Skip to main content
Download PDF

Association between prehospital shock index variation and 28-day mortality among patients with septic shock

BMC Emergency Medicine volume 22, Article number: 87 (2022) Cite this article

Abstract

Purpose

Septic shock (SS) hyperdynamic phase is characterized by tachycardia and low-blood pressure reflecting the relative hypovolemia. Shock index (SI), the ratio between heart rate and systolic blood pressure, is a simple objective tool, usable for SS prognosis assessment.

This study aims to evaluate the relationship between prehospital SI variation and 28-day mortality of SS patients initially cared for in prehospital setting by a mobile intensive care unit (mICU).

Methods

From April 6th, 2016 to December 31st, 2020, 406 patients with SS requiring prehospital mICU were retrospectively analyzed. Initial SI, i.e. first measurement after mICU arrival to the scene, and final SI, i.e. last measurement of the prehospital stage, were used to calculate delta SI (initial SI—final SI) and to define positive and negative delta SI. A survival analysis after propensity score matching compared the 28-day mortality of SS patients with positive/negative delta SI.

Results

The main suspected origins of infection were pulmonary (42%), digestive (25%) and urinary (17%). The 28-day overall mortality reached 29%.

Cox regression analysis revealed a significant association between 28-day mortality and delta SI. A negative delta SI was associated with an increase in mortality (adjusted hazard ratio (HRa) of 1.88 [1.07–3.31] (p = 0.03)), whereas a positive delta SI was associated with a mortality decrease (HRa = 0.53 [0.30–0.94] (p < 10–3)).

Conclusion

Prehospital hemodynamic delta SI among SS patients cared for by a mICU is associated with 28-day mortality. A negative prehospital delta SI could help physicians to identify SS with higher risk of 28-day mortality.

Peer Review reports

Introduction

Every year, sepsis affects more than 30 million people worldwide [1,2,3] leading to around 11 million deaths [3]. Despite progress in the prevention, diagnosis and care, sepsis related mortality rate still ranges from 10 to 20% and from 50 to 60% for septic shock [4,5,6].

The World Health Assembly, the World Health Organization and the “SEPSIS-3” conference emphasize prevention, early recognition, severity assessment and treatment of septic patients to decrease mortality [7, 8]. Early recognition of septic shock (SS) is the first step prior diagnosis, severity assessment and treatment implementation [7], in both prehospital and in-hospital setting [3, 6]. In the prehospital setting, severity assessment influences the orientation to the appropriate ward, i.e. emergency department (ED) for less severe patients or intensive care unit (ICU) for the most severe patients [9]. In the prehospital setting, the early recognition and prognostication of sepsis remains a daily challenge, most of the time based on clinical examination [10]. Among tools that can be used to recognize and to assess the severity, we previously reported that skin mottling score and capillary refill time, and initial prehospital shock index (SI), ratio between heart rate and systolic blood pressure [11], normal range from 0.5 to 0.7 in healthy adult [11], are associated with increased mortality of patients with SS initially cared for in the prehospital setting [12, 13].

By similarity with the relative blood lactate clearance, which is a prognostic tool usable in the pre and in-hospital setting for sepsis severity assessment [14, 15], for treatment effect assessment [7, 15,16,17,18] and to guide sepsis resuscitation [7, 19, 20], we investigated in this study the association between prehospital SI variation and 28-day mortality of patients presenting with septic shock initially cared for in prehospital setting by a mobile intensive care unit (mICU). The aim of the study was to show that, in the same way as lactate clearance, is an indirect tool for treatment effect assessment [7, 21], the change in SI is a clinical tool allowing of treatment effect assessment.

Methods

Background

As previously reported [22], the French prehospital emergency medical service (EMS) is based on the SAMU (Urgent Medical Aid Service), a public health control organization, which provides medical response to prehospital emergency situations. The SAMU is made up of emergency physicians and assistants answer calls who respond to the patients' complaints [23]. In the case of life-threatening emergencies, a mICU, the SMUR (Mobile Emergency and Resuscitation Service) staffed with an emergency physician and equipped with medical devices and drugs allowing initial management of major organ deficiency, is dispatched to the scene [24] in order to provide out-of-hospital treatment and transport to definitive in-hospital care, either the ED or the ICU.

