Articles: phenotype.
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Multicenter Study
Comorbidity patterns and mortality in atrial fibrillation: a latent class analysis of the EURopean study of Older Subjects with Atrial Fibrillation (EUROSAF).
Most older patients with atrial fibrillation (AF) have comorbidities. However, it is unclear whether specific comorbidity patterns are associated with adverse outcomes. We identified comorbidity patterns and their association with mortality in multimorbid older AF patients with different multidimensional frailty. ⋯ We observed an association between comorbidity phenotypes identified using LCA and mortality in older AF patients. Further research is warranted to identify the mechanisms underpinning such associations.
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Eur. J. Clin. Invest. · Mar 2025
Multicenter StudyThe impact of clinical phenotypes of coronary artery disease on outcomes in patients with atrial fibrillation: A post-hoc analysis of GLORIA-AF registry.
Coronary artery disease (CAD) and atrial fibrillation (AF) often coexist, but the impact of clinical phenotypes of CAD on outcomes in AF patients in the non-vitamin K antagonist oral anticoagulant drugs (NOACs) era is less well understood. ⋯ CAD was prevalent in patients with AF, and clinical phenotypes of CAD influenced outcomes in patients with AF, with a history of MI/unstable angina being associated with a significantly increased risk of CV events, compared to stable angina. NOACs were superior to VKA in terms of the effectiveness and safety outcomes in patients with AF and concomitant CAD.
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Chinese medical journal · Feb 2025
Machine learning-based unsupervised phenotypic clustering analysis of patients with IgA nephropathy: Distinct therapeutic responses of different groups.
Immunoglobulin A nephropathy (IgAN) has a heterogeneous clinical presentation. Comparison of different IgAN subgroups may facilitate the application of more targeted therapies. This study was aimed to distinct disease phenotypes in IgAN and to develop prognostic models for renal composite outcomes. ⋯ The unsupervised clustering method provided reliable classification of IgAN patients into different subgroups according to clinical features, prognoses, and treatment responsiveness. Our subgroup-based prediction model has significant clinical utility for the assessment of risk and treatment in patients with IgAN.
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Reg Anesth Pain Med · Feb 2025
ReviewRecognizing pain phenotypes: biopsychosocial sources of variability in the transition to chronic postsurgical pain.
Chronic postsurgical pain (CPSP) is a cause of new chronic pain, with a wide range of reported incidence. Previous longitudinal studies suggest that development of CPSP may depend more on the constellation of risk factors around a patient (pre-existing pain phenotype) rather than on the extent of surgical injury itself. The biopsychosocial model of pain outlines a broad array of factors that modulate the severity, longevity, and impact of pain. ⋯ Early preoperative identification of a patient's pain phenotype allows estimation of their constellation of risk factors and may greatly enhance successful, personalized prevention of postoperative pain. Effective preoperative employment of behavioral interventions like cognitive-behavioral therapy, stress reduction, and physical and mental prehabilitation may particularly require knowledge of a patient's pain phenotype. Preoperative assessment of patients' pain phenotypes will not only inform high-quality personalized perioperative care clinically, but it will enable enriched testing of novel therapies in future scientific studies.
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The application of sepsis subtypes to enhance personalized medicine in critically ill patients is hindered by the lack of validation across diverse cohorts and the absence of a simple classification model. We aimed to validate the previously identified SENECA clinical sepsis subtypes in multiple large ICU cohorts, and to develop parsimonious classifier models for δ-type adjudication in clinical practice. ⋯ The distribution and mortality rates of clinical sepsis subtypes varied between US and European cohorts. A three-variable model could accurately identify the δ-type sepsis patients.