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Digoxin is commonly used to treat acute heart failure (AHF), especially in patients with concurrent atrial fibrillation (AF). Nonetheless, there is little consensus about in which patients digoxin should be given, the proper time for digoxin initiation, and whether digoxin initiation is associated with improved outcomes. We investigated factors related to digoxin initiation after an episode of AHF and whether patients receiving digoxin presented better short-term outcomes. We analyzed digoxin-naïve AHF patients from a Spanish and Swiss database, who were dichotomized into cohorts based on their receipt of digoxin treatment at discharge. The relationship between digoxin initiation and 23 additional patient covariates, including chronic treatment, was investigated, as well as its association with 90-day combined adverse events (defined as all-cause death or AHF hospitalization). Of 13,105 patients (10,600/2505 from the Spanish/Swiss cohorts, respectively), the median (interquartile range) age was 83 (74.87) years, and 51% were women. Of these, 484 (3.7%) received digoxin at discharge, which was associated with AF, female sex, left ventricular ejection fraction (LVEF) < 50%, and coming from the Spanish cohort. Parameters inversely associated with receiving digoxin at discharge included some chronic treatments, diabetes mellitus (DM), and chronic kidney disease (CKD). Digoxin initiation was not association with 90-day adverse events, adjusted hazard ratio (aHR) = 0.939 (0.769-1.146), but there was an interaction for CKD, aHR = 1.390 (0.831-2.325) vs. 0.854 (0.682-1.183), p = 0.039, and for cohort pertinence, with higher risk in the Swiss cohort; aHR = 1.405 (0.827-2.386) vs. 0.862 (0.689-1.077), p = 0.046. Digoxin initiation after an AHF episode was more frequent in the Spanish cohort and was associated with certain patient characteristics (AF, female sex, reduced LVEF, no DM, no CKD), but had no effect on 90-day outcomes.

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BACKGROUND: Point-of-care (POC) high-sensitivity cardiac troponin assays may further accelerate the diagnosis of myocardial infarction (MI). OBJECTIVES: This study sought to assess the clinical and analytical performance of the novel high-sensitivity cardiac troponin I (hs-cTnI)-SPINCHIP POC test. METHODS: Adult patients presenting with acute chest discomfort to the emergency department were enrolled in an international, diagnostic, multicenter study. The final diagnosis was centrally adjudicated by 2 independent cardiologists using all clinical information. We compared the discriminatory performance of hs-cTnI-SPINCHIP with current established central laboratory assays and derived an assay-specific hs-cTnI-SPINCHIP 0/1-hour algorithm. Secondary analyses included sample type comparisons (whole blood, fresh/frozen plasma, and capillary finger prick) and precision analysis. RESULTS: MI was the adjudicated final diagnosis in 214 (19%) of 1,102 patients. Area under the receiver-operating characteristic curve was 0.94 (95% CI: 0.92-0.95) for hs-cTnI-SPINCHIP vs 0.94 (95% CI: 0.92-0.95) for hs-cTnI-Architect (P = 0.907) and 0.93 (95% CI: 0.91-0.95) for high-sensitivity cardiac troponin T Elecsys (P = 0.305). A cutoff <7 ng/L at presentation (if chest pain onset was >3 hours) or <7 ng/L together with a 0/1-hour delta of <4 ng/L ruled out 51% with a sensitivity and negative predictive value of 100% (95% CI: 97.7%-100%) and 100% (95% CI: 99.0%-100%), respectively. A hs-cTnI-SPINCHIP concentration ≥36 ng/L or a 0/1-hour delta ≥11 ng/L ruled in 27% with a specificity and positive predictive value of 90.9% (95% CI: 88.3%-92.9%) and 72.9% (95% CI: 66.4%-78.6%), respectively. Bootstrap internal validation confirmed excellent diagnostic performance. High agreement was observed between different sample types. CONCLUSIONS: The SPINCHIP hs-cTnI POC test has very high diagnostic accuracy. Its assay-specific 0/1-hour algorithm achieved very high sensitivity/negative predictive value and specificity/positive predictive value for rule-out/in MI. (Advantageous Predictors of Acute Coronary Syndromes Evaluation [APACE] Study [APACE]; NCT00470587).

