Objective: This study aimed to assess the population at risk of infection by extended-spectrum beta-lactamase (ESBL)-producing organisms, using clinical criteria.
Materials And Methods: All urine cultures positive for Enterobacteriaceae in a Spanish hospital department from January 2010 to 2014 were reviewed. All isolates with ESBL-positive strains were collected, and isolates received during the first week of each month with ESBL-negative strains from symptomatic patients hospitalized or admitted to the emergency room. Multivariate analysis of the factors involved was undertaken and a nomogram developed to predict the probability of infection by ESBL-producing microorganisms.
Results: The study included 1524 patients with urinary tract infection (UTI): 416 ESBL-positive and 1108 ESBL-negative. In univariate analysis, risk factors were: male gender (p = 0.036), age (p < 0.0001), nursing home (p < 0.0001), previous antimicrobial therapy (p < 0.0001) or hospitalization (p < 0.0001), diabetes (p < 0.0001), chronic renal insufficiency (p < 0.0001), severe underlying disease (p < 0.0001), neoplasia (p = 0.0005), urological (p < 0.0001) and non-urological invasive procedure (p = 0.0003), recurrent UTI (p < 0.0001), urological (p < 0.0001) or abdominal surgery (p < 0.0001) and permanent urethral catheter (p < 0.0001). In multivariate analysis, the data set was split into a development cohort of 1067 patients and a validation cohort of 457 cases. A nomogram was developed to predict the probability of infection by ESBL-producing bacteria, which included seven variables: age (p < 0.0001), gender (p = 0.004), nursing home (p < 0.0001), previous antimicrobial therapy (p = 0.04) or hospitalization (p < 0.0001), recurrent UTI (p < 0.0001) and non-urological invasive procedure (p = 0.005). The discriminative accuracy was 0.79 (95% confidence interval 0.77-0.83).
Conclusions: A nomogram was developed that predicts the risk of infection by ESBL-producing Enterobacteriaceae with reasonable accuracy. It could improve clinical decision making and enable more efficient empirical treatment.
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http://dx.doi.org/10.1080/21681805.2017.1373698 | DOI Listing |
Eur J Med Res
December 2024
Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
Background: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.
Methods: Data were collected from two centers and categorized into development and validation cohorts. Using the development cohort, candidate variables were selected via the Recursive Feature Elimination (RFE) method.
Signal Transduct Target Ther
December 2024
School of Basic Medical Science, Tsinghua University, 30 Shuangqing Rd., Haidian District, Beijing, 100084, China.
Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail.
View Article and Find Full Text PDFClin Pharmacokinet
December 2024
Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Background And Objective: Vancomycin is a glycopeptide antibiotic used for the treatment of severe gram-positive infections. Despite decades of clinical experience, optimized dosing for vancomycin in pediatric populations still warrants further investigation. Patients admitted to the pediatric intensive care unit (PICU) after cardiac surgery are often treated with vancomycin in case of (suspected) infection.
View Article and Find Full Text PDFBackground: A multivariate predictive model was constructed using baseline and 12-week clinical data to evaluate the rate of clearance of hepatitis B surface antigen (HBsAg) at the 48-week mark in patients diagnosed with chronic hepatitis B who are receiving treatment with pegylated interferon α (PEG-INFα).
Methods: The study cohort comprised CHB patients who received pegylated interferon treatment at Mengchao Hepatobiliary Hospital, Fujian Medical University, between January 2019 and April 2024. Predictor variables were identified (LASSO), followed by multivariate analysis and logistic regression analysis.
Virol J
December 2024
Department of Hepatology, Qilu Hospital of Shandong University, Wenhuaxi Road 107#, Jinan, 250012, China.
Background: Oxidative stress plays a crucial role in the pathogenesis of HBV. This study aimed to investigate the value of fibroblast growth factor 21 (FGF21) promoter methylation in the occurrence and development of chronic hepatitis B (CHB) oxidative stress.
Methods: A total of 241 participants including 221 patients with CHB and 20 healthy controls (HCs) were recruited.
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