In addition to what metrics to use, there are some additional aspects to be considered when evaluating a specific EWS.
The likelihood ratio can also be considered for the evaluation of an EWS. Likelihood ratios are the multiplier that needs to be applied to the pre-test odds to calculate the post-test odds (the positive or negative likelihood ratio in the case of a positive or a negative result in the test, respectively). These ratios are one step closer to providing a clear cost–benefit analysis, because they only need to be multiplied by a prevalence or event rate to provide an estimation of cost in terms of false alerts. However, they still do not make the tradeoff evident.
Metrics that focus on missed events, such as the negative predictive value, are mainly useful if the intended use of the EWS is to rule out the possibility of physiological deterioration. This does not seem to be the current intended use, which, rather, is to add “an additional layer of early detection” [4, 20].
Reclassification indices can also be considered. These indices can offer good comparisons between two different scores, by showing how many additional patients would be correctly classified as having an event or not when one score is used over another. However, reclassification indices are limited in that they are only able to compare scores one-to-one, and they provide only comparisons, not results in absolute terms: a score may correctly classify double the number of patients, but this does not mean the resulting PPV will be actionable. Reclassification indices do not allow for direct evaluation of the tradeoff between detection and false alerts in absolute terms.
Just as the measures used to evaluate a diagnostic test (e.g., to measure the accuracy of a specific HIV diagnostic test) are different from the evaluation of the strategy (answering the question “does testing blood for HIV reduce infections?”), the pre-implementation metrics discussed in this paper (aimed at evaluating the accuracy of the EWS) are different from post-implementation “success measures” of the strategy (aimed at answering the question “does the use of EWS improve patient outcomes?”).
EWSs are really trying to predict instances of physiological deterioration. Surrogate measures of physiological deterioration include ICU transfers and cardiorespiratory arrests, and some authors also include the calls to the rapid response team. These proxy outcomes vary locally by hospital and patient population, but they are within the same order of magnitude (0.02) so the arguments made in this article still hold true despite those variances. We nonetheless recommend reporting the prevalence of physiological deterioration in studies comparing EWSs.
Our article assumes selection of a threshold to trigger an escalation of care. Threshold selection has been described as a function of the test’s properties (sensitivity and specificity), the prevalence of the condition, and the benefit or harm of identifying or missing the diagnosis of a condition . Different hospitals may have different priorities or constraints that may affect any of these variables, but we believe the metrics should make evident the tradeoff between detection of physiological deterioration and the practice constraints.