deparment of surgery, division of ...scripties.umcg.eldoc.ub.rug.nl/files/root/geneeskunde/...the...

DEPARMENT OF SURGERY, DIVISION OF TRAUMATOLOGY, UNIVERSITY MEDICAL CENTER GRONINGEN

Fibrinogen level at admission is associated

with 24-hour mortality in polytraumatized

adults

Name: B. Gareb

Student number: S1870041

Supervisor: M. el Moumni, MD, trauma surgeon

Department: Department of Surgery, Division of Traumatology

Institution: University Medical Center Groningen

Period: September 2014 – February 2015

Fibrinogen level at admission is associated with 24-hour mortality in polytraumatized adults

1

SUMMARY

BACKGROUND Trauma is one of the leading causes of death worldwide. Central

nervous system (CNS) injury and hemorrhage are the most common causes of mortality

within the first 24 hours after trauma. Hemorrhage is the leading cause of (potentially)

preventable death after injury, most often due to delay in treatment. Acute Traumatic

Coagulopathy (ATC) is a coagulopathy which results in a hypocoaguable and

hyperfibrinolytic state. Fibrinogen is the most vulnerable coagulation factor resulting in a

rapid fall in fibrinogen concentration after admission. The objective of this study was to

estimate the adjusted effect of fibrinogen level at admission on 24-hour mortality in

polytraumatized patients. Additionally, we tried to determine if this relationship is

different for sex, age, and traumatic brain injury.

MATERIAL AND METHODS Patients treated in 2004-2013 at the University Medical

Center Groningen with New Injury Severity Score (NISS) higher than 15 and age of 18-

80 years were included. Endpoint of this study was mortality within 24 hours after

admission. Patient’s characteristics consisted of demographics and initial shock-related

and coagulation parameters. For descriptive statistics, patients were divided into two

groups based on their outcome: survivors and non-survivors. Multivariable Cox

regression model was used to investigate the adjusted effect of fibrinogen level at

admission on 24-hour mortality in polytraumatized patients.

RESULTS Out of 1491 included patients, 1377 (92.4%) survived and 114 patients

(7.6%) died within the first 24 hours after admission. Median age of all subjects was 45

(25th-75th percentile [P25-P75] 28-60). The vast majority of patients were male (76.3%)

and had mainly blunt trauma (95.3%). Median Glasgow Coma Scale (GCS) was 13 (P25-

P75, 6-15). Median NISS (P25-P75) was 33 (25-43). Mean fibrinogen level (standard

deviation) of all patients was 2.0 (1.0). Of all characteristics, sex (P=0.819), cause of

injury (P=0.230), mechanism of injury (P=0.134), and pulse (P=0.878) were not

significantly different between both subgroups. Multivariable Cox regression analysis

showed a significant adjusted effect of fibrinogen level at admission on 24-hour mortality

(hazard ratio [HR] 0.475, 95% confidence interval [95% CI] 0.305-0.738; P=0.001). No

relevant effect modification was found.

CONCLUSION The present study demonstrates a significant adjusted association

between fibrinogen level at admission and 24-hour mortality in polytraumatized adults.

This effect of fibrinogen was not modified by sex, age, or traumatic brain injury.

Monitoring fibrinogen levels routinely at admission and actively supplementing

fibrinogen could reduce (potential) preventable deaths.


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SAMENVATTING

ACHTERGROND Trauma is één van de belangrijkste doodsoorzaken wereldwijd.

Schade aan het centraal zenuwstelsel en verbloedingen zijn de meest voorkomende

doodsoorzaken binnen 24 uur na trauma. Verbloeding is de belangrijkste (potentiaal)

vermijdbare doodsoorzaak, voornamelijk door een te laat ingezette behandeling. Acute

Traumatische Coagulopathie (ATC) is een coagulopathie wat resulteert in een

hypocoagulabele en hyperfibrinolytische toestand. Fibrinogeen is de meest kwetsbare

coagulatiefactor, wat resulteert in een snelle daling van fibrinogeenspiegel na opvang.

Het doel van deze studie was het schatten van het gecorrigeerde effect van fibrinogeen bij

opname op de 24-uurs mortaliteit bij polytrauma patiënten. Daarnaast wilden wij

vaststellen of deze relatie anders is voor geslacht, leeftijd en schedelhersenletsel.

MATERIAAL EN METHODES Patiënten die in de periode van 2004-2013 behandeld

zijn in het Universitair Medisch Centrum Groningen met een New Injury Severity Score

(NISS) hoger dan 15 en een leeftijd van 18-80 jaar werden geïncludeerd. Eindpunt van

deze studie was mortaliteit binnen 24 uur na opvang. Patiëntkarakteristieken bestonden

uit demografische gegevens, en shock gerelateerde en coagulatie parameters bij aanvang

van opname. Voor beschrijvende statistiek werden de patiënten opgedeeld in twee

subgroepen: overlevenden en niet-overlevenden. Multivariabele Cox regressie model

werd gebruikt om het gecorrigeerde effect van fibrinogeenspiegel bij opname op 24-uurs

mortaliteit bij polytrauma patiënten te onderzoeken.

RESULTATEN Van de 1491 geïncludeerde patiënten overleefden 1377 (92.4%) en

overleden 114 (7.6%) patiënten binnen 24 uur na opname. Mediane leeftijd van alle

geïncludeerde patiënten was 45 (25e-75e percentiel [P25-P75] 28-60). The meerderheid

van de patiënten was mannelijk (76.3%) en had voornamelijk stomp trauma (95.3%).

Mediane Glascow Come Scale (GCS) was 13 (P25-P75, 6-15). Mediane NISS (P25-P75)

was 33 (25-43). Gemiddelde fibrinogeenspiegel (standaard deviatie) van alle patiënten

was 2.0 (1.0). Van alle karakteristieken verschilden alleen geslacht (P=0.819), trauma-

oorzaak (P=0.230), traumamechanisme (P=0.134) en hartfrequentie (P=0.878) niet

significant tussen beide subgroepen. Multivariabele Cox regressie analyse toonde een

significante gecorrigeerde effect van fibrinogeenspiegel bij opvang op 24-uurs mortaliteit

(hazard ratio [HR] 0.475, 95% betrouwbaarheidsinterval [95% BI] 0.305-0.738;

P=0.001). Relevante effect modificatie werd niet gevonden.

CONCLUSIE De huidige studie toont een significante gecorrigeerde associatie tussen

fibrinogeenspiegel bij opvang en 24-uurs mortaliteit bij polytrauma patiënten. Dit effect

van fibrinogeen werd niet gemodificeerd door geslacht, leeftijd of schedelhersenletsel.

Het routinematig monitoren van fibrinogeenspiegels bij opvang en het actief

supplementeren van fibrinogeen zou (potentiaal) vermijdbare sterfgevallen kunnen

verlagen.


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TABLE OF CONTENTS

1. INTRODUCTION 5

1.1 Epidemiology 5

1.2 Haemostasis 5

1.2.1 Classical coagulation cascade 6

1.2.2 Cell-based model 7

1.2.3 The anticoagulant system 8

1.2.4 Fibrinolysis 9

1.3 Pathogenesis 9

1.3.1 Phenotypic variation of DIC 9

1.3.2 Overactive thrombomodulin-protein C pathway 10

1.3.3 The neurohormonal hypothesis 10

1.4 Risk factors 10

1.5 Diagnostics 11

1.6 Treatment 12

1.7 Prognosis 12

1.8 Fibrinogen level and prognosis 12

1.9 Aims 14

2. MATERIAL AND METHODS 16

2.1 Patients 16

2.2 Trauma protocol 16

2.3 Study parameters 16

2.4 Statistical analyses 17

2.4.1. Descriptive statistics 17

2.4.2 Association model 17

3. RESULTS 19

3.1 Patient’s characteristics 19

3.2 Survival analyses 19

4. DISCUSSION 24

4.1 Main findings 24


4

4.2 Strengths and limitations 28

4.3 Clinical message and recommendations 28

4.4 Conclusion 29

5. REFERENCES 30

APPENDIX 38


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CHAPTER 1

INTRODUCTION

1.1 Epidemiology Trauma is one of the leading causes of death worldwide, with an

estimated mortality rate of 83.7 per 100.000 citizens per year. In 2000, these injuries

accounted for 5 million deaths worldwide, resulting in 9% of world’s deaths. Men are

affected twice as much compared to women. Furthermore, these injuries were responsible

for 12% of world’s burden of disease (1). Over 90% of the current injuries occur in low-

and middle-income nations. However, trauma also has a significant impact on morbidity

and mortality in industrialized nations. In 2003, 29 million people in the United States

(U.S.) were involved in injuries (i.e. more than 10% of the population). Among the

population aged 1 to 44 years, injury was the leading cause of death. In all age strata,

injury was the third leading cause of death. As a consequence, injury contributes to 30%

of potential life years lost which is the largest contribution of any cause of death and

about twice that of cancer, the second leading cause of death (2).

