Epidemiology of Antimicrobial-use and Antimicrobial-resistant Infections in Four Hospitals in Thailand
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorUniversity of Oxford
About this trial
The main goal of this retrospective observational study is to understand how stepping down antibiotic treatment (called antibiotic de-escalation) affects patients who receive it compared to those who don't after received a short-course (≤7 days) of parenteral antibiotics. The investigators will use past medical records from four public referral hospitals in Thailand from the year 2019 to 2024. The investigators will firstly evaluate which types of patients are more likely to receive antibiotic de-escalation. Then, the investigators will estimate the impact of antibiotic de-escalation, while taking those differences into account. This way, it will help us understand the impact of antibiotic de-escalation in real-world clinical practice. The investigators also aim to assess how accurate automated outbreak detection systems are at detecting outbreaks, evaluate patterns of antimicrobial use and antimicrobial-resistant infections, and develop new indicators for antimicrobial stewardship that are applicable for local and national actions in low and middle-income countries.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Admitted to four collaborating hospitals from 1 Jan 2019 to 31 Dec 2024
Received a parenteral antibiotic for at least four consecutive days
Were still hospitalized on day 8 after starting a parenteral antibiotic
Disqualifiers
Admitted as day admissions to four collaborating hospitals from 1 Jan 2019 to 31 Dec 2024
Had a clinical specimen collected within 2 calendar days of starting a parenteral antibiotic culture positive for an antimicrobial-resistant organism or Staphylococcus aureus
All age and gender
Admitted to four collaborating hospitals from 1 Jan 2019 to 31 Dec 2024
Trial population
All patients admitted to four collaborating hospitals from 1 Jan 2019 to 31 Dec 2024. We expect that the total sample size would be about 10,800,000 inpatients.
Trial design
Design model
Other
Time perspective
Retrospective
Treatments tested in this trial
Not listed
Trial groups
108,000 Participants
are grouped into 3 trial groupsGroup A: No antibiotic de-escalation
Group B: De-escalation by using Access or Low Watch parenteral antibiotics
Group C: De-escalation by cessation of parenteral antibiotics
Trial outcomes
Primary outcomes
1
New antimicrobial-resistant bloodstream infection
Antimicrobial-resistant (AMR) bloodstream infection (BSI) is defined as having blood culture positive for methicillin-resistant S. aureus (MRSA), vancomycin-resistant Enterococcus spp., 3rd-generation cephalosporin-resistant Gram-negative bacterium and carbapenem-resistant Gram-negative bacterium from day 8 to day 30 after starting a parenteral antibiotic therapy.
Time frame
6 years2
In-hospital 30-day mortality
In-hospital mortality is defined as having discharge outcomes recorded as 'death without autopsy', 'death with autopsy' or 'died'. In hospital 30-day mortality will be defined as having in-hospital mortality from day 8 to day 30 after starting a parenteral antibiotic therapy from day 8 to day 30 after starting a parenteral antibiotic therapy.
Time frame
6 yearsSecondary outcomes
1
Sensitivity of automated outbreak detection systems
Sensitivity is defined as the proportion of positive cluster signals identified by automated outbreak systems among all clusters defined by expert opinion. Data from the year 2019 is used as the initial baseline for automated outbreak detection systems.
Time frame
5 years, from 2020 to 20242
Specificity of automated outbreak detection systems
Specificity is defined as the proportion of ward-weeks without positive cluster signals identified by automated outbreak systems among all ward-weeks without clusters defined by expert opinion. Data from the year 2019 is used as the initial baseline for automated outbreak detection systems.
Time frame
5 years, from 2020 to 20243
Positive predictive value (PPV) of automated outbreak detection systems
PPV is defined as the proportion of clusters defined by expert opinion among all positive cluster signals. Data from the year 2019 is used as the initial baseline for automated outbreak detection systems.
Time frame
5 years, from 2020 to 20244
Negative predictive value (NPV) of automated outbreak detection systems
NPV is defined as the proportion of ward-weeks without positive cluster signals defined by expert opinion among all ward-weeks without positive cluster signals. Data from the year 2019 is used as the initial baseline for automated outbreak detection systems.
Time frame
5 years, from 2020 to 2024Sponsors and contacts
Click on the lead sponsor to view all of their trials.
University of Oxford
Lead sponsor
Wellcome Trust
Collaborator
Source ClinicalTrials.gov
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