Article, Emergency Medicine

Prehospital triage of septic patients at the SAMU regulation: Comparison of qSOFA, MRST, MEWS and PRESEP scores

a b s t r a c t

Purpose: A couple of scoring systems have been developed for risk stratification of septic patients. Their perfor- mance in the management of out-of-hospital initial care delivery is not documented. This study try to evaluate the Predictive ability of Quick Sequential Organ Failure Assessment , Robson Screening Tool (RST), Mod- ified Early Warning Score (MEWS) and Prehospital Early Sepsis Detection (PRESEP) scores on out of-hospital tri- age of septic patients, to predict intensive care unit admission.

Methods: A retrospective study using call records received by the SAMU 15 regulation call centre including all pa- tients with presumed septic shock was performed. The primary outcome was the admission to the ICU. Results: Among the 47 000 reports received, 37 patients with presumed septic shock were included. Twenty-two patients (59%) were admitted to ICU. AUCs of qSOFA, RST, MEWS and PRESEP scores were respectively 0.40 [0.22- 0.59], 0.60 [0.43-0.78], 0.66 [0.47-0.85] and 0.67 [0.51-0.84]. RST outperformed PRESEP, MEWS and qSOFA for

sensitivity (1, 0.92, 0.85 and 0.62 respectively). MEWS showed better specificity than PRESEP, MRST and qSOFA (0.33, 0.29, 0.16 and 0.16). MEWS showed comparable positive predictive value than PRESEP and outperformed MRST and qSOFA (0.41, 0.41, 0.39 and 0.29 respectively). Negative predictive value of MRST outperformed PRESEP, MEWS and qSOFA (1, 0.88, 0.80 and 0.44 respectively).

Conclusion: Our findings suggest that Screening patients at SAMU 15 regulation call centre using qSOFA, MRST, MEWS and PRESEP scores to predict ICU admission is irrelevant. Development of a specific scoring system for out-of-hospital triage of septic patients is needed.

(C) 2017

Introduction

Sepsis is responsible for N 30% of all hospitalizations and approxi- mately 50% of Intensive care unit admissions [1]. The outcome of patients with sepsis in terms of care-related morbidity and mortality is well characterized in the ICU [2-10]. Despite a trend towards an im- provement in the survival rate of patients affected by sepsis in the last decade [3,5], mortality still reaches 30% at day 28 [10,11], with an over- all hospital mortality rate of 40% [1]. Strikingly, sepsis exhibits a higher mortality rate than the mortality rates from cardiac arrest [11].

A decrease in the mortality rates was demonstrated to rely on early diagnosis of sepsis and prompt initiation of an appropriate therapy [11-16]. However, the diagnosis of sepsis in an out-of-hospital setting

* Corresponding author at: Intensive Care Unit, Anaesthesiology Department, SAMU of Paris, Hopital Necker, Assistance Publique Hopitaux de Paris, Paris Descartes University, 149 Rue de Sevres, Paris 75730, France.

E-mail address: [email protected] (R. Jouffroy).

1 RJ and AS wrote the manuscript.

2 RJ made statistical analysis.

3 SE and AC collected data.

4 MM, PC and BV read back the manuscript.

still remains a challenge and an accurate diagnosis scoring system is lacking in this setting up to the present moment.

In Prehospital emergencies, proper triage of septic patients is to avert “under-triage”, defined as an admission to the Emergency Depart- ment (ED) when ICU admission would have been more appropriate, on the one hand. On the other hand, an “over-triage” is defined as an ad- mission to an ICU when appropriate care would have been provided in the ED [17]. Not only does under-diagnosis of sepsis impact on mor- tality, but the delay between the first medical contact and diagnosis of the sepsis is also crucial. Inappropriate management of septic patients could therefore be harmful. [17].