Central nervous system (CNS) injury and hemorrhage are currently the most

common causes of mortality within the first 24 hours after trauma, representing 40-50%

and 21-50% of deaths, respectively (3-6). Severe CNS injury often has devastating

outcomes, with few possible interventions for survivability and functional recovery

resulting in high pre-hospital mortality. Hemorrhage, however, is more susceptible to

intervention to decrease mortality, making it the leading cause of (potentially)

preventable death after injury, most often due to delay in treatment (3,7-9). In up to one

third of the patients arriving in hospital, abnormal coagulation is present (10). Compared

to mortality of patients without coagulopathy after trauma, patients with coagulopathy

have three- to eight-fold increased mortality (10).

Although CNS injury, particularly traumatic brain injury (TBI), is the primary

cause of death, hypotension due to bleeding doubles to triples mortality in this group (11-

13). This significant contribution to TBI deaths makes hemorrhage a major contributor to

overall mortality of trauma patients. Since the World Health Organization (WHO)

predicts that in 2020 traumatic injuries will rise dramatically, this traumatic injured

population will only have more impact on public health (1).

1.2 Haemostasis In 1964, the classical coagulation cascade was introduced by two

independent research groups (14,15). This model was called the cascade or waterfall

model because activation of one clotting factor resulted in activation of other clotting

factors. Eventually, this cascade leads to production of thrombin (factor IIa), which

converts fibrinogen (factor I) into fibrin (factor Ia). Fibrinogen is produced in the liver

and the norm value of fibrinogen in plasma level is 2.0 – 4.5 g/L (16). This model was

later divided into two pathways: an intrinsic and extrinsic pathway (17). This model was

a major discovery in understanding coagulation and provided an accurate prediction of

coagulation in vitro. However, it had several flaws when predicting coagulation in vivo.

Further research suggested a form of dependency between both pathways (18-22). As a

result, a new model of haemostasis was introduced in 1992 which was named the cell-

based model of haemostasis in 2001 (23,24). This model does not divide coagulation into

two pathways, but rather into three overlapping phases: the initiation, amplification and

propagation phase (19,23,25). Both models are described below.


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1.2.1 Classical coagulation cascade

The classical model of coagulation divides the coagulation sequence into two pathways:

the intrinsic and extrinsic pathway (figure 1). The intrinsic pathway consists of

components which are all present the blood. This pathway is measured using the

activated partial thromboplastin time (aPTT). The extrinsic pathway consists of factor

VIIa and a subendothelial cell membrane protein, tissue factor (TF). The latter is not in

contact with blood in healthy individuals, hence the name extrinsic pathway. The

prothrombin time (PT) is used to measure this pathway. Both pathways will eventually

assemble to the common pathway (19).

The intrinsic pathway starts with activation of factor XII (19,25,26). Factor XII is

produced in the liver and circulates in the bloodstream. It is activated by negatively

charged surfaces. During this conversion, it uses high-molecular-weight kininogen

(HMWK) as co-factor by using it as an anchor to negatively charged surfaces. Factor

XIIa, an endopeptidase, will cleave pre-kallikrein into kallikrein, which accelerates the

conversion of factor XII into factor XIIa (i.e. positive feedback). When a threshold of

factor XIIa is reached, it activates factor XI which activates factor IX. Factor IXa,

together with factor VIIIa, calcium, and phospholipid, activates factor X. All these

inactivated factors are zymogens (pro-enzyms) with the exception of factor V and VIII,

which are co-factors of the formed enzymes.

Figure 1. The classical coagulation model, divided into two pathways: the intrinsic and extrinsic pathway.

Solid lines imply activation pathways; interrupted lines imply inhibitory effects of anticoagulants. Ca:

calcium; PL: phospholipid; a: activated (19).


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Figure 2. Initiation phase of the cell-

based model of coagulation (26).

The extrinsic pathway consists only of TF and factor VII. TF is constantly

expressed by certain cells in vessel walls, like vascular smooth muscle cells, pericytes,

and adventitial fibroblasts (27-30). TF is also expressed in several other organs, like the

brain, lung, kidney, and heart (30,31). Upon vessel injury, TF binds circulating factor VII

and activates it. The activated TF/VIIa complex will activate factor IX from the intrinsic

pathway and factor X. The latter is activated using calcium (19,30).

The common pathway starts after activation of factor X. Factor Xa along with

factor Va, calcium, and phospholipids, converts prothrombin (factor II) to thrombin

(factor IIa). Thrombin converts fibrinogen into fibrin, which along with factor XIIIa will

form a stable fibrin clot (19,25,26,30).

As mentioned before, this model has several flaws. For example, deficiencies of

HMWK, pre-kallikrein, and factor XII prolong aPTT, but does not lead to a clinical

bleeding disorder, indicating that the intrinsic pathway is not essential (19,25,32).

However, patients with factor XI deficiency might have severe hemorrhage after surgery

or trauma, suggesting that factor XI is also activated through another mechanism (25). In

addition, deficiency of factor VIIIa (i.e. haemophilia A) or factor IXa (i.e. haemophilia B)

causes severe bleedings. This model can not explain why activation of factor X through

the extrinsic pathway can not compensate the deficiencies of the abovementioned factors

(19,26). Similar, factor VII deficiency also results in severe bleeding, although the

intrinsic pathway is intact. Thus, it seems improbable that these pathways operate

independently in vivo (19,26).

1.2.2 Cell-based model

The cell-based model strongly suggests that coagulation occurs in three overlapping

phases: the initiation, amplification, and propagation phase. In this model, two cells are

essential: a TF-bearing cell (extravascular) and platelets (intravascular). Activation of

coagulation is prevented until these two cells make contact with each other at the site of

injury. This model addresses the abovementioned flaws and questions (19,25,26).

The initiation phase (figure 2) starts when

TF is exposed to blood due to injury. Factor VII will

bind to TF, activating factor VII. The formed

TF/VIIa complex activates a low amount of factor

IX and X. Factor Xa, together with factor Va, binds

to the TF-bearing cell forming prothrombinase

complexes on the surface of the TF-bearing cell

(26,33). Factor V is activated by factor Xa or non-

coagulation enzymes (26,34,35). The produced

factor Xa generates a small amount of thrombin

(factor IIa) (25,26,36). However, antithrombin (AT)

neutralizes the produced factor Xa and thrombin.

Furthermore, tissue factor pathway inhibitor (TFPI)

inhibits the TF/VIIa/Xa-complex. As a result, the initiation phase only leads to a very

small amount of thrombin. Inhibition by AT and TFPI prevent overproduction of

thrombin. Thus, procoagulant triggering only continues if TF is exposed to high levels of

VIIa (i.e. massive injury) to overcome the inhibition by AT and TFPI (25,26).


8

Figure 3. Amplification phase of the cell-


Figure 4. Propagation phase of the cell-


One of the major functions of the

amplification phase (figure 3) is activation of

platelets. Activation occurs by the small amount

of thrombin produced in the initiation phase.

Thrombin activates factor V on the activated

platelet. Furthermore, thrombin dissociates von

Willebrand factor (vWF) from factor VIII and

activating factor VIII. Factor VIIIa

subsequently binds to the surface of the

activated platelet. Free vWF allows additional

platelet adhesion and aggregation at the injured

tissue. These platelets express procoagulant

phospholipds on their surfaces, making

assembly of other coagulation factors possible

(25,26,37).

During the propagation phase (figure 4), large number of platelets are recruited.

This phase takes place at the surface of the

platelets. Factor IXa, activated during the

initiation phase, binds to factor VIIIa at the

platelet surface. Additionally, factor IXa can be

produced by factor XIa, which is bound to

platelets. The IXa/VIIIa-complex activates factor

X. Factor Xa binds to factor Va, forming the

prothrombinase complex on the surface of

platelets. This complex can form a burst of

thrombin, which will be sufficient to clot

fibrinogen into fibrin. At this moment, the platelet

is the only cell type known on which the

propagation phase could be carried out properly

due to the specialized coordination of the

VIIIa/IXa- and Va/Xa-complex (25,26).

1.2.3 The anticoagulant system

The major components of the anticoagulant system are protein C, protein S, and

antithrombin III. Their inhibitory effects are presented in figure 1. Its effects has not been

changed in the cell-based model (19,26).

Protein C is activated on the endothelial cell surface by thrombin and

thrombomodulin, an endothelial cell receptor. Activated protein C (APC) degrades factor

Va and VIIIa using protein S as co-factor. Protein S is mainly synthesized in the liver and

forms a complex with APC on negatively charged membranes, resulting in a ten-fold

higher affinity to these membranes compared to APC alone. Degradation of these factors

inhibits thrombin formation. As a result, thrombin is unable to convert fibrinogen into

fibrin, which is essential for clot formation (26).


9

Antithrombin III binds to various glycosaminoglycans, which are expressed on

healthy endothelial cells. These glycosaminoglycans function as high affinity binding

sites for antithrombin III. Binding of these two components is crucial for rapid inhibition

of thrombin, factor IXa, and Xa.

