Biomarkers in Acute High-risk AbdoMinAl Surgery
Conditions: General Surgery, Inflammation, Troponins, Biomarkers
Sponsor: University of Copenhagen

Official Title: 
Study protocol: Biomarkers in Acute High-risk 
AbdoMinAl Surgery (BAHAMAS) 
Version: 7  
Date: July 5th 2017 
Unique Protocol ID: BAHAMAS1 
Clinical trials identifier: NCT03582631 
Authors:  
Rune Børch Hasselager1 
Nicolai Bang Foss1 
Ove Andersen3 
Morten Bay-Nielsen2 
Hans Jørgen Nielsen2 
Linda Camilla Andresen3 
Line Toft Tengberg2 
Institutions: 
1Department of Anaesthesiology and Intensive Care, Hvidovre University Hospital, 
Hvidovre, Denmark 
2Department of Surgical Gastroenterology, Hvidovre University Hospital, Hvidovre, 
Denmark 
3Department of Clinical Research, Hvidovre University Hospital, Hvidovre, Denmark 

 
2
 
 
Introduction: 
Acute high-risk abdominal surgery (AHA) is performed in hospitals worldwide. 
Ethiologies are heterogeneous, but overall emergency surgery carries a very high 
mortality rate (1)(2). In particular, emergency laparotomies performed on elderly 
people has a high mortality rate(3)(4). Different quality improvement programmes for 
that particular patient group have been suggested, but the quality of care and thereby 
mortality varies considerably between hospitals(5)(6). The use of postoperative 
intensive care and monitoring seem to be inadequate for this high risk population in a 
variety of hospitals(1)(7)(8). It is of paramount importance to identify the frailest and 
most acutely deranged patients, who are in risk of poor outcome, in order to allocate 
resources for improved monitoring and optimisation postoperatively. It has been 
shown that failure to rescue patients after having developed postoperative 
complications and inability to escalate care intensity affects the outcome. 
Organisation, teamwork and culture is important in the postoperative phase to be able 
to escalate care especially in standard care wards(9)(10). However, it is found 
difficult to predict which patients will experience complications in a standard care 
ward. 
Different risk assessment tools have been proposed for patients undergoing 
AHA(11)(12). The APACHE-II score, even though developed for critical care, seems 
to be the one with best prediction of outcome. Risk assessment tools are important to 
support clinical decision making by the perioperative team as subjective clinical 
assessment often underestimates the risk for the patients in highest risk of 
complications and death(13). Good clinical decision-making is likely to improve the 
clinical outcome by allocating appropriate resources. Prognostic tools are also useful 
to inform patients about what to expect in the immediate postoperative phase and of 
long-term outcome. Especially in the elder population with increased risk of long 
hospitalisation and loss of function or independency this can be useful to give 
informed consent to treatment strategy. Furthermore, good risk assessment is 

 
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important to optimize palliative care after end-of-life decisions, which is often 
ignored in research, but highly relevant in clinical work. 
During recent years use of prognostic biomarkers in other high mortality populations 
have received much attention for risk stratification(14). An ideal biomarker should be 
readily available upon decision-making, easy to measure, reliable and biologically 
stable. Furthermore it has to be able to accurately differentiate prognosis for patients 
to have value in the clinical decision-making and guide the treatment of the patient. It 
should be linked to the clinical outcomes.  
We aim to identify serological AHA biomarkers that are prognostic or predictive for 
postoperative morbidity, mortality and length of hospitalisation. 
Potential biomarkers: 
Inflammation markers:  
Soluble urokinase-type plasminogen activator receptor (SUPAR) is a relatively new 
potential biomarker for inflammation. It is related to mortality in critically ill 
patients(15) especially in combination with the APACHE II score(16)(17). SUPAR is 
thought to be related to the inflammation reaction and endothelial dysfunction 
affected in critically ill patients(18). It is furthermore related to cardiovascular disease 
and subclinical organ damage(19) and is shown to be relatively stable throughout the 
day without circadian variations. It is related to all-cause mortality in major cohorts 
from different parts of the world(20). It has been suggested that the biomarker can be 
used to strengthen risk stratification for patients presenting acutely at the emergency 
department(21).  
IL-10, IL-6, Tumor Necrosis Factor alpha, IP-10 are other markers of inflammation 
that regulate the inflammatory response to disease. It is proposed that increased 
synthesis or reduced metabolization of IL-6 is related to poor outcome in sepsis(22).  
Troponin: 