Patients

From April 06th, 2016 to December 31st, 2020, septic shock patients to the 2012 sepsis-2 conference [25] cared for by a mICU teams of 7 hospital centres (Necker-Enfants Malades Hospital, Lariboisière Hospital, La Pitié-Salpêtrière Hospital, Hôtel Dieu Hospital, APHP, Paris – France; The Paris Fire Brigade Paris, – France; the Toulouse University Health Centre, Toulouse – France and the Castres Hospital, Castres – France), were retrospectively analyzed. Septic shock diagnosis was established after hospital admission and patient identification based on medical hospital reports. In the prehospital setting, septic shock diagnosis was presumed on clinical history, clinical signs and lactate measurement of available according to the sepsis-2 conference [25].

Patients younger than 18 years, and/or pregnant, and/or at the terminal stage of any comorbidity and/or with guardianship or curatorship were not included in this study [26]. One-hundred and fourteen among the 406 patients included in this study were previously retrospectively analyzed [13].

Patients’ demographic characteristics (age, weight, height, and gender), supposed prehospital origin of sepsis, initial prehospital (i.e., the first mICU contact), and final prehospital (i.e., at the end of prehospital stage) vital sign values (systolic (SBP), diastolic (DBP) and mean arterial pressure (MAP)) were measured with a non-invasive automated devices in all centres. Heart rate (HR), pulse oximetry (SpO2), respiratory rate (RR), temperature and Glasgow coma scale (GCS)), plasma blood glucose level, duration of prehospital care, and prehospital treatments delivered (antibiotic therapy type and dose, fluid volume expansion type and dose, as well as catecholamine type and dose), were collected from mICU prehospital medical reports. Previous underlying comorbidities (chronic cardiac failure, chronic renal failure, chronic obstructive pulmonary disease, diabetes mellitus, and history of cancer) were also collected [27].

Initial SI corresponds to the SI measured by mICU after their arrival to the scene, whereas final SI corresponds to the SI measured just prior hospital arrival. Delta SI represents the difference between initial SI and final SI. Delta SI was encoded as a categorial variable (0 for negative delta SI or 1 for positive delta SI).

Initial blood lactate is the blood lactate value measured after mICU arrival to the scene and final blood lactate the value measured just prior hospital arrival using a point of care medical device (StatStrip® Lactates, Nova Biomedical, Waltham, MA, USA) with correct comparability and transferability according to the central laboratory analyzers [28]. Delta lactate was estimated by the following equation: ((initial blood lactate—final blood lactate) / prehospital duration) (mmol.l−1.minutes−1). Delta lactate was encoded as a categorial variable (0 for negative delta lactate or 1 for positive delta lactate).

The in-hospital length of stay (LOS) and the 28-day mortality were retrieved from medical reports in case of death in hospital or by phone call when the patient was discharged from the hospital. The Sequential Organ Failure Assessment (SOFA) score [29] and the Simplified Acute Physiology Score (SAPS 2) [30] were calculated 24 h after ICU admission.

In order to minimize the bias in data abstraction [31], data collection was performed by a single investigator (RJ) using a standardized abstraction template.

Ethical considerations

The study was approved by the French Society of Anesthesia and Intensive Care ethics committee on December 12th, 2017 (Ref number: IRB 00,010,254–2017-026). The French Society of Anesthesia and Intensive Care ethics committee waived the need of informed consent.

Statistical Analysis

Results are expressed as mean with standard deviation for quantitative parameters with a normal distribution, as median with interquartile range [Q1-Q3] for parameters with a non-Gaussian distribution, and as absolute value and percentage for qualitative parameters.

The primary outcome was the 28-day mortality rate.

Statistical analysis was a priori decided, and the statistical analysis plan developed prior to analysis.

Univariate and multivariate analyses were performed to evaluate the relationship between each covariate and the 28-day mortality rate.

To reduce the effect of confounders on 28-day mortality and on delta SI, a propensity score matching was used to balance the differences in baseline characteristics between patients with positive SI and those with negative SI [32]. The propensity score was estimated using logistic regression based on potential confounders on 28-day mortality and on SI variation: age, prehospital duration, prehospital catecholamine infusion [26], prehospital fluid expansion [26], hypertension [26], chronic cardiac failure, chronic renal failure, chronic obstructive pulmonary disease, history of cancer, diabetes mellitus, immunodepression, prehospital antiobiotic administration [9] and hospital length of stay. "Hospital centre" variable was also added in the propensity score in order to take into account the heterogeneity between centres’ practices and the number of patients included by each centre. The nearest neighbour matching method was used to match patients based on the logit of the propensity score [32]. The balance of covariates after matching was assessed by absolute mean differences with a considered acceptable threshold of 15% [33].