1.2.4 Fibrinolysis

Fibrinolysis is crucial for clot dissolution. Plasminogen is synthesized by the liver and

released into the systemic circulation. Plasminogen activators convert plasminogen into

plasmin. The two physiological plasminogen activators are tissue plasminogen activator

(TPA) and urokinase plasminogen activator (UPA). TPA requires fibrin as co-factor,

whereas UPA can activate plasminogen without the presence of fibrin. Plasmin is a fibrin

degrading enzyme which results in dissolution of clots (25).

1.3 Pathogenesis Acute Traumatic Coagulopathy (ATC) is most commonly defined as a

prothrombin time ratio>1.2 (i.e. international normalized ratio, INR). However, other

definitions, like fibrinogen < 200 mg/dL, are also used (11). Traditionally, coagulopathy

associated with trauma was thought to be one of the three components of the ‘triad of

death’. Together with acidemia (pH<7.2) and hypothermia (< 33°C), it raises mortality

dramatically (38,39). Coagulopathy would occur due to loss, dilution or dysfunction of

coagulation factors. Loss of coagulation factors is caused by bleeding, whereas dilution

of these factors occurs after massive fluid and blood transfusion. Furthermore,

dysfunction of coagulation factors may occur due to hypothermia and acidemia resulting

in coagulation enzymes dysfunction (40-42). This description of ATC suggests that it

occurs late after injury and as a consequence of continuous hemorrhage and its treatment

(38,39).

However, recent studies suggest that ATC is a distinct coagulopathy (6,39,43-51).

These studies have shown that ATC developed before resuscitation of patients,

independent of loss, consumption, or dilution of coagulation factors or platelets.

Furthermore, ATC developed prior to development of acidosis or hypothermia. Also, the

median time from injury to the emergency department was short. These studies suggest

that ATC is a coagulopathy based on anticoagulation and hyperfibrinolysis rather than a

consumptive disorder. It must be noted that, although ATC can develop before these

factors are present, these factors exacerbate ATC.

At this moment, controversy about the underlying mechanism of ATC is still

present among researchers. The three main hypotheses are: a disseminated intravascular

coagulation (DIC) with fibrinolytic phenotype, an overactive thrombomodulin-protein C

pathway, and a neurohormonal hypothesis of cathecholamine-induced endothelial

damage (52).

1.3.1 Phenotypic variation of DIC

One of the three main hypotheses of ATC is a phenotypic variation of classical DIC.

Originally, DIC is described as a prothrombotic and hypercoaguable disorder. If the

underlying condition persists, it progresses into a consumptive, hypocoaguable disorder,

resulting in a hemorrhagic disorder (52). In the context of ATC, researchers describe DIC

with a fibronolytic phenotype. During the first 24-48 hours posttrauma, thrombin is

produced as a result of endothelial injury, hypoxia, and ischaemia. However, due to


10

massive release of plasminogen activators, plasminogen is converted in large amounts to

plasmin. These two events result in a hyperfibrinolytic and hypocoaguable state after

trauma. Furthermore, hyperfibrinogenolysis occurs, inhibiting conversion of fibrinogen to

fibrin due to lack of substrate (4,52-54).

1.3.2 Overactive thrombomodulin-protein C pathway

The second hypothesis contends that ATC occurs due to decreased thrombin degradation

and increased thrombomodulin activity. The underlying mechanism of these two events is

unknown. Thrombin itself is a prothrombotic agent. However, as presented in figure 1,

together with thrombomodulin and inactive protein C, this event leads to overactivation

of protein C. APC inhibits coagulation factors Va and VIIIa. Furthermore, it enhances

fibrinolysis through inhibition of plasminogen activator inhibitors, resulting in higher

intravascular concentrations of plasmin and thus in fibrinolysis (4,39,52,55-58).

1.3.3 The neurohormonal hypothesis

This final hypothesis describes catecholamine-induced endothelial damage as the initiator

of ATC. Tissue injury after trauma results in release of catecholamines (i.e. adrenalin and

noradrenalin) into the systemic circulation. These catecholamines change endothelial

cells from an antithrombotic to a prothrombotic state. This is beneficial and necessary at

the site of injury. A counterbalance system is also activated to prevent systemic

coagulation. This system counters the prothrombotic state of the endothelial cells,

inhibiting coagulation in blood. Major components of this system are thrombomodulin

and TPA, which results in anticoagulation and fibrinolysis, respectively. However, this

counterbalance system is poorly adapted as the degree of tissue injury increases. As a

result, the counter-regulatory response is overactive, leading to hypocoagulation and

hyperfibrinolysis (59-61).

These different hypotheses have lead to controversy about the underlying mechanism of

ATC. Although the initiator and mechanism of these hypotheses differ vastly, all three

hypotheses achieve a hypocoaguable and hyperfibrinolytic state. Several studies have

reported an association between low fibrinogen levels and increased morbidity and

mortality (5,62-64). Also, fibrinogen is the most vulnerable coagulation factor resulting

in a rapid fall in fibrinogen concentration after admission (62,65,66). Thus, researchers

and clinicians have focused mainly on fibrinogen to identify, treat and estimate prognosis

of ATC (5,52,57,58,63,67-76).

1.4 Risk factors Factors associated with low fibrinogen levels are high injury severity

score (ISS), time from injury, young age, male gender, injuries to extremities and pelvic

girdle, hypothermia, shock, and a low base excess due to hypoperfusion (5,39). Also,

massive fluid and blood transfusions are associated with low fibrinogen levels (67).

Risk factors in combination with low fibrinogen concentration for mortality are

high ISS, low base excess, high INR, and low Glasgow coma scale (GCS) (5,47,63).

However, controversy regarding gender, age, and platelet count exists as risk factors for

mortality (5,47,63).


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1.5 Diagnostics Early identification of ATC leads to early treatment and guidance of

treatment. This may lead to a reduction in mortality and morbidity. Various tests have

been used to identify ATC, which will be described below.

Traditionally, several tests were used to monitor coagulopathy, such as PT, aPTT,

INR, and platelet count. PT and aPTT, which are based on the classical coagulation

cascade, are measured using platelet-poor plasma at 37°C and normal pH. These tests

usually require 30-60 minutes (39). However, these tests only measure factor deficiencies

and do not take account possible hyperfibrinolysis. In addition, they often do not

represent in vivo coagulation of trauma patients, because they fail to account for

hypothermia and acidemia (52). Platelet count only provides quantitative information but

it does not reflect functional activity. Finally, trauma patients with ATC require acute

intervention. Tests that require 30-60 minutes to complete do not contribute to the

required acute intervention. Altogether, the abovementioned flaws of traditional

coagulation test result in poor early prediction of ATC (39,52,55,77,78).

Detection of these flaws and the conversion of the classical coagulation cascade to

the cell-based model of coagulation have led to interest in viscoelastic tests for trauma

patients. These tests (i.e. thromboelastography [TEG] and rotational thromboelastometry

[ROTEM]) are considered more global tests for coagulation (4,39,52,79,80). In contrast

to traditional coagulation tests, viscoelastic tests are able to monitor the entire

coagulation process from fibrin formation to fibrinolysis (81). This is an essential

advantage compared to traditional coagulation tests as hyperfibrinolysis plays a central

role in ATC. It has been used in clinical practice for several years, but recently it has

been available into the resuscitation room now that the technique is stable and rapid

enough for injured patients. These tests plot graphs which represent functional activity of

coagulation factors and platelet and fibrinogen function (39). Many variables could be

determined from the graph, which represent functional activity of the different phases of

coagulation (i.e. initiation, amplification and propagation phase) (52). The amplitude of

the graph, which represents the clot strength, appears to be essential in the context of

ATC. Woolley et al. have shown that amplitudes after 5 and 10 minutes (A5 and A10,

respectively) have sensitivities/specificities in predicting ATC of 0.96/0.58 and 1.00/0.70,

respectively (82). Other studies have compared the proportion of detection of ATC using

ROTEM and INR, defining ATC as A5 ≤ 35mm or INR > 1.2. The proportion of

individuals with ATC who tested positive was 71% versus 43%, respectively (4,79,83).

Although these results are promising, large studies validating these tests to

identify ATC are absent. Also, evidence of using ROTEM or TEG to improve morbidity

or mortality are lacking (52,80). Furthermore, viscoelastic tests could be less specific to

identify major deficiency of coagulation factors (<30% of norm values) compared to

traditional coagulation tests (84). Finally, these tests remain in vitro tests, which fail to

take physiological effects (e.g. endothelial effects) into account (52).

As a result of the abovementioned, no validated test is currently available that can

rapidly identify ATC (39). This deficiency makes timely and goal-directed therapy

difficult, contributing to high potentially preventable mortality due to hemorrhage (52).

Therefore, clinicians focus mainly on fibrinogen level to identify, treat, and estimate

prognosis of ATC (5,51,52,57,58,63,67-75,85,86).


