Skip to main content
  • Original research
  • Open access
  • Published:

A longitudinal, retrospective registry-based validation study of RETTS©, the Swedish adult ED context version



Triage and triage related work has been performed in Swedish Emergency Departments (EDs) since the mid-1990s. The Rapid Emergency Triage and Treatment System (RETTS©), with annual updates, is the most applied triage system. However, the national implementation has been performed despite low scientific foundation for triage as a method, mainly related to the absence of adjustment to age and gender. Furthermore, there is a lack of studies of RETTS© in Swedish ED context, especially of RETTS© validity. Hence, the aim the study was to determine the validity of RETTS©.


A longitudinal retrospective register study based on cohort data from a healthcare region comprising two EDs in southern Sweden. Two editions of RETTS© was selected; year 2013 and 2016, enabling comparison of crude data, and adjusted for age-combined Charlson comorbidity index (ACCI) and gender. All patients ≥ 18 years visiting either of the two EDs seeing a physician, was included. Primary outcome was ten-day mortality, secondary outcome was admission to Intensive Care Unit (ICU). The data was analysed with descriptive, and inferential statistics.


Totally 74,845 patients were included. There was an increase in patients allocated red or orange triage levels (unstable) between the years, but a decrease of admission, both to general ward and ICU. Of all patients, 1031 (1.4%) died within ten-days. Both cohorts demonstrated a statistically significant difference between the triage levels, i.e. a higher risk for ten-day mortality and ICU admission for patients in all triage levels compared to those in green triage level. Furthermore, significant statistically differences were demonstrated for ICU admission, crude as well as adjusted, and for adjusted data ten-day mortality, indicating that ACCI explained ten-day mortality, but not ICU admission. However, no statistically significant difference was found for the two annual editions of RETTS© considering ten-day mortality, crude data.


The annual upgrade of RETTS© had no statistically significant impact on the validity of the triage system, considering the risk for ten-day mortality. However, the inclusion of ACCI, or at least age, can improve the validity of the triage system.

Graphical Abstract


During the last decades many Emergency Departments (EDs) report crowding problems [1]. One intervention to address ED crowding is triage, defined as sorting patients according to their medical needs using a triage scale [2]. Most commonly known scales are the Australasian Triage Scale (ATS) [3], the Canadian Triage and Acuity Scale (CTAS) [4, 5], the Manchester Triage System (MTS) [6] and the Emergency Severity Index (ESI) [7], which all have demonstrated validity [8,9,10].

In Sweden, triage was spread across the country in the mid-2000s, initially with MTS, and thereafter with nationally developed triage scales such as Adaptive Process Triage (ADAPT) and the Medical Emergency Triage and Treatment System (METTS) [11]. These Swedish triage scales spread to adjacent countries; a modified Danish version of ADAPT, Danish Emergency Process Triage (DEPT) [12], and METTS in Norway [13]. In Sweden, METTS subsequently became the Rapid Emergency Triage and Treatment System (RETTS©) [14], as well as in Norway [15]. A version called Rapid Emergency Triage and Treatment System—Hospital Unit West (RETTS-HEV) was implemented in Denmark [16]. RETTS© is a process-orientated five-level triage system, using the triage levels of red, orange, yellow, green and blue, in declining priority of acuity, i.e. red is the highest priority, etc. The system combines chief complaints, called Emergency Symptoms and Signs (ESS), with vital signs (VS), and both ESS and VS have cut-off levels that indicate levels of acuity (Fig. 1). The most pronounced deviation in either VS or ESS determines the triage level. RETTS© uses a defined boundary for unstable and stable patients, i.e. a boundary that determines whether or not a patient can wait for physician examination without their condition deteriorating, although without stipulating any specific time frames except for the red triage level. Furthermore, there are versions of RETTS© for Adult, Paediatric, Psychiatric and Pre-hospital triage [14] and it is continuously updated in order to improve the assessment [17] but it is not mandatory to apply each upgrade.

