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Aims: To develop a suite of quality indicators (QIs) for the evaluation of the care and outcomes for adults undergoing cardiac pacing. Methods and results: Under the auspice of the Clinical Practice Guideline Quality Indicator Committee of the European Society of Cardiology (ESC), the Working Group for cardiac pacing QIs was formed. The Group comprised Task Force members of the 2021 ESC Clinical Practice Guidelines on Cardiac Pacing and Cardiac Resynchronization Therapy, members of the European Heart Rhythm Association, international cardiac device experts, and patient representatives. We followed the ESC methodology for QI development, which involved (i) the identification of the key domains of care by constructing a conceptual framework of the management of patients receiving cardiac pacing, (ii) the development of candidate QIs by conducting a systematic review of the literature, (iii) the selection of the final set of QIs using a modified-Delphi method, and (iv) the evaluation of the feasibility of the developed QIs. Four domains of care were identified: (i) structural framework, (ii) patient assessment, (iii) pacing strategy, and (iv) clinical outcomes. In total, seven main and four secondary QIs were selected across these domains and were embedded within the 2021 ESC Guidelines on Cardiac Pacing and Cardiac Resynchronization therapy. Conclusion: By way of a standardized process, 11 QIs for cardiac pacing were developed. These indicators may be used to quantify adherence to guideline-recommended clinical practice and have the potential to improve the care and outcomes of patients receiving cardiac pacemakers.

BACKGROUND: Diagnosing Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) at an early stage can be challenging even after ECG recording and a combination of several imaging techniques. The purpose of this study was to explore if a Body Surface Mapping (BSM) system with 252-leads could identify repolarization abnormalities and thereby diagnose early stages of ARVC. METHODS: ARVC patients, gene carriers without signs of ARVC and controls underwent a 12 lead resting ECG, signal-averaged ECG, echocardiography, 24-hours Holter monitoring and BSM with electrocardiographic imaging (ECGI). All 252-leads, divided into four quadrants of the vest, were analyzed regarding concordances between T wave polarity and QRS main vector. RESULTS: Of 40 patients included there were 12 ARVC patients, 20 gene carriers and 8 controls. The ARVC patients had two different repolarization patterns, one with more pronounced negative T waves at the lower left panel and another with mixed changes that clearly differed from the controls, all of whom had a normal 12 lead ECGs and consistent repolarization patterns on their BSM recordings. The patterns observed in ARVC patients were also present in 5/20 (25%) gene carriers, three of whom had normal resting ECG. A novel repolarization index successfully detected all ARVC patients and 88% of gene carriers with pathologic repolarization pattern. CONCLUSIONS: The finding that abnormal repolarization patterns could be unmasked by BSM in 25% of healthy gene carriers, suggests that it may potentially be a useful tool for identifying early manifestations of ARVC. Further and larger studies are warranted to assess its diagnostic accuracy.

Atrial fibrillation (AF) is common and increases the risk for stroke and heart failure (HF). The early identification of patients at risk may prevent the development of AF and improve prognosis. This study, therefore, aimed to test the effect of the association between P-wave and PR-interval on the ECG and incident AF. The PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (1016 individuals all aged 70 years; 50% women) was used to identify whether the ECG variables P-wave duration (Pdur) and PR-duration in lead V1 were related to new-onset AF. Exclusion criteria were prevalent AF, QRS-duration >= 130 milliseconds (msec), atrial tachyarrhythmias and implanted pacemaker/defibrillator. Cox proportional-hazards models were used for analyses. Adjustments were made for gender, RR-interval, beta-blocking agents, systolic blood pressure, body mass index, and smoking. Of 877 subjects at risk, 189 individuals developed AF during a 15-year follow-up. There was a U-shaped relationship between the Pdur and incident AF (P = 0.017) following multiple adjustment. Values below 60 msec were significantly associated with incident AF, with a hazard ratio of 1.55 (95% confidence interval 1.15-2.09) for a Pdur <= 42 msec. There was no significant relationship between incident AF and the PR-interval. A short Pdur derived from the ECG in V1 may be a useful marker for new-onset AF, enabling the early identification of at-risk patients.

