Objective: This study aimed to understand predictors of inadequate response (IR) to low-dose febuxostat treatment based on clinical variables.
Methods: We pooled data from 340 patients of an observational cohort and two clinical trials who received febuxostat 20 mg/day for at least 3 months. IR was defined as failure to reach the target serum urate level (sUA<6 mg/dL) at any time point during 3 months treatment. The potential predictors associated with short- or mid-term febuxostat IR after pooling the three cohorts were explored using mixed-effect logistic analysis. Machine learning models were performed to evaluate the predictors for IR using the pooled data as the discovery set and validated in an external test set.
Results: Of the 340 patients, 68.9% and 51.8% were non-responders to low-dose febuxostat during short- and mid-term follow-up, respectively. Serum urate and triglyceride (TG) levels were significantly associated with febuxostat IR, but were also selected as significant features by LASSO analysis combined with age, BMI, and C-reactive protein (CRP). These five features in combination, using the best-performing stochastic gradient descent classifier, achieved an area under the receiver operating characteristic curve of 0.873 (95% CI [0.763, 0.942]) and 0.706 (95% CI [0.636, 0.727]) in the internal and external test sets, respectively, to predict febuxostat IR.
Conclusion: Response to low-dose febuxostat is associated with early sUA improvement in individual patients, as well as patient age, BMI, and levels of TG and CRP.
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http://dx.doi.org/10.2147/JIR.S458250 | DOI Listing |
Front Nutr
December 2024
Department of Reproductive Health and Nutrition, School of Public Health, College of Health Science and Medicine, Wolaita Sodo University, Wolaita Sodo, Ethiopia.
Background: The primary cause of vitamin A deficiency in developing countries like Ethiopia is the inadequate consumption of vitamin A-rich foods. Preschool children are particularly vulnerable due to their higher nutritional requirements and increased susceptibility to infections. This study aims to assess the prevalence of inadequate consumption of vitamin A-rich foods and identify the associated factors among preschool children in Wolaita Sodo, Southern Ethiopia.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Orthopaedic Surgery, Hebei Medical University Third Hospital, Shijiazhuang, China.
Background: Different from other parts of long bone fractures, surgical site infections (SSI) often occurs in open fractures of the hand (OFH) due to the anatomical characteristics and injury mechanisms. Our aim of the study is to investigate the particular risk factors of SSI after emergency surgery in OFH and develop a prediction nomogram model.
Methods: In our traumatic center, patients with OFH not less than 18 years old were retrieved between October 2020 and April 2024.
J Orthop Trauma
January 2025
Department of Orthopaedic Surgery, Nassau University Medical Center, East Meadow, NY, USA.
Objectives: To evaluate the effect of perioperative variables including PT and walking distance on length of stay (LOS) in hip fracture patients.
Methods: Design: A retrospective review.
Setting: Single level I trauma center.
BMC Res Notes
January 2025
Ragon Institute of MGH, MIT, and Harvard, 600 Main Street, Cambridge, MA, 02139, USA.
Background: Immune reconstitution following the initiation of combination antiretroviral therapy (cART) significantly impacts the prognosis of individuals infected with human immunodeficiency virus (HIV). Our previous studies have indicated that the baseline CD4 T cells count and percentage before cART initiation are predictors of immune recovery in TB-negative children infected with HIV, with TB co-infection potentially causing a delay in immune recovery. However, it remains unclear whether these predictors consistently impact immune reconstitution during long-term intensive cART treatment in TB-negative/positive children infected with HIV.
View Article and Find Full Text PDFMAbs
December 2025
Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalizability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported.
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