Fig. 1
figure 1

Cut off levels due to vital signs and ESS no. 6, abdominal pain, according to the manual for RETTS©, edition 2014

In Sweden and Norway RETTS© is the most commonly used triage system [15, 18]. Validity studies of RETTS© in Norway (paediatric version) and Denmark (adult version) report the system to valid based on significant associations between hospitalisation, and admission to intensive care for children with high triage levels [19] and a strong association between triage levels and admission to hospital and in-hospital mortality [20].

In Sweden, RETTS© has mainly been studied in a pre-hospital context. A pre-hospital paediatric version demonstrated a moderate sensitivity, since two-thirds of the patients triaged with red or orange triage level later were identified as non-emergent [21]. Furthermore, a version of RETTS© adult, demonstrated a higher sensitivity (73%) to detecting time-sensitive conditions, but lower specificity (54%) compared to NEWS and NEWS2 [22]. In the Swedish ED context, a study of METTS (i.e. the predecessor of RETTS), demonstrated validity through significantly longer hospital LOS, and in-hospital mortality, associated with higher triage levels [23]. According to a review of triage and patient flow in Swedish EDs by the Swedish Council of Health Technology Assessment (SBU) in 2010, the scientific evidence for triage was insufficient mainly related to the absence of adjustment for age and gender, and that the scientific basis for METTS was deficient [24]. In summary, despite the nationwide establishment of triage in general, and RETTS© in particular, we have only been able to identify one study about the validity of RETTS© regarding adult patients in a Swedish ED context. Hence, there is a knowledge gap regarding RETTS© in a Swedish ED context, particularly regarding its validity. The aim of the study was to determine the validity of RETTS©.


Design and setting

This study had a longitudinal retrospective register-based design using cohort data from a healthcare region in Southern Sweden comprising two EDs. Together, these EDs had approximately 42,000 visits annually. At both settings, physicians had on-call duty whilst RNs and assistant nurses were employed at the EDs working shifts, rotating between medical specialities and emergency rooms. RETTS© edition 1.0 was implemented at the EDs in 2011, as well as a four-hour target, which is why the fifth RETTS© (blue) triage level was redundant. At the implementation of RETTS© edition 1.0, a 4-h education session was performed, while a two-hour update session followed the upgrading to the 2014 edition. Staff employed after 2014 only received the 2-h update session. The major difference between the two editions used in the study was the increased number of symptoms for describing red and orange triage levels in 40% of the ESS in the 2014 edition.

Data sources and collection

The two editions of RETTS© (1.0 and 2014) had been applied 2 years respectively prior to data collection. The data (Fig. 2) were collected from the administrative system connected to the general computerised medical records through the Department of healthcare data analytics and quality assessments in the region in 2018. Inclusion criteria were adult patients (≥ 18 years) who visited either of the two EDs during 2013 or 2016, and who were seen by a physician.

Fig. 2
figure 2

Variables extracted from the computerised medical record system

Since the computerised medical record system is connected to the national population register, all requested data were retrieved from this system, including mortality status.

The initial data comprised N = 75,930 contacts for the two sites together: 2013; n = 36,777 and 2016; n = 39,153. However, 1085 contacts were found to be invalid data due to duplicates, misregistration and consultations, i.e. refers to hospitalized patients in need of assessment from a physician stationed at the ED, leaving n = 74,845 distributed as n = 36,323 from 2013, and n = 38,522 from 2016.

Since the extracted data were not from an established quality register, an algorithm for data control was constructed by the research group: 15 variables multiplied by ten patients, multiplied by two cohorts. This yielded 300 randomly selected charts, extracted from the data set by the Department of healthcare data analytics and quality assessments, which became subject to chart audits. The research group decided that if less than 20% of the variables contained incorrect data, the data should be regarded as acceptable, and that an initial quality test would be performed halfway through the charts. After reviewing 160 charts (80 per year) of the 300 predetermined charts, the quality test demonstrated that seven (47%) of the 15 variables included were 100% correct. The remaining eight (53%) variables were correct on average 98% (ranging between 93 and 99%). Based on the results, the research group determined that the data were of high quality and refrained from further auditing of the remaining predetermined charts.


