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IRIS
Background: Real-world uptake of guideline-directed medical therapy (GDMT) at hospital discharge and clinical predictors of complete decongestion in acute heart failure (AHF) populations remain insufficiently described. Methods: The BRING-UP 3 HF study is an observational, prospective, nationwide investigation involving 179 Italian cardiology sites. This report summarizes baseline data from the first enrollment phase hospitalized cohort and assesses the predictors of decongestion via a machine learning model. Results: Among 1373 patients (mean age 71 years; 30% females; 43% de-novo HF), HF with reduced ejection fraction (HFrEF) predominated (70%). Hypertension, atrial fibrillation, diabetes mellitus, and chronic kidney disease were reported in 75%, 43%, 35%, and 33% of patients, respectively. In HFrEF, discharge prescriptions rose markedly with respect to admission, with 57% of patients receiving all four pillars of GDMT. Successful decongestion was achieved in 469/681 evaluable patients (69%). A random-forest model identified higher estimated glomerular filtration rate, younger age, lower urea/creatinine ratio, lower C-reactive protein, and smaller left-atrial volumes as the strongest predictors of a successful decongestion, with good discrimination (AUC 0.80). Conclusions: Contemporary Italian cardiology practice shows high adherence to discharge GDMT across the spectrum of EF in AHF. Nevertheless, nearly one-third of patients leaves the hospital with residual congestion. The identified machine learning model predictors may provide an objective framework for risk stratification. These variables may help clinicians identify a high-risk patient profile that requires intensified in-hospital decongestive strategies and more aggressive post-discharge transitional care to reduce the risk of early rehospitalization. Clinicaltrial: GOV: NCT06279988.
Discharge medical treatment implementation and predictors of a successful decongestion in patients with acute heart failure: first data from the BRING-UP 3 Heart Failure Study
Background: Real-world uptake of guideline-directed medical therapy (GDMT) at hospital discharge and clinical predictors of complete decongestion in acute heart failure (AHF) populations remain insufficiently described. Methods: The BRING-UP 3 HF study is an observational, prospective, nationwide investigation involving 179 Italian cardiology sites. This report summarizes baseline data from the first enrollment phase hospitalized cohort and assesses the predictors of decongestion via a machine learning model. Results: Among 1373 patients (mean age 71 years; 30% females; 43% de-novo HF), HF with reduced ejection fraction (HFrEF) predominated (70%). Hypertension, atrial fibrillation, diabetes mellitus, and chronic kidney disease were reported in 75%, 43%, 35%, and 33% of patients, respectively. In HFrEF, discharge prescriptions rose markedly with respect to admission, with 57% of patients receiving all four pillars of GDMT. Successful decongestion was achieved in 469/681 evaluable patients (69%). A random-forest model identified higher estimated glomerular filtration rate, younger age, lower urea/creatinine ratio, lower C-reactive protein, and smaller left-atrial volumes as the strongest predictors of a successful decongestion, with good discrimination (AUC 0.80). Conclusions: Contemporary Italian cardiology practice shows high adherence to discharge GDMT across the spectrum of EF in AHF. Nevertheless, nearly one-third of patients leaves the hospital with residual congestion. The identified machine learning model predictors may provide an objective framework for risk stratification. These variables may help clinicians identify a high-risk patient profile that requires intensified in-hospital decongestive strategies and more aggressive post-discharge transitional care to reduce the risk of early rehospitalization. Clinicaltrial: GOV: NCT06279988.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/95843
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.