Introduction of Soluble Urokinase Plasminogen Activating Receptor in Acute Care as a Prognostic Biomarker to Strengthen Risk Stratification of Acutely Admitted Patients
Phase: NA
Conditions: Triage, Risk Stratification With Biomarker
Sponsor: Herlev Hospital

 
 
Study protocol and Statistical analysis Plan 
 
Risk Stratification in Acute Care: The Meaning of 
suPAR Measurement in Triage 
(TRIAGE III) 
 
Clinical Trials ID: NCT0264345 
 
January 4, 2018 

1 
 
 
 
 
Protocol and Statistical analysis Plan 
 
TRIAGE III trial 
 
 
 
All documents included are from the TRIAGE III trial. 
 
 
 

2 
 
 
Table of contents 
 
Original protocol (In Danish) ................................................................................................ 3 
Original Protocol (translated from Danish) ......................................................................... 12 
Final Protocol (in Danish) ................................................................................................... 21 
Final Protocol (translated from Danish) .............................................................................. 29 
Original Statistical analysis plan ........................................................................................ 37 
Final Statistical analysis plan ............................................................................................. 39 
Summary of changes to the protocol ................................................................................. 45 
Summary of changes to the Statistical Analysis Plan ........................................................ 46 
Summary of changes at clinicaltrials.gov: .......................................................................... 47 
 
 
 

3 
 
Original protocol (In Danish) 
 
 
 
Betydningen af suPAR-måling i akutmodtagelser 
(TRIAGE III) 
 
Teknisk protokol, V1 
15. Sep 2015 
 
 
 
 

4 
 
Generelle oplysninger 
 
Styregruppe 
Seniorforsker, phd Jesper Eugen-Olsen, Hvidovre Hospital. 
Professor, dr.med Lars S Rasmussen, Rigshospitalet. 
Professor, dr.med Lars Køber, Rigshospitalet. 
Overlæge, dr.med Kasper Iversen, Herlev Hospital 
Overlæge dr.med Erik Kjøller, Herlev Hospital 
Ledende overlæge Lisbet Ravn, Herlev Hospital 
Ledende overlæge Birgitte Nybo Jensen, Bispebjerg Hospital 
 
Lokalisation 
Projektet udføres på Herlev Hospital, Bispebjerg Hospital og Nordsjælland Hospital 
 
Tidsplan 
Planlagt projektstart er 11. januar 2016 og ventes afsluttet 16. Maj 2015 kl 08.00 
 
Baggrund 
Risikovurdering er central ved modtagelse og vurderingen af akut indlagte patienter skal 
sikre, at de mest akut syge bliver behandlet først og observeres tættest. Risikovurdering 
foretages i dag ved hjælp af systematiseret triage. Systematiseret triage foregår typisk ved 
en gennemgang af patientens primære symptom samt deres vitale parametre. Både 
symptom og vitale parametre tillægges en numerisk værdi ud fra et scoringssystem, 
bestemt af triagemodellen. På denne måde opdeles patienterne i kategorier, som vha. 
farver eller tal signalerer, hvor hurtigt de skal tilses, samt hvor tæt de skal observeres1.  
Gennem de seneste år er der kommet tiltagende fokus på, om man kan forbedre 
risikovurderingen i hospitalernes modtagelser ved at addere resultater fra 
biomarkørmålinger til den eksisterende triage. Det ser ud som om, at en sådan kombineret 
strategi er brugbar både ved specifikke diagnoser2-4 og mere generelt5-7. Hvilke 
biomarkører der skal bruges, og om det rent faktisk gør en forskel for patienterne, er der 
indtil videre ingen data på. 
Soluble urokinase plasminogen activating receptor (suPAR) er den opløselige form af 

5 
 
urokinase-type plasminogen activator receptor (uPAR) og primært udtrykt på immunceller 
som neutrofile leukocytter, aktiverede T-celler og makrofager8. Studier af suPAR har vist, 
at proteinet er stærkt associeret med mortalitet og morbiditet i en lang række specifikke 
sygdomme (sepsis, leversygdom, hjertesygdom, lungesygdom)9-19, generelt ved indlagte 
patienter20,21 og i den generelle befolkning22,23. Derudover har suPAR vist sig at kunne 
identificere patienter med manifest eller med høj risiko for at udvikle cancer24,25 
Det ser således ud til, at suPAR er helt uspecifik og derfor ikke kan bruges til at 
diagnosticere nogen bestemt sygdom, men udelukkende til at identificere lav- og høj-
risikopatienter generelt. Dermed kunne suPAR potentielt være en god markør i 
akutmodtagelser til at identificere risikopatienter, der kræver tættere observation, hurtigere 
behandling og/eller videre udredning. På den anden side ser det også ud til at patienter 
med lav suPAR har en overordentlig god prognose, og suPAR vil derfor potentielt også 
kunne identificere patienter, der hurtigt kan udskrives. Enkelte steder (eksempelvis 
Hvidovre Hospital) har man allerede indført suPAR som en rutinemarkør, der tages på alle 
indlagte patienter i akutmodtagelsen. 
Selvom det er vist, at suPAR både kan identificere høj- og lavrisiko-patienter, er det aldrig 
vist, at man kan bruge denne viden til at forebygge dødsfald eller alvorlige komplikationer 
og ej heller om det kan forbedre patientflowet på hospitalet. 
 
Formål 
At undersøge om måling af suPAR og information herom til læger, der arbejder i 
akutmodtagelsen, kan forebygge dødsfald eller alvorlige komplikationer samt forbedre 
patientflow hos akut indlagte medicinske og kirurgiske patienter. 
 
Metode 
Studiet er et åbent clusterrandomiseret studie af betydningen af suPAR-måling i 
akutmodtagelsen. 
 
Målepunkter 
Primære endepunkt 
• Død inden for 12 måneder af alle årsager 
 

6 
 
Sekundære endepunkter 
• Død inden for 1 måned 
• Død inden for 3 mdr 
• Død inden for 2 år 
• Antal patienter udskrevet indenfor 24 timer 
• Indlæggelser på medicinske afdelinger indenfor 30 dage 
• Indlæggelser på intensivafdeling indenfor 30 dage 
• Cancerdiagnoser inden for 3 måneder 
• Cancerdiagnoser fra 3 – 24 måneder 
• Indlæggelsesvarighed 
• Akutte genindlæggelser inden for 30 dage  
 
Patienter 
suPAR indføres som rutinemarkør på alle indlagte patienter i 2 x 21 dages perioder på 3 
matrikler i akutmodtagelsen på henholdsvis Bispebjerg, Nordsjælland og Herlev Hospital 
(Tabel 1). De resterende 4 x 21 dages perioder i studieperioden fungerer som 
kontrolperioder. 
 
Måling af suPAR 
Materiale til måling af suPAR tages fra i forbindelse med rutine-blodprøvetagning og 
medfører hverken ekstra stik til patienter eller udtagning af en større mængde blod fra 
patienterne end vanligt.  
suPAR måles på EDTA-plasma fra overskydende materiale fra rutineblodprøverne. 
Analysen udføres med suPARnostic® Quick Triage stick (QT stick) på Klinisk Biokemisk 
Afdeling. Der udtages 10 mikroliter (µl) EDTA-plasma fra rutine-blodprøverne. Disse 10 µl 
overføres til et præfabrikeret rør, der indeholder 100 µl running buffer. Herfra pipetteres 60 
µl på QT sticken, som sættes i analyseapparatet, og der trykkes start. Efter 20 minutter 

7 
 
aflæses resultatet automatisk og rapporteres på den tilkoblede PC. Den benyttede QT 
stick smides ud, og apparatet er klar til ny måling. Der opstilles 4 analyseapparater, 
således at 4 analyser kan køres sideløbende, og op til 12 prøver analyseres i timen. Så 
snart resultatet på suPAR foreligger på den til analyseapparatet tilkoblede PC, vil svaret 
blive skrevet ind i de elektroniske blodprøve- og journalsystemer LABKA og OPUS. Det 
tilstræbes, at alle suPAR-analysesvar er indskrevet i LABKA/OPUS hurtigst muligt og max 
2 timer efter blodprøvetagning.  
Afpippetering og analyse af suPAR foretages af studiepersonale og vil ikke medføre ekstra 
arbejde for de implicerede hospitaler. 
 
Information til hospitalets læger om suPAR 
Forud for studieperioden vil læger på de implicerede hospitaler blive undervist i den 
prognostiske betydning af suPAR både i forhold til specifikke diagnoser og i forhold til 
uselekterede patienter. Undervisningen vil foregå på de enkelte afdelinger på sygehuset. 
Derudover vil alle læger få tilsendt et resume over de data, der forligger om suPAR’s 
prognostiske betydning, og alle læger får derudover udleveret lommekort, der beskriver 
risiko for 30- og 90-dages mortalitet ved forskellige niveauer af suPAR, baseret på data fra 
Hvidovre Hospital (Figur 1). 
 
Dataopsamling 
Resultater fra suPAR-måling samt øvrige biokemiske målinger vil være registreret i 
LABKA-databasen, og data vil blive trukket herfra.  
Information om primært og sekundære endepunkter indhentes fra centrale registre. 
 
Styrkeberegning 
I et tidligere endnu upubliceret studie af patienter indlagt i akutmodtagelsen på 
Nordsjælland Hospital var dødeligheden inden for 3 måneder 7,5 %. Vores hypotese er, at 
vi kan reducere mortaliteten med 1 % ved at måle suPAR på alle indlagte patienter. Ved et 
signifikansniveau på 5 % og en power på 80 % skal man, såfremt grupperne er lige store, 
bruge en stikprøvestørrelse på 10.042 personer i hver gruppe for at vise denne forskel. Da 
ratio mellem interventionsgruppen og kontrolgruppen er 1: 2 ændres gruppestørrelserne til 
7665 i interventionsgruppen og 15.329 i kontrolgruppen.  