Demographic data were analysed using descriptive statistics. Since patients ≤ 18 years were excluded, the data became skewed and reported with median and interquartile range (IQR). Pearson’s chi-square test was used for comparison between the annual cohorts. The triage data were analysed using multiple logistic regression, both crude and adjusted for the predictors of co-morbidity and age according to the age-combined Charlson comorbidity index (ACCI) [25], together with gender. Female gender, green triage level, zero ACCI points and the year 2013 were used as references in the calculations. Ten-day mortality was the primary outcome and admission to an intensive care unit (ICU) the secondary outcome. A crude version was calculated based on triage levels, both years together, and divided by years, followed by an equal calculation based on the adjusted data. ACCI was applied as described by Sundararajan et al. [26], i.e. with ICD 10 implemented (“Appendix”). The results were reported by odds ratio (OR) and a 95% confidence interval (C.I.), with two-sided p < 0.05, considered as statistically significant. Receiver operating characteristic (ROC) curves were calculated and reported in combination with area under curve (AUC), with values from 0.7 to 0.8 considered as acceptable, 0.8 to 0.9 as excellent and > 0.9 as outstanding [27]. The ROC and AUC calculations were performed on crude data, all triage levels and both years together. Missing data occurred only in the variables arrival mode and time to physician, and, since they are not included in the validity analyses, were treated as missing. All statistics were performed using IBM’s SPSS, version 27. Since the study was a total population study, a power calculation was redundant.


Background characteristics

A total of 74,845 patients met the inclusion criteria and are therefore included in the study (Table 1). The yearly census had increased by 2199 (3%) patients between 2013 and 2016.There was no statistically significant difference between the two cohorts regarding age. In both annual cohorts the most commonly allocated triage level was yellow. The number of patients with comorbidity or high age, i.e. having one or more ACCI points, decreased per triage level. Patients who left without being seen by a physician increased from 2013 to 2016 by more than 100%. However, they did not exceed 0.5% of the total. In 2016, patients visit at the EDs were in median 27 min longer, compared to 2013. The most common discharge destinations in both annual cohorts were the patients’ home. There was a decrease in admission rates, both to general wards and ICUs, although there was an increase in patients assessed as unstable (red and orange triage level) during the same period. Of all 74,845 ED patients, almost one fifth (18%) were assessed as being unstable. ED mortality was low in both annual cohorts.

Table 1 Description of the two emergency departments and characteristics of patients visiting in 2013 and 2016

The ability of RETTS© to identify patients at risk for ten-day mortality or ICU admission over time

In total, 1031 (1.4%) of all patients died within ten days after the ED visit (Fig. 3). The majority of these patients were assessed as unstable upon arrival to the ED, n = 587 (57%). The remaining patients (n = 444; 43%) were assessed as stable, i.e. allocated a yellow or green triage level. The risk for ten-day mortality decreased from 12% in the red triage level, to less than 1% in the yellow and green triage levels.

Fig. 3
figure 3

Ten-day mortality in total, crude data APercentage from the total of ED visits. BPercentage per triage level. CPercentage of ten-day mortality

As depicted in Table 2, ten-day mortality and admission to ICU demonstrated statistically significant differences between each triage level compared to the reference in both annual cohorts. Thus, patients allocated red, orange and yellow triage levels had a significantly higher risk of mortality within ten days, or being admitted to an ICU, compared to those patients in the green triage level. The OR declined and CI decreased over time in all triage levels, except for the red triage level considering admission to an ICU. Statistically significant differences were found between the two annual cohorts for ICU admission, but not for ten-day mortality.

Table 2. The effect of triage level on primary and secondary outcomes, crude data, multiple logistic regression

The impact of predictors added to RETTS©

When adjusting for ACCI and gender, a statistically significant difference between the two annual cohorts was seen for ten-day mortality also (Table 3). The OR declined and CI decreased, i.e. the risk of ten-day mortality after the ED visit per triage level was lower in both annual cohorts when adding gender and ACCI, with the largest effect in the 2016 cohort. The opposite was displayed for ICU admission. Adding ACCI and gender yielded an increase in OR and wider CI in both annual cohorts, i.e. the risk of ICU admission increased.