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BACKGROUND: Intravenous digoxin is still used in emergency departments (EDs) to treat patients with acute heart failure (AHF), especially in those with rapid atrial fibrillation. Nonetheless, many emergency physicians are reluctant to use intravenous digoxin in patients with advanced age, impaired renal function, and potassium disturbances due to its potential capacity to increase adverse outcomes. OBJECTIVE: We investigated whether intravenous digoxin used to treat rapid atrial fibrillation in patients with AHF may influence mortality in patients with specific age, estimated glomerular filtration rate (eGFR), and serum potassium classes. DESIGN: A secondary analysis of patients included in in the Spanish EAHFE cohort, which includes patients diagnosed with AHF in the ED. SETTING: 45 Spanish EDs. PARTICIPANTS: Two thousand one hundred ninety-four patients with AHF and rapid atrial fibrillation (heart rate ≥100 bpm) not receiving digoxin at home, divided according to whether they were or were not treated with intravenous digoxin in the ED. OUTCOME: The relationships between age, eGFR, and potassium with 30-day mortality were investigated using restricted cubic spline (RCS) models adjusted for relevant patient and episode variables. The impact of digoxin use on such relationships was assessed by checking interaction. MAIN RESULTS: The median age of the patients was 82 years [interquartile range (IQR) = 76-87], 61.4% were women, 65.2% had previous episodes of atrial fibrillation, and the median heart rate at ED arrival was 120 bpm (IQR = 109-135). Digoxin and no digoxin groups were formed by 864 (39.4%) and 1330 (60.6%) patients, respectively. There were 191 deaths within the 30-day follow-up period (8.9%), with no differences between patients receiving or not receiving digoxin (8.5 vs. 9.1%, P  = 0.636). Although analysis of RCS curves showed that death was associated with advanced age, worse renal function, and hypo- and hyperkalemia, use of intravenous digoxin did not interact with any of these relationships ( P  = 0.156 for age, P  = 0.156 for eGFR; P  = 0.429 for potassium). CONCLUSION: The use of intravenous digoxin in the ED was not associated with significant changes in 30-day mortality, which was confirmed irrespective of patient age or the existence of renal dysfunction or serum potassium disturbances.

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BACKGROUND: The myocardial-ischaemic-injury-index (MI3) is a novel machine learning algorithm for the early diagnosis of type 1 non-ST-segment elevation myocardial infarction (NSTEMI). The performance of MI3, both when using early serial blood draws (eg, at 1 h or 2 h) and in direct comparison with guideline-recommended algorithms, remains unknown. Our aim was to externally validate MI3 and compare its performance with that of the European Society of Cardiology (ESC) 0/1h-algorithm. METHODS: In this secondary analysis of a multicentre international diagnostic cohort study, adult patients (age >18 years) presenting to the emergency department with symptoms suggestive of myocardial infarction were prospectively enrolled from April 21, 2006, to Feb 27, 2019 in 12 centres from five European countries (Switzerland, Spain, Italy, Poland, and Czech Republic). Patients were excluded if they presented with ST-segment-elevation myocardial infarction, did not have at least two serial high-sensitivity cardiac troponin I (hs-cTnI) measurements, or if the final diagnosis remained unclear. The final diagnosis was centrally adjudicated by two independent cardiologists using all available medical records, including serial hs-cTnI measurements and cardiac imaging. The primary outcome was type 1 NSTEMI. The performance of MI3 was directly compared with that of the ESC 0/1h-algorithm. FINDINGS: Among 6487 patients, (median age 61·0 years [IQR 49·0-73·0]; 2122 [33%] female and 4365 [67%] male), 882 (13·6%) patients had type 1 NSTEMI. The median time difference between the first and second hs-cTnI measurement was 60·0 mins (IQR 57·0-70·0). MI3 performance was very good, with an area under the receiver-operating-characteristic curve of 0·961 (95% CI 0·957 to 0·965) and a good overall calibration (intercept -0·09 [-0·2 to 0·02]; slope 1·02 [0·97 to 1·08]). The originally defined MI3 score of less than 1·6 identified 4186 (64·5%) patients as low probability of having a type 1 NSTEMI (sensitivity 99·1% [95% CI 98·2 to 99·5]; negative predictive value [NPV] 99·8% [95% CI 99·6 to 99·9]) and an MI3 score of 49·7 or more identified 915 (14·1%) patients as high probability of having a type 1 NSTEMI (specificity 95·0% [94·3 to 95·5]; positive predictive value [PPV] 69·1% [66·0-72·0]). The sensitivity and NPV of the ESC 0/1h-algorithm were higher than that of MI3 (difference for sensitivity 0·88% [0·19 to 1·60], p=0·0082; difference for NPV 0·18% [0·05 to 0·32], p=0·016), and the rule-out efficacy was higher for MI3 (11% difference, p<0·0001). Specificity and PPV for MI3 were superior (difference for specificity 3·80% [3·24 to 4·36], p<0·0001; difference for PPV 7·84% [5·86 to 9·97], p<0·0001), and the rule-in efficacy was higher for the ESC 0/1h-algorithm (5·4% difference, p<0·0001). INTERPRETATION: MI3 performs very well in diagnosing type 1 NSTEMI, demonstrating comparability to the ESC 0/1h-algorithm in an emergency department setting when using early serial blood draws. FUNDING: Swiss National Science Foundation, Swiss Heart Foundation, the EU, the University Hospital Basel, the University of Basel, Abbott, Beckman Coulter, Roche, Idorsia, Ortho Clinical Diagnostics, Quidel, Siemens, and Singulex.

