Elsevier

Resuscitation

Volume 77, Issue 2, May 2008, Pages 170-179
Resuscitation

Review paper
Review and performance evaluation of aggregate weighted ‘track and trigger’ systems

https://doi.org/10.1016/j.resuscitation.2007.12.004Get rights and content

Summary

Objectives

There is no up-to-date literature review of physiologically based, aggregate weighted ‘track and trigger’ systems (AWTTS) and few data on their predictive ability for serious adverse outcomes. The aim of this study was to describe the AWTTS in clinical use and assess their ability to discriminate between survivors and non-survivors of hospital admission, based on an initial set of vital signs.

Materials and methods

A systematic review of the literature was performed, to describe the AWTTS, their components and their differences. Their ability to discriminate between survivors and non-survivors was evaluated using the area under the receiver-operating characteristics (AUROC) curve, and a database of 9987 vital signs datasets.

Results

A total of 33 unique AWTTS were identified with AUROC (±95% CI) ranging from 0.657 (0.636–0.678) to 0.782 (0.767–0.797). 12 AWTTS (36%) discriminated reasonably well between survivors and non-survivors, the top four performing AWTTS incorporated age as a component (AUROCs ranging from 0.722 to 0.782). The top two systems also incorporated temperature.

Conclusions

There is a wide range of unique, but very similar, AWTTS in clinical use. There is no consistency regarding their physiological components, but the majority differ only in minor variations in the weightings for physiological derangement and/or the cut-off points between physiological weighting bands. The performance of most systems tested was poor when used to discriminate between survivors and non-survivors, although 36% discriminated reasonably well. Our results suggest that physiology can be used to predict outcome, but that further work is required to improve the AWTTS models.

Introduction

Many simple, physiologically based, ‘track and trigger’ systems (TTS) have been developed to facilitate the early identification and management of at-risk or deteriorating adult patients, and to predict adverse clinical outcomes.1 These can be categorised as single-parameter systems, multiple-parameter systems, aggregate weighted scoring systems or combination systems.1 Aggregate weighted TTS (AWTTS) allocate points in a weighted manner, based on the derangement of patients’ vital signs variables from an arbitrarily agreed ‘normal’ range. The sum of the allocated points is known as the early warning score (EWS). This is used to direct care, e.g. to increase vital signs monitoring, to involve more experienced staff or to call a rapid response team (e.g. outreach or medical emergency team). The variables that are allocated points in AWTTS typically include pulse rate, breathing rate, blood pressure and a measure of level of consciousness. There is variation in the other variables included, the weightings assigned and the thresholds for triggering specific responses. With few exceptions, the inclusion of variables and their weightings are solely based on clinical experience and intuition.

The performance of some AWTTS has been studied in groups of unselected patients2, 3, 4 early on in their hospital stay, but the majority of AWTTS have either not been validated or have only been assessed in small studies where patients have experienced a specific clinical outcome, e.g. intensive care unit (ICU) admission or visit by a rapid response team. There is no clear indication which is the best performing AWTTS but, generally, they have low sensitivities and positive predictive values, and acceptable specificities and negative predictive values.5 The rationale for using an AWTTS is that a high or rising EWS heralds an adverse outcome (e.g. cardiac arrest, unanticipated ICU admission or death). However, the exact relationship between EWS values and subsequent outcomes has been difficult to establish because of the intermittent nature of most ward-based monitoring and the lack of completeness of the vital signs datasets. As the first part of a stepwise approach to analysing this relationship, this study was designed to review the published AWTTS and evaluate their ability to discriminate between survivors and non-survivors of hospital admission, based on an initial set of vital signs recorded in a medical assessment unit (MAU).

Section snippets

Vital signs database

Local research ethics committee approval was obtained before the commencement of the study. A vital signs database was developed from clinical data obtained from consecutive patients on admission to a bed in the MAU (58 beds) of a large hospital in the UK between 8 May 2006 and 31 December 2006. Having excluded those people who were well enough to be discharged from hospital before midnight on the day of admission, we were left with those who died in hospital or who stayed in hospital past

Review of AWTTS

Our literature search identified 59 publications which described a single AWTTS,3, 4, 6, 10, 13, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77 five publications that described two AWTTS22, 78, 79, 80, 81 and one that described three AWTTS.82 Therefore, we identified a total of 65 publications and 72 AWTTS. Of the 72 AWTTS, 23

Discussion

We analysed the ability of 35 AWTTS to discriminate between hospital survivors and non-survivors, based on vital signs data collected on admission to a MAU. Some might question the hypothesis that data taken early in a person's admission could be a strong determinant of hospital outcome some days later. However, admission vital signs,3, 4 early laboratory data83, 84, 85, 86 and/or combinations of these87 do appear to have a strong predictive ability. Overall, only 36% of AWTTS tested here

Conclusions

There is wide range of unique, but very similar, AWTTS in clinical use. There is no consistency regarding their physiological components, but the majority differ only in minor variations in the weightings for physiological derangement and/or the cut-off points between physiological weighting bands. The performance of most systems tested was poor when used to discriminate between survivors and non-survivors, although 36% discriminated reasonably well. Our results suggest that physiology can be

Conflict of interest

The electronic vital signs data gathering system used in this study, VitalPAC™, is a collaborative development of The Learning Clinic Ltd. and Portsmouth Hospitals NHS Trust. Dr Paul Schmidt and the wives of both Prof. Smith and Dr Prytherch are creditors of The Learning Clinic Ltd.

Acknowledgements

The authors would like to acknowledge the cooperation of the nursing and medical staff of the Medical Assessment Unit of Portsmouth Hospitals NHS Trust.

The study was not supported by any financial grant.

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    A Spanish translated version of the summary of this article appears as Appendix in the final online version at doi:10.1016/j.resuscitation.2007.12.004.

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