 
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Troponin is a well-established biomarker for myocardial infarction. Recent studies 
show that it is related to the APACHE II score and could be useful as a biomarker for 
overall mortality in critically ill patients with severe sepsis(23)(24)(25).  
Endothelial dysfunction 
Markers of endothelial function have been suggested to predict poor outcome. 
Syndecan-1, a transmembrane endothelial proteoglycan, which in serum is a marker 
of glycocalyx degradation, is related to mortality in trauma patients(27).   
Endocrine and other markers 
The endocrine system is highly involved in the surgical stress response and 
imbalances in hormonal response to stress could potentially predict outcome. 
Especially catecholamines are involved in the inflammatory response and related to 
endothelial dysfunction(27).  
Furthermore, combination of different molecular biomarkers could be of interest to 
create the best possible molecular risk assessment tool(14). 
 
Methods 
Manuscripts will be prepared using the STROBE-ME statement guidelines(29).  
Approvals and ethical considerations: 
Danish legislation “Lov om behandling af personoplysninger” will be followed and 
the study will be reported to the Danish Data Protection Agency. 
Data used in these studies are collected in the quality improvement program “Acute 
High-Risk Abdominal Surgery study – an optimized Perioperative Course (AHA)” 
(ClinicalTrials.gov Identifier: NCT01899885) approved by the Danish Data 
Protection Agency (jr.no. 2007-58-0015) HVH-2013-052 in 2013. In this program 
patients were enrolled in a standardized protocol of perioperative optimization. The 
standardized protocol included collection of blood samples for future research. 

 
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Patients were informed about the AHA protocol and gave informed consent where 
possible. If patients were in a state where informed consent was not possible informed 
consent was sought from relatives. 
As this was a qualitative improvement programme, it did not require approval by an 
ethical committee according to Danish law. Blood samples were drawn and collected 
with the standard preoperative tests and stored at -80C in a biobank.  
We will establish a new research bio bank based on material from the samples 
collected in relation to the standard treatment according to the AHA-protocol (one 
EDTA-tube and one tube with coagulation enhancer). The research biobank will be 
reported to the Danish Data Protection Agency. After approval the biological material 
will be kept in the research biobank until the end of the study, at which time the 
samples have been used entirely or will be transferred back to the biobank from which 
they originally were taken. The present study is expected finalized by May 1, 2022.  
Analysis of the samples from the biobank will require approval by the Ethics 
Committee including dispensation from obtaining consent from involved patients. 
Retrospective consent is impossible to obtain as a large proportion of patients have 
died since the data collection started, and analysis of the data set without the patients, 
who have died is worthless as a predictive tool. Research on this vulnerable 
population with high mortality and morbidity is very limited. It is therefore an 
important ethical aspect that the collected data can be used for research, which may be 
beneficial for future critically ill, emergency patients.  
Approval for these studies will be applied from the Ethics Committee of the Capital 
Region of Denmark. Referral of data from the AHA quality improvement project to 
this study will be registered and approved by the Danish Data Protection Agency. The 
biological material and non-anonymous data, from both the research bio bank and the 
samples collected in relation to standard treatment, will the destroyed when the 
approval from the Danish Data Protection Agency expires. 
We will search the national register for usage of biological tissue, 
“Vævsanvendelsesregistret”, for patients who only wish their tissue is used for their 
own treatment. Samples from these patients will not be included in this project. 

 
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Risks, side effects and harms 
Potential harms for the participants are limited as the research is focused on 
preoperative risk assessment and not long-term prediction of future diseases. There is 
therefore a low risk of findings that may affect the participants. The biomarkers we 
wish to study have in similar studies of critically ill patients not shown any random 
findings affecting the participants. 
Population 
We will include patients with a preoperative blood sample stored in the bio bank; the 
patients did undergo AHA at Hvidovre Hospital, Copenhagen, Denmark, from June 
1st 2013 when the AHA study protocol was initiated.  
Inclusion criteria were: Patients aged 18 or older with the suspicion for abdominal 
pathology requiring immediate emergency laparotomy or laparoscopy including 
reoperations after elective surgery and reoperations after previous AHA surgery.  
We excluded the following procedures: appendectomies; negative laparoscopies and 
laparotomies; cholecystectomies; simple herniotomies following incarceration 
(without intestinal resection); reoperation due to fascial separation with no other 
abdominal pathology; internal herniation after roux-en-y gastric bypass surgery; sub-
acute surgery (planned within 48 hours) for inflammatory bowel disease; and sub-
acute colorectal cancer surgery.  
We excluded, traumas, pregnant woman, uro-genital, gynaecological and vascular 
pathology except for mesenteric ischemia.  
Data collection 
Data on mortality was obtained using the Danish Civil Registration System(30). This 
register is maintained by the Danish government and assigns a unique personal 
identification number to all Danish citizens. It contains data on address, immigration 
emigration, gender, date of birth and exact day of death. It is updated within days of 
any change in information.  