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Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnostic accuracy or expose patients to possibly harmful radiation. Here we show how machine learning (ML) can outperform cardiologists in predicting the presence of stress-induced fCAD in terms of area under the receiver operating characteristic (AUROC: 0.71 vs. 0.64, p = 4.0E-13). We present two ML approaches, the first using eight static clinical variables, whereas the second leverages electrocardiogram signals from exercise stress testing. At a target post-test probability for fCAD of <15%, ML facilitates a potential reduction of imaging procedures by 15-17% compared to the cardiologist's judgement. Predictive performance is validated on an internal temporal data split as well as externally. We also show that combining clinical judgement with conventional ML and deep learning using logistic regression results in a mean AUROC of 0.74.

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AIMS: We hypothesized that the current gold standard for risk stratification of patients with acute heart failure (AHF), the Multiple Estimation of risk based on the Emergency department Spanish Score In patients with AHF (MEESSI-AHF) risk score, can be further improved by adding systemic inflammation as quantified by C-reactive protein (CRP). METHODS AND RESULTS: In a prospective multicentre diagnostic study (BASEL V), AHF was centrally adjudicated by two independent cardiologists. The MEESSI-AHF risk score was calculated using an established reduced and recalibrated model containing 12 independent risk factors. Model extension was performed by refitting and adding CRP in the logistic regression model with 30-day mortality as binary outcome. Discrimination, calibration and clinical usefulness were used to assess the performance of the extended Multiple Estimation of risk based on the Emergency department Spanish Score In patients (MEESSI) model. Validation was performed in an independent, retrospective and single-centre AHF cohort. Among 1208 AHF patients with complete data allowing calculation of the recalibrated MEESSI and the extended MEESSI models, the prognostic accuracy for 30-day mortality of the extended MEESSI model (c-statistic 0.83, 95% confidence interval [CI] 0.79-0.87) was significantly higher compared to the recalibrated model (c-statistic 0.79, 95% CI 0.75-0.83, p = 0.013). The extended model allowed to stratify a higher percentage of patients into the lowest risk group compared to the recalibrated model (33.1% vs. 20.3%). Demonstrating a calibration plot's slope of 1.00 (95% CI 0.81-1.19) and an intercept of 0.0 (95% CI -0.22 to 0.22), the extended MEESSI model achieved excellent and improved calibration. Results were confirmed in the independent validation cohort (n = 575). CONCLUSIONS: Quantifying inflammation using CRP concentration provided incremental value in AHF risk stratification using the established MEESSI model.

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BACKGROUND: The characterization of the different pathophysiological mechanisms involved in normotensive versus hypertensive acute heart failure (AHF) might help to develop individualized treatments. METHODS: The extent of hemodynamic cardiac stress and cardiomyocyte injury was quantified by measuring the B-type natriuretic peptide (BNP), N-terminal proBNP (NT-proBNP), and high-sensitivity cardiac troponin T (hs-cTnT) concentrations in 1152 patients presenting with centrally adjudicated AHF to the emergency department (ED) (derivation cohort). AHF was classified as normotensive with a systolic blood pressure (SBP) of 90-140 mmHg and hypertensive with SBP > 140 mmHg at presentation to the ED. Findings were externally validated in an independent AHF cohort (n = 324). RESULTS: In the derivation cohort, with a median age of 79 years, 43% being women, 667 (58%) patients had normotensive and 485 (42%) patients hypertensive AHF. Hemodynamic cardiac stress, as quantified by the BNP and NT-proBNP, was significantly higher in normotensive as compared to hypertensive AHF [1105 (611-1956) versus 827 (448-1419) pg/mL, and 5890 (2959-12,162) versus 4068 (1986-8118) pg/mL, both p < 0.001, respectively]. Similarly, the extent of cardiomyocyte injury, as quantified by hs-cTnT, was significantly higher in normotensive AHF as compared to hypertensive AHF [41 (24-71) versus 33 (19-59) ng/L, p < 0.001]. A total of 313 (28%) patients died during 360 days of follow-up. All-cause mortality was higher in patients with normotensive AHF vs. patients with hypertensive AHF (hazard ratio 1.66, 95%CI 1.31-2.10; p < 0.001). Normotensive patients with a high BNP, NT-proBNP, or hs-cTnT had the highest mortality. The findings were confirmed in the validation cohort. CONCLUSION: Biomarker profiling revealed a higher extent of hemodynamic stress and cardiomyocyte injury in patients with normotensive versus hypertensive AHF.

