Antimicrobial Resistance Prediction in Intensive Care Unit for Pseudomonas Aeruginosa using Temporal Data-Driven Models.

Àlvar Hernàndez Carnerero; Miquel Sànchez Marrè; Inmaculada Mora Jiménez; Cristina Soguero Ruiz; Sergio Martínez Agüero; Joaquín Álvarez Rodríguez

doi:10.9781/ijimai.2021.02.012

Authors

Àlvar Hernàndez Carnerero Universitat Politècnica de Catalunya
Miquel Sànchez Marrè Universitat Politècnica de Catalunya
Inmaculada Mora Jiménez Universidad Rey Juan Carlos
Cristina Soguero Ruiz Universidad Rey Juan Carlos
Sergio Martínez Agüero Universidad Rey Juan Carlos
Joaquín Álvarez Rodríguez Hospital Universitario de Fuenlabrada

DOI:

https://doi.org/10.9781/ijimai.2021.02.012

Keywords:

Antimicrobial Resistance, Intensive Care Unit, Prediction, Pseudomonas Aeruginosa, Temporal Data-Driven Modeling

Supporting Agencies

We are thankful to the University Hospital of Fuenlabrada in Madrid, Spain, for providing us the database used in this research. This work has been partly supported by the Spanish Thematic Network “Learning Machines for Singular Problems and Applications (MAPAS)” (TIN2017-90567-REDT, MINECO/FEDER EU), by the IDEAI-UPC Consolidated Research Group Grant from Catalan Agency of University and Research Grants (AGAUR, Generalitat de Catalunya) (2017 SGR 574), by the Science and Innovation Ministry Grants Klinilycs (TEC2016-75361-R), AAVis-BMR (PID2019-107768RA-I00) and BigTheory (PID2019-106623RB-C41), by the Spanish Institute of Health Carlos III (grant DTS 17/00158), by Project Ref. F656 financed by Rey Juan Carlos University, and by the Youth Employment Initiative R&D Project (TIC-11649) financed by the Community of Madrid (Spain). Funded action by the Community of Madrid in the framework of the Multiannual Agreement with the Rey Juan Carlos University in line of action 1, “Encouragement of Young Phd students investigation” Project Ref. F661 Acronym Mapping-UCI.

Abstract

One threatening medical problem for human beings is the increasing antimicrobial resistance of some microorganisms. This problem is especially difficult in Intensive Care Units (ICUs) of hospitals due to the vulnerable state of patients. Knowing in advance whether a concrete bacterium is resistant or susceptible to an antibiotic is a crux step for clinicians to determine an effective antibiotic treatment. This usual clinical procedure takes approximately 48 hours and it is named antibiogram. It tests the bacterium resistance to one or more antimicrobial families (six of them considered in this work). This article focuses on cultures of the Pseudomonas Aeruginosa bacterium because is one of the most dangerous in the ICU. Several temporal data-driven models are proposed and analyzed to predict the resistance or susceptibility to a determined antibiotic family previously to know the antibiogram result and only using the available past information from a data set. This data set is formed by anonymized electronic health records data from more than 3300 ICU patients during 15 years. Several data-driven classifier methods are used in combination with several temporal modeling approaches. The results show that our predictions are reasonably accurate for some antimicrobial families, and could be used by clinicians to determine the best antibiotic therapy in advance. This early prediction can save valuable time to start the adequate treatment for an ICU patient. This study corroborates the results of a previous work pointing that the antimicrobial resistance of bacteria in the ICU is related to other recent resistance tests of ICU patients. This information is very valuable for making accurate antimicrobial resistance predictions.

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