8 
 
Tidsplan 
Studiet vil starte klokken 8:00 den 11/1 2016 og vil være færdigt klokken 8:00 den 16/5 
2016. 
Opgørelse af data til det primære endepunkt forventes færdiggjort 1/6 2017. Første udkast 
til den første artikel vil være færdigt inden udgangen af 2017. 
Baseret på tal fra de 3 akutmodtagelser indlægges der ca. 240 patienter dagligt på de 3 
hospitaler tilsammen. I studieperioden vil der således kunne opsamles data på 10.080 
patienter i den aktive arm og 20.160 patienter i kontrolarmen. Dette vil være tilstrækkeligt 
til at undersøge den primære hypotese.  
 
Tilladelser 
Studiet er ikke anmeldelsespligtigt til videnskabsetisk komité (jr. nummer FSP-15003590). 
Studiet vil blive anmeldt til Sundhedsstyrelsen, Datatilsynet samt registreret på 
clinicaltrials.gov.  
 
Økonomi 
 
Udgifter i forbindelse med studiet er finansieret af firmaet ViroGates A/S. Der vil sammen 
med ViroGates A/S blive søgt ekstern finansiering til dele af eller hele projektet. 
 
Referencer 
1. 
Farrohknia N, Castren M, Ehrenberg A, et al. Emergency department triage scales and their components: a systematic review 
of the scientific evidence. Scandinavian journal of trauma, resuscitation and emergency medicine 2011;19:42. 
2. 
Albrich WC, Ruegger K, Dusemund F, et al. Biomarker-enhanced triage in respiratory infections: a proof-of-concept feasibility 
trial. The European respiratory journal 2013;42:1064-75. 
3. 
Sibon I, Rouanet F, Meissner W, Orgogozo JM. Use of the Triage Stroke Panel in a neurologic emergency service. The 
American journal of emergency medicine 2009;27:558-62. 
4. 
Vanni S, Polidori G, Pepe G, et al. Use of biomarkers in triage of patients with suspected stroke. The Journal of emergency 
medicine 2011;40:499-505. 
5. 
Gans SL, Atema JJ, Stoker J, Toorenvliet BR, Laurell H, Boermeester MA. C-reactive protein and white blood cell count as 
triage test between urgent and nonurgent conditions in 2961 patients with acute abdominal pain. Medicine 2015;94:e569. 
6. 
Herlitz J, Svensson L. The value of biochemical markers for risk stratification prior to hospital admission in acute chest pain. 
Acute cardiac care 2008;10:197-204. 
7. 
Seymour CW, Cooke CR, Wang Z, et al. Improving risk classification of critical illness with biomarkers: a simulation study. 
Journal of critical care 2013;28:541-8. 
8. 
Huai Q, Mazar AP, Kuo A, et al. Structure of human urokinase plasminogen activator in complex with its receptor. Science 
(New York, NY) 2006;311:656-9. 
9. 
Borne Y, Persson M, Melander O, Smith JG, Engstrom G. Increased plasma level of soluble urokinase plasminogen activator 
receptor is associated with incidence of heart failure but not atrial fibrillation. European journal of heart failure 2014;16:377-83. 

9 
 
10. 
Eapen DJ, Manocha P, Ghasemzedah N, et al. Soluble urokinase plasminogen activator receptor level is an independent 
predictor of the presence and severity of coronary artery disease and of future adverse events. Journal of the American Heart 
Association 2014;3:e001118. 
11. 
Gumus A, Altintas N, Cinarka H, et al. Soluble urokinase-type plasminogen activator receptor is a novel biomarker predicting 
acute exacerbation in COPD. International journal of chronic obstructive pulmonary disease 2015;10:357-65. 
12. 
Lyngbaek S, Andersson C, Marott JL, et al. Soluble urokinase plasminogen activator receptor for risk prediction in patients 
admitted with acute chest pain. Clinical chemistry 2013;59:1621-9. 
13. 
Lyngbaek S, Marott JL, Moller DV, et al. Usefulness of soluble urokinase plasminogen activator receptor to predict repeat 
myocardial infarction and mortality in patients with ST-segment elevation myocardial infarction undergoing primary 
percutaneous intervention. The American journal of cardiology 2012;110:1756-63. 
14. 
Molkanen T, Ruotsalainen E, Thorball CW, Jarvinen A. Elevated soluble urokinase plasminogen activator receptor (suPAR) 
predicts mortality in Staphylococcus aureus bacteremia. European journal of clinical microbiology & infectious diseases : 
official publication of the European Society of Clinical Microbiology 2011;30:1417-24. 
15. 
Persson M, Ostling G, Smith G, et al. Soluble urokinase plasminogen activator receptor: a risk factor for carotid plaque, 
stroke, and coronary artery disease. Stroke; a journal of cerebral circulation 2014;45:18-23. 
16. 
Raggam RB, Wagner J, Pruller F, et al. Soluble urokinase plasminogen activator receptor predicts mortality in patients with 
systemic inflammatory response syndrome. Journal of internal medicine 2014;276:651-8. 
17. 
Reichsoellner M, Raggam RB, Wagner J, Krause R, Hoenigl M. Clinical evaluation of multiple inflammation biomarkers for 
diagnosis and prognosis for patients with systemic inflammatory response syndrome. Journal of clinical microbiology 
2014;52:4063-6. 
18. 
Suberviola B, Castellanos-Ortega A, Ruiz Ruiz A, Lopez-Hoyos M, Santibanez M. Hospital mortality prognostication in sepsis 
using the new biomarkers suPAR and proADM in a single determination on ICU admission. Intensive care medicine 
2013;39:1945-52. 
19. 
Zimmermann HW, Koch A, Seidler S, Trautwein C, Tacke F. Circulating soluble urokinase plasminogen activator is elevated 
in patients with chronic liver disease, discriminates stage and aetiology of cirrhosis and predicts prognosis. Liver international 
: official journal of the International Association for the Study of the Liver 2012;32:500-9. 
20. 
Donadello K, Scolletta S, Taccone FS, et al. Soluble urokinase-type plasminogen activator receptor as a prognostic biomarker 
in critically ill patients. Journal of critical care 2014;29:144-9. 
21. 
Haupt TH, Petersen J, Ellekilde G, et al. Plasma suPAR levels are associated with mortality, admission time, and Charlson 
Comorbidity Index in the acutely admitted medical patient: a prospective observational study. Critical care (London, England) 
2012;16:R130. 
22. 
Eugen-Olsen J, Andersen O, Linneberg A, et al. Circulating soluble urokinase plasminogen activator receptor predicts cancer, 
cardiovascular disease, diabetes and mortality in the general population. Journal of internal medicine 2010;268:296-308. 
23. 
Lyngbaek S, Marott JL, Sehestedt T, et al. Cardiovascular risk prediction in the general population with use of suPAR, CRP, 
and Framingham Risk Score. International journal of cardiology 2013;167:2904-11. 
24. 
Langkilde A, Hansen TW, Ladelund S, et al. Increased plasma soluble uPAR level is a risk marker of respiratory cancer in 
initially cancer-free individuals. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for 
Cancer Research, cosponsored by the American Society of Preventive Oncology 2011;20:609-18. 
25. 
Chounta A, Ellinas C, Tzanetakou V, et al. Serum soluble urokinase plasminogen activator receptor as a screening test for the 
early diagnosis of hepatocellular carcinoma. Liver international : official journal of the International Association for the Study of 
the Liver 2015;35:601-7. 
 
 
 

10 
 
 
Tabel 1 
 
Hospital 
Periode 1 
Periode 2 
Periode 3 
Periode 4 
Periode 5 
Periode 6 
Herlev 
+ suPAR 
Kontrol 
Kontrol 
+ suPAR 
Kontrol 
Kontrol 
Bispebjerg 
Kontrol 
+ suPAR 
Kontrol 
Kontrol 
+ suPAR 
+ Kontrol 
Nordsjælland Kontrol 
Kontrol 
+ suPAR 
Kontrol 
Kontrol 
+ suPAR 
 
 
 
Figur 1: 
suPAR og risiko for dødelighed hos akutmedicinske patienter 
 
Patienter < 70 år: 
Mortalitetsrisiko (% (95%CI)) i henhold til suPAR-værdier hos patienter <70 år 
suPAR (ng/ml) 
30 dage 
90 dage 
Alle (n=2652) 
1,5% (1,1-2,0) 
2,9% (2,2-3,6) 
0-3 (n=1705) 
0,3 % (0,1-0,7; n=5) 
0,8 % (0,5-1,4; n=14) 
3-6 (n=744) 
1,6 % (0,8-2,8; n=12) 
3,6 % (2,4-5,2; n=27) 
6-9 (n=127) 
7,1 % (3,3-13,0; n=9) 
11,8 % (6,8-18,7; n=15) 
>9 (n=76) 
19,7 % (11,5-30,5; n=15) 
27,6 % (18,0-39,1; n=21) 
 
Eksempler: 
En mand/kvinde på 63 år med suPAR på 2,7 ng/ml har en risiko for 30 dages mortalitet på 
0,3%, hvilket er fem gange mindre end den gennemsnitlige dødelighed (1.5%) for 
personer under 70 år. 
En mand/kvinde på 63 år med suPAR på 7,7 ng/ml har en risiko for 30 dages mortalitet på 
7,1%, hvilket er fem gange højere end den gennemsnitlige dødelighed (1.5%) for personer 
under 70 år. 
 
 
 

11 
 
 
Patienter > 70 år: 
Mortalitetsrisko (% (95% CI)) i henhold til suPAR-værdier hos patienter >70 år 
suPAR ng/ml 
30 dage 
90 dage 
Alle (n=1691) 
10,8% (9,3-12,3) 
18,3% (16,4-20,1) 
0-3 (n=339) 
2,7 % (1,2-5,0; n=9) 
3,5 % (1,8-6,1; n=12) 
3-6 (n=913) 
7,1 % (5,5-9,0; n=65) 
13,7 % (11,5-16,1; n=125) 
6-9 (n=293) 
19,8 % (15,4-24,8; n=58) 
32,8 % (27,4-38,5; n=96) 
>9 (n=146) 
34,9 % (27,2-43,3; n=51) 
52,1 % (43,6-60,4; n=76) 
 
Eksempler: 
En mand/kvinde på 75 år med suPAR på 2.7 ng/ml har en risiko for 30 dages mortalitet på 
2.7%, hvilket er fire gange mindre end den gennemsnitlige dødelighed (10.8%) for 
personer over 70 år. 
En mand/kvinde på 75 år med suPAR på 7.7 ng/ml har en risiko for 30 dages mortalitet på 
19.8%, hvilket er ca. dobbelt så høj risiko end den gennemsnitlige dødelighed (10.8%) 
personer over 70 år.  
 
