| Author | Dantas, Leila Figueiredo | |
| Author | Peres, Igor Tona | |
| Author | Antunes, Bianca Brandão de Paula | |
| Author | Bastos, Leonardo S. L. | |
| Author | Hamacher, Silvio | |
| Author | Kurtz, Pedro | |
| Author | Martin-Loeches, Ignacio | |
| Author | Bozza, Fernando A. | |
| Access date | 2024-10-05T00:31:52Z | |
| Available date | 2024-10-05T00:31:52Z | |
| Document date | 2024 | |
| Citation | DANTAS, Leila Figueiredo et al. Prediction of multidrug-resistant bacteria (MDR) hospital-acquired infection (HAI) and colonisation: A systematic review. Infection, Disease & Health, p. 1-11, Aug. 2024. | en_US |
| ISSN | 2468-0451 | en_US |
| URI | https://www.arca.fiocruz.br/handle/icict/66331 | |
| Sponsorship | This work was supported by the Brazilian National Council for Scientific and Technological Development (CNPq) [310940/2019-2, 403863/2016-3 and 444968/2023-7 to S.H.; 420096/2023-0 to L.S.L.B]; the Carlos Chagas Filho Foundation for Research Support in Rio de Janeiro State (FAPERJ) [E-26/210.858/2024 to I.T.P]; the Coordination for the Improvement of Higher Education Personnel (CAPES); and the Pontifical Catholic University of Rio de Janeiro. | en_US |
| Language | eng | en_US |
| Publisher | Elsevier | en_US |
| Rights | restricted access | |
| Title | Prediction of multidrug-resistant bacteria (MDR) hospital-acquired infection (HAI) and colonisation: A systematic review | en_US |
| Type | Article | |
| DOI | 10.1016/j.idh.2024.07.003 | |
| Abstract | Background: Hospital-Acquired Infections (HAI) represent a public health priority in most countries worldwide. Our main objective was to systematically review the quality of the predictive modeling literature regarding multidrug-resistant gram-negative bacteria in Intensive Care Units (ICUs). Methods: We conducted and reported a Systematic Literature Review according to the recommendations of the PRISMA statement. We analysed the quality of the articles in terms of adherence to the TRIPOD checklist. Results: The initial search identified 1935 papers and 15 final articles were included in the review. Most studies analysed used traditional prediction models (logistic regression), and only three developed machine-learning techniques. We noted poor adherence to the main methodological issues recommended in the TRIPOD checklist to develop prediction models, such as handling missing data (20% adherence), model-building procedures (20% adherence), assessing model performance (47% adherence), and reporting performance measures (33% adherence). Conclusions: Our review found few studies that use efficient alternatives to predict the acquisition of multidrug-resistant gram-negative bacteria in ICUs. Furthermore, we noted a lack of strategies for dealing with missing data, feature selection, and imbalanced datasets, a common problem in HAI studies. | en_US |
| Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Pontifical Catholic University of Rio de Janeiro. Department of Industrial Engineering. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | D'Or Institute for Research and Education. IDOR. Rio de Janeiro, RJ, Brazil. | en_US |
| Affilliation | Department of Intensive Care Medicine. Multidisciplinary Intensive Care Research Organization. St James' Hospital. Dublin, Ireland. | en_US |
| Affilliation | Oswaldo Cruz Foundation. Evandro Chagas National Institute of Infectious Disease. Clinical Research Laboratory in Intensive Medicine. Rio de Janeiro, RJ, Brazil / D'Or Institute for Research and Education. IDOR. Rio de Janeiro, RJ, Brazil. | en_US |
| Subject | Multidrug resistant | en_US |
| Subject | MDR | en_US |
| Subject | Prediction model | en_US |
| Subject | TRIPOD | en_US |
| Subject | Systematic review | en_US |
| e-ISSN | 2468-0869 | |
| Embargo date | 2030-12-31 | |
