Article, Emergency Medicine

Prehospital shock index to assess 28-day mortality for septic shock

a b s t r a c t

Context: In the prehospital setting, early identification of septic shock (SS) with high risk of mortality aims to ini- tiate early treatments and to decide delivery unit (emergency department (ED) or intensive care unit (ICU)). In this context, there is a need for a prognostic measure of severity and death in order to early detect patients with a higher risk of pejorative evolution.

In this study, we describe the association between prehospital Shock Index and mortality at day 28 of patients with SS initially cared for in the prehospital setting by a mobile intensive care unit (MICU).

Methods: Patients with SS cared for by a MICU between January 2016 and May 2019 were retrospectively ana- lyzed. Using propensity score, the association between SI and mortality was assessed by Odd Ratio (OR) with 95 percent confidence interval [95 CI].

Results: One-hundred and fourteen patients among which 78 males (68%) were analysed. The mean age was

71 +- 14 years old. SS was mainly associated with pulmonary (55%), digestive (20%) or urinary (11%) infection. Overall mortality reached 33% (n = 38) at day 28.

Median SI [interquartile range] differed between alive and deceased patients: 0.73 [0.61-1.00] vs 0.80 [0.66-1.10], p b 0.001*). After adjusting for confounding factors, the OR of SI N 0.9 was 1.17 [1.03-1.32].

Conclusion: In this study, we report an association between prehospital SI and mortality of patients with prehospital SS. A SI N 0.9 is a readily available tool correlated with increased mortality of patients with SS initially cared for in the prehospital setting.

(C) 2019

  1. Introduction

On Friday, May 26th, 2017, the World Health Assembly and the World Health Organization made sepsis a global health priority, by adopting a resolution to improve, prevent, diagnose, and manage sepsis [1]. Sepsis is a major public health issue with an estimated incidence of 240 per 100 000 inhabitants in the United States [2]. Most of the time, sepsis comes from respiratory tract (50%), from digestive tract (25%) and less frequently from urinary tract (5%). Sepsis related mortality re- mains high, around 30% at 28 days [3,4]. One-third to one-half of all in-

* Corresponding author.

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

hospital deaths are related to septic shock [5], contributing to 180,000 deaths each year in the United States of America [6].

Early recognition of patients with a high mortality risk is one of the key-element to improve the survival rate of sepsis [7]. The early identi- fication aims to help physicians in their decision making for the initia- tion of effective and appropriate treatments (hemodynamic optimization and antibiotics administration) since prehospital setting [3,6] and the orientation to the most appropriate ward, i.e. emergency department (ED) or intensive care unit (ICU) [8]. Recognition and prog- nostication of sepsis is based on clinical judgement and scoring [9]. Many scores alone or associated with biological values were used to prognosticate sepsis outcome in ICU, ED or in ward [10-12]. Neverthe- less, validation of these scores remains under debate [13] and none is, to date, efficient to prognosticate sepsis outcome since the prehospital

https://doi.org/10.1016/j.ajem.2019.11.004

0735-6757/(C) 2019

setting [14,15]. In a previous study, we report that prehospital skin mot- tling score and capillary refill time could be used to predict mortality of septic shock initially cared in the prehospital setting by a mobile inten- sive care unit [16].

Shock Index is a simple clinical tool, initially describe in 1967, corresponding to the ratio between heart rate and systolic blood pres- sure with a normal range of 0.5 to 0.7 in healthy adult.

In the ED, a SI >=1.0 indicates a poorer outcome in case of acute circu- latory failure [17]. The association between SI and mortality has not been evaluated in the prehospital setting.

The aim of this study was to describe the association between SI and mortality at day 28 of patients with SS initially cared for in prehospital setting.