Kilde: Data er beregnet ud fra samtlige akutmedicinske patienter (n=4343) i 
Akutmodtagelsen, Hvidovre Hospital, i perioden 18. november 2013 – 31. marts 2014. 
 
 
 

12 
 
Original Protocol (translated from Danish) 
 
 
 
 
 
 
The impact of suPAR measurement 
in emergency departments 
(TRIAGE III) 
 
Technical protocol V.1 
September 15 2015 /MSC 
 
 
 

13 
 
General information 
Steering committee:  
Senior researcher, PhD Jesper Eugen-Olsen, Hvidovre Hospital 
Professor, DMSc. Lars S Rasmussen, Rigshospitalet 
Professor, DMSc. Lars Køber, Rigshospitalet 
M.D., DMSc. Kasper Iversen, Herlev Hospital 
M.D., DMSc. Erik Kjøller, Herlev Hospital 
Chief Physician Lisbet Ravn, Herlev Hospital 
Chief Physician Birgitte Nybo Jensen, Bispebjerg Hospital 
 
Location 
The study will take place at Herlev Hospital, Bispebjerg Hospital and Hillerød Hospital. 
 
Time plan 
Planned start of inclusion is on January 11 2016 at 08:00 and is expected to be concluded 
on May 16 2016 at 08:00. 
 
Background 
Risk assessment is a central concept at admittance of acutely admitted patients to ensure 
that the sickest patients are treated first and observed the closest. Today, systematized 
risk assessment is being performed with triage algorithms. According to the primary 
complaint and vitals sign of the patient an aggregated score is determined by the triage 
algorithm. Patients are then stratified in groups of acuity that signals how fast they should 
be assessed by doctors and the frequency of observation1. In recent years it has been 
suggested that the risk assessment in the emergency departments can be strengthened 
by adding results from blood-based biomarkers to the triage process.  A combined strategy 
looks viable both in specific diagnoses2-4 and in general5-7. However, there is no data to 
support whether this strategy makes a difference to the patients or which biomarkers that 
should be used. 
Soluble urokinase plasminogen activating receptor (suPAR) is the soluble form of the 
urokinase-type plasminogen activator receptor (uPAR), which primary is expressed on the 
surface of cells in the immune system (Neutrophiles cells, activated T-cells and 

14 
 
macrophages)8. Studies have shown that suPAR is strongly associated with mortality and 
morbidity in a wide range of specific diseases (sepsis, hepatic-, cardiac- and lung-
diseases) 9-19, among admitted patients20,21 and in the general population22,23. In addition 
suPAR have been shown to identify patients at low and high risk of developing cancer24,25. 
It appears that suPAR is a nonspecific marker of outcome and not suited as a diagnostic 
tool but solely to identify patients at low and high risk.  
Thus, suPAR could potentially be a good marker in the emergency department to identify 
risk-patients requiring close observation, fast treatment and further investigation. On the 
other hand, it appears that patients with low suPAR level have a very good prognosis and 
are potentially suitable for rapid discharge. In few places (e.g. Hvidovre Hospital) suPAR 
have already been implemented as a routine analysis of all acutely admitted medical 
patients. 
Although it has been shown that suPAR is suitable for identification of patients at high and 
low risk, it has never been shown if this information can be used to prevent deaths or 
serious complications, nor if it can improve patient flow in the emergency department (ED). 
 
Objective 
To assess if measurement of the suPAR level and information to the attending ED doctors 
can prevent serious complications and deaths among acutely admitted medical- and 
surgical patients and improve flow in the emergency department. 
  
Methods 
Triage III is an open, cluster-randomized, interventional study on the impact of introducing 
suPAR measurement in the emergency department.  
 
Outcomes 
Primary outcome: 
• 
All-cause mortality within 12 months  
Secondary outcomes: 
• 
All-cause mortality at one month, three months and two years of follow-up 
• 
Number of patients discharged within 24 hours  

15 
 
• 
Number of admissions at the medical wards within 30 days 
• 
Number of transfers to the intensive care unit within 30 days 
• 
New cancer diagnoses assessed three months after admission 
• 
New cancer diagnoses assessed 24 months after admission 
• 
Length of stay 
• 
Number of readmissions within 30  days 
 
Patients: 
suPAR will be introduced as a routine analysis on all acutely admitted patients in two 21-
day periods at the emergency departments at Herlev, Hillerød and Bispebjerg hospital. 
The remaining four periods of 21 will serve as control periods (Table 1). 
   
Measurement of suPAR 
Plasma for suPAR measurement will be obtained in connection with routine blood test and 
will not result in additional discomfort for the patients.  
The suPAR level is measured on excess EDTA-plasma from the routine blood tests. The 
analysis is done with the suPARnostic® Quick Triage stick (QT stick). From routine blood-
tests 10 microliter (µl) EDTA-plasma is extracted and transferred to a prefabricated test 
tube containing 100 µl ”running buffer”. From the mixture, 60 µl is pipetted to the QT stick 
which is inserted in the analyzer. The suPAR level is automatically reported on the 
connected PC after 20 minutes. During the inclusion period it is possible to analyze four 
tests simultaneously at a minimum of twelve tests an hour. When the test result is 
available it will be reported to the electronic Laboratory system ”LABKA” and electronical 
hospital records (“OPUS”) as soon as possible and within two hours. Measurement of 
suPAR is done by study staff, which is present 24 hours every day during inclusion. 
 
Information to attending doctors regarding the suPAR level 
Prior to the study period, doctors will receive written information regarding the prognostic 
value of suPAR in general and with regard to specific diagnoses. In addition, all 
participating departments will be offered formalized teaching sessions. Doctors will receive 

16 
 
a review of the published literature and pocket cards describing risk of 30- and 90-day 
mortality depending on the suPAR level based on data from Hvidovre Hospital and 
Hillerød hospital (Figure 1). 
 
Data 
SuPAR levels as well as all other biochemical test are registered in the LABKA database 
and can be extracted. Data on all outcomes will be obtained from the central Danish 
registries. 
  
Power calculation 
In a previous unpublished study of patients acutely admitted to the ED at Copenhagen 
University Hospital North Zealand, Denmark, 3-month mortality was 7.5 %. 
Our main hypothesis is that we can reduce all-cause mortality with an absolute of 1% by 
measuring suPAR on admitted patients. Using a 5 % level of significance, a power of 
80 %, and an assumption of equal group size we will need a sample of 10,042 patients in 
each randomization group to detect this difference. Ratio between the intervention group 
and the control group is 1:2, equal to 7,665 patients in the intervention group and 15,329 in 
the control group. 
Timeline 
The trial will start on January 11 2016 at 08:00 and be concluded on May 16 2016 at 
08:00. Data on the primary outcome can be assessed at June 1 2017. First draft of the 
manuscript is expected at the end of 2017. 
 Based upon numbers from the three emergency departments, approximately 240 patients 
are admitted on a daily basis. During the inclusion period it will be possible to collect data 
on 10,080 patients in the intervention group and 20,160 patients in the control periods. 
This will be sufficient to assess the primary hypothesis. 
 
 
 

17 
 
Ethics and approvals 
The trial was presented to the Regional Ethics Committee, who decided that no formal 
approval was needed in accordance with Danish law (Jr. Nr.: FSP-15003590). The trial will 
be registered at the Danish Data Protection Agency, the Danish Patient Safety Authority, 
and at clinicaltrials.org. 
 
Economy  
Expenses related to the study are financed by ViroGates A / S. Together with ViroGates 
external funding will be sought for parts of or the entire project. 
 
References 
1. 
Farrohknia N, Castren M, Ehrenberg A, et al. Emergency department triage scales and their components: a 
systematic review of the scientific evidence. Scandinavian journal of trauma, resuscitation and emergency medicine 
2011;19:42. 
2. 
Albrich WC, Ruegger K, Dusemund F, et al. Biomarker-enhanced triage in respiratory infections: a proof-of-concept 
feasibility trial. The European respiratory journal 2013;42:1064-75. 
3. 
Sibon I, Rouanet F, Meissner W, Orgogozo JM. Use of the Triage Stroke Panel in a neurologic emergency service. 
The American journal of emergency medicine 2009;27:558-62. 
4. 
Vanni S, Polidori G, Pepe G, et al. Use of biomarkers in triage of patients with suspected stroke. The Journal of 
emergency medicine 2011;40:499-505. 
5. 
Gans SL, Atema JJ, Stoker J, Toorenvliet BR, Laurell H, Boermeester MA. C-reactive protein and white blood cell 
count as triage test between urgent and nonurgent conditions in 2961 patients with acute abdominal pain. Medicine 
2015;94:e569. 
6. 
Herlitz J, Svensson L. The value of biochemical markers for risk stratification prior to hospital admission in acute 
chest pain. Acute cardiac care 2008;10:197-204. 
7. 
Seymour CW, Cooke CR, Wang Z, et al. Improving risk classification of critical illness with biomarkers: a simulation 
study. Journal of critical care 2013;28:541-8. 
8. 
Huai Q, Mazar AP, Kuo A, et al. Structure of human urokinase plasminogen activator in complex with its receptor. 
Science (New York, NY) 2006;311:656-9. 
9. 
Borne Y, Persson M, Melander O, Smith JG, Engstrom G. Increased plasma level of soluble urokinase plasminogen 
activator receptor is associated with incidence of heart failure but not atrial fibrillation. European journal of heart 
failure 2014;16:377-83. 
10. 
Eapen DJ, Manocha P, Ghasemzedah N, et al. Soluble urokinase plasminogen activator receptor level is an 
Independent predictor of the presence and severity of coronary artery disease and of future adverse events. Journal 
of the American Heart Association 2014;3:e001118. 
11. 
Gumus A, Altintas N, Cinarka H, et al. Soluble urokinase-type plasminogen activator receptor is a novel biomarker 
predicting acute exacerbation in COPD. International journal of chronic obstructive pulmonary disease 2015;10:357-
65. 
12. 
Lyngbaek S, Andersson C, Marott JL, et al. Soluble urokinase plasminogen activator receptor for risk prediction in 
patients admitted with acute chest pain. Clinical chemistry 2013;59:1621-9. 
13. 
Lyngbaek S, Marott JL, Moller DV, et al. Usefulness of soluble urokinase plasminogen activator receptor to predict  
repeat myocardial infarction and mortality in patients with ST-segment elevation myocardial infarction undergoing 
primary percutaneous intervention. The American journal of cardiology 2012;110:1756-63. 

