Risk assessment of sepsis through measurement of proAVP (copeptin): a secondary analysis of the TRIAGE study

in Endocrine Connections
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  • 1 Medical University Department of Medicine, Kantonsspital Aarau, Aarau, Switzerland
  • | 2 Faculty of Medicine, University of Basel, Basel, Switzerland

Correspondence should be addressed to M Kloter: milena.kloter@hotmail.com
Open access

Objective

Systemic infections and sepsis lead to strong activation of the vasopressin system, which is pivotal for stimulation of the endocrine stress response and, in addition, has vasoconstrictive and immunomodulatory effects. Our aim was to assess the significance of the vasopressor system through measurement of C-terminal proAVP (copeptin) regarding mortality prediction in a large prospective cohort of patients with systemic infection.

Design and methods

This secondary analysis of the observational cohort TRIAGE study included consecutive, adult, medical patients with an initial diagnosis of infection seeking emergency department care. We used multivariable regression analysis to assess associations of copeptin levels in addition to the Sequential Organ Failure Assessment (SOFA) score with 30-day mortality. Discrimination was assessed by calculation of the area under the curve (AUC).

Results

Overall, 45 of 609 (7.4%) patients with infection died within 30 days. Non-survivors had a marked upregulation of the vasopressin system with a more than four-fold increase in admission copeptin levels compared to non-survivors (199.9 ± 204.7 vs 46.6 ± 77.2 pmol/L). In a statistical model, copeptin was significantly associated with mortality (adjusted odds ratio of 1.04, 95% CI 1.01 to 1.07, P = 0.002). Regarding discrimination, copeptin alone showed an AUC of 0.82, while adding copeptin to the SOFA score significantly improved its prognostic ability (AUC 0.83 vs 0.86, P = 0.027).

Conclusion

Activation of the vasopressin system mirrored by an increase in copeptin levels provided significant information regarding mortality risk and improved the SOFA score for prediction of sepsis mortality.

Abstract

Objective

Systemic infections and sepsis lead to strong activation of the vasopressin system, which is pivotal for stimulation of the endocrine stress response and, in addition, has vasoconstrictive and immunomodulatory effects. Our aim was to assess the significance of the vasopressor system through measurement of C-terminal proAVP (copeptin) regarding mortality prediction in a large prospective cohort of patients with systemic infection.

Design and methods

This secondary analysis of the observational cohort TRIAGE study included consecutive, adult, medical patients with an initial diagnosis of infection seeking emergency department care. We used multivariable regression analysis to assess associations of copeptin levels in addition to the Sequential Organ Failure Assessment (SOFA) score with 30-day mortality. Discrimination was assessed by calculation of the area under the curve (AUC).

Results

Overall, 45 of 609 (7.4%) patients with infection died within 30 days. Non-survivors had a marked upregulation of the vasopressin system with a more than four-fold increase in admission copeptin levels compared to non-survivors (199.9 ± 204.7 vs 46.6 ± 77.2 pmol/L). In a statistical model, copeptin was significantly associated with mortality (adjusted odds ratio of 1.04, 95% CI 1.01 to 1.07, P = 0.002). Regarding discrimination, copeptin alone showed an AUC of 0.82, while adding copeptin to the SOFA score significantly improved its prognostic ability (AUC 0.83 vs 0.86, P = 0.027).

Conclusion

Activation of the vasopressin system mirrored by an increase in copeptin levels provided significant information regarding mortality risk and improved the SOFA score for prediction of sepsis mortality.

Introduction

Systemic infection leading to sepsis contributes to a major portion of patients seeking care in the emergency department (ED) and the intensive care unit (ICU) (1, 2). Sepsis has been defined as life-threatening organ dysfunction caused by a dysregulated host response to infection and is associated with increased use of healthcare resources and mortality (3, 4). Sepsis leads to strong physiological activation of the vasopressin system aiming to co-stimulate the endocrine stress response and stabilize blood hemostasis through vasoconstrictive effects and the overwhelming immune response through immunomodulatory effects (5). While pharmacological treatment of sepsis patients with vasopressin did not appear to lower mortality (6), measuring the activation of the vasopressin system could provide important prognostic information regarding the severity of the infection and associated mortality risks.

Estimating severity and risk for mortality has become the main focus in the initial assessment of sepsis. It has become clear that organ dysfunction is both a hallmark of severe systemic infection and a main prognostic indicator (7, 8). According to current guidelines, the diagnosis of sepsis thus also relies on the sequential organ failure assessment (SOFA), which reflects the individual degree of organ dysfunction (9). However, relying on SOFA still is not perfect, and there is misclassification of patients regarding their true mortality risk. Thus, improving SOFA by the addition of other prognostic indicators is important. Herein, novel biomarkers mirroring fluid and endocrine activation may be helpful (1, 10, 11, 12). Hemodynamic instability, including vascular tone loss, decreased arterial blood pressure and tissue perfusion, occurs in sepsis and in septic shock resulting in activation of counteracting mediators (13, 14). This includes activation of the arginine vasopressin (AVP) on the hypothalamic–pituitary–adrenal (HPA) axis (15, 16, 17). As AVP is hard to measure due to the instability and short half-life, the more stable pre-hormone copeptin (39-amino acid C-terminal portion of proAVP) may be measured instead (14, 18, 19, 20). Copeptin has previously been shown to provide prognostic information in patients with stroke and infection of the lung (10, 21, 22, 23, 24), as well as for critically ill patients with sepsis (13, 25, 26, 27, 28). However, to our knowledge, there is a lack of studies investigating whether the addition of copeptin to SOFA could improve the prognostic assessment of patients by providing information regarding activation of the vasopressin system.

Our aim was to assess the significance of the activation of the vasopressor system through measurement of copeptin in addition to SOFA regarding mortality prediction in a large prospective cohort of patients with systemic infection.

Materials and methods

Study design, setting and patient sample

This is a secondary analysis of the prospective TRIAGE study (1), a multi-national, observational cohort study, which recruited consecutive ED patients with any symptoms in Aarau (Switzerland), Paris (France), and Clearwater (FL, USA) between March 2013 and October 2014. The study protocol (29) and main results (1) have been published previously. The Institutional Review Board of all centers approved the protocol and waived the need for individual information content due to the observational design of the study (main Swiss IRB: Ethic Commission of the Canton Aargau: registration number: EK-2012/059). The TRIAGE study was registered at the “ClinicalTrials.gov” website (http://www.clinicaltrials.gov/ct2/show/NCT01768494, last access 19.02.2021).

For this secondary analysis, only medical patients presenting at the tertiary care hospital in Aarau (Switzerland) with a main diagnosis of infection were included. Thus, all patients had a main infection diagnosis, which was verified through their Swiss diagnosis-related groups (DRG) coding at discharge (from: https://www.swissdrg.org, last access 19.02.2021). Surgical and pediatric patients were not part of the study.

Data collection and selection

All included participants provided a medical history and underwent a physical examination, including measurement of vital signs, laboratory assessment and collection of leftover blood samples. We also recorded socio-demographical data, clinical symptoms, complaints, and comorbidities. Patient’ outcomes, including admission toICU and length of hospital stay (LOS), were collected by chart review, if necessary. Missing data was supplemented through chart abstraction and automatic export from the internal medical data system. All included patients were contacted 30 days after hospital admission via telephone interview to assess their vital status.

Primary and secondary endpoints

Consistent with the initial study, the primary endpoint of this analysis was defined as all-cause 30-day mortality. Secondary endpoints were defined as admission to the ICU within 30 days following ED admission and positive blood cultures during the hospital stay.

Definitions of infection at ED admission

For this analysis, we grouped patients into pre-specified groups based on the main focus of infection, namely respiratory tract infection (including community-acquired pneumonia, chronic obstructive pulmonary disease (COPD)-exacerbation, asthma-exacerbation, bronchitis), urinary tract infection, skin infection, gastrointestinal infection, CNS infection, and other types of infections.

SOFA score calculation

In order to identify organ dysfunction caused by a dysregulated host response to infection, the SOFA score as proposed by the 'Third International Consensus Definitions for Sepsis and Septic Shock' was used (30). The score evaluates different organ systems (respiratory, coagulation, liver, cardiovascular, central nervous, and renal), which require laboratory and clinical variables for assessment and computation (9). Because the main entry point of the study was the ED and not all patients had an arterial blood gas analysis taken, we used an adapted score as previously proposed relying on the SO2/FiO2 index instead of the pO2/FiO2 index (9). If the route of O2 administration was unknown, we assumed a FiO2 of 0.3 for patients with nasal O2 administration according to the previous study (31). For the cardiovascular system, 0 points were assigned for mean arterial pressure (MAP) ≥ 70 mmHg and 1 point for MAP < 70 mmHg as specified in the original SOFA score calculation.

