Understanding and using confidence intervals in clinical research

The Modified Early Warning Score as a Predictive Tool During Unplanned Surgical Intensive Care Unit Admission

Background: The Modified Early Warning Score (MEWS) has been proposed to warn healthcare providers of potentially serious adverse events. We evaluated this scoring system during unplanned escalation of care in hospitalized surgical patients during a 1-year period.

Methods: Following institutional review board approval, all consecutive, unplanned surgical admissions into the surgical intensive care unit (SICU) during 2016 were entered into this study. MEWS and patient demographics during bedside evaluation for SICU admission were extracted from electronic medical records. Logistic regression was used to analyze the association of MEWS with the incidence of future mortality. P values were set at <0.01 for statistical significance.

Results: In this series of 263 consecutive patients, the incidence of mortality following unplanned escalation of care was 29.3% (confidence interval [CI] 24.1% to 35.0%), ranging from 22% to 57%, with all positive MEWS values. The association of MEWS with future mortality was not statistically significant (P=0.0107). A misclassification rate of 0.29 (CI 0.24 to 0.35) was observed with this association.

Conclusion: MEWS provided no clinical benefit as an early warning system, as mortality was elevated throughout the MEWS scale in this clinical setting. The high misclassification rate indicates MEWS does not provide discriminatory support for patients at risk for mortality.

A practical guide for understanding confidence intervals and P values

The 95 percent confidence interval about the mean demarcates the range of values in which the mean would fall if many samples from the universal parent population were taken. In other words, if the same observation, experiment, or trial were done over and over with a different sample of subjects, but with the same characteristics as the original sample, 95 percent of the means from those repeated measures would fall within this range. This gives a measure of how confident we are in the original mean. It tells us not only whether the results are statistically significant because the CI falls totally on one side or the other of the no difference marker (0 if continuous variables; 1 if proportions), but also the actual values so that we might determine if the data seem clinically important. In contrast, the P value tells us only whether the results are statistically significant, without translating that information into values relative to the variable that was measured. Consequently, the CI is a better choice to describe the results of observations, experiments, or trials.