To optimize the triage of septic patients requiring ICU admission, many scoring systems have been extensively explored and used for the assessment of the severity of an illness, to determine long and short-term prognosis of critically ill patients, and to predict mortality. Among them, the modified early warning score is a nonspecif- ic score validated in hospitalized patients to detect the risk of severe de- terioration [18]. In addition, the modified Robson screening tool (mRST) was developed to detect sepsis in an out-of-hospital setting [19]. The Prehospital Early Sepsis Detection (PRESEP) score was developed based on consensus criteria for sepsis and organ failure [20].

https://doi.org/10.1016/j.ajem.2017.10.030

0735-6757/(C) 2017

During the third international consensus definitions for sepsis and septic shock, the need for further guidelines to improve the manage- ment of septic patients was highlighted [21]. On this occasion, the quick Sepsis-related Organ Failure Assessment score (qSOFA) was de- fined as a specific tool to identify patients with suspected infection at risk of in-hospital mortality. This score replaced the old criteria defining Systemic Inflammatory Response Syndrome , sepsis, severe sepsis and septic shock used in 1992 and 2001 [16,22]. The qSOFA score was mainly studied in the ED and in ICU [21,23,24] and its performance in an out-of-hospital setting is still very controversial.

Despite a couple of attempts to develop a generic risk adjustment score for out-of-hospital management of septic patients, currently, no accurate score exists to identify at-risk patients during initial pre hospi- tal care. The primary objective of this study was to evaluate the efficacy of MEWS, mRST, PRESEP and qSOFA scores on out of-hospital triage of septic patients to predict admission to the ICU.

Study design and setting

We performed a retrospective, observational study using data col- lected between January 1st and March 31st, 2017 by the Paris central control function of the emergency medical services called SAMU.

In France, dialling the national emergency number 15 connects the public to the emergency medical assistance service, known as the “Ser- vice d’Aide Medicale d’Urgence” (SAMU) [25].

Once aware of the main complaint of the call, hospital-based medical teams determine the appropriate level of care to dispatch to the scene, based on the patient’s medical history and symptoms. For life-threaten- ing emergencies, such as cardiac arrests and poly-trauma, the “Service Mobile d’Urgence et de Reanimation” (SMUR), corresponding to a mo- bile intensive care unit (MICU), enables direct admission of the patient to an ICU [26]. The MICU is composed of a driver, a nurse, and an emer- gency physician or an internal medical specialist [25]. For less severe cases, an emergency mobile team (EMT) (firefighters) or a basic life support ambulance is dispatched to the scene. When the appropriate care support arrives on the scene, the patient’s clinical evaluation is communicated to the regulating call centre to decide on the best course of action. Thereafter, patients are either transferred to the ED or to the ICU.

Population and data

The Paris SAMU regulation call centre provided us access to the data collected from January 1st to March 31st 2017 recorded during prehospital care delivery.

Patients under 18, pregnant women and patients with incomplete data sets were excluded.

All patients with suspected infection (fever and/or hypothermia) and signs of shock (marbling, tachycardia, low blood pressure, tachypnea, cyanosis, confusion), and no alternative diagnosis, during prehospital assessment by a MICU/EMT/Ambulance team were includ- ed in the study, and were followed for 28 days.

The MICU/EMT/Ambulance team communicated patient’s character- istics such as demographic characteristics and comorbidities, patient’s vital signs (blood pressure, heart rate, respiratory rate, temperature and Glasgow coma scale, pulse oximetry) to the SAMU regulation call centre over the phone.

Upon ED/ICU admission, bacteriological investigations (blood cul- tures, urine cultures and chest X-ray) were performed for all patients.

The results and patient’s outcomes including death were retrieved from medical reports.

Diagnosis of infectious disease was based on demographic (age, sex) and clinical features (systolic blood pressure, diastolic blood pressure, mean blood pressure, heart rate, respiratory rate, body temperature, ox- ygen saturation and neurological evaluation based on Glasgow coma scale) using data extracted from prehospital records. The site of

infection was suspected according to the patient’s medical history and clinical signs. The final diagnosis was made using the discharge letter (source of infection, agent, outcome), as well as the biological and the microbiological data from medical records.

The severity of the infection was assessed during prehospital care delivery allowing classification of the patients into 2 groups according to the Third International Consensus definition [21]: infection and sepsis.