Table 3. The effect of triage level on primary and secondary outcomes, crude and adjusted per year, multiple logistic regression

The impact of RETTS©, gender and ACCI on ten-day mortality and ICU admission is illustrated by ROC curves calculated on all patients together (Fig. 4). The AUC value considering ten-day mortality for the RETTS© triage system alone was 0.735, which indicates an acceptable predictive ability for detecting patients at risk of ten-day mortality. By adding ACCI, AUC increased to 0.878, where age alone generated an AUC value of 0.873. When gender was added, the AUC value increased to 0.880, indicating excellent predictive ability. Regarding ICU admission, the AUC value for RETTS© alone was 0.838, which is excellent. The AUC increased to 0.855 when adding ACCI, where the co-morbidity had a higher impact than age, with an AUC of 0.837. When gender was added to RETTS© and ACCI, the AUC further increased to 0.858.

figure 4

The effect of triage, gender and ACCI on ten-day mortality and ICU admission, 2013 and 2016 together


The main results of this study demonstrate that the annual upgrade of RETTS© had no statistically significant impact on the validity of the triage system, at least for ten-day mortality. However, the results demonstrated the decisive impact of age and co-morbidity, with statistical significance in both outcomes, i.e. both ten-day mortality and admission to an ICU.

In the primary outcome, ten-day mortality, the OR and CI decreased in the adjusted data, meaning morbidity and higher age explain mortality more than RETTS©. However, the opposite difference is displayed in the ICU admission, which showed an increase in OR and CI, particularly on the red triage level. This increase can be interpreted to mean that ICU admission is more a result of the event and/or morbidity, rather than of high age, considering the AUC values. For both annual cohorts, both ten-day mortality and ICU admission, the OR is highest in the red triage level. Regarding the fact that the median age in this study was 61 years, a large number of patients had two ACCI points from the start, without considering any comorbidity. This might explain the effect on the outcomes. Age has been discussed as a predictor to be considered in triage earlier in a study where the discriminative ability of the triage system decreased when the patient’s age increased [28]. Interestingly, in a Swedish study of associations between VS and mortality using RETTS, a side finding was that age was an independent risk factor for mortality [29]. This risk was confirmed by Ruge et al. [30], who identified a strong association with mortality in all triage levels defined by RETTS©, defining older as patients > 60 years. The results of this study confirm that age is a major predictor for outcomes in RETTS© and should be taken into consideration when revising the triage scale again.

Regarding the validity of RETTS© in ED context, the study by Widgren et al. [23] is the most cited. However, the effect of co-morbidity was not investigated and age was reported as descriptive information associated with arrival mode and per triage levels. The study also demonstrated a total mortality rate of 5.2%, which is noticeably higher than the 1.4% in the present study. However, this difference might be associated with different measurements; ten-day mortality versus in-hospital mortality. Nevertheless, Widgren et al. demonstrated statistically significant differences between triage levels and in-hospital mortality [23], which is comparable to the results of the present study. A comparison with Pérez et al. [20] is not completely feasible since they applied outcomes such as admission to a general ward, prolonged in-hospital LOS, and four different mortality rates—none of them ten days. The choice of primary outcome differs with regards to time perspective compared to many other ED triage studies [20, 31]. The reason to use 10-day mortality stems from ED research in which it was found that the majority of adverse events after an ED visit occurs within ten days [32], which a Swedish study confirmed by demonstrating that one third of short time mortality occurred between the eighth and tenth day after the ED visit [33]. However, Pérez et al. did adjust their data, but applied a version of the Charlson index, without age, scoring the patients from zero to 2+ [20]. However, both studies [20, 23] demonstrate a statistical significance between triage levels, which corresponds well with the present study.

In summary, it is difficult to compare validity studies, mainly because there is a lack of consensus about how to define the validity of triage scales and systems, and what outcomes to use [34, 35], as well as the range of study design, study samples and reference standards, which makes comparison even more difficult [36]. One commonly used proxy is over and under triage. Over triage is defined as non-urgent patients who are incorrectly classified as urgent [37], which can result in the inappropriate use of resources, and can potentially have an adverse impact on other patients [38]. Under triage is defined as patients allocated with lower triage levels than their needs require, leading to prolonged waiting time to being assessed by a physician, and therefore increased risk of adverse outcomes [39]. However, there is no consensus about how high degree of under respectively over triage is acceptable. Several studies use the American College of Surgeons Committee on Trauma (ACS COT) as a guideline with figures for acceptable under triage ranging from 1 to 10% and over triage ranging from 25 to 50% [37, 39,40,41]. The results of this study demonstrate that almost one fifth (n = 13,658) of all patients (n = 74,845) were assessed as unstable, i.e. were assessed as having a life-threatening or potentially life-threatening condition. However, only 587 patients (4.3%) died within ten days, which might be considered as over triage. Furthermore, since there are no guidelines for the ED context about over or under triage, it is difficult to determine. Nevertheless, to some extent, over or under triage is probably needed in order to ensure that no patients will be missed, but has the disadvantage that overuse can reduce compliance with the system [22].

