In-Hospital Antibiotic Use for COVID-19: Facts and Rationales Assessed through a Mixed-Methods Study.
Laura Elena StoichitoiuLarisa PinteAlexandr CeasovschihRoxana Carmen CernatNicoleta-Dorina VladVlad PadureanuLaurentiu SorodocAdriana HristeaAdrian PurcareaCamelia BadeaCristian BăicuşPublished in: Journal of clinical medicine (2022)
It is well known that during the coronavirus disease 2019 (COVID-19) pandemic, antibiotics were overprescribed. However, less is known regarding the arguments that have led to this overuse. Our aim was to understand the factors associated with in-hospital antibiotic prescription for COVID-19, and the rationale behind it. We chose a convergent design for this mixed-methods study. Quantitative data was prospectively obtained from 533 adult patients admitted in six hospitals (services of internal medicine, infectious diseases and pneumology). Fifty-six percent of the patients received antibiotics. The qualitative data was obtained from interviewing 14 physicians active in the same departments in which the enrolled patients were hospitalized. Thematic analysis was used for the qualitative approach. Our study revealed that doctors based their decisions to prescribe antibiotics on a complex interplay of factors regarding the simultaneous appearance of consolidation on the chest computer tomography together with a worsening of clinical conditions suggestive of bacterial infection and/or an increase in inflammatory markers. Besides these features which might suggest bacterial co-/suprainfection, doctors also prescribed antibiotics in situations of uncertainty, in patients with severe disease, or with multiple associated comorbidities.
Keyphrases
- coronavirus disease
- end stage renal disease
- healthcare
- sars cov
- ejection fraction
- newly diagnosed
- primary care
- chronic kidney disease
- systematic review
- prognostic factors
- clinical trial
- emergency department
- high resolution
- young adults
- respiratory syndrome coronavirus
- mental health
- electronic health record
- deep learning
- machine learning
- study protocol
- mass spectrometry
- artificial intelligence
- medical students