Team:Sheffield/episode2
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Background
Our initial research focused on human antibiotic misprescription - the prescription of antibiotics where they will be of no benefit to the individual taking them. The Center For Disease Control and Prevention (CDC) reported that 1 in 3 courses of antibiotics in US for outpatients was unnecessary1. The review on antimicrobial resistance chaired by Lord O’Neill also found that 700,000 deaths were caused by drug-resistant strains of common bacterial infections, HIV, TB and malaria every year 2.
Reports such as these supported our decision to tackle the growing issue of antibiotic resistance from the point of antibiotic misprescription. Reducing unnecessary prescriptions through the use of our device would help maximise efficient usage and therefore minimise the rate of antibiotic resistance development.
References
(1) Fleming-Dutra K.E., Hersh A.L., Shapiro D.J., et al., (2016). Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010-2011. Cosgrove: Journal of the American Medical Association. 315(17):1864-1873.
(2) O'Neill, J., (2016). Tackling drug-resistant infections globally: final report and recommendations. London: Wellcome Trust & HM Government.
The Problem
When we started our project we envisaged that we would create a point-of-care device that would aid GPs in determining which patients required antibiotics.
The device would detect a biomarker of bacterial infection giving an objective likelihood of a patient having a bacterial infection. This would give doctors greater confidence when prescribing antibiotics, as well as denying antibiotics to those who do not require them. We hoped our device could help alleviate the international issue of antibiotic-resistance.
In order to breakdown the thought process that a GP goes through when deciding whether or not to prescribe antibiotics, we created a flow diagram. We wanted to use this to determine whether our device could have impact.
Fig 2. The process a GP undergoes when making antibiotic prescription decision as hypothesised by our team.
There are four outcomes of the doctor's decision: Justified use of antibiotics, antibiotics misprescribed, misprescription avoided, and bacterial infection left untreated. With the current level of information available to doctors, a significant group of patients will receive antibiotics unnecessarily whilst another group will not receive necessary antibiotics. A diagnostic device providing additional information to doctors could reduce the occurrence these undesirable outcomes.
Currently, the most accurate tests involve taking bodily fluid samples in a hospital for culturing. Results from these tests can take weeks, at considerable cost. A point-of-care device would be able to provide similar information considerably faster.
This next flow diagram models where our device would have most influence in a doctor's decision making process.
Fig 3. Input of our device into the antibiotic decision making process. In this model, misprescription is avoided and patients requiring antibiotics get the treatment they require. The device will compare concentration of the biomarker, K, in the blood sample (Blood [K]), with the concentration of biomarker expected in a non-bacterial infected blood sample (Baseline [K]) to determine the likelihood of a bacterial infection.
