Publicação
Prediction of Antimicrobial Resistance for Personalized Prevention and Clinical Management of Infectious Diseases
| Resumo: | Antibiotics are a very important class of drugs in modern Medicine; its discovery and introduction constituted a major revolution in Medicine. As such, the decrease of their effectiveness due to the rising levels of antibiotic resistance is a great concern to our society. Hospital-Acquired Infections (HAI), also known as nosocomial infections, are infections that a patient acquires in the context of medical treatments. Nosocomial infections are closely related to the problem of resistance to antibiotics, because a large part of these infections are caused by antibiotic resistant bacteria. This aspect motivates the tackling of these two problems jointly. This work was performed in the frame of the RESISTIR project, that aims to develop a Decision Support System for intelligent control of infection and personalized antibiotherapy. The data was collected by a portuguese software company, Maxdata Software, S.A.; then, in the scope of this project, it was pre-processed and inserted into a database. The database contains information generated by clinical episodes with origin in three portuguese hospitals, dated from 2013 to 2016. The available data allowed us to develop predictive models of risk of antibiotic resistance (AMR models) and risk of nosocomial infection (HAI model). For the AMR models, we aggregated the antibiotics into the level 4 of the ATC classification system, and focused on four classes of antibiotics: J01MA (fluoroquinolones), J01CA (penicillins with extended spectrum), J01DC (second-generation cephalosporins) and J01DH (carbapenems). In all predictive models developed, the features generated related to the clinical history of the patients and the health units involved in the episodes were found to be the most significant. We concluded that the main goals of this project were achieved, with the development of prototype predictive models of risk of antibiotic resistance and risk of nosocomial infection. The predictive power of the models, as measured by the ROC-AUC, ranged from 0.720 to 0.857 for the AMR models, and was 0.915 for the HAI model. These prototype predictive models that we were able to develop show that it is possible to deploy in healthcare units a Decision Support System that can help to monitor and reduce the problem of resistance to antibiotics and the nosocomial infections. For the HAI predictive model developed, we made an estimate of its benefits if it would be deployed in production in the three hospitals involved in this project. We drew the conclusion that the following savings would be obtained, in 2007 euros and considering only the hospitalizations: about 20 million euros and a reduction of approximately 60 000 days in the hospital stays, per year. |
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| Autores principais: | Braz, Nuno Gonçalo Viegas Ludovico |
| Assunto: | Resistência a Antibióticos Modelação Preditiva Previsão de Resistência a Antibióticos Previsão de Infeções Nosocomiais Análise Económica de Modelos Preditivos Teses de mestrado - 2023 |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | Antibiotics are a very important class of drugs in modern Medicine; its discovery and introduction constituted a major revolution in Medicine. As such, the decrease of their effectiveness due to the rising levels of antibiotic resistance is a great concern to our society. Hospital-Acquired Infections (HAI), also known as nosocomial infections, are infections that a patient acquires in the context of medical treatments. Nosocomial infections are closely related to the problem of resistance to antibiotics, because a large part of these infections are caused by antibiotic resistant bacteria. This aspect motivates the tackling of these two problems jointly. This work was performed in the frame of the RESISTIR project, that aims to develop a Decision Support System for intelligent control of infection and personalized antibiotherapy. The data was collected by a portuguese software company, Maxdata Software, S.A.; then, in the scope of this project, it was pre-processed and inserted into a database. The database contains information generated by clinical episodes with origin in three portuguese hospitals, dated from 2013 to 2016. The available data allowed us to develop predictive models of risk of antibiotic resistance (AMR models) and risk of nosocomial infection (HAI model). For the AMR models, we aggregated the antibiotics into the level 4 of the ATC classification system, and focused on four classes of antibiotics: J01MA (fluoroquinolones), J01CA (penicillins with extended spectrum), J01DC (second-generation cephalosporins) and J01DH (carbapenems). In all predictive models developed, the features generated related to the clinical history of the patients and the health units involved in the episodes were found to be the most significant. We concluded that the main goals of this project were achieved, with the development of prototype predictive models of risk of antibiotic resistance and risk of nosocomial infection. The predictive power of the models, as measured by the ROC-AUC, ranged from 0.720 to 0.857 for the AMR models, and was 0.915 for the HAI model. These prototype predictive models that we were able to develop show that it is possible to deploy in healthcare units a Decision Support System that can help to monitor and reduce the problem of resistance to antibiotics and the nosocomial infections. For the HAI predictive model developed, we made an estimate of its benefits if it would be deployed in production in the three hospitals involved in this project. We drew the conclusion that the following savings would be obtained, in 2007 euros and considering only the hospitalizations: about 20 million euros and a reduction of approximately 60 000 days in the hospital stays, per year. |
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