Open AccessEvaluation of a hypothetical decision-support tool for intensive care triage of patients with coronavirus disease 2019 (COVID-19)
Author(s) -
Emily Simon Thomas,
Bryony Peiris,
Leon Di Stefano,
Matthew Rowland,
Dominic Wilkinson
Publication year - 2021
Publication title -
wellcome open research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.298
H-Index - 21
ISSN - 2398-502X
DOI - 10.12688/wellcomeopenres.16939.1
Subject(s) - medicine , triage , intensive care unit , confidence interval , pandemic , covid-19 , emergency medicine , intensive care medicine , disease , infectious disease (medical specialty)
Background: At the start of the coronavirus disease 2019 (COVID-19) pandemic there was widespread concern about potentially overwhelming demand for intensive care and the need for intensive care unit (ICU) triage. In March 2020, a draft United Kingdom (UK) guideline proposed a decision-support tool (DST). We sought to evaluate the accuracy of the tool in patients with COVID-19. Methods: We retrospectively identified patients in two groups: referred and not referred to intensive care in a single UK national health service (NHS) trust in April 2020. Age, Clinical Frailty Scale score (CFS), and co-morbidities were collected from patients’ records and recorded, along with ceilings of treatment and outcome. We compared the DST, CFS, and age alone as predictors of mortality, and treatment ceiling decisions. Results: In total, 151 patients were included in the analysis, with 75 in the ICU and 76 in the non-ICU-reviewed groups. Age, clinical frailty and DST score were each associated with increased mortality and higher likelihood of treatment limitation (p-values all 8 had 65% (95% confidence interval (CI) 51%-79%) sensitivity and 63% (95% CI 54%-72%) specificity for predicting mortality. It had a sensitivity of 80% (70%-88%) and specificity of 96% (95% CI 90%-100%) for predicting treatment limitation. The DST was more discriminative than age alone (p<0.001), and potentially more discriminative than CFS (p=0.08) for predicting treatment ceiling decisions. Conclusions: During the first wave of the COVID-19 pandemic, in a hospital without severe resource limitations, a hypothetical decision support tool was limited in its predictive value for mortality, but appeared to be sensitive and specific for predicting treatment limitation.