Aims: The 4S-AF classification scheme comprises of four domains: stroke risk (St), symptoms (Sy), severity of atrial fibrillation (AF) burden (Sb), and substrate (Su). We sought to examine the implementation of the 4S-AF scheme in the EORP-AF General Long-Term Registry and compare outcomes in AF patients according to the 4S-AF-led decision-making process. Methods and results: Atrial fibrillation patients from 250 centres across 27 European countries were included. A 4S-AF score was calculated as the sum of each domain with a maximum score of 9. Of 6321 patients, 8.4% had low (St), 47.5% EHRA I (Sy), 40.5% newly diagnosed or paroxysmal AF (Sb), and 5.1% no cardiovascular risk factors or left atrial enlargement (Su). Median follow-up was 24 months. Using multivariable Cox regression analysis, independent predictors of all-cause mortality were (St) [adjusted hazard ratio (aHR) 8.21, 95% confidence interval (CI): 2.60-25.9], (Sb) (aHR 1.21, 95% CI: 1.08-1.35), and (Su) (aHR 1.27, 95% CI: 1.14-1.41). For CV mortality and any thromboembolic event, only (Su) (aHR 1.73, 95% CI: 1.45-2.06) and (Sy) (aHR 1.29, 95% CI: 1.00-1.66) were statistically significant, respectively. None of the domains were independently linked to ischaemic stroke or major bleeding. Higher 4S-AF score was related to a significant increase in all-cause mortality, CV mortality, any thromboembolic event, and ischaemic stroke but not major bleeding. Treatment of all 4S-AF domains was associated with an independent decrease in all-cause mortality (aHR 0.71, 95% CI: 0.55-0.92). For each 4S-AF domain left untreated, the risk of all-cause mortality increased substantially (aHR 1.35, 95% CI: 1.16-1.56). Conclusion: Implementation of the novel 4S-AF scheme is feasible, and treatment decisions based on this scheme improve mortality rates in AF.

The improvement of Patient-reported outcomes, such as health-related quality of life, is the main indication for atrial fibrillation ablation. Despite this guideline derived indication for an AF ablation procedure the current standardized primary endpoint in AF ablation trials is still rhythm-related, and primarily a 30-second long AF episode. The review presents reflections on the non-rational arguments of using rhythm related endpoints rather than Patient-reported outcomes in AF ablation procedure trials despite the mismatch between many of the rhythm related variables and symptoms. Arguments for health-related quality of life as the most optimal primary endpoint in clinical trials are presented while atrial fibrillation burden is presented as the most optimal electrical complementary endpoint, apart from being the major variable in mechanistic trials.

Infections related to cardiac implantable electronic devices (CIED) are associated with significant morbidity and mortality. Despite optimal use of antimicrobials and other preventive strategies, the incidence of CIED infections is increasing over time leading to considerable costs to the healthcare systems. Recently, antibiotic-eluting envelopes (AEEs) have been introduced as a promising technology to prevent CIED infections. This review will address the current evidence on stratification of CIED infection risk, present the rationale behind AEE, and summarize the currently available evidence for CIED infection prevention as well as demonstrate the cost-effectiveness of this novel technology.

Aims: A third of non-valvular atrial fibrillation (NVAF) patients are non-adherent to direct oral anticoagulants (DOACs). Estimates of the economic value of full adherence and the cost of two types of adherence improving interventions are important to healthcare planners and decision-makers. Methods: A cost-utility analysis estimated the impact of non-adherence over a 20-year horizon, for a patient cohort with a mean age of 77 years, based on data from the Stockholm Healthcare database of NVAF patients with incident stroke between 2011 and 2018. Adherence was defined using a medication possession ratio (MPR) cut-off of 90%; primary outcomes were the number of ischemic strokes and associated incremental cost–utility ratio. Results: Hypothetical comparisons between cohorts of 1,000 patients with varying non-adherence levels and full adherence (MPR >90%) predicted an additional number of strokes ranging from 117 (MPR = 81–90%) to 866 (MPR <60%), and years of life lost ranging from 177 (MPR = 81– 90%) to 1,318 (MPR < 60%; discounted at 3%). Chronic disease co-management intervention occurring during each DOAC prescription renewal and patient education intervention at DOAC initiation will be cost-saving to the health system if its cost is below SEK 143 and SEK 4,655, and cost-effective if below SEK 858 and SEK 28,665, respectively. Conclusion: Adherence improving interventions for NVAF patients on DOACs such as chronic disease co-management and patient education can be cost-saving and cost-effective, within a range of costs that appear reasonable to the Swedish healthcare system.

