Dataset Information

Failure of vital sign normalization is more strongly associated than single measures with mortality and outcomes.

ABSTRACT:

Background

Modified Early Warning Systems (MEWS) scores offer proxies for morbidity and mortality that are easily acquired, but there are limited data on what changing MEWS scores within the ED indicate. We examined the correlation of changing MEWS scores during resuscitation in the ED and in-hospital morbidity and mortality.

Methods

We conducted a retrospective analysis on medical ED patients with simplified MEWS scores (without urine output or mental status) admitted to a single academic tertiary care center over one year. Triage-to-Last delta MEWS score and Triage-to-Max delta MEWS scores were calculated and correlated to in-hospital mortality, ICU admission, length of stay (LOS) and diagnosis of sepsis.

Results

Our analysis included 8322 ED patients with an ICU admission rate of 17% and a mortality rate of 2%. Every point of worsened MEWS after triage was more strongly associated with all-cause mortality (OR 2.41, 95% CI 1.96-2.97) than triage MEWS alone (OR 1.33, 95% CI 1.23-1.44; p < 0.001). Likewise, each point of worsened MEWS was associated with increased odds of ICU admission (Triage-to-Last: OR 2.12, 95% CI 1.92-2.33 and Triage-to-Max: OR 1.52, 95% CI 1.45-1.60, respectively). Among patients with suspected infection, similar associations are found.

Conclusions

Dynamic vital signs in the emergency department, as categorized by delta MEWS, and failure to normalize abnormalities, were associated with increased mortality, ICU admission, LOS, and the diagnosis of sepsis. Our results suggest that MEWS scores that do not normalize, from triage onward, are more strongly associated with outcome than any single score.

SUBMITTER: Levin N

PROVIDER: S-EPMC6938586 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Pediatric early warning systems using expert-derived vital sign parameters demonstrate limited sensitivity and specificity in identifying deterioration. We hypothesized that modified tools using data-driven vital sign parameters would improve the performance of a validated tool.Retrospective case control.Quaternary-care children's hospital.Hospitalized, noncritically ill patients less than 18 years old. Cases were defined as patients who experienced an emergent transfer to an ICU or out-of-ICU cardiac arrest. Controls were patients who never required intensive care. Cases and controls were split into training and testing groups.The Bedside Pediatric Early Warning System was modified by integrating data-driven heart rate and respiratory rate parameters (modified Bedside Pediatric Early Warning System 1 and 2). Modified Bedside Pediatric Early Warning System 1 used the 10th and 90th percentiles as normal parameters, whereas modified Bedside Pediatric Early Warning System 2 used fifth and 95th percentiles.The training set consisted of 358 case events and 1,830 controls; the testing set had 331 case events and 1,215 controls. In the sensitivity analysis, 207 of the 331 testing set cases (62.5%) were predicted by the original tool versus 206 (62.2%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 191 (57.7%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. For specificity, 1,005 of the 1,215 testing set control patients (82.7%) were identified by original Bedside Pediatric Early Warning System versus 1,013 (83.1%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 1,055 (86.8%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. There was no net gain in sensitivity and specificity using either of the modified Bedside Pediatric Early Warning System tools.Integration of data-driven vital sign parameters into a validated pediatric early warning system did not significantly impact sensitivity or specificity, and all the tools showed lower than desired sensitivity and specificity at a single cutoff point. Future work is needed to develop an objective tool that can more accurately predict pediatric decompensation.