However, according to van Veen and Moll [42], the validity of a triage system is determined by its reliability and ability to predict the true level urgency. Further, its reliability depends on how uniform and complete it is, and how it is understood and applied. Good training and instructions can optimise the use and interpretation of the triage system [42]. Previous studies of RETTS© have demonstrated problems with both education [18] and reliability [43], which may impact the validity of RETTS©.

Strengths and limitations

The major strength of this study is the adjustment of data according to ACCI in the data analysis around the multiple logistic regression analyses. Another strength is the adjustment of gender. The rather big sample size of 74,845 is another strength. However, the low number of patients who were actually included in the analyses of ten-day mortality and ICU admission may explain the wide CI values, which can be considered as a limitation. The movements of just a few patients between the cohorts can generate a significantly impact on the OR. Hence, the wide CI values warrant caution when interpreting the results. Another limitation is that the study is restricted to two EDs, which might limit the generalisability of the study.

A selection bias is inevitable when patients ≤ 18 years are excluded, but also when considering the fact that elderly patients are rarely admitted to an ICU. The lack of consensus about how to validate a triage scale can be considered as a limitation that affects all validity studies [34]. The use of different references [35] and different ED contexts [44] as well as mortality rates [20, 31, 32], make comparisons with other studies difficult.


The results of this study demonstrate that the annual upgrade of RETTS© had no statistically significant impact on the validity of the triage system, at least with regards to the risk of dying within 10 days of an ED visit. However, the inclusion of ACCI, or at least age, can improve the validity of RETTS©. Furthermore, the results show a statistical significance between triage levels, i.e. that there is a statistically higher risk of both ten-day mortality and ICU admission for all triage levels compared to the green triage level, whether or not adjusted for gender and ACCI. Finally, the results demonstrate a suspected over triage. However, it is difficult to evaluate as there is no consensus on over or under triage in an ED context.

Availability of data materials

The dataset generated during and/or analysed during the current study are not publicly available due to the ethical approval but are available from the corresponding author on reasonable request.



Age Combined Comorbidity Index


Adaptive process triage


Australasian Triage Scale


Area under curve


Confidence Interval


Canadian Triage and Acuity Scale


Danish emergency process triage


Emergency Department


Emergency Severity Index


Emergency symptoms and signs


International Statistical Classification of Diseases and Related Health Problems


Intensive Care Unit


Length of stay


Left without being seen


Medical emergency triage and treatment system


Manchester triage system


Odds ratio


Rapid emergency triage and treatment system


Rapid Emergency Triage and Treatment System—Hospital Unit West


Registered nurse


Receiver operating characteristics


The Swedish Council of Health Technology Assessment


Vital signs


  1. Pines JM, Iyer S, Disbot M, Hollander JE, Shofer FS, Datner EM. The effect of emergency department crowding on patient satisfaction for admitted patients. Acad Emerg Med. 2008;15(9):825–31.

    Article  PubMed  Google Scholar 

  2. Fernandes CM, Tanabe P, Gilboy N, Johnson LA, McNair RS, Rosenau AM, et al. Five-level triage: a report from the ACEP/ENA five-level triage task force. J Emerg Nurs. 2005;31(1):39–50.

    Article  PubMed  Google Scholar 

  3. Australasian College for Emergency Medicine A. Policy on the Australasian Triage Scale. Melbourne: Australasian College for Emergency Medicine; 2013.

  4. Beveridge R. The Canadian triage and acuity scale: a new and critical element in health care reform. J Emerg Med. 1998;16:507–11.