Copeptin measurement and other markers of the osmotic system

For the analysis of these copeptin values, there were no new measurements performed. The analyzed copeptin data were measured during the TRIAGE study and are original data from there. For this, leftover samples of routinely collected blood samples upon admission were immediately centrifuged, aliquoted and frozen at −20°C for later batch analysis of copeptin. Copeptin was batch-measured in plasma with a new sandwich immunoassay as described elsewhere (14, 32). The assays have analytical detection limits of 0.4 pmol/L. We also recorded other markers influencing the osmotic system, such as sodium, osmolality and glomerular filtration rate (GFR) from the routine laboratory assessment.

Statistical analysis

All statistical analyses were performed using STATA 15.1 (StataCorp LLC). For descriptive statistics, discrete variables are expressed as frequency (percentage) and continuous variables are expressed as mean with s.d. or as medians with interquartile range (IQR). Imputation methods were used to complete data missing less than 10% of values. Univariable and multivariable logistic regression models with primary and secondary endpoints were used to examine the association of copeptin and other markers. Laboratory values with non-normal distribution were normalized through log-transformation before being entered into the statistical models. Odds ratios (OR), including the corresponding 95% CIs were reported as a measure of association. We predefined three types of regression models, namely an unadjusted model (model 1), a model adjusted for age, sex, type of infection and comorbidities (model 2), and a model adjusted for age, sex, type of infection, comorbidities and SOFA score (model 3). The area under the receiver-operator-curve (ROC-AUC) was calculated as a measure of discrimination. Moreover, we also investigated subgroups for differences in performance based on socio-demographic factors (age and sex), type of infection and fluid balance makers.

Results

Patient population

This analysis includes a total of 654 medical inpatients presenting with a main diagnosis of infection to the ED of the Cantonal Hospital Aarau (Switzerland). The median age was 61 ± 20 years and 56% (n = 365) of the patients were male. The mean SOFA score was 1.5 points (±2) and 62.4% of patients had a SOFA score < 2 points. Regarding focus of infection, respiratory tract infection (n = 272, 41.6%) and urinary tract infection (n = 154, 23.5%) were most frequent. Almost 30% of patients had chronic kidney disease (n = 184). Overall, a total of 45 patients (6.9%) reached the primary endpoint of all-cause 30-day mortality. Baseline characteristics of the patient population overall and stratified according to the primary endpoint are presented in Table 1.

Table 1

Baseline characteristics of the overall cohort and stratified by primary endpoint.

All (n = 654)Survivors (n = 609)Non-survivors (n = 45)P-value
Sociodemographics
 Age (years)61.0 (20.3)59.8 (20.3)78.3 (10.2)<0.001
 Male sex365 (55.8%)336 (55.2%)29 (64.4%)0.23
Clinical presentation at ED admission
 Blood pressure systolic (mmHg)129.9 (22.6)130.7 (22.0)119.3 (27.2)0.001
 Blood pressure diastolic (mmHg)74.8 (15.3)75.5 (14.3)64.6 (23.1)<0.001
 Pulse rate (bpm)90.4 (19.5)89.8 (19.0)97.7 (24.4)0.010
 SpO2 (%)93.8 (4.5)94.0 (4.3)91.2 (5.6)<0.001
 O2administration119 (18.3%)95 (15.7%)24 (54.5%)<0.001
 Temperature (°C)37.9 (1.1)37.9 (1.1)37.7 (1.0)0.26
 GCS14.7 (1.2)14.8 (0.8)13.4 (3.1)<0.001
SOFA score at ED admission
 Total SOFA score1.5 (2.0)1.3 (1.8)4.2 (2.7)<0.001
  SOFA score < 2 points408 (62.4%)401 (65.8%)7 (15.5%)
  SOFA score 2–5 points208 (31.8%)187 (30.7%)21 (46.6%)
  SOFA score > 5 points38 (5.8%)21 (3.5%)17 (37.9%)
Origin of infection0.12
 Respiratory tract infection272 (41.6%)246 (40.4%)26 (57.8%)0.023
  Pneumonia135 (49.6%)117 (47.6%)18 (69.2%)
  Asthma exacerbation, bronchitis, others120 (44.1%)115 (46.7%)5 (19.2%)
  COPD exacerbation17 (6.3%)14 (5.7%)3 (11.5%)
 Urinary tract infection154 (23.5%)145 (23.8%)9 (20.0%)
 Skin infection59 (9.0%)56 (9.2%)3 (6.7%)
 Gastrointestinal tract infection39 (6.0%)35 (5.7%)4 (8.9%)
 CNS infection17 (2.6%)17 (2.8%)0 (0.0%)
 Other infection113 (17.3%)110 (18.1%)3 (6.7%)
Comorbidities
 Anemia316 (48.3%)279 (42.7%)37 (5.7%)<0.001
 Hypertension305 (46.6%)278 (45.6%)27 (60.0%)0.063
 Chronic renal failure184 (28.1%)155 (25.5%)29 (64.4%)<0.001
 Cancer122 (18.7%)106 (17.4%)16 (35.6%)0.003
 Diabetes mellitus119 (18.2%)108 (17.7%)11 (24.4%)0.26
 Coronary heart disease89 (13.6%)73 (12.0%)16 (35.6%)<0.001
 COPD53 (8.1%)42 (6.9%)11 (24.4%)<0.001
 Congestive heart failure47 (7.2%)42 (6.9%)5 (11.1%)0.29
 Dementia45 (6.9%)37 (6.1%)8 (17.8%)0.003
 Substance abuse27 (4.1%)26 (4.3%)1 (2.2%)0.51
 Stroke26 (4.0%)24 (3.9%)2 (4.4%)0.87
Initial blood sample
Na (mmol/L)136.8 (4.3)136.7 (4.2)137.4 (6.1)0.34
  Hyponatremia (<135 mmol/L)214 (32.7%)199 (32.7%)15 (33.3%)0.003
  Normonatremia (136–143 mmol/L)414 (63.3%)390 (64.0%)24 (53.3%)
  Hypernatremia (>143 mmol/L)26 (4.0%)20 (3.3%)6 (13.3%)
 Osmolality (mosmol/kg)289.1 (11.2)288.4 (10.6)297.6 (14.9)<0.001
  Hypoosmolality (<280 mosmol/kg)101 (15.4%)99 (16.3%)2 (4.4%)<0.001
  Normoosmolality (280–300 mosmol/kg)483 (73.9%)458 (75.2%)25 (55.6%)
  Hyperosmolality (>300 mosmol/kg)70 (10.7%)52 (8.5%)18 (40.0%)
 GFR MDRD (mL/min/1.73 m2)52.2 (14.9)53.4 (13.9)37.2 (18.7)<0.001
  CKD Stage 1 + 2 (>60)410 (62.7%)399 (65.5%)11 (24.4%)<0.001
  CKD Stage 3 (30–60)175 (26.8%)158 (25.9%)17 (37.8%)
  CKD Stage 4–5 (<30)69 (10.6%)52 (8.5%)17 (37.8%)

COPD, chronic obstructive pulmonary disease; ED, emergency department; GCS, Glasgow Coma Scale; GFR, glomerular filtration rate; MDRD, modification of diet in renal disease; SOFA score, sequential organ failure assessment score; SpO2, peripheral capillary oxygen saturation.

Association of SOFA, copeptin and fluid balance markers with primary and secondary endpoints

Overall, initial SOFA score values were three-fold higher in patients who died compared to survivors (4.2 ± 2.7 vs 1.3 ± 1.8, P < 0.001) (Table 2). Also, copeptin levels upon admission were four-fold higher in non-survivors compared to survivors (199.9 ± 204.7 pmol/L vs 46.6 ± 77.2 pmol/L). In an unadjusted logistic regression analysis (model 1), we found an association of copeptin with an OR of 1.08 (95% CI 1.06, 1.10, P < 0.001) for the primary endpoint 30-day mortality. These results remained robust in the multivariable model 2 (OR 1.06 (95% CI 1.04, 1.09), P < 0.001), adjusted for age, sex, type of infection and comorbidities, as well as in the multivariable model 3 adjusted for age, sex, type of infection, comorbidities and SOFA score (OR 1.04 (95% CI 1.01, 1.07), P = 0.002). Copeptin also showed an association for the two secondary endpoints – ICU admission and blood culture positivity – in the unadjusted model (model 1). However, in the two adjusted models (model 2 and model 3), the association was no longer significant for both secondary endpoints.

Table 2

Univariable and multivariable logistic regression analysis for the primary and secondary endpoints.

