body temperature of trauma patients on admission to hospital

Body temperature of trauma patients on admission to hospital:

a comparison of anaesthetised and non-anaesthetised patients

Emerg Med J 2010;emj.2009.086967

Published Online First: 20 October 2010

Background

• Hypothermia at admission independently predicts mortality in severe trauma irrespective of:– ISS– Shock– Other coexisting factors

• Induction of general anaesthesia typically results in a 1°C fall in core temperature within 30 minutes

• Researchers suspected that anaesthetised patients with trauma would be at high risk of hypothermia

Objective 1

To determine the incidence of hypothermia in prehospital trauma patients

P prehospital patientsE traumaO incidence of hypothermia

Research question – What is the incidence of hypothermia among prehospital patients with trauma?

Objective 2

To establish if trauma patients anaesthetised in the prehospital phase have a higher rate of hypothermia at admission than non-anaesthetised patients

P prehospital trauma patientsI anaesthetisedC not anaesthetisedO rate of hypothermia on ED admission

Research question - Among prehospital trauma patients is there a higher rate of hypothermia on admission to ED in anaesthetised patients compared to non-anaesthetised patients?

Methods

• Retrospective review• 1292 consecutive prehospital trauma patients• 494 tympanic body temperatures recorded on ED

admission– 207 RSI (anaesthetised)– 287 Non-RSI (non-anaesthetised)

• Tympanic thermometers calibrated every 3 months• SPSS V16.0, normality assumed, Student’s t test or one

way ANOVA for numerical data (Kruskal-Wallis used for NP data); χ2 test for proportions; α=0.05

“For patients with a temperature recorded (n=494), a higher percentage of patients with CNS injury (AIS for head ≥3) was found for the RSI group than for the non-RSI group.”“The percentage of patients with ISS>15 was higher for the RSI group than for the non-RSI group (n=1292?).”

RSI patients…

were older on average,were more seriously injured on average,experienced longer on-scene times on average,were more likely to be elderly,were more likely to have had a serious head injury,were more likely to have sustained a serious or critical injury,

when compared to their non-RSI counterparts

All cases in which body temp was recorded (n=474)

Q3

Median (Q2)

Q1

More than 3xIQRFrom Q1

Maximum valueThat lies within1.5xIQR from Q3

Minimum valueThat lies within1.5xIQR from Q1

Between 1.5 and3xIQR from Q1

RSI group (n=207)

“No significant seasonal body temperature variation was demonstrated”“For our data, no robust seasonal body temperature variation was found, which is in accordance with previous observations.”

Findings

“Patients anaesthetised in the prehospital phase of care had a significantly lower admission body temperature.”Based on a comparison of means

35.0 vs 36.2°C, p<0.001“No significant seasonal body temperature variation was demonstrated.”Based on a comparison of medians

35.0 vs 36.3°C, p=0.05

Did this study produce valid findings?

There are broadly three reasons why findings may not be valid – 1) Chance– 2) Bias– 3) Confounding

Chance

The measurements we make while doing research are nearly always subject to random variation. Determining whether findings are due to chance is a key feature of statistical analysis. The best way to avoid error due to random variation is to ensure your sample size is adequate.Could the findings of this study have been affected by chance?

Bias

• Whereas chance is caused by random variation, bias is caused by systematic variation. A systematic error in the way we select our patients, measure our outcomes, or analyse our data will lead to results that are inaccurate. There are numerous types of bias that may affect a study.

Types of bias

• These can broadly be divided into three categories -

1) Selection bias

2) Measurement bias

3) Analysis bias

1) Selection bias

• The selection of subjects into your sample or their allocation to treatment group produces a sample that is not representative of the population, or treatment groups that are systematically different. Random selection and random allocation are the keys to avoiding this bias.

• Could the findings of this study have been affected by selection bias?

2) Measurement bias

• Measurement of outcomes is inaccurate. This may be due to inaccuracy in the measurement instrument or bias in the expectations of study participants, carers or researchers. The latter may be addressed by blinding participants, carers or researchers.

• Could the findings of this study have been affected by measurement bias?

Confounding

One definition of confounding (R McNamee)

Bias in the estimation of the effect of exposure on disease, due to inherent differences in risk between exposed and unexposed groups

What is a confounder?

A confounder is a factor that is prognostically linked to the outcome of interest and is unevenly distributed between the study groups.

Random allocation to study groups is the best way to control for confounding

Example…

A classic example of confounding is to interpret the finding that people who carry matches are more likely to develop lung cancer as evidence of an association between carrying matches and lung cancer. Smoking is a confounder in this relationship - smokers are more likely to carry matches and they are also more likely to develop lung cancer.

Did this study have confounders?

• External temperature

• Severity of injury

• Scene time

• Age

• CNS trauma

• Gender

Known confounders

Dealing with confounding is relatively easy if you know what the likely confounders are. You could stratify your results - i.e. in our previous example you could analyse smokers and non-smokers separately, or you could use statistical techniques to adjust for confounding.Could the findings of this study have been affected by known confounders?

Unknown confounders

Dealing with unknown confounders is obviously much trickier. There is always a risk that an apparent association between a risk factor, or an intervention, and an outcome is being mediated by an unknown confounder. This is particularly true of observational studies where patients may be selected to one treatment group or another, not according to any explicit criteria, but by some unknown process, such as a care providers 'gut feeling'. The best defence against unknown confounders is randomisation. This ensures that both known and unknown confounders are randomly distributed between treatment groups.Could the findings of this study been affected by unknown confounders?

Did this research produce valid findings?

“Patients anaesthetised in the prehospital phase of care had a significantly lower admission body temperature.”

“No significant seasonal body temperature variation was demonstrated.”– 1) Chance– 2) Bias– 3) Confounding

Research questions re-visited

• What is the incidence of hypothermia among prehospital trauma patients?

• Among prehospital trauma patients is there a higher rate of hypothermia on admission to ED in anaesthetised patients compared to non-anaesthetised patients?

Can the results of this study be applied to our patients?