Elsevier

Resuscitation

Volume 102, May 2016, Pages 1-5
Resuscitation

Clinical paper
The value of vital sign trends for detecting clinical deterioration on the wards

https://doi.org/10.1016/j.resuscitation.2016.02.005Get rights and content

Abstract

Aim

Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient's current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends.

Methods

Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC).

Results

A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p < 0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC −0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05).

Conclusion

Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores.

Introduction

Early detection of critical illness is key to achieving timely transfer to the intensive care unit (ICU) and decreasing the rate of preventable in-hospital cardiac arrest. Vital signs have been shown to be the most accurate predictors of clinical deterioration.1 Early warning scores consisting of vital sign severity thresholds have been implemented across the United States and around the world in order to accurately detect high-risk ward patients.2, 3 These scores typically utilize only the current vital sign values and rarely include trends of vital signs over time.3, 4 Although clinicians often include the trend in a patient's condition over time when assessing a patient, the additional value of vital sign trends to risk scores containing a patient's current values is poorly characterized, but has the potential to increase accuracy and decrease false alarms.

Although the idea of including vital sign trends in early warning scores sounds intuitive and straightforward, the low frequency of monitoring (e.g., every four hours), interventions provided to patients, and manual assessment of some of the variables add additional complexity. For example, treatments are often administered in an attempt to “normalize” vital signs, such as acetaminophen for fever and fluid boluses for hypotension. In addition, vital signs may be collected soon after a patient was ambulatory, which may cause a patient to meet the systemic inflammatory response criteria, or may not be accurately quantified, such as always inputting a respiratory rate of 18.5, 6 Therefore, simply including the change of a vital sign since last collection may not adequately capture a patient's true physiologic trajectory and additional methods, such as including vital sign variability, the most deranged previous values, and even smoothing the trajectory, may prove to be more accurate.

The aim of this study was to utilize a large, multicentre dataset to compare the accuracy of different methods of modelling vital sign trends for detecting clinical deterioration on the wards.

Section snippets

Study population and data sources

The study population and data sources have been described previously.1, 7 Briefly, we included all ward patients at the University of Chicago and four NorthShore University HealthSystem hospitals between November 2008 and January 2013. Patient vital sign data, which were both time- and location-stamped, were obtained from the Electronic Data Warehouse at NorthShore and the electronic health record (EPIC, Verona, WI) at the University of Chicago. Demographic information was obtained from

Results

A total of 269,999 patient admissions were included in the study, which resulted in 16,452 outcomes (424 ward cardiac arrests, 13,188 ICU transfers, and 2840 deaths on the ward) occurring during the study period. Our study population was 60% female, 52% white, and had an average age of 60 years. Additional details have been described elsewhere.1, 14

During univariate analysis, respiratory rate was the most accurate vital sign when using the current value (AUC 0.70 (95% CI 0.70–0.70)), and the

Discussion

In this large, multicentre study evaluating the value of vital sign trends, we found that trajectories of these variables significantly improved the accuracy of detecting clinical deterioration compared to the current vital sign values alone. The optimal method of modelling trend varied across the different vital signs. Importantly the simplest method, taking the difference from the previous value, was the least accurate method of modelling trends of the different techniques we studied. Methods

Conclusions

In this large, multicentre study, we found that adding trends of vital signs significantly increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians interpreting vital sign trends at the bedside, as well as for the development of early warning scores. Accuracy is paramount with these scores in order to get the right people to the bedside while avoiding alarm fatigue, and our study shows that trends in physiology

Author contributions

Study concept and design: M.C., D.P.E.; acquisition of data: D.P.E.; analysis and interpretation of data: all authors; first drafting of the manuscript: M.C.; critical revision of the manuscript for important intellectual content: all authors; statistical analysis: R.A., M.C.; obtained funding: M.C., D.P.E.; administrative, technical, and material support: all authors; study supervision: M.C., D.P.E.; Dr. Churpek had full access to all the data in the study and takes responsibility for the

Conflict of interest statement

This research was funded in part by an institutional Clinical and Translational Science Award grant (UL1 RR024999; PI: Solway). Dr. Churpek is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080). Drs. Churpek and Edelson have a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. In addition, Dr. Edelson has received research support and honoraria from Philips Healthcare (Andover, MA), research

Acknowledgments

This research was funded in part by an institutional Clinical and Translational Science Award grant (UL1 RR024999; PI: Solway). Dr. Churpek is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080).

We would like to thank Timothy Holper, Justin Lakeman, and Contessa Hsu for assistance with data extraction and technical support, PoomeChamnankit, MS, CNP, Kelly Bhatia, MSN, ACNP, and Audrey Seitman, MSN, ACNP for performing manual chart review of

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