18 
 
14. 
Molkanen T, Ruotsalainen E, Thorball CW, Jarvinen A. Elevated soluble urokinase plasminogen activator receptor 
(suPAR) predicts mortality in Staphylococcus aureus bacteremia. European journal of clinical microbiology & 
infectious diseases : official publication of the European Society of Clinical Microbiology 2011;30:1417-24. 
15. 
Persson M, Ostling G, Smith G, et al. Soluble urokinase plasminogen activator receptor: a risk factor for carotid 
plaque, stroke, and coronary artery disease. Stroke; a journal of cerebral circulation 2014;45:18-23. 
16. 
Raggam RB, Wagner J, Pruller F, et al. Soluble urokinase plasminogen activator receptor predicts mortality in 
patients with systemic inflammatory response syndrome. Journal of internal medicine 2014;276:651-8. 
17. 
Reichsoellner M, Raggam RB, Wagner J, Krause R, Hoenigl M. Clinical evaluation of multiple inflammation 
biomarkers for diagnosis and prognosis for patients with systemic inflammatory response syndrome. Journal of 
clinical microbiology 2014;52:4063-6. 
18. 
Suberviola B, Castellanos-Ortega A, Ruiz Ruiz A, Lopez-Hoyos M, Santibanez M. Hospital mortality prognostication 
In sepsis using the new biomarkers suPAR and proADM in a single determination on ICU admission. Intensive care 
medicine 2013;39:1945-52. 
19. 
Zimmermann HW, Koch A, Seidler S, Trautwein C, Tacke F. Circulating soluble urokinase plasminogen activator is 
elevated in patients with chronic liver disease, discriminates stage and aetiology of cirrhosis and predicts prognosis. 
Liver international : official journal of the International Association for the Study of the Liver 2012;32:500-9. 
20. 
Donadello K, Scolletta S, Taccone FS, et al. Soluble urokinase-type plasminogen activator receptor as a prognostic 
biomarker in critically ill patients. Journal of critical care 2014;29:144-9. 
21. 
Haupt TH, Petersen J, Ellekilde G, et al. Plasma suPAR levels are associated with mortality, admission time, and 
Charlson Comorbidity Index in the acutely admitted medical patient: a prospective observational study. Critical care 
(London, England) 2012;16:R130. 
22. 
Eugen-Olsen J, Andersen O, Linneberg A, et al. Circulating soluble urokinase plasminogen activator receptor predicts 
cancer, cardiovascular disease, diabetes and mortality in the general population. Journal of internal medicine 
2010;268:296-308. 
23. 
Lyngbaek S, Marott JL, Sehestedt T, et al. Cardiovascular risk prediction in the general population with use of suPAR, 
CRP, and Framingham Risk Score. International journal of cardiology 2013;167:2904-11. 
24. 
Langkilde A, Hansen TW, Ladelund S, et al. Increased plasma soluble uPAR level is a risk marker of respiratory 
cancer in initially cancer-free individuals. Cancer epidemiology, biomarkers & prevention : a publication of the 
American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 
2011;20:609-18. 
25. 
Chounta A, Ellinas C, Tzanetakou V, et al. Serum soluble urokinase plasminogen activator receptor as a screening 
test for the early diagnosis of hepatocellular carcinoma. Liver international : official journal of the International 
Association for the Study of the Liver 2015;35:601-7. 
 
 
 
 
 

19 
 
 
Table 1 
 
Hospital 
Period 1 
Period 2 
Period 3 
Period 4 
Period 5 
Period 6 
Herlev 
+ suPAR 
Control 
Control 
+ suPAR 
Control 
Control 
Bispebjerg 
Control 
+ suPAR 
Control 
Control 
+ suPAR 
Control 
Hillerød 
Control 
Control 
+ suPAR 
Control 
Control 
+ suPAR 
 
 
Figure 1: 
suPAR level and risk of mortality in acutely admitted medical patients 
Patients < 70 years: 
Mortality risk (% (95%CI)) stratified according to suPAR levels in patients below 70 years 
suPAR (ng/ml) 
30 days 
90 days 
Alle (n=2652) 
1.5% (1.1-2.0) 
2.9% (2.2-3.6) 
0-3 (n=1705) 
0.3 % (0.1-0.7; n=5) 
0.8 % (0.5-1.4; n=14) 
3-6 (n=744) 
1.6 % (0.8-2.8; n=12) 
3.6 % (2,4-5,2; n=27) 
6-9 (n=127) 
7.1 % (3.3-13.0; n=9) 
11.8 % (6.8-18.7; n=15) 
>9 (n=76) 
19.7 % (11.5-30.5; n=15) 
27.6 % (18.0-39.1; n=21) 
 
Examples: 
A man/women of 63 years with a suPAR level of 2.7 ng/ml has a risk of mortality within 30 
days of 0.3%, which is five times less than the average mortality (1.5%) in patients below 
70 years of age. 
A man/women of 63 years with a suPAR level of 7.7 ng/ml has a risk of mortality within 30 
days of 7.1%, which is five times the average mortality (1.5%) in patients below 70 years 
of age. 
 
 
 
 
 

20 
 
Patients > 70 years: 
Mortality risk (% (95%CI)) stratified according to suPAR levels in patients above 70 years 
suPAR ng/ml 
30 days 
90 days 
Alle (n=1691) 
10.8% (9.3-12.3) 
18,3% (16.4-20.1) 
0-3 (n=339) 
2.7 % (1.2-5.0; n=9) 
3,5 % (1.8-6.1; n=12) 
3-6 (n=913) 
7.1 % (5.5-9.0; n=65) 
13,7 % (11.5-16.1; n=125) 
6-9 (n=293) 
19.8 % (15.4-24.8; n=58) 
32,8 % (27.4-38.5; n=96) 
>9 (n=146) 
34.9 % (27.2-43.3; n=51) 
52,1 % (43.6-60.4; n=76) 
 
Examples: 
A man/women of 75 years with a suPAR level of 2.7 ng/ml has a risk of mortality within 30 
days of 2.7%, which is five times less than the average mortality (10.8%) in patients above 
70 years of age. 
A man/women of 75 years with a suPAR level of 7.7 ng/ml has a risk of mortality within 30 
days of 19.8%, which is twice as high as the average mortality (10.8%) in patients above 
70 years of age. 
 
Source: Data is calculated from a cohort of acutely admitted medical patients (n=4343) 
from the emergency department at Hvidovre Hospital, from November 18 2013 until March 
31 2014. 
 
 
 

21 
 
Final Protocol (in Danish) 
 
 
 
 
Introduktion af suPAR-måling i akutmodtagelser 
(TRIAGE III) 
 
Teknisk protokol V.5 (Final) 
13. Mar. 2016 /MSC 
 
 
 

22 
 
Generelle oplysninger 
Styregruppe 
Seniorforsker, phd Jesper Eugen-Olsen, Hvidovre Hospital. 
Professor, dr.med Lars S Rasmussen, Rigshospitalet. 
Professor, dr.med Lars Køber, Rigshospitalet. 
Overlæge, dr.med Kasper Iversen, Herlev Hospital 
Overlæge dr.med Erik Kjøller, Herlev Hospital 
Ledende overlæge Lisbet Ravn, Herlev Hospital 
Ledende overlæge Birgitte Nybo Jensen, Bispebjerg Hospital 
 
Lokalisation 
Projektet udføres på Herlev Hospital og Bispebjerg Hospital 
 
Tidsplan 
Planlagt projektstart er 11. januar 2016 og ventes afsluttet 6. juni 2016 kl 08.00 
 
Baggrund 
Risikovurdering er central ved modtagelse og vurderingen af akut indlagte patienter skal 
sikre, at de mest akut syge bliver behandlet først og observeres tættest. Risikovurdering 
foretages i dag ved hjælp af systematiseret triage. Systematiseret triage foregår typisk ved 
en gennemgang af patientens primære symptom samt deres vitale parametre. Både 
symptom og vitale parametre tillægges en numerisk værdi ud fra et scoringssystem, 
bestemt af triagemodellen.  På denne måde opdeles patienterne i kategorier, som vha. 
farver eller tal signalerer, hvor hurtigt de skal tilses, samt hvor tæt de skal observeres1.  
Gennem de seneste år er der kommet tiltagende fokus på, om man kan forbedre 
risikovurderingen i hospitalernes modtagelser ved at addere resultater fra 
biomarkørmålinger til den eksisterende triage. Det ser ud som om, at en sådan kombineret 
strategi er brugbar både ved specifikke diagnoser2-4 og mere generelt5-7. Hvilke biomarkører 
der skal bruges, og om det rent faktisk gør en forskel for patienterne, er der indtil videre 
ingen data på. 
Soluble urokinase plasminogen activating receptor (suPAR) er den opløselige form af 
urokinase-type plasminogen activator receptor (uPAR) og primært udtrykt på immunceller 

23 
 
som neutrofile leukocytter, aktiverede T-celler og makrofager8. Studier af suPAR har vist, 
at proteinet er stærkt associeret med mortalitet og morbiditet i en lang række specifikke 
sygdomme (sepsis, leversygdom, hjertesygdom, lungesygdom)9-19, generelt ved indlagte 
patienter20,21 og i den generelle befolkning22,23. Derudover har suPAR vist sig at kunne 
identificere patienter med manifest eller med høj risiko for at udvikle cancer24,25 
Det ser således ud til, at suPAR er helt uspecifik og derfor ikke kan bruges til at 
diagnosticere nogen bestemt sygdom, men udelukkende til at identificere lav- og høj-
risikopatienter generelt. Dermed kunne suPAR potentielt være en god markør i 
akutmodtagelser til at identificere risikopatienter, der kræver tættere observation, hurtigere 
behandling og/eller videre udredning. På den anden side ser det også ud til at patienter 
med lav suPAR har en overordentlig god prognose, og suPAR vil derfor potentielt også 
kunne identificere patienter, der hurtigt kan udskrives. Enkelte steder (eksempelvis 
Hvidovre Hospital) har man allerede indført suPAR som en rutinemarkør, der tages på alle 
indlagte patienter i akutmodtagelsen. 
Selvom det er vist, at suPAR både kan identificere høj- og lavrisiko-patienter, er det aldrig 
vist, at man kan bruge denne viden til at forebygge dødsfald eller alvorlige komplikationer 
og ej heller om det kan forbedre patient flowet. 
 
