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2.
Europace ; 26(7)2024 Jul 02.
Artículo en Inglés | MEDLINE | ID: mdl-38979560

RESUMEN

AIMS: Recommendations on cardiac resynchronization therapy (CRT) in patients with atrial fibrillation or flutter (AF) are based on less robust evidence than those in sinus rhythm (SR). We aimed to assess the efficacy of CRT upgrade in the BUDAPEST-CRT Upgrade trial population by their baseline rhythm. METHODS AND RESULTS: Heart failure patients with reduced ejection fraction (HFrEF) and previously implanted pacemaker (PM) or implantable cardioverter defibrillator (ICD) and ≥20% right ventricular (RV) pacing burden were randomized to CRT with defibrillator (CRT-D) upgrade (n = 215) or ICD (n = 145). Primary [HF hospitalization (HFH), all-cause mortality, or <15% reduction of left ventricular end-systolic volume] and secondary outcomes were investigated. At enrolment, 131 (36%) patients had AF, who had an increased risk for HFH as compared with those with SR [adjusted hazard ratio (aHR) 2.99; 95% confidence interval (CI) 1.26-7.13; P = 0.013]. The effect of CRT-D upgrade was similar in patients with AF as in those with SR [AF adjusted odds ratio (aOR) 0.06; 95% CI 0.02-0.17; P < 0.001; SR aOR 0.13; 95% CI 0.07-0.27; P < 0.001; interaction P = 0.29] during the mean follow-up time of 12.4 months. Also, it decreased the risk of HFH or all-cause mortality (aHR 0.33; 95% CI 0.16-0.70; P = 0.003; interaction P = 0.17) and improved the echocardiographic response (left ventricular end-diastolic volume difference -49.21 mL; 95% CI -69.10 to -29.32; P < 0.001; interaction P = 0.21). CONCLUSION: In HFrEF patients with AF and PM/ICD with high RV pacing burden, CRT-D upgrade decreased the risk of HFH and improved reverse remodelling when compared with ICD, similar to that seen in patients in SR.


Asunto(s)
Fibrilación Atrial , Terapia de Resincronización Cardíaca , Desfibriladores Implantables , Insuficiencia Cardíaca , Volumen Sistólico , Humanos , Fibrilación Atrial/terapia , Fibrilación Atrial/fisiopatología , Fibrilación Atrial/mortalidad , Fibrilación Atrial/complicaciones , Fibrilación Atrial/diagnóstico , Masculino , Femenino , Terapia de Resincronización Cardíaca/métodos , Anciano , Insuficiencia Cardíaca/fisiopatología , Insuficiencia Cardíaca/terapia , Insuficiencia Cardíaca/mortalidad , Resultado del Tratamiento , Persona de Mediana Edad , Función Ventricular Derecha , Función Ventricular Izquierda , Dispositivos de Terapia de Resincronización Cardíaca , Factores de Riesgo , Hospitalización/estadística & datos numéricos , Cardioversión Eléctrica/efectos adversos , Cardioversión Eléctrica/instrumentación , Factores de Tiempo , Anciano de 80 o más Años
3.
BMC Health Serv Res ; 24(1): 637, 2024 May 17.
Artículo en Inglés | MEDLINE | ID: mdl-38760673

RESUMEN

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. It is also a major risk factor for ischemic stroke. The main objective of our study was to identify direct and indirect costs of AF and AF-related stroke in Slovakia. METHODS: We conducted a retrospective population-based study of AF and stroke related costs both from the third-party healthcare payers and societal perspective. The prevalence and incidence of AF and stroke were determined from central government run healthcare database. Further we estimated both indirect and direct costs of AF and stroke. All costs and healthcare resources were assessed from 2015 through 2019 and were expressed in the respective year. RESULTS: Over the 5-year study period, the prevalence of AF increased by 26% to a total of 149,198 AF cases in 2019, with an estimated total annual economic burden of €66,242,359. Direct medical costs accounted for 94% of the total cost of AF. The total cost of treating patients with stroke in 2019 was estimated at €89,505,669. As a result, the medical costs of stroke that develops as a complication of AF have been estimated to be €25,734,080 in 2019. CONCLUSIONS: Our study shows a substantial economic burden of AF and AF-related stroke in Slovakia. In view of the above, both screening for asymptomatic AF in high-risk populations and effective early management of AF with a focused on thromboprophylaxis rhythm control should be implemented.


