schuetz proadm review cclm 2015

Clin Chem Lab Med 2015; 53(4): 521–539

Review

Philipp Schuetz*, Robert J. Marlowe and Beat Mueller

The prognostic blood biomarker proadrenomedullin for outcome prediction in patients with chronic obstructive pulmonary disease (COPD): a qualitative clinical reviewDOI 10.1515/cclm-2014-0748Received July 20, 2014; accepted August 24, 2014; previously published online September 25, 2014

Abstract: Plasma proadrenomedullin (ProADM) is a blood biomarker that may aid in multidimensional risk assess-ment of patients with chronic obstructive pulmonary dis-ease (COPD). Co-secreted 1:1 with adrenomedullin (ADM), ProADM is a less biologically active, more chemically stable surrogate for this pluripotent regulatory peptide, which due to biological and ex vivo physical character-istics is difficult to reliably directly quantify. Upregulated by hypoxia, inflammatory cytokines, bacterial products, and shear stress and expressed widely in pulmonary cells and ubiquitously throughout the body, ADM exerts or mediates vasodilatory, natriuretic, diuretic, antioxidative, anti-inflammatory, antimicrobial, and metabolic effects. Observational data from four separate studies totaling 1366 patients suggest that as a single factor, ProADM is a significant independent, and accurate, long-term all-cause mortality predictor in COPD. This body of work also suggests that combined with different groups of demo-graphic/clinical variables, ProADM provides significant incremental long-term mortality prediction power rela-tive to the groups of variables alone. Additionally, the lit-erature contains indications that ProADM may be a global cardiopulmonary stress marker, potentially supplying

prognostic information when cardiopulmonary exercise testing results such as 6-min walk distance are unavailable due to time or other resource constraints or to a patient’s advanced disease. Prospective, randomized, controlled interventional studies are needed to demonstrate whether ProADM use in risk-based guidance of site-of-care, moni-toring, and treatment decisions improves clinical, quality-of-life, or pharmacoeconomic outcomes in patients with COPD.

Keywords: 6-min walk test (6MWT); cardiopulmonary exercise testing; chronic obstructive pulmonary disease (COPD); risk stratification; mortality prediction; proadre-nomedullin (ProADM).

IntroductionIn patients with chronic obstructive pulmonary disease (COPD), as in other medical settings, risk stratification helps caregivers to more appropriately direct diagnostic, monitoring, or therapeutic interventions. More person-alized, better-targeted health-care resource application offers opportunities to improve safety, efficacy, and cost-effectiveness of care, as well as quality of life of patients and their loved ones.

COPD’s complexity and heterogeneity have led the Global Initiative for Chronic Obstructive Lung Disease (GOLD) [1] and other groups, e.g., [2–5], to move beyond strictly spirometry-based prognostication to multidimen-sional risk assessment of patients with this condition. Considerable interest has arisen in using blood biomark-ers within this framework [6–9].

One such analyte that may have a role in COPD multidimensional risk assessment is plasma proadre-nomedullin (ProADM), a surrogate for the pluripotent reg-ulatory peptide adrenomedullin (ADM) [10]. There exists

*Corresponding author: Prof. Dr. med. Philipp Schuetz, MD, MPH, University Department of Medicine, Kantonsspital Aarau, Tellstr., 5001 Aarau, Switzerland, Phone: +41 62 838 4141, Fax: +41 62 838 4100, E-mail: [email protected]; and Division of General Internal and Emergency Medicine, Medical University Department, Kantonsspital Aarau, Aarau, SwitzerlandBeat Mueller: Division of General Internal and Emergency Medicine, Medical University Department, Kantonsspital Aarau, Aarau, SwitzerlandRobert J. Marlowe: Spencer-Fontayne Corporation, Jersey City, NJ, USA

- 10.1515/cclm-2014-0748Downloaded from PubFactory at 08/31/2016 11:46:19PM

via free access

mailto:[email protected]

522 Schuetz et al.: ProADM in COPD

a substantial observational literature regarding ProADM use as an all-cause mortality predictor in patients with sepsis [11–19], in patients with a variety of underlying dis-eases presenting to the emergency department (ED) with acute dyspnea [20–26], and especially in patients with community-acquired pneumonia (CAP) [13, 27–38].