The qSOFA, mRST, MEWS and PRESEP scores were calculated using the data collected by the SAMU call centre during prehospital care delivery.

The qSOFA includes three clinical signs as the following: systolic blood pressure (SBP) <= 100 mm Hg, respiratory rate (RR) >= 22/min and altered mental function determined by a Glasgow Coma Scale (GCS) b 13, with one point awarded to each item. A qSOFA >= 2 predicts an increased risk of prolonged ICU stay or mortality [11].

The mRST includes temperature, heart rate (HR), respiratory rate (RR), altered mental status and a history suggestive of a new infection [27].

The MEWS uses Blood pressure , HR, RR, body temperature, and mental status. One point was awarded for each of these conditions, giv- ing a score ranging from 0 to 5 [18].

The PRESEP takes into account temperature N 38 and b 36 ?C, SpO2 b 92%, RR N 22 breaths/min, HR N 90 beats/min, BP b 90 mm Hg and GCS score b 15. One point was awarded for each of these conditions, giv- ing a score ranging from 0 to 6 [20].

The primary outcome was ICU admission. Three researchers, two residents, and one emergency physician ver-

ified the diagnosis according to consensus criteria [21] using the data provided by the SAMU regulation call centre.

Statistical analysis

Results are expressed as median with standard deviation for quanti- tative parameters or absolute values and percentages for qualitative parameters.

Evaluation of the predictive accuracy of each score was performed by receiver operator characteristic (ROC) analyses. We analyzed the area under the curve (AUC) with a 95% confidence interval (95CI) ob- tained after a resampling procedure based on a smoothed bootstrap due to the small size of sample. The corresponding sensitivity, specific- ity, positive predictive value (PPV) and negative predictive value (NPV) were calculated to evaluate the Predictive validity of the each scores, using a threshold of 2 for the qSOFA score, of 2 for the mRST score, of 5 for the MEWS score and of 4 for PRESEP score.

Data analyses were performed using R(C) version 3.2.3.

In keeping with French legislation, our local ethical committee con- sidered that consent of patients was waived for participation in this ob- servational study (Number 2017-A00354-49).

Results

Over the study period, the Paris SAMU regulation call centre received

47.000 calls (Fig. 1). Thirty-eight calls concerned patients with septic shock and were eligible for the study. One patient had missing data and was excluded. In the end, 37 patients were included (Fig. 1).

Twenty-three patients (62%) were transferred to a hospital by a MICU. Twenty-four (65%) patients were admitted to an ED, while 13 (35%) were admitted to an ICU. Detailed patient baseline characteristics and clinical outcomes are summarized in Table 1.

Pre hospital mean temperature was 38.2 +- 1.5 ?C for patients admit- ted to the ICU and 37.8 +- 1.4 ?C for patients to the ED. Pre hospital mean SBP was 96 +- 33 mm Hg and 93 +- 36 mm Hg, mean DBP 59 +- 19 mm Hg and 59 +- 22 mm Hg, mean MBP 72 +- 22 mm Hg and 71

+- 26 mm Hg, mean HR 103 +- 29/min and 112 +- 28/min, mean RR was 33 +- 10/min and 30 +- 10/min, mean GCS was 12 +- 3 and 12 +-

Fig. 1. Flow chart of the study.

4 in the overall population and in patients admitted to the ICU, respec- tively (Table 1).

The suspected origin of sepsis was pulmonary for 14 patients (38%), urinary for 12 (32%) and abdominal for 5 (14%) (Table 2). Infection was microbiologically documented in 26 cases (70%) and was in fact pulmo- nary for 15 patients (60%), urinary for 7 patients (25%), abdominal for 4 patients (14%) and undocumented for 11 patients (Table 2).

By day 28, 9 patients (24%) had died. All deaths had a documented history of sepsis and no other cause of death was identified.

Among the patients hospitalized in the ICU, 8 patients had a qSOFA

>= 2, 13 patients had a mRST >= 2, 11 patients had a MEWS >=5 and 12 pa- tients had a PRESEP >= 4 (Table 3).

