International journal of cardiology
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Multicenter Study
Meaningful and feasible composite clinical worsening definitions in paediatric pulmonary arterial hypertension: An analysis of the TOPP registry.
Composite clinical worsening (cCW) outcomes might allow measurement of disease progression in paediatric pulmonary arterial hypertension (PAH). This TOPP registry analysis investigated three cCW outcomes and their predictive strength for lung transplantation/death. ⋯ These data support the use of cCW outcomes in paediatric PAH research. WHO FC deterioration, PAH-related hospitalisation, occurrence/worsening of ≥2 PAH symptoms may be important for risk assessment during clinical management.
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Multicenter Study
Real-world persistence with direct oral anticoagulants (DOACs) in naïve patients with non-valvular atrial fibrillation.
Anticoagulation therapy is central for the management of stroke in patients with non-valvular atrial fibrillation (NVAF). Persistence with oral anticoagulation is essential to prevent thromboembolic complications. ⋯ In this study of real world data, one out four naive patients stopped treatment with DOACs within 12 months. Some characteristics may identify patients with poor persistence.
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We investigated the predictive value of preoperative computed tomography (CT)-derived tricuspid annular and right ventricular (RV) parameters for postoperative RV dysfunction in patients undergoing tricuspid valve (TV) surgery. ⋯ Preoperative assessment of cardiac CT imaging-based TV annular diameter and RV volume can provide independent information for predicting postoperative RV dysfunction in patients undergoing TV surgery.
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The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and increasing costs. Therefore, accurately predicting LOS would have a positive impact on healthcare metrics. The aim of this study is to develop a machine learning-based model approach for predicting in-hospital LOS for cardiac patients. ⋯ We showed that machine learning methods provide accurate prediction of LOS for cardiac patients. This is can be used in clinical bed management and resources allocation.