Formål 
At undersøge om måling af suPAR og information herom til læger, der arbejder i 
akutmodtagelsen, kan forebygge dødsfald eller alvorlige komplikationer samt forbedre 
patient flow hos akut indlagte medicinske og kirurgiske patienter i akutmodtagelsen. 
 
Metode 
Studiet er et åbent overkrydsnings cluster-randomiseret interventionsstudie af betydningen 
af suPAR-måling i akutmodtagelsen. 
 
Målepunkter 
Primære endepunkt: 
• 
Død af alle årsager opgjort 10 måneder efter inklusion af sidste patient. 
Sekundære endepunkter: 
• 
Død inden for en måned 

24 
 
• 
Antal patienter udskrevet indenfor 24 timer 
• 
Indlæggelser på medicinske afdelinger indenfor 30 dage 
• 
Indlæggelser på intensivafdeling indenfor 30 dage 
• 
Nye cancerdiagnoser opgjort 10 måneder efter inklusion af sidste patient 
• 
Indlæggelsesvarighed 
• 
Akutte genindlæggelser inden for 30 og 90 dage  
• 
Justerede analyser for aldersgrupper og specifikke sygdomme med hensyn til 
center, A-diagnose og køn. 
 
Patienter: 
suPAR indføres som rutinemarkør på alle patienter, der indlægges akut i akutmodtagelsen 
på Bispebjerg- og Herlev Hospital og får taget blodprøver i 3 x 21 dages perioder på de 2 
matrikler. Perioder på 21 dage før, imellem og efter studieperioderne fungerer som 
kontrolperioder (Tabel 1).   
 
Inklusionskriterier:  Patienter der indlægges akut og som får taget Hæmoglobin, CRP og 
Kreatinin indenfor 6 timer efter registrering i det grønne system. Alder 
på 16 eller over. 
 
Eksklusionskriterier: Patienter som indlægges i gyn./obs.- og pædiatri regi eksluderes.  
 
Måling af suPAR 
Materiale til måling af suPAR tages fra i forbindelse med rutine-blodprøvetagning og 
medfører ikke ekstra stik til patienter.  
suPAR måles på EDTA-plasma fra overskydende materiale fra rutineblodprøverne. 
Analysen udføres med suPARnostic® Quick Triage stick (QT stick). Der udtages 10 
mikroliter (µl) EDTA-plasma fra rutine-blodprøverne. Disse 10 µl overføres til et 
præfabrikeret rør, der indeholder 100 µl ”running buffer”. Herfra pipetteres 60 µl på QT 
sticken, som sættes i analyseapparatet, og der trykkes start. Efter 20 minutter aflæses 

25 
 
resultatet automatisk og rapporteres på den tilkoblede PC. Den benyttede QT stick smides 
ud, og apparatet er klar til ny måling. Der opstilles 3 analyseapparater, således at 3 
analyser kan køres sideløbende, og der kan som minimum analyseres 9 prøver i timen. Så 
snart resultatet på suPAR foreligger på den til analyseapparatet tilkoblede PC, vil svaret 
blive skrevet ind i de elektroniske blodprøve- og journalsystemer LABKA og OPUS. Det 
tilstræbes, at alle suPAR-analysesvar er indskrevet i LABKA/OPUS hurtigst muligt og max 
2 timer efter blodprøvetagning.  
Afpippetering og analyse af suPAR foretages af studiepersonale der er til stede døgnet 
rundt. 
 
Information til hospitalets læger om suPAR 
Forud for studieperioden vil læger på de implicerede hospitaler blive informeret skriftligt 
om den prognostiske betydning af suPAR både i forhold til specifikke diagnoser og i 
forhold til uselekterede patienter. Der vil desuden blive tilbudt undervisning på de enkelte 
afdelinger på sygehuset. Alle læger vil få tilsendt et resume over de data, der forligger om 
suPAR’s prognostiske betydning, og få udleveret lommekort, der beskriver risiko for 
mortalitet ved 30- og 90-dage ved forskellige niveauer af suPAR, baseret på data fra 
Hvidovre - og Nordsjælland Hospital. 
 
Dataopsamling 
Resultater fra suPAR-målingen samt øvrige biokemiske målinger vil være registreret i 
LABKA-databasen, og data vil blive trukket herfra. Information om primært og sekundære 
endepunkter indhentes fra centrale registre. 
 
Styrkeberegning 
I et tidligere endnu upubliceret studie af patienter indlagt i akutmodtagelsen på 
Nordsjælland Hospital var dødeligheden inden for 12 måneder 12,7 %. Vores hypotese er, 
at vi kan reducere mortaliteten til 11,2 % ved at måle suPAR på indlagte patienter. På 
samme kohorte var Genindlæggelsesprocenten 16 % efter 5 måneder. Ved et 
signifikansniveau på 5 % og en power på 80% skal man, såfremt grupperne er lige store, 
bruge en stikprøvestørrelse på 7340 personer i hver gruppe for at vise denne forskel.  

26 
 
Baseret på tal fra de 2 akutmodtagelser indlægges der ca. 170 patienter dagligt på de 2 
hospitaler tilsammen. I studieperioden vil der således kunne opsamles data på 10.700 
patienter i både den aktive og i kontrolarmen. Dette vil være tilstrækkeligt til at undersøge 
den primære hypotese.  
 
Tilladelser 
Studiet er godkendt af direktionerne på de deltagende hospitaler samt af de enkelte 
afdelingsledelser. Studiet er vurderet, men ikke fundet anmeldelsespligtigt af 
videnskabsetisk komité i henhold til gældende lovgivning (j. nummer FSP-15003590). 
Databehandling er anmeldt til og godkendt af Datatilsynet( HGH-2015-042 I-Suite nr. 
04087) og vil blive anmeldt til Sundhedsstyrelsen. Studiet er registeret på clinicaltrials.gov 
(NCT02643459). 
 
Økonomi 
 
Udgifter i forbindelse med studiet er finansieret af firmaet ViroGates A/S. Der vil sammen 
med ViroGates A/S blive søgt ekstern finansiering til dele af eller hele projektet. 
 
Referencer 
1. 
Farrohknia N, Castren M, Ehrenberg A, et al. Emergency department triage scales and their components: a 
systematic review of the scientific evidence. Scandinavian journal of trauma, resuscitation and emergency medicine 2011;19:42. 
2. 
Albrich WC, Ruegger K, Dusemund F, et al. Biomarker-enhanced triage in respiratory infections: a proof-of-concept 
feasibility trial. The European respiratory journal 2013;42:1064-75. 
3. 
Sibon I, Rouanet F, Meissner W, Orgogozo JM. Use of the Triage Stroke Panel in a neurologic emergency service. 
The American journal of emergency medicine 2009;27:558-62. 
4. 
Vanni S, Polidori G, Pepe G, et al. Use of biomarkers in triage of patients with suspected stroke. The Journal of 
emergency medicine 2011;40:499-505. 
5. 
Gans SL, Atema JJ, Stoker J, Toorenvliet BR, Laurell H, Boermeester MA. C-reactive protein and white blood cell 
count as triage test between urgent and nonurgent conditions in 2961 patients with acute abdominal pain. Medicine 2015;94:e569. 
6. 
Herlitz J, Svensson L. The value of biochemical markers for risk stratification prior to hospital admission in acute 
chest pain. Acute cardiac care 2008;10:197-204. 
7. 
Seymour CW, Cooke CR, Wang Z, et al. Improving risk classification of critical illness with biomarkers: a simulation 
study. Journal of critical care 2013;28:541-8. 
8. 
Huai Q, Mazar AP, Kuo A, et al. Structure of human urokinase plasminogen activator in complex with its receptor. 
Science (New York, NY) 2006;311:656-9. 
9. 
Borne Y, Persson M, Melander O, Smith JG, Engstrom G. Increased plasma level of soluble urokinase plasminogen 
activator receptor is associated with incidence of heart failure but not atrial fibrillation. European journal of heart failure 2014;16:377-83. 
10. 
Eapen DJ, Manocha P, Ghasemzedah N, et al. Soluble urokinase plasminogen activator receptor level is an 
independent predictor of the presence and severity of coronary artery disease and of future adverse events. Journal of the American 
Heart Association 2014;3:e001118. 

