Background: A few simple and pre-procedural risk models have been developed for predicting contrast-induced nephropathy (CIN), which allow for early administration of preventative strategies before coronary angiography (CAG). The study aims to develop and validate simple pre-procedure tools for predicting risk of CIN following CAG.
Methods: We retrospectively analyzed the data from 3,469 consecutive patients undergoing CAG, who were randomly assigned to a development dataset (n=2,313) and a validation dataset (n=1,156). CIN was defined as an increase in serum creatinine (SCr) ≥0.5 mg/dL from baseline within 72 hours after CAG. Multivariate logistic regression was applied to identify independent predictors of CIN to develop risk models. The possible predictors included age >75 years, hypotension, acute myocardial infarction (AMI), SCr ≥1.5 mg/dL, and congestive heart failure (CHF).
Results: The incidences of CIN were 3.20% and 3.55% in the training and validation dataset respectively. Compared to classical Mehran' and ACEF CIN risk score, the new score across the validation dataset exhibited similar discrimination and predictive ability on CIN (c-statistic: 0.829, 0.832, 0.812 respectively) and in-hospital mortality (c-statistic: 0.909, 0.937, 0.866 respectively) (all P>0.05).
Conclusions: The easy-to-use pre-procedural prediction model only containing 5 factors had similar predictive ability on CIN and mortality.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6531701 | PMC |
| http://dx.doi.org/10.21037/jtd.2019.04.69 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Integrating RNA-seq and scRNA-seq to explore the prognostic features and immune landscape of exosome-related genes in breast cancer metastasis.
Ann Med
December 2025
Department of Neurosurgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, PR China.
Objective: This study aims to explore the role of exosome-related genes in breast cancer (BRCA) metastasis by integrating RNA-seq and single-cell RNA-seq (scRNA-seq) data from BRCA samples and to develop a reliable prognostic model.
Methods: Initially, a comprehensive analysis was conducted on exosome-related genes from the BRCA cohort in The Cancer Genome Atlas (TCGA) database. Three prognostic genes (JUP, CAPZA1 and ARVCF) were identified through univariate Cox regression and Lasso-Cox regression analyses, and a metastasis-related risk score model was established based on these genes.
Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.
Transl Vis Sci Technol
January 2025
School of Optometry and Vision Science, University of New South Wales, Sydney, Australia.
Purpose: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.
Methods: The dataset included 2265 participants aged 40 years and above from a population-based study in South India. The dataset included ocular and systemic clinical parameters, dilated retinal fundus images, and hematological data such as complete blood counts and Hb concentration levels.
Icelandic translation, adaptation and validation of the Parental Burnout Assessment (PBA-IS).
Scand J Occup Ther
January 2025
School of Health, Business and Natural Sciences, University of Akureyri, Akureyri, Iceland.
Background: As parental burnout is increasingly recognised for its severe impact on parents and children, identifying factors that exacerbate or alleviate this condition is crucial. Reliable assessment tools in clinical settings are essential to detect those at risk of or experiencing burnout, enabling timely intervention.
Aims/objectives: This study aims to adapt the Parental Burnout Assessment for use in Iceland and evaluate its psychometric properties while exploring how personal and socio-demographic factors influence parental burnout.
Artificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs.
Clin Implant Dent Relat Res
February 2025
SEMRUK Technology Inc., Cumhuriyet Teknokent, Sivas, Turkiye.
Objectives: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.
Materials And Methods: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized.
Research on atrial fibrillation diagnosis in electrocardiograms based on CLA-AF model.
Eur Heart J Digit Health
January 2025
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, No. 88 West Taishan Road, Zhuzhou 412007, Hunan, China.
Aims: The electrocardiogram (ECG) is the primary method for diagnosing atrial fibrillation (AF), but interpreting ECGs can be time-consuming and labour-intensive, which deserves more exploration.
Methods And Results: We collected ECG data from 6590 patients as YY2023, classified as Normal, AF, and Other. Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), and Attention construct the AF recognition model CNN BiLSTM Attention-Atrial Fibrillation (CLA-AF).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!