0000000000428596

AUTHOR

Núria Alonso

showing 12 related works from this author

Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional decline

2020

Abstract Background Left ventricular ejection fraction (LVEF) trajectories and functional recovery with current heart failure (HF) management is increasingly recognized. Type 2 diabetes mellitus (T2D) leads to a worse prognosis in HF patients. However, it is unknown whether T2D interferes with LVEF trajectories. The aim of this study was to prospectively assess very long-term (up to 15 years) LVEF trajectories in patients with and without T2D and underlying HF. Methods Ambulatory patients admitted to a multidisciplinary HF clinic were prospectively evaluated by scheduled two-dimensional echocardiography at baseline, 1 year, and then every 2 years afterwards, up to 15 years. Statistical anal…

Malelcsh:Diseases of the circulatory (Cardiovascular) systemmedicine.medical_specialtyEjection fractionTime FactorsEtiologyDiabetic CardiomyopathiesEndocrinology Diabetes and MetabolismHeart failureType 2 diabetesVentricular Function LeftVentricular Dysfunction LeftDiabetes mellitusLong-termRisk FactorsInternal medicineDiabetes mellitusDiabetic cardiomyopathymedicineHumansProspective StudiesOriginal InvestigationAngiologyAgedHeart FailureEjection fractionbusiness.industryFollow-upType 2 Diabetes MellitusStroke VolumeMiddle Agedmedicine.diseaseProgression-Free SurvivalDiabetes Mellitus Type 2lcsh:RC666-701Heart failureAmbulatoryDisease ProgressionFemaleCardiology and Cardiovascular Medicinebusiness
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An in-depth analysis shows a hidden atherogenic lipoprotein profile in non-diabetic chronic kidney disease patients

2019

Background: Chronic kidney disease (CKD) is an independent risk factor for atherosclerotic disease. We hypothesized that CKD promotes a proatherogenic lipid profile modifying lipoprotein composition and particle number. Methods: Cross-sectional study in 395 non-diabetic individuals (209 CKD patients and 186 controls) without statin therapy. Conventional lipid determinations were combined with advanced lipoprotein profiling by nuclear magnetic resonance, and their discrimination ability was assessed by machine learning. Results: CKD patients showed an increase of very-low-density (VLDL) particles and a reduction of LDL particle size. Cholesterol and triglyceride content of VLDLs and intermed…

0301 basic medicineMaleVery low-density lipoproteinMagnetic Resonance SpectroscopyClinical BiochemistryMachine LearningPCSK9chemistry.chemical_compound0302 clinical medicineLp(a)Risk FactorsDrug DiscoveryProspective Studiesmedicine.diagnostic_testMiddle AgedLipids030220 oncology & carcinogenesisMolecular MedicineFemalelipids (amino acids peptides and proteins)Proprotein Convertase 9Adultmedicine.medical_specialtylipoprotein subfractionsLipoproteins03 medical and health sciencesInternal medicinemedicineHumansRisk factorRenal Insufficiency ChronicAgedPharmacologybusiness.industryCholesterolPCSK9dyslipidemiamedicine.diseaseAtherosclerosis030104 developmental biologyEndocrinologyCross-Sectional StudieschemistryCase-Control StudiesbusinessLipid profileDyslipidemiachronic kidney diseaseLipoproteinKidney disease
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Head-to-head comparison of contemporary heart failure risk scores.

2021

Altres ajuts: acords transformatius de la UAB Aims: Several heart failure (HF) web-based risk scores are currently used in clinical practice. Currently, we lack head-to-head comparison of the accuracy of risk scores. This study aimed to assess correlation and mortality prediction performance of Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, which includes clinical variables + medications; Seattle Heart Failure Model (SHFM), which includes clinical variables + treatments + analytes; PARADIGM Risk of Events and Death in the Contemporary Treatment of Heart Failure (PREDICT-HF) and Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, which also include biomarke…

medicine.medical_specialtyCalibration (statistics)Head to headHeart failureSpearman's rank correlation coefficientRisk AssessmentCorrelationRisk FactorsInternal medicineNatriuretic Peptide BrainmedicineRisk modelsHumansOverall performanceMortalityNatriuretic PeptidesHeart FailureFramingham Risk Scorebusiness.industrymedicine.diseasePrognosisRisk predictionPeptide FragmentsClinical PracticeHeart failureCardiology and Cardiovascular MedicinebusinessBiomarkersEuropean journal of heart failureReferences
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Trends in modes of death in heart failure over the last two decades: less sudden death but cancer deaths on the rise.