12

1.6 Treatment As mentioned before, fibrinogen plays a crucial role in coagulation as it is

converted to fibrin, which forms a stable blood clot with platelets (87,88). Low levels of

fibrinogen can be supplemented and different studies have reported that this is effective

(73,89). Fibrinogen can be supplemented using cryoprecipitate, fresh frozen plasma

(FFP) or fibrinogen concentrates (67,90). Concentrations of fibrinogen differ between

these three supplements. Cryoprecipitate contains 15 to 17 g/L while FFP contains 2.0 to

4.5 g/L fibrinogen. Fibrinogen concentrate is a pasteurized concentrate (i.e. powder) and

is available in vials containing 900 to 1300 mg fibrinogen. After reconstitution with

sterile water, it often contains 15-20 g/L (67,91). Using one of the three supplements, it is

believed that the environment of coagulation is improved due to sufficient substrate

concentration. This enhances speed and strength of clot forming, which can be analyzed

using ROTEM or TEG (67,92).

At this moment, strong evidence regarding the optimal source of

supplementation is absent (67,93). However, several studies suggest that fibrinogen

concentrate has the potential effect of reducing allogeinic blood products and may be the

most efficient way to correct fibrinogen deficiency (94). Avoiding transfusions with

allogeneic blood products have beneficial effects as it reduces morbidity and mortality

(67). Additionally, no increased risk of adverse effects have been reported (62,67,91).

Furthermore, it has some practical benefits compared to cryoprecipitate and FFP, for

example no need for defrosting and blood group matching, low administration volume,

virally inactivated as standard, and it can be delivered as a standard dose. The latter is

unlikely if cryoprecipitate or FFP is used due to the variable amount of fibrinogen

concentration (67,91). A counterargument to use fibrinogen concentrate is that is would

be more expensive than cryoprecipitate or FFP. However, no study have been performed

which prospectively compares the direct and indirect costs of these supplements.

Therefore, conclusions about cost-effectiveness may not be drawn (94-96).

Currently, European guidelines recommend fibrinogen supplementation if

fibrinogen concentration is less than 1.5-2.0 g/L (97). However, the evidence of these

guidelines is limited and it is based on data of elective surgery and postpartum

hemorrhage, rather than hemorrhage after trauma (5). Several authors suggest that early

and aggressive supplementation of fibrinogen can have beneficial effects (93,98-101).

Consequently, the optimal fibrinogen level as a trigger to initiate treatment is not clear.

Also, the fibrinogen level as treatment goal is unknown (63).

1.7 Prognosis Outcomes after ATC are devastating. ATC has a mortality approaching

50% (4,5). If death is not rapid (often due to hypovolemia), prolonged shock raises the

incidence of multi-organ failure (MOF) and late mortality. Late mortality is primarily

contributed to hypoxia, MOF, sepsis, ongoing internal hemorrhage or cardiovascular

failure (52). Furthermore, these patients have greater organ injury, more transfusion

requirements, and longer critical care stay (4).

1.8 Fibrinogen level and prognosis Researchers have focused mainly on fibrinogen to

estimate prognosis of ATC (5,57,58,63,67-75,85,86). The relationship between

fibrinogen and mortality is the most interesting relationship, as it can reduce potentially

preventable deaths.


13

In 2014, Hagemo et al. built two regression models to determine the association

of fibrinogen level and mortality within 28 days after admission based on mainly

prospectively obtained data (5). The first model was a logistic regression model which

resulted in a significant association between fibrinogen level and mortality (odds ratio

[OR]: 0.46, 95% confidence interval [95% CI]: 0.31-0.67). The second model was a

piecewise logistic model divided into a lower and upper segment. The OR of the lower

and upper segment were 0.08 (95% CI 0.03-0.20) and 1.77 (95% CI 0.94-3.32),

respectively. Breakpoint of these two segments was a fibrinogen level of 2.29 g/L (95%

CI 1.93-2.64). Only the P-value of the lower segment was significant. The relationship of

fibrinogen level and mortality was adjusted for ISS, age, time from injury, mechanism of

injury, base excess, INR, platelet count, and gender in both models. The authors

mentioned a dramatic increase in mortality if fibrinogen level falls below their

breakpoint, and they suggest aggressive treatment if such fibrinogen level is found. The

CI of the OR of the upper segment is relatively wide and not significant and should

therefore be interpreted with caution.

Inaba et al. also built a regression model in 2013 containing fibrinogen level as

independent variable and 24-hour mortality as dependent variable (63). Fibrinogen level

was converted into a dichotomic variable: fibrinogen level≤100 mg/dL or >100 mg/L.

The authors found that low fibrinogen level (≤100 mg/dL) was associated with poorer

survival compared to a high fibrinogen level (OR: 3.97, 95% CI 1.34-11.74). This effect

was adjusted for GCS (≤8), laparotomy, ISS (>15), platelet count (<100*109/L), age,

systolic blood pressure, Abbreviated Injury Scale head, tracheostomy, units of

transfusion, aPTT and time from emergency department to intensive care unit. The

authors concluded that aggressive treatment is necessary if fibrinogen falls below 100

mg/dL, as this was a strong predictor of mortality. However, the CI is wide, this study

only consisted of patients who received massive transfusion, and patients were only

included if fibrinogen level was known. Also, patients who died in the emergency room

were excluded. This could have resulted in a biased conclusion (i.e. selection bias).

A study conducted by Rourke et al. in 2012 has associated fibrinogen level on

admission with 24-hour and 28-day mortality (both P<0.001) (62). However, only the OR

of 28-day mortality is given (OR 0.22; 95% CI 0.10-0.47). Other variables in this model

were ISS, aPTT, age, and gender. They suggest that there may be role for early and

aggressive supplementation for patients with hypofibrinogenemia.

Kimura et al. regressed fibrinogen on 7-day mortality in 2014 (64). Other

determinants in that model were New Injury Severity Score (NISS), Triage Revised

Trauma Score (T-RTS), BE, and lactate, resulting in HR of 0.99 (95% CI 0.98-0.998;

P=0.01). The authors also concluded that repeated measurements of fibrinogen levels and

appropriate supplementation are recommended.

In 2011, Tauber et al. have also tried to identify the adjusted effect of fibrinogen

level on mortality (75). They found a non-significant association between fibrinogen

level and 24-hour mortality (OR: 0.997, 95% CI 0.988-1.007). The authors found,

however, a significant relationship between fibrinogen level and red blood cell

requirements during the first 6h after admission (OR: 0.994, 95% CI 0.991-0.998).

Innerhofer et al. conducted a study in 2013. They found that the exclusive administration

of coagulation factor compared to fresh frozen plasma reduces transfusion requirement

after major trauma (73). The authors also monitored fibrinogen level over time during


14

critical care stay. However, no regression model was build, which could have resulted in

a misestimated conclusion (i.e. not adjusted for other patient’s characteristics).

Fibrinogen is also associated with morbidity and mortality in other clinical

settings. Karlsson et al. found the preoperative fibrinogen concentration was a significant

independent predictor of transfusion requirement after coronary artery bypass grafting

surgery (71). The authors found an OR of 2.0 (95% CI 1.1-3.7) per 1g/L decrease of

fibrinogen level. This OR was adjusted for sex, and aortic class-clamp time. Ucar et al.

and Blome et al. also suggested that preoperative fibrinogen level may be a potential risk

factor for postoperative bleeding after coronary artery bypass surgery (68,72). However,

the authors did not build a regression model. Finally, Charbit et al. found that fibrinogen

level could predict the risk of severe postpartum hemorrhage (PPH) (69). The authors

built a model of fibrinogen level and PPH at four moments. These four moments were

H0, H1, H2, and H4 which are defined as the intravenous administration of sulprostone

(i.e. start of partus) and 1, 2, and 4 hours afterwards, respectively. The corresponding

adjusted OR were 2.63 (95% CI 1.66-4.16), 2.70 (95% CI 1.75-4.16), 3.70 (95% CI 2.17-

6.25), and 5.00 (95% CI 2.63-9.09). These OR were calculated for each 1 g/L decrease of

fibrinogen level.

At this moment, the adjusted effect of fibrinogen on outcome, and especially

mortality, in trauma patients is not clear. The studies mentioned above have tried to

identify the association between fibrinogen level and mortality or transfusion

requirements. Hagemo et al., Inaba et al., Rourke et al., and Kimura et al. found an

association of fibrinogen and mortality. However, the conclusions of Hagemo et al. and

Inaba et al. differ. Hagemo et al. concludes that a fibrinogen level of 2.29 g/L or lower is

a trigger for aggressive treatment, while Inaba et al. defined 100 mg/dL (i.e. 1 g/L) as

crucial fibrinogen level. Also, the models lack important variables which could have

resulted in a misestimated conclusion. For example, the presence of TBI, pH of blood,

and temperature are not taken into account in the abovementioned models. Temperature

and pH of blood are two main contributors of the ‘triad of death’ and studies have shown

that mortality increases dramatically if hypothermia or acidemia are present (38,39).

