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.

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.

Weight as an assay-independent predictor of poor response to enteric aspirin in cardiovascular patients.

Aspirin non-response is associated with poor outcome but there is no agreement between the different methods to asses it. Weight has been shown to be a predictor of poor response but only using one method. In this study, we determine the effects of weight on different assays of platelet function. The response to aspirin was determined in 138 cardiology patients using serum thromboxane, arachidonic acid-induced platelet aggregation and VerifyNow©. Twenty-five percent of patients showed an inadequate response to aspirin in at least one assay on the initial test. After ensuring patient compliance only 5% of patients were considered to be non-responders. Only 9% of non-responders were non-responsive in all three assays. When switched to plain aspirin, only 2% of patients were non-responsive. All patients responded adequately to 150 mg aspirin. The non-responders were significantly heavier than responders (78.5 kg ± 14.0 (SD); BMI: 28.4 kg/m2± 4.4 v's 102.6 kg ± 20.6, P = .0016; BMI: 38.3 kg/m2 ± 7.6, P= .0015). A rule-based approach of using plain aspirin in patients over 90 kg or BMI 32 along with patient education to ensure compliance will ensure that all patients respond to their aspirin without the need for testing.

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.

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.

An exploratory analysis investigating blood protein biomarkers to augment ECG diagnosis of ACS.

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 6 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-AUC = 0.96). Six proteins discriminated significantly between the stable angina and the low risk control groups (ROC-AUC = 1.0). Additionally, 16 proteins together perfectly discriminated between the STEMI and NSTEMI patients (ROC-AUC = 1). ECG comparisons with the protein biomarker data for a subset of patients (STEMI n = 6 and NSTEMI n = 6), demonstrated that 21 features (20 proteins + ST elevation) resulted in the highest classification accuracy 91.7% (ROC-AUC = 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-AUC = 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.

Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12­lead electrocardiogram.

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 n = 151, Left Ventricular Hypertrophy (LVH) n = 151, Myocardial Infarction n = 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.

Automation bias in medicine: The influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms.

INTRODUCTION: Interpretation of the 12­lead Electrocardiogram (ECG) is normally assisted with an automated diagnosis (AD), which can facilitate an 'automation bias' where interpreters can be anchored. In this paper, we studied, 1) the effect of an incorrect AD on interpretation accuracy and interpreter confidence (a proxy for uncertainty), and 2) whether confidence and other interpreter features can predict interpretation accuracy using machine learning. METHODS: This study analysed 9000 ECG interpretations from cardiology and non-cardiology fellows (CFs and non-CFs). One third of the ECGs involved no ADs, one third with ADs (half as incorrect) and one third had multiple ADs. Interpretations were scored and interpreter confidence was recorded for each interpretation and subsequently standardised using sigma scaling. Spearman coefficients were used for correlation analysis and C5.0 decision trees were used for predicting interpretation accuracy using basic interpreter features such as confidence, age, experience and designation. RESULTS: Interpretation accuracies achieved by CFs and non-CFs dropped by 43.20% and 58.95% respectively when an incorrect AD was presented (p < 0.001). Overall correlation between scaled confidence and interpretation accuracy was higher amongst CFs. However, correlation between confidence and interpretation accuracy decreased for both groups when an incorrect AD was presented. We found that an incorrect AD disturbs the reliability of interpreter confidence in predicting accuracy. An incorrect AD has a greater effect on the confidence of non-CFs (although this is not statistically significant it is close to the threshold, p = 0.065). The best C5.0 decision tree achieved an accuracy rate of 64.67% (p < 0.001), however this is only 6.56% greater than the no-information-rate. CONCLUSION: Incorrect ADs reduce the interpreter's diagnostic accuracy indicating an automation bias. Non-CFs tend to agree more with the ADs in comparison to CFs, hence less expert physicians are more effected by automation bias. Incorrect ADs reduce the interpreter's confidence and also reduces the predictive power of confidence for predicting accuracy (even more so for non-CFs). Whilst a statistically significant model was developed, it is difficult to predict interpretation accuracy using machine learning on basic features such as interpreter confidence, age, reader experience and designation.

Comparing ST-segment elevation myocardial infarction care between patients residing in central and remote locations: a retrospective case series.