Imbalance matching was assessed with standardized mean deviation, based on the following formulae to assess the standardized mean deviation (SMD):

$$SMD=100* \frac{|\mathrm{x }(\mathrm{cases})-\mathrm{x }(\mathrm{controls})|}{\sqrt{{\left(s cases\right)}^{2}+s {\left(controls\right)}^{2}}}$$

where x denotes the mean or proportion for binary variables and classes of categorical variables et s the variance.

Thereafter, in the matched sample, baseline characteristics were compared between cases (patients with negative SI) and controls (patients with positive SI) by paired tests.

Finally, in the propensity score–matched cohort, a survival analysis using Cox proportional hazards regression was used to compare the 28-day mortality of patients according to (i) the positive or negative delta SI, and (ii) the positive or negative delta lactate. Proportional hazards assumption was verified for each Cox model variable by Kaplan Meier curves and the log-rank test.

Results are expressed by adjusted Hazard ratio (HRa) with 95 percent confidence intervals [95 CI].

All tests were 2-sided with a statistically significant p-value of < 0 0.05. All analyses were performed using R 3.4.2 (http://www.R-project.org; the R Foundation for Statistical Computing, Vienna, Austria).

Results

Population characteristics

Among, the 406 patients analyzed with septic shock requiring action by the mICU, 268 patients (68%) were male, and the mean age was 69 ± 15 years old (Table 1). One-hundred and thirty-five patients (33%) were admitted to the ED and 271 patients (67%) admitted to the ICU after mICU intervention.

Table 1 Population characteristics. Results were expressed as mean and standard deviation for quantitative parameters (normal distribution), as median and interquartile range for quantitative parameters (non-gaussian distribution) and, as absolute value and percentage for qualitative parameters. P-value corresponds to the comparison between deceased and living patients
Full size table

The median length of stay in a hospital was 12 [7,8,9,10,11,12,13,14,15,16,17,18,19,20] days and the median ICU length of stay was 5 [2,3,4,5,6,7,8,9] days (Table 1).

Pulmonary, digestive and urinary infections were the suspected cause of the SS in 42%, 25% and 17% of the cases, respectively (Table 1).

The 28-day overall mortality rate reached 29%.

No significant difference in the duration of the prehospital stage was observed between patients who survived and those who died on day-28 (64 ± 32 min vs 67 ± 31 min, p > 0.05; Table 1).

One-hundred and fourteen patients (28%) received prehospital antibiotic therapy, no significant difference was observed between patients who survived (n = 83, 29%) and those who died on day-28 (n = 31, 27%—p = 0.701) (Table 1). Among the 114 patients (28%) who received antibiotics prior to hospital admission, 74% were treated with 3rd generation cephalosporin, 42% with cefotaxime and 31% with ceftriaxone.

All patients received crystalloids infusion for hemodynamic optimization in the prehospital setting. No significant difference in prehospital fluid expansion was observed between alive and deceased patients on day-28: 675 [500 – 1000] vs 500 [500 – 1000] ml respectively, p = 0.762) (Table 1).

One hundred patients (25%) received norepinephrine in the prehospital setting, among which 32 patients (28%) were deceased and 83 patients (29%) were alive on day-28 (p = 0.357). No other catecholamine was used in the prehospital setting.

No significant difference was observed for norepinephrine dose between alive and deceased patients on day-28: 1.0 [0.5 – 2.0] vs 1.0 [0.7 – 2.0] mg.h−1 respectively, p = 0.668 (Table 1).

Bivariate analysis reported a significant association between 28-day mortality and age, prehospital initial and final body core temperature, final prehospital respiratory rate, pulse oximetry, blood lactate, delta blood lactate, Glasgow coma scale, SOFA score, IGS-2 score and in-hospital length of stay (Table 1).

Main measurement

In the overall population, the mean initial SI was 1.2 ± 0.5, the mean final SI was 1.1 ± 0.7 and the mean delta SI was 0.1 ± 0.7. Two hundred and forty-eight patients (61%) had a positive delta SI, and 158 patients (39%) had a negative delta SI. Comparisons between patients with a positive and a negative delta SI are reported in Table 2.