Additionally, recent observational studies demon-strated that in patients during or just recovering from COPD exacerbation [39–41] or in patients with stable COPD [40, 42], ProADM is a powerful independent prog-nosticator of long-term non-survival [39–42]. Some of this work also shows that when combined with demographic and clinical variables, ProADM provides significant incremental mortality prediction accuracy [41, 42]. Addi-tional data [43–45] suggest that ProADM may be a global cardiopulmonary stress marker [45]. As such, this blood biomarker may supply prognostic information when cardiopulmonary exercise testing (CPET) results such as 6-min walk distance (6MWD) are unavailable. Indeed, ProADM may even serve as a simpler, less “invasive” substitute for the 6-min walk test (6MWT) or other CPET in settings where time or other resource constraints or a patient’s advanced disease render such examinations infeasible, albeit this hypothesis awaits confirmation by prospective, randomized, controlled interventional studies.

Aims of the review and methodologyThe present review’s goal is to provide clinicians with an overview of ProADM in risk stratification of COPD patients. We begin by briefly describing ADM and explain-ing why ProADM serves as a surrogate for this regulatory peptide. Next, we summarize observational data regard-ing ProADM in risk stratification of COPD and other pulmonary diseases/disorders and the analyte’s rela-tionship with cardiopulmonary stress, exercise capacity, and physical activity. We conclude by outlining clinical considerations and future research directions regard-ing ProADM in patients with COPD. Literature discussed in this article was partly identified through a system-atic literature search of English-language publications indexed in PubMed through 12 May 2014 under the terms “adrenomedullin” or “proadrenomedullin” or “ProADM” together with each of the terms “lung”, “pulmonary”, “chronic obstructive pulmonary disease”, “COPD”, “exacerbation”, “dyspnea”, “emphysema”, “bronchitis”, “asthma”, “pneumonia”, “cardiac”, “cardiovascular”, or

“exercise”. Notwithstanding this formal methodology, the present paper is a qualitative rather than a systematic review.

ADM and ProADMFirst isolated in the early 1990s [46], ADM is a 52-amino acid ringed peptide with C-terminal amidation belonging to the calcitonin superfamily [47, 48]. ADM is ubiquitously expressed in pulmonary, cardiovascular, renal, gastroin-testinal, and endocrine tissue and by endothelial cells, vascular smooth muscle cells, cardiomyocytes, fibro-blasts, leukocytes, and placental trophoblast cells, among others [49–52]. In pulmonary tissue, ADM expression has been found in endothelial cells including type II pneumo-cytes, chondrocytes, smooth muscle cells, the columnar epithelium, alveolar macrophages, monocytes, T cells, and neurons of the pulmonary parasympathetic nervous system, as well as small-cell and non-small-cell neoplasia [42, 53].

ADM’s widespread expression throughout the body reflects this molecule’s great variety of biological activi-ties: the peptide seems to act as both a hormone and a cytokine and thus can be seen as a “hormokine”. It acts systemically and in autocrine and paracrine fashion [54, 55], exerting or mediating vasodilatory, natriuretic, diu-retic, antioxidative, anti-inflammatory, antimicrobial, and metabolic effects [42, 50, 56–58]. Upregulated by hypoxia, inflammatory cytokines, bacterial products, and shear stress, ADM has in preclinical and animal models been shown to reduce hypoxic pulmonary vascular structural remodeling and fibrosis and to inhibit bronchoconstric-tion; the molecule also has been shown to stabilize barrier function in the lungs by downregulating pro-inflammatory factors and reactive oxidative species [42, 50, 51, 59–62]. Circulating ADM elevation, e.g., in end-stage pulmonary disease [63], is believed to reflect “high demand” for these compensatory/counter-regulatory effects [28, 42].

Based on the hormone’s biological importance and effects, the utility of measuring ADM in blood seems evident; however, abundant binding to peripheral and local receptors, a short half-life, and ex vivo physical char-acteristics including instability and “stickiness” make cir-culating ADM difficult to reliably directly quantify [10, 64].

ADM, however, is only one of five peptides contained on the ADM precursor molecule (Figure 1). During ADM’s processing into mature hormone, it and an adjoining peptide, mid-regional proadrenomedullin (MR-ProADM; referred to here and elsewhere as ProADM), are cleaved


via free access

Schuetz et al.: ProADM in COPD 523

off of the precursor in a 1:1 ratio. This stoichiometric secre-tion, the peptide’s apparently minimal if any biological activity, and hence binding, and considerable chemical stability render ProADM an easily and robustly quantifi-able surrogate biomarker for ADM [10, 65].