BACKGROUND: Atrial fibrillation (AF) and renal impairment share a bidirectional relationship with important pathophysiological interactions. We evaluated the impact of renal impairment in a contemporary cohort of patients with AF. METHODS: We utilised the ESC-EHRA EORP-AF Long-Term General Registry. Outcomes were analysed according to renal function by CKD-EPI equation. The primary endpoint was a composite of thromboembolism, major bleeding, acute coronary syndrome and all-cause death. Secondary endpoints were each of these separately including ischaemic stroke, haemorrhagic event, intracranial haemorrhage, cardiovascular death and hospital admission. RESULTS: A total of 9,306 patients were included. The distribution of patients with no, mild, moderate and severe renal impairment at baseline were 16.9%, 49.3%, 30.0% and 3.8%, respectively. AF patients with impaired renal function were older, more likely to be females, had worse cardiac imaging parameters and multiple comorbidities. Among patients with an indication for anticoagulation, prescription of these agents was reduced in those with severe renal impairment,p<0.001. Over 24 months, impaired renal function was associated with significantly greater incidence of the primary composite outcome and all secondary outcomes. Multivariable Cox regression analysis demonstrated an inverse relationship between eGFR and the primary outcome (HR 1.07[95% CI,1.01-1.14] per 10 mL/min/1.73m2 decrease), that was most notable in patients with eGFR <30mL/min/1.73m2 (HR 2.21[95% CI,1.23-3.99] compared to eGFR ≥90mL/min/1.73m2 ). CONCLUSION: A significant proportion of patients with AF suffer from concomitant renal impairment which impacts their overall management. Furthermore, renal impairment is an independent predictor of major adverse events including thromboembolism, major bleeding, acute coronary syndrome and all-cause death in patients with AF.

Atrial fibrillation (AF) and renal impairment share a bidirectional relationship with important pathophysiological interactions. We evaluated the impact of renal impairment in a contemporary cohort of patients with AF. We utilised the ESC-EHRA EORP-AF Long-Term General Registry. Outcomes were analysed according to renal function by CKD-EPI equation. The primary endpoint was a composite of thromboembolism, major bleeding, acute coronary syndrome and all-cause death. Secondary endpoints were each of these separately including ischaemic stroke, haemorrhagic event, intracranial haemorrhage, cardiovascular death and hospital admission. A total of 9306 patients were included. The distribution of patients with no, mild, moderate and severe renal impairment at baseline were 16.9%, 49.3%, 30% and 3.8%, respectively. AF patients with impaired renal function were older, more likely to be females, had worse cardiac imaging parameters and multiple comorbidities. Among patients with an indication for anticoagulation, prescription of these agents was reduced in those with severe renal impairment, p < .001. Over 24 months, impaired renal function was associated with significantly greater incidence of the primary composite outcome and all secondary outcomes. Multivariable Cox regression analysis demonstrated an inverse relationship between eGFR and the primary outcome (HR 1.07 [95% CI, 1.01-1.14] per 10 ml/min/1.73 m decrease), that was most notable in patients with eGFR <30 ml/min/1.73 m (HR 2.21 [95% CI, 1.23-3.99] compared to eGFR ≥90 ml/min/1.73 m ). A significant proportion of patients with AF suffer from concomitant renal impairment which impacts their overall management. Furthermore, renal impairment is an independent predictor of major adverse events including thromboembolism, major bleeding, acute coronary syndrome and all-cause death in patients with AF.