Table 2 Comparison between patients with a positive and a negative delta SI. Results were expressed as mean and standard deviation for quantitative parameters (normal distribution), as median and interquartile range for quantitative parameters (non-gaussian distribution) and, as absolute value and percentage for qualitative parameters. P-value corresponds to the comparison between deceased and living patients
Full size table

Conversely, the mean initial blood lactate was 0.02 ± 0.04 mmol.l−1.min−1, the mean final blood lactate was 0.02 ± 0.05 mmol.l−1.min−1 and the mean delta blood lactate was 0.002 ± 0.1 mmol.l−1.min−1 (0.002 ± 0.1 for alive patients and 0.001 ± 0.1 mmol.l-1.min-1 for alive and deceased patients respectively, p = 0.215). One-hundred and fifteen patients (28%) had a positive delta blood lactate, and 54 patients (13%) had a negative delta blood lactate.

After propensity score matching for positive SI, 337 patients: i.e., 240 negative delta SI and 97 positive delta SI were compared. Comparisons are reported in Table 3 and the absolute mean differences between subgroups after propensity score matching are depicted in Fig. 1.

Table 3 Comparison of predictive variable for 28-day mortality included in the propensity score before and after matching. Values are expressed as mean ± SD or number (%). d corresponds to the standard mean deviation
Full size table
Fig. 1
figure 1

Absolute mean differences between patients with prehospital hemodynamic optimisation achievement and those without prehospital hemodynamic optimisation achievement after matching

Full size image

Using Cox regression analysis on matched population, we observed a significant association between 28-day mortality and negative delta SI with an adjusted hazard ratio (HRa) of 1.88 [1.07–3.31] (p = 0.03) as for positive delta SI: HRa = 0.53 [0.30–0.94] (p < 10–3).

Figure 2 depicts Kaplan Meier curves after confounder adjustment for 28-day survival between positive delta SI and negative delta SI patients (Fig. 2).

Fig. 2
figure 2

Kaplan Meier curves of 28-day survival between patients with negative SI and those with positive SI

Full size image

Figure 3 represents Kaplan Meier curves after confounder adjustment for 28-day survival between positive delta lactate and negative delta lactate patients (Fig. 3).

Fig. 3
figure 3

Kaplan Meier curves of 28-day survival between patients with negative delta lactate and those with positive delta lactate

Full size image

Discussion

In this study, we observed a significant association between 28-day mortality and prehospital delta shock index. Negative prehospital delta SI is associated with a 1.9-fold 28-day mortality increase whereas positive prehospital delta SI is associated with a 1.9-fold 28-day mortality decrease among septic shock patients cared for by a mICU in the prehospital setting.

In order to reduce mortality related to sepsis, the World Health Assembly, the World Health Organization and the “SEPSIS-3” conference recommand early sepsis recognition and severity assessment, as key elements prior to early treatment initiation [7, 8].

In-hospital studies reported an association between septic shock patient poor outcome and clinical signs, biomarkers and severity scores, i.e., SOFA and IGS-2 [29, 30, 34,35,36,37]. Nevertheless, in the prehospital setting, only clinical signs and few biomarkers are available [12]. Elsewhere, the qSOFA score, which do not require biological criteria as opposed to SOFA and IGS-2, has been suggested for assessing sepsis severity, but its validity remains under debate [38,39,40,41,42,43,44]. To date, for sepsis severity assessment, blood lactate is the best biomarker [45, 46], available in the prehospital setting [28], associated with survival [15, 28, 47, 48]. In addition, lactate clearance represents an indirect tool for treatment effect assessment [7, 21], usable to guide sepsis resuscitation [7, 19, 20], although subjected to controversies [49, 50]. However, blood lactate point of care testing is not worldwide available in the prehospital setting.

To bypass biomarkers’ limits, clinical signs have been proposed. On the one hand, capillary refill time and skin mottling score are associated with 28-day mortality [12]. SI, a simple clinical objective [51] tool, has an higher ability than hemodynamic physical signs (heart rate and blood pressure) for septic shock severity assessment [10] and is usable for early triage [13, 52]. To the best of our knowledges, this is the first study reporting a dynamic analysis of the SI in the prehospital stage of septic shock patients cared for by a mICU.

Nevertheless, the current study suffers from several limitations restricting the conclusions generalization. We cannot rule out a bias from misclassification of covariates, because data were collected from prehospital and in-hospital medical reports. Data accuracy may be compromised because data abstraction was collected by a single investigator [53]. Patients were only adults, consequently, the conclusions are not directly transposable to pediatric populations. Beta-blockers are widely prescribed, limiting SI increase despite an underlying illness. Despite no significant difference between alive and deceased patients, the fluid volume expansion is lower than recommended [7]. The study is retrospective; thus, no therapeutic goal (antibiotic therapy and/or hemodynamic optimization) was required for the mICU teams. In addition, the study focused on patients with septic shock, not on sepsis or other shock-etiologies. The external validity is affected by the specificity of the French prehospital EMS, based on SAMU and mICU intervention in the prehospital setting, contrary to others prehospital EMS organization based on paramedics.