INTRODUCTION: People who experience an ST-elevation myocardial infarction (STEMI) due to an occluded coronary artery require prompt treatment. Treatments to open a blocked artery are called reperfusion therapies (RTs) and can include intravenous pharmacological thrombolysis (TL) or primary percutaneous coronary intervention (pPCI) in a cardiac catheterisation laboratory (cath lab). Optimal RT (ORT) with pPCI or TL reduces morbidity and mortality. In remote areas, a number of geographical and organisational barriers may influence access to ORT. These are not well understood and the exact proportion of patients who receive ORT and the relationship to time of day and remoteness from the cardiac cath lab is unknown. The aim of this retrospective study was to compare the characteristics of ORT delivery in central and remote locations in the north of Scotland and to identify potential barriers to optimal care with a view to service redesign. METHOD: The study was set in the north of Scotland. All patients who attended hospital with a STEMI between March 2014 and April 2015 were identified from national coding data. A data collection form was developed by the research team in several iterative stages. Clinical details were collected retrospectively from patients' discharge letters. Data included treatment location, date of admission, distance of patient from the cath lab, route of access to health care, left ventricular function and RT received. Distance of patients from the cath lab was described as remote if they were more than 90 minutes of driving time from the cardiac cath lab and central if they were 90 minutes or less of driving time from the regional centre. For patients who made contact in a pre-hospital setting, ORT was defined as pre-hospital TL (PHT) or pPCI. For patients who self-presented to the hospital first, ORT was defined as in-hospital TL or pPCI. Data were described as mean (standard deviation) as appropriate. Chi-squared and student's t-test were used as appropriate. Each case was reviewed to determine if ORT was received; if ORT was not received, the reasons for this were recorded to identify potentially modifiable barriers. RESULTS: Of 627 acute myocardial infarction patients initially identified, 131 had a STEMI, and the others were non-STEMI. From this STEMI cohort, 82 (62%) patients were classed as central and 49 (38%) were remote. In terms of initial therapy, 26 (20%) received pPCI, 19 (15%) received PHTs, 52 (40%) received in-hospital TL, while 33 (25%) received no initial RT. ORT was received by 53 (65%) central and 20 (41%) remote patients; χ²=7.05, degrees of freedom =130, p<0.01).Several recurring barriers were identified. CONCLUSION: This study has demonstrated a significant health inequality between the treatment of STEMI in remote compared to central locations. Potential barriers identified include staffing availability and training, public awareness and inter-hospital communication. This suggests that there remain significant opportunities to improve STEMI care for people living in the north of Scotland.

A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogram.

BACKGROUND: The 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named 'Interactive Progressive based Interpretation' (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreter's annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation. OBJECTIVES: To improve interpretation accuracy and reduce missed co-abnormalities. METHODS: The Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI+DDA approach. RESULTS: A total of 375 interpretations were collected. The IPI+DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value=0.1852), the IPI+DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated. CONCLUSION: Although results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerised diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size.

A computer-human interaction model to improve the diagnostic accuracy and clinical decision-making during 12-lead electrocardiogram interpretation.

INTRODUCTION: The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. METHODS: An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model 'Interactive Progressive based Interpretation' (IPI) as the user cannot 'progress' unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). RESULTS: For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI=42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD=42.4%; CI=49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI=4.45, 22.35) An N-1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort. CONCLUSIONS: We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.

Human factors analysis of the CardioQuick Patch®: A novel engineering solution to the problem of electrode misplacement during 12-lead electrocardiogram acquisition.

