Many bacterial typing methods are specific for one species only, time-consuming, or poorly reproducible. DiversiLab (DL; bioMérieux) potentially overcomes these limitations. In this study, we evaluated the DL system for the identification of hospital outbreaks of a number bacterial species. Appropriately typed clinical isolates were tested with DL. DL typing agreed with pulsed-field gel electrophoresis (PFGE) for Acinetobacter (n = 26) and Stenotrophomonas maltophilia (n = 13) isolates. With two exceptions, DL typing of Klebsiella isolates (n = 23) also correlated with PFGE, and in addition, PFGE-nontypeable (PFGE-NT) isolates could be typed. Enterobacter (n = 28) results also correlated with PFGE results; also, PFGE-NT isolates could be clustered. In a larger study (n = 270), a cluster of 30 isolates was observed that could be subdivided by PFGE. The results for Escherichia coli (n = 38) correlated less well with an experimental multilocus variable number of tandem repeats analysis (MLVA) scheme. Pseudomonas aeruginosa (n = 52) showed only a limited number of amplification products for most isolates. When multiple Pseudomonas isolates were assigned to a single type in DL, all except one showed multiple multilocus sequence types. Methicillin-resistant Staphylococcus aureus generally also showed a limited number of amplification products. Isolates that belonged to different outbreaks by other typing methods, including PFGE, spa typing, and MLVA, were grouped together in a number of cases. For Enterococcus faecium, the limited variability of the amplification products obtained made interpretation difficult and correlation with MLVA and esp gene typing was poor. All of the results are reflected in Simpson's index of diversity and adjusted Rand's and Wallace's coefficients. DL is a useful tool to help identify hospital outbreaks of Acinetobacter spp., S. maltophilia, the Enterobacter cloacae complex, Klebsiella spp., and, to a somewhat lesser extent, E. coli. In our study, DL was inadequate for P. aeruginosa, E. faecium, and MRSA. However, it should be noted that for the identification of outbreaks, epidemiological data should be combined with typing results.
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http://dx.doi.org/10.1128/JCM.01191-10 | DOI Listing |
JMIR Res Protoc
January 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a nonuser-oriented development approach, resulting in reduced user acceptance.
View Article and Find Full Text PDFJMIR Infodemiology
January 2025
Salzburg University of Applied Sciences, Puch/Salzburg, Austria.
Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
JMIR Aging
January 2025
Department of Geriatrics, Guangdong Provincial Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China, 0898-66571684.
Background: The utility of aging metrics that incorporate cognitive and physical function is not fully understood.
Objective: We aim to compare the predictive capacities of 3 distinct aging metrics-motoric cognitive risk syndrome (MCR), physio-cognitive decline syndrome (PCDS), and cognitive frailty (CF)-for incident dementia and all-cause mortality among community-dwelling older adults.
Methods: We used longitudinal data from waves 10-15 of the Health and Retirement Study.
Neurology
February 2025
Department of Medicine and Geriatrics, Tuen Mun Hospital, Hong Kong, People's Republic of China.
Background And Objectives: Mitochondrial disorders are multiorgan disorders resulting in significant morbidity and mortality. We aimed to characterize death-associated factors in an international cohort of deceased individuals with mitochondrial disorders.
Methods: This cross-sectional multicenter observational study used data provided by 26 mitochondrial disease centers from 8 countries from January 2022 to March 2023.
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