Developing a model for decision-making around antibiotic prescribing for patients with COVID-19 pneumonia in acute NHS hospitals during the first wave of the COVID-19 pandemic: qualitative results from the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients (PEACH Study)

Abstract

Objective: To explore and model factors affecting antibiotic prescribing decision-making early in the pandemic.

Design: Semistructured qualitative interview study.

Setting: National Health Service (NHS) trusts/health boards in England and Wales.

Participants: Clinicians from NHS trusts/health boards in England and Wales.

Method: Individual semistructured interviews were conducted with clinicians in six NHS trusts/health boards in England and Wales as part of the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients study, a wider study that included statistical analysis of procalcitonin (PCT) use in hospitals during the first wave of the pandemic. Thematic analysis was used to identify key factors influencing antibiotic prescribing decisions for patients with COVID-19 pneumonia during the first wave of the pandemic (March to May 2020), including how much influence PCT test results had on these decisions.

Results: During the first wave of the pandemic, recommendations to prescribe antibiotics for patients with COVID-19 pneumonia were based on concerns about secondary bacterial infections. However, as clinicians gained more experience with COVID-19, they reported increasing confidence in their ability to distinguish between symptoms and signs caused by SARS-CoV-2 viral infection alone, and secondary bacterial infections. Antibiotic prescribing decisions were influenced by factors such as clinician experience, confidence, senior support, situational factors and organisational influences. A decision-making model was developed.

Conclusion: This study provides insight into the decision-making process around antibiotic prescribing for patients with COVID-19 pneumonia during the first wave of the pandemic. The importance of clinician experience and of senior review of decisions as factors in optimising antibiotic stewardship is highlighted. In addition, situational and organisational factors were identified that could be optimised. The model presented in the study can be used as a tool to aid understanding of the complexity of the decision-making process around antibiotic prescribing and planning antimicrobial stewardship support in the context of a pandemic.

Trial registration number: ISRCTN66682918.

Keywords: COVID-19; infection control; infectious diseases.

Figure 1

The complex factors which influence decisions to prescribe antibiotics. The blue boxes indicate the combination of factors contributing to the likelihood of prescribing antibiotics. The three main factors are: the acuity of the illness (how sick they are), the vulnerability of the patient to infection (age, immunosuppression, risk factors) and the likelihood of bacterial infection (based on clinical signs, laboratory tests and radiology). Other factors: clinician experience, confidence and support; situational factors; laboratory tests; organisational influences. The directional colour scale (orange) indicates the likelihood for prescribing antibiotics, with darker shaded areas weighted towards a higher likelihood of prescribing, and areas of lighter shading towards lower likelihood of prescribing. *Situational factors include time of day, family/patient pressure to prescribe antibiotics, etc. (The question of how much impact family/patient pressure had on decision-making was presented to clinicians during the interviews. The majority expressed that they were senior and confident enough to ignore pressure to prescribe, which could be expressed during telephone calls from relatives. However, they might take patient wishes into consideration in terms of intolerances or preferences against particular antibiotics.) **Organisational factors include staffing levels, busyness of emergency department (ED), availability of laboratory test results in real time, etc.