Asunto(s)
Fibrilación Atrial , Accidente Cerebrovascular , Humanos , Eslovaquia/epidemiología , Fibrilación Atrial/epidemiología , Fibrilación Atrial/economía , Fibrilación Atrial/terapia , Estudios Retrospectivos , Accidente Cerebrovascular/epidemiología , Accidente Cerebrovascular/economía , Femenino , Masculino , Anciano , Persona de Mediana Edad , Costos de la Atención en Salud/estadística & datos numéricos , Costo de Enfermedad , Incidencia , Prevalencia , Anciano de 80 o más Años , Adulto
4.
Eur Heart J Digit Health ; 5(2): 123-133, 2024 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-38505483

RESUMEN

Aims: A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods and results: An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)]. Conclusion: The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.

5.
Bratisl Lek Listy ; 125(3): 159-165, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38385541

RESUMEN

OBJECTIVES:  This study aimed to predict individual COVID-19 patient prognosis at hospital admission using artificial intelligence (AI)-based quantification of computed tomography (CT) pulmonary involvement. BACKGROUND: Assessing patient prognosis in COVID-19 pneumonia is crucial for patient management and hospital and ICU organization. METHODS: We retrospectively analyzed 559 patients with PCR-verified COVID-19 pneumonia referred to the hospital for a severe disease course. We correlated the CT extent of pulmonary involvement with patient outcome. We also attempted to define cut-off values of pulmonary involvement for predicting different outcomes. RESULTS:  CT-based disease extent quantification is an independent predictor of patient morbidity and mortality, with the prognosis being impacted also by age and cardiovascular comorbidities. With the use of explored cut-off values, we divided patients into three groups based on their extent of disease: (1) less than 28 % (sensitivity 65.4 %; specificity 89.1 %), (2) ranging from 28 % (31 %) to 47 % (sensitivity 87.1 %; specificity 62.7 %), and (3) above 47 % (sensitivity 87.1 %; specificity, 62.7 %), representing low risk, risk for oxygen therapy and invasive pulmonary ventilation, and risk of death, respectively. CONCLUSION: CT quantification of pulmonary involvement using AI-based software helps predict COVID-19 patient outcomes (Tab. 4, Fig. 4, Ref. 38).


Asunto(s)
COVID-19 , Neumonía , Humanos , COVID-19/diagnóstico por imagen , Inteligencia Artificial , SARS-CoV-2 , Estudios Retrospectivos , Pulmón/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos
6.
J Electrocardiol ; 82: 147-154, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38154405

RESUMEN

BACKGROUND: The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools used to assess cardiac patients at the first point of contact. Despite advances in computerized interpretation of the electrocardiogram (CIE), its accuracy remains inferior to physicians. This study evaluated the diagnostic performance of an artificial intelligence (AI)-powered ECG system and compared its performance to current state-of-the-art CIE. METHODS: An AI-powered system consisting of 6 deep neural networks (DNN) was trained on standard 12­lead ECGs to detect 20 essential diagnostic patterns (grouped into 6 categories: rhythm, acute coronary syndrome (ACS), conduction abnormalities, ectopy, chamber enlargement and axis). An independent test set of ECGs with diagnostic consensus of two expert cardiologists was used as a reference standard. AI system performance was compared to current state-of-the-art CIE. The key metrics used to compare performances were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score. RESULTS: A total of 932,711 standard 12­lead ECGs from 173,949 patients were used for AI system development. The independent test set pooled 11,932 annotated ECG labels. In all 6 diagnostic categories, the DNNs achieved high F1 scores: Rhythm 0.957, ACS 0.925, Conduction abnormalities 0.893, Ectopy 0.966, Chamber enlargement 0.972, and Axis 0.897. The diagnostic performance of DNNs surpassed state-of-the-art CIE for the 13 out of 20 essential diagnostic patterns and was non-inferior for the remaining individual diagnoses. CONCLUSIONS: Our results demonstrate the AI-powered ECG model's ability to accurately identify electrocardiographic abnormalities from the 12­lead ECG, highlighting its potential as a clinical tool for healthcare professionals.


Asunto(s)
Síndrome Coronario Agudo , Inteligencia Artificial , Humanos , Electrocardiografía , Redes Neurales de la Computación , Benchmarking
8.
Eur Heart J ; 44(40): 4259-4269, 2023 Oct 21.
Artículo en Inglés | MEDLINE | ID: mdl-37632437