    Article  CAS  PubMed  Google Scholar 

  5. Bullard MJ, Musgrave E, Warren D, Unger B, Skeldon T, Grierson R, et al. Revisions to the Canadian Emergency Department Triage and Acuity Scale (CTAS) Guidelines 2016. CJEM. 2017;19(S2):18–27.

    Article  Google Scholar 

  6. Mackway-Jones K, Marsden J, Windle J. Emergency Triage. 3rd ed. Oxford: Wiley; 2014.

    Google Scholar 

  7. Gilboy N, Tanabe P, Travers D, Rosenau AM. ESI, Emergency Severity Index. A Triage Tool for Emergency Department Care. Version 4. Emergency Nurses Association; 2020.

  8. Worster A, Fernandes CM, Eva K, Upadhye S. Predictive validity comparison of two five-level triage acuity scales. Eur J Emerg Med. 2007;14:188–92.

    Article  PubMed  Google Scholar 

  9. Zachariasse JM, Seiger N, Rood PPM, Alves CF, Freitas P, Smit FJ, Roukema GR, Moll HA. Validity of the Manchester Triage System in emergency care: a prospective observational study. PLoS One. 2017;12:e0170811.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Forero R, Nugus P. Australasian College for Emergency Medicine (ACEM) Literature review on the Australasian Triage Scale (ATS). 2011.

  11. Farrokhnia N, Göransson KE. Swedish emergency department triage and interventions for improved patient flows: a national update. Scand J Trauma Resusc Emerg Med. 2011;19:72.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Plesner LL, Iversen AK, Langkjær S, Nielsen TL, Østervig R, Warming PE, et al. The formation and design of the TRIAGE study—baseline data on 6005 consecutive patients admitted to hospital from the emergency department. Scand J Trauma Resusc Emerg Med. 2015;23:106.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Engebretsen S, Røise O, Ribu L. Triage in Norwegian emergency departments. J Nor Med Assoc. 2013;3:285–9.

    Google Scholar 

  14. Widgren BR. RETTS: akutsjukvård direkt [RETTS: Emergency care Directly]. Lund: Studentlitteratur; 2012.

    Google Scholar 

  15. Stadheim HK, Nilsen JE, Olsen JÅ. Triage i den akuttmedisinske kjeden [Triage in the emergency medicine chain] Oslo: Helsedirektoratet; 2014. Report No.: 2. 2014.

  16. Nissen L, Kirkegaard H, Perez N, Horlyk U, Larsen LP. Inter-rater agreement of the triage system RETTS-HEV. Eur J Emerg Med. 2014;21(1):37–41.

    PubMed  Google Scholar 

  17. Predicare. Om RETTS.

  18. Wireklint SC, Elmqvist C, Göransson KE. An updated national survey of triage and triage related work in Sweden: a cross-sectional descriptive and comparative study. Scand J Trauma Resusc Emerg Med. 2021;29:89.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ødegård Steinsmo S, Tran T, Næss-Pleym LE, Risnes K, Døllner H. A validity study of the rapid emergency Triage and treatment system for children. Scand J Trauma Resusc Emerg Med. 2021;29:18.

    Article  Google Scholar 

  20. Pérez N, Nissen L, Nielsen RF, Petersen P, Biering K. The predictive validity of RETTS-HEV as an acuity triage tool in the emergency department of a Danish Regional Hospital. Eur J Emerg Med. 2014;23(1):33–7.

    Article  Google Scholar 

  21. Magnusson C, Herlitz J, Karlsson T, Jiménez-Herrera M, Axelsson C. The performance of the EMS triage (RETTS-p) and the agreement between the field assessment and final hospital diagnosis: a prospective observational study among children < 16 years. BMC Pediatr. 2019. 19:1.

  22. Magnusson C, Herlitz J, Axelsson C. Pre-hospital triage performance and emergency medical services nurse’s field assessment in an unselected patient population attended to by the emergency medical services: a prospective observational study. Scand J Trauma Resusc Emerg Med. 2020;28:81.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Widgren BR, Jourak M. Medical Emergency Triage and Treatment System (METTS): a new protocol in primary triage and secondary priority decision in emergency medicine. J Emerg Med. 2011;40(6):623–8.

