Objectives: Chronic kidney disease (CKD) is associated with disability, low quality of life, and mortality. However, most cases are asymptomatic, often detected incidentally, or only recognized when they have progressed to the later stages with complications. The present study aimed to determine the prevalence of CKD and develop a predictive nomogram for CKD in Vietnamese adults.
Methods: This cross-sectional, population-based study involved 533 men and 957 women aged 18 years and older who were screened for CKD. The CKD was diagnosed using the albumin-to-creatinine ratio and/or estimated glomerular filtration rate by the CKD-EPI 2009 equation based on serum creatinine, age, gender, and race (these tests included a baseline measurement and a repeat measurement after 3 months) according to the KDIGO 2012 guideline. We used the Bayesian Model Averaging method to identify the optimal model for predicting CKD. A predictive nomogram was also developed to enable risk prediction.
Results: The overall CKD prevalence was 13.1% (95% confidence interval [CI] = 11.6-14.6), with a prevalence of 11.8% (95% CI = 9.1-14.5) in men and 13.8% (95% CI = 11.6-16) in women. The optimal model for predicting CKD included age (odds ratio [OR] per 5-year increase = 1.19; 95% CI = 1.11-1.28), hypertension (OR = 2.08; 95% CI = 1.50-2.89), and diabetes (OR = 1.69; 95% CI = 1.18-2.43). The area under the receiver operating characteristic curve was 0.7, with a 95% CI ranging from 0.65 to 0.73.
Conclusions: The CKD is relatively common among Vietnamese adults. A simple model-including age, hypertension, and diabetes-is helpful for the selective screening of CKD in Vietnamese individuals.
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http://dx.doi.org/10.1177/20543581241309979 | DOI Listing |
Abdom Radiol (NY)
January 2025
Department of Hepato-Biliary-Pancreatic Surgery, Huadong Hospital affiliated to Fudan University, Shanghai, China.
Background: The prognostic prediction of pancreatic ductal adenocarcinoma (PDAC) remains challenging. This study aimed to develop a radiomics model to predict Ki-67 expression status in PDAC patients using radiomics features from dual-phase enhanced CT, and integrated clinical characteristics to create a radiomics-clinical nomogram for prognostic prediction.
Methods: In this retrospective study, data were collected from 124 PDAC patients treated surgically at a single center, from January 2017 to March 2023.
Objectives: Combining Computed Tomography (CT) intuitive anatomical features with Three-Dimensional (3D) CT multimodal radiomic imaging features to construct a model for assessing the aggressiveness of pancreatic neuroendocrine tumors (pNETs) prior to surgery.
Methods: This study involved 242 patients, randomly assigned to training (170) and validation (72) cohorts. Preoperative CT and 3D CT radiomic features were used to develop a model predicting pNETs aggressiveness.
Aim: Many combinations of inflammation-based markers have been reported their prognostic ability. The prognostic value of albumin-to-gama-glutamyltransferase ratio (AGR), an inflammation-related index, has been identified for several cancers. However, the predictive value of AGR for high-grade glioma patients remains unclear.
View Article and Find Full Text PDFFront Pediatr
January 2025
Department of Neonatology, The First Affiliated Hospital of Zheng Zhou University, Zhengzhou, China.
Objective: To explore the risk factors for the reactivate of retinopathy of prematurity (ROP) after intravitreal injection of anti-vascular endothelial growth factor (VEGF) and to construct a nomogram model to predict the risk of ROP reactivate.
Methods: A retrospective analysis was conducted on 185 ROP children who underwent anti-VEGF treatment at the First Affiliated Hospital of Zhengzhou University from January 2017 to October 2023. They were randomly divided into a training set (129 cases) and a validation set (56 cases) at a ratio of 7:3.
Front Surg
January 2025
Department of Cardio-Thoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Objective: This study aimed to explore the risk factors of hypokalemia after radical resection of esophageal cancer (EC) and establish a nomogram risk prediction model to evaluate hypokalemia risk after esophagectomy. Thus, this study provides a reference for the clinical development of intervention measures.
Methods: Clinical data of EC patients who underwent radical surgery from January 2020 to November 2022 in the First Affiliated Hospital of Guangxi Medical University were retrospectively collected.
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