Although Hagemo et al. did include base excess, pH blood gives extra information about

the nature of the alkalemia or acidemia. Base excess does not appropriately take the

metabolic responses into account and could lead to a misinterpreted conclusion (102).

Thus, these characteristics should have been included in these models. Furthermore, in

isolated TBI, approximately 35% of patients show acute traumatic coagulopathy, which

states that the presence of TBI may be important to identify the effect of fibrinogen (103).

Also, Tauber et al. have not found a significant relationship between fibrinogen level and

mortality at all. In other clinical settings, fibrinogen is associated with morbidity and

mortality. As a result, controversy is still present about the effect of fibrinogen level on

prognosis. Also, the adjusted effect of fibrinogen level on mortality after trauma is not

clear.

1.9 Aims Taking the devastating outcome of ATC and controversy of performed studies

into account, it is of paramount importance to clarify the effect of fibrinogen level on

mortality in these patients. The purpose of this study was to determine the adjusted effect

of fibrinogen level at admission on early mortality in trauma patients. Additionally, we

tried to determine if this relationship is different for sex, age, and traumatic brain injury.


15

Our aim was to identify patients at increased risk of dying as a result of ATC using

fibrinogen concentrations. We hypothesized that lower levels of fibrinogen are associated

with higher mortality rates in trauma patients.


16

CHAPTER 2

MATERIAL AND METHODS

2.1 Patients This was a retrospective study which included polytraumatized patients.

Trauma patients treated in the time period 2004-2013 at the University Medical Center

Groningen (UMCG) were eligible for inclusion. Inclusion criteria were New Injury

Severity Score (NISS) higher than 15 and age from 18 to 80 years. Exclusion criteria

were pregnant women and if time from injury to hospital was greater than 180 minutes.

Also, patients with known clotting disorders were excluded, but not patients using

anticoagulants for other reasons.

2.2 Trauma protocol Patients were initially assessed according the ATLS (104). This

protocol is based on the principle ‘treat first what kills first’ and follows a fixed order of

assessment of injuries. The most immediate life-threatening injuries are quickly identified

and treated in order of their risk potential. The next step of assessment was only

performed if the previous step of assessment was proven stable. If massive hemorrhage

was proven or expected (i.e. blood loss of 2 liters), massive transfusion protocol (MTP)

was followed (appendix, figure A1.1). MTP consisted of infusion of red blood cells, fresh

frozen plasma, and thrombocytes (ratio 4:4:1). Both ATLS protocol and MTP are

described in detail in the ATLS and UMCG trauma guidelines (104,105).

2.3 Study parameters Endpoint of this study was mortality within 24 hours after

admission. Patient’s characteristics were determined from electronic medical records.

Demographics consisted of gender, age, cause of injury, mechanism of injury, GCS, and

NISS. Shock-related parameters comprised pulse, systolic blood pressure, hemoglobin

level, arterial bicarbonate, and arterial pH. Finally, coagulation parameters used in this

study were INR, aPTT, PT, platelet count, and fibrinogen level. Shock-related and

coagulation parameters were obtained at the emergency department. For each parameter,

the first measured value within 3 hours after admission was used for analyses.

Cause of injury was divided into five categories: motor vehicle accidents, falls

from 3 meters and higher, falls from lower than 3 meters, violence, and other. Injury

mechanism was categorized into blunt or penetrating injury.

Injury severity was scored using the NISS. Severity of injuries was scored from 1

to 5: minor, moderate, serious (not life-threatening), severe (life-threatening, survival

probable), or critical (survival uncertain). The NISS is calculated by squaring and

summing up the highest grade of the three most severe injuries, regardless of the body

region in which they occur (106). The latter is an important difference compared to the

ISS, in which the highest grade of the three most severed body regions are squared and

summed up. Thus, NISS scores ranges from 1 to 75.

The GCS is measured by testing eye, motor, and verbal responses. Ranges of

scores of each test are 1 to 4, 1 to 6, and 1 to 5, respectively. The lowest possible total

score is 3 (deep coma) while the maximum score is 15 (fully awake person) (107). The

GCS of the emergency department was used in our analyses. However, missings were

supplemented with pre-hospital scores, which are highly correlated (108).


17

PT and aPTT were determined using the regular method described in literature

(109,110). Norm values are 9-12 and 23-33 seconds (depending on laboratory),

respectively. INR was calculated by dividing the patient’s PT through the norm value of

PT, raised to the power of the international sensitivity index (ISI). ISI is a correction

factor to compare INRs between different laboratories with each other, making it an

international standard. Finally, hemodynamic instability was defined as systolic blood

pressure lower than 90 mmHg.

2.4 Statistical analyses

2.4.1. Descriptive statistics

Patients were divided into two subgroups based on their outcome 24 hours after

admission at the emergency department: survivors and non-survivors. Descriptive

statistics were used to describe the main characteristics of the patients. All normally

distributed variables were presented as means and standard deviations (SD). Mean values

of both groups (i.e. survivors and non-survivors) were compared using the independent-

samples t-test. Non-parametric continuous data were presented as medians and

interquartile range (25th-75th percentile, P25-P75). These data were compared between

both groups using the Mann-Whitney U test. All nominal or categorical variables were

described as frequencies and percentages. Comparison between both groups was

performed using the Fisher’s exact test.

2.4.2 Association model

An association model reveals the relationship between an independent variable A (i.e.

fibrinogen) and dependent variable B (i.e. mortality). This relationship is the crude effect

as confounders and effect modifiers can influence this relationship. A confounder

correlates with both the dependent and independent variable. Not taking into account

confounders leads to a misestimated effect of the independent variable on the dependent

variable. Effect modifiers also result in misestimating the relationship of the independent

and dependent variable. This happens due to a different relationship within independent

variable A on the dependent variable B. For example, different effects of variable A on

variable B for both sexes.

Survival was presented using a Kaplan-Meier curve. Only for this survival curve,

fibrinogen level was categorized into two groups based on European trauma guidelines:

normal (>1.5 g/L) and critical (≤1.5 g/L) levels (97,111). These groups were compared

by the log-rank test. Further analyses of fibrinogen levels were performed using the

continuous variable as this is superior compared to dichotomizing a continuous variable

(112). Multivariable Cox regression model with stepwise forward selection was used to

build an association model. The endpoint of the model was hours to death within 24

hours after admission. Firstly, a univariable Cox regression model with fibrinogen levels

as independent variable was built to determine the crude effect of fibrinogen level on

mortality. The following clinical relevant effect modifiers were tested: sex, age, TBI

(measured using the GCS), NISS, and mechanism of injury. A variable was considered an

effect modifier when the regression coefficient of the interaction term was significant.

The crude effect of fibrinogen was adjusted for patient’s characteristics mentioned above,

identifying confounders. These characteristics were added in three blocks: demographics,


18

shock-related parameters, and coagulation parameters. Categorical variables with more

than 2 categories were converted to dummy variables based on reference group coding.

Relevant confounding was found present when the regression coefficient of fibrinogen

changed more than 10% compared to the unadjusted regression coefficient of fibrinogen.

The proportional hazard assumption was tested graphically for categorical variables and

using a time-dependent covariate for continuous variables. Results are presented as

regression coefficient, standard error, and hazard ratios (HRs) with 95% confidence

interval. The Wald test was used to determine statistical significance.

Alpha (α) was set at 0.05, which states the probability to incorrectly reject a true null

hypothesis. Thus, P<0.05 was considered significant for all analyses. All performed tests

were two-tailed tests. All analyses were performed in SPSS 22 (IBM SPSS Statistics for

Windows, Version 22.0. Armonk, NY: IBM Corp.).


19

CHAPTER 3

RESULTS

3.1 Patient’s characteristics In the time period 2004-2013, 2073 trauma patients were

treated at the UMCG. Of these, 582 patients (28.1%) met the exclusion criteria and were

therefore excluded from analyses. The remaining 1491 patients (71.9%) met the inclusion

criteria of this study. Patient’s characteristics are presented in table 1. Of all included

patients, 1377 patients (92.4%) survived and 114 patients (7.6%) died within the first 24

hours after admission. Median age (P25-P75) of all subjects was 45 (26-60). The vast

majority of included patients were male (76.3%). Mechanism of injury was mainly blunt

trauma (95.3%). Median GCS (P25-P75) of all patients was 13 (6-15). Median NISS

(P25-P75) was 33 (25-43). Mean fibrinogen level (SD) of all patients was 2.0 (1.0).

Age differed significantly between both subgroups, with a median age (P25-P75)

of 45 (28-59) of the survivors and 51 (31-68) of the non-survivors (P=0.008). Median

GCS (P25-P75) of the survivors and non-survivors were 14 (7-15) and 3 (3-4),

respectively, which resulted in a significant difference (P<0.001). Also, difference of

NISS between both subgroups was significant, with a median NISS (P25-P75) of 33 (25-

43) and 59 (50-75), respectively (P<0.001). Furthermore, mean fibrinogen level of the

survivors and non-survivors differed significantly (2.0 [SD 0.99] versus 1.2 [SD 0.85],

respectively; P<0.001). Of all characteristics, sex (P=0.819), cause of injury (P=0.230),

mechanism of injury (P=0.134), and pulse (P=0.878) were not significantly different

between both subgroups (table 1).