Beyond these limitations, in a similar manner to lactate clearance, the ability of delta SI (i) to be an indirect tool for treatment effect assessment, and (ii) enabling sepsis resuscitation guiding, requires larger prospective trials.

Conclusion

Delta shock index in the prehospital stage of septic shock patients cared for by a mICU is significantly associated with 28-day mortality. A negative prehospital delta SI is associated with a 1.9-fold 28-day mortality increase whereas positive delta SI is associated with a 1.9-fold 28-day mortality decrease. Further studies are needed to evaluate the ability of prehospital delta SI to assess hemodynamic optimization treatment effect assessment and its usefulness for sepsis resuscitation guiding during the prehospital setting.

Authors' information

not applicable

Availability of data and materials

The dataset analyzed during the current study are not publicly available because their containing information that could compromise the privacy of research participants but are available from the corresponding author on reasonable request.

Abbreviations

SS:

Septic shock

SI:

Shock index

mICU:

Mobile intensive care unit

HRa:

Adjusted hazard ratio

ED:

Emergency department

ICU:

Intensive care unit

SAMU:

Urgent Medical Aid Service

SMUR:

Mobile Emergency and Resuscitation Service

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

MAP:

Mean arterial pressure

HR:

Heart rate

SpO2:

Pulse oximetry

RR:

Respiratory rate

GCS:

Glasgow coma scale

LOS:

Length of stay

SOFA:

Sequential Organ Failure Assessment

SAPS 2:

Simplified Acute Physiology Score

SMD:

Standardized mean deviation

References

  1. Fleischmann C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. Am J Respir Crit Care Med. 2016;193(3):259–72.

    Article  CAS  PubMed  Google Scholar 

  2. Gaieski DF, Edwards JM, Kallan MJ, Carr BG. Benchmarking the incidence and mortality of severe sepsis in the United States. Crit Care Med. 2013;41(5):1167–74.

    Article  PubMed  Google Scholar 

  3. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Busani S, Damiani E, Cavazzuti I, Donati A, Girardis M. Intravenous immunoglobulin in septic shock: review of the mechanisms of action and meta-analysis of the clinical effectiveness. Minerva Anestesiol. 2016;82(5):559–72.

    PubMed  Google Scholar 

  5. Luhr R, Cao Y, Soderquist B, Cajander S. Trends in sepsis mortality over time in randomised sepsis trials: a systematic literature review and meta-analysis of mortality in the control arm, 2002–2016. Crit Care. 2019;23(1):241.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sakr Y, Elia C, Mascia L, Barberis B, Cardellino S, Livigni S, et al. Epidemiology and outcome of sepsis syndromes in Italian ICUs: a muticentre, observational cohort study in the region of Piedmont. Minerva Anestesiol. 2013;79(9):993–1002.

    CAS  PubMed  Google Scholar 

  7. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. World Health Organization. Improving the prevention, diagnosis and clinical management of sepsis. 2017. Available at: https://www.who.int/activities/improving-the-prevention-diagnosis-and-clinical-management-ofsepsis.

  9. Pottecher T, Calvat S, Dupont H, Durand-Gasselin J, Gerbeaux P, workgroup SS. Haemodynamic management of severe sepsis: recommendations of the French Intensive Care Societies (SFAR/SRLF) Consensus Conference, 13 October 2005, Paris, France. Crit Care. 2006;10(4):311.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Guirgis FW, Jones L, Esma R, Weiss A, McCurdy K, Ferreira J, et al. Managing sepsis: Electronic recognition, rapid response teams, and standardized care save lives. J Crit Care. 2017;40:296–302.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Allgöwer MBC. Schock index Deutsche Medizinische Wodenschrift. 1967;46:1–10.

    Google Scholar 

  12. Jouffroy R, Saade A, Tourtier JP, Gueye P, Bloch-Laine E, Ecollan P, et al. Skin mottling score and capillary refill time to assess mortality of septic shock since pre-hospital setting. Am J Emerg Med. 2019;37(4):664–71.