SurvivorsNon-survivorsP-valueAUCModel 1: Univariable OR (95% CI), P-valueModel 2a: Multivariable OR (95% CI), P-valueModel 3b: Multivariable OR (95% CI), P-value
Primary endpoint: 30-day mortalityn = 609n = 45
 SOFA score1.3 (1.8)4.2 (2.7)<0.0010.831.66 (1.46, 1.89), P < 0.0011.64 (1.38, 1.95), P < 0.001
 Copeptin (pmol/L)46.6 (77.2)199.9 (204.7)<0.0010.821.08 (1.06, 1.10), P < 0.0011.06 (1.04, 1.09), P < 0.0011.04 (1.01, 1.07), P = 0.002
Na (mmol/L)136.7 (4.2)137.4 (6.1)0.340.571.04 (0.96, 1.11), P = 0.3401.01 (0.94, 1.09), P = 0.7761.01 (0.94, 1.09), P = 0.843
 Osmolality (mosmol/kg)288.4 (10.6)297.6 (14.9)<0.0010.741.06 (1.03, 1.08), P < 0.0011.03 (1.00, 1.06), P = 0.0331.01 (0.98, 1.04), P = 0.724
 GFR MDRD (mL/min/1.73 m2)53.4 (13.9)37.2 (18.7)<0.0010.751.05 (1.03, 1.07), P < 0.0011.05 (1.02, 1.08), P < 0.0011.01 (0.98, 1.04), P = 0.579
Secondary endpoint: ICU admissionn = 588n = 66
 SOFA score1.3 (1.7)4.0 (2.4)<0.0010.831.68 (1.49, 1.89), P < 0.0011.65 (1.43, 1.90), P < 0.001
 Copeptin (pmol/L)51.8 (97.2)105.0 (106.5)<0.0010.751.04 (1.02, 1.05), P < 0.0011.02 (1.00, 1.05), P = 0.0500.99 (0.96, 1.02), P = 0.417
Na (mmol/L)136.8 (4.2)136.4 (5.0)0.460.520.98 (0.92, 1.04), P = 0.4551.00 (0.94, 1.06), P = 0.8991.01 (0.95, 1.07), P = 0.791
 Osmolality (mosmol/kg)288.5 (10.7)294.3 (13.6)<0.0010.651.04 (1.02, 1.06), P < 0.0011.02 (1.00, 1.05), P = 0.0831.00 (0.98, 1.03), P = 0.828
 GFR MDRD (mL/min/1.73 m2)53.6 (13.5)40.6 (20.2)<0.0010.701.04 (1.03, 1.06), P < 0.0011.04 (1.02, 1.06), P < 0.0011.00 (0.97, 1.02), P = 0.710
Secondary endpoint: blood culture positivityn = 400n = 81
 SOFA score1.7 (2.0)2.9 (2.5)<0.0010.651.28 (1.15, 1.41), P < 0.0011.27 (1.11, 1.45), P = 0.001
 Copeptin (pmol/L)57.2 (95.2)109.3 (131.9)<0.0010.681.04 (1.02, 1.06), P < 0.0011.02 (1.00, 1.05), P = 0.0521.01 (0.99, 1.04), P = 0.398
Na (mmol/L)136.3 (4.3)136.3 (4.5)0.910.511.00 (0.94, 1.05), P = 0.9121.00 (0.95, 1.06), P = 0.9341.00 (0.94, 1.06), P = 0.968
 Osmolality (mosmol/kg)288.7 (11.4)290.3 (13.3)0.270.571.01 (0.99, 1.03), P = 0.2711.00 (0.98, 1.02), P = 0.9240.99 (0.97, 1.01), P = 0.333
 GFR MDRD (mL/min/1.73 m2)51.4 (15.8)45.9 (16.1)0.0050.621.02 (1.01, 1.03), P = 0.0061.01 (0.98, 1.03), P = 0.6560.98 (0.96, 1.01), P = 0.196

aAdjusted for age, sex, type of infection and comorbidities; badjusted for age, sex, type of infection and comorbidities and SOFA score.

SOFA score, sequential organ failure assessment score; GF, glomerular filtration rate; MDRD, modification of diet in renal disease; AUC, area under the curve; OR, odds ratio.

Regarding discrimination, copeptin was the strongest for 30-day mortality, with an AUC of 0.82 compared to the other fluid balance markers including Na (AUC 0.57), osmolality (AUC 0.74) and GFR (AUC 0.75) and similar to the SOFA score (AUC 0.82). We also compared AUCs of copeptin among different predefined patient subgroups. As demonstrated in Fig. 1, results were similar in the subgroup stratified by gender, type of infection, Na concentration and osmolality. However, a significant effect modification was found for urinary tract infection and CKD stage 3 (P for interaction < 0.05). We also investigated the same subgroups regarding SOFA score, where a subgroup effect was found for patients aged under 60 years (AUC 0.94) and for patients with CKD stages 4 and 5 (AUC of 0.53 (Fig. 2).

Figure 1
Figure 1

Prognostic performance of copeptin as predictor for 30-day mortality stratified by age, sex, type of infection, and fluid balance markers (sodium, osmolality and kidney function) to evaluate subgroup differences. The vertical reference line mirrors the overall AUC. AUCs to the right of the reference line indicate higher levels of discrimination, AUCs to the left of the reference line indicate lower levels of discrimination. The forest plot shows different levels of discrimination with their respective 95% CIs by subgroups. P for interaction indicates the level of effect modification by subgroups.

Citation: Endocrine Connections 10, 9; 10.1530/EC-21-0211

Figure 2
Figure 2

Prognostic performance of SOFA score as predictor for 30-day mortality stratified by age, sex, type of infection, and fluid balance markers (sodium, osmolality and kidney function) to evaluate subgroup differences. The vertical reference line mirrors the overall AUC. AUCs to the right of the reference line indicate higher levels of discrimination, AUCs to the left of the reference line indicate lower levels of discrimination. The forest plot shows different levels of discrimination with their respective 95% CIs by subgroups. P for interaction indicates the level of effect modification by subgroups.

Citation: Endocrine Connections 10, 9; 10.1530/EC-21-0211

In the next step, we investigated whether the combination of SOFA score with copeptin and other fluid balance markers would further improve its prognostic potential. Table 3 shows the AUC of different bivariable and multivariable models combining different parameters. Adding copeptin to SOFA significantly improved its AUC from 0.83 to 0.86 (P = 0.028). Further addition of markers did only slightly provide better prognostication as assessed by improvements in AUC and none of the other markers improved the SOFA score, except MR-proADM which showed the same improvement as copeptin (AUC from 0.83 to 0.86, P = 0.002). Also, regarding blood culture positivity, copeptin improved the SOFA score from AUC 0.65 to 0.68, while none of the other markers had a similar effect. Finally, regarding admission to ICU, osmolality but not copeptin improved the model.

Table 3

Association of various combinations of SOFA score with copeptin and fluid balance biomarkers for primary and secondary endpoints.

AUC (95% CI)P-value
Primary endpoint 30-day mortality
  SOFA score0.83 (0.77, 0.88)
 Change in AUC in bivariate analysis
  SOFA score and Na0.83 (0.77, 0.88)0.7448
  SOFA score and GFR0.84 (0.78, 0.89)0.0321
  SOFA score and osmolality0.84 (0.79, 0.89)0.0861
  SOFA score and copeptin0.86 (0.81, 0.91)0.0277
  SOFA score and MR-proADM0.86 (0.81, 0.90)0.0016
 Change in AUC in multivariate analysis
  SOFA score and copeptin and osmolality and GFR0.87 (0.82, 0.92)0.0281
  SOFA score and copeptin and osmolality and GFR and Na0.87 (0.82, 0.92)0.0289
  SOFA score and copeptin and GFR0.87 (0.82, 0.92)0.0144
  SOFA score and copeptin and MR-proADM0.87 (0.83, 0.92)0.0075
Secondary endpoint admission to ICU
  SOFA score0.83 (0.78, 0.88)
 Change in AUC in bivariate analysis
  SOFA score and copeptin0.82 (0.77, 0.87)0.0546
  SOFA score and Na0.82 (0.77, 0.87)0.3127
  SOFA score and GFR0.82 (0.78, 0.87)0.4976
  SOFA score and osmolality0.84 (0.80, 0.88)0.0155
 Change in AUC in multivariate analysis
  SOFA score and osmolality and GFR and Na and copeptin0.83 (0.79, 0.88)0.3516
  SOFA score and osmolality and GFR and Na0.84 (0.79, 0.88)0.1387
  SOFA score and osmolality and GFR0.84 (0.79, 0.88)0.0194
Secondary endpoint blood culture positivity
  SOFA score0.65 (0.59, 0.72)
 Change in AUC in bivariate analysis
  SOFA score and GFR0.64 (0.57, 0.71)0.0589
  SOFA score and osmolality0.65 (0.58, 0.71)0.7496
  SOFA score and sodium0.65 (0.58, 0.72)0.9707
  SOFA score and copeptin0.68 (0.62, 0.74)0.0144
 Change in AUC in multivariate analysis
  SOFA score and copeptin and Na and osmolality and GFR0.68 (0.61, 0.74)0.0900
  SOFA score and copeptin and Na and osmolality0.68 (0.61, 0.74)0.0876
  SOFA-score and copeptin and Na0.68 (0.62, 0.75)0.0104

AUC, area under the curve; GFR, glomerular filtration rate; MR-proADM, mid-regional pro-adrenomedullin; SOFA score, sequential organ failure assessment score.