INTRODUCTION: The CardioQuick Patch® (CQP) has been developed to assist operators in accurately positioning precordial electrodes during 12-lead electrocardiogram (ECG) acquisition. This study describes the CQP design and assesses the device in comparison to conventional electrode application. METHODS: Twenty ECG technicians were recruited and a total of 60 ECG acquisitions were performed on the same patient model over four phases: (1) all participants applied single electrodes to the patient; (2) all participants were then re-trained on electrode placement and on how to use the CQP; (3) participants were randomly divided into two groups, the standard group applied single electrodes and the CQP group used the CQP; (4) after a one day interval, the same participants returned to carry out the same procedure on the same patient (measuring intra-practitioner variability). Accuracy was measured with reference to pre-marked correct locations using ultra violet ink. NASA-TLK was used to measure cognitive workload and the Systematic Usability Scale (SUS) was used to quantify the usability of the CQP. RESULTS: There was a large difference between the minimum time taken to complete each approach (CQP=38.58s vs. 65.96s). The standard group exhibited significant levels of electrode placement error (V1=25.35mm±29.33, V2=18.1mm±24.49, V3=38.65mm±15.57, V4=37.73mm±12.14, V5=35.75mm±15.61, V6=44.15mm±14.32). The CQP group had statistically greater accuracy when placing five of the six electrodes (V1=6.68mm±8.53 [p<0.001], V2=8.8mm±9.64 [p=0.122], V3=6.83mm±8.99 [p<0.001], V4=14.90mm±11.76 [p<0.001], V5=8.63mm±10.70 [p<0.001], V6=18.13mm±14.37 [p<0.001]). There was less intra-practitioner variability when using the CQP on the same patient model. NASA TLX revealed that the CQP did increase the cognitive workload (CQP group=16.51%±8.11 vs. 12.22%±8.07 [p=0.251]). The CQP also achieved a high SUS score of 91±7.28. CONCLUSION: The CQP significantly improved the reproducibility and accuracy of placing precordial electrodes V1, V3-V6 with little additional cognitive effort, and with a high degree of usability.

α2A-Adrenergic receptor polymorphism potentiates platelet reactivity in patients with stable coronary artery disease carrying the cytochrome P450 2C19*2 genetic variant.

OBJECTIVE: Platelet α2A-adrenergic receptors (ARs) mediate platelet aggregation in response to sympathetic stimulation. The 6.3-kb variant of α2A-AR gene is associated with increased epinephrine-induced platelet aggregation in healthy volunteers. The cytochrome P450 2C19*2 (CYP2C19*2) loss-of-function allele influences P2Y12-mediated platelet inhibition and hence the rate of major adverse cardiovascular events. We assessed the influence of 6.3-kb α2A-AR gene variant on platelet aggregation and its interaction with CYP2C19*2 loss-of-function allele in patients with stable angina on aspirin and clopidogrel (dual antiplatelet therapy). APPROACH AND RESULTS: Aggregation to 5 increasing doses of epinephrine (from 0.156 to 10 µmol/L) was assessed in aggregation units by Multiplate Analyzer and platelet reactivity in P2Y12 reactivity units and % inhibition by VerifyNow P2Y12 assay before percutaneous revascularization. Gene polymorphisms were analyzed with TaqMan Drug Metabolism assay. Of 141 patients, aggregation was higher in 6.3-kb carriers (n=52) when compared with wild types (n=89) at all epinephrine doses (P<0.05) apart from 10 µmol/L (P=0.077). Percentage inhibition was lower (P=0.048) in 6.3-kb α2A-AR carriers. Percentage inhibition was lower (P=0.005) and P2Y12 reactivity units was higher (P=0.012) in CYP2C19*2 allele carriers. Higher P2Y12 reactivity units (P=0.037) and lower percentage inhibition (P=0.009) were observed in carriers of both 6.3-kb α2A-AR variant and CYP2C19*2 allele when compared with wild-type or with either mutation on its own. CONCLUSIONS: The 6.3-kb α2A-AR variant is associated with increased platelet reactivity to epinephrine and has an additive effect along with CYP2C19*2 loss-of-function allele on P2Y12-mediated platelet responses in patients with stable angina on dual antiplatelet therapy.

Using computerised interactive response technology to assess electrocardiographers and for aggregating diagnoses.

The 12-lead electrocardiogram (ECG) is a crucial diagnostic tool. However, the ideal method to assess competency in ECG interpretation remains unclear. We sought to evaluate whether keypad response technology provides a rapid, interactive way to assess ECG knowledge. 75 participants were enrolled [32 (43%) Primary Care Physicians, 24 (32%) Hospital Medical Staff and 19 (25%) Nurse Practitioners]. Nineteen ECGs with 4 possible answers were interpreted. Out of 1425 possible decisions 1054 (73.9%) responses were made. Only 570/1425 (40%) of the responses were correct. Diagnostic accuracy varied (0% to 78%, mean 42%±21%) across the entire cohort. Participation was high, (median 83%, IQR 50%-100%). Hospital Medical Staff had significantly higher diagnostic accuracy than nurse practitioners (50±20% vs. 38±19%, p=0.04) and Primary Care Physicians (50±20% vs. 40±21%, p=0.07) although not significant. Interactive voting systems can be rapidly and successfully used to assess ECG interpretation. Further education is necessary to improve diagnostic accuracy.