ProADM in risk assessment of patients with COPDClinical research regarding plasma ProADM in patients with COPD, and particularly, regarding ProADM in risk assessment of such patients, was initially inspired by a study [63] showing significantly elevated ADM concentra-tions in patients with end-stage pulmonary diseases and a trend toward this finding in the COPD subgroup, rela-tive to controls [39]. As of May, 2014, observational data regarding ProADM in COPD have been published by four groups [39–42] (Table 1). Two of these teams, Stolz et al. at University Hospital Basel and Zuur-Telgen et al. at Medisch Spectrum Twente, Enschede, The Netherlands, reported single-center data; the other two, the Predicting Outcome Using Systemic Markers in Severe Exacerbations of Chronic Obstructive Pulmonary Disease Study (PROM-ISE-COPD) investigators and the ProHOSP investigators, reported multicenter data. The PROMISE-COPD data derived from 11 centers in 8 European countries, and the ProHOSP data, from 6 Swiss centers. The University Hos-pital Basel and Enschede study samples were relatively small (n = 167 and n = 181, respectively); the multicenter samples were substantially larger, 549 (PROMISE-COPD) and 469 (ProHOSP). All cohorts comprised predominantly, or exclusively, patients with moderate to severe COPD. However, unlike the other samples, the ProHOSP patients with COPD lacked dedicated spirometric confirmation of this diagnosis because their baseline data derived from a secondary analysis of an antibiotic stewardship trial in patients with a mixed group of lower respiratory tract infections (LRTIs) [41]. In all four of these studies, the

1Signal 21 45 92 95 146 Adrenotensin 185PAMP ADM

MR-proADM

Figure 1 Schematic representation of the ADM precursor molecule.The diagram illustrates that the precursor is occupied by five pep-tides including MR-ProADM, more commonly referred to as ProADM. When the molecule is processed, ProADM and ADM are co-secreted in a 1:1 ratio. The black numerals refer to the amino acids on the pre-cursor molecule. PAMP, proadrenomedullin N-terminal 20 peptide.

analyses evaluated the risk prediction power of single ProADM measurements in samples obtained when patients were in one or more of stable [40, 42], (severely) exacerbated [39, 40], or “recovery” states [39, 41]. In two of the studies [39, 41], ProADM was measured using a manual luminoimmunoassay [10], while in the other two studies [40, 42], the analyte was quantitated with an auto-mated homogeneous sandwich fluoroimmunoassay [70]. Because these two assays were shown to have a Spear-man correlation coefficient of r = 0.97, their results may be viewed as interchangeable [70].

Studies focusing on ProADM in COPD risk stratifica-tion have had very consistent results and, collectively, have had the following main findings (summarized in Supplemental Data, Panel 1). First, even in the stable state, COPD patients almost always have ProADM concen-trations elevated above healthy adult reference values. In the Basel study [39], ProADM levels exceeded a mean ref-erence value [10] in 98% of patients (163/167) at admission for COPD exacerbation resulting in median [interquartile range (IQR) 25th–75th percentile] hospitalization of 9 [1–15] days. The corresponding figures were 90% [140/156] at “recovery” 14–18 days post-admission and 79% [114/144] at “stable state” 6 months post-admission. The Enschede investigators reported that during the stable state, defined as freedom from COPD exacerbation, antibiotics, or pred-nisolone over the prior > 4 weeks, 99% of patients had elevation compared with healthy individuals [40].

Second, as suggested by the three serial measure-ments in the Basel investigation [39], ProADM values appear to be repeatable. That study’s median [IQR] “recovery” and “stable state” values, respectively, 0.72 [0.55–0.98] and 0.66 [0.49–0.95] nmol/L, did not differ (p = 0.350) [39]. Additionally, the Enschede investigators found exacerbated- and stable-state ProADM to be signifi-cantly correlated (r = 0.73, p < 0.001), another sign of inter-nal consistency among values of this biomarker. Further, measurements obtained within the exacerbated, recovery, or stable states seem to be comparable across studies and assays (Table 2).