Association of copeptin ICU admission and blood culture positivity

The SOFA score alone showed the best discrimination for the secondary endpoint of ICU admission as well (AUC 0.83) followed by copeptin with an AUC of 0.75. In contrast, regarding blood culture positivity, copeptin performed the best (AUC 0.68) followed by SOFA score (AUC 0.65), which nevertheless remained very weak. Results of the regression analysis and discrimination values are shown in Table 2.

Figure 3 illustrates the SOFA score together with copeptin, whereby stratification by age found higher AUC for 60 to 80 years (AUC 0.98) and a P for interaction of < 0.05 for stage 1 and 2 CKD.

Figure 3
Figure 3

Prognostic performance of SOFA score and copeptin as predictor for 30-day mortality stratified by age, sex-, type of infection, and fluid balance markers (sodium, osmolality and kidney function) to evaluate subgroup differences. The vertical reference line mirrors the overall AUC. AUCs to the right of the reference line indicate higher levels of discrimination, AUCs to the left of the reference line indicate lower levels of discrimination. The forest plot shows different levels of discrimination with their respective 95% CIs by subgroups. P for interaction indicates the level of effect modification by subgroups.

Citation: Endocrine Connections 10, 9; 10.1530/EC-21-0211

Discussion

The key findings of this analysis are two-fold. First, we found that the activation of the vasopressin system mirrored by an increase in admission copeptin levels provided prognostic information regarding mortality. Secondly, when added to the SOFA score, this information further improved the early risk stratification of patients. The addition of fluid balance biomarkers, such as osmolality or Na, did not provide such prognostic information.

Early and reliable risk stratification in patients presenting with signs and symptoms of infection and possible sepsis is important to reduce time to effective treatment and improve the site of care decisions (1, 2). For this purpose, SOFA is a well-established score with high prognostic accuracy regarding mortality (8). It has been shown that the vasoactive peptide mid-regional pro-adrenomedullin (MR-proADM) is able to improve the mortality risk stratification in patients with infection presenting to the ED beyond SOFA score alone and may further improve initial therapeutic site of care decisions (33). These findings were also shown for copeptin, as already mentioned above. Moreover, we have observed a further significant improvement of discrimination when adding both MR-proADM and copeptin to the SOFA score.

Physiopathologically, systemic infections lead to strong activation of the vasopressin system in order to balance vasodilatation by its vasoconstrictive and volume-retention effects. Often neglected vasopressin stimulates adreno-corticotropic hormone (ACTH) secretion in synergy with hypothalamic corticotroph-releasing hormone (CRH). Thus, vasopressin mediates and amplifies the hypothalamic–pituitary-adrenal stress response. Vasopressin also has immunomodulatory effects (5). Vasopressin also controls Na balance by free water retention in the kidney. In addition to the production of vasopressin in response to volume and osmolarity effects (5, 34), it is also a stress hormone that increases under physiological conditions of disease, including acute infections (27, 28). Physiological stress caused by infections or severe disease triggers the release of copeptin aiming to increase free water resorption in the kidney and thus maintaining blood pressure homeostasis through V2 receptors and inducing vasoconstriction of blood vessels through V1 receptors (35, 36, 37). The physiological role of copeptin is not yet known. However, this peptide may have a role during intracellular processing of provasopressin, which contributes to the correct structural formation of the AVP precursor, which in turn leads to efficient proteolytic maturation (38). Despite unknown function, copeptin has been described as a surrogate of AVP for physiological conditions and in different diseases (39). Several studies found copeptin to be increased in different types of infections and to be associated with short-term mortality (40, 41). Our finding of higher copeptin levels in infections as a marker and mediator of the stress response is, thus, not surprising. To our knowledge, however, this is the first large-scale study investigating the additive effects of the vasopressor system through measurement of copeptin to the SOFA score, the current gold standard for sepsis diagnosis, regarding mortality risk.

Our results are in line with previous studies showing that fluid balance markers (i.e. sodium, osmolality, GFR) may improve risk prediction for infection in addition to organ dysfunction markers (8, 12). Interestingly, patients with respiratory, gastrointestinal or hepatic infections had the most benefit when applying SOFA score together with copeptin levels. Especially remarkable is the impact of kidney function on predicting 30-day mortality when the SOFA score is combined with copeptin. Mortality prediction remains significant regardless of the stage of chronic kidney disease, indicating dysregulated fluid homeostasis, which is also in line with other investigations (42). Taken together with other study results, this is evidence of the strong correlation of body fluid balance with infection and mortality (43).

It represents also a fluid balance marker, associated with urine osmolality and sodium, released by increased plasma osmolality, decreased arterial pressure and reductions in cardiac volume (44). Earlier investigations of copeptin focused primarily on vasopressin-dependent disorders of fluid homeostasis, such as hyponatremia, polydipsia and diabetes insipidus in the outpatient setting with a generally low stress level (5, 34) or acute cardiovascular illness (45, 46).

Based on the above-mentioned findings, one may hypothesize that risk stratification can be improved by assessing the SOFA score together with biomarkers, such as copeptin, in the initial assessment of patients with infection. One can expect better patient flow and a more adequate estimation of triage priority, which in turn may lead to lower ICU admissions and 30-day mortality rates. However, these findings must be investigated in further studies.

Our study has some limitations. First, because of the retrospective design of this analysis, we did not have all laboratory parameters and characteristics available, which would be of value in the context of copeptin measurement. We also used an adapted version of the SOFA score as not all patients had an arterial gas analysis done upon ED admission. Secondly, we limited all results to admission values only and no follow-up information regarding kinetics was available. Thirdly, we did not have a control patient population without infection to understand whether copeptin can discriminate between infection-related and non-infection-related deterioration in patients.

Conclusion

In conclusion, activation of the vasopressin system mirrored by an increase in copeptin levels provided significant information regarding mortality risk and improved the SOFA score for prediction of sepsis mortality.

Declaration of interest

P S and B M received research support paid to the Institution from Thermofisher, bioMerieux, Roche Diagnostics, Nestle Health Science and Abbott Nutrition. All other authors reported no conflicts of interest.

Funding

Thermofisher provided an unrestricted research grand for the initial TRIAGE study. P S was supported by the Swiss National Science Foundation (SNSF Professorship, PP00P3 15031/1). This TRIAGE study was supported by the Swiss Academies of Arts and Sciences (SAMW).

Author contribution statement

M K, C G and E H managed the data collection. M K, C G and P S performed the statistical analyses and M K and C G drafted the manuscript. E H, A K, B M and P S, amended and commented on the manuscript. All authors approved the final version.

Acknowledgements

This multidisciplinary and interprofessional trial was only possible in close collaboration of social services (Anja Keller, Regina Schmid), the nursing department (Susanne Schirlo, Petra Tobias), the central laboratory (Martha Kaeslin, Renate Hunziker), medical controlling (Juergen Froehlich, Thomas Holler, Christoph Reemts), IT (Roger Wohler, Kurt Amstad, Ralph Dahnke, Sabine Storost) of the Cantonal Hospital Aarau, Clinical Trial Unit (CTU), University Hospital Basel (Thomas Fabbro, Guido Stirnimann, Patrick Simon), the department of Health Economics of the University of Basel (Stefan Felder, Timo Tondelli), as well as all participating patients, nurses and physicians. The TRIAGE study group includes members from the University Department of Internal Medicine, Cantonal Hospital Aarau, Switzerland (Ulrich Buergi, MD, Petra Tobias, RN, Eva Grolimund, MD, Ursula Schild, RN, Zeljka Caldara, RN, Katharina Regez, RN, Martha Kaeslin, Ursina Minder, RN, Renate Hunziker, RN, Andriy Zhydkov, MD, Timo Kahles, MD, Krassen Nedeltchev, MD, Petra Schäfer-Keller, PhD) the Clinical Trial Unit University Hospital Basel (Stefanie von Felten, PhD), the Institute of Nursing Science, University of Basel, Switzerland (Sabina De Geest, PhD); the Department of Psychology, University of Berne (Pasqualina Perrig-Chiello, PhD). The authors thank Erica Holt for native English review.