  1. Methods
    1. Context

As previously described [16,18], in France, the management of out- of-hospital emergencies is based on the Service d’Aide Medicale d’Urgence (SAMU) corresponding to the American pre-hospital emer- gency medical service. The SAMU hospital-based team is composed of switchboard operators and physicians reached dialling the national number “15”. Over the phone, the physician determines the appropriate level of care. For life-threatening emergencies, a mobile intensive care unit (MICU), composed of a driver, a nurse and an emergency physician, equipped with medical devices and drugs allowing initial management of main organ deficiencies is dispatched to the scene [19]. The physician decides since prehospital the destination ward either to intensive care unit (MICU) or to the emergency department (ED). To date, prehospital lactate measurement is not available in all French MICU. For sepsis, the MICU is allowed to initiate the following in the prehospital setting: he- modynamic resuscitation (Fluid expansion and/or norepinephrine infu- sion), antibiotics administration (amoxicillin-clavulanate or ceftriaxone or cefoxitine) and mechanical ventilation after endotracheal intubation and sedation if required.

Study population

Between January 2016 and May 2019, we performed a multi- centric retrospective observational study including all consecutive patients with SS cared for by a prehospital MICU from SAMU 75 Paris Necker University Hospital, SAMU 75 Paris Hotel Dieu Univer- sity Hospital, Fire Brigade of Paris, SAMU 38 Grenoble University Hospital, SAMU 31 Toulouse University Hospital, SAMU 972 La Mar- tinique University Hospital and SAMU 31 Castres Hospital. SS was identified from prehospital forms fulfilled by the MICU retrieved in the prehospital EMS database.

SS was defined according to the surviving sepsis campaign criteria adapted for French practices by the French Intensive Care Society and the French Anaesthesia and Intensive Care Society considering a septic shock as a severe sepsis with hypotension uncorrected by fluid expan- sion [8].

Ethical considerations

In keeping with the French legislation, our local ethical committee (Comite de Protection des Personnes, Ile de France, Paris – Number: 2015-08-03-SC) and the National Heart Agency (ID RCB number: 2015-A10068-41) considered that consent of patients was waived for participation in this retrospective study.

Data collection

Variables were defined prior to data collection and included pa- tients’ demographic characteristics (age, weight, size, and gender),

immunosuppression status, Prehospital vital signs (mean blood pres- sure, diastolic and systolic blood pressure, heart rate, pulse oximetry, re- spiratory rate, and temperature) collected by MICU first contact, duration of prehospital care, length of stay in the ICU, Sequential Organ Failure.

Assessment (SOFA) score and mortality at day 28. Immunosuppres- sion status was defined by the presence of at least diabetes mellitus and/ or chronic renal failure.

Shock index (SI) was calculated by the ratio between heart rate and systolic blood pressure [20] initially measured by MICU after their ar- rival to the scene. SOFA score was calculated 24 h after ICU admission [21]. Mortality at 28 days, a common variable used in ICU studies, was retrieved from hospital medical reports.

In order to minimize the bias in data abstraction [22], data collection was performed by a single investigator (RJ) using a standardized ab- straction template.

Statistical analysis

The primary outcome was the mortality rate at day 28 of patients with SS initially cared for by a MICU in prehospital setting.

First, univariate and multivariable analyses were conducted to eval- uate the relationship between covariates and mortality at day 28. SI was analysed as a continuous variable and as a binary variable using a threshold of SI N0.9 for abnormal value according to previous studies [23-25]. Results are expressed as mean with standard deviation for quantitative gaussian parameters, as median with interquartile range [Q1-Q3] for non-gaussian parameters and, as absolute value and per- centage for qualitative parameters.

Second, to reduce the effect of confounders, a propensity score anal- ysis was performed using “optimal” method [26]. Potential confounders included in the propensity score were: age, duration of prehospital care, in-hospital length of stay in the ICU, immunosuppression, HIV infection, Chronic Obstructive Pulmonary Disease (COPD), previous or actual his- tory of cancer, norepinephrine infusion (binary covariable) in the prehospital setting and volume of isotonic chlorine saline infused in the prehospital setting. Standardized mean deviation was used for sta- tistical testing, to reduce the influence of sample size on p value [27].