 
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Other data were collected in the medical records of patients. Complications were 
defined using the Clavien-Dindo Classification for Surgical Complications(31). 
Outcome measures 
Primary outcomes are short and long-term mortality (30 days and 180 days 
respectively) and major complications during index hospitalization (Clavien-Dindo 
>2). Secondary outcomes are length of hospitalisation, length of intensive care 
admission as well as perioperative need for inotropic or vasopressor support and high 
volume fluid resuscitation. 
Description of biological material 
Blood samples were collected at the time of induction of anaesthesia. For AHA-
patients a pre-operative optimisation protocol was introduced in the hospital 
throughout the data collection period, which stated that the patient was optimized with 
goal directed fluid therapy before induction of anaesthesia. The blood samples were 
usually taken from the arterial line used for invasive blood pressure monitoring but 
some were obtained from venous puncture with standard preoperative tests. 
The blood samples were collected in tubes with EDTA as anti-coagulant for plasma 
and in tubes with coagulation enhancer for serum. After centrifugation for 10 mins at 
room temperature the plasma and serum, respectively were transferred to freezing 
tubes for subsequent long-term storage at -80C. 
 
 
Biochemical analysis 
A predefined biochemical analysis protocol for each biomarker will be followed. The 
methods used will be described in the specific published studies. 
Statistical analysis protocol 

 
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Data will be analysed using the SPSS, STATA, R or SAS software. Tests will be two-
sided with p<0.05 considered to be significant. 
Missing data will be assessed using Little’s test for data missing completely at 
random(32). If data is not missing completely at random multiple imputation will be 
performed(33). 
All patients or random samples (with a size defined by a preoperative sample size 
estimation) from the AHA cohort with blood samples taken preoperatively will be 
included in the analysis. Baseline characteristics will be presented and for biomarkers 
with continuous data patients will be stratified into quartiles or tertiles. Characteristics 
of populations will be presented as frequencies for categorical variables and medians 
with inter-quartile rage for continuous variables. Differences between groups will be 
presented using appropriate statistical tests.  Kaplan-Meier curves will be produced 
for the primary outcomes and compared using the log-rank test. COX-regression will 
be used to perform multivariate analyses adjusted for potential confounders with 
Bonferroni correction. 
Receiver operating characteristic curves for each biomarker will be produced for the 
primary outcomes. Area under the curve will be measured to evaluate the prognostic 
value of the biomarkers. 
Limitations of the study 
Data collection during clinical work including blood samples requires resources and is 
susceptible to selection and information bias. It is possible that resources for 
collecting blood samples correctly where reduced for the most unstable patients at the 
time of induction of anaesthesia. Patients who were considered too frail to undergo 
surgery, where a palliative or conservative approach was chosen, were not included in 
this study. Postoperatively, patients in the AHA-cohort were assigned to postoperative 
intensive monitoring in the post-operative care unit if they had American Society of 
Anaesthesiologist physical performance score(34) above two. This stratification is 
susceptible to information-bias because of inter-observer variation(35).  
The data is collected in a single medical facility with and optimized care protocol for 
AHA and can therefore not uncritically be applied to other populations. 