Background: Guideline recommendations are the accepted reference for selection of therapies for rhythm control of atrial fibrillation (AF). This study was designed to understand physicians' treatment practices and adherence to guidelines. Methods and Results: The AIM-AF (Antiarrhythmic Medication for Atrial Fibrillation) study was an online survey of clinical cardiologists and electrophysiologists that was conducted in the United States and Europe (N=629). Respondents actively treated >= 30 patients with AF who received drug therapy, and had received or were referred for ablation every 3 months. The survey comprised 96 questions on physician demographics, AF types, and treatment practices. Overall, 54% of respondents considered guidelines to be the most important nonpatient factor influencing treatment choice. Across most queried comorbidities, amiodarone was selected by 60% to 80% of respondents. Other nonadherent usage included sotalol by 21% in patients with renal impairment; dofetilide initiation (16%, United States only) outside of hospital; class Ic agents by 6% in coronary artery disease; and dronedarone by 8% in patients with heart failure with reduced ejection fraction. Additionally, rhythm control strategies were frequently chosen in asymptomatic AF (antiarrhythmic drugs [AADs], 35%; ablation, 8%) and subclinical AF (AADs, 38%; ablation, 13%). Despite guideline algorithms emphasizing safety first, efficacy (48%) was selected as the most important consideration for AAD choice, followed by safety (34%). Conclusions: Despite surveyed clinicians recognizing the importance of guidelines, nonadherence was frequently observed. While deviation may be reasonable in selected patients, in general, nonadherence has the potential to compromise patient safety. These findings highlight an underappreciation of the safe use of AADs, emphasizing the need for interventions to support optimal AAD selection.

Background: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that AI can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. Objective: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). Methods: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, 63.4±16.9 years). Records were labeled by mortality (death vs. discharge) or MACE (no events vs. arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. Results: 245 (17.7%) patients died (67.3% male, 74.5±14.4 years); 352 (24.4%) experienced at least one MACE (119 arrhythmic; 107 HF; 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60±0.05 and 0.55±0.07, respectively; these were comparable to AUC values for conventional models (0.73±0.07 and 0.65±0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. Conclusion: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.

AIMS: To develop a suite of quality indicators (QIs) for the evaluation of the care and outcomes for adults undergoing cardiac pacing. METHODS AND RESULTS: Under the auspice of the Clinical Practice Guideline Quality Indicator Committee of the European Society of Cardiology (ESC), the Working Group for cardiac pacing QIs was formed. The Group comprised Task Force members of the 2021 ESC Clinical Practice Guidelines on Cardiac Pacing and Cardiac Resynchronization Therapy, members of the European Heart Rhythm Association, international cardiac device experts, and patient representatives. We followed the ESC methodology for QI development, which involved (i) the identification of the key domains of care by constructing a conceptual framework of the management of patients receiving cardiac pacing, (ii) the development of candidate QIs by conducting a systematic review of the literature, (iii) the selection of the final set of QIs using a modified-Delphi method, and (iv) the evaluation of the feasibility of the developed QIs. Four domains of care were identified: (i) structural framework, (ii) patient assessment, (iii) pacing strategy, and (iv) clinical outcomes. In total, seven main and four secondary QIs were selected across these domains and were embedded within the 2021 ESC Guidelines on Cardiac Pacing and Cardiac Resynchronization therapy. CONCLUSION: By way of a standardized process, 11 QIs for cardiac pacing were developed. These indicators may be used to quantify adherence to guideline-recommended clinical practice and have the potential to improve the care and outcomes of patients receiving cardiac pacemakers.

Background: The current practice of atrial fibrillation ablation (AFA) as a treatment option for atrial fibrillation (AF) in patients with heart failure (HF) across Europe, their clinical profiles and outcomes is still undefined. Methods: The European Society of Cardiology (ESC) led a prospective observational registry of consecutive patients undergoing AFA, in 27 member countries. The subgroup of patients with HF, followed-up for 1 year, was analyzed and the results are reported. Results: Of the 3582 AF patients in the Registry, 537 (14.9%) had HF. Diabetes, hypertension, hypercholesterolemia, CHA2DS2-VASc score ≥ 2, structural heart disease and persistent AF were more common in HF than non-HF patients (all p < 0.001). However the in-hospital complications were less frequent in HF patients (5.0% vs. 8.2% p = 0.01). Both in-hospital and 1-year outcomes, including 1-year AF recurrence (15.4%) and repeat ablations (9.5%), were similar in both groups. We subdivided HF patients according to their left ventricular ejection fraction (EF) at baseline into reduced (HFrEF, <40%), mid-range (HFmEF, 40-49%), or preserved EF (HFpEF, ≥ 50%). Most patients were HFpEF (n 375, 77%), 72 (15%) were HFmEF and 8% HFrEF. The most frequent underlying conditions in HFpEF were hypertension and ischemic heart disease, while those most common in HFmEF and HFrEF were valvular and dilated cardiomyopathy. Conclusion: In routine care in Europe, HF patients represent a minority of patients undergoing AFA, and most belong to the HFpEF phenotype. The limited clinical research on AFA HFpEF patients is reflected by the uncertainty expressed in the current AF Guidelines and Expert statements.