RESUMEN

BACKGROUND AND AIMS: De novo implanted cardiac resynchronization therapy with defibrillator (CRT-D) reduces the risk of morbidity and mortality in patients with left bundle branch block, heart failure and reduced ejection fraction (HFrEF). However, among HFrEF patients with right ventricular pacing (RVP), the efficacy of CRT-D upgrade is uncertain. METHODS: In this multicentre, randomized, controlled trial, 360 symptomatic (New York Heart Association Classes II-IVa) HFrEF patients with a pacemaker or implantable cardioverter defibrillator (ICD), high RVP burden ≥ 20%, and a wide paced QRS complex duration ≥ 150 ms were randomly assigned to receive CRT-D upgrade (n = 215) or ICD (n = 145) in a 3:2 ratio. The primary outcome was the composite of all-cause mortality, heart failure hospitalization, or <15% reduction of left ventricular end-systolic volume assessed at 12 months. Secondary outcomes included all-cause mortality or heart failure hospitalization. RESULTS: Over a median follow-up of 12.4 months, the primary outcome occurred in 58/179 (32.4%) in the CRT-D arm vs. 101/128 (78.9%) in the ICD arm (odds ratio 0.11; 95% confidence interval 0.06-0.19; P < .001). All-cause mortality or heart failure hospitalization occurred in 22/215 (10%) in the CRT-D arm vs. 46/145 (32%) in the ICD arm (hazard ratio 0.27; 95% confidence interval 0.16-0.47; P < .001). The incidence of procedure- or device-related complications was similar between the two arms [CRT-D group 25/211 (12.3%) vs. ICD group 11/142 (7.8%)]. CONCLUSIONS: In pacemaker or ICD patients with significant RVP burden and reduced ejection fraction, upgrade to CRT-D compared with ICD therapy reduced the combined risk of all-cause mortality, heart failure hospitalization, or absence of reverse remodelling.

9.
Physiol Meas ; 44(3)2023 03 10.
Artículo en Inglés | MEDLINE | ID: mdl-36787645

RESUMEN

Objective. The objective of the present study is to investigate the feasibility of using heart rate characteristics to estimate atrial fibrillatory rate (AFR) in a cohort of atrial fibrillation (AF) patients continuously monitored with an implantable cardiac monitor. We will use a mixed model approach to investigate population effect and patient specific effects of heart rate characteristics on AFR, and will correct for the effect of previous ablations, episode duration, and onset date and time.Approach. The f-wave signals, from which AFR is estimated, were extracted using a QRST cancellation process of the AF episodes in a cohort of 99 patients (67% male; 57 ± 12 years) monitored for 9.2(0.2-24.3) months as median(min-max). The AFR from 2453 f-wave signals included in the analysis was estimated using a model-based approach. The association between AFR and heart rate characteristics, prior ablations, and episode-related features were modelled using fixed-effect and mixed-effect modelling approaches.Main results. The mixed-effect models had a better fit to the data than fixed-effect models showing h.c. of determination (R2 = 0.49 versusR2 = 0.04) when relating the variations of AFR to the heart rate features. However, when correcting for the other factors, the mixed-effect model showed the best fit (R2 = 0.04). AFR was found to be significantly affected by previous catheter ablations (p< 0.05), episode duration (p< 0.05), and irregularity of theRRinterval series (p< 0.05).Significance. Mixed-effect models are more suitable for AFR modelling. AFR was shown to be faster in episodes with longer duration, less organizedRRintervals and after several ablation procedures.


Asunto(s)
Fibrilación Atrial , Humanos , Masculino , Femenino , Fibrilación Atrial/cirugía , Frecuencia Cardíaca/fisiología , Electrocardiografía , Factores de Tiempo , Prótesis e Implantes
10.
Med Biol Eng Comput ; 61(2): 317-327, 2023 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-36409405

RESUMEN

Methods for characterization of atrial fibrillation (AF) episode patterns have been introduced without establishing clinical significance. This study investigates, for the first time, whether post-ablation recurrence of AF can be predicted by evaluating episode patterns. The dataset comprises of 54 patients (age 56 ± 11 years; 67% men), with an implantable cardiac monitor, before undergoing the first AF catheter ablation. Two parameters of the alternating bivariate Hawkes model were used to characterize the pattern: AF dominance during the monitoring period (log(mu)) and temporal aggregation of episodes (beta1). Moreover, AF burden and AF density, a parameter characterizing aggregation of AF burden, were studied. The four parameters were computed from an average of 29 AF episodes before ablation. The risk of AF recurrence after catheter ablation using the Hawkes parameters log(mu) and beta1, AF burden, and AF density was evaluated. While the combination of AF burden and AF density is related to a non-significant hazard ratio, the combination of log(mu) and beta1 is related to a hazard ratio of 1.95 (1.03-3.70; p < 0.05). The Hawkes parameters showed increased risk of AF recurrence within 1 year after the procedure for patients with high AF dominance and high episode aggregation and may be used for pre-ablation risk assessment.


Asunto(s)
Fibrilación Atrial , Ablación por Catéter , Masculino , Humanos , Persona de Mediana Edad , Anciano , Femenino , Fibrilación Atrial/cirugía , Resultado del Tratamiento , Medición de Riesgo , Ablación por Catéter/métodos , Electrocardiografía
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