Third, ProADM values are significantly higher in the exacerbated state vs. the stable state of COPD. For example, in the Basel study [39], median [IQR] ProADM concentration at admission, 0.84 [0.59–1.22] nmol/L, sig-nificantly exceeded that at “recovery” or “stable state” (p = 0.002).

Fourth, ProADM appears not to be associated with underlying COPD severity. In the Basel study [39], no cor-relation was seen between GOLD grade and ProADM con-centration at admission for severe exacerbation (r = –0.066, p = 0.406); indeed, GOLD grade IV patients had the lowest


via free access


Tabl

e 1

Sum

mar

y of

sel

ecte

d lit

erat

ure

rele

vant

to P

roAD

M in

risk

stra

tific

atio

n of

pat

ient

s w

ith C

OPD.

Firs

t aut

hor,

year

Re

fere

nce

St

udy

type

(nam

e or

acr

onym

)

Patie

nts

Se

ttin

g

Key

findi

ngs

ADM

in e

nd-s

tage

COP

D an

d ot

her p

ulm

onar

y di

seas

es

Vizz

a, 2

005

[6

3]

Sing

le-c

ente

r, pr

ospe

ctiv

e,

obse

rvat

iona

l cas

e-co

ntro

l st

udy

56

pat

ient

s w

ith e

nd-s

tage

lung

dis

ease

ev

alua

ted

for l

ung

trans

plan

tatio

n, 1

1 (2

0%) w

ith C

OPD;

9 co

ntro

ls w

ith h

isto

ry o

f ex

cisi

on o

f one

ben

ign

lung

nod

ule

Ita

lian

acad

emic

ce

nter

–

Veno

us a

nd p

ulm

onar

y ar

teria

l pla

sma

ADM

co

ncen

tratio

n w

as h

ighe

r in

COPD

pat

ient

s vs

. con

trols

, tre

ndin

g to

war

d st

atis

tical

si

gnifi

canc

ePr

oADM

in ri

sk s

tratif

icat

ion,

focu

sing

on

patie

nts

with

COP

D

Stol

z, 2

008

[3

9]

Sing

le-c

ente

r, pr

ospe

ctiv

e ob

serv

atio

nal c

ohor

t stu

dy

(sub

stud

y of

an

antib

iotic

st

ewar

dshi

p tri

al [6

6])

16

7 pa

tient

s ho

spta

lized

for s

ever

e AE

COPD

Sw

iss

acad

emic

ce

nter

–

ProA

DM (d

urin

g AE

COPD

, at r

ecov

ery

or s

tabl

e st

ate

not r

epor

ted)

was

the

only

inde

pend

ent

2-ye

ar a

ll-ca

use

mor

talit

y pr

edic

tor (

HR 2

.368

, 95

% C

I 1.1

67–4

.803

, p = 0

.017

) in

mul

tivar

iate

an

alys

is in

clud

ing

11 o

ther

dem

ogra

phic

, cl

inic

al, s

piro

met

ric, a

nd la

bora

tory

var

iabl

es

– M

edia

n Pr

oADM

was

sig

nific

antly

hig

her

(p = 0

.002

) dur

ing

AECO

PD th

an a

fter r

ecov

ery

or in

sta

ble

stat

e, b

ut th

e re

cove

ry a

nd s

tabl

e st

ate

mea

sure

men

ts w

ere

sim

ilar (

p = 0.

350)

Zu

ur-T

elge

n,

2013

[4

0]

Sing

le-c

ente

r, pr

ospe

ctiv

e ob

serv

atio

nal c

ohor

t stu

dy

(COM

IC s

ubst

udy)

18

1 w

ith p

aire

d m

easu

rem

ents

dur

ing

exac

erba

ted

and

stab

le s

tate

s

Dutc

h ac

adem

ic

cent

er

– As

sin

gle

fact

or, P

roAD

M in

sta

ble

stat

e w

as

mod

erat

ely

pred

ictiv

e (c

sta

tistic

0.7

6) o

f al

l-cau

se m

orta

lity

with

a 2

9-m

onth

med

ian

follo

w-u

p

– Pr

oADM

was

sig

nific

antly

hig

her i

n no

n-su

rviv

ors

than

in s

urvi

vors

in b

oth

exac

erba

ted-

and

sta

ble-

stat

e m

easu

rem

ents

(p

< 0.0

01 fo

r eac

h co

mpa

rison

)