    Article  PubMed  Google Scholar 

  24. Swedish Council on Health Technology Assessment (SBU). Triage Methods and Patient Flow Processes in Emergency Departments: A Systematic Review. Stockholm; 2010.

  25. Charlson ME, Pompei P, Ales KL, MacKenzie CRA. new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.

    Article  CAS  PubMed  Google Scholar 

  26. Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson Comorbidity Index predicted in-hospital mortality. J Clin Epidemiol. 2004;57:1288–94.

    Article  PubMed  Google Scholar 

  27. Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315–6.

    Article  PubMed  Google Scholar 

  28. Kuriyama A, Ikegami T, Nakayama T. Impact of age on the discriminative ability of an emergency triage system: a cohort study. Acta Anaesthesiol Scand. 2019;63:781–8.

    Article  PubMed  Google Scholar 

  29. Ljunggren M, Castrén M, Nordberg M, Kurland L. The association between vital signs and mortality in a retrospective cohort study of an unselected emergency department population. Scand J Trauma Resusc Emerg Med. 2016;24(21):1.

    Google Scholar 

  30. Ruge T, Malmer G, Wachtler C, Ekelund U, Westerlund E, Svensson P, et al. Age is associated with increased mortality in the RETTS-A triage scale. BMC Geriatr. 2019;19(139):1.

    Google Scholar 

  31. Obermeyer Z, Cohn B, Wilson M, Jena AB, Cutler DM. Early death after discharge from emergency departments: analysis of national US insurance claims data. BMJ. 2017;356:j239.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Forster AJ, Rose NGW, van Walraven C, Stiell I. Adverse events following an emergency department visit. Qual Saf Health Care. 2007;16:17–22.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Berg LM, Ehrenberg A, Florin J, Östergren J, Discacciati A, Göransson KE. Associations between crowding and ten-day mortality among patients allocated lower triage acuity levels without need of acute hospital care on departure from the emergency department. Ann Emerg Med. 2019;74:345.

    Article  PubMed  Google Scholar 

  34. Moll HA. Challenges in the validation of triage systems at emergency departments. J Clin Epidemiol. 2010;63(4):384–8.

    Article  PubMed  Google Scholar 

  35. Kuriyama A, Urushidani S, Nakayama T. Five-level emergency triage systems: variation in assessment of validity. Emerg Med J. 2017;34:703–10.

    Article  PubMed  Google Scholar 

  36. Zachariasse JM, van der Hagen V, Seiger N, Mackway-Jones K, van Ween M, Moll HA. Performance of triage systems in emergency care: a systematic review and meta-analysis. BMJ. 2019;9:e026471.

    Google Scholar 

  37. Meyer GD, Meyer Nadishani T, Gaunt CB. Validity of the South African Triage Scale in a rural district hospital. AfJEM. 2018;8:145–9.

    PubMed  PubMed Central  Google Scholar 

  38. Considine J, Ung L, Thomas S. Triage nurses’ decisions using the National Triage Scale for Australian emergency departments. Accid Emerg Nurs. 2000;8(4):201–9.

    Article  CAS  PubMed  Google Scholar 

  39. Twomey M, Wallis LA, Thopmson ML, Myers JE. The South African triage scale (adult version) provides valid acuity ratings when used by doctors and enrolled nursing assistants. AfJEM. 2012;2:3–12.

    Google Scholar 

  40. Bruijns SR, Wallis LA, Burch VC. A prospective evaluation of the Cape triage score in the emergency department of an urban public hospital i South Africa. Emerg Med J. 2008;25:398–402.

    Article  CAS  PubMed  Google Scholar 

  41. Torlén K, Kurland L, Castrén M, Olanders K, Bohm K. A comparison of two emergency medical dispatch protocols with respect to accuracy. Scand J Trauma Resusc Emerg Med. 2017.

  42. van Veen M, Moll HA. Reliability and validity of triage systems in paediatric emergency care. Scand J Trauma Resusc Emerg Med. 2009;17(38):1.

