Uncertainty and decision-making in critical care: lessons from managing COVID-19 ARDS in preparation for the next...

Coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) was an emergent syndrome that led to high volumes of critically ill ventilated patients. We explored influences on decision-making regarding management of COVID-19 ARDS mechanical ventilation to identify modifiable factors to improve preparedness for future pandemics.

A systematic review and small group interviews informed the development of an international questionnaire (UK, Italy, Germany and Netherlands) on factors influencing COVID-19 ARDS ventilation decision-making in critical care professionals. Participants ranked four themes in order of importance: disease (uncertainties around COVID-19 ARDS), contextual (cognitive strain), environmental (structural logistics) and team factors. Participants also ranked the subthemes within each theme. Thematic analysis was used to derive findings from qualitative data. Kruskal-Wallis, Mann-Whitney U and Kendall's tau were used for quantitative data analysis.

Patient factors (comorbidities, clinical/biochemical parameters) were the most studied influences in the extant literature on decision-making; uncertainty was one of the least studied. 371 critical care professionals responded to the questionnaire. Disease uncertainty (lack of applicable guidelines, unfamiliarity with pathophysiology) was ranked as the most important influence on ventilation decision-making for COVID-19 ARDS across regions, professions and experience levels (p<0.001). Participants expressed underconfidence in their decision-making (median score: 9/20); this was unaffected by experience (p=0.79) or profession (p=0.58). Qualitative findings supported and extended the initial proposed influences, including the impact of team factors (+ve) and resource limitations (-ve) on disease uncertainty.

Future pandemic preparedness programmes should target modifiable influences such as information sharing, teamworking and resource limitations to mitigate against the negative influence of uncertainty and thereby improve decision-making overall.