Background And Objectives: Renal dysfunction is a common comorbidity in patients with advanced heart failure who may benefit from left ventricular assist device (LVAD) therapy. The effect of preoperative renal dysfunction on clinical outcomes after LVAD implantation remains uncertain. We conducted a systematic review and meta-analysis to compare outcomes post-LVAD in patients with and without renal dysfunction.
Methods: PubMed, MEDLINE, and Embase databases were searched for studies comparing outcomes in patients with and without renal dysfunction who underwent LVAD implantation for advanced heart failure. The primary outcome of all-cause mortality was reported as random effects risk ratio (RR) with 95% confidence interval (CI).
Results: Our search yielded 5,229 potentially eligible studies. We included 7 studies reporting on 26,652 patients. Patients with renal dysfunction (glomerular filtration rate [GFR] <60 mL/min/1.73 m) (n=4,630) had increased risk of all-cause mortality (RR, 2.21; 95% CI, 1.39-3.51; p<0.01) compared to patients with normal renal function (GFR >60 mL/min/1.73 m) (n=22,019).
Conclusions: Patients with renal dysfunction have increased mortality after LVAD implantation when compared to patients with normal renal function. GFR can be used to risk stratify patients and guide decision making prior to LVAD therapy.
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http://dx.doi.org/10.36628/ijhf.2020.0030 | 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.
Background: Acute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI.
Objective: This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.
PLoS 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.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Nephrology, Institute of Kidney Diseases, West China Hospital, Sichuan University, Chengdu, China.
Introduction: Chronic Kidney Disease (CKD) is a growing global health issue, affecting approximately 9.1% of the world's population. Oxidative stress is believed to play a key role in CKD development, with indicators such as the Oxidative Balance Score (OBS), Pro-Oxidant-Antioxidant Balance (PAB), and Total Antioxidant Capacity (TAC) being of particular interest.
View Article and Find Full Text PDFFood Funct
January 2025
Department of Nutrition and Food Hygiene, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
Gut dysbiosis serves as an underlying risk factor for the development of hypertension. The resolution of this dysbiosis has emerged as a promising strategy in improving hypertension. Food-derived bioactive protein peptides have become increasingly more attractive in ameliorating hypertension, primarily due to their anti-inflammatory and anti-oxidant activities.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Importance: People with kidney failure have a high risk of death and poor quality of life. Mortality risk prediction models may help them decide which form of treatment they prefer.
Objective: To systematically review the quality of existing mortality prediction models for people with kidney failure and assess whether they can be applied in clinical practice.
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