Stol

z, 2

014

[4

2]

Mul

ticen

ter,

mul

tinat

iona

l, pr

ospe

ctiv

e, o

bser

vatio

nal

coho

rt st

udy

(PRO

MIS

E-CO

PD)

54

9 pa

tient

s w

ith a

vaila

ble

biom

arke

r and

BO

DE d

ata

in s

tabl

e st

ate

11

hos

pita

l pn

eum

olog

y de

partm

ents

in

8 E

urop

ean

coun

tries

–

Addi

ng P

roAD

M to

BOD

E pr

ovid

ed s

igni

fican

t in

crem

enta

l pre

dict

ive

accu

racy

com

pare

d w

ith u

sing

BOD

E al

one

for b

oth

1- a

nd 2

-yea

r al

l-cau

se m

orta

lity:

resp

ectiv

e c s

tatis

tics

0.81

8 vs

. 0.7

45, 0

.750

vs.

0.6

79, b

oth

p < 0.

001

–

In m

ultiv

aria

te a

naly

sis

also

incl

udin

g th

e fo

ur

BODE

com

pone

nts,

Pro

ADM

inde

pend

ently

pr

edic

ted

2-ye

ar a

ll-ca

use

mor

talit

y: H

R 1.

77,

95%

CI 1

.30–

2.42

, lik

elih

ood

ratio

χ2 1

3.0,

p <

0.00

1

Grol

imun

d,

2014

[4

1]

Mul

ticen

ter,

pros

pect

ive

obse

rvat

iona

l coh

ort s

tudy

/se

cond

ary

char

t ana

lysi

s (P

roHO

SP [6

7] s

ubgr

oup

with

CO

PD a

s co

mor

bidi

ty)

46

9 pa

tient

s at

dis

char

ge fr

om

hosp

italiz

atio

n fo

r pne

umon

ic o

r sev

ere

non-

pneu

mon

ic A

ECOP

D

6

Swis

s ho

spita

ls

– In

mul

tivar

iate

ana

lysi

s, co

mpa

red

with

six

de

mog

raph

ic/c

linic

al v

aria

bles

alo

ne, a

ddin

g Pr

oADM

pro

vide

d si

gnifi

cant

incr

emen

tal

pred

ictiv

e ac

cura

cy fo

r 1-,

3-, a

nd 5

- to

7-ye

ar

all-c

ause

mor

talit

y: re

spec

tive

AUC

0.75

9 vs

. 0.

715,

p = 0

.045

, 0.7

07 v

s. 0

.674

, p = 0

.037

, 0.

742

vs. 0

.725

, p = 0

.049


via free access


Firs

t aut

hor,

year

Re

fere

nce

St

udy

type

(nam

e or

acr

onym

)

Patie

nts

Se

ttin

g

Key

findi

ngs

ProA

DM in

risk

stra

tific

atio

n in

oth

er p

ulm

onar

y co

nditi

ons,

incl

udin

g su

bsta

ntia

l pro

port

ions

of p

atie

nts

with

COP

D

Mai

sel,

2011

[2

3]

Mul

ticen

ter,

mul

tinat

iona

l, pr

ospe

ctiv

e, o

bser

vatio

nal

coho

rt st

udy

(BAC

H)

16

41 p

atie

nts

pres

entin

g to

ED

with

ac

ute

dysp

nea,

∼29

.5%

with

COP

D as

co

mor

bidi

ty

15

cent

ers

in

6 Eu

rope

an

coun

tries

, USA

, an

d Ne

w Z

eala

nd

–

ProA

DM w

as s

igni

fican

tly s

uper

ior t

o ca

rdia

c tro

poni

n or

BNP

in p

redi

ctin

g 90

-day

all-

caus

e m

orta

lity,

resp

ectiv

e c i

ndic

es: 0

.755

vs.

0.

