98%
921
2 minutes
20
Background: The International IgA nephropathy (IgAN) Prediction Tool was recently updated to predict the risk of a 30% decline in estimated glomerular filtration rate (eGFR) or kidney failure in children with IgAN. We aimed to evaluate the clinical performance of this tool in a Korean cohort of children with IgAN.
Methods: We calculated the predicted risk for biopsy-proven IgAN children from 20 Korean centers. The primary outcome was a 30% decline in eGFR or kidney failure. Discrimination and calibration performances of two pre-developed models were first evaluated. Subsequently, we constructed an updated model for Korean children using clinically meaningful variables.
Results: The study included 472 children with a mean age of 11.4 years. During a median follow-up period of 47.5 months, 58 patients (14.0%) reached the primary outcome. The two prediction models from the International IgA Nephropathy Prediction Tool exhibited suboptimal prediction power, with an integrated area under the curve (AUC) level of 0.57 (model with race) and 0.55 (model without race), respectively. The updated model, incorporating additional coefficients (sex, body mass index, serum albumin, presenting symptoms), showed good agreement between predicted risk and observed outcomes for Korean children (integrated AUC level of 0.70), significantly better than the IgAN International tool. Various model performance assessments showed consistent results. External validation with 145 children also demonstrated a superior fit for our model.
Conclusion: The updated International IgA Nephropathy Prediction Tool for children had suboptimal prediction ability in Korean IgAN children whereas our proposed model showed acceptable prediction ability in this population.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.23876/j.krcp.24.262 | DOI Listing |
Gastric Cancer
September 2025
Department of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Background: Immune checkpoint inhibitors (ICIs) play a pivotal role in the treatment of advanced gastric cancer (GC). However, the biomarkers used to predict ICI efficacy are limited due to their reliance on single or static tumor characteristics. This study aims to develop a machine learning (ML) model that incorporates dynamic changes in clinlabomics data to optimize the predictive accuracy of ICI efficacy.
View Article and Find Full Text PDFJ Clin Ultrasound
September 2025
Second Department of Anesthesiology, Medical School, National and Kapodistrian University of Athens, NKUA, Athens, Greece.
Sonographic examination of major vessels can be a valuable bedside tool for perioperative hemodynamic assessment. In the present review, we present the anatomic and physiological aspects of internal jugular vein ultrasonography, its utility in assessing central venous pressure, intravascular volume status, fluid responsiveness, and its predictive value regarding post-spinal anesthesia hypotension. The existing literature is primarily comprised of small, observational studies with great heterogeneity in their methodology and shortcomings in data development and analysis, rendering the generalization of their results difficult to interpret for daily clinical practice.
View Article and Find Full Text PDFGeroscience
September 2025
Department of Biological Sciences, College of Natural Sciences, Kangwon National University, Kangwon, 24341, Republic of Korea.
Alzheimer's disease (AD) represents a growing global health burden, underscoring the urgent need for reliable diagnostic and prognostic biomarkers. Although several disease-modifying treatments have recently become available, their effects remain limited, as they primarily delay rather than halt disease progression. Thus, the early and accurate identification of individuals at elevated risk for conversion to AD dementia is crucial to maximize the effectiveness of these therapies and to facilitate timely intervention strategies.
View Article and Find Full Text PDFInt J Pharm
September 2025
Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, Texas A&M University, College Station, TX 77843, USA. Electronic address:
Quality control of drug products is an essential step in pharmaceutical manufacturing. It is often time-consuming and requires expensive equipment. Process analytical technology tools are typically integrated into the manufacturing process to monitor quality, thereby reducing time and costs.
View Article and Find Full Text PDFExp Cell Res
September 2025
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital and Institute, Beijing, China. Electronic address:
Background: Enteric glial cells (EGCs) have been implicated in colorectal cancer (CRC) progression. This study aimed to develop and validate a prognostic model integrating EGC- and CRC-associated gene expression to predict patient survival, recurrence, metastasis, and therapy response.
Methods: Bulk and single-cell RNA sequencing data were analyzed, and a machine learning-based model was constructed using the RSF random forest algorithm.