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BACKGROUND: Atrial fibrillation (AF) and heart failure frequently coexist. Prediction of left ventricular ejection fraction (LVEF) recovery after catheter ablation (CA) for AF remains difficult. OBJECTIVE: The purpose of this study was to evaluate the value of biomarkers, alone and in combination with the Antwerp score, to predict LVEF recovery after CA for AF. METHODS: Patients undergoing CA for AF with depressed LVEF (<50%) were included. Plasma levels of 13 biomarkers were measured immediately before CA. Patients were categorized into "responders" and "nonresponders" in a similar fashion to the Antwerp score performance derivation and validation cohorts. The predictive power of the biomarkers alone and combined in outcome prediction was evaluated. RESULTS: A total of 208 patients with depressed LVEF were included (median age 63 years; 39-19% female; median indexed left atrial volume 42 (33-52) mL/m2; median LVEF 43 (38-46)%). At a median follow-up time of 30 (20-34) months, 161 (77%) were responders and 47 (23%) were nonresponders. Of 13 biomarkers, -4-angiopoietin 2 (ANG2), growth differentiation factor 15 (GDF15), fibroblast growth factor 23, and myosin binding protein C3-were significantly different between responders and nonresponders (P ≤ .001) and their combination could predict the end point with an area under the curve of 0.72 (95% confidence interval [CI] 0.64-0.81) overall, 0.69 (95% CI 0.59-0.78) in heart failure with mildly reduced ejection fraction, and 0.88 (95% CI 0.77-0.98) in heart failure with reduced ejection fraction. Only ANG2 and GDF15 remained significantly associated with LVEF recovery after adjustment for age, sex, and Antwerp score and significantly improved the accuracy of the Antwerp score predictions (P < .001). The area under the curve of the Antwerp score in the outcome prediction improved from 0.75 (95% CI 0.67-0.83) to 0.78 (95% CI 0.70-0.86). CONCLUSION: A biomarker panel (ANG2 and GDF15) significantly improved the accuracy of the Antwerp score.

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AIMS: We aimed to test the hypothesis if combining coronary artery calcium score (Ca-score) as a quantitative anatomical marker of coronary atherosclerosis with high-sensitivity cardiac troponin as a quantitative biochemical marker of myocardial injury provided incremental value in the detection of functionally relevant coronary artery disease (fCAD) and risk stratification. METHODS AND RESULTS: Consecutive patients undergoing myocardial perfusion single-photon emission computed tomography (MPS) without prior CAD were enrolled. The diagnosis of fCAD was based on the presence of ischaemia on MPS and coronary angiography; fCAD was centrally adjudicated in the diagnostic and prognostic domain. Diagnostic accuracy was evaluated using the area under the receiver-operating characteristic curve (AUC). The composite of cardiovascular death and non-fatal acute myocardial infarction (AMI) within 730 days was the primary prognostic endpoint. Among 1715 patients eligible for the diagnostic analysis, 399 patients had fCAD. The combination of Ca-score and high-sensitivity cardiac troponin T (hs-cTnT) had good diagnostic accuracy for the diagnosis of fCAD (AUC 0.79, 95% confidence interval (CI) 0.77-0.81), but no incremental value compared with the Ca-score alone (AUC 0.79, 95% CI 0.77-0.81, P = 0.965). Similar results were observed using high-sensitivity cardiac troponin I (AUC 0.80, 95% CI 0.77-0.82) instead of hs-cTnT. Among 1709 patients (99.7%) with available follow-up, 59 patients (3.5%) suffered the composite primary prognostic endpoint (non-fatal AMI, n = 34; CV death, n = 28). Both Ca-score and hs-cTnT had independent prognostic value. Increased risk was restricted to patients with elevation in both markers. CONCLUSION: The combination of the Ca-score with hs-cTnT increases the prognostic accuracy for future events but does not provide incremental value vs. the Ca-score alone for the diagnosis of fCAD. STUDY REGISTRATION: Clinical trial registration: NCT00470587.