    Article  PubMed  Google Scholar 

  13. Jouffroy R, Pierre Tourtier J, Gueye P, Bloch-Laine E, Bounes V, Debaty G, et al. Prehospital shock index to assess 28-day mortality for septic shock. Am J Emerg Med. 2019.

  14. Bakker J, Coffernils M, Leon M, Gris P, Vincent JL. Blood lactate levels are superior to oxygen-derived variables in predicting outcome in human septic shock. Chest. 1991;99(4):956–62.

    Article  CAS  PubMed  Google Scholar 

  15. Jones AE. Lactate clearance for assessing response to resuscitation in severe sepsis. Acad Emerg Med. 2013;20(8):844–7.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Bao L, Zhang M, Yan P, Wu X, Shao J, Zheng R. Retrospective analysis of the value of arterial blood lactate level and its clearance rate on the prognosis of septic shock patients. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2015;27(1):38–42.

    PubMed  Google Scholar 

  17. Nguyen HB, Kuan WS, Batech M, Shrikhande P, Mahadevan M, Li CH, et al. Outcome effectiveness of the severe sepsis resuscitation bundle with addition of lactate clearance as a bundle item: a multi-national evaluation. Crit Care. 2011;15(5):R229.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Tian HH, Han SS, Lv CJ, Wang T, Li Z, Hao D, et al. The effect of early goal lactate clearance rate on the outcome of septic shock patients with severe pneumonia. Zhongguo Wei Zhong Bing Ji Jiu Yi Xue. 2012;24(1):42–5.

    PubMed  Google Scholar 

  19. Pan J, Peng M, Liao C, Hu X, Wang A, Li X. Relative efficacy and safety of early lactate clearance-guided therapy resuscitation in patients with sepsis: A meta-analysis. Medicine (Baltimore). 2019;98(8):e14453.

    Article  CAS  Google Scholar 

  20. Ryoo SM, Lee J, Lee YS, Lee JH, Lim KS, Huh JW, et al. Lactate Level Versus Lactate Clearance for Predicting Mortality in Patients With Septic Shock Defined by Sepsis-3. Crit Care Med. 2018;46(6):e489–95.

    Article  CAS  PubMed  Google Scholar 

  21. Shapiro NI, Howell MD, Talmor D, Nathanson LA, Lisbon A, Wolfe RE, et al. Serum lactate as a predictor of mortality in emergency department patients with infection. Ann Emerg Med. 2005;45(5):524–8.

    Article  PubMed  Google Scholar 

  22. Jouffroy R, Gilbert B, Tourtier JP, Bloch-Laine E, Ecollan P, Bounes V, et al. Impact of Prehospital Antibiotic Therapy on Septic Shock Mortality. Prehosp Emerg Care. 2020:1–8.

  23. Jouffroy R, Saade A, Muret A, Philippe P, Michaloux M, Carli P, et al. Fluid resuscitation in pre-hospital management of septic shock. Am J Emerg Med. 2018;36(10):1754–8.

    Article  PubMed  Google Scholar 

  24. Adnet F, Lapostolle F. International EMS systems: France. Resuscitation. 2004;63(1):7–9.

    Article  PubMed  Google Scholar 

  25. Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 2013;39(2):165–228.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Jouffroy RGB, Gueye P, Tourtier JP, Bloch-laine E, et al. Prehospital hemodynamic optimisation is associated with a 30-day mortality decrease in patients with septic shock. Am J Emerg Med. 2021;45:105–11.

    Article  PubMed  Google Scholar 

  27. Salvatore F. The shift of the paradigm between ageing and diseases. Clin Chem Lab Med. 2020.

  28. Leguillier T, Jouffroy R, Boisson M, Boussaroque A, Chenevier-Gobeaux C, Chaabouni T, et al. Lactate POCT in mobile intensive care units for septic patients? A comparison of capillary blood method versus venous blood and plasma-based reference methods. Clin Biochem. 2018;55:9–14.

    Article  CAS  PubMed  Google Scholar 

  29. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonca A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22(7):707–10.

    Article  CAS  PubMed  Google Scholar 

  30. Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270(24):2957–63.

    Article  PubMed  Google Scholar 

  31. Gearing RE, Mian IA, Barber J, Ickowicz A. A methodology for conducting retrospective chart review research in child and adolescent psychiatry. J Can Acad Child Adolesc Psychiatry. 2006;15(3):126–34.

    PubMed  PubMed Central  Google Scholar 

  32. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res. 2011;46(3):399–424.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083–107.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ait-Oufella H, Bige N, Boelle PY, Pichereau C, Alves M, Bertinchamp R, et al. Capillary refill time exploration during septic shock. Intensive Care Med. 2014;40(7):958–64.

