the relationship between SI and 90-day modified Rankin Scale (mRS). RESULTS: 979 patients were included. Baseline SI was not associated with mortality. 24 h SI > 0.7 was associated with 7-day mortality (hazard ratio (95% confidence interval) = 3.14 (1.37-7.19)). 48 h and 72 h SI > 0.7 were associated with 7-day (4.23 (2.07-8.66) and 3.24 (1.41-7.42) respectively) and 90-day mortality (2.97 (1.82-4.85) and 2.05 (1.26-3.61) respectively). SI < 0.5 at baseline, 48 h and 72 h was associated with decreased pneumonia risk. 24 h and 48 h SI > 0.7was associated with increased MACE risk. 48 h and 72 h SI > 0.7 was associated with increased odds of higher 90-day mRS. CONCLUSION: Higher-than-normal SI subsequent to initial encounter was associated with higher post-ICH mortality at 3, 7, and 90 days. Lower-than-normal SI was associated with a decreased risk of incident pneumonia.
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Pressão Sanguínea/fisiologia , Hemorragia Cerebral/diagnóstico , Hemorragia Cerebral/mortalidade , Frequência Cardíaca/fisiologia , Idoso , Idoso de 80 Anos ou mais , Hemorragia Cerebral/fisiopatologia , Ensaios Clínicos como Assunto/métodos , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mortalidade/tendências , Estudos Retrospectivos , Fatores de Risco , Fatores de TempoRESUMO
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.
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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.
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OBJECTIVE: An accurate prediction tool may facilitate optimal management of patients with acute stroke from an early stage. We evaluated the association between admission modified early warning score (MEWS) and mortality in patients with acute stroke. METHOD: Data from the Anglia Stroke Clinical Network Evaluation Study (ASCNES) were analysed. We evaluated the association between admission MEWS and four outcomes; in-patient, 7-day, 30-day and 1-year mortality. Logistic regression models were used to calculate the odds of all mortality timeframes, whereas Cox proportional hazards models were used to calculate mortality at 1 year. Five univariate and multivariate models were constructed, adjusting for confounders. Patients with a moderate (2-3) or high (≥4) scores were compared to patients with a low score (0-1). RESULTS: The study population consisted of 2006 patients. A total of 1196 patients had low MEWS, 666 had moderate MEWS and 144 had a high MEWS. A high MEWS was associated with increased mortality as an in-patient (OR 4.93, 95 % CI: 2.88-8.42), at 7 days (OR 7.53, 95 % CI: 4.24-13.38), at 30 days (OR 5.74, 95 % CI: 3.38-9.76) and 1-year (HR 2.52, 95 % CI 1.88-3.39). At 1 year, model 5 had a 1.02 OR (95 % CI 0.83-1.24) with moderate MEWS and 2.52 (95 % CI 1.88-3.39) with high MEWS. CONCLUSION: Elevated MEWS on admission is a potential marker for acute-stroke mortality and may therefore be a useful risk prediction tool, able to guide clinicians attempting to prognosticate outcomes for patients with acute-stroke.
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Escore de Alerta Precoce , Acidente Vascular Cerebral Hemorrágico/fisiopatologia , Mortalidade Hospitalar , AVC Isquêmico/fisiopatologia , Idoso , Idoso de 80 Anos ou mais , Feminino , Hospitalização , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Mortalidade , Análise Multivariada , Modelos de Riscos Proporcionais , Acidente Vascular Cerebral/fisiopatologiaRESUMO
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.
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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.
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Síndrome Coronariana Aguda/diagnóstico , Algoritmos , Sistemas de Apoio a Decisões Clínicas/organização & administração , Eletrocardiografia , Serviços Médicos de Emergência/métodos , Humanos , Valor Preditivo dos TestesRESUMO
This article retrospectively analyses a primary percutaneous coronary intervention dataset comprising patient referrals that were accepted for percutaneous coronary intervention and those who were turned down between January 2015 and December 2018 at Altnagelvin Hospital (United Kingdom). Time series analysis of these referrals was undertaken for analysing the referral rates per year, month, day and per hour. The overall referrals have 70 per cent (n = 1466, p < 0.001) males. Of total referrals, 65 per cent (p < 0.001) of referrals were 'out of hours'. Seasonality decomposition shows a peak in referrals on average every 3 months (standard deviation = 0.83). No significant correlation (R = 0.03, p = 0.86; R = -0.11, p = 0.62) was found between the referral numbers and turndown rate. Being female increased the probability of being out of hour in all the groups. The 30-day mortality was higher in the turndown group. The time series of all the referrals depict variation over the months or days which is not the same each year. The average age of the patients in the turndown group is higher. The number of referrals does not impact on the turndown rate and clinical decision making. Most patients are being referred out of hours, especially females. This analysis leads to the emphasis on the importance of working 24/7 CathLab service.
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Intervenção Coronária Percutânea , Infarto do Miocárdio com Supradesnível do Segmento ST , Feminino , Humanos , Masculino , Encaminhamento e Consulta , Estudos Retrospectivos , Fatores de Risco , Fatores de Tempo , Resultado do Tratamento , Reino UnidoRESUMO
Atrial fibrillation is the most common heart-rhythm disorder, affecting about 1.5% to 2% of the population with an increased risk of mortality and morbidity due to stroke, thromboembolism, and heart failure. If the conversion back to sinus rhythm does not happen spontaneously, pharmacological or electrical cardioversion (ECV) is the next available treatment options for some patients. However, the long-term success following ECV is variable. This review describes the factors that are associated with maintenance of sinus rhythm following ECV and proposes a clinical strategy based on the available evidence.