Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Concerns about contrast-associated acute kidney injury (CA-AKI) may lead to individuals not undergoing necessary contrast-enhanced imaging. Effective risk stratification plays a crucial role in patient management.
Objectives: To develop a prediction model combining the effects of multiple risk factors to predict risk of CA-AKI for individuals undergoing intravenous contrast-enhanced computed tomography (CECT) imaging.
Methods: In this retrospective cohort study, 906 inpatient and outpatient adults who underwent CECT imaging in two tertiary centers, St. Luke's Medical Center-Quezon City and St. Luke's Medical Center-Global City, located in Manila, Philippines, were included. Multivariate logistic regression was used to identify independent predictors for CA-AKI. A p-value ≤ 0.05 was considered significant. Based on the odds ratio, five parameters were identified and included in the model.
Results: The incidence of CA-AKI was 10.38%. A significant association was found between CA-AKI with in-hospital mortality (odds ratio [OR]: 4.23, confidence interval [CI]: 2.59-6.90, p = 0.001) and AKI requiring renal replacement therapy (OR: 3.57, CI: 1.94-6.59, p = 0.001) among admitted patients. Multivariate analysis included reduced estimated glomerular filtration rate (OR = 17.13, p = 0.005), acute heart failure (OR = 9.21, p = 0.006), hypotension (OR = 5.15, p = 0.011), anemia (OR = 4.34, p = 0.004), and use of nephrotoxic antibiotics (OR = 5.82, p = 0.009) in the final prediction model. The prediction model had fair predictive power (area under the curve = 0.737) and good calibration (Hosmer-Lemeshow p-value = 0.418).
Conclusions: This prediction model, incorporating clinical and laboratory parameters, provides a practical tool for estimating CA-AKI risk with fair discrimination and good internal calibration. It may support informed decision-making regarding the risks and benefits of intravenous CECT. External validation is recommended.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/jhm.70136 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The AI and Automation in Anaesthesia (TRIPLE-A) Perioperative Database in Eastern Denmark 2017-: A Platform for Epidemiology, AI-Driven Prediction, Quality Control and Automated Data Retrieval.
Acta Anaesthesiol Scand
October 2025
Copenhagen Trial Unit, Centre for Clinical Intervention Research, The Capital Region, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
Introduction: Electronic health records can be used to create high-quality databases if data are structured and well-registered, which is the case for most perioperative data in the Capital and Zealand Regions of Denmark. We present the purpose and development of the AI and Automation in Anaesthesia (TRIPLE-A) database-a platform designed for epidemiology, prediction, quality control, and automated research data collection.
Methods: Data collection from the electronic medical record (EPIC Systems Corporation, WI, USA) was approved by the Capital Region, Denmark, and ethical approval was waived.
Clinical applicability and generalizability of an AI-based model predicting surgical site infection after metastatic spine surgery: a correspondence.
Int J Surg
September 2025
Department of Orthopaedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Predictive Modeling of Heart Failure Risk Based on Dietary Antioxidants: A Machine Learning Approach.
Mol Nutr Food Res
September 2025
The Hubei Key Laboratory of Tumor Microenvironment and Immunotherapy, China Three Gorges University, Yichang, China.
This study investigates the relationship between dietary antioxidants and heart failure (HF) risk using nationally representative National Health and Nutrition Examination Survey data (2005-2018). It aims to identify key dietary antioxidants and develop a machine-learning-based predictive model for HF. Among 9279 participants (434 HF cases), 44 dietary antioxidant variables were extracted from two 24-h dietary recalls.
View Article and Find Full Text PDFNext-Generation Food Drying: Specialized and Smart Approaches to Boost Efficiency and Quality.
Compr Rev Food Sci Food Saf
September 2025
Department of Life Science (Food Science and Technology Division), GITAM University, Visakhapatnam, Andhra Pradesh, India.
Drying is a critical unit operation in food processing, essential for extending shelf life, ensuring microbial safety, and preserving the nutritional and sensory attributes of food products. However, conventional convective drying techniques are often energy-intensive and lead to undesirable changes such as texture degradation, loss of bioactive compounds, and reduced product quality, thereby raising concerns regarding their sustainability and efficiency. In response, recent advancements have focused on the development of innovative drying technologies that offer energy-efficient, rapid, and quality-preserving alternatives.
View Article and Find Full Text PDFLiving near dumpsites: A machine learning and econometric assessment of how environmental conditions shape rental prices.
Waste Manag Res
September 2025
Department of Economics, John Cabot University, Rome, Italy.
This research examines the impact of environmental (dis)amenities on residential rental values in the urban areas of Rawalpindi and Islamabad, Pakistan. Using a unique dataset of 849 households and geospatial data on 35 irregular dumpsites, we quantify how proximity to environmental disamenities depresses rental prices. Specifically, results confirm that irregular dumpsites significantly depress rental values, especially for properties situated near the closest distance rings.
View Article and Find Full Text PDF