References

  • 1

    Schuetz P, Hausfater P, Amin D, Amin A, Haubitz S, Faessler L, Kutz A, Conca A, Reutlinger B & Canavaggio P et al.Biomarkers from distinct biological pathways improve early risk stratification in medical emergency patients: the multinational, prospective, observational TRIAGE study. Critical Care 2015 19 377. (https://doi.org/10.1186/s13054-015-1098-z)

    • Search Google Scholar
    • Export Citation
  • 2

    Julian-Jimenez A, Candel-Gonzalez FJ, Gonzalez Del Castillo J. Usefulness of inflammation and infection biomarkers in the Emergency Department. Enfermedades Infecciosas y Microbiologia Clinica 2014 32 1771 90. (https://doi.org/10.1016/j.eimc.2013.01.005)

    • Search Google Scholar
    • Export Citation
  • 3

    Prescott HC, Langa KM, Liu V, Escobar GJ, Iwashyna TJ. Increased 1-year healthcare use in survivors of severe sepsis. American Journal of Respiratory and Critical Care Medicine 2014 190 626 9. (https://doi.org/10.1164/rccm.201403-0471OC)

    • Search Google Scholar
    • Export Citation
  • 4

    Prescott HC, Osterholzer JJ, Langa KM, Angus DC, Iwashyna TJ. Late mortality after sepsis: propensity matched cohort study. BMJ 2016 353 i2375. (https://doi.org/10.1136/bmj.i2375)

    • Search Google Scholar
    • Export Citation
  • 5

    Christ-Crain M, Fenske W. Copeptin in the diagnosis of vasopressin-dependent disorders of fluid homeostasis. Nature Reviews: Endocrinology 2016 12 1681 76. (https://doi.org/10.1038/nrendo.2015.224)

    • Search Google Scholar
    • Export Citation
  • 6

    Russell JA, Walley KR, Singer J, Gordon AC, Hebert PC, Cooper DJ, Holmes CL, Mehta S, Granton JT & Storms MM et al.Vasopressin versus norepinephrine infusion in patients with septic shock. New England Journal of Medicine 2008 358 8778 8 7. (https://doi.org/10.1056/NEJMoa067373)

    • Search Google Scholar
    • Export Citation
  • 7

    Kadziolka I, Swistek R, Borowska K, Tyszecki P, Serednicki W. Validation of Apache II and SAPS II scales at the intensive care unit along with assessment of SOFA scale at the admission as an isolated risk of death predictor. Anaesthesiology Intensive Therapy 2019 51 10711 1. (https://doi.org/10.5114/ait.2019.86275)

    • Search Google Scholar
    • Export Citation
  • 8

    Innocenti F, Bianchi S, Guerrini E, Vicidomini S, Conti A, Zanobetti M, Pini R. Prognostic scores for early stratification of septic patients admitted to an emergency department-high dependency unit. European Journal of Emergency Medicine 2014 21 25425 9. (https://doi.org/10.1097/MEJ.0000000000000075)

    • Search Google Scholar
    • Export Citation
  • 9

    Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD & Coopersmith CM et al.The Third International Consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016 315 8018 10. (https://doi.org/10.1001/jama.2016.0287)

    • Search Google Scholar
    • Export Citation
  • 10

    Kruger S, Ewig S, Giersdorf S, Hartmann O, Suttorp N, Welte T & German Competence Network for the Study of Community Acquired Pneumonia. Cardiovascular and inflammatory biomarkers to predict short- and long-term survival in community-acquired pneumonia: results from the German Competence Network, CAPNETZ. American Journal of Respiratory and Critical Care Medicine 2010 182 142614 34. (https://doi.org/10.1164/rccm.201003-0415OC)

    • Search Google Scholar
    • Export Citation
  • 11

    Tigabu BM, Davari M, Kebriaeezadeh A, Mojtahedzadeh M. Fluid volume, fluid balance and patient outcome in severe sepsis and septic shock: a systematic review. Journal of Critical Care 2018 48 15315 9. (https://doi.org/10.1016/j.jcrc.2018.08.018)

    • Search Google Scholar
    • Export Citation
  • 12

    Sakr Y, Rubatto Birri PN, Kotfis K, Nanchal R, Shah B, Kluge S, Schroeder ME, Marshall JC, Vincent JL & Intensive Care Over Nations Investigators. IHigher fluid balance increases the risk of death from sepsis: results from a Large International Audit. Critical Care Medicine 2017 45 3863 94. (https://doi.org/10.1097/CCM.0000000000002189)

    • Search Google Scholar
    • Export Citation
  • 13

    Struck J, Morgenthaler NG, Bergmann A. Copeptin, a stable peptide derived from the vasopressin precursor, is elevated in serum of sepsis patients. Peptides 2005 26 2500250 4. (https://doi.org/10.1016/j.peptides.2005.04.019)

    • Search Google Scholar
    • Export Citation
  • 14

    Morgenthaler NG, Muller B, Struck J, Bergmann A, Redl H, Christ-Crain M. Copeptin, a stable peptide of the arginine vasopressin precursor, is elevated in hemorrhagic and septic shock. Shock 2007 28 2192 2 6. (https://doi.org/10.1097/SHK.0b013e318033e5da)

    • Search Google Scholar
    • Export Citation
  • 15

    Palmiere C, Augsburger M. Copeptin as a diagnostic biomarker for sepsis-related deaths. Peptides 2014 59 757 8. (https://doi.org/10.1016/j.peptides.2014.07.011)

    • Search Google Scholar
    • Export Citation
  • 16

    Jochberger S, Dorler J, Luckner G, Mayr VD, Wenzel V, Ulmer H, Morgenthaler NG, Hasibeder WR, Dunser MW. The vasopressin and copeptin response to infection, severe sepsis, and septic shock. Critical Care Medicine 2009 37 4764 82. (https://doi.org/10.1097/CCM.0b013e3181957532)

    • Search Google Scholar
    • Export Citation
  • 17

    Lesur O, Roussy JF, Chagnon F, Gallo-Payet N, Dumaine R, Sarret P, Chraibi A, Chouinard L, Hogue B. Proven infection-related sepsis induces a differential stress response early after ICU admission. Critical Care 2010 14 R131. (https://doi.org/10.1186/cc9102)

    • Search Google Scholar
    • Export Citation
  • 18

    Morgenthaler NG, Struck J, Jochberger S, Dunser MW. Copeptin: clinical use of a new biomarker. Trends in Endocrinology and Metabolism 2008 19 434 9. (https://doi.org/10.1016/j.tem.2007.11.001)

    • Search Google Scholar
    • Export Citation
  • 19

    Schuetz P, Muller B. The hypothalamic-pituitary-adrenal axis in critical illness. Endocrinology and Metabolism Clinics of North America 2006 35 82338, x. (https://doi.org/10.1016/j.ecl.2006.09.013)

    • Search Google Scholar
    • Export Citation
  • 20

    Nickel CH, Bingisser R, Morgenthaler NG. The role of copeptin as a diagnostic and prognostic biomarker for risk stratification in the emergency department. BMC Medicine 2012 10 7. (https://doi.org/10.1186/1741-7015-10-7)

    • Search Google Scholar
    • Export Citation
  • 21

    Schuetz P, Wolbers M, Christ-Crain M, Thomann R, Falconnier C, Widmer I, Neidert S, Fricker T, Blum C & Schild U et al.Prohormones for prediction of adverse medical outcome in community-acquired pneumonia and lower respiratory tract infections. Critical Care 2010 14 R106. (https://doi.org/10.1186/cc9055)

    • Search Google Scholar
    • Export Citation
  • 22

    De Marchis GM, Katan M, Weck A, Fluri F, Foerch C, Findling O, Schuetz P, Buhl D, El-Koussy M & Gensicke H et al.Copeptin adds prognostic information after ischemic stroke: results from the CoRisk study. Neurology 2013 80 127812 86. (https://doi.org/10.1212/WNL.0b013e3182887944)

    • Search Google Scholar
    • Export Citation
  • 23

    Ostadal P, Kruger A, Zdrahalova V, Janotka M, Vondrakova D, Neuzil P, Prucha M. Blood levels of copeptin on admission predict outcomes in out-of-hospital cardiac arrest survivors treated with therapeutic hypothermia. Critical Care 2012 16 R187. (https://doi.org/10.1186/cc11671)

    • Search Google Scholar
    • Export Citation
  • 24

    Potocki M, Breidthardt T, Mueller A, Reichlin T, Socrates T, Arenja N, Reiter M, Morgenthaler NG, Bergmann A & Noveanu M et al.Copeptin and risk stratification in patients with acute dyspnea. Critical Care 2010 14 R213. (https://doi.org/10.1186/cc9336)