    Article  CAS  PubMed  Google Scholar 

  35. Ait-Oufella H, Lemoinne S, Boelle PY, Galbois A, Baudel JL, Lemant J, et al. Mottling score predicts survival in septic shock. Intensive Care Med. 2011;37(5):801–7.

    Article  CAS  PubMed  Google Scholar 

  36. Sakr Y, Dubois MJ, De Backer D, Creteur J, Vincent JL. Persistent microcirculatory alterations are associated with organ failure and death in patients with septic shock. Crit Care Med. 2004;32(9):1825–31.

    Article  PubMed  Google Scholar 

  37. Charlton M, Sims M, Coats T, Thompson JP. The microcirculation and its measurement in sepsis. J Intensive Care Soc. 2017;18(3):221–7.

    Article  PubMed  Google Scholar 

  38. Askim A, Moser F, Gustad LT, Stene H, Gundersen M, Asvold BO, et al. Poor performance of quick-SOFA (qSOFA) score in predicting severe sepsis and mortality - a prospective study of patients admitted with infection to the emergency department. Scand J Trauma Resusc Emerg Med. 2017;25(1):56.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Finkelsztein EJ, Jones DS, Ma KC, Pabon MA, Delgado T, Nakahira K, et al. Comparison of qSOFA and SIRS for predicting adverse outcomes of patients with suspicion of sepsis outside the intensive care unit. Crit Care. 2017;21(1):73.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Dorsett M, Kroll M, Smith CS, Asaro P, Liang SY, Moy HP. qSOFA Has Poor Sensitivity for Prehospital Identification of Severe Sepsis and Septic Shock. Prehosp Emerg Care. 2017;21(4):489–97.

    Article  PubMed  Google Scholar 

  41. Harada M, Takahashi T, Haga Y, Nishikawa T. Comparative study on quick sequential organ failure assessment, systemic inflammatory response syndrome and the shock index in prehospital emergency patients: single-site retrospective study. Acute Med Surg. 2019;6(2):131–7.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Koyama S, Yamaguchi Y, Gibo K, Nakayama I, Ueda S. Use of prehospital qSOFA in predicting in-hospital mortality in patients with suspected infection: A retrospective cohort study. PLoS ONE. 2019;14(5):e0216560.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lane DJ, Lin S, Scales DC. Classification versus Prediction of Mortality Risk using the SIRS and qSOFA Scores in Patients with Infection Transported by Paramedics. Prehosp Emerg Care. 2019:1–8.

  44. Silcock DJ, Corfield AR, Staines H, Rooney KD. Superior performance of National Early Warning Score compared with quick Sepsis-related Organ Failure Assessment Score in predicting adverse outcomes: a retrospective observational study of patients in the prehospital setting. Eur J Emerg Med. 2019;26(6):433–9.

    Article  PubMed  Google Scholar 

  45. Kushimoto S, Akaishi S, Sato T, Nomura R, Fujita M, Kudo D, et al. Lactate, a useful marker for disease mortality and severity but an unreliable marker of tissue hypoxia/hypoperfusion in critically ill patients. Acute Med Surg. 2016;3(4):293–7.

    Article  PubMed  PubMed Central  Google Scholar 

  46. April MD, Donaldson C, Tannenbaum LI, Moore T, Aguirre J, Pingree A, et al. Emergency department septic shock patient mortality with refractory hypotension vs hyperlactatemia: A retrospective cohort study. Am J Emerg Med. 2017;35(10):1474–9.

    Article  PubMed  Google Scholar 

  47. Jouffroy R, Leguillier T, Gilbert B, Tourtier JP, Bloch-Laine E, Ecollan P, et al. Prehospital lactate clearance is associated with reduced mortality in patients with septic shock. Am J Emerg Med. 2020.

  48. Jouffroy R, Leguillier T, Gilbert B, Tourtier JP, Bloch-Laine E, Ecollan P, et al. Pre-Hospital Lactatemia Predicts 30-Day Mortality in Patients with Septic Shock-Preliminary Results from the LAPHSUS Study. J Clin Med. 2020;9(10).

