Purpose: To derive Regional Diagnostic Reference Levels (RDRL) for paediatric conventional and CT examinations using weight-based DRL curves and compare the outcome with DRL derived using the weight groups.
Methods: Data from 1722 examinations performed at 29 hospitals in four countries were included. DRL was derived for four conventional x-ray (chest, abdomen, pelvis, hips/joints) and two types of CT examinations (thorax, abdomen). DRL curves were derived using an exponential fit to the data using weight as an independent variable and the respective radiation dose indices (P, CTDI, DLP) as dependent variables. DRL was also derived for weight groups for comparison. The result was compared with national diagnostic reference level (NDRL) curves.
Results: The derived curves show similarities with the NDRL curves available and corresponded sufficiently well with DRL for weight groups using the same data set, if sufficient number of data was available.
Conclusions: We conclude that weight-based DRL curves are a feasible approach and could be used together with DRL for weight groups. The main advantage of DRL curves is its application in the clinic. When the examination frequency is low, time to collect enough data to establish typical values for one or several weight groups may be unreasonably long. The curve provides the means to compare dose level faster and with fewer data points.
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http://dx.doi.org/10.1016/j.ejmp.2021.05.035 | DOI Listing |
Circ Arrhythm Electrophysiol
November 2024
Duke Clinical Research Institute and Duke University Medical Center, Durham, NC (J.P.P.).
Background: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertable cardiac monitors (ICMs), may be useful in predicting near-term CVH.
Methods: Using Optum's deidentified Clinformatics Data Mart Database (2007-2019), linked with the Medtronic CareLink ICM database, we identified patients with >1 days of ICM-detected atrial fibrillation.
Circ Arrhythm Electrophysiol
November 2024
Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL (R.S.P.).
Background: Atrial fibrillation (AF) is associated with an increased risk of stroke, yet the limitations of conventional monitoring have restricted our understanding of AF burden risk thresholds. Predictive algorithms incorporating continuous AF burden measures may be useful for predicting stroke. This study evaluated the performance of temporal AF burden trends as predictors of stroke from a large cohort with insertable cardiac monitors.
View Article and Find Full Text PDFEur J Med Res
September 2024
Dermatology Center, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, China.
Heliyon
August 2024
Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169, Donghu Road, Wuchang District, Wuhan, Hubei Province, 430071, China.
Background: Depression and long non-coding RNA (lncRNA) have been reported to be associated with tumor progression and prognosis in gastric cancer (GC). This study aims to build a GC risk classification and prognosis model based on depression-related lncRNAs (DRLs).
Methods: To develop a risk model, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses using RNA sequencing data of GC from The Cancer Genome Atlas (TCGA) and depression-related genes (DRGs) from previous studies.
Medicine (Baltimore)
July 2024
Department of Urology, Suining Central Hospital, Suining, Sichuan, China.
Background: Bladder cancer (BLCA) is a prevalent and aggressive cancer associated with high mortality and poor prognosis. Currently, studies on the role of disulfidptosis-related long non-coding RNAs (DRLs) in BLCA are limited. This study aims to construct a prognostic model based on DRLs to improve the accuracy of survival predictions for patients and identify novel targets for therapeutic intervention in BLCA management.
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