    • Search Google Scholar
    • Export Citation
  • 25

    Katan M, Christ-Crain M. The stress hormone copeptin: a new prognostic biomarker in acute illness. Swiss Medical Weekly 2010 140 w13101. (https://doi.org/10.4414/smw.2010.13101)

    • Search Google Scholar
    • Export Citation
  • 26

    Stolz D, Christ-Crain M, Morgenthaler NG, Leuppi J, Miedinger D, Bingisser R, Muller C, Struck J, Muller B, Tamm M. Copeptin, C-reactive protein, and procalcitonin as prognostic biomarkers in acute exacerbation of COPD. Chest 2007 131 105810 67. (https://doi.org/10.1378/chest.06-2336)

    • Search Google Scholar
    • Export Citation
  • 27

    Muller B, Morgenthaler N, Stolz D, Schuetz P, Muller C, Bingisser R, Bergmann A, Tamm M, Christ-Crain M. Circulating levels of copeptin, a novel biomarker, in lower respiratory tract infections. European Journal of Clinical Investigation 2007 37 1451 52. (https://doi.org/10.1111/j.1365-2362.2007.01762.x)

    • Search Google Scholar
    • Export Citation
  • 28

    Katan M, Morgenthaler N, Widmer I, Puder JJ, Konig C, Muller B, Christ-Crain M. Copeptin, a stable peptide derived from the vasopressin precursor, correlates with the individual stress level. Neuro Endocrinology Letters 2008 29 34134 6.

    • Search Google Scholar
    • Export Citation
  • 29

    Schuetz P, Hausfater P, Amin D, Haubitz S, Fassler L, Grolimund E, Kutz A, Schild U, Caldara Z & Regez K et al.Optimizing triage and hospitalization in adult general medical emergency patients: the triage project. BMC Emergency Medicine 2013 13 12. (https://doi.org/10.1186/1471-227X-13-12)

    • Search Google Scholar
    • Export Citation
  • 30

    Raith EP, Udy AA, Bailey M, McGloughlin S, MacIsaac C, Bellomo R, Pilcher DV & Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcomes and Resource Evaluation (CORE). Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA 2017 317 290300. (https://doi.org/10.1001/jama.2016.20328)

    • Search Google Scholar
    • Export Citation
  • 31

    Pandharipande PP, Shintani AK, Hagerman HE, St Jacques PJ, Rice TW, Sanders NW, Ware LB, Bernard GR, Ely EW. Derivation and validation of SpO2/FiO2 ratio to impute for PaO2/FiO2 ratio in the respiratory component of the sequential organ failure assessment score. Critical Care Medicine 2009 37 131713 21. (https://doi.org/10.1097/CCM.0b013e31819cefa9)

    • Search Google Scholar
    • Export Citation
  • 32

    Morgenthaler NG, Struck J, Alonso C, Bergmann A. Assay for the measurement of copeptin, a stable peptide derived from the precursor of vasopressin. Clinical Chemistry 2006 52 11211 9. (https://doi.org/10.1373/clinchem.2005.060038)

    • Search Google Scholar
    • Export Citation
  • 33

    Haag E, Gregoriano C, Molitor A, Kloter M, Kutz A, Mueller B, Schuetz P. Does mid-regional pro-adrenomedullin (MR-proADM) improve the sequential organ failure assessment-score (SOFA score) for mortality-prediction in patients with acute infections? Results of a prospective observational study. Clinical Chemistry and Laboratory Medicine 2021 59 116511 76. (https://doi.org/10.1515/cclm-2020-1566)

    • Search Google Scholar
    • Export Citation
  • 34

    Christ-Crain M, Morgenthaler NG, Fenske W. Copeptin as a biomarker and a diagnostic tool in the evaluation of patients with polyuria-polydipsia and hyponatremia. Best Practice and Research: Clinical Endocrinology and Metabolism 2016 30 2352 47. (https://doi.org/10.1016/j.beem.2016.02.003)

    • Search Google Scholar
    • Export Citation
  • 35

    Michell RH, Kirk CJ, Billah MM. Hormonal stimulation of phosphatidylinositol breakdown with particular reference to the hepatic effects of vasopressin. Biochemical Society Transactions 1979 7 86186 5. (https://doi.org/10.1042/bst0070861)

    • Search Google Scholar
    • Export Citation
  • 36

    Penit J, Faure M, Jard S. Vasopressin and angiotensin II receptors in rat aortic smooth muscle cells in culture. American Journal of Physiology 1983 244 E72E 82. (https://doi.org/10.1152/ajpendo.1983.244.1.E72)

    • Search Google Scholar
    • Export Citation
  • 37

    Thibonnier M Use of vasopressin antagonists in human diseases. Kidney International: Supplement 1988 26 S48S 51.

  • 38

    Barat C, Simpson L, Breslow E. Properties of human vasopressin precursor constructs: inefficient monomer folding in the absence of copeptin as a potential contributor to diabetes insipidus. Biochemistry 2004 43 81918 203. (https://doi.org/10.1021/bi0400094)

    • Search Google Scholar
    • Export Citation
  • 39

    Gomes DA, de Almeida Beltrao RL, de Oliveira Junior FM, da Silva Junior JC, de Arruda EPC, Lira EC, da Rocha MJA. Vasopressin and copeptin release during sepsis and septic shock. Peptides 2021 136 170437. (https://doi.org/10.1016/j.peptides.2020.170437)

    • Search Google Scholar
    • Export Citation
  • 40

    Saeed K, Wilson DC, Bloos F, Schuetz P, van der Does Y, Melander O, Hausfater P, Legramante JM, Claessens YE & Amin D et al.The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study. Critical Care 2019 23 40. (https://doi.org/10.1186/s13054-019-2329-5)

    • Search Google Scholar
    • Export Citation
  • 41

    Angeletti S, Spoto S, Fogolari M, Cortigiani M, Fioravanti M, De Florio L, Curcio B, Cavalieri D, Costantino S, Dicuonzo G. Diagnostic and prognostic role of procalcitonin (PCT) and MR-pro-adrenomedullin (MR-proADM) in bacterial infections. APMIS 2015 123 74074 8. (https://doi.org/10.1111/apm.12406)

    • Search Google Scholar
    • Export Citation
  • 42

    Wang H, Kang X, Shi Y, Bai ZH, Lv JH, Sun JL, Pei HH. SOFA score is superior to Apache-II score in predicting the prognosis of critically ill patients with acute kidney injury undergoing continuous renal replacement therapy. Renal Failure 2020 42 6386 45. (https://doi.org/10.1080/0886022X.2020.1788581)

    • Search Google Scholar
    • Export Citation
  • 43

    Acheampong A, Vincent JL. A positive fluid balance is an independent prognostic factor in patients with sepsis. Critical Care 2015 19 251. (https://doi.org/10.1186/s13054-015-0970-1)

    • Search Google Scholar
    • Export Citation
  • 44

    Treschan TA, Peters J. The vasopressin system: physiology and clinical strategies. Anesthesiology 2006 105 599612; quiz 3940. (https://doi.org/10.1097/00000542-200609000-00026)

    • Search Google Scholar
    • Export Citation
  • 45

    Annborn M, Dankiewicz J, Nielsen N, Rundgren M, Smith JG, Hertel S, Struck J, Friberg H. CT-proAVP (copeptin), MR-proANP and peroxiredoxin 4 after cardiac arrest: release profiles and correlation to outcome. Acta Anaesthesiologica Scandinavica 2014 58 4284 36. (https://doi.org/10.1111/aas.12282)

    • Search Google Scholar
    • Export Citation
  • 46

    Mockel M, Searle J. The positive predictive value of ct-proAVP (copeptin) in patients with STEMI. Heart 2013 99 1475. (https://doi.org/10.1136/heartjnl-2013-304217)

    • Search Google Scholar
    • Export Citation

 

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  • View in gallery

    Prognostic performance of copeptin as predictor for 30-day mortality stratified by age, sex, type of infection, and fluid balance markers (sodium, osmolality and kidney function) to evaluate subgroup differences. The vertical reference line mirrors the overall AUC. AUCs to the right of the reference line indicate higher levels of discrimination, AUCs to the left of the reference line indicate lower levels of discrimination. The forest plot shows different levels of discrimination with their respective 95% CIs by subgroups. P for interaction indicates the level of effect modification by subgroups.

  • View in gallery

    Prognostic performance of SOFA score as predictor for 30-day mortality stratified by age, sex, type of infection, and fluid balance markers (sodium, osmolality and kidney function) to evaluate subgroup differences. The vertical reference line mirrors the overall AUC. AUCs to the right of the reference line indicate higher levels of discrimination, AUCs to the left of the reference line indicate lower levels of discrimination. The forest plot shows different levels of discrimination with their respective 95% CIs by subgroups. P for interaction indicates the level of effect modification by subgroups.

