Elsevier

Resuscitation

Volume 78, Issue 2, August 2008, Pages 109-115
Resuscitation

Clinical paper
Should age be included as a component of track and trigger systems used to identify sick adult patients?

https://doi.org/10.1016/j.resuscitation.2008.03.004Get rights and content

Summary

Aim of study

Few published “track and trigger systems” used to identify sick adult patients incorporate patient age as a variable. We investigated the relationship between vital signs, patient age and in-hospital mortality and investigated the impact of patient age on the function as predictors of in-hospital mortality of the two most commonly used track and trigger systems.

Materials and methods

Using a database of 9987 vital signs datasets, we studied the relationship between admission vital signs and in-hospital mortality for a range of selected vital signs, grouped by patient age. We also used the vital signs data set to study the impact of patient age on the relationship between patient triggers using the “MET criteria” and “MEWS”, and in-hospital mortality.

Results

At hospital discharge, there were 9152 (91.6%) survivors and 835 (8.4%) non-survivors. As admission vital signs worsened, mortality increased for each age range. Where groups of patients had triggered a certain MET criterion, mortality was higher as patient age increased. Mortality varied significantly with age (p < 0.05; Fishers exact test) for breathing rate >36 breaths min−1, systolic BP < 90 mmHg and decreased conscious level. For each age group, mortality also increased as total MEWS score increased. As the number of simultaneously occurring MEWS abnormalities, or simultaneously occurring MET criteria, increased, mortality increased for each age range.

Conclusions

Age has a significant impact on in-hospital mortality. Our data suggest that the inclusion of age as a component of these systems could be advantageous in improving their function.

Introduction

Simple, physiologically based, “track and trigger “systems [TTS] are now commonly used to assist in the identification of sick or deteriorating, hospitalised patients.1 There are several types of TTS, but the most commonly used are the single-parameter systems [SPTTS], and the aggregate weighted scoring systems [AWTTS].1 SPTTS consist of a range of specific conditions, physiological/pathological abnormalities and other criteria, the occurrence of any of which “triggers” a call for expert help. The rationale for using a SPTTS is that reaching a predetermined value for a given physiological variable presages an adverse outcome (e.g., cardiac arrest, unanticipated ICU admission, death). 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] and the use of AWTTS is based upon the premise that a high or rising early warning score (EWS) also presages an adverse event. However, there are few data about the performance of either type of TTS as predictive tools.

Generally, TTS used for adult patients incorporate heart rate, breathing rate, blood pressure and a measure of level of consciousness, but there is marked variability in the inclusion of other parameters.2, 3 Only four published TTS – all of them AWTTS – describe the inclusion of patient age,4, 5, 6, 7 despite chronological age being an important component of many other outcome prediction scores used in acute or critical care.8, 9, 10, 11, 12, 13, 14, 15, 16 In a recent review of AWTTS, the four scores that best discriminated hospital mortality all included age.3

Portsmouth Hospitals NHS Trust has established a large, and growing, database of vital signs data and outcomes, permitting investigation of TTS and their components. The objectives of this study were twofold. Firstly, to investigate the relationship between vital signs recorded on admission to a Medical Assessment Unit (MAU), patient age and in-hospital mortality in adults. Secondly, to investigate the impact of patient age on the function of the two most commonly used track and trigger systems – one a SPTTS (the Medical Emergency Team [MET] calling criteria17) and the other, an AWTTS (the modified early warning score [MEWS]18) – as predictors of in-hospital mortality.

Section snippets

Materials and methods

Having obtained research ethics committee approval, we collected the vital signs of consecutive adult patients on admission to a bed in the MAU (58 beds) of a large, UK hospital between 08 May 2006 and 31 December 2006. Having excluded those patients who were well enough to be discharged from hospital before midnight on the day of admission, we were left with patients who died in hospital on the day of admission or who stayed in hospital past midnight on their admission day. The MAU admits all

Results

After exclusion of 64 (0.6%) admission vital signs datasets from the analysis, because the submitted physiological values were outside clinically possible ranges, we were left with a final database of 9987 vital signs datasets from 9987 patient episodes. There were 4761 males (47.7%) and 5226 females (52.3%), with mean ages of 65.8 and 75.0 years, respectively. At hospital discharge, 9152 (91.6%) admissions were alive and 835 (8.4%) admissions were dead. 91.9% of males and 91.4% of females

Discussion

Our study shows a strong relationship between the extent of a patient's physiological derangement on admission to a Medical Assessment Unit and the risk of subsequent in-hospital mortality. There were so few deaths in the age group 16–39 years, that it was impossible to determine any relationship. However, for older patients, a worse outcome was more likely to occur if there was a high respiratory or pulse rate, a low systolic blood pressure or their conscious level was abnormal, and mortality

Conclusions

Although absent from most of the commonly used TTS, age has a significant impact on in-hospital mortality. Further work is required to investigate how age should be incorporated appropriately in these trigger systems.

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 Professor Smith and Dr Prytherch are creditors of The Learning Clinic Ltd.

Acknowledgements

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

References (30)

  • H. Gao et al.

    Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward

    Intens Care Med

    (2007)
  • C. Subbe et al.

    ASSIST: a screening tool for critically ill patients on general medical wards

    Intens Care Med

    (2002)
  • C.P. Subbe et al.

    Validation of a modified Early Warning Score in medical admissions

    QJM

    (2001)
  • C.P. Subbe et al.

    Reproducibility of physiological track-and-trigger warning systems for identifying at-risk patients on the ward

    Intens Care Med

    (2007)
  • Bakir A, Duckitt R, Buxton-Thomas R, et al. A simple physiological scoring system for medical in-patients derived by...
  • Cited by (0)

    A Spanish translated version of the summary of this article appears as Appendix in the final online version at doi:10.1016/j.resuscitation.2008.03.004.

    View full text