The predictive ability for ICU admission based on the AUC area was

0.40 [0.22-0.59] for qSOFA, 0.60 [0.43-0.78] for mRST, 0.66 [0.47-

0.85] for MEWS and 0.67 [0.51-0.84] for PRESEP.

Sensitivity, specificity, negative and positive predictive values of qSOFA, mRST, MEWS and PRESEP are summarized in Table 4. The mRST outperformed PRESEP, MEWS and qSOFA for sensitivity (1, 0.92, 0.85 and 0.62 respectively). The MEWS (0.33) had a better specificity than PRESEP (0.29), mRST (0.16) and qSOFA (0.16) scores to predict ICU admission. The MEWS (0.41) showed comparable PPV than PRESEP (0.41) and outperformed mRST and qSOFA (0.39 and 0.29 respectively). The NPV of the mRST score outperformed PRESEP, MEWS and qSOFA (1, 0.88, 0.80 and 0.44 respectively) (Table 4).

Table 1

demographic and clinical features of the overall population, the patients admitted to the intensive care unit and to the emergency department. Data are expressed as means with standard deviation. GCS: Glasgow Coma Scale, SBP: systolic blood pressure, DBP = diastol- ic blood pressure, MBP: mean blood pressure, HR: heart rate (beats per minute), RR: respi- ratory rate (breaths per minute) and SpO2: percutaneous oxygen saturation (%)

Discussion

In this study, we observed that triage of patients with septic shock at the SAMU regulation call centre using the qSOFA, mRST, MEWS or PRESEP scores is irrelevant to predict ICU admission. The mRST outperformed the PRESEP, MEWS and qSOFA scores for sensitivity (1, 0.92, 0.85 and 0.62 respectively) with yet an insufficient ability to pre- dict ICU admission. Taking into account the AUC, all scores performed poorly and had AUC b 0.7. Consequently, the need for a valuable tool for early identification of septic patients at-risk of poor outcome remains.

Early identification of septic patients significantly impacts on patient’s outcomes [14]. Actually, an important prognosis element is the time lapse between the first medical contact and the diagnosis of sepsis which triggers the initiation of treatments, especially antibiotics [11,13,21]. Therefore, early and efficient screening and triaging are cru- cial to improve the diagnosis and the management of sepsis [11,13]. De- spite the available guidelines on sepsis [21,28], no reliable screening tool is available for pre hospital identification of septic patients, unlike those in the standard guidelines concerning acute coronary syndromes [29-31]. The qSOFA score was recently proposed as an early Predictive tool for severe outcome. However, its performance in a pre hospital set- ting is still controversial [32]. Actually, Dorsett, Innocenti and Churpek reported poor sensitivity of qSOFA score in the pre hospital chain of care [32-34]. The other scores were also reported to lack accuracy [20]. Despite the absence of an optimal screening score, simple clinical characteristics with the use of biological measurements are often suffi- cient in the process of decision-making [35]. Still, an efficient tool to aid detection of at-risk septic patients could significantly reduce time to de- livery of the appropriate therapy early in the pre hospital chain of care. Our study has several limitations. Firstly, this is a single region, retro- spective study. Secondly, the small number of patients may impact on the results of the study. Thirdly, the number of monitored variables within each score can account for the differences in the predictive

All patients (n = 37)

Intensive care unit (n = 13)

Emergency department (n = 24)

Table 2

Age (years) 76 +- 18 75 +- 19 77 +- 18

Gender (M:F) 19:18 (51%) 8:5 (62%) 11:13 (46%)

Suspected and confirmed primary sources of sepsis. Data are expressed as absolute values with their percentages. NA = not applicable

Suspected origin of sepsis (n = 37)

DBP (mm Hg)

59 +- 19

59 +- 22

59 +- 17

MBP (mm Hg)

72 +- 22

71 +- 26

72 +- 20

Pulmonary

14 (37%)

15 (58%)

HR (bpm)

