Aim: Chronic kidney disease (CKD) is an emerging major public health issue that leads to end-stage kidney disease (ESRD). Factors influencing the self-management and self-efficacy of ESRD patients are still under investigation. The objective of this study is to evaluate the association of depression and anxiety with self-management and self-efficacy in patients with pre-ESRD.
Methods: Patients in the department of nephrology of a regional hospital in Taiwan were invited to participate and were included in our study if they had a confirmed diagnosis of early-stage CKD, were more than 20 years old, and could converse in Mandarin Chinese or Taiwanese. Patients diagnosed with depression, who could not execute self-care, or who had cognitive deficits were excluded. In total, this cross-sectional study included 112 pre-ESRD patients. We used the Chinese versions of the hospital anxiety and depression scale (HADS), the chronic kidney disease self-efficacy instrument (CKD-SE), and the chronic kidney disease self-management instrument (CKD-SM) as the questionnaire. Spearman's rank correlation and logistic regressions were used to analyze the data.
Results: The top quartile of self-management and self-efficacy scores (28 patients) was defined as high self-management and -efficacy, respectively, and the lower three quartiles as low self-management and -efficacy. The logistic regression analysis showed that having depression decreased the odds of having high self-management by 75.4% and high self-efficacy by 75.1%. Having an education level of senior high school or above increased the odds ratios for having high self-management and high self-efficacy to 4.47 and 3.56 (all p-values < 0.05).
Conclusion: Controlling depression as well as increasing the level of education can potentially increase self-management and self-efficacy in pre-ESRD patients.
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http://dx.doi.org/10.3390/healthcare9030266 | DOI Listing |
J Med Internet Res
January 2025
Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.
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Objective: This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.
Kidney360
January 2025
Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
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View Article and Find Full Text PDFDiabetes
January 2025
Centre de recherche, Centre hospitalier de l'Université de Montréal (CRCHUM) and Département de médecine, Université de Montréal, 900 Saint Denis Street, Montréal, QC Canada H2X 0A9.
The role of the intrarenal renin-angiotensin system (iRAS) in diabetic kidney disease (DKD) progression remains unclear. In this study, we generated mice with renal tubule-specific deletion of angiotensinogen (Agt; RT-Agt-/-) in both Akita and streptozotocin (STZ)-induced mouse model of diabetes. Both Akita RT-Agt-/- and STZ-RT-Agt-/- mice exhibited significant attenuation of glomerular hyperfiltration, urinary albumin/creatinine ratio, glomerulomegaly and tubular injury.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Laboratory of Obesity and Aging Research, Cardiovascular Branch, National Heart Lung and Blood Institute, NIH, Bethesda, MD 20892.
Mitochondrial endonuclease G (EndoG) contributes to chromosomal degradation when it is released from mitochondria during apoptosis. It is presumed to also have a mitochondrial function because EndoG deficiency causes mitochondrial dysfunction. However, the mechanism by which EndoG regulates mitochondrial function is not known.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Müunster, Müunster, Germany.
Objective: Acute kidney injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series, requires researchers to implement classification algorithms of their own which is resource intensive and might impact study quality by introducing different interpretations of edge cases. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation.
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