655

vs. 0

.691

, p < 0

.000

1

Chris

t-Cra

in,

2006

[2

7]

Sing

le-c

ente

r, pr

ospe

ctiv

e,

obse

rvat

iona

l coh

ort s

tudy

30

2 pa

tient

s ad

mitt

ed to

ED

with

CAP

, 25

.2%

with

COP

D as

com

orbi

dity

Sw

iss

acad

emic

ce

nter

–

In th

is fi

rst p

ublis

hed

stud

y in

CAP

, Pro

ADM

as

sin

gle

fact

or a

ccur

atel

y pr

edic

ted

appr

oxim

atel

y 6-

wee

k al

l-cau

se m

orta

lity:

AUC

0.

76 (9

5% C

I 0.7

1–0.

81)

Be

llo, 2

012

[3

3]

Sing

le-c

ente

r, pr

ospe

ctiv

e,

obse

rvat

iona

l stu

dy

228

patie

nts

with

CAP

, 31.

6% w

ith C

OPD

as

com

orbi

dity

Sp

anis

h ac

adem

ic ce

nter

–

In m

ultiv

aria

te a

naly

sis,

Pro

ADM

was

a s

trong

, si

gnifi

cant

pre

dict

or o

f 30-

, 90-

, and

180

-day

an

d 1-

year

all-

caus

e m

orta

lity:

resp

ectiv

e AU

C 0.

859,

0.8

25, 0

.792

, 0.8

03, a

ll p <

0.00

01

– M

edia

n Pr

oADM

conc

entra

tion

did

not d

iffer

am

ong

patie

nts

with

bac

teria

l, vi

ral/

atyp

ical

, or

mix

ed C

AP e

tiolo

gy: 0

.909

vs.

0.8

75 v

s.

0.94

9 nm

ol/L

, all

com

paris

ons

p > 0.

05Pr

oADM

as

a ca

rdio

pulm

onar

y st

ress

mar

ker

St

olz,

201

4

[42]

M

ultic

ente

r, m

ultin

atio

nal,

pros

pect

ive,

obs

erva

tiona

l co

hort

stud

y (P

ROM

ISE-

COPD

)

54

9 pa

tient

s w

ith a

vaila

ble

biom

arke

r and

BO

DE d

ata

in s

tabl

e st

ate

11

hos

pita

l pn

eum

olog

y de

partm

ents

in

8 E

urop

ean

coun

tries

–

Sugg

estin

g th

at P

roAD

M co

uld

subs

titut

e fo

r the

6M

WT

if ne

eded

, Pro

ADM

plu

s “B

OD”

was

mor

e pr

edic

tive

of 1

- or 2

-yea

r all-

caus

e m

orta

lity

than

was

the

orig

inal

BOD

E,

incl

udin

g 6M

WD:

resp

ectiv

e c s

tatis

tics:

0.8

15

vs. 0

.759

, 0.7

64 v

s. 0

.711

Je

hn, 2

013

[4

3]

Mul

ticen

ter,

mul

tinat

iona

l, pr

ospe

ctiv

e, o

bser

vatio

nal

coho

rt st

udy

(PRO

MIS

E-CO

PD

subs

tudy

)

10

5 pa

tient

s w

ith s

tabl

e CO

PD

Swis

s ac

adem

ic

cent

er

– In

sep

arat

e st

epw

ise

mul

tivar

iate

regr

essi

on

anal

yses

, eac

h in

clud

ing

one

acce

lom

etry

va

riabl

e pl

us p

atie

nt a

ge, a

ge-a

djus

ted

Char

lson

com

orbi

dity

sco

re, m

MRC

dys

pnea

sc

ore,

and

St.

Geor

ge’s

Res

pira

tory

Qu

estio

nnai

re s

core

, acc

elom

etric

ally

de

term

ined

tota

l wal

king

min

utes

/day

or s

teps

w

alke

d/da

y w

ere

sole

fact

ors

inde

pend

ently

as

soci

ated

with

Pro

ADM

conc

entra

tion:

re

gres

sion

coef

ficie

nts

–0.0

004

(95%

CI

–0.0

007

to –

0.00

02) a

nd –

0.00

04 (9

5% C

I –0

.000

6 to

–0.