 
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Economical support and budget: 
The clinical study as well as establishment of the biobank has been supported by a 
grant from the Capital Region of Denmark (Region H). The cost of biochemical 
analysis will be covered by the Department of Clinical Biochemistry, Hvidovre 
Hospital. Other expenses will be covered by the AHA research group, Department of 
Anaesthesiology and Intensive Care and Department of Surgical Gastroenterology, 
Hvidovre Hospital. 
Publication of results: 
Results from the studies will be published in medical journals indexed in the 
PubMed/EMBASE database.  
References 
1.  
Pearse RM, Moreno RP, Bauer P, Pelosi P, Metnitz P, Spies C, et al. Mortality 
after surgery in Europe: a 7 day cohort study. Lancet. Elsevier; 2012 Sep 
22;380(9847):1059–65.  
2.  
Tolstrup M-B, Watt SK, Gögenur I. Morbidity and mortality rates after 
emergency abdominal surgery: an analysis of 4346 patients scheduled for 
emergency laparotomy or laparoscopy. Langenbeck’s Arch Surg. 2016 Aug 9  
3.  
Stoneham M, Murray D, Foss N. Emergency surgery: the big three--abdominal 
aortic aneurysm, laparotomy and hip fracture. Anaesthesia. 2014 Jan;69 Suppl 
1:70–80.  
4.  
Jeppesen MH, Tolstrup M-B, Kehlet Watt S, Gögenur I. Risk factors affecting 
morbidity and mortality following emergency laparotomy for small bowel 
obstruction: A retrospective cohort study. Int J Surg. 2016 Apr;28:63–8.  
5.  
Saunders DI, Murray D, Pichel AC, Varley S, Peden CJ, UK Emergency 
Laparotomy Network. Variations in mortality after emergency laparotomy: the 
first report of the UK Emergency Laparotomy Network. Br J Anaesth. 2012 
Sep;109(3):368–75.  
6.  
Symons NRA, Moorthy K, Almoudaris AM, Bottle A, Aylin P, Vincent CA, et 
al. Mortality in high-risk emergency general surgical admissions. Br J Surg. 

 
10
2013 Sep;100(10):1318–25.  
7.  
Jhanji S, Thomas B, Ely A, Watson D, Hinds CJ, Pearse RM. Mortality and 
utilisation of critical care resources amongst high-risk surgical patients in a 
large NHS trust*. Anaesthesia. 2008 May 16;63(7):695–700.  
8.  
Vester-Andersen M, Lundstrøm LH, Møller MH, Waldau T, Rosenberg J, 
Møller AM, et al. Mortality and postoperative care pathways after emergency 
gastrointestinal surgery in 2904 patients: a population-based cohort study. Br J 
Anaesth. 2014 May;112(5):860–70.  
9.  
Johnston MJ, Arora S, King D, Bouras G, Almoudaris AM, Davis R, et al. A 
systematic review to identify the factors that affect failure to rescue 
and escalation of care in surgery. Surgery. 2015 Apr;157(4):752–63.  
10.  
Ghaferi AA, Dimick JB. Importance of teamwork, communication and culture 
on failure-to-rescue in the elderly. Br J Surg. 2016 Jan;103(2):e47–51.  
11.  
Oliver CM, Walker E, Giannaris S, Grocott MPW, Moonesinghe SR. Risk 
assessment tools validated for patients undergoing emergency laparotomy: a 
systematic review. Br J Anaesth. 2015 Dec;115(6):849–60.  
12.  
Al-Temimi MH, Griffee M, Enniss TM, Preston R, Vargo D, Overton S, et al. 
When Is Death Inevitable after Emergency Laparotomy? Analysis of the 
American College of Surgeons National Surgical Quality Improvement 
Program Database. J Am Coll Surg. 2012 Oct;215(4):503–11.  
13.  
Hobson SA, Sutton CD, Garcea G, Thomas WM. Prospective comparison of 
POSSUM and P-POSSUM with clinical assessment of mortality following 
emergency surgery. Acta Anaesthesiol Scand. 2007 Jan;51(1):94–100.  
14.  
Sandquist M, Wong HR. Biomarkers of sepsis and their potential value in 
diagnosis, prognosis and treatment. Expert Rev Clin Immunol. 2014 
Oct;10(10):1349–56.  
15.  
Donadello K, Scolletta S, Taccone FS, Covajes C, Santonocito C, Cortes DO, 
et al. Soluble urokinase-type plasminogen activator receptor as a prognostic 
biomarker in critically ill patients. J Crit Care. 2014 Feb;29(1):144–9.  
16.  
Liu X, Shen Y, Li Z, Fei A, Wang H, Ge Q, et al. Prognostic significance of 
APACHE II score and plasma suPAR in Chinese patients with sepsis: a 

