Transcript The discriminatory effect of Manchester Triage System: How

Manchester Triage System:
How does it affect hospital mortality?
PROTOCOL
SERVIÇO DE BIOESTATÍSTICA E INFORMÁTICA MÉDICA – INTRODUÇÃO À MEDICINA
SUMMARY
 Introduction
 Research Question
 Objectives
 Methods and Participants
 Statistical Analysis
 Discussion
INTRODUCTION
Background
 The contribution of
Dominique Larrey was very
important to the creation of
triage systems. 1
 MTS was introduced in UK
in 1996.
 Nowadays it is widespread
especially in Europe. 2
1 - Iserson KV, Moskop JC. Triage in medicine, part I: Concept, history, and types. Ann Emerg Med. March 2007; 49 (3): 275–81.
2- Martins HM, Cuña LM, Freitas P. Is Manchester (MTS) more than a Triage System? A study of its association with mortality and admission to a large
Portuguese Hospital. Emerg. Med. J.. March 2009; 26(3): 183-186.
Manchester
Triage System
What is it?
Manchester
Triage System
Monitorize
emergency
activity
Adequate
measures
MTS
To
prioritize
patients
3- Mackway-Jones K, Marsden J, Windle J. Emergency Triage. Blackwell Publishing. 2006; 2nd ed.
Waiting
time
Manchester
Triage System
How does it
work?
Manchester
Triage System
Inclusion
Questions
Objective
Observation
Color
Assignment
4- George S, Read S, Westlake L, Fraser-Moodie A, Pritty P, Williams B. Differences in priorities assigned to patients by triage nurses and by consultant
physicians in accident and emergency departments. J Epidemiol Community Health. 1993; 4:312-5.
Manchester
Triage System
Main aims:
 Prioritize emergencies 5 ;
 Treat and care emergency patients efficiently;
 Decrease mortality rate. 6
5- Dong SL, Bullard MJ, Meurer DP, Blitz S, Ohinmaa A, Holroyd BR. Reliability of computerized emergency triage. Acad Emerg Med. 2006; 3:269-75.
6- Maconochie I, Dawood M. Manchester Triage System in pediatric emergency care. BMJ. 2008; 337: 1507.
Evaluation
Is there a way to
determine how good
triage is?
Evaluation
The evaluation of Manchester Triage
System is very important and relevant since
it enfolds people’s health.
7
7- Maconochie I, Dawood M. Manchester Triage System in pediatric emergency care. BMJ. 2008; 337: 1507.
Evaluation
Example of a study evaluating MTS
Conclusion: mortality is related with the emergency
of the case.
8
However, there are no studies that relate the rate of
mortality with every emergency level and the time of wait in
the emergency room, in a Portuguese hospital using MTS.
8- Van der Wulp I, Schrijvers AJ, van Stel HF. Predicting admission and mortality with the Emergency Severity Index and the Manchester Triage
System: a retrospective observational study. Emerg Med J. 2009 Jul; 26(7):506-9.
RESEARCH QUESTION
Is MTS correctly
optimized to decrease
mortality?
OBJECTIVES
Objectives

Analyze the rate of mortality in the Emergency
room by priority;