AIMS: The aims of this study is to characterize the transvenous lead extraction (TLE) population with active (A) compared with passive fixation (PFix) leads and to compare the safety, efficacy, and ease of extracting active fixation (AFix) compared with PFix right atrial (RA) and right ventricular (RV) leads. METHODS AND RESULTS: The European Lead Extraction ConTRolled Registry (ELECTRa) was analysed. Patients were divided into three groups; those with only AFix, only PFix, and combined Fix leads. Three outcomes were defined. Difficult extraction, complete radiological, and clinical success. Multivariate model was used to analyse the independent effect of Fix mechanism on these outcomes. The study included 2815 patients, 1456 (51.7%) with only AFix leads, 982 (34.9%) with only PFix leads, and 377 (13.4%) with combined Fix leads. Patients with AFix leads were younger with shorter lead dwelling time. Infection was the leading cause for TLE among the combined Fix group with lowest rates among AFix group. No difference in complications rates was noted between patients with only AFix vs. PFix leads. Overall, there were 1689 RA (1046 AFix and 643 PFix) and 2617 RV leads (1441 AFix and 1176 PFix). Multivariate model demonstrated that PFix is independently associated with more difficult extraction for both RA and RV leads, lower radiological success in the RA but has no effect on clinical success. CONCLUSION: Mechanism of Fix impact the ease of TLE of RA and RV leads and rates of complete radiological success in the RA but not clinical success. These findings should be considered during implantation and TLE procedures.

Aims: The safety of digoxin therapy in atrial fibrillation (AF) remains ill-defined. We aimed to evaluate the effects of digoxin over beta-blocker therapy in AF. Methods and results: Patients with AF who were treated with either digoxin or beta-blocker from the ESC-EHRA EORP-AF General Long-Term Registry were included. Outcomes of interest were all-cause mortality, cardiovascular (CV) mortality, non-CV mortality, quality of life and number of patients with unplanned hospitalisations. Of 6377 patients, 549(8.6%) were treated with digoxin. Over 24 months, there were 550(8.6%) all-cause mortality events and 1304(23.6%) patients with unplanned emergency hospitalisations. Compared to beta-blocker, digoxin therapy was associated with increased all-cause mortality (HR 1.90 [95%CI,1.48-2.44], CV mortality (HR 2.18 [95%CI,1.47-3.21] and non-CV mortality (HR 1.68 [95%CI,1.02-2.75] with reduced quality of life (Health Utility Score 0.555[±0.406] vs. 0.705[±0.346], P<0.001) but no differences in emergency hospitalisations (HR 1.00 [95%CI,0.56-1.80]) or AF-related hospitalisations (HR 0.95 [95%CI,0.60-1.52]).On multivariable analysis, there were no differences in any of the outcomes between both groups, after accounting for potential confounders. Similar results were obtained in the subgroups of patients with permanent AF and coexisting heart failure. There was no differences in outcomes between AF patients receiving digoxin with and without chronic kidney disease. Conclusion: Poor outcomes related to the use of digoxin over beta-blocker therapy in terms of excess mortality and reduced quality of life are associated with the presence of other risk factors rather than digoxin per se. The choice of digoxin or beta-blocker therapy had no influence on the incidence of unplanned hospitalisations.