Third, after matching, baseline characteristics included in the pro-

pensity score were compared between cases (deceased patients) and controls (aLive patients) by paired tests. In order to estimate the average treatment effect, odds ratio (OR) with 95 percent confidence interval [95 CI] of mortality at day 28 was evaluated for SI N0.9.

All analyses were performed using R 3.4.2 (http://www.R-project. org; the R Foundation for Statistical Computing, Vienna, Austria).

  1. Results
    1. Study characteristics
      1. One-hundred and fourteen patients initially cared for septic shock by a MICU in the prehospital were retrospectively analysed

Seventy-six patients (67%) were male and the mean age was of 71 +- 15 years. The mean duration from dispatch to delivery in the prehospital was 81 +- 31 min. Deceased patients were older than alive patients (76 +- 12 vs 69 +- 15 years, p = 0.016).

Populations? demographic and clinical characteristics are summa- rized in Table 1.

Septic shock was mainly associated with pulmonary (55%), abdom- inal (20%) or urinary (11%) infection (Table 2).

The mean duration in the prehospital setting was of 81 +- 31 min; the median in-hospital length of stay was of 14 [7-22] days with a me- dian in-ICU length of stays of 6 [3-10] days (Table 1).

Overall mortality reached 33% (n = 38) at day 28.

Table 1

Populations’ demographic and clinical characteristics. Results are expressed as mean with standard deviation for quantitative gaussian parameters, as median with interquartile range for quantitative non-normal parameters and, as absolute value and percentage for qualitative parameters. p-value corresponds to the comparison between deceased and alive patients.

Overall population

Alive

(n = 76)

Deceased (n = 38)

p value

(n = 114)

Age (years)

71 +- 15

69 +- 15

76 +- 12

0.016*

Weight (kg)

68 +- 12

67 +- 11

69 +- 15

0.486

Size (cm)

170 [170-175]

170 +- 8

169 +- 8

0.898

SBP (mmHg)

92 +- 30

92 +- 30

93 +- 29

0.924

DBP (mmHg)

54 +- 20

54 +- 19

55 +- 23

0.882

MBP (mmHg)

65 +- 23

64 +- 23

67 +- 24

0.614

HR (beats.min-1)

114 +- 32

116 +- 29

109 +- 38

0.298

RR (movements min-1)

30 [20-36]

28 [20-35]

34 [25-38]

0.203

Pulse oximetry (%)

91 [82-96]

91 [84-97]

89 [81-94]

0.192

Body core temperature

38.6

38.0 +- 2.1

37.5 +- 2.1

0.464

(?C)

[36.9-39.2]

Glycemia (mmol/l)

10.3 +- 6.1

10.5 +- 6.3

10.0 +- 5.8

0.260

in-hospital length of stay was longer for alive than for deceased pa- tients: 16 [10-29] vs 6 [2-13], p = 0.001*; Table 1).

Mean prehospital fluid expansion reached 998 +- 523 ml in the over- all population; no difference was observed between alive and deceased patients (1043 +- 543 ml vs 910 +- 478 ml respectively, p = 0.21;

Table 1).

In the prehospital setting, 41 (36%) patients received norepineph- rine infusion (Table 1) with a median dose of 1.0 [0.5-2.0] mg h-1. Twenty-three patients (20%) received antibiotic prior hospital admis- sion and no patient had an initial lactate measurement.

Table 3 reports the comparison results between cases (deceased pa- tients) and controls (alive patients) after matching.

Using a threshold of 0.1 for absolute mean differences, after matching, no differences remain between cases (deceased patients) and controls (alive patients) as depict by Fig. 1 and reported in Table 3. No variable included in the propensity score significantly dif- fered between the cases and the controls after propensity score matching. Thereby the alone variable varying between cases and con-

Glasgow coma scale

14 [12-15]

14 [13-15]

14 [11-15]

0.102

trols was SI value of N 0.9.

Prehospital fluid expansion (ml)

998 +- 523

1043 +- 543

910 +- 478

0.207

After adjusting for confounding factors using propensity score, the

odd ratio of mortality at day 28 was 1.17 [1.03-1.32] for SI N 0.9.