Study the relationship between waiting times
and the rate of mortality.
METHODS
AND
PARTICIPANTS
PARTICIPANTS
Target Population:
 Adult population attending in emergency
services with MTS.
 All patients attended in a convenient hospital
between a specific period.
9- Kumar R.. Research Methodology – A step-by-step guide for beginners. SAGE Publications. 1996; 147-166.
PARTICIPANTS
Inclusion Criteria:
 Patients attending the emergency cares of
“Santa Maria da Feira” Hospital between
October's 1st of 2005 and September's 30th of
2008;
 Age over 16 years.
Study Design
Study defined as:
Statistical analysis with:
 Observational;
 SPSS 17.0 program 10;
 Retrospective;
 Microsoft Excel;
 Transversal;
 Microsoft Visio:
Flowcharts.
 Analytical.
10- SPSS for Windows, Rel. 15.0.0.2006. Chicago (IL): SPSS Inc.
20
General
methods
Variables of data:
 Emergency;
 Discharge ;
 Birth date;
 Hospitalization;
 Gender;
 Result of Hospitalization;
 Priority;
 Date of triage;
 Flowchart;
 Medical Observation;
 Result;
 Discharge;
 Ward;
 Readmission in 48h/72h.
METHODS
Variables created:
 Patients’ age;
 Waiting Time;
 Final Result;
 Out of Time.
METHODS
Problems:
 Several missing cases (we could only calculate
waiting time in half of the patients);
 Absurd results:
o Negative waiting times;
o Patients waited too long (we excluded
cases when patients waited more 24
hours).
STATISTICAL
ANALYSIS
Patient sex
The database has a total of 336526 records.
42,1%
57,9%
Graph 1: Pie Chart - Percentage of Patient’s Sex
Female
Male
Distribution
of age
Median = 43 years
Minimum= 16 years
Maximum= 106 years
Graph 2: Histogram - Distribution of Frequencies by Age
Colour
48.8 %
Red = 1146 patients
Orange = 27490 patients
Yellow = 126474 patients
Green = 164294 patients
37.6 %
Blue = 4318 patients
White = 12455 patients
8.2 %
0.4 %
13 %
3.7 %
Graph 3: Histogram - Distribution of patients by priority
Statistical
Analysis
Mortality in n(%)
Died
Total
1781 (0,5)
336526 (100)
Table 1: The Total Rate of
Mortality in ER and
Inpatient Service
Female
Male
Mortality in n(%)
812 (0,2)
969 (0,3)
Table 2: Total Rate of Mortality by
Sex
Qui-squared Test, p<0,001
This only happens because the database has a wide number
of cases, making the differences statistically significative.
Statistical
Analysis
Graph 4: BoxPlot – Mortality by Age
We observed that the median of the dead patients’ ages is 77,
while the one of those who have survived is 43.
Statistical
Analysis
Colour
Expected
Mortality in Mortality in
Mortality in
Emergency
Inpatient
Emergency
Room
Service
Room 11
n (%)
Red
416 (29)
71 (4,9)
245 (10)
Orange
199 (1)
522 (1,9)
32 (0,04)
Yellow
81 (0,1)
430 (0,34)
6 (0,003)
Green
6 (0,004)
28 (0,002)
1 (0,002)
Blue
0 (0)
1 (0,02)
0(0)
Table 3: Rate of Mortality by Colour in The Emergency Room and
in Inpatient Service and Expected Mortality in ER
11- Martins H. M. G., Castro L. M., Dominguez Cuña, Freitas P. Is Manchester (MTS) more than a triage system? A study of its association with mortality and
admission to a large Portuguese hospital. Emerg Med J. 2009; 26: 183-186.
Statistical
Analysis
Comparison between the rate of Mortality
in the Emergency Room and the expected one
By analysing the relative frequencies we can conclude that:
× there are more deaths in the red color rather than in any
other one, not only in the database that was analyzed by
our group, but also in previous studies;
× the highest emergency levels have a higher rate of
mortality than the lowest ones in both studies.
Statistical
Analysis
Mortality
n(%)
Died
In Time
Out of Time
26 (3,1)
808 (97)
Survived 57326 (39)
90862 (61)
Table 4: Mortality in Inpatient Service
Statistical
Analysis
Colour
Mortality in
Emergency
Room
Mortality in
Inpatient
Service
n (%)
Red
Orange
Yellow
Green
Blue
IN TIME
7 (100) *
0 (0,0)
OUT OF TIME
130 (14) *
61 (7)
IN TIME
7 (2) *
2 (0,6) *
OUT OF TIME
136 (0,8) *
414 (2) *
IN TIME
5 (0,0) *
21 (0,1) *
OUT OF TIME
49 (0,1) *
314 (0,5) *
IN TIME
0 (0,0)
2 (0,0) *
OUT OF TIME
1 (0,0)
19 (0,1) *
IN TIME
0 (0,0)
1 (0,1)
OUT OF TIME
0 (0,0)
0 (0,0)
Table 5: Rate of Mortality according Priority Level and Waiting Times.
The results are statistically significant when *p< 0,05.
Statistical
Analysis
Year
Total Episodes
Mortality
2005
26828
155 (0,6)
2006
120214
583 (0,5)
2007
103267
581 (0,6)
2008
86217
462 (0,5)
Table 6: Rate of Mortality by year.
Pearson Chi-Square Test: p=0,047.
Statistical
Analysis
 In the Inpatient Service, 23567 (7%) episodes
were registered.
 There is a 0,3% rate of mortality in this service.
DISCUSSION
DISCUSSION
Rate of mortality vs age and gender:
 A higher rate of mortality in elderly
population;
 Because of the large number of cases, the
differences between genders are statistically
significative.
DISCUSSION
The evolution in mortality rate:
 There was no significative evolution or decrease
of mortality rate in the Urgency Service, as it
remains similar for the three years.
 We can conclude that internment service
quality has been the same for that period of
time, which means that possible triage errors
were not fixed.
DISCUSSION
Who has the red color:
 has a serious illness;
 needs more healthcares;
 needs faster treatment;
 has a higher probability of dying.
We verified a higher rate of
mortality in the red color, as
expected.
DISCUSSION
Who has the red color:
 Waiting time according to MTS: 0 min;
 The majority of cases in our database
exceeded this time.
DISCUSSION
Rate of mortality per color in inpatients service:
 The fact that patients exceed or not the waiting
time purposed by MTS does not make any
difference in red (p=0,618) and blue (p=0,944)
colors.
 In green, yellow and orange colors, we saw that
exceeding or not exceeding the waiting time
makes difference because p<0,05.
DISCUSSION
 MTS is been functioning right, but still has a lot
to improve: when patients are attributed with
a less urgent level of priority they do not expect
to die.
 If the waiting time is determined, it must be
respected, to decrease mortality efficiently.
DISCUSSION
 The rate of mortality may vary because it is
predicted the hospital quality system to
improve, due to the advances of medicine.
DISCUSSION
 We hope this information to be useful in
posterior studies, which can lead to the
optimization of MTS.
 To evaluate and to improve the Manchester
Triage System, the insertion of data in hospital’s
data bases has to be more accurate.
WORK ACCOMPLISHED BY:
 Ana Isabel Gonçalves Ferreira
[email protected]
 Bárbara Ferreira Mendes
[email protected]
 Carolina Botelho Amaral Rebelo Félix
 Catarina Torres Monteiro
[email protected]
[email protected]
 Cláudia Inês de Sousa Amorim Sampaio da Costa
 João Tiago Ramos Paulino
[email protected]
[email protected]
 Mariana Andreia Guimarães Rocha
[email protected]
 Pedro Nuno de Frias Marques Gonçalves [email protected]
 Tânia Raquel Sousa Rodrigues
[email protected]
 Tatiana Filipa Martins de Melo
[email protected]
 Vera Lúcia da Rocha Teixeira
[email protected]
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