 
11
prospective observational study. BMC Anesthesiol. 2015 Dec 29;16(1):46.  
17.  
Zeng M, Chang M, Zheng H, Li B, Chen Y, He W, et al. Clinical value of 
soluble urokinase-type plasminogen activator receptor in the diagnosis, 
prognosis, and therapeutic guidance of sepsis. Am J Emerg Med. 2016 
Mar;34(3):375–80.  
18.  
Donadello K, Scolletta S, Covajes C, Vincent J-L. suPAR as a prognostic 
biomarker in sepsis. BMC Med. 2012 Dec 5;10(1):2.  
19.  
Hodges GW, Bang CN, Wachtell K, Eugen-Olsen J, Jeppesen JL. suPAR: A 
New Biomarker for Cardiovascular Disease? Can J Cardiol. 2015 
Oct;31(10):1293–302.  
20.  
Botha S, Fourie CMT, Schutte R, Eugen-Olsen J, Pretorius R, Schutte AE. 
Soluble urokinase plasminogen activator receptor as a prognostic marker of all-
cause and cardiovascular mortality in a black population. Int J Cardiol. 2015 
Apr 1;184:631–6.  
21.  
Sandø A, Schultz M, Eugen-Olsen J, Rasmussen LS, Køber L, Kjøller E, et al. 
Introduction of a prognostic biomarker to strengthen risk stratification of 
acutely admitted patients: rationale and design of the TRIAGE III cluster 
randomized interventional trial. Scand J Trauma Resusc Emerg Med. 2016 Aug 
5;24:100.  
22.  
Lahiri R, Derwa Y, Bashir Z, Giles E, Torrance HDT, Owen HC, et al. 
Systemic Inflammatory Response Syndrome After Major Abdominal Surgery 
Predicted by Early Upregulation of TLR4 and TLR5. Ann Surg. 2016 
May;263(5):1028–37.  
23.  
John J, Woodward DB, Wang Y, Yan SB, Fisher D, Kinasewitz GT, et al. 
Troponin-I as a prognosticator of mortality in severe sepsis patients. J Crit 
Care. 2010 Jun;25(2):270–5.  
24.  
Landesberg G, Jaffe AS, Gilon D, Levin PD, Goodman S, Abu-Baih A, et al. 
Troponin elevation in severe sepsis and septic shock: the role of left ventricular 
diastolic dysfunction and right ventricular dilatation*. Crit Care Med. 2014 
Apr;42(4):790–800.  
25.  
Sheyin O, Davies O, Duan W, Perez X. The prognostic significance of troponin 

 
12
elevation in patients with sepsis: A meta-analysis. Hear Lung J Acute Crit 
Care. 2015 Jan;44(1):75–81. 
26.  
Benz F, Roy S, Trautwein C, Roderburg C, Luedde T. Circulating MicroRNAs 
as Biomarkers for Sepsis. Int J Mol Sci. Multidisciplinary Digital Publishing 
Institute  (MDPI); 2016 Jan 9;17(1).  
27.  
Johansson PI, Stensballe J, Rasmussen LS, Ostrowski SR. A High Admission 
Syndecan-1 Level, A Marker of Endothelial Glycocalyx Degradation, Is 
Associated With Inflammation, Protein C Depletion, Fibrinolysis, and 
Increased Mortality in Trauma Patients. Ann Surg. 2011 Aug;254(2):194–200.  
28.  
Nemeth E, Rivera S, Gabayan V, Keller C, Taudorf S, Pedersen BK, et al. IL-6 
mediates hypoferremia of inflammation by inducing the synthesis of the iron 
regulatory hormone hepcidin. J Clin Invest. 2004 May;113(9):1271–6.  
29.  
Gallo V, Egger M, Mccormack V, Farmer PB, Ioannidis JPA, Kirsch-volders 
M, et al. STrengthening the Reporting of OBservational studies in 
Epidemiology – Molecular Epidemiology ( STROBE- ME ): An Extension of 
the STROBE Statement. PLoS Med. 2011;8(10).  
30.  
Pedersen CB. The Danish Civil Registration System. Scand J Public Health. 
2011;39(7 Suppl):22–5.  
31.  
Dindo D, Demartines N, Clavien P-A. Classification of surgical complications: 
a new proposal with evaluation in a cohort of 6336 patients and results of a 
survey. Ann Surg. 2004 Aug;240(2):205–13.  
32.  
Little RJA. A Test of Missing Completely at Random for Multivariate Data 
With Missing Values. J Am Stat Assoc. Taylor & Francis Group; 1988 Mar 
12;83(404):1198–202.  
33.  
Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol 
Methods. 2002 Jun;7(2):147–77.  
34.  
Saklad M. Grading of patients for surgical procedures. Anesthesiology. 1941 
May 30;2(3):281–4.  
35.  
Sankar A, Johnson SR, Beattie WS, Tait G, Wijeysundera DN. Reliability of 
the American Society of Anesthesiologists physical status scale in clinical 
practice. Br J Anaesth. 2014 Sep;113(3):424–32.  

 
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