1. M anchester T riage S ystem: How does it affect hospital mortality? INTRODUÇÃO À MEDICINA SERVIÇO DE BIOESTATÍSTICA E INFORMÁTICA MÉDICA – INTRODUÇÃO À MEDICINA PROTOCOL

2. SUMMARY  Introduction  Research Question  Objectives  Methods and Participants  Statistical Analysis  Discussion

3. INTRODUCTION

4. 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.

5. Manchester Triage System What is it?

6. MTS Adequate measures Waiting time To prioritize patients Monitorize emergency activity 3- Mackway-Jones K, Marsden J, Windle J. Emergency Triage. Blackwell Publishing. 2006; 2nd ed. Manchester Triage System

7. How does it work?

8. InclusionQuestions 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

9. Main aims:  Prioritize emergencies 5 ;  Treat and care emergency patients efficiently;  Decrease mortality rate. 6 Manchester Triage System 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.

10. Evaluation Is there a way to determine how good triage is?

11. 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.

12. Evaluation 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. Example of a study evaluating MTS Conclusion: 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.

13. RESEARCH QUESTION Is MTS correctly optimized to decrease mortality? Is MTS correctly optimized to decrease mortality?

14. OBJECTIVES

15.  Analyze the rate of mortality in the Emergency room by priority;  Study the relationship between waiting times and the rate of mortality. Objectives

16. METHODS AND PARTICIPANTS

17. 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

18. 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. PARTICIPANTS

19. Study defined as:  Observational;  Retrospective;  Transversal;  Analytical. Statistical analysis with:  SPSS 17.0 program 10 ;  Microsoft Excel;  Microsoft Visio: Flowcharts. 10- SPSS for Windows, Rel. 15.0.0.2006. Chicago (IL): SPSS Inc. Study Design

20. Variables of data:  Emergency;  Birth date;  Gender;  Priority;  Flowchart;  Result;  Ward; 20  Discharge ;  Hospitalization;  Result of Hospitalization;  Date of triage;  Medical Observation;  Discharge;  Readmission in 48h/72h. Generalmethods

21. Variables created: METHODS  Patients’ age;  Waiting Time;  Final Result;  Out of Time.

22. 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). METHODS

23. STATISTICAL ANALYSIS

24. Female Male Patient sex 57,9% 42,1% The database has a total of 336526 records. Pie Chart Graph 1: Pie Chart - Percentage of Patient’s Sex

25. Distribution of age Histogram Graph 2: Histogram - Distribution of Frequencies by Age Median = 43 years Minimum= 16 years Maximum= 106 years

26. Colour 8.2 % 0.4 % 3.7 % 48.8 % 37.6 % 13 % Histogram Graph 3: Histogram - Distribution of patients by priority Red = 1146 patients Orange = 27490 patients Yellow = 126474 patients Green = 164294 patients Blue = 4318 patients Whit e = 12455 patients

27. Statistical Analysis Mortality in n(%) Died 1781 (0,5) Total 336526 (100) Mortality in n(%) Female 812 (0,2) Male 969 (0,3) Table 1: The Total Rate of Mortality in ER and Inpatient Service Table 2: Total Rate of Mortality by Sex Qui-squared Test, p<0,001 Qui-squared Test, p<0,001 This only happens because the database has a wide number of cases, making the differences statistically significative.

28. Statistical Analysis BoxPlot 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.

29. Statistical Analysis Table 3: Rate of Mortality by Colour in The Emergency Room and in Inpatient Service and Expected Mortality in ER Colour Mortality in Emergency Room Mortality in Inpatient Service Expected Mortality in Emergency 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) 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.

30. Comparison between the rate of Mortality in the Emergency Room and the expected one Statistical Analysis 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.

31. Table 4: Mortality in Inpatient Service Statistical Analysis In TimeOut of Time Mortality n(%) Died 26 (3,1)808 (97) Survived 57326 (39)90862 (61)

32. Table 5: Rate of Mortality according Priority Level and Waiting Times. * The results are statistically significant when *p< 0,05. Statistical Analysis Colour Mortality in Emergency Room Mortality in Inpatient Service n (%) Red IN TIME * 7 (100) *0 (0,0) OUT OF TIME * 130 (14) *61 (7) Orange IN TIME * 7 (2) * * 2 (0,6) * OUT OF TIME * 136 (0,8) * * 414 (2) * Yellow IN TIME * 5 (0,0) * * 21 (0,1) * OUT OF TIME * 49 (0,1) * * 314 (0,5) * Green IN TIME 0 (0,0) * 2 (0,0) * OUT OF TIME 1 (0,0) * 19 (0,1) * Blue IN TIME 0 (0,0)1 (0,1) OUT OF TIME 0 (0,0)

33. Statistical Analysis YearTotal EpisodesMortality 2005 26828155 (0,6) 2006 120214583 (0,5) 2007 103267581 (0,6) 2008 86217462 (0,5) Table 6: Rate of Mortality by year. Pearson Chi-Square Test Pearson Chi-Square Test: p=0,047.

34. Statistical Analysis  In the Inpatient Service, 23567 (7%) episodes were registered.  There is a 0,3% rate of mortality in this service.

35. DISCUSSION

36. 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

37. 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

38. Who has the red color:  has a serious illness;  needs more healthcares;  needs faster treatment;  has a higher probability of dying. DISCUSSION We verified a higher rate of mortality in the red color, as expected.

39. Who has the red color:  Waiting time according to MTS: 0 min;  The majority of cases in our database exceeded this time. DISCUSSION

40. 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

41.  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

42.  The rate of mortality may vary because it is predicted the hospital quality system to improve, due to the advances of medicine. DISCUSSION

43. 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.

44. WORK ACCOMPLISHED BY:  Ana Isabel Gonçalves Ferreiramimed09196@med.up.pt  Bárbara Ferreira Mendesmimed09223@med.up.pt  Carolina Botelho Amaral Rebelo Félixmimed09234@med.up.pt  Catarina Torres Monteiromimed09073@med.up.pt  Cláudia Inês de Sousa Amorim Sampaio da Costamimed09079@med.up.pt  João Tiago Ramos Paulinomimed09065@med.up.pt  Mariana Andreia Guimarães Rochamimed09104@med.up.pt  Pedro Nuno de Frias Marques Gonçalvesmimed09136@med.up.pt  Tânia Raquel Sousa Rodriguesmimed09183@med.up.pt  Tatiana Filipa Martins de Melomimed09182@med.up.pt  Vera Lúcia da Rocha Teixeiramimed09081@med.up.pt Turma 21