  49. Hernandez G, Ospina-Tascon GA, Damiani LP, Estenssoro E, Dubin A, Hurtado J, et al. Effect of a Resuscitation Strategy Targeting Peripheral Perfusion Status vs Serum Lactate Levels on 28-Day Mortality Among Patients With Septic Shock: The ANDROMEDA-SHOCK Randomized Clinical Trial. JAMA. 2019;321(7):654–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Angus DC. How Best to Resuscitate Patients With Septic Shock? JAMA. 2019;321(7):647–8.

    Article  PubMed  Google Scholar 

  51. Espinoza ED, Welsh S, Dubin A. Lack of agreement between different observers and methods in the measurement of capillary refill time in healthy volunteers: an observational study. Rev Bras Ter Intensiva. 2014;26(3):269–76.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Tseng J, Nugent K. Utility of the shock index in patients with sepsis. Am J Med Sci. 2015;349(6):531–5.

    Article  PubMed  Google Scholar 

  53. Worster A, Bledsoe RD, Cleve P, Fernandes CM, Upadhye S, Eva K. Reassessing the methods of medical record review studies in emergency medicine research. Ann Emerg Med. 2005;45(4):448–51.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

None

Funding

none

Author information

Authors and Affiliations

  1. Intensive Care Unit, Ambroise Paré Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France

    Romain Jouffroy

  2. IRMES - Institute for Research in Medicine and Epidemiology of Sport, INSEP, Paris, France

    Romain Jouffroy

  3. INSERM U-1018, Centre de Recherche en Epidémiologie Et Santé Des Populations - U1018 INSERM, Paris Saclay University, Paris, France

    Romain Jouffroy

  4. Université de Paris, 7329, Paris, EA, France

    Romain Jouffroy

  5. Intensive Care Unit, Anaesthesiology, SAMU, Necker Enfants Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France

    Romain Jouffroy & Benoit Vivien

  6. Department of Emergency Medicine, University Hospital of Toulouse, SAMU 31, Toulouse, France

    Basile Gilbert & Vincent Bounes

  7. Hôpital d‘Instruction Des Armées Bégin, Paris, France

    Léa Thomas

  8. Emergency Department, Cochin Hospital, Paris, France & Emergency Department, SMUR, Hôtel Dieu Hospital, Paris, France

    Emmanuel Bloch-Laine

  9. Intensive Care Unit, SMUR, Pitie Salpêtriere Hospital, 47 Boulevard de l‘Hôpital, 75013, Paris, France

    Patrick Ecollan

  10. Centre Hospitalier Intercommunal Castres-Mazamet, Paris, France

    Josiane Boularan

  11. SAMU 972 CHU de Martinique Pierre Zobda Quitman Hospital, Fort-de-France Martinique, France

    Papa-Ngalgou Gueye

Authors
  1. Romain Jouffroy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Basile Gilbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Léa Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emmanuel Bloch-Laine
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Patrick Ecollan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Josiane Boularan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vincent Bounes
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Benoit Vivien
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Papa-Ngalgou Gueye
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Romain Jouffroy, Papa Gueye Methodology: Romain Jouffroy Data curation: Romain Jouffroy, Basile Gilbert, Léa Thomas, Emmanuel Bloch-Laine, Patrick Ecollan, Vincent Bounes, Josiane Boularan, Papa Gueye, Benoît Vivien Writing- Original draft preparation: Romain Jouffroy, Basile Gilbert, Papa Gueye- Investigation: Romain Jouffroy & Papa Gueye Supervision: Romain Jouffroy, Papa Gueye Validation: Romain Jouffroy & Papa Gueye Writing- Reviewing and Editing: Romain Jouffroy, Basile Gilbert, Léa Thomas, Emmanuel Bloch-Laine, Patrick Ecollan, Vincent Bounes, Josiane Boularan, Papa Gueye, Benoît Vivien All authors read and approved the final manuscript.

Corresponding author

Correspondence to Romain Jouffroy.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the French Society of Anaesthesia and Intensive Care ethics committee on 2017, December 12th (Ref number: IRB 00010254–2017-026). The French Society of Anesthesia and Intensive Care ethics committee waived the need of informed consent.

Consent for publication

Not applicable.

Competing interests

none author has any competing interests. All methods were performed in accordingly to the relevant guidelines and regulations.

Additional information

Publisher's Note

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

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 http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) 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

Jouffroy, R., Gilbert, B., Thomas, L. et al. Association between prehospital shock index variation and 28-day mortality among patients with septic shock. BMC Emerg Med 22, 87 (2022). https://doi.org/10.1186/s12873-022-00645-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12873-022-00645-1

Keywords

BMC Emergency Medicine

ISSN: 1471-227X

Contact us