Results 201 to 210 of about 882,867 (351)

Morbid Obesity: Increased Risk of Failure After Aseptic Revision TKA

open access: yesClinical Orthopaedics and Related Research, 2015
Chad D. Watts   +4 more
semanticscholar   +1 more source

Sleeve gastrectomy for morbid obesity: weight loss trajectory and failure predictors over a decade. [PDF]

open access: yesFuture Sci OA
Sghaier A   +7 more
europepmc   +1 more source

European Journal of Heart Failure consensus statement. Heart failure pharmacotherapy for patients with heart failure with reduced ejection fraction and concomitant atrial fibrillation: Review of evidence and call to action

open access: yesEuropean Journal of Heart Failure, EarlyView.
Heart failure (HF) and atrial fibrillation (AF) are major global health challenges with rising prevalence and significant morbidity, mortality, and healthcare burden. Despite advances in HF management, AF remains a critical comorbidity that worsens outcomes and requires ad hoc treatment strategies, increasing the risk of non‐adherence and side effects.
Mark Luedde   +24 more
wiley   +1 more source

Morbid Obesity [PDF]

open access: yes, 2020
Minna Ferrari Schleu   +1 more
openaire   +2 more sources

Contemporary medical therapy for heart failure across the ejection fraction spectrum: The OPTIPHARM‐HF registry

open access: yesEuropean Journal of Heart Failure, EarlyView.
Medical therapy use across the left ventricular ejection fraction spectrum in the OPTIPHARM‐HF registry. ACEi, angiotensin‐converting enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor–neprilysin inhibitor; GDMT, guideline‐directed medical therapy; HF, heart failure; HFmrEF, heart failure with mildly reduced ejection ...
Riccardo M. Inciardi   +152 more
wiley   +1 more source

Mining the risk: early cardiovascular detection in workers. [PDF]

open access: yesFront Med (Lausanne)
Jorquera R   +6 more
europepmc   +1 more source

Unsupervised machine learning for cardiovascular disease: A framework for future studies

open access: yesEuropean Journal of Heart Failure, EarlyView.
Unsupervised machine learning can improve the characterization and stratification of patients with cardiovascular diseases (CVDs). Clustering algorithms, which group patients based on patterns in clinical data, can reveal distinct subgroups that may differ in prognosis and treatment response.
Emmanuel Bresso   +7 more
wiley   +1 more source

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