3.2 Survival analyses

Survival was plotted using a

Kaplan-Meier curve (figure

5). Log-rank test showed that

lower fibrinogen levels were

associated with poorer 24-

hour survival compared to

normal fibrinogen levels

(P<0.001; figure 5).

Of all non-survivors,

78 patients (68.4%) died

because of TBI. Hemorrhage

was also a major contributor

to mortality, responsible for

the death of 25 patients

(21.9%). Other causes of

deaths were suicide (3.5%),

asystole with unknown cause

(1.8%), spinal cord lesion

(1.8%), hypothermia (0.9%),

and no identifiable cause

(1.8%).

Figure 5. Kaplan-Meier survival curve for overall survival of

polytraumatized patients from admission to 24 hours after

admission, categorized by fibrinogen level at admission. Note that

probability of survival (i.e. y-axis) ranges from 80% to 100%.


20

The Kaplan-Meier survival curve (figure 5) showed an “elbow point” at 5 hours

after admission. Therefore, all non-survivors were divided into two groups: early (≤5

hours) and late deaths (>5 hours but ≤24 hours; table 2). No significant difference of

cause of death was found between these two subgroups (P=0.424). Of the characteristics

presented in table 2, only INR, aPTT, PT, and fibrinogen level differed significantly

between early and late deaths (P=0.004, P=0.037, P<0.001, and P=0.034, respectively).

Median GCS (P25-P75) of deaths due to TBI of early and late deaths were both 3

(3-3; P=0.489). Furthermore, hemodynamic instability was present in 10 (52.6%) and 1

patient(s) (20.0%) in early and late hemorrhagic deaths, respectively (P=0.327). Focusing

on patients with critical fibrinogen level, no significant difference was found between

early and late deaths regarding GCS and hemodynamic instability (P=0.890 and P=0.099,

respectively).

In univariable Cox regression analysis, fibrinogen level at admission was

significantly associated with mortality within 24 hours after admission at the emergency

department (HR 0.343, 95% CI 0.265-0.444, P<0.001; table 3). The interaction with sex,

age, GCS, NISS, and mechanism of injury were tested to identify effect modification.

None of these interaction terms were significant (P=0.300, P=0.076, P=0.301, P=0.853,

and P=0.484, respectively). After stepwise adjusting for demographics, shock-related

parameters, and coagulation parameters, fibrinogen level was still significantly associated

with mortality at every step of analyses (table 3). All three adjusted regression

coefficients of fibrinogen level differed more than 10% compared to the unadjusted

regression coefficient. Thus, every step identified relevant confounding characteristics.

INR was omitted due to collinearity between INR and PT and because of a lower Wald

score than PT. All HRs are calculated per 1 g/L increase of fibrinogen level. The final

multivariable model resulted in a HR (95% CI) of 0.475 (0.305-0.738; P=0.001; table 3).

This states that at every point of time within 24 hours after admission, an increase of

admission fibrinogen level of 1 g/L reduces the probability of death by a factor 0.475.


21

Table 1. Patient’s characteristics of all patients and the comparison of these characteristics between

patients based on their outcome. Patient’s

characteristics

Total (n = 1491) Survivors (n = 1377) Non-survivors

(n = 114)

P-value*

Age (years)a 45 (28-60) 45 (28-59) 51 (31-68) 0.008

Sex, n (%) 0.819

Male 1138 (76.3%) 1052 (76.4%) 86 (75.4%)

Female 353 (23.7%) 325 (23.6%) 28 (24.6%)

Cause of injury, n (%) 0.230

Motor Vehicle

Accident

837 (56.1%) 775 (56.3%) 62 (54.4%)

Fall ≥3m height 236 (15.8%) 214 (15.5%) 22 (19.3%)

Fall <3m height 200 (13.4%) 188 (13.7%) 12 (10.5%)

Violence 56 (3.8%) 54 (3.9%) 2 (1.8%)

Other 129 (8.7%) 114 (8.3%) 15 (13.2%)

Unknown 33 (2.2%) 32 (2.3%) 1 (0.9%)

Mechanism of Injury,

n (%)

0.134

Penetrating 42 (2.8%) 36 (2.6%) 6 (5.3%)

Blunt 1421 (95.3%) 1313 (95.4%) 108 (94.7%)

Unknown 28 (1.9%) 28 (2.0%) 0 (0.0%)

GCS (points) a 13 (6-15) 14 (7-15) 3 (3-4) <0.001

NISS (points) a 33 (25-43) 29 (24-41) 59 (50-75) <0.001

Shock-related

parameters

Pulse (bpm)b 85.2 (20.7) 85.2 (20.0) 85.6 (28.3) 0.878

SBP (mmHg)b 124.9 (28.2) 126.0 (25.9) 111.0 (45.6) 0.001

Hemoglobin level

(mmol/L)a

7.8 (6.7-8.6) 7.9 (6.8-8.7) 6.8 (4.7-7.7) <0.001

Arterial HCO3-

level (mmol/L)a

22.0 (19.0-24.0) 22.0 (20.0-24.0) 20.0 (15.0-22.0) <0.001

Arterial pHa 7.32 (7.25-7.37) 7.33 (7.26-7.38) 7.25 (7.08-7.31) <0.001

Coagulation

parameters

INRa 1.1 (1.1-1.2) 1.1 (1.0-1.2) 1.3 (1.2-1.9) <0.001

aPTT (sec)a 26.0 (23.8-30.0) 26.0 (23.0-29.0) 34.0 (27.0-60.0) <0.001

PT (sec)a 12.0 (11.2-13.5) 11.9 (11.2-13.2) 14.8 (12.5-20.9) <0.001

Platelet count

(x109/L)

b

206.9 (74.2) 210.5 (73.6) 165.6 (67.4) <0.001

Fibrinogen (g/L)b 2.0 (1.0) 2.1 (0.99) 1.2 (0.85) <0.001

GCS: Glasgow Coma Scale, NISS: New Injury Severity Score, SBP: Systolic Blood Pressure, INR: International

Normalized Ratio, aPTT: Activated Partial Thromboplastin Time, PT: Prothrombin Time. a values are given in median (25th-75th percentile). b values are given as mean (standard deviation). *Mann–Whitney

U test, independent-samples t-test or Fisher’s exact test, as appropriate.

Bold P-values are statistically significant (P<0.05, two-tailed).


22

Table 2. Patient’s characteristics of early and late deaths and the comparison of these characteristics based

on their outcome.

Patient’s characteristics Early deaths (n = 87) Late deaths (n = 27) P-value*

Age (years)a 54 (31-68) 46 (32-66) 0.459

Sex, n (%) 0.076

Male 62 (71.3%) 24 (88.9%)

Female 25 (28.7%) 3 (11.1%)

NISS (points)a

59 (50-75) 66 (50-75) 0.415

GCS (points)a 3 (3-4) 3 (3-4) 0.872

Cause of death, n (%) 0.424

TBI 58 (66.7%) 20 (74.1%)

Hemorrhage 20 (23.0%) 5 (18.5%)

Suicide 4 (4.6%) 0 (0.0%)

Asystole with

unknown cause

1 (1.1%) 1 (3.7%)

Spinal cord lesion 2 (2.3%) 0 (0.0%)

Hypothermia 0 (0.0%) 1 (3.7%)

No identifiable cause 2 (2.3%) 0 (0.0%)

Hemodynamic instability,

n (%) 0.164

Yes 33 (39.3%) 6 (22.2%)

No 51 (60.7%) 21 (77.8%)

Shock-related parameters

Pulse (bpm)a 85 (61-109) 83 (74-100) 0.694

SBP (mmHg)a 100 (75-135) 125 (98-150) 0.119

Hemoglobin level

(mmol/L)a

6.8 (4.3-7.5) 6.8 (6.0-8.0) 0.123

Arterial HCO3-

level

(mmol/L)a

19.0 (15.0-22.0) 20.0 (17.0-22.0) 0.296

Arterial pHa 7.22 (7.06-7.32) 7.26 (7.18-7.31) 0.517

Coagulation parameters

INRa 1.5 (1.2-2.1) 1.2 (1.1-1.4) 0.004

aPTT (sec)a 37.0 (27.7-71.0) 31.0 (25.0-39.0) 0.037

PT (sec)a 17.0 (12.7-22.8) 12.6 (12.1-15.0) <0.001

Platelet count

(x109/L)

a

159.0 (112.0-202.0) 170.0 (115.0-240.0) 0.406

Fibrinogen (g/L)a 1.1 (0.5-1.6) 1.4 (0.9-1.8) 0.034

GCS: Glasgow Coma Scale, NISS: New Injury Severity Score, SBP: Systolic Blood Pressure. a values are given in median (25th-75th percentile). *Mann–Whitney U test or Fisher’s exact test, as appropriate.