27 
 
11. 
Gumus A, Altintas N, Cinarka H, et al. Soluble urokinase-type plasminogen activator receptor is a novel biomarker 
predicting acute exacerbation in COPD. International journal of chronic obstructive pulmonary disease 2015;10:357-65. 
12. 
Lyngbaek S, Andersson C, Marott JL, et al. Soluble urokinase plasminogen activator receptor for risk prediction in 
patients admitted with acute chest pain. Clinical chemistry 2013;59:1621-9. 
13. 
Lyngbaek S, Marott JL, Moller DV, et al. Usefulness of soluble urokinase plasminogen activator receptor to predict 
repeat myocardial infarction and mortality in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous 
intervention. The American journal of cardiology 2012;110:1756-63. 
14. 
Molkanen T, Ruotsalainen E, Thorball CW, Jarvinen A. Elevated soluble urokinase plasminogen activator receptor 
(suPAR) predicts mortality in Staphylococcus aureus bacteremia. European journal of clinical microbiology & infectious diseases : 
official publication of the European Society of Clinical Microbiology 2011;30:1417-24. 
15. 
Persson M, Ostling G, Smith G, et al. Soluble urokinase plasminogen activator receptor: a risk factor for carotid 
plaque, stroke, and coronary artery disease. Stroke; a journal of cerebral circulation 2014;45:18-23. 
16. 
Raggam RB, Wagner J, Pruller F, et al. Soluble urokinase plasminogen activator receptor predicts mortality in 
patients with systemic inflammatory response syndrome. Journal of internal medicine 2014;276:651-8. 
17. 
Reichsoellner M, Raggam RB, Wagner J, Krause R, Hoenigl M. Clinical evaluation of multiple inflammation 
biomarkers for diagnosis and prognosis for patients with systemic inflammatory response syndrome. Journal of clinical microbiology 
2014;52:4063-6. 
18. 
Suberviola B, Castellanos-Ortega A, Ruiz Ruiz A, Lopez-Hoyos M, Santibanez M. Hospital mortality prognostication 
in sepsis using the new biomarkers suPAR and proADM in a single determination on ICU admission. Intensive care medicine 
2013;39:1945-52. 
19. 
Zimmermann HW, Koch A, Seidler S, Trautwein C, Tacke F. Circulating soluble urokinase plasminogen activator is 
elevated in patients with chronic liver disease, discriminates stage and aetiology of cirrhosis and predicts prognosis. Liver international : 
official journal of the International Association for the Study of the Liver 2012;32:500-9. 
20. 
Donadello K, Scolletta S, Taccone FS, et al. Soluble urokinase-type plasminogen activator receptor as a prognostic 
biomarker in critically ill patients. Journal of critical care 2014;29:144-9. 
21. 
Haupt TH, Petersen J, Ellekilde G, et al. Plasma suPAR levels are associated with mortality, admission time, and 
Charlson Comorbidity Index in the acutely admitted medical patient: a prospective observational study. Critical care (London, England) 
2012;16:R130. 
22. 
Eugen-Olsen J, Andersen O, Linneberg A, et al. Circulating soluble urokinase plasminogen activator receptor predicts 
cancer, cardiovascular disease, diabetes and mortality in the general population. Journal of internal medicine 2010;268:296-308. 
23. 
Lyngbaek S, Marott JL, Sehestedt T, et al. Cardiovascular risk prediction in the general population with use of suPAR, 
CRP, and Framingham Risk Score. International journal of cardiology 2013;167:2904-11. 
24. 
Langkilde A, Hansen TW, Ladelund S, et al. Increased plasma soluble uPAR level is a risk marker of respiratory 
cancer in initially cancer-free individuals. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for 
Cancer Research, cosponsored by the American Society of Preventive Oncology 2011;20:609-18. 
25. 
Chounta A, Ellinas C, Tzanetakou V, et al. Serum soluble urokinase plasminogen activator receptor as a screening 
test for the early diagnosis of hepatocellular carcinoma. Liver international : official journal of the International Association for the Study 
of the Liver 2015;35:601-7. 
  
 
 
 
 
 
 

28 
 
Figur 1 
 
 
 
 
 
 
 

29 
 
Final Protocol (translated from Danish) 
 
 
 
 
 
 
Introducing suPAR measurement in the 
emergency departments 
(TRIAGE III) 
 
Technical protocol V.5 (Final) 
March 13 2016 /MSC 
 
 
 

30 
 
General information 
Steering committee:  
Senior researcher, PhD Jesper Eugen-Olsen, Hvidovre Hospital 
Professor, DMSc. Lars S Rasmussen, Rigshospitalet 
Professor, DMSc. Lars Køber, Rigshospitalet 
M.D., DMSc. Kasper Iversen, Herlev Hospital 
M.D., DMSc. Erik Kjøller, Herlev Hospital 
Chief Physician Lisbet Ravn, Herlev Hospital 
Chief Physician Birgitte Nybo Jensen, Bispebjerg Hospital 
 
Location 
The study will take place at Herlev Hospital and Bispebjerg Hospital. 
 
Time plan 
Planned start of inclusion is on January 11 2016 and is expected to be concluded on June 
6 2016. 
 
Background 
Risk assessment is a central concept at admittance of acutely admitted patients to ensure 
that the sickest patients are treated first and observed the closest. Today, systematized 
risk assessment is being performed with triage algorithms. According to the primary 
complaint and vitals sign of the patient an aggregated score is determined by the triage 
algorithm. Patients are then stratified in groups of acuity that signals how fast they should 
be assessed by doctors and the frequency of observation1. In recent years it has been 
suggested that the risk assessment in the emergency departments can be strengthened 
by adding results from blood-based biomarkers to the triage process.  A combined strategy 
looks viable both in specific diagnoses2-4 and in general5-7. However, there is no data to 
support whether this strategy makes a difference to the patients or which biomarkers that 
should be used. 
Soluble urokinase plasminogen activating receptor (suPAR) is the soluble form of the 
urokinase-type plasminogen activator receptor (uPAR), which primary is expressed on the 
surface of cells in the immune system (Neutrophiles cells, activated T-cells and 

31 
 
macrophages)8. Studies have shown that suPAR is strongly associated with mortality and 
morbidity in a wide range of specific diseases (sepsis, hepatic-, cardiac- and lung-
diseases) 9-19, among admitted patients20,21 and in the general population22,23. In addition 
suPAR have been shown to identify patients at low and high risk of developing cancer24,25. 
It appears that suPAR is a nonspecific marker of outcome and not suited as a diagnostic 
tool but solely to identify patients at low and high risk.  
Thus, suPAR could potentially be a good marker in the emergency department to identify 
risk-patients requiring close observation, fast treatment and further investigation. On the 
other hand, it appears that patients with low suPAR level have a very good prognosis and 
are potentially suitable for rapid discharge. In few places (e.g. Hvidovre Hospital) suPAR 
have already been implemented as a routine analysis of all acutely admitted medical 
patients. 
Although it has been shown that suPAR is suitable for identification of patients at high and 
low risk, it has never been shown if this information can be used to prevent deaths or 
serious complications, nor if it can improve patient flow in the emergency department (ED). 
 
Objective 
To assess if measurement of the suPAR level and information to the attending ED doctors 
can prevent serious complications and deaths among acutely admitted medical- and 
surgical patients and improve flow in the emergency department. 
  
Methods 
Triage III is an open, cross-over, cluster-randomized, interventional study on the impact of 
introducing suPAR measurement in the emergency department.  
 
Outcomes 
Primary outcome: 
• 
All-cause mortality assessed 10 months prior to inclusion of the last patient  
Secondary outcomes: 
• 
All-cause mortality at one month follow-up 
• 
Number of patients discharged within 24 hours  

32 
 
• 
Number of admissions at the medical wards within 30 days 
• 
Number of transfers to the intensive care unit within 30 days 
• 
New cancer diagnoses assessed 10 months prior to inclusion of the last patient 
• 
Length of stay 
• 
Number of readmissions within 30 and 90 days 
• 
Analyses adjusted for gender, age, hospital and specific diseases. 
 
Patients: 
suPAR will be introduced as a routine analysis on all acutely admitted patients at the 
emergency department at Herlev – and Bispebjerg hospital in three 21-day periods on 
both hospitals. Periods of 21 days before, in between, and after will serve as control 
periods (Figure 1). 
   
Inclusion criteria:  
Acutely admitted patients, who have routine blood-tests (including 
hemoglobin, C-reactive protein and creatinine) analyzed within six 
hours of registration.  Age of 16 years or above. 
Exclusion criteria:  
Patients admitted in gynecological-/obstetrics-/pediatric regimen.   
 
Measurement of suPAR 
Plasma for suPAR measurement will be obtained in connection with routine blood test and 
will not result in additional discomfort for the patients.  
The suPAR level is measured on excess EDTA-plasma from the routine blood tests. The 
analysis is done with the suPARnostic® Quick Triage stick (QT stick). From routine blood-
tests 10 microliter (µl) EDTA-plasma is extracted and transferred to a prefabricated test 
tube containing 100 µl ”running buffer”. From the mixture, 60 µl is pipetted to the QT stick 
which is inserted in the analyzer. The suPAR level is automatically reported on the 
connected PC after 20 minutes. During the inclusion period it is possible to analyze three 
tests simultaneously at a minimum of nine tests an hour. When the test result is available it 
will be reported to the electronic Laboratory system ”LABKA” and electronical hospital 

33 
 
records (“OPUS”) as soon as possible and within two hours. Measurement of suPAR is 
done by study staff, which is present 24 hours every day during inclusion. 
 
Information to attending doctors regarding the suPAR level 
Prior to the study period, doctors will receive written information regarding the prognostic 
value of suPAR in general and with regard to specific diagnoses. In addition, all 
participating departments will be offered formalized teaching sessions. Doctors will receive 
a review of the published literature and pocket cards describing risk of 30- and 90-day 
mortality depending on the suPAR level based on data from Hvidovre Hospital and 
Hillerød hospital 
 
Data 
SuPAR levels as well as all other biochemical test are registered in the LABKA database 
and can be extracted. Data on all outcomes will be obtained from the central Danish 
registries. 
  
Power calculation 
In a previous unpublished study of patients acutely admitted to the ED at Copenhagen 
University Hospital North Zealand, Denmark, 12-month mortality was 12.7 % and the 
frequency of readmissions was 16 % within 5 months. 
Our main hypothesis is that we can reduce all-cause mortality to 11.2% by measuring 
suPAR on admitted patients. Using a 5 % level of significance and a power of 80 %, we 
will need a sample of 7340 patients in each randomization group to detect this difference 
an absolute risk reduction in mortality at least 10 months after admission of 1.5 %. 
Based upon numbers from the two emergency departments, approximately 170 patients 
are admitted on a daily basis. During the inclusion period it will be possible to collect data 
on 10,700 patients in both active- and control periods. This will be sufficient to assess the 
primary hypothesis. 
 
 

34 
 
Ethics and approvals 
The study was approved by the board of directors at each participating hospital and 
department heads of the emergency departments gave consent. The study was presented 
to the Regional Ethics Committee, who decided that no formal approval was needed in 
accordance with Danish law (Jr. Nr.: FSP-15003590). All processing of personal data 
followed national guidelines in accordance with Danish law and the study was approved by 
the Danish Data Protection Agency (HGH-2015-042, I-Suite nr.04087) and will be 
registered at the Danish Patient Safety Authority. The study is registered at 
clinicaltrials.org (NCT02643459). 
 
Economy  
Expenses related to the study are financed by ViroGates A / S. Together with ViroGates 
external funding will be sought for parts of or the entire project. 
 