SBP = systolic blood pressure, DBP = diastolic blood pressure, MBP = mean blood pres- sure, HR = heart rate, RR = respiratory rate, ICU = intensive care unit, SOFA = sequential organ failure assessment, HIV = human immunodeficiency virus, COPD = chronic ob- structive pulmonary disease, min = minutes, *=p-value b 0.05 between deceased and alive patients.

Norepinephrine

41 (36%)

29 38%)

12 (32%)

0.979

Prehospital duration

81 +- 31

80 +- 34

82 +- 26

0.840

(min)

Shock index

0.75

0.73

0.80

b0.001*

[0.62-1.05]

[0.61-1.00]

[0.66-1.10]

In-ICU length of stay

6 [3-10]

6 [3-10]

4 [2-11]

0.708

(days)

In-hospital length of stay

14 [7-22]

16 [10-29]

6 [2-13]

0.001*

(days)

SOFA score

9 [3-12]

8 [2-12]

11 [6-15]

0.305

Male gender

78 (68%)

55 (72%)

23 (60%)

0.202

Immunosuppression

25 (22%)

15 (20%)

10 (26%)

0.425

Diabetes Mellitus

31 (27%)

21 (28%)

10 (26%)

0.882

HIV infection

3 (3%)

1 (1%)

2 (5%)

0.250

Cancer

37 (32%)

26 (36%)

11 (29%)

0.572

COPD

11 (10%)

8 (11%)

3 (8%)

0.655

Chronic Renal Failure

11 (10%)

7 (9%)

4 (10%)

0.823

Shock index N0.9

40 (35%)

24 (32%)

16 (42%)

0.268

Main measurements

Median SI differed between alive and deceased patients: 0.73 [0.61-1.00] vs 0.80 [0.66-1.10], p b 0.001*).

No significant difference was observed in the mean duration of prehospital medical care between alive and deceased patients (80 +- 34 min vs 82 +- 26 min respectively, p = 0.84; Table 1).

In this study including 114 patients with septic shock initially cared for by a MICU in the prehospital setting, shock index was associated with a 17% increase in mortality at day 28. This study is the first to de- scribe an association, since prehospital setting, between a shock index of N 0.9 and an increase in mortality at day 28.

For sepsis Severity assessment, excepting septic shock, clinical signs may be not sufficient. Scores, SOFA and IGS2 [21,28], were developed and validated in the ICU and in the ED to improve clinical examination. Nevertheless, these scores require biological criteria needing sometime few hours to get the results and to establish the score and thus are not useful in prehospital assessment. To avoid this time-delay, the qSOFA score has been proposed to evaluate the severity of sepsis [7] outside ICU [29] even if qSOFA validity remains under debate [30,31]. To date, no score is validated in the prehospital setting [14,15]. To overcome Clinical scores deficiency, biomarker have been added to scores, in order to improve their efficiency. Currently, blood lactate measurement represents the better biomarker for shock severity assessment indepen- dently of its origin [32]. Unfortunately, lactate point of care testing is not always available easily in the out-of-hospital setting.

SI is a simple clinical tool aiming to assess the degree of hypovolemia in hemorrhagic and infectious shock states [20]. In the ED, a SI >=1.0 is as- sociated with poorer outcome in case of acute circulatory failure [17] and indicate persistent failure despite resuscitation [33]. The ability of the SI to predict poor outcome has been studied and validated in

Table 3 Comparison of predictive variables for mortality at day 28 included in the propensity score before and after matching. Values are expressed as mean +- SD or number (%).

