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Träfflista för sökning "WFRF:(Maggioni Aldo Pietro) "

Sökning: WFRF:(Maggioni Aldo Pietro)

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1.
  • Levi, Nir, et al. (författare)
  • Lead fixation mechanism impacts outcome of transvenous lead extraction : Data from the European Lead Extraction ConTRolled Registry
  • 2022
  • Ingår i: Europace. - : Oxford University Press (OUP). - 1099-5129 .- 1532-2092. ; 24:5, s. 817-827
  • Tidskriftsartikel (refereegranskat)abstract
    • 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.
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  • Norhammar, Anna, et al. (författare)
  • Prevalence, outcomes and costs of a contemporary, multinational population with heart failure
  • 2023
  • Ingår i: Heart. - : BMJ Publishing Group Ltd. - 1355-6037 .- 1468-201X. ; 109:7, s. 548-556
  • Tidskriftsartikel (refereegranskat)abstract
    • Objective; Digital healthcare systems could provide insights into the global prevalence of heart failure (HF). We designed the CardioRenal and Metabolic disease (CaReMe) HF study to estimate the prevalence, key clinical adverse outcomes and costs of HF across 11 countries.Methods: Individual level data from a contemporary cohort of 6 29 624 patients with diagnosed HF was obtained from digital healthcare systems in participating countries using a prespecified, common study plan, and summarised using a random effects meta-analysis. A broad definition of HF (any registered HF diagnosis) and a strict definition (history of hospitalisation for HF) were used. Event rates were reported per 100 patient years. Cumulative hospital care costs per patient were calculated for a period of up to 5 years.Results: The prevalence of HF was 2.01% (95% CI 1.65 to 2.36) and 1.05% (0.85 to 1.25) according to the broad and strict definitions, respectively. In patients with HF (broad definition), mean age was 75.2 years (95% CI 74.0 to 76.4), 48.8% (40.9-56.8%) had ischaemic heart disease and 34.5% (29.4-39.6%) had diabetes. In 51 442 patients with a recorded ejection fraction (EF), 39.1% (30.3-47.8%) had a reduced, 18.8% (13.5-24.0%) had a mildly reduced and 42.1% (31.5-52.8%) had a preserved left ventricular EF. In 1 69 518 patients with recorded estimated glomerular filtration rate, 49% had chronic kidney disease (CKD) stages III-V. Event rates were highest for cardiorenal disease (HF or CKD) and all cause mortality (19.3 (95% CI 11.3 to 27.1) and 13.1 (11.1 to 15.1), respectively), and lower for myocardial infarction, stroke and peripheral artery disease. Hospital care costs were highest for cardiorenal diseases.Conclusions: We estimate that 1-2% of the contemporary adult population has HF. These individuals are at significant risk of adverse outcomes and associated costs, predominantly driven by hospitalisations for HF or CKD. There is considerable public health potential in understanding the contemporary burden of HF and the importance of optimising its management.
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  • Saglietto, Andrea, et al. (författare)
  • AFA-Recur : an ESC EORP AFA-LT registry machine-learning web calculator predicting atrial fibrillation recurrence after ablation
  • 2023
  • Ingår i: Europace. - : Oxford University Press. - 1099-5129 .- 1532-2092. ; 25:1, s. 92-100
  • Tidskriftsartikel (refereegranskat)abstract
    • Aims: Atrial fibrillation (AF) recurrence during the first year after catheter ablation remains common. Patient-specific prediction of arrhythmic recurrence would improve patient selection, and, potentially, avoid futile interventions. Available prediction algorithms, however, achieve unsatisfactory performance. Aim of the present study was to derive from ESC-EHRA Atrial Fibrillation Ablation Long-Term Registry (AFA-LT) a machine-learning scoring system based on pre-procedural, easily accessible clinical variables to predict the probability of 1-year arrhythmic recurrence after catheter ablation.Methods and results: Patients were randomly split into a training (80%) and a testing cohort (20%). Four different supervised machine-learning models (decision tree, random forest, AdaBoost, and k-nearest neighbour) were developed on the training cohort and hyperparameters were tuned using 10-fold cross validation. The model with the best discriminative performance on the testing cohort (area under the curve-AUC) was selected and underwent further optimization, including re-calibration. A total of 3128 patients were included. The random forest model showed the best performance on the testing cohort; a 19-variable version achieved good discriminative performance [AUC 0.721, 95% confidence interval (CI) 0.680-0.764], outperforming existing scores (e.g. APPLE score: AUC 0.557, 95% CI 0.506-0.607). Platt scaling was used to calibrate the model. The final calibrated model was implemented in a web calculator, freely available at http://afarec.hpc4ai.unito.ti/.Conclusion: AFA-Recur, a machine-learning-based probability score predicting 1-year risk of recurrent atrial arrhythmia after AF ablation, achieved good predictive performance, significantly better than currently available tools. The calculator, freely available online, allows patient-specific predictions, favouring tailored therapeutic approaches for the individual patient.
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