Understanding the influences on clinical decisions regarding ventilation of critically ill COVID-19 patients

ABSTRACT

Introduction: National Service Evaluations of COVID-19 ARDS care in the US and UK showed significant variability in clinical practice, and adherence to existing guidelines. To better understand the basis for this, we explored factors influencing decision-making around mechanical ventilation in COVID-19. Method(s) We conducted interprofessional focus groups identifying factors that influenced decision-making through thematic analysis. From this, we developed a questionnaire to validate these themes with a larger sample of critical care professionals across the UK. Kruskal- Wallis or Mann-Whitney U tests were used for data analysis. Result(s) There were 179 complete responses from doctors, nurses and physios. In their usual practice, 66% of clinicians reported adherence to national ARDS guidelines. However, 80% thought COVID-19 ARDS presented differently to their previous clinical knowledge/experience of ARDS and 72% thought deviating from usual practice was necessary. Doctors were more likely to think deviation was necessary (p < 0.001) but there was no difference across level of ICU experience (p = 0.845). Clinicians reported their ventilatory decision-making was most influenced by disease factors, followed by team then contextual and least by environmental factors (p < 0.001). Disease factor was seen as most important across profession and experience level. During COVID-19, 68% of clinicians reported not being confident in their ventilatory decision-making;however, clinicians who felt COVID-19 ARDS presentation fitted with their previous clinical knowledge/experience of ARDS reported greater confidence (p < 0.001). Confidence was not affected by experience (p = 0.522) or profession (p = 0.294) (Fig. 1). Conclusion(s) Clinicians were influenced by the uncertain understanding of COVID-19 ARDS, especially when they considered previous experiences to be less relevant. In the event of another novel disease, developing a consistent, understandable clinical models of disease should be prioritised to optimise decision making.