0001

), bo

th p

< 0.0

001

St

olz,

201

4

[44]

M

ultic

ente

r, m

ultin

atio

nal,

pros

pect

ive,

obs

erva

tiona

l co

hort

stud

y (P

ROM

ISE-

COPD

)

54

9 pa

tient

s w

ith a

vaila

ble

biom

arke

r and

BO

DE d

ata

in s

tabl

e st

ate

11

hos

pita

l pn

eum

olog

y de

partm

ents

in

8 E

urop

ean

coun

tries

–

Mul

tivar

iabl

e lin

ear l

ogis

tic re

gres

sion

an

alys

is o

f 123

3 6M

WTs

per

form

ed o

ver

2 ye

ars

by 5

74 P

ROM

ISE-

COPD

par

ticip

ants

w

ith s

tabl

e CO

PD, f

ound

Pro

ADM

in

depe

nden

tly p

redi

cted

exe

rtio

nal

(Tab

le 1

Cont

inue

d)


via free access


Firs

t aut

hor,

year

Re

fere

nce

St

udy

type

(nam

e or

acr

onym

)

Patie

nts

Se

ttin

g

Key

findi

ngs

h

ypox

emia

, def

ined

as

nadi

r arte

rial o

xyge

n sa

tura

tion

< 88%

on

cont

inuo

us tr

ansc

utan

eous

pu

lse

oxim

etry

: HR

6.85

(95%

CI 2

.91–

16.0

8)

per l

ogar

ithm

ic ch

ange

, p < 0

.001

–

This

ass

ocia

tion

pers

iste

d ev

en a

fter i

nclu

sion

in

to m

ultiv

aria

te m

odel

ing

of th

e an

nual

CO

PD e

xace

rbat

ion

rate

plu

s the

age

-adj

uste

d Ch

arls

on co

mor

bidi

ty sc

ore,

or o

f any

or a

ll of

co

nges

tive

hear

t fai

lure

, cor

onar

y arte

ry d

isea

se,

or h

isto

ry o

f acu

te m

yoca

rdia

l inf

arct

ion

Zu

rek,

201

4

[45]

Si

ngle

-cen

ter p

rosp

ectiv

e ob

serv

atio

nal s

tudy

16

2 co

nsec

utiv

e el

igib

le p

atie

nts

with

m

ixed

card

iac o

r pul

mon

ary

cond

ition

s un

derg

oing

CPE

T to

ass

ess

subj

ectiv

e ch

roni

c exe

rcis

e in

tole

ranc

e, a

ppro

xim

atel

y 24

% w

ith C

OPD

as co

mor

bidi

ty

Sw

iss

acad

emic

ce

nter

–

Pre-

exer

cise

Pro

ADM

was

sig

nific

antly

as

soci

ated

with

impa

ired

peak

oxy

gen

cons

umpt

ion

( < 14

mL/

min

/kg)

– in

clud

ing

afte

r mul

tivar

iabl

e ad

just

men

t for

fact

ors

incl

udin

g ag

e, B

MI,

and

FEV 1

–

ProA

DM w

as co

nsis

tent

ly s

igni

fican

tly

asso

ciat

ed w

ith o

ther

var

iabl

es re

flect

ing

impa

ired

card

iac o

utpu

t res

erve

, ven

tilat

ory

effic

ienc

y, a

nd d

iffus

ion

capa

city

Aven

ues

for f

utur

e re

sear

ch o

n Pr

oADM

in C

OPD

M

aise

l, 20

11

[23]

M

ultic

ente

r, m

ultin

atio

nal,

pros

pect

ive,

obs

erva

tiona

l co

hort

stud

y (B

ACH)

16

41 p

atie

nts

pres

entin

g to

ED

with

acu

te

dysp

nea,

app

roxi

mat

ely

29.5

% w

ith C

OPD

as co

mor

bidi

ty

15

cent

ers

in

6 Eu

rope

an

coun

tries

, USA

, an

d Ne

w Z

eala

nd

–

In th

e su

bgro

up w

ith P

roAD

M m

easu

rem

ents

at

adm

issi

on a

nd d

isch

arge

(n = 9

64),

Kapl

an-

Mei

er a

naly

sis

of 9

0-da

y su

rviv

al s

how

ed th

at

com

bine

d “l

ow”

or “

high

” Pr

oADM

(cut

off

1.98

5 nm

ol/L

) at a

dmis

sion

and

dis

char

ge

esse

ntia

lly d

elin

eate

d th

ree

dist

inct

sur

viva

l cu

rves

, sug

gest

ing

addi

tiona

l inf

orm

ativ

enes

s fo

r ser

ial v

s. s

ingl

e Pr

oADM

mea

sure

men

ts

Hartm

ann,

20

12

[34]