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INTRODUCTION: Cardiac complications after major noncardiac surgery are common and associated with high morbidity and mortality. How preoperative use of beta-blockers may impact perioperative cardiac complications remains unclear. METHODS: In a multicentre prospective cohort study, preoperative beta-blocker use was ascertained in consecutive patients at elevated cardiovascular risk undergoing major noncardiac surgery. Cardiac complications were prospectively monitored and centrally adjudicated by two independent experts. The primary endpoint was perioperative myocardial infarction or injury attributable to a cardiac cause (cardiac PMI) within the first three postoperative days. The secondary endpoints were major adverse cardiac events (MACE), defined as a composite of myocardial infarction, acute heart failure, life-threatening arrhythmia, and cardiovascular death and all-cause death after 365 days. We used inverse probability of treatment weighting to account for differences between patients receiving beta-blockers and those who did not. RESULTS: A total of 3839/10 272 (37.4%) patients (mean age 74 yr; 44.8% female) received beta-blockers before surgery. Patients on beta-blockers were older, and more likely to be male with established cardiorespiratory and chronic kidney disease. Cardiac PMI occurred in 1077 patients, with a weighted odds ratio of 1.03 (95% confidence interval [CI] 0.94-1.12, P=0.55) for patients on beta-blockers. Within 365 days of surgery, 971/10 272 (9.5%) MACE had occurred, with a weighted hazard ratio of 0.99 (95% CI 0.83-1.18, P=0.90) for patients on beta-blockers. CONCLUSION: Preoperative use of beta-blockers was not associated with decreased cardiac complications including cardiac perioperative myocardial infarction or injury and major adverse cardiac event. Additionally, preoperative use of beta-blockers was not associated with increased all-cause death within 30 and 365 days. CLINICAL TRIAL REGISTRATION: NCT02573532.

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Background: Infranodal conduction disorders are common after transcatheter aortic valve replacement (TAVR). Risk factors are incompletely understood. Objective: The purpose of this study was to assess the impact of valve implantation depth and calcium burden of the device landing zone on infranodal conduction intraprocedure pre- and post-TAVR. Methods: In all patients undergoing TAVR between June 2020 and June 2021, the His-ventricle (HV) interval was measured pre- and post-valve deployment. The difference between the 2 measurements defined delta HV, whereas infranodal conduction delay was defined as HV interval >55 ms. Valve implantation depth was measured as the distance between the aortic annular plane and the ventricular prosthesis end. Calcium burden was quantified as the volume of calcium in 6 regions of interest: the non-, right, and left coronary cusps (NCC, RCC, and LCC, respectively) and the corresponding regions of the left ventricular outflow tract (LVOT) underlying each cusp (LVOTNCC, LVOTRCC, LVOTLCC, respectively). Results: Of 101 patients (mean age 81 ± 5.7 years; 47% women), 37 demonstrated infranodal conduction delay intraprocedure post-TAVR. Overall, mean implantation depth was 5 ± 3.1 mm, median calcium volume was 2080 mm3 [interquartile range 632-2400]. Delta HV showed no correlation with implantation depth or calcium burden (r = -0.08 and r = 0.12, respectively). However, LVOTNCC calcification was a significant predictor for infranodal conduction delay post-valve deployment in a multivariable logistic regression model (odds ratio 1.62 per 100-mm3 increase (95% confidence interval 1.06-2.69; P = .04). Conclusion: Assessment of LVOTNCC calcification may identify patients at risk for infranodal conduction delay after TAVR, whereas implantation depth did not predict infranodal conduction delay.