Bold P-values are statistically significant (P<0.05, two-tailed).


23

Table 3. Uni- and multivariable Cox regression analysis to estimate the effect of fibrinogen on mortality

within 24 hours after admission.

Determinant Regression

coefficient

Standard error Hazard Ratio (95% CI) P-value

Fibrinogen (g/L)* -1.069 0.131 0.343 (0.265-0.444) <0.001

Fibrinogen (g/L)** -0.667 0.158 0.513 (0.376-0.700) <0.001

Fibrinogen (g/L)*** -0.670 0.205 0.511 (0.342-0.765) 0.001

Fibrinogen (g/L)**** -0.745 0.225 0.475 (0.305-0.738) 0.001

*Unadjusted effect.

** Adjusted for demographic characteristics: sex, age, New Injury Severity Score, Glasgow Coma Scale, cause of

injury, and mechanism of injury.

*** Adjusted for demographic characteristics, plus for shock-related parameters: pulse, systolic blood pressure,

hemoglobin level, arterial pH, and arterial bicarbonate.

**** Adjusted for demographic characteristics and shock-related parameters, plus for coagulation parameters:

platelet count, prothrombin time, and activated partial thromboplastin time.

Bold P-values are statistically significant (P<0.05).


24

CHAPTER 4

DISCUSSION

4.1 Main findings The main objective of this study was to estimate the adjusted effect of

fibrinogen level at admission on 24-hour mortality in polytraumatized patients.

Additionally, we tried to determine if this relationship is different for sex, age, and

traumatic brain injury. Univariable analysis showed that an increase of admission

fibrinogen level is associated with a lower chance of dying within 24 hours after

admission (HR 0.343, 95% CI 0.265-0.444; P<0.001). This relationship was not modified

by TBI, sex, and age. However, adjusting for demographics, shock-related parameters,

and coagulation parameters is necessary as they confounded the effect of fibrinogen level

on early mortality (table 3). The HR of the final model was 0.475 (95% CI 0.305-0.738;

P=0.001). Thus, lower fibrinogen levels are associated with higher mortality,

independently of demographics, shock-related parameters, and coagulation parameters.

As preventable trauma deaths mainly occur after hemorrhage with uncontrolled bleeding

and low fibrinogen levels (111,113), results of this study indicate clinical importance as it

could reduce potential preventable deaths. Monitoring fibrinogen level routinely to

identify patients at increased risk of dying as a result of ATC and actively supplementing

fibrinogen may reduce mortality of these patients.

The study population mainly consisted of males and blunt trauma patients. Only

sex, pulse, cause and mechanism of injury were not significantly different between

survivors and non-survivors (table 1). Distribution of all characteristics between

survivors and non-survivors are similar when compared to literature, except for pulse

(5,47,62-64,114,115). Higher pulse is associated with higher mortality in literature

(116,117) while the present study found no significant difference between survivors and

non-survivors. Although pulse did not differ significantly between both groups, systolic

blood pressure did. Non-survivors had lower systolic blood pressure with the same pulse

as survivors. Hence, these patients were probably hypovolemic and thus may explain the

abovementioned differences with literature.

The study populations of studies investigating the association between fibrinogen

level at admission and mortality are different compared to the population studied in the

present study. Firstly, two studies reported younger patients (62,63) whereas a third study

investigated an older population (64). Of the first mentioned two studies, one did not

have an age inclusion criterion (63) while the other had an inclusion criterion of ≥16

years (62). This study included patients of age 18-80 years because our population of

interest was adults. This may explain the difference in age between those and the present

study. The third study examined older patients compared to the present study (64). We

could not find differences in study design which could explain this difference. Secondly,

compared to the present study, lower median or mean ISS (5,62,63) or NISS (64) were

reported elsewhere. This may be explained due to the inclusion of patients with

(N)ISS<16 by the mentioned studies. We did not include patients with NISS<16 because

those patients had insufficient and inadequate records for evaluation. We used NISS

instead on ISS in the present study because it is more accurate to predict in-hospital death

(118). Furthermore, due to the differences in calculation of the ISS and NISS (see section

2.3), NISS is generally higher than ISS (118). This may also contribute to the difference


25

in mean or median (N)ISS compared to other studies. Lastly, one study only included

massively transfused patients (63). These patients are, by definition, in a critical state and

mortality in this group of patients is higher compared to non-massively transfused

patients (119). Due to these differences, comparison with the present study is difficult.

The Kaplan-Meier survival curve for overall survival and the log-rank test showed

that critical fibrinogen levels are associated with poorer 24-hour survival compared to

normal fibrinogen levels (figure 5). To our knowledge, only one study have performed a

similar analysis (63). These results correspond with our results. However, as mentioned

before, the authors only included massively transfused patients and thus, comparison

must be done with caution. The Kaplan-Meier curve showed an elbow point at 5 hours

after admission in both subgroups. Other studies have found similar results

(114,120,121). Therefore, we focused on causes of death. The distribution of causes of

death is similar to the literature (3,120-122). No significant difference in cause of death

between patients who died within 5 hours or 5 to 24 hours after admission was found.

This corresponds to literature as CNS injury and hemorrhage remains the main causes of

early deaths (3,121). Of all TBI deaths, early and late deaths had minimum GCS and no

significant difference was found. This is in accordance with literature as well, as CNS

injury is the primary cause of death from 1 to 72 hours after trauma (121). Also, GCS did

not differ significantly between early and late deaths when focusing on patients with

critical fibrinogen level. Focusing on hemorrhagic deaths, we found no significant

difference in the presence of hemodynamic instability between early and late deaths.

Additionally, the presence of hemodynamic instability did not differ significantly

between early and late deaths of patients with critical fibrinogen levels. This may be the

result of a small sample size of hemorrhagic deaths (n = 25) because the present study

found that INR, aPTT, PT, and fibrinogen level are significantly different between early

and late deaths (table 2). All of these significantly different characteristics were worse in

the subgroup of early deaths. Furthermore, other studies have shown that the presence of

hemodynamic instability is associated with mortality within 2 to 6 hours after trauma

(114,115). However, these studies are relatively old and further research is therefore

needed to clarify this.

Univariable analysis showed that fibrinogen level at admission is significantly

associated with 24-hour mortality (HR 0.343, 95% CI 0.265-0.444; P<0.001; table 3).

Only one study has presented an unadjusted effect of fibrinogen level on mortality (64).

The authors regressed fibrinogen level on 7-day mortality (HR 0.98, 95% CI 0.978-0.987;

P<0.001). Hemorrhage is a major contributor to mortality within 24 hours after

admission. Mortality beyond 24 hours and within 28 days is more often the result of

sepsis and multi-organ failure (3,8,121,123). Very few hemorrhagic deaths occur after 24

hours (3,4,6,8,63,121,123). Hence, 24-hour mortality is a better endpoint to study

hemorrhagic mortality. In addition, the study included patients with lower NISS and older

age. Hypofibrinogenemia is more frequently found in younger patients compared to older

patients (5,39,64), which could be due to higher plasma fibrinogen levels in the latter

population (124). In addition, injury severity is correlated with a pathophysiological

response indicative of ATC in younger trauma patients and older patients show a

nonadaptive response irrespectively of severity of injury. These older, less severely

injured patients could, therefore, partly explain why the authors found an HR close to 1

(125).


26

Multivariable analysis of this study showed that lower levels of fibrinogen are

associated with higher mortality, independent of demographics, shock-related

parameters, and coagulation parameters (HR 0.475, 95% CI 0.305-0.738; P=0.001; table

3). No relevant effect modification was found. However, stepwise addition of

demographics, shock-related and coagulation parameters in blocks revealed relevant

confounding at each step. Thus, adjusting for these characteristics is necessary. Several

authors have investigated whether fibrinogen level is an independent predictor of

mortality in trauma patients by building a prediction model, adjusting for several

patient’s characteristics (5,62-64). All of these studies have shown a significant

association between fibrinogen level and mortality. However, the degree of association

differs and this may be explained by the difference in study designs and characteristics

included in these models. As mentioned before, endpoint of this study was 24-hour

mortality due to the high hemorrhagic mortality in this time frame and few hemorrhagic

deaths after 24 hours. Other studies have focused on 24-hour (63), 7-day (64) or 28-day

mortality (5,62). Inaba et al. focused on 24-hour mortality (OR 3.97; 95% CI 1.34-11.74;

P=0.013) (63). Kimura et. al regressed fibrinogen on 7-day mortality (HR 0.99, 95% CI

0.98-0.998; P=0.01) (64). Hagemo et al. regressed fibrinogen level on 28-day mortality

(OR 0.46; 95% CI 0.31-0.67; P<0.001) (5). Rourke et al. also focused on 28-day

mortality (OR 0.22; 95% CI 0.10-0.47; P<0.001) (62). Although Inaba et al. focused on

24-hour mortality, all continuous variables were converted to dichotomous variables (e.g.

fibrinogen into ≤100 mg/dL and >100 mg/dL), resulting in a loss of power and a possible

biased estimate (112). Conversion to dichotomous variables may also explain the wide CI

(112). Furthermore, the present study showed the importance of adjusting for important

characteristics (table 3). Several important characteristics are lacking in the

abovementioned studies, like GCS (5,62,64), shock-related parameters (5,62-64), and

coagulation parameters (64). Kimura et al. did include the Triage Revised Trauma Score

(T-RTS). Although this score is based on GCS, systolic blood pressure, and pulse, it does

not include these three parameters in the model independently. Consequently, the effect

of fibrinogen level is not adjusted for these three important parameters independently to

predict mortality and thus could explain the difference of results. TBI is associated with

coagulopathy and GCS should therefore be included in a model to predict mortality

(11,103). Shock-related parameters are crucial as septic shock may occur, especially if

focused on mortality beyond 24 hours. Furthermore, several studies used ISS instead of

NISS (5,62,63). Altogether, this may have contributed to the differences in results.