References 
1. 
Farrohknia N, Castren M, Ehrenberg A, et al. Emergency department triage scales and their components: a 
systematic review of the scientific evidence. Scandinavian journal of trauma, resuscitation and emergency medicine 2011;19:42. 
2. 
Albrich WC, Ruegger K, Dusemund F, et al. Biomarker-enhanced triage in respiratory infections: a proof-of-concept 
feasibility trial. The European respiratory journal 2013;42:1064-75. 
3. 
Sibon I, Rouanet F, Meissner W, Orgogozo JM. Use of the Triage Stroke Panel in a neurologic emergency service. 
The American journal of emergency medicine 2009;27:558-62. 
4. 
Vanni S, Polidori G, Pepe G, et al. Use of biomarkers in triage of patients with suspected stroke. The Journal of 
emergency medicine 2011;40:499-505. 
5. 
Gans SL, Atema JJ, Stoker J, Toorenvliet BR, Laurell H, Boermeester MA. C-reactive protein and white blood cell 
count as triage test between urgent and nonurgent conditions in 2961 patients with acute abdominal pain. Medicine 2015;94:e569. 
6. 
Herlitz J, Svensson L. The value of biochemical markers for risk stratification prior to hospital admission in acute 
chest pain. Acute cardiac care 2008;10:197-204. 
7. 
Seymour CW, Cooke CR, Wang Z, et al. Improving risk classification of critical illness with biomarkers: a simulation 
study. Journal of critical care 2013;28:541-8. 
8. 
Huai Q, Mazar AP, Kuo A, et al. Structure of human urokinase plasminogen activator in complex with its receptor. 
Science (New York, NY) 2006;311:656-9. 
9. 
Borne Y, Persson M, Melander O, Smith JG, Engstrom G. Increased plasma level of soluble urokinase plasminogen 
activator receptor is associated with incidence of heart failure but not atrial fibrillation. European journal of heart failure 2014;16:377-83. 
10. 
Eapen DJ, Manocha P, Ghasemzedah N, et al. Soluble urokinase plasminogen activator receptor level is an 
independent predictor of the presence and severity of coronary artery disease and of future adverse events. Journal of the American 
Heart Association 2014;3:e001118. 
11. 
Gumus A, Altintas N, Cinarka H, et al. Soluble urokinase-type plasminogen activator receptor is a novel biomarker 
predicting acute exacerbation in COPD. International journal of chronic obstructive pulmonary disease 2015;10:357-65. 
12. 
Lyngbaek S, Andersson C, Marott JL, et al. Soluble urokinase plasminogen activator receptor for risk prediction in 
patients admitted with acute chest pain. Clinical chemistry 2013;59:1621-9. 

35 
 
13. 
Lyngbaek S, Marott JL, Moller DV, et al. Usefulness of soluble urokinase plasminogen activator receptor to predict 
repeat myocardial infarction and mortality in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous 
intervention. The American journal of cardiology 2012;110:1756-63. 
14. 
Molkanen T, Ruotsalainen E, Thorball CW, Jarvinen A. Elevated soluble urokinase plasminogen activator receptor 
(suPAR) predicts mortality in Staphylococcus aureus bacteremia. European journal of clinical microbiology & infectious diseases : 
official publication of the European Society of Clinical Microbiology 2011;30:1417-24. 
15. 
Persson M, Ostling G, Smith G, et al. Soluble urokinase plasminogen activator receptor: a risk factor for carotid 
plaque, stroke, and coronary artery disease. Stroke; a journal of cerebral circulation 2014;45:18-23. 
16. 
Raggam RB, Wagner J, Pruller F, et al. Soluble urokinase plasminogen activator receptor predicts mortality in 
patients with systemic inflammatory response syndrome. Journal of internal medicine 2014;276:651-8. 
17. 
Reichsoellner M, Raggam RB, Wagner J, Krause R, Hoenigl M. Clinical evaluation of multiple inflammation 
biomarkers for diagnosis and prognosis for patients with systemic inflammatory response syndrome. Journal of clinical microbiology 
2014;52:4063-6. 
18. 
Suberviola B, Castellanos-Ortega A, Ruiz Ruiz A, Lopez-Hoyos M, Santibanez M. Hospital mortality prognostication 
in sepsis using the new biomarkers suPAR and proADM in a single determination on ICU admission. Intensive care medicine 
2013;39:1945-52. 
19. 
Zimmermann HW, Koch A, Seidler S, Trautwein C, Tacke F. Circulating soluble urokinase plasminogen activator is 
elevated in patients with chronic liver disease, discriminates stage and aetiology of cirrhosis and predicts prognosis. Liver international : 
official journal of the International Association for the Study of the Liver 2012;32:500-9. 
20. 
Donadello K, Scolletta S, Taccone FS, et al. Soluble urokinase-type plasminogen activator receptor as a prognostic 
biomarker in critically ill patients. Journal of critical care 2014;29:144-9. 
21. 
Haupt TH, Petersen J, Ellekilde G, et al. Plasma suPAR levels are associated with mortality, admission time, and 
Charlson Comorbidity Index in the acutely admitted medical patient: a prospective observational study. Critical care (London, England) 
2012;16:R130. 
22. 
Eugen-Olsen J, Andersen O, Linneberg A, et al. Circulating soluble urokinase plasminogen activator receptor predicts 
cancer, cardiovascular disease, diabetes and mortality in the general population. Journal of internal medicine 2010;268:296-308. 
23. 
Lyngbaek S, Marott JL, Sehestedt T, et al. Cardiovascular risk prediction in the general population with use of suPAR, 
CRP, and Framingham Risk Score. International journal of cardiology 2013;167:2904-11. 
24. 
Langkilde A, Hansen TW, Ladelund S, et al. Increased plasma soluble uPAR level is a risk marker of respiratory 
cancer in initially cancer-free individuals. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for 
Cancer Research, cosponsored by the American Society of Preventive Oncology 2011;20:609-18. 
25. 
Chounta A, Ellinas C, Tzanetakou V, et al. Serum soluble urokinase plasminogen activator receptor as a screening 
test for the early diagnosis of hepatocellular carcinoma. Liver international : official journal of the International Association for the Study 
of the Liver 2015;35:601-7. 
 
 
 
 
 
 
 
 
 
 
 
 
 

36 
 
Figure 1: 
 

37 
 
Original Statistical analysis plan 
 
The original statistical analysis plan was published with the design article on August 5 
2016. 
Patients admitted in each intervention or control cycle will be followed as a single cohort 
and data will be analyzed as randomized. The two groups will be assessed for 
comparability of the following variables: age, sex, and Charlson score. Differences in mean 
age of more than 5 years and/or an absolute Charlson Comorbidity Index score of 2 or 
more will be adjusted for in the final analysis. 
Patient data will be analyzed according to which arm of the trial the patient is admitted to 
during index admission according to the randomization scheme and corresponding to the 
intention-to-treat principle. 
A weighted Cox model will be used to compare mortality at 10 months after inclusion of the 
last patient. Patients are censored if their first readmission is in the opposite group of their 
index admission. As this censoring is likely to be dependent censoring (a readmission is 
rarely a positive prognostic signal), we will employ Inverse Probability of Censoring 
Weighting (IPCW) where patients readmitted to their own treatment group will be up-
weighted to compensate. We will employ stabilized weights such that the reweighted 
sample has the same implied sample size throughout follow-up. Due to the design, time 
since index admission is the only covariate that needs to be included in the weights. 
Reweighing will be done for every two weeks of follow-up. We will not censor nor reweight 
for 2nd or later readmissions, since the weights would become highly unstable and it is not 
likely that the presence or absence of an initial suPAR measurement will be important for 
clinical decisions at this stage. 
Furthermore, a traditional intention-to-treat analysis will be performed. Notable difference 
between the results of the two analysis strategies will be considered critically. 
Kaplan-Meier plots will be used to illustrate survival. Unpaired T-test will be used to 
compare length of stay. P < 0.05 will be considered significant. 

38 
 
Subgroup analysis of the following groups will be performed: patients aged 65 year and 
above, and patients discharged with diagnoses of surgical conditions, cancer, infections, 
and cardiovascular disease. 
 

39 
 
Final Statistical analysis plan 
 
April 5 2017 
 
This document contains the statistical analysis plan for the TRIAGE III trial. The statistical 
analysis plan has been completed prior to the availability of the outcome data. The plan 
was made available on April 5 2017 on a public website:  
https://www.hvidovrehospital.dk/afdelinger-og-klinikker/Klinisk-Forskningscenter/om-
centret/Til-forskere-paa-hospitalet/Documents/TRIAGE%20III%20Analysis%20plan.pdf 
 and at clinicaltrials.gov. 
 
Time-line: 
Study start: January 11 2016. Study completion: June 6 2016. The primary analysis will 
be conducted 10 months after inclusion of the last patient, which is expected to be 
April 6 2017. 
  
Background: 
The primary research question is: Can the introduction of a prognostic biomarker 
strengthen risk stratification of acutely admitted patients leading to fewer deaths. 
The study is a cluster randomized controlled interventional trial conducted at two major 
emergency departments of unselected patients acutely admitted. 
 
Design: 
Cluster-randomized, cross-over, parallel interventional two center trial, conducted around 
six cycles of three weeks in a 1:1 allocation ratio of intervention versus control at each 
cluster. 
 
Inclusion:  
1. ≥16 years at admittance.  
2. Acute arrival at the emergency department within the inclusion period.  
3. Have routine blood tests analyzed, including hemoglobin, C-reactive protein, and 
creatinine, within 6 hours of admission to the emergency department. 

40 
 
4. As the results of the blood tests can take up to two hours to provide, the doctors 
cannot change outcome based on biomarker levels until those data are provided. 
Hence, inclusion time will be calculated as time for blood sampling + 2 hours.  
Exclusion:  
1. Arrival at the gynecological-, obstetric-, and pediatric department.  
 
Ethics:  
The study was presented to the Regional Ethics Committee, who decided that no formal 
approval was needed in accordance with Danish law. The study has been approved by the 
Danish Patient Safety Authority (3-3013-1744/1) and data management has been 
approved by the Danish Data Protection Agency (ID: HGH-2015-042, I-suite: 04087).  
 
