Bacteraemia, Bloodstream Infection, Sepsis
Conditions
Brief summary
The goal of this study is to create a computer simulation of patients with bloodstream infection to understand how changes in healthcare policies and resources affect patient treatment. This simulation will help doctors and health-care decision makers make better choices in treating these patients and avoid overusing antibiotics that can lead to antibiotic resistance. Antibiotic resistance is when bacteria can't be killed by antibiotics anymore. Participants will not receive treatments as this is an observational study, but the study will involve: * Interviews with healthcare staff to understand patient care pathways. * Analysis of historical data on bacteria causing infections and antibiotic treatments. * A 30-day observational study to observe patient treatment for bloodstream infections.
Detailed description
The main aim of this research is to create a computer simulation of patients with bloodstream infection. Bloodstream infection is often a severe infection (blood poisoning) that can lead to sepsis, which is a significant global health concern, causing around 66,096 deaths each year in the United Kingdom alone. Doctors and healthcare decision-makers have to make difficult decisions that balance the effective treatment of patients with sepsis and avoid the overuse of broad-spectrum antibiotics, which can lead to antibiotic resistance. By generating a virtual environment to test out different scenarios, this computer simulation model will provide insight into the projected effects of changes to hospital policy, changes in hospital resources (such as staff) and the impact of laboratory diagnostic tests. Proposed changes can be simulated in this virtual environment prior to implementation in the real healthcare system. This study will be carried out in three parts. First, there will be interviews with healthcare staff over six months to comprehensively describe the patient pathways involved in management of bloodstream infection. Next, the project will analyse historical pseudonymised data from Liverpool University Hospitals NHS Foundation and Liverpool Clinical Laboratories to inform the characteristics of the model (in particular on the bacteria causing infections, the antibiotic treatments used, and the time it takes for patients to be treated). Finally, there will be a 30-day observational study where researchers observe the treatment of patients with bloodstream infection (without intervening or recording identifying details) to ensure that the computer model captures all the important steps involved in patient management.
Interventions
None listed
Sponsors
Liverpool University Hospitals NHS Foundation Trust
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Sub-study 2 (retrospective data collection) * Patients who were managed within a LUFT acute hospital site (Aintree or Royal) and had a concurrent blood culture investigation requested * Age ≥ 18 years at the time of the study. Sub-study 3 (observational study) * Patients with blood culture investigation requested during the observation period, and staff caring for the patients.
Exclusion criteria
* Age \< 18 years at the time of the study * Blood culture requested but patient not managed on an acute hospital site.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Generation of a software simulation model that is fine tuned to the observed data. | 2 years | The main outcome is the generation of a software simulation model that is calibrated to the observed data. The study team consists of individuals with experience in model construction and calibration. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Exploration of the performance of the model in different hypothesised scenarios. | 6 months | Secondary outcomes of this study are the exploration of the performance of the model in different hypothesised scenarios. The scenarios and outcome metrics are dependant on the final model delivered, but as an example may include the following: Time from presentation to hospital to time to administration of antibiotics active against the infecting pathogen in a simulated model which includes a novel diagnostic test for antimicrobial resistance, vs a model that does not include one. |
Countries
United Kingdom
Contacts
Primary ContactEsha D Sheth
Outcome results
None listed