Improved recording of atrial activity by modified bipolar leads derived from the 12-lead electrocardiogram.

This study investigates the use of multivariate linear regression to estimate three bipolar ECG leads from the 12-lead ECG in order to improve P-wave signal strength. The study population consisted of body surface potential maps recorded from 229 healthy subjects. P-waves were then isolated and population based transformation weights developed. A derived P-lead (measured between the right sternoclavicular joint and midway along the costal margin in line with the seventh intercostal space) demonstrated significant improvement in median P-wave root mean square (RMS) signal strength when compared to lead II (94µV vs. 76µV, p<0.001). A derived ES lead (from the EASI lead system) also showed small but significant improvement in median P-wave RMS (79µV vs. 76µV, p=0.0054). Finally, a derived modified Lewis lead did not improve median P-wave RMS when compared to lead II. However, this derived lead improved atrioventricular RMS ratio. P-wave leads derived from the 12-lead ECG can improve signal-to-noise ratio of the P-wave; this may improve the performance of detection algorithms that rely on P-wave analysis.

Thresholds for platelet reactivity to predict clinical events after coronary intervention are different in patients with and without diabetes mellitus.

Patients with diabetes mellitus (DM) have increased baseline platelet reactivity and impaired response to antiplatelet drugs, compared to non-diabetics. Aim of the present study was to investigate whether thresholds for high platelet reactivity (HPR) that predict clinical outcomes after percutaneous coronary intervention (PCI) are similar in diabetic compared to non-diabetic patients. A total of 640 (32.6% with DM) consecutive patients taking aspirin and clopidogrel undergoing elective PCI were recruited. Platelet reactivity was measured immediately before the procedure with the VerifyNow P2Y12 assay. Primary end point was the 30-day incidence of major adverse cardiac events (MACE) in relation to the presence of DM and to P2Y12 reaction units (PRU) distribution. The optimal cut-off to predict 30-day MACE was a PRU value of >256 in diabetics, and a PRU value of >229 in non-diabetics. Accordingly, we redefined HPR on the basis of these two specific thresholds (HPR-ST), now including 60/209 (29%) diabetic patients with PRU >256, and 130/431 (30%) non-diabetic patients with PRU >229. HPR-ST discriminates significantly (p < 0.001) patients with and without MACE, with a diagnostic accuracy of 73%. The combination of DM and HPR-ST resulted in the highest incidence of MACE (23.3%; p for trend <0.001). At multivariate analysis, HPR-ST was the strongest independent predictor of 30-day MACE (odds ratio 4.80, 95% confidence interval 2.58-8.93; p < 0.001). Redefining HPR based on specific thresholds for patients with and without DM significantly improves prediction of MACE post-PCI. Patients with HPR-ST, especially in the presence of DM, are at increased risk for ischemic events and may benefit from more aggressive antiplatelet strategies.

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.

Increased platelet reactivity in HIV-1-infected patients receiving abacavir-containing antiretroviral therapy.

BACKGROUND: Current or recent use of abacavir for treating human immunodeficiency virus type 1 (HIV-1) infection has been associated with increased rates of myocardial infarction (MI). Given the role of platelet aggregation in thrombus formation in MI and the reversible nature of the abacavir association, we hypothesized that patients treated with abacavir would have increased platelet reactivity. METHODS: In a prospective study in adult HIV-infected patients, we determined associations between antiretrovirals (ARVs), and in particular the nucleoside reverse transcriptase inhibitor abacavir, and platelet reactivity by measuring time-dependent platelet aggregation in response to agonists: adenosine diphosphate (ADP), thrombin receptor-activating peptide (TRAP), collagen, and epinephrine. RESULTS: Of 120 subjects, 40 were ARV-naive and 80 ARV-treated, 40 of whom were receiving abacavir. No consistent differences in platelet reactivity were observed between the ARV-naive and ARV-treated groups. In contrast, within the ARV-treated group, abacavir-treated subjects had consistently higher percentages of platelet aggregation upon exposure to ADP, collagen, and epinephrine (P = .037, P = .022, and P = .032, respectively) and had platelets that were more sensitive to aggregation upon exposure to TRAP (P = .025). CONCLUSIONS: The consistent increases in platelet reactivity observed in response to a range of agonists provides a plausible underlying mechanism to explain the reversible increased rates of MI observed in abacavir-treated 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.