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

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Aims: It has been demonstrated that several cardiac pathologies, including myocardial ischaemia, can be detected using smartwatch electrocardiograms (ECGs). Correct placement of bipolar chest leads remains a major challenge in the outpatient population. Methods and results: In this feasibility trial, we propose an augmented reality-based smartphone app that guides the user to place the smartwatch in predefined positions on the chest using the front camera of a smartphone. A machine-learning model using MobileNet_v2 as the backbone was trained to detect the bipolar lead positions V1-V6 and visually project them onto the user's chest. Following the smartwatch recordings, a conventional 10 s, 12-lead ECG was recorded for comparison purposes. All 50 patients participating in the study were able to conduct a 9-lead smartwatch ECG using the app and assistance from the study team. Twelve patients were able to record all the limb and chest leads using the app without additional support. Bipolar chest leads recorded with smartwatch ECGs were assigned to standard unipolar Wilson leads by blinded cardiologists based on visual characteristics. In every lead, at least 86% of the ECGs were assigned correctly, indicating the remarkable similarity of the smartwatch to standard ECG recordings. Conclusion: We have introduced an augmented reality-based method to independently record multichannel smartwatch ECGs in an outpatient setting.

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AIMS: The presence of accompanying dyspnoea is routinely assessed and common in patients presenting with acute chest pain/discomfort to the emergency department (ED). We aimed to assess the association of accompanying dyspnoea with differential diagnoses, diagnostic work-up, and outcome. METHODS AND RESULTS: We enrolled patients presenting to the ED with chest pain/discomfort. Final diagnoses were adjudicated by independent cardiologists using all information including cardiac imaging. The primary diagnostic endpoint was the final diagnosis. The secondary diagnostic endpoint was the performance of high-sensitivity cardiac troponin (hs-cTn) and the European Society of Cardiology (ESC) 0/1h-algorithms for the diagnosis of myocardial infarction (MI). The prognostic endpoints were cardiovascular and all-cause mortality at two years. Among 6045 patients, 2892/6045 (48%) had accompanying dyspnoea. The prevalence of acute coronary syndrome (ACS) in patients with vs. without dyspnoea was comparable (MI 22.4% vs. 21.9%, P = 0.60, unstable angina 8.7% vs. 7.9%, P = 0.29). In contrast, patients with dyspnoea more often had cardiac, non-coronary disease (15.3% vs. 10.2%, P < 0.001). Diagnostic accuracy of hs-cTnT/I concentrations was not affected by the presence of dyspnoea (area under the curve 0.89-0.91 in both groups), and the safety of the ESC 0/1h-algorithms was maintained with negative predictive values >99.4%. Accompanying dyspnoea was an independent predictor for cardiovascular and all-cause death at two years [hazard ratio 1.813 (95% confidence intervals, 1.453-2.261, P < 0.01)]. CONCLUSION: Accompanying dyspnoea was not associated with a higher prevalence of ACS but with cardiac, non-coronary disease. While the safety of the diagnostic work-up was not affected, accompanying dyspnoea was an independent predictor for cardiovascular and all-cause death. CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT00470587, number NCT00470587.

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PURPOSE: The objective of this study was to quantify secondary prevention care by creating a secondary prevention benchmark (2PBM) score for patients undergoing ambulatory cardiac rehabilitation (CR) after acute coronary syndrome (ACS). METHODS: In this observational cohort study, 472 consecutive ACS patients who completed the ambulatory CR program between 2017 and 2019 were included. Benchmarks for secondary prevention medication and clinical and lifestyle targets were predefined and combined in the comprehensive 2PBM score with maximum 10 points. The association of patient characteristics and achievement rates of components and the 2PBM were assessed using multivariable logistic regression analysis. RESULTS: Patients were on average 62 ± 11 yr of age and predominantly male (n = 406; 86%). The types of ACS were ST-elevation myocardial infarction (STEMI) in 241 patients (51%) and non-ST-elevation myocardial infarction in 216 patients (46%). Achievement rates for components of the 2PBM were 71% for medication, 35% for clinical benchmark, and 61% for lifestyle benchmark. Achievement of medication benchmark was associated with younger age (OR = 0.979: 95% CI, 0.959-0.996, P = .021), STEMI (OR = 2.05: 95% CI, 1.35-3.12, P = .001), and clinical benchmark (OR = 1.80: 95% CI, 1.15-2.88, P = .011). Overall ≥8 of 10 points were reached by 77% and complete 2PBM by 16%, which was independently associated with STEMI (OR = 1.79: 95% CI, 1.06-3.08, P = .032). CONCLUSIONS: Benchmarking with 2PBM identifies gaps and achievements in secondary prevention care. ST-elevation myocardial infarction was associated with the highest 2PBM scores, suggesting best secondary prevention care in patients after ST-elevation myocardial infarction.