BACKGROUND Transvenous lead extraction (TLE) remains a high-risk procedure. OBJECTIVE The purpose of this study was to develop a machine learning (ML)-based risk stratification system to predict the risk of major adverse events (MAEs) after TLE. A MAE was defined as procedure-related major complication and procedure-related death. METHODS We designed and evaluated an ML-based risk stratification system trained using the European Lead Extraction ConTRolled (ELECTRa) registry to predict the risk of MAEs in 3555 patients undergoing TLE and tested this on an independent registry of 1171 patients. ML models were developed, including a self-normalizing neural network (SNN), stepwise logistic regression model ("stepwise model"), support vector machines, and random forest model. These were compared with the ELECTRa Registry Outcome Score (EROS) for MAEs. RESULTS There were 53 MAEs (1.7%) in the training cohort and 24 (2.4%) in the test cohort. Thirty-two clinically important features were used to train the models. ML techniques were similar to EROS by balanced accuracy (stepwise model: 0.74 vs EROS: 0.70) and superior by area under the curve (support vector machines: 0.764 vs EROS: 0.677). The SNN provided a finite risk for MAE and accurately identified MAE in 14 of 169 "high (<80%) risk" patients (8.3%) and no MAEs in all 198 "low (,20%) risk" patients (100%). CONCLUSION ML models incrementally improved risk prediction for identifying those at risk of MAEs. The SNN has the additional advantage of providing a personalized finite risk assessment for patients. This may aid patient decision making and allow better preoperative risk assessment and resource allocation.

Background: The prevalence of atrial fibrillation(AF) and diabetes mellitus is rising to epidemic proportions. We aimed to assess the impact of diabetes on the management and outcomes of patients with AF. Methods: The EORP-AF General Long-Term Registry is a prospective, observational registry from 250 centres across 27 European countries. Outcomes of interest were as follows: i)rhythm control interventions; ii)quality of life; iii)healthcare resource utilisation; and iv)major adverse events. Results: Of 11,028 patients with AF, the median age was 71 (63-77) years and 2537 (23.0%) had diabetes. Median follow-up was 24 months. Diabetes was related to increased use of anticoagulation but less rhythm control interventions. Using multivariable analysis, at 2-year follow-up, patients with diabetes were associated with greater levels of anxiety (p = 0.038) compared to those without diabetes. Overall, diabetes was associated with worse health during follow-up, as indicated by Health Utility Score and Visual Analogue Scale. Healthcare resource utilisation was greater with diabetes in terms of length of hospital stay (8.1 (+/- 8.2) vs. 6.1 (+/- 6.7) days); cardiology and internal medicine/general practitioner visits; and emergency room admissions. Diabetes was an independent risk factor of major adverse cardiovascular event (MACE; HR 1.26 [95% CI, 1.04-1.52]), all-cause mortality (HR 1.28 [95% CI, 1.08-1.52]), and cardiovascular mortality (HR 1.41 [95% CI, 1.09-1.83]). Conclusion: In this contemporary AF cohort, diabetes was present in 1 in 4 patients and it served as an independent risk factor for reduced quality of life, greater healthcare resource utilisation and excess MACE, all-cause mortality and cardiovascular mortality. There was increased use of anticoagulation therapy in diabetes but with less rhythm control interventions.

BACKGROUND: Early identification of patients at risk of congestive heart failure (HF) may alter their poor prognosis. The aim was therefore to test whether simple electrocardiographic variables, the P-wave and PR-interval, could predict incident HF. METHODS: The PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (1016 individuals all aged 70 years, 50% women) was used to identify predictors of HF. Subjects with prevalent HF, QRS duration z >= 130 ms, atrial tachyarrhythmias, implanted pacemaker/defibrillator, second- and third-degree atrioventricular block or delta waves at baseline were excluded. Cox proportional hazard analysis was used to relate the PR interval, P-wave duration (Pdur) and amplitude (Pamp), measured in lead V1, to incident HF. Adjustment was performed for gender, RR-interval, beta-blocking agents, systolic blood pressure, body mass index and smoking. RESULTS: Out of 836 subjects at risk, 107 subjects were diagnosed with HF during a follow-up of 15 years. In the multivariate analysis, there was a strong U-shaped correlation between Pdur in lead V1 and incident HF (P= 0.0001) which was significant for a Pdur < 60 ms [HR = 2.75; 95% CI: 1.87-4.06, at Pdur 40 ms] but not for prolonged Pdur. There was no significant relationship between incident HF and the PR-interval or the Pamp. A Pdur < 60 ms improved discrimination by 3.7% when added to the traditional risk factors including sex, RR-interval, beta-blocking agents, systolic blood pressure, BMI and smoking (P= 0.048). CONCLUSIONS: A short Pdur, an easily measured parameter on the ECG, may potentially be a useful marker of future HF, enabling its early detection and prevention, thus improving outcomes.