Development of an algorithm to aid triage decisions for intensive care unit admission: a clinical vignette and retrospective cohort study

Abstract

BackgroundIntensive care unit (ICU) admission triage is performed routinely and is often based solely on clinical judgment, which could mask biases. A computerized algorithm to aid ICU triage decisions was developed to classify patients into the Society of Critical Care Medicine’s prioritization system. In this study, we sought to evaluate the reliability and validity of this algorithm.MethodsNine senior physicians evaluated forty clinical vignettes based on real patients. The reference standard was defined as the priorities ascribed by two investigators with full access to patients’ records. Agreement of algorithm-based priorities with the reference standard and with intuitive priorities provided by the physicians were evaluated. Correlations between algorithm prioritization and physicians’ judgment of the appropriateness of ICU admissions in scarcity and nonscarcity settings were also evaluated. Validity was further assessed by retrospectively applying this algorithm to 603 patients with requests for ICU admission for association with clinical outcomes.ResultsAgreement between algorithm-based priorities and the reference standard was substantial, with a median κ of 0.72 (interquartile range [IQR] 0.52–0.77). Algorithm-based priorities demonstrated higher interrater reliability (overall κ 0.61, 95 % confidence interval [CI] 0.57–0.65; median percentage agreement 0.64, IQR 0.59–0.70) than physicians’ intuitive prioritization (overall κ 0.51, 95 % CI 0.47–0.55; median percentage agreement 0.49, IQR 0.44–0.56) (p = 0.001). Algorithm-based priorities were also associated with physicians’ judgment of appropriateness of ICU admission (priorities 1, 2, 3, and 4 vignettes would be admitted to the last ICU bed in 83.7 %, 61.2 %, 45.2 %, and 16.8 % of the scenarios, respectively; p < 0.001) and with actual ICU admission, palliative care consultation, and hospital mortality in the retrospective cohort.ConclusionsThis ICU admission triage algorithm demonstrated good reliability and validity. However, more studies are needed to evaluate a difference in benefit of ICU admission justifying the admission of one priority stratum over the others.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-016-1262-0) contains supplementary material, which is available to authorized users.

Evaluation of bed vacancy and occupancy times as governance parameters in patients admitted to a public intensive care unit

“…This finding differs from other studies, in which longer times for processes performed at night and on weekends were observed, explained by fewer staff in those periods. ( 8 - 11 ) However, the of vacancy-to-occupancy time increased when the bed was offered on holidays, in line with the cited studies.…”

Burden of hospitalisation among older people in the Brazilian public health system: a big data analysis from 2009 to 2015

“…A possible explanation for this is the scarcity of ICU beds in the SUS, leading the hospitals to use screening criteria to admit patients to ICU beds 22. Since most of these triage tools used age-related comorbidities (eg, cancer and decompensated chronic diseases) and functionality status to prioritise patients for ICU admission, it is expected that older patients, especially the very older (≥80 years), will receive a lower priority than younger patients 23.…”

ICU physicians’ and internists’ survival predictions for patients evaluated for admission to the intensive care unit

“…In our study higher accuracy in prediction is likely explained by the fact that physicians estimated survival for patients they had actually assessed. We could not compare physicians’ performance with a scoring system as no validated score exists for estimating patient prognosis at the time of triage [ 10 , 11 ]. However, physicians’ clinical judgment has been shown to be at least as accurate as objective risk scores for predicting mortality in patients admitted to the ICU [ 5 ] and for predicting medical in-patients’ deterioration [ 12 ].…”