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                             <h2>What was our understanding at the start of our project?</h2>
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                             <h2>Background</h2>
 
                             <p>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 unnecessary<sup>1</sup>. 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 <sup>2</sup>.</p>
 
                             <p>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 unnecessary<sup>1</sup>. 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 <sup>2</sup>.</p>
  
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                                 <h2>What was our device trying to address?</h2>
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                                 <h2>The Problem</h2>
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                             <p>When we started our project we envisaged that we would create a point-of-care device that would benefit GPs in determining which patients required antibiotics.</p>
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                             <p>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.</p>
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                             <p>The device would detect a biomarker of bacterial infection giving an objective likelihood of a patient having a bacterial infection. This type of device would be the first of its kind, and would give doctors greater confidence when prescribing antibiotics, as well as denying antibiotics to those who do not require them. Ultimately, we hoped this tool would considerably alleviate the international issue of antibiotic-resistant bacteria.</p>
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                             <p>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.</p>
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                           <p>In order to breakdown the thought process that a GP goes through when deciding whether to prescribe antibiotics we created a flow diagram. We wanted to use this to determine where our device could have impact.</p>
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                           <p>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.</p>
  
 
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                         <p><b>Fig 2. Thought process a GP undergoes when making antibiotic prescription decision as hypothesised by our team.</b>There are four outputs that will directly affect the patient once the doctor has decided on the best course of action; Justified use of antibiotic, misprescription caused, misprescription avoided and patient with bacterial infection does not get the necessary treatment - in this case antibiotics. It is clear that with the current prescription decisions made a group of patients will receive antibiotics unnecessarily whilst another group will not receive necessary antibiotics, a device that could reduce this would have impact in this model.</p>
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                         <p><b>Fig 2. The process a GP undergoes when making antibiotic prescription decision as hypothesised by our team.</b> <br>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.</p>
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                        <p>The flow diagram above shows that a GP has to make a decision either to prescribe antibiotics as a precaution in cases where it is not clear if a patient has a bacterial infection or not from just the clinical symptoms. Doctors are experts at determining the nature of an infection however some bacterial and viral infections have very similar symptoms making it impossible to determine the exact nature of the infection on symptoms alone.</p>
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                         <p>This uncertainty can currently only be addressed by taking a blood sample that is sent to a lab for testing. This may require cell culturing which can take up to a week or longer for example in the case of tuberculosis. A point-of-care device would target this uncertainty and make the process much more accurate.</p>
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                         <p>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.</p>
  
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                         <p>This next flow diagram (below) predicted where our device would influence the decision making of doctors.</p>
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                         <p>This next flow diagram models where our device would have most influence in a doctor's decision making process.</p>
  
 
                         <img src="https://static.igem.org/mediawiki/2016/7/77/T--Sheffield--P%2BP-simpleprocess-chart.png">
 
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                       <p><b>Fig 3. Input of our device into the antibiotic decision making process.</b> Misprescription is avoided and patients requiring antibiotics get the treatment they require. K is the biomarker used in our device. The device will compare concentration of the biomarker 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.
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                       <p><b>Fig 3. Input of our device into the antibiotic decision making process.</b> 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.
 
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Latest revision as of 18:35, 19 October 2016

A template page

MIND MAPPING

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.