The median in-ICU length of stay wasn’t different for alive than for deceased patients: 6 [3-10] vs 4 [2-11], p = 0.71) whereas the median

Before Matching n = 114

After Matching n = 80

Table 2

PS covariate

Controls

Cases

p value

Controls

Cases

p value

n = 76

n = 38

n = 52

n = 28

Age

69 +- 15

76 +- 12

0.016

72 +- 13

77 +- 13

0.026

Prehospital duration

80 +- 34

82 +- 26

0.840

77 +- 34

82 +- 27

0.56

In-hospital LOS

Prehospital fluid

16

[10-29]

1043

6 [2-13]

910

0.001

0.207

12

[15-30]

938

10

[2-19]

861

0.104

0.166

expansion

+- 543

+- 478

+- 431

+- 512

Origin of septic shock. Data are expressed as absolute value with percentage. Due to rounding, the percentages don’t add up to 100%.

Origin n (percentage)

Pulmonary

63 (55%)

NE

29 38%)

12 (32%)

0.979

16 (31%)

7 (25%)

0.197

Digestive

23 (20%)

HIV

1 (1%)

2 (5%)

0.250

1 (2%)

2 (7%)

0.234

Urinary

14 (11%)

Cancer

26 (36%)

11 (29%)

0.572

9 (17%)

3 (11%)

0.432

Cutaneous

5 (5%)

Immunosuppression

15 (20%)

10 (26%)

0.425

7 (13%)

6 (21%)

0.905

PS: propensity score, LOS: length of stay, NE: norepinephrine administration (yes or no), HIV: human immunodeficiency virus. Cases for deceased patients and controls for alive patients.

Meningeal

3 (3%)

Dental

1 (1%)

Unknown

5 (5%)

Covariate Balance

distance

age

prehospital.fluid.expansion

Cancer

norepinephrine

in.hospital.LOS

Sample

Unadjusted Adjusted

HIV

COPD

Immunosuppression

0.00

0.25

0.50

Absolute Mean Differences

0.75

Fig. 1. Standardized mean deviation between cases and controls after matching.

hospital as in the prehospital setting, especially for haemorrhagic shock related to trauma, [34-38]. Otherwise, SI is, also a usefulness tool for tri- age in the ED [23,24,33,39]. In the herein study, we report the interest of prehospital SI for the early triage and prognostication of SS. SI or modi- fied shock index [40] appears, as skin mottling score and capillary refill time [16,40], to be a simple tool for the SS patients triage in the prehospital setting. These three simple tools have a higher ability than classical, especially haemodynamic physical signs (heart rate and blood pressure), to assess SS severity since the prehospital setting [9].

Nevertheless, using medical devices, SI evaluation isn’t submitted to the same subjectivity as others clinical tool; for example skin mottling score and capillary refill time [41]. In order to the early screening of SS, particularly in the early and hyperkinetic phase of SS, SI appears to be a simple tool useful since the prehospital setting.

Larger Prospective trials are required to confirm these results and to further explore the potential benefit to include SI in the early triage and prognostication for SS in the prehospital setting.

Limitations

This study presents limitations limiting the generalization of the conclusions. First, this is a retrospective study. Second, bias from mis- classification of covariates might exist, because data were collected from prehospital and in-hospital medical reports. Third, the study was not designed to conclude in a causality link between mortality and SI.

Fourth, an intrinsic limitation of SI should be kept in mind. To date, nu- merous patients receive beta-blockers, limiting the heart rate increase despite an underlying illness and finally SI interest for the early detec- tion of SS. Fifth, this study focused only patients with SS, not other shock aetiologies. Sixth, a single investigator collected data, thus, data accuracy may be compromised [42].

Beyond these limitations, shock index may be used since prehospital setting in order to, earlier, screen patients with a higher risk of pejorative evolution and begin effective treatments: antibi- otics and Hemodynamic resuscitation (fluid expansion and/or norepinephrine infusion).

  1. Conclusion

Shock index is associated with mortality at day 28 of patients with prehospital septic shock cared for in the prehospital setting. A shock index value greater than 0.9 appears to be a simple tool to help physi- cians in order to define an increased risk of mortality. Shock index may be used since prehospital setting to, earlier, screen patients with a higher risk of pejorative evolution and instore effective treatments.

Nevertheless, these results need to be confirmed by further studies.

Declaration of Competing Interest

The authors declared that there is no conflict of interest.

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