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BACKGROUND: Multiple smart devices capable to detect atrial fibrillation (AF) are presently available. Sensitivity and specificity for the detection of AF may differ between available smart devices, and this has not yet been adequately investigated. OBJECTIVES: The aim was to assess the accuracy of 5 smart devices in identifying AF compared with a physician-interpreted 12-lead electrocardiogram as the reference standard in a real-world cohort of patients. METHODS: We consecutively enrolled patients presenting to a cardiology service at a tertiary referral center in a prospective, diagnostic study. RESULTS: We prospectively analyzed 201 patients (31% women, median age 66.7 years). AF was present in 62 (31%) patients. Sensitivity and specificity for the detection of AF were comparable between devices: 85% and 75% for the Apple Watch 6, 85% and 75% for the Samsung Galaxy Watch 3, 58% and 75% for the Withings Scanwatch, 66% and 79% for the Fitbit Sense, and 79% and 69% for the AliveCor KardiaMobile, respectively. The rate of inconclusive tracings (the algorithm was unable to determine the heart rhythm) was 18%, 17%, 24%, 21%, and 26% for the Apple Watch 6, Samsung Galaxy Watch 3, Withings Scan Watch, Fitbit Sense, and AliveCor KardiaMobile (P < 0.01 for pairwise comparison), respectively. By manual review of inconclusive tracings, the rhythm could be determined in 955 (99%) of 969 single-lead electrocardiograms. Regarding patient acceptance, the Apple Watch was ranked first (39% of participants). CONCLUSIONS: In this clinical validation of 5 direct-to-consumer smart devices, we found differences in the amount of inconclusive tracings diminishing sensitivity and specificity of the smart devices. In a clinical setting, manual review of tracings is required in about one-fourth of cases.

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STUDY OBJECTIVE: The diagnostic performance of T-wave amplitudes for the detection of myocardial infarction is largely unknown. We aimed to address this knowledge gap. METHODS: T-wave amplitudes were automatically measured in 12-lead ECGs of patients presenting with acute chest discomfort to the emergency department within a prospective diagnostic multicenter study. The final diagnosis was centrally adjudicated by 2 independent cardiologists. Patients with left ventricular hypertrophy, complete left bundle branch block, or paced ventricular depolarization were excluded. The performance for lead-specific 95th-percentile thresholds were reported as likelihood ratios (lr), specificity, and sensitivity. RESULTS: Myocardial infarction was the final diagnosis in 445 (18%) of 2457 patients. In most leads, T-wave amplitudes tended to be greater in patients without myocardial infarction than those with myocardial infarction, and T-wave amplitude exceeding the 95th percentile had positive and negative lr close to 1 or with confidence intervals (CIs) crossing 1. The exceptions were leads III, aVR, and V1, which had positive lrs of 3.8 (95% CI, 2.7 to 5.3), 4.3 (95% CI, 3.1 to 6.0) and 2.0 (95% CI, 1.4 to 2.9), respectively. These leads normally have inverted T waves, so T-wave amplitude exceeding the 95th percentile reflects upright rather than increased-amplitude hyperacute T waves. CONCLUSION: Hyperacute T waves, when defined as increased T-wave amplitude exceeding the 95th percentile, did not provide useful information in diagnosing myocardial infarction in this sample.

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AIMS: Systemic inflammation may be central in the pathophysiology of acute heart failure (AHF). We aimed to assess the possible role of systemic inflammation in the pathophysiology, phenotyping, and risk stratification of patients with AHF. METHODS AND RESULTS: Using a novel Interleukin-6 immunoassay with unprecedented sensitivity (limit of detection 0.01 ng/L), we quantified systemic inflammation in unselected patients presenting with acute dyspnoea to the emergency department in a multicentre study. One-year mortality was the primary prognostic endpoint. Among 2042 patients, 1026 (50.2%) had an adjudicated diagnosis of AHF, 83.7% of whom had elevated interleukin-6 concentrations (>4.45 ng/L). Interleukin-6 was significantly higher in AHF patients compared to patients with other causes of dyspnoea (11.2 [6.1-26.5] ng/L vs. 9.0 [3.2-32.3] ng/L, p < 0.0005). Elevated interleukin-6 concentrations were independently predicted by increasing N-terminal pro-B-type natriuretic peptide and high-sensitivity cardiac troponin T, as well as the clinical diagnosis of infection. Among the different AHF phenotypes, interleukin-6 concentrations were highest in patients with cardiogenic shock (25.7 [14.0-164.2] ng/L) and lowest in patients with hypertensive AHF (9.3 [4.8-21.6] ng/L, p = 0.001). Inflammation as quantified by interleukin-6 was a strong and independent predictor of 1-year mortality both in all AHF patients, as well as those without clinically overt infection at presentation (adjusted hazard ratio [95% confidence interval] 1.45 [1.15-1.83] vs. 1.48 [1.09-2.00]). The addition of interleukin-6 significantly improved the discrimination of the BIOSTAT-CHF risk score. CONCLUSION: An unexpectedly high percentage of patients with AHF have subclinical systemic inflammation as quantified by interleukin-6, which seems to contribute to AHF phenotype and to the risk of death.