    Google Scholar 

  43. Wireklint SC, Elmqvist C, Parenti N, Göransson KE. A descriptive study of registered nurses’ application of the triage scale RETTS©; a Swedish reliability study. Int Emerg Nurs. 2018;38:21–8.

    Article  PubMed  Google Scholar 

  44. Twomey M, Wallis LA, Myers JE. Limitations in validating emergency department triage scales. Emerg Med J. 2007;24:477–9.

    Article  PubMed  PubMed Central  Google Scholar 

  45. WMA Declaration of Helsinki-ethcial principles for medical research involving human subjects. 2018.

Download references


We would like to express our sincere gratitude to Thomas Frisk at the Department of healthcare data analytics and quality assessments, Region Kronoberg, for providing us with the data, and to statistician Anna Lindgren, Lund University, for providing statistical consultations.


Open access funding provided by Linnaeus University. This study was funded by the Medical Research Council of Southeast Sweden (FORSS), the Department of Research and Development (FoU), Region Kronoberg and the Emergency Department, Region Kronoberg.

Author information

Authors and Affiliations



The research group designed and prepared the study together. The first author was responsible for data collection and for performing the statistical analyses together with a statistician. The results were discussed in the research group. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sara C. Wireklint.

Ethics declarations

Ethics approval and consent to participate

The study is conducted with ethical considerations according with the Helsinki Declaration [45] regarding risks/benefits, voluntariness, informed consent and confidentiality. The obtained data were anonymised by the Department of healthcare data analytics and quality assessments, and the research group had no access to any code key. The study was approved by the Regional Ethics Committee in Linköping (Ref. No. 2017/348-31).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



A longitudinal, retrospective registry-based validation study of RETTS©, the Swedish adult ED context version.



Age-combined co-morbidity index, according to Charlson et al. [25]


 41–49 years

0 points

 50–59 years


 60–69 years

2 points

 70–79 years

3 points

 80–89 years

4 points

  ≥ 90 (5p)

5 points


ICD-10 code

Charlson co-morbidity Index ICD 10 version according to Sundararajan et al. [26]


 Acute myocardial infarction (1p)

I21, I22, I252

 Congestive heart failure (1p)


 Peripheral vascular disease (1p)

I71, I790, I739, R02, Z958, Z959

 Cerebrovascular insult (1p)

I60, I61, I62, I63, I65, I66, G450, G451, G452, G458, G459, G46, I64, G454, I670, I671, I672, I674, I675, I676, I677 I678, I679, I681, I682, I688, I69

 Dementia (1p)

F00, F01, F02, F051

 Pulmonary disease (1p)

J40, J41, J42, J44, J43, J45, J46, J47, J67, J60, J61, J62, J63, J66, J64, J65

 Connective tissue disease (1p)

M32, M34, M332, M053, M058, M059, M060, M063, M069, M050, M052, M051, M353

 Gastric ulcer (1p)

K25, K26, K27, K28

 Hepatic disease (1p)

K702, K703, K73, K717, K740, K742, K746, K743, K744, K745

 Diabetes mellitus (1p)

E109, E119, E139, E149, E101, E111, E131, E141, E105, E115, E135, E145

 Complications of diabetes mellitus (2p)

E102, E112, E132, E142 E103, E113, E133, E143 E104, E114, E134, E144

 Paraplegia (2p)

G81 G041, G820, G821, G822

 Renal disease (2p)

N03, N052, N053, N054, N055, N056, N072, N073, N074, N01, N18, N19, N25

 Cancer (2p)

C0, C1, C2, C3, C40, C41, C43, C45, C46, C47, C48, C49, C5, C6, C70, C71, C72, C73, C74, C75, C76, C80, C81, C82, C83, C84, C85, C883, C887, C889, C900, C901, C91, C92, C93, C940, C941, C942, C943, C9451, C947, C95, C96

 Metastatic cancer (3p)

C77, C78, C79, C80

 Advanced hepatic disease (3p)

K729, K766, K767, K721

 HIV (6p)

B20, B21, B22, B23, B24

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wireklint, S.C., Elmqvist, C., Fridlund, B. et al. A longitudinal, retrospective registry-based validation study of RETTS©, the Swedish adult ED context version. Scand J Trauma Resusc Emerg Med 30, 27 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: