Journal of investigative medicine : the official publication of the American Federation for Clinical Research
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Natriuretic peptide levels are elevated in persons with chronic kidney disease (CKD) stages 1-3, but it remains unclear whether this is associated with extracellular volume excess or early cardiovascular changes. We hypothesized that patients with CKD stages 1-3 would have evidence of cardiovascular changes, which would associate with brain natriuretic peptide (BNP), amino-terminal-pro-BNP (NT-pro-BNP), and patient-reported symptoms. Outpatients with CKD stages 1-3 and non-CKD controls were enrolled. ⋯ TPRI and blood pressure correlated moderately with symptoms. Elevated natriuretic peptides may coincide with low cardiac index and elevated peripheral resistance in patients with CKD stages 1-3. The role of these biomarkers to detect subclinical cardiovascular changes needs to be further explored.
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Family history of coronary artery disease (FHxCAD) is a critical risk factor for CAD, underscoring the contribution of genetic factors to disease pathogenesis and susceptibility. Takotsubo cardiomyopathy (TCM) simulates the clinical features of and frequently coexists with CAD. However, the association between FHxCAD and TCM is unclear. ⋯ TCM with FHxCAD patients had a reduced incidence of cardiogenic shock, acute kidney injury (AKI), and acute respiratory failure (ARF); lower mortality rates; shorter length of stay (LOS); and decreased total charge compared with TCM without FHxCAD patients (p<0.05). In the matched cohort, TCM with FHxCAD patients (vs TCM without FHxCAD patients) had a lower incidence of cardiogenic shock (2.2% vs 6.3%, p<0.001; OR 0.33, 95% CI 0.18 to 0.61), AKI (5.1% vs 8.7%, p=0.016; OR 0.57, 95% CI 0.36 to 0.88), and ARF (5.7% vs 12.7%, p<0.001; OR 0.42, 95% CI 0.28 to 0.63); decreased in-hospital mortality (<11% vs 3.1%, p=0.002; OR 0.2, 95% CI 0.07 to 0.57); shorter LOS (2.66±1.96 days vs 3.40±3.05 days, p<0.001); and a reduced total charge (p=0.001), respectively. FHxCAD was associated with favorable outcomes in both unmatched and propensity-matched cohorts.
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Different demographic, clinical and laboratory variables have been related to the severity and mortality following SARS-CoV-2 infection. Most studies applied traditional statistical methods and in some cases combined with a machine learning (ML) method. This is the first study to date to comparatively analyze five ML methods to select the one that most closely predicts mortality in patients admitted with COVID-19. ⋯ The GNB algorithm shows relatively low classification performance. The variables with the greatest weight in predicting mortality were C reactive protein, procalcitonin, glutamyl oxaloacetic transaminase, glutamyl pyruvic transaminase, neutrophils, D-dimer, creatinine, lactic acid, ferritin, days of non-invasive ventilation, septic shock and age. Based on these results, XGB is a solid candidate for correct classification of patients with COVID-19.