8 research outputs found

    Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

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    This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

    Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

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    Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

    Cardiopoietic cell therapy for advanced ischemic heart failure: results at 39 weeks of the prospective, randomized, double blind, sham-controlled CHART-1 clinical trial

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    Cardiopoietic cells, produced through cardiogenic conditioning of patients' mesenchymal stem cells, have shown preliminary efficacy. The Congestive Heart Failure Cardiopoietic Regenerative Therapy (CHART-1) trial aimed to validate cardiopoiesis-based biotherapy in a larger heart failure cohort

    Additional file 1 of Clinical complexity and impact of the ABC (Atrial fibrillation Better Care) pathway in patients with atrial fibrillation: a report from the ESC-EHRA EURObservational Research Programme in AF General Long-Term Registry

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    Additional file 1: Table S1. Items Included into the Frailty Index. Table S2. Baseline Characteristics of the Cohort. Table S3. Cox Regression for the risk of major outcomes according to clinical complexity and subgroups. Table S4. Baseline characteristics according to cluster allocation. Figure S1. Kaplan Meier Curves for the risk of MACE according to cluster analysis. Figure S2. Kaplan Meier Curves for the risk of composite outcome according to cluster analysis. Figure S3. Delay of Event analysis for MACE, ABC adherent vs. non-adherent in cluster 1 subgroup. Figure S4. Delay of Event analysis for Composite Outcome, ABC adherent vs. non-adherent in cluster 1 subgroup

    Comprehensive Transcriptional Analysis of Early Stage Urothelial Carcinoma using whole transcriptome sequencing

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    The expression profile and sequence variants of 476 early stage urothelial carcinoma were studied using whole transcriptome sequencing. RNA-Seq libraries were prepared by Ribo-Zero treatment of total-RNA (to reduce the rRNA content) followed by library preparation using ScriptSeq. RNA-Seq libraries were paired-end sequenced (2x 101 bp) on Illumina HiSeq 2000 and the resulting fastq files were processed using tools from the Genome Analysis Toolkit (GATK and from the Tuxedo suite. Access to the sequence data (bam and vcf files), containing person identifying information, needs signature on a controlled access form, and can be accessed at The European Genome-phenome Archive (EGA) using the study ID EGAS00001001236 following request. An expression matrix of FPKM values are available without restriction at ArrayExpress

    Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

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    Abstract Background Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients’ clinical phenotypes and analyse the differential clinical course. Methods We performed a hierarchical cluster analysis based on Ward’s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients’ prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27–3.62; HR 3.42, 95%CI 2.72–4.31; HR 2.79, 95%CI 2.32–3.35), and Cluster 1 (HR 1.88, 95%CI 1.48–2.38; HR 2.50, 95%CI 1.98–3.15; HR 2.09, 95%CI 1.74–2.51) reported a higher risk for the three outcomes respectively. Conclusions In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

    Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

    No full text
    Abstract Background Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients’ clinical phenotypes and analyse the differential clinical course. Methods We performed a hierarchical cluster analysis based on Ward’s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients’ prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P < .001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27–3.62; HR 3.42, 95%CI 2.72–4.31; HR 2.79, 95%CI 2.32–3.35), and Cluster 1 (HR 1.88, 95%CI 1.48–2.38; HR 2.50, 95%CI 1.98–3.15; HR 2.09, 95%CI 1.74–2.51) reported a higher risk for the three outcomes respectively. Conclusions In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

    Impact of malignancy on outcomes in European patients with atrial fibrillation: A report from the ESC‐EHRA EURObservational research programme in atrial fibrillation general long‐term registry

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