Observational multi-centre, prospective study to characterize novel pathogen-and host-related factors in hospitalized patients with lower respiratory tract infections and/or sepsis - the "TAILORED-Treatment" study

C B van Houten, K Oved, E Eden, A Cohen, D Engelhard, S Boers, R Kraaij, R Karlsson, D Fernandez, E Gonzalez, Y Li, A Stubbs, E R B Moore, J P Hays, L J Bont, C B van Houten, K Oved, E Eden, A Cohen, D Engelhard, S Boers, R Kraaij, R Karlsson, D Fernandez, E Gonzalez, Y Li, A Stubbs, E R B Moore, J P Hays, L J Bont

Abstract

Background: The emergence and spread of antibiotic resistant micro-organisms is a global concern, which is largely attributable to inaccurate prescribing of antibiotics to patients presenting with non-bacterial infections. The use of 'omics' technologies for discovery of novel infection related biomarkers combined with novel treatment algorithms offers possibilities for rapidly distinguishing between bacterial and viral infections. This distinction can be particularly important for patients suffering from lower respiratory tract infections (LRTI) and/or sepsis as they represent a significant burden to healthcare systems. Here we present the study details of the TAILORED-Treatment study, an observational, prospective, multi-centre study aiming to generate a multi-parametric model, combining host and pathogen data, for distinguishing between bacterial and viral aetiologies in children and adults with LRTI and/or sepsis.

Methods: A total number of 1200 paediatric and adult patients aged 1 month and older with LRTI and/or sepsis or a non-infectious disease are recruited from Emergency Departments and hospital wards of seven Dutch and Israeli medical centres. A panel of three experienced physicians adjudicate a reference standard diagnosis for all patients (i.e., bacterial or viral infection) using all available clinical and laboratory information, including a 28-day follow-up assessment. Nasal swabs and blood samples are collected for multi-omics investigations including host RNA and protein biomarkers, nasal microbiota profiling, host genomic profiling and bacterial proteomics. Simplified data is entered into a custom-built database in order to develop a multi-parametric model and diagnostic tools for differentiating between bacterial and viral infections. The predictions from the model will be compared with the consensus diagnosis in order to determine its accuracy.

Discussion: The TAILORED-Treatment study will provide new insights into the interplay between the host and micro-organisms. New host- or pathogen-related biomarkers will be used to generate a multi-parametric model for distinguishing between bacterial and viral infections. This model will be helpful to better guide antimicrobial therapy for patients with LRTI and sepsis. This study has the potential to improve patient care, reduce unnecessary antibiotic prescribing and will contribute positively to institutional, national and international healthcare economics.

Trial registration: NCT02025699 . Registration Date: January, 1, 2014.

Keywords: Antibiotic resistance; Lower respiratory tract infections; Rapid diagnostics; Sepsis.

Conflict of interest statement

Ethics approval and consent to participate

Protocol version 4, date 08–08-2014. Approval for this protocol was obtained prior to study start from the Medical Ethical Committee of UMCU (14–104, approval date: 09–09-2014) and the Institutional Review Boards of Hillel Yaffe Medical Centre (HYMC-0108-13 and HYMC-0107-13), Bnai Zion Medical Centre (BNZ-0107-14 and BNZ-0011-14) and Hadassah University Medical Centre (HMO-0007-14 and HMO-0006-14). Written, informed consent will be obtained from each patient-participant by research staff (by research nurse, research fellow or the principal investigator) prior to enrolment in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Limitations of current diagnostic tools
Fig. 2
Fig. 2
Schematic diagram of the study workflow
Fig. 3
Fig. 3
Development of the multi-parametric model

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