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AIMS: Pulmonary transit time (PTT) is the time blood takes to pass from the right ventricle to the left ventricle via pulmonary circulation. We aimed to quantify PTT in routine cardiovascular magnetic resonance imaging perfusion sequences. PTT may help in the diagnostic assessment and characterization of patients with unclear dyspnoea or heart failure (HF). METHODS AND RESULTS: We evaluated routine stress perfusion cardiovascular magnetic resonance scans in 352 patients, including an assessment of PTT. Eighty-six of these patients also had simultaneous quantification of N-terminal pro-brain natriuretic peptide (NTproBNP). NT-proBNP is an established blood biomarker for quantifying ventricular filling pressure in patients with presumed HF. Manually assessed PTT demonstrated low inter-rater variability with a correlation between raters >0.98. PTT was obtained automatically and correctly in 266 patients using artificial intelligence. The median PTT of 182 patients with both left and right ventricular ejection fraction >50% amounted to 6.8 s (Pulmonary transit time: 5.9-7.9 s). PTT was significantly higher in patients with reduced left ventricular ejection fraction (<40%; P < 0.001) and right ventricular ejection fraction (<40%; P < 0.0001). The area under the receiver operating characteristics curve (AUC) of PTT for exclusion of HF (NT-proBNP <125 ng/L) was 0.73 (P < 0.001) with a specificity of 77% and sensitivity of 70%. The AUC of PTT for the inclusion of HF (NT-proBNP >600 ng/L) was 0.70 (P < 0.001) with a specificity of 78% and sensitivity of 61%. CONCLUSION: PTT as an easily, even automatically obtainable and robust non-invasive biomarker of haemodynamics might help in the evaluation of patients with dyspnoea and HF.

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OBJECTIVE: To investigate the relationship between ambient temperature and atmospheric pressure (AP) and the severity of heart failure (HF) decompensations. METHODS: We analysed patients coming from the Epidemioloy Acute Heart Failure Emergency (EAHFE) Registry, a multicentre prospective cohort study enrolling patients diagnosed with decompensated HF in 26 emergency departments (EDs) of 16 Spanish cities. We recorded patient and demographic data and maximum temperature (Tmax) and AP (APmax) the day before ED consultation. Associations between temperature and AP and severity endpoints were explored by logistic regression. We used restricted cubic splines to model continuous non-linear associations of temperature and AP with each endpoint. RESULTS: We analysed 16,545 patients. Daily Tmax and APmax (anomaly) of the day before patient ED arrival ranged from 0.8 to 41.6° and from - 61.7 to 69.9 hPa, respectively. A total of 12,352 patients (75.2%) were hospitalised, with in-hospital mortality in 1171 (7.1%). The probability of hospitalisation by HF decompensation showed a U-shaped curve versus Tmax and an increasing trend versus APmax. Regarding temperature, hospitalisation significantly increased from 20 °C (reference) upwards (25 °C: OR = 1.12, 95% CI = 1.04-1.21; 40 °C: 1.65, 1.13-2.40) and below 5.4 °C (5 °C: 1.21, 1.01-1.46). Concerning the mean AP of the city (anomaly = 0 hPa), hospitalisation increased when APmax (anomaly) was above + 7.0 hPa (atmospheric anticyclone; + 10 hPa: 1.14, 1.05-1.24; + 30 hPa: 2.02. 1.35-3.03). The lowest probability of mortality also corresponded to cold-mild temperatures and low AP, with a significant increased risk only found for Tmax above 24.3 °C (25 °C: 1.13, 1.01-1.27; 40 °C: 2.05, 1.15-3.64) and APmax (anomaly) above + 3.4 hPa (+ 10 hPa: 1.21, 1.07-1.36; + 30 hPa: 1.73, 1.06-2.81). Sensitivity analysis confirmed the main analysis results. CONCLUSION: Temperature and AP are independently associated with the severity of HF decompensations, with possible different effects on the need for hospitalisation and in-hospital mortality.

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