  • View in gallery

    Prognostic performance of SOFA score and copeptin as predictor for 30-day mortality stratified by age, sex-, type of infection, and fluid balance markers (sodium, osmolality and kidney function) to evaluate subgroup differences. The vertical reference line mirrors the overall AUC. AUCs to the right of the reference line indicate higher levels of discrimination, AUCs to the left of the reference line indicate lower levels of discrimination. The forest plot shows different levels of discrimination with their respective 95% CIs by subgroups. P for interaction indicates the level of effect modification by subgroups.

  • 1

    Schuetz P, Hausfater P, Amin D, Amin A, Haubitz S, Faessler L, Kutz A, Conca A, Reutlinger B & Canavaggio P et al.Biomarkers from distinct biological pathways improve early risk stratification in medical emergency patients: the multinational, prospective, observational TRIAGE study. Critical Care 2015 19 377. (https://doi.org/10.1186/s13054-015-1098-z)

    • Search Google Scholar
    • Export Citation
  • 2

    Julian-Jimenez A, Candel-Gonzalez FJ, Gonzalez Del Castillo J. Usefulness of inflammation and infection biomarkers in the Emergency Department. Enfermedades Infecciosas y Microbiologia Clinica 2014 32 1771 90. (https://doi.org/10.1016/j.eimc.2013.01.005)

    • Search Google Scholar
    • Export Citation
  • 3

    Prescott HC, Langa KM, Liu V, Escobar GJ, Iwashyna TJ. Increased 1-year healthcare use in survivors of severe sepsis. American Journal of Respiratory and Critical Care Medicine 2014 190 626 9. (https://doi.org/10.1164/rccm.201403-0471OC)

    • Search Google Scholar
    • Export Citation
  • 4

    Prescott HC, Osterholzer JJ, Langa KM, Angus DC, Iwashyna TJ. Late mortality after sepsis: propensity matched cohort study. BMJ 2016 353 i2375. (https://doi.org/10.1136/bmj.i2375)

    • Search Google Scholar
    • Export Citation
  • 5

    Christ-Crain M, Fenske W. Copeptin in the diagnosis of vasopressin-dependent disorders of fluid homeostasis. Nature Reviews: Endocrinology 2016 12 1681 76. (https://doi.org/10.1038/nrendo.2015.224)

    • Search Google Scholar
    • Export Citation
  • 6

    Russell JA, Walley KR, Singer J, Gordon AC, Hebert PC, Cooper DJ, Holmes CL, Mehta S, Granton JT & Storms MM et al.Vasopressin versus norepinephrine infusion in patients with septic shock. New England Journal of Medicine 2008 358 8778 8 7. (https://doi.org/10.1056/NEJMoa067373)

    • Search Google Scholar
    • Export Citation
  • 7

    Kadziolka I, Swistek R, Borowska K, Tyszecki P, Serednicki W. Validation of Apache II and SAPS II scales at the intensive care unit along with assessment of SOFA scale at the admission as an isolated risk of death predictor. Anaesthesiology Intensive Therapy 2019 51 10711 1. (https://doi.org/10.5114/ait.2019.86275)

    • Search Google Scholar
    • Export Citation
  • 8

    Innocenti F, Bianchi S, Guerrini E, Vicidomini S, Conti A, Zanobetti M, Pini R. Prognostic scores for early stratification of septic patients admitted to an emergency department-high dependency unit. European Journal of Emergency Medicine 2014 21 25425 9. (https://doi.org/10.1097/MEJ.0000000000000075)

    • Search Google Scholar
    • Export Citation
  • 9

    Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD & Coopersmith CM et al.The Third International Consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 2016 315 8018 10. (https://doi.org/10.1001/jama.2016.0287)

    • Search Google Scholar
    • Export Citation
  • 10

    Kruger S, Ewig S, Giersdorf S, Hartmann O, Suttorp N, Welte T & German Competence Network for the Study of Community Acquired Pneumonia. Cardiovascular and inflammatory biomarkers to predict short- and long-term survival in community-acquired pneumonia: results from the German Competence Network, CAPNETZ. American Journal of Respiratory and Critical Care Medicine 2010 182 142614 34. (https://doi.org/10.1164/rccm.201003-0415OC)

    • Search Google Scholar
    • Export Citation
  • 11

    Tigabu BM, Davari M, Kebriaeezadeh A, Mojtahedzadeh M. Fluid volume, fluid balance and patient outcome in severe sepsis and septic shock: a systematic review. Journal of Critical Care 2018 48 15315 9. (https://doi.org/10.1016/j.jcrc.2018.08.018)

    • Search Google Scholar
    • Export Citation
  • 12

    Sakr Y, Rubatto Birri PN, Kotfis K, Nanchal R, Shah B, Kluge S, Schroeder ME, Marshall JC, Vincent JL & Intensive Care Over Nations Investigators. IHigher fluid balance increases the risk of death from sepsis: results from a Large International Audit. Critical Care Medicine 2017 45 3863 94. (https://doi.org/10.1097/CCM.0000000000002189)

    • Search Google Scholar
    • Export Citation
  • 13

    Struck J, Morgenthaler NG, Bergmann A. Copeptin, a stable peptide derived from the vasopressin precursor, is elevated in serum of sepsis patients. Peptides 2005 26 2500250 4. (https://doi.org/10.1016/j.peptides.2005.04.019)

    • Search Google Scholar
    • Export Citation
  • 14

    Morgenthaler NG, Muller B, Struck J, Bergmann A, Redl H, Christ-Crain M. Copeptin, a stable peptide of the arginine vasopressin precursor, is elevated in hemorrhagic and septic shock. Shock 2007 28 2192 2 6. (https://doi.org/10.1097/SHK.0b013e318033e5da)

    • Search Google Scholar
    • Export Citation
  • 15

    Palmiere C, Augsburger M. Copeptin as a diagnostic biomarker for sepsis-related deaths. Peptides 2014 59 757 8. (https://doi.org/10.1016/j.peptides.2014.07.011)

    • Search Google Scholar
    • Export Citation
  • 16

    Jochberger S, Dorler J, Luckner G, Mayr VD, Wenzel V, Ulmer H, Morgenthaler NG, Hasibeder WR, Dunser MW. The vasopressin and copeptin response to infection, severe sepsis, and septic shock. Critical Care Medicine 2009 37 4764 82. (https://doi.org/10.1097/CCM.0b013e3181957532)

    • Search Google Scholar
    • Export Citation
  • 17

    Lesur O, Roussy JF, Chagnon F, Gallo-Payet N, Dumaine R, Sarret P, Chraibi A, Chouinard L, Hogue B. Proven infection-related sepsis induces a differential stress response early after ICU admission. Critical Care 2010 14 R131. (https://doi.org/10.1186/cc9102)

    • Search Google Scholar
    • Export Citation
  • 18

    Morgenthaler NG, Struck J, Jochberger S, Dunser MW. Copeptin: clinical use of a new biomarker. Trends in Endocrinology and Metabolism 2008 19 434 9. (https://doi.org/10.1016/j.tem.2007.11.001)

    • Search Google Scholar
    • Export Citation
  • 19

    Schuetz P, Muller B. The hypothalamic-pituitary-adrenal axis in critical illness. Endocrinology and Metabolism Clinics of North America 2006 35 82338, x. (https://doi.org/10.1016/j.ecl.2006.09.013)

    • Search Google Scholar
    • Export Citation
  • 20

    Nickel CH, Bingisser R, Morgenthaler NG. The role of copeptin as a diagnostic and prognostic biomarker for risk stratification in the emergency department. BMC Medicine 2012 10 7. (https://doi.org/10.1186/1741-7015-10-7)

    • Search Google Scholar
    • Export Citation
  • 21

    Schuetz P, Wolbers M, Christ-Crain M, Thomann R, Falconnier C, Widmer I, Neidert S, Fricker T, Blum C & Schild U et al.Prohormones for prediction of adverse medical outcome in community-acquired pneumonia and lower respiratory tract infections. Critical Care 2010 14 R106. (https://doi.org/10.1186/cc9055)

    • Search Google Scholar
    • Export Citation
  • 22

    De Marchis GM, Katan M, Weck A, Fluri F, Foerch C, Findling O, Schuetz P, Buhl D, El-Koussy M & Gensicke H et al.Copeptin adds prognostic information after ischemic stroke: results from the CoRisk study. Neurology 2013 80 127812 86. (https://doi.org/10.1212/WNL.0b013e3182887944)