Study variables and endpoints:  
The following data are collected for the included patients:  
• Age  
• Results of the routine blood tests at the index admission including suPAR where 
available. Routine biomarkers: Hemoglobin, Leukocytes, thrombocytes, sodium, 
potassium, C-reactive protein, Creatinine, ALAT, Albumin, Calcium, lactate. suPAR 
in intervention periods 
• Gender  
• Personal Identification number (Danish Civil Registration number (CPR) 
 
• At follow-up (10 months after inclusion of last patient) the following data is collected 
from the National Patient Registry (NPR) 
 
• Contacts with the hospital system (including all historical contacts)  
• Information regarding admissions (date, time and place of admission and 
discharge)  
 

41 
 
• Diagnoses (follow-up during study period, historical and in relation to index 
admission) 
• Date of death or immigration (only very few immigrations are expected)  
• Charlson score will be calculated using registered diagnoses in NPR dating two 
years back from index admission 
 
Sample size calculation  
In a previous unpublished study of patients acutely admitted to an Emergency Department 
in Denmark 12-month mortality was 12.7% and readmission rate was 16% within 5 
months. Using a 5% level of significance and a power of 80%, the study will need to 
include 7,340 patients in each group to detect an absolute risk reduction in mortality of 
1.5% at 10 months after admission. The two participating hospitals approximately admit 
170 patients on a daily basis. During the study period approximately 10,700 patients will 
be included in every group.  
 
Study end  
The follow-up for all patients ends at April 6 2017 and this date will be used as the 
censoring time for the main analysis.  
 
 
Statistical plan for main outcome paper  
Statistical analyses will be performed using SAS and R.  
 
Consort diagram:  
A modified CONSORT trail profile will be used to describe patient flow and total number 
patients in each group. All patients not included in analysis will be explained.  
 
Baseline description of groups:  

42 
 
Table of summary statistics for each group (described separately for each hospital in 
appendices) will be presented with a number of baseline variables (age, sex, results of 
routine blood tests, comorbidities).  
Continuous variables will be summarized with: n (non-missing sample size), mean, 
standard deviation, median, interquartile range, number of missing values. Categorical 
variables will be reported as frequency and percentages (based on non-missing sample 
size) and number of missing values.  
Continuous and ordinal variables will be tested by Wilcoxon analysis for differences 
between groups. Categorical variables will be tested with chi-square.  
Primary outcome analysis:  
The primary outcome will be an analysis of all-cause mortality between the 
intervention and control groups. Patients will be followed as one cohort and data will be 
analyzed as randomized. Patients will be included at their first contact with the emergency 
department (index admission) and are followed until the April 6 2017. Any patient lost to 
follow-up will be censored at the last time known to be alive.  
Patients admitted in the intervention period without a suPAR measurement will remain in 
the intervention group for analyses corresponding to the intention-to-treat principle.  
The main analysis of the primary outcome will use a weighted Cox model to compare the 
primary outcome of all-cause mortality. Patients are artificially censored if they are 
readmitted at one of the study emergency departments in the opposite group of their index 
admission. As this censoring is likely to be dependent censoring (a readmission is rarely a 
positive prognostic signal), we will employ Inverse Probability of Censoring Weighting 
(IPCW) where patients readmitted to their own treatment group will be up-weighted to 
compensate. We will employ stabilized weights such that the reweighted sample has the 
same implied sample size throughout follow-up. Due to the design, time since index 
admission is the only covariate that needs to be included in the weights. Reweighting will 
be done for every two weeks of follow-up. We will not censor nor reweight for 2nd or later 
readmissions, since the weights would become highly unstable and it is not likely that the 
presence or absence of an initial suPAR measurement will be important for clinical 
decisions at this stage.  

43 
 
A secondary analysis will be performed where the observational period ends if a patient is 
readmitted within the inclusion period. We anticipate, that the patients in the secondary 
analysis will seem healthier and the possible effect of the intervention weaker, although 
showing the same trend as the main analysis. Notable difference between the results of 
the main and the secondary analysis strategies will be considered critically.  
Finally a per protocol weighted analysis is planned. In this analysis, only patients with a 
suPAR measurement are included in the intervention group. In order to investigate the 
difference between these results and the main analysis, we will analyze the mortality 
differences between patients with and without a suPAR measurement in the intervention 
periods.  
Kaplan-Meier plots will be used to illustrate survival. Robust standard errors will be 
employed to account for the clustering. P < 0.05 will be considered statistically significant 
 Adjusted analyses of primary endpoint:  
Three additional models will also be examined to further examine any role of differences in 
baseline values of the two groups. The first will include age, sex, CRP and center. The 
second will further include Charlson score. The third will include all variables used in the 
descriptive statistics. Results will be presented as hazard ratios with 95% confidence 
intervals. A forest plot will illustrate the results.  
Sensitivity analyses of the primary endpoint:  
Sensitivity analyses will be performed: One analysis where all readmissions are censored 
and a second analysis with only 10 months follow-up for all patients.  
Secondary endpoints:  
The predefined secondary endpoints of the study are: all-cause mortality at one month of 
admission; short admission (<24 h) to the emergency department; admission to the 
medical wards; admission to the intensive care unit; new (not previously registered) cancer 
diagnosis at the end of the study; length of stay during admission; readmission at one and 
three months; and economical expenses.  
All-cause mortality at one month will be analyzed similar to the primary outcome. The 
discriminative ability of suPAR with regard to mortality at one and ten months will be 
assessed by using area under the curve for receiver operating characteristics curves as 
well as sensitivity, specificity, negative- and positive predictive value. To compare 

44 
 
readmission risk, we will use Cox regression analysis. Cumulative incidence rates adjusted 
for competing risk (death, readmission) will be estimated. Unpaired t-test will be used to 
compare length of stay between groups.  
Subgroup analyses:  
Primary and secondary endpoints (mortality, readmissions, length of stay, discharge from 
the ED) will be analyzed in the following subgroups according to primary diagnosis at 
admission: cardiovascular disease, cancer, infections, neurological disease, surgery done 
during current admission, and patients aged 65 years or older. When Triage data is 
available, outcomes will be assessed according to DEPT triage categories, as suPAR 
previously has shown strongest prognostic effect in patients with lowest DEPT triage. All-
cause mortality will be analyzed similar to the primary endpoint. 
 Handling of missing data:  
The primary outcome analysis should be subject to no or little missing data as it is based 
on Danish register data.  
Handling of diagnoses for outcome and subgroups:  
Diagnoses obtained from the National Patient Registry are coded with the ICD-10 system. 
The original chapters will be used to group patients according to diagnoses. The primary 
diagnosis will be used when constructing subgroups and both primary and secondary 
diagnoses will be used to calculate Charlson score. The following will define the 
subgroups:  
Cancer: Chapter II: Neoplasms (C00-C97 + D37-D48).  
Cardiovascular disease: Chapter IX: Diseases of the circulatory system (I09-I52 + I70-
I89)  
Infections: Chapter I: (A00-B99 + J00-J22 + N10-N11+ N30-N31 + G00-G09)  
Neurological disease: Chapter VI: Diseases of the nervous system (G09-G47 + I60-I69)  
Surgical conditions: Presence of surgical procedure codes divided into different 
specialties (general, orthopedic, other)  
 
 
 

45 
 
Summary of changes to the protocol 
 
Changes to the protocol were made before outcome data was available. The final protocol 
was published as a design article on August 5 2016: 
https://www.ncbi.nlm.nih.gov/pubmed/27491822 
 
• The study was originally planned to be conducted at three separate hospitals. Due 
to internal factors and changes in management, Nordsjælland hospital could not 
continue to participate in the study and withdrew after four days of inclusion. 
Although attempts were made, it was not possible to find a replacement. Therefore 
the Steering Committee together with the chief statistician decided to change the 
study design to a two center model with 1:1 ratio of intervention and control. The 
new design had three active periods at each center instead of two, which meant 
that the inclusion period was prolonged until June 6 2016. A new power calculation 
reflected this change. 
 
• The original follow-up period was planned to be 12 months, but based upon 
statistical advice the Steering Committee decided to adjust it to 10 months after 
inclusion of the last patient (median 12 months). It was also decided to include 
subgroups and adjusted analyses and to reduce the number of secondary 
endpoints. 
 
• Inclusion- and exclusion-criteria were added. 
 
• The reduction in centers also meant a reduced number of patients eligible for 
inclusion. 
 
 
 

46 
 
Summary of changes to the Statistical Analysis Plan 
 
Changes to the Statistical Analysis Plan were made before outcome data was available. 
 
• 
Elaboration of the entire analysis plan. 
• Added sensitivity and adjusted analyses of the primary outcome as well as a ”per-
protocol” analysis. 
• Specification of subgroup analyses and definitions of subgroups. 
 
 
 

47 
 
Summary of changes at clinicaltrials.gov: 
 
Changes were made before outcome data was available. 
 
• 
February 5, 2016: Status of study changed to recruiting. The study started inclusion 
on January 11, 2016. 
 
• 
March 1, 2016: The secondary endpoint “Economical expenses” was added after 
the Steering Committee decided on inclusion of this outcome. 
 
• 
April 27, 2016: Subgroup analysis was added. Analyses on the following subgroups: 
Cardiovascular-, infectious-, surgical-, neurological diseases and patients aged 
older than 65 year. Patients will be eligible for a subgroup based on their primary 
diagnosis at discharge. 
 
• 
May 3, 2016: The follow-up period for each patient was changed from 12 months 
from discharge to one date for all patients, where follow-up ends: 10 months after 
admission of the last patient: April 6, 2017. This is approximately equal to a median 
follow-up of 12 months. 
 
• 
May 31, 2016: Change from 3 to 2 participating centers, which also meant a change 
in the design, reducing number of control periods to be equal the number of 
intervention periods. It was clear in the beginning of March that Nordsjælland 
Hospital had withdrawn from the study. The Steering Committee tried in the 
subsequent months to find a replacement, which was not possible. The late change 
on clinicaltrials.gov reflected this. 
 
• 
June 6, 2016: Status of study changed because of planned stop in the inclusion 
period. 
 
• 
April 5, 2017: Original Statistical Analysis plan was made available online.