Preparing for the Worst-Case Scenario in a Pandemic: Intensivists Simulate Prioritization and Triage of Scarce ICU Resources.

Abstract

Simulation and evaluation of a prioritization protocol at a German university hospital using a convergent parallel mixed methods design. Prospective single-center cohort study with a quantitative analysis of ICU patients and qualitative content analysis of two focus groups with intensivists. Five ICUs of internal medicine and anesthesiology at a German university hospital. Adult critically ill ICU patients ( n = 53). After training the attending senior ICU physicians ( n = 13) in rationing, an impending ICU congestion was simulated. All ICU patients were rated according to their likelihood to survive their acute illness (good-moderate-unfavorable). From each ICU, the two patients with the most unfavorable prognosis ( n = 10) were evaluated by five prioritization teams for triage. Patients nominated for prioritization visit ( n = 10) had higher Sequential Organ Failure Assessment scores and already a longer stay at the hospital and on the ICU compared with the other patients. The order within this worst prognosis group was not congruent between the five teams. However, an in-hospital mortality of 80% confirmed the reasonable match with the lowest predicted probability of survival. Qualitative data highlighted the tremendous burden of triage and the need for a team-based consensus-oriented decision-making approach to ensure best possible care and to support professionals. Transparent communication within the teams, the hospital, and to the public was seen as essential for prioritization implementation. To mitigate potential bias and to reduce the emotional burden of triage, a consensus-oriented, interdisciplinary, and collaborative approach should be implemented. Prognostic comparative assessment by intensivists is feasible. The combination of long-term ICU stay and consistently high Sequential Organ Failure Assessment scores resulted in a greater risk for triage in patients. It remains challenging to reliably differentiate between patients with very low chances to survive and requires further conceptual and empirical research.