Aims/hypothesis: It is argued that GFR estimation (eGFR) using cystatin C-based equations (eGFRcys) is superior to that using creatinine-based equations (eGFRcre). We investigated whether eGFRcys are superior to eGFRcre in patients with type 2 diabetes.
Methods: GFR was measured in 448 type 2 diabetic patients using (51)Cr-EDTA-measured GFR (mGFR) as the reference standard. Bias, precision and accuracy of eGFRcys and eGFRcre were compared.
Results: The most accurate eGFRcre equation (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]), which produced the highest proportion of estimates that were within 30% and 10% of the reference standard (80.7% and 38.0% of samples, respectively) had a bias of 7.1 and precision of 12.0 ml min(-1) 1.73 m(-2). The calibrated eGFRcys with the highest accuracy (Tan-C), which produced the highest proportion of estimates that were within 30% (78.8%) and within 10% (39.0%) of the reference standard had a bias of -3.5 and precision of 18.0 ml min(-1) 1.73 m(-2). Moreover, the areas under the receiver operating curve were higher with eGFRcre (CKD-EPI and Modification of Diet in Renal Disease [MDRD]) than with eGFRcys for the diagnosis of mild (mGFR <90 ml min(-1) 1.73 m(-2)) and moderate (mGFR <60 ml min(-1) 1.73 m(-2)) chronic kidney disease. In patients with mGFR ≥90 ml min(-1) 1.73 m(-2), CKD-EPI was the least biased, the most precise and the most accurate equation.
Conclusions/interpretation: In patients with type 2 diabetes, eGFRcys do not currently provide better eGFR than eGFRcre. At present, compared with eGFRcys, eGFRcre are better at predicting the stage of chronic kidney disease. In addition, CKD-EPI seems to be the best equation for eGFR in patients with normal renal function.
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http://dx.doi.org/10.1007/s00125-011-2307-1 | DOI Listing |
BMC Health Serv Res
January 2025
Department of Biological Sciences, Faculty of Science, Kyambogo University, Kampala, Uganda.
Background: A key concern for global public health is nosocomial infections. Essential to the fight against nosocomial infection, is healthcare professionals' knowledge and attitudes. Therefore, this study investigated healthcare professionals' knowledge and attitudes toward nosocomial infection at the Kiruddu Referral Hospital, Kampala, Uganda.
View Article and Find Full Text PDFItal J Pediatr
January 2025
Department of Neonatology, Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital, Zhengzhou Children's Hospital, Henan, China.
Background: Severe pulmonary infection is the primary cause of death in children aged < 5 years. The early identification of pathogenic bacteria and targeted anti-infective therapies can significantly improve the prognosis of children with severe infections. This study aims to provide a reference for the rational use of antibiotics at an early stage in children with severe pulmonary infections.
View Article and Find Full Text PDFBMC Cancer
January 2025
Faculty of Medicine, University of Cologne and Institute for Health Economics and Clinical Epidemiology, University Hospital Cologne, Cologne, Germany.
Background: Patients who actively engage in their medical decision-making processes can experience better health outcomes. This exploratory study aimed to identify predictors of preferred and actual roles in decision-making in healthy women with BRCA1/2 pathogenic variants (PVs).
Methods: Women with BRCA1/2 PVs without a history of breast and/or ovarian cancer were recruited in six centres across Germany.
BMC Nephrol
January 2025
Department of Internal Medicine, Naguru Referral Hospital, Kampala, Uganda.
Background: Limited studies have explored the relationship between estimated Glomerular Filtration Rate(eGFR) and in-hospital mortality(IHM) in low-income sub-Saharan African countries. This study aimed to explores this association, offering insights into its impact in resource-limited settings.
Methods And Results: We retrospectively included 226 patients(age 45.
Radiol Med
January 2025
Department of Translational Medicine, University of Ferrara, Ferrara, Italy.
Purpose: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
Material And Methods: Primary tumor and the most significant lymph node metastasis were manually segmented in baseline [F]FDG PET/CT of 52 newly diagnosed BC patients. Clinical parameters, NAC and conventional semiquantitative PET parameters were collected.
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