Understanding clinical decision-making during the COVID-19 pandemic: A cross-sectional worldwide survey

Abstract

Background: The lack of evidence-based recommendations for therapeutic decisions during the early weeks of the COVID-19 pandemic creates a unique scenario of clinical decision making which is worth to analyze. We aim to identify the drivers of therapeutic aggressiveness during the first weeks of the COVID-19 pandemic.

Methods: This cross-sectional worldwide survey (conducted April 12 to 19, 2020) was aimed at physicians who managed patients diagnosed with COVID-19. Treatment preferences were collected in five different clinical scenarios. We used multilevel mixed-effects ordered logistic regression to identify variables that were associated with the use of more aggressive therapies.

Findings: The survey was completed by 852 physicians from 44 different specialties and 29 countries. The heterogeneity of therapeutic decisions increased as the clinical scenario worsened. Factors associated with aggressive therapeutic decisions were higher self-perceived expertise (high vs. null, OR 1.95, 95%CI 1.31-2.89), perceived quality of COVID-19 publications (high vs. null, OR 1.92, 95%CI 1.17-3.16), and female sex (OR 1.17, 95%CI 1.02-1.33). Conversely, Infectious Diseases specialty, Latin American and North American origin, lower confidence in the treatments chosen, and having published articles indexed in PubMed as the first-author were associated with the use of less aggressive therapies.

Interpretation: Our study provides insight into the drivers of the decision-making process during a new and extreme health emergency. Different factors including the perceived expertise and quality of publications, gender, geographic origin, medical specialty and implication in medical research influenced this process. The clinical severity attenuated the physician's tolerance for uncertainty.

Funding: No funding was required.

Keywords: COVID-19; Clinical decision-making; Surveys and questionnaires; Therapeutics; Uncertainty.

Fig. 1

Main hallmarks of COVID-19 in the context of the survey timeframe [–[19], [20], [21], [22], [23]].

Fig. 2

Two-dimensional correspondence plots representing the correlation between treatment options in each scenario. The closer the arrows (smaller angles) in the orthogonal coordinates, the more correlated are the treatments. Uncorrelated treatments are represented by orthogonal vectors (angles close to 90°). Negatively correlated treatments are represented by vectors with angles close to 180°. The amount of variance explained by each component (or dimension) is presented as a footnote. In all scenarios, this total explained variance is small (lower than 50%).

Fig. 3

Degree of aggressiveness category by scenario.

Fig. 4

Variables associated with the use of more aggressive therapies in the multilevel mixed-effects ordered logistic regression. aOR, adjusted odds ratio; CI, confidence interval; Ref, reference category.