A study conducted in 2011 showed an insignificant association between

fibrinogen level at admission and mortality within 24 hours after admission (75).

However, the authors focused on predicting mortality based on coagulation parameters.

Demographics (e.g. age, gender, and [N]ISS) and shock-related parameters were not

included in their analyses. This may have resulted in the insignificant association

between fibrinogen level and mortality.

The present study demonstrates that the association of fibrinogen level and 24-

hour mortality is not modified by age, sex, or TBI. The latter was measured using the

GCS. We tested these effect modifiers in particular as several studies have mentioned that

these characteristics may play an important role in ATC (5,11,62,64).

Hypofibrinogenemia is observed more frequently in younger patients (5,39,64).

Furthermore, TBI is associated with coagulopathy in both isolated TBI and


27

polytraumatized patients (11,63). Male gender is associated with higher mortality

compared to female gender (5,62). Although demographics revealed relevant

confounding in the present study, these characteristics did not modify the effect of

fibrinogen level on 24-hour mortality. Thus, the effect of fibrinogen level found in the

final model demonstrated in this study (table 3) is applicable to all patients included in

this study, regardless of age, sex, or TBI.

This study, like the abovementioned studies (62-64), assumed a linear relationship

between fibrinogen level and mortality. However, several authors suggest a piecewise

logistic relationship between fibrinogen and mortality (5,126). Besides the mentioned

logistic model, Hagemo et al. also built a piecewise logistic model (i.e. a segmented

regression model) (5). Breakpoint of this model was a fibrinogen level of 2.29 g/L. The

association of fibrinogen level and 28-day mortality of the lower segment was significant

(OR 0.08; 95% CI 0.03-0.20; P<0.001) while this was insignificant in the upper segment

(OR 1.77; 95% CI 0.94-3.32; P=0.076). This suggests that mortality increases rapidly if

fibrinogen level falls below their breakpoint and that fibrinogen level is not associated

with mortality in the upper segment. The present study found one HR of the final model

for all patients. This may explain the difference in results. The discrepancy between a

linear and piecewise logistic model indicates that our results may underestimate the

negative impact of low fibrinogen levels in trauma patients. Further research is needed to

identify the type of model (e.g. linear, piecewise logistic, or polynomial) which is best to

estimate this relationship.

The cause of low fibrinogen level is assumed multifactorial. ATC results in a

hypocoagulation and hyperfibrinolysis. Hyperfibrinolysis lowers fibrin level, driving the

conversion of fibrinogen into fibrin (59-61). Additionally, hyperfibrinogenolysis occurs,

which lowers fibrinogen levels (4,52-54). Furthermore, fibrinogen is lost due to

hemorrhage. Dilution through resuscitation with fibrinogen-poor fluids also lowers

fibrinogen levels (63). Lastly, acidosis enhances fibrinogen breakdown (41) and

hypothermia affects fibrinogen levels by inhibiting synthesis of it (40).

Noteworthy, all the abovementioned studies have built a prediction model (5,62-

64). In this study, however, an association model was built to estimate the adjusted effect

of fibrinogen on early mortality. The differences between these models is that an

association model estimates the effect of a central determinant (i.e. fibrinogen level) on

outcome (i.e. early mortality) as purely as possible whereas a prediction model is used to

predict an outcome as best as possible using several different determinants. To our

knowledge, no association model has been built that estimates the adjusted effect of

fibrinogen level on (early) mortality. Furthermore, the present study used a Cox

regression model instead of a logistic regression model. Only the model of Kimura et al.

(2014) was built using Cox regression. A logistic model does not take time to event (i.e.

death) into account, only the occurrence of event (dichotomous variable). Time to death

is important, especially in an acute situation when treating polytraumatized patients. If

patients are more probable to die sooner, early, aggressive treatment may reduce

mortality. The difference in used models may explain the difference in results between

the current study and literature. Further research including studies with association

models is needed to clarify this.


28

4.2 Strengths and limitations This study has several strengths and limitations. One of

the strengths of this study was the large sample size (n = 1491). Furthermore, we built an

association model instead of a prediction model, which is necessary to estimate the

adjusted effect of fibrinogen level on mortality. Also, we tested clinically relevant effect

modifiers and we adjusted for several important characteristics based on literature.

Finally, we looked at mortality within 24 hours after trauma. Several studies suggest that

hemorrhage is a major contributor to mortality within 24 hours after trauma and that late

in-hospital deaths (e.g. >24 hours) have other causes of death (3,4,6,63). Thus, to

determine the effect of fibrinogen levels on hemorrhagic mortality, it is necessary to

focus on early mortality.

An important limitation of this study was bias inherent to retrospective studies.

Furthermore, patients who died at the site of injury were not included in this study,

potentially resulting in selection bias. In addition, we recruited all patients from one

medical center. This could have a potential biased effect based on location and treatment.

Additionally, this study did not include temperature in the final model due to a large

number of missing values. Body core temperature is a main contributor of the ‘triad of

death’ and studies have shown that mortality increases dramatically in the presence of

hypothermia (38,39). Thus, temperature may be a confounder which is not taken into

account in this current study. Other studies have also mentioned a large number of

missing temperature values in trauma patients (5,6). Prospective research could avoid this

limitation. Finally, we assumed a linear relation between fibrinogen level and mortality.

However, several authors demonstrated a piecewise logistic relation between fibrinogen

and mortality (5,126). As mentioned before, this may have underestimated the effect of

low fibrinogen levels on mortality in trauma patients.

4.3 Clinical message and recommendations Since preventable trauma deaths mainly

occur after hemorrhage with uncontrolled bleeding and low fibrinogen levels, results of

this study have clinical impact as it could reduce potentially preventable deaths

(111,113). The present study shows an adjusted HR of 0.475 for every 1 g/L increase of

fibrinogen level at admission on 24-hour mortality. Therefore, monitoring fibrinogen

level routinely to identify patients at increased risk of dying as a result of ATC and

actively supplementing fibrinogen may reduce mortality of these patients. Other authors

agree with early and aggressive treatment of hypofibrinogenemia (93,98-101). However,

these results, including the results of the present study, should be interpreted with caution

due to the retrospective nature of these studies. Prospective research is therefore

recommended to provide additional evidence. Furthermore, the fibrinogen level as a

trigger to initiate treatment and the target level in trauma patients is not clear (5,63,97).

Thus, further research, preferably prospective, is needed to identify this. Additionally, the

studies mentioned above and the present study only focused on fibrinogen level at

admission. However, fibrinogen level fluctuates during critical care stay as a result of

continuous hemorrhage and its treatment (73). Hence, research regarding this fluctuation

and the effect on mortality is needed. Finally, high-quality studies regarding the optimal

source of supplementation are absent (67,93,127). More research, especially double-

blinded randomized controlled trials are required to determine the optimal source of

fibrinogen.


29

4.4 Conclusion In summary, the present retrospective study demonstrates a significant

adjusted association between fibrinogen level at admission and 24-hour mortality in

polytraumatized patients. This relationship was not modified by traumatic brain injury,

sex, or age. Monitoring fibrinogen level routinely and actively supplementing fibrinogen

may reduce mortality of polytraumatized patients with ATC. Further research is needed

to determine the fibrinogen level as trigger to initiate treatment and to determine the

target level of treatment. Finally, research regarding the fluctuation of fibrinogen levels

during critical care stay and the optimal source of fibrinogen supplementation is

recommended.


30

CHAPTER 5

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APPENDIX

Figure A1.1: Massa transfusion protocol of the University Medical Center Groningen. RBC: Red Blood

Cells, FFP: Fresh Frozen Plasma, TC: Thrombocytes, SBP: Systolic Blood Pressure, HR: Heart Rate, Hb:

Hemoglobin, PT: Prothrombin Time, aPTT: Activated Partial Thromboplastin Time, Ht: Hematocrit.

Translated from “Massa Transfusie Trauma Protocol” by K.W. Wendt, 2011 (105).

deparment of surgery, division of ...scripties.umcg.eldoc.ub.rug.nl/files/root/geneeskunde/...the...

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