103 +- 29

112 +- 28

98 +- 29

Urinary

12 (32%)

7 (27%)

RR (mpm)

28 +- 9

30 +- 10

26 +- 9

Abdominal

5 (14%)

4 (15%)

SpO2

90 +- 10

80 +- 12

94 +- 9

Dermatological

1 (3%)

0 (0%)

Temperature (?C)

37.9 +- 1.4

38.2 +- 1.5

37.8 +- 1.4

Others

5 (14%)

NA

GCS

12 +- 3

12 +- 4

12 +- 3

SBP (mm Hg)

96 +- 33

93 +- 36

98 +- 31

Confirmed origin of sepsis (n = 26)

Table 3

Number of patients admitted to the ICU and to the ED according to prehospital mRST, qSOFA, MEWS or PRESEP scores. mRST: Modified Robson screening tool, qSOFA: quick Se- quential (Sepsis-related) Organ Failure Assessment, MEWS: Modified Early Warning Score, PRESEP: Prehospital Early Sepsis Detection.

Intensive care unit Emergency department

mRST

>= 2

13/33

20/33

33/33

b 2

0/4

4/4

4/4

13/13

24/24

37/37

qSOFA

>= 2

8/28

20/28

28/28

b 2

5/9

4/9

9/9

13/13

24/24

37/37

MEWS

>= 5

11/27

16/27

27/27

b 5

2/10

8/10

10/10

13/13

24/24

37/37

PRESEP

>= 4

12/29

17/29

29/29

b 4

1/8

7/8

8/8

13/13

24/24

37/37

ability between the mRST, qSOFA, MEWS and PRESEP scores. Another explanation may be due to coupling between parameters. When one value is abnormal, the others values often follow the abnormal value,

i.e. Glasgow coma scale and respiratory rate [35]. Additionally, the dif- ferent thresholds of the common parameters, i.e. systolic blood pres- sure, may affect the sensitivity of each score [35] and lead to over treatment of non-septic patients [17].

According to the literature, the performance of these scoring sys- tems is very heterogeneous among studies. This observation may result from substantial heterogeneity in the setting of each study [36]. Most studies include ED, hospital wards or ICU patients [20,37-39].

In our work, we tested the predictive ability of these score in the pre hospital chain of care. In this context, the tested scores displayed a low AUC. Therefore, all scores lacked clinical relevance in the pre hospital setting. Consequently, no statistical test was performed.

The need to develop a reliable tool for the management of pre hospi- tal septic patients is still needed. An ongoing retrospective study (NCT03237403) is trying to identify the clinical signs and the medical history elements, i.e. immunosuppression, associated with ICU admis- sion. For this purpose, a machine learning approach described by Desautels et al. may be of great use in the ICU [40]. The latter study may unveil a new perspective to validate a screening tool to predict ICU admission of septic patients in a pre hospital setting.

Conclusion

Despite the existence of many scoring systems, not one system can stand-alone to define sepsis and improve the recognition of septic pa- tients early in the pre hospital chain of care. Neither the qSOFA, nor the mRST, MEWS and PRESEP score is relevant in this setting to identify septic patients requiring ICU admission. Early identification of patients in the pre hospital chain of care is the first step in the management of

Table 4 Sensitivity, specificity, positive and negative predictive values of prehospital mRST, qSOFA, MEWS and PRESEP scores for ICU admission. mRST: Modified Robson screening tool, qSOFA: quick Sequential (Sepsis-related) Organ Failure Assessment, MEWS: Modified Ear- ly Warning Score, PRESEP: Prehospital Early Sepsis Detection.

mRST >= 2

qSOFA >= 2

MEWS >= 5

PRESEP >= 4

Sensitivity

100%

62%

85%

92%

Specificity

16%

16%

33%

29%

Positive predictive value

39%

29%

41%

41%

Negative predictive value

100%

44%

80%

88%

septic shock urging the need to develop a specific tool to improve the prognosis of these patients.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgments

Misses Camila Flores (MD) and Isabelle Spiller (nurse) for the reviewing of English.

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