    • Search Google Scholar
    • Export Citation
  • 23

    Ostadal P, Kruger A, Zdrahalova V, Janotka M, Vondrakova D, Neuzil P, Prucha M. Blood levels of copeptin on admission predict outcomes in out-of-hospital cardiac arrest survivors treated with therapeutic hypothermia. Critical Care 2012 16 R187. (https://doi.org/10.1186/cc11671)

    • Search Google Scholar
    • Export Citation
  • 24

    Potocki M, Breidthardt T, Mueller A, Reichlin T, Socrates T, Arenja N, Reiter M, Morgenthaler NG, Bergmann A & Noveanu M et al.Copeptin and risk stratification in patients with acute dyspnea. Critical Care 2010 14 R213. (https://doi.org/10.1186/cc9336)

    • Search Google Scholar
    • Export Citation
  • 25

    Katan M, Christ-Crain M. The stress hormone copeptin: a new prognostic biomarker in acute illness. Swiss Medical Weekly 2010 140 w13101. (https://doi.org/10.4414/smw.2010.13101)

    • Search Google Scholar
    • Export Citation
  • 26

    Stolz D, Christ-Crain M, Morgenthaler NG, Leuppi J, Miedinger D, Bingisser R, Muller C, Struck J, Muller B, Tamm M. Copeptin, C-reactive protein, and procalcitonin as prognostic biomarkers in acute exacerbation of COPD. Chest 2007 131 105810 67. (https://doi.org/10.1378/chest.06-2336)

    • Search Google Scholar
    • Export Citation
  • 27

    Muller B, Morgenthaler N, Stolz D, Schuetz P, Muller C, Bingisser R, Bergmann A, Tamm M, Christ-Crain M. Circulating levels of copeptin, a novel biomarker, in lower respiratory tract infections. European Journal of Clinical Investigation 2007 37 1451 52. (https://doi.org/10.1111/j.1365-2362.2007.01762.x)

    • Search Google Scholar
    • Export Citation
  • 28

    Katan M, Morgenthaler N, Widmer I, Puder JJ, Konig C, Muller B, Christ-Crain M. Copeptin, a stable peptide derived from the vasopressin precursor, correlates with the individual stress level. Neuro Endocrinology Letters 2008 29 34134 6.

    • Search Google Scholar
    • Export Citation
  • 29

    Schuetz P, Hausfater P, Amin D, Haubitz S, Fassler L, Grolimund E, Kutz A, Schild U, Caldara Z & Regez K et al.Optimizing triage and hospitalization in adult general medical emergency patients: the triage project. BMC Emergency Medicine 2013 13 12. (https://doi.org/10.1186/1471-227X-13-12)

    • Search Google Scholar
    • Export Citation
  • 30

    Raith EP, Udy AA, Bailey M, McGloughlin S, MacIsaac C, Bellomo R, Pilcher DV & Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcomes and Resource Evaluation (CORE). Prognostic accuracy of the SOFA score, SIRS criteria, and qSOFA score for in-hospital mortality among adults with suspected infection admitted to the intensive care unit. JAMA 2017 317 290300. (https://doi.org/10.1001/jama.2016.20328)

    • Search Google Scholar
    • Export Citation
  • 31

    Pandharipande PP, Shintani AK, Hagerman HE, St Jacques PJ, Rice TW, Sanders NW, Ware LB, Bernard GR, Ely EW. Derivation and validation of SpO2/FiO2 ratio to impute for PaO2/FiO2 ratio in the respiratory component of the sequential organ failure assessment score. Critical Care Medicine 2009 37 131713 21. (https://doi.org/10.1097/CCM.0b013e31819cefa9)

    • Search Google Scholar
    • Export Citation
  • 32

    Morgenthaler NG, Struck J, Alonso C, Bergmann A. Assay for the measurement of copeptin, a stable peptide derived from the precursor of vasopressin. Clinical Chemistry 2006 52 11211 9. (https://doi.org/10.1373/clinchem.2005.060038)

    • Search Google Scholar
    • Export Citation
  • 33

    Haag E, Gregoriano C, Molitor A, Kloter M, Kutz A, Mueller B, Schuetz P. Does mid-regional pro-adrenomedullin (MR-proADM) improve the sequential organ failure assessment-score (SOFA score) for mortality-prediction in patients with acute infections? Results of a prospective observational study. Clinical Chemistry and Laboratory Medicine 2021 59 116511 76. (https://doi.org/10.1515/cclm-2020-1566)

    • Search Google Scholar
    • Export Citation
  • 34

    Christ-Crain M, Morgenthaler NG, Fenske W. Copeptin as a biomarker and a diagnostic tool in the evaluation of patients with polyuria-polydipsia and hyponatremia. Best Practice and Research: Clinical Endocrinology and Metabolism 2016 30 2352 47. (https://doi.org/10.1016/j.beem.2016.02.003)

    • Search Google Scholar
    • Export Citation
  • 35

    Michell RH, Kirk CJ, Billah MM. Hormonal stimulation of phosphatidylinositol breakdown with particular reference to the hepatic effects of vasopressin. Biochemical Society Transactions 1979 7 86186 5. (https://doi.org/10.1042/bst0070861)

    • Search Google Scholar
    • Export Citation
  • 36

    Penit J, Faure M, Jard S. Vasopressin and angiotensin II receptors in rat aortic smooth muscle cells in culture. American Journal of Physiology 1983 244 E72E 82. (https://doi.org/10.1152/ajpendo.1983.244.1.E72)

    • Search Google Scholar
    • Export Citation
  • 37

    Thibonnier M Use of vasopressin antagonists in human diseases. Kidney International: Supplement 1988 26 S48S 51.

  • 38

    Barat C, Simpson L, Breslow E. Properties of human vasopressin precursor constructs: inefficient monomer folding in the absence of copeptin as a potential contributor to diabetes insipidus. Biochemistry 2004 43 81918 203. (https://doi.org/10.1021/bi0400094)

    • Search Google Scholar
    • Export Citation
  • 39

    Gomes DA, de Almeida Beltrao RL, de Oliveira Junior FM, da Silva Junior JC, de Arruda EPC, Lira EC, da Rocha MJA. Vasopressin and copeptin release during sepsis and septic shock. Peptides 2021 136 170437. (https://doi.org/10.1016/j.peptides.2020.170437)

    • Search Google Scholar
    • Export Citation
  • 40

    Saeed K, Wilson DC, Bloos F, Schuetz P, van der Does Y, Melander O, Hausfater P, Legramante JM, Claessens YE & Amin D et al.The early identification of disease progression in patients with suspected infection presenting to the emergency department: a multi-centre derivation and validation study. Critical Care 2019 23 40. (https://doi.org/10.1186/s13054-019-2329-5)

    • Search Google Scholar
    • Export Citation
  • 41

    Angeletti S, Spoto S, Fogolari M, Cortigiani M, Fioravanti M, De Florio L, Curcio B, Cavalieri D, Costantino S, Dicuonzo G. Diagnostic and prognostic role of procalcitonin (PCT) and MR-pro-adrenomedullin (MR-proADM) in bacterial infections. APMIS 2015 123 74074 8. (https://doi.org/10.1111/apm.12406)

    • Search Google Scholar
    • Export Citation
  • 42

    Wang H, Kang X, Shi Y, Bai ZH, Lv JH, Sun JL, Pei HH. SOFA score is superior to Apache-II score in predicting the prognosis of critically ill patients with acute kidney injury undergoing continuous renal replacement therapy. Renal Failure 2020 42 6386 45. (https://doi.org/10.1080/0886022X.2020.1788581)

    • Search Google Scholar
    • Export Citation
  • 43

    Acheampong A, Vincent JL. A positive fluid balance is an independent prognostic factor in patients with sepsis. Critical Care 2015 19 251. (https://doi.org/10.1186/s13054-015-0970-1)

    • Search Google Scholar
    • Export Citation
  • 44

    Treschan TA, Peters J. The vasopressin system: physiology and clinical strategies. Anesthesiology 2006 105 599612; quiz 3940. (https://doi.org/10.1097/00000542-200609000-00026)

    • Search Google Scholar
    • Export Citation
  • 45

    Annborn M, Dankiewicz J, Nielsen N, Rundgren M, Smith JG, Hertel S, Struck J, Friberg H. CT-proAVP (copeptin), MR-proANP and peroxiredoxin 4 after cardiac arrest: release profiles and correlation to outcome. Acta Anaesthesiologica Scandinavica 2014 58 4284 36. (https://doi.org/10.1111/aas.12282)

    • Search Google Scholar
    • Export Citation
  • 46

    Mockel M, Searle J. The positive predictive value of ct-proAVP (copeptin) in patients with STEMI. Heart 2013 99 1475. (https://doi.org/10.1136/heartjnl-2013-304217)

    • Search Google Scholar
    • Export Citation