2019

AIMS Better management of heart failure (HF) over the past two decades has improved survival, mainly by reducing the incidence of death due to cardiovascular (CV) causes. Deaths due to non-CV causes, particularly cancer, may be increasing. This study explored the modes of death of consecutive patients who attended a HF clinic over 17 years. METHODS AND RESULTS A total of 935 deaths were ascertained from 2002 to 2018 among 1876 patients (mean age 65.8 ± 12.5 years, 75% men, left ventricular ejection fraction < 50%) admitted to our HF clinic. Median follow-up was 4.2 years [1.9-7.8]. Mode of death was curated from patient health records and verified by the Catalan and Spanish health system da…

MalePediatricsmedicine.medical_specialtyManagement of heart failure030204 cardiovascular system & hematologyHealth recordsSudden death03 medical and health sciencesDeath Sudden0302 clinical medicineNeoplasmsmedicineHumansLongitudinal StudiesMortalityLung cancerAgedHeart FailureEjection fractionbusiness.industryIncidence (epidemiology)CancerMiddle Agedmedicine.diseaseSpainHeart failureFemaleCardiology and Cardiovascular MedicinebusinessEuropean journal of heart failureReferences
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Additional file 2 of Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional …

2020

Additional file 2: Table S1. Demographic, clinical, and therapeutic characteristics at baseline and treatments during follow-up according to etiology of heart failure and presence of diabetes mellitus. Table S2. Paired wise means data analysis in diabetic patients. Table S3. Causes of death of the studied cohort during the 15-year follow-up, according the presence or absence of diabetes mellitus. Table S4. Multivariable Cox regression analysis for all-cause death and the composite end-point all-cause death or heart failure hospitalization.

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Additional file 8 of Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional …

2020

Additional file 8: Figure S4. Survival and event-free survival curves related to the presence of diabetes mellitus and to etiology (ischemic vs. non-ischemic). Panel B: Event-free survival curves (composite end-point of all-cause death or HF hospitalizations). Diabetic patients from ischemic etiology (dark purple) showed the worse prognosis, while non-diabetic from non-ischemic etiology (blue) showed the best. Remarkably diabetic patients from non-ischemic etiology (soft orange) showed slightly worse prognosis than non-diabetic patients from ischemic etiology (green).

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Additional file 1 of Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional …

2020

Additional file 1: Figure S1. Distribution of the number of echocardiograms performed per patient. Number of echocardiograms per patient ranged from 2 (minimum inclusion criteria) to 9 (patients with all the per protocol pre-specified echocardiograms).

congenital hereditary and neonatal diseases and abnormalitiescardiovascular systemcardiovascular diseasescirculatory and respiratory physiology
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Additional file 6 of Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional …

2020

Additional file 6: Figure S3. Loess spline curves of long-term LVEF trajectories based on sex. Panel B: Women. P value for trajectory changes on LVEF &lt;0.001 for both groups. P for comparison between groups (interaction between trajectory changes and diabetes) = 0.14. Shaded regions displayed around curves represent the confidence interval at level = 0.95.

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Additional file 5 of Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional …

2020

Additional file 5: Figure S3. Loess spline curves of long-term LVEF trajectories based on sex. Panel A: Men. Diabetic (orange) vs. non-diabetic (blue) patients. P value for trajectory changes on LVEF

cardiovascular systemcardiovascular diseaseshumanitiescirculatory and respiratory physiology
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Additional file 3 of Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional …

2020

Additional file 3: Figure S2. Loess spline curves of long-term LVEF trajectories based on heart failure duration. Panel A: Patients with HF duration ≤ 12 months; Diabetic (orange) vs. non-diabetic (blue) patients. P value for trajectory changes on LVEF

cardiovascular systemcardiovascular diseasescirculatory and respiratory physiology
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Additional file 4 of Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional …

2020

Additional file 4: Figure S2. Loess spline curves of long-term LVEF trajectories based on heart failure duration. Panel B: Patients with HF duration &gt; 12 months. P value for trajectory changes on LVEF

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Additional file 7 of Long-term LVEF trajectories in patients with type 2 diabetes and heart failure: diabetic cardiomyopathy may underlie functional …

2020

Additional file 7: Figure S4. Survival and event-free survival curves related to the presence of diabetes mellitus and to etiology (ischemic vs. non-ischemic). Panel A: All-cause death survival curves.

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