Se

cond

ary

anal

ysis

of a

Pr

oHOS

P su

bstu

dy [3

0, 6

7]

1303

pat

ient

s w

ith a

cute

LRT

I (∼3

9%

with

COP

D as

com

orbi

dity

) with

ava

ilabl

e ba

selin

e Pr

oADM

val

ues

6

Swis

s ho

spita

ls

– In

nes

ted

time-

depe

nden

t Cox

regr

essi

on

anal

ysis

, whe

n co

mbi

ned

with

bas

elin

e Pr

oADM

val

ues,

eac

h of

inpa

tient

day

3,

5, o

r 7 m

easu

rem

ents

alo

ne, o

r the

thre

e m

easu

rem

ents

toge

ther

, add

ed s

igni

fican

t pr

edic

tive

valu

e: re

spec

tive

adde

d lik

elih

ood

ratio

χ2 : 1

4.3,

14.

8, 1

5.3,

17.

2, p

≤ 0.

0006

Al

bric

h, 2

013

[6

8]

Pros

pect

ive,

rand

omiz

ed,

cont

rolle

d in

terv

entio

nal t

rial

31

3 pa

tient

s w

ith m

ixed

LRT

I, ap

prox

imat

ely

14%

with

AEC

OPD

3

Swis

s ce

nter

s

– Ov

eral

l and

in te

sted

sub

grou

ps, s

ite-o

f-tre

atm

ent a

ssig

nmen

t gui

ded

by d

ynam

ic

inte

rpro

fess

iona

l ris

k as

sess

men

t inc

ludi

ng

ProA

DM d

ata

cons

iste

ntly

redu

ced

hosp

ital

leng

th-o

f-sta

y rel

ativ

e to

the

sam

e as

sess

men

t w

ithou

t Pro

ADM

dat

a

(Tab

le 1

Cont

inue

d)


via free access


Firs

t aut

hor,

year

Re

fere

nce

St

udy

type

(nam

e or

acr

onym

)

Patie

nts

Se

ttin

g

Key

findi

ngs

–

How

ever

, diff

eren

ces

did

not a

ttain

si

gnifi

canc

e

Möc

kel,

2013

[6

9]

Suba

naly

sis

of m

ultic

ente

r, m

ultin

atio

nal,

pros

pect

ive,

ob

serv

atio

nal c

ohor

t stu

dy

(BAC

H), c

ompa

ring

actu

al

site

-of-c

are

assi

gnm

ent w

ith a

hy

poth

etic

al a

ssig

nmen

t bas

ed

sole

ly o

n Pr

oADM

cuto

ffs

83

1 US

pat

ient

s an

d 72

6 Eu

rope

an p

atie

nts

pres

entin

g to

ED

with

acu

te d

yspn

ea,

appr

oxim

atel

y 11

% d

ue to

COP

D

8

US ce

nter

s,

6 Eu

rope

an

cent

ers

–

Usin

g Pr

oADM

cuto

ffs w

ould

hav

e in

crea

sed

num

ber o

f dis

char

ged

patie

nts

by 1

6.7%

in

the

US (3

84 v

s. 3

29) a

nd 1

5.9%

in E

urop

e (2

19 v

s. 1

89)

– Ov

eral

l, th

e th

eore

tical

NRI

was

12.

0% (9

5%

CI 5

.7%

–18.

4%) f

or th

e US

and

16%

(95%

CI

8.2%

–23.

9%) f

or E

urop

e

–

9.7%

(81/

831)

of U

S pa

tient

s an

d 9.

9%

(72/

726)

of E

urop

ean

patie

nts

wou

ld h

ave

chan

ged

site

of c

are

6MW

D, 6

-min

wal

k di

stan

ce; 6

MW

T, 6

-min

wal

k te

st; A

ECOP

D, a

cute

exa

cerb

atio

ns o

f chr

onic

obs

truct

ive

pulm

onar

y di

seas

e; A

UC, a

rea

unde

r the

rece

iver

ope

ratin

g ch

arac

teris

tics

curv

e;

BACH

, Bio

mar

kers

in A

cute

Hea

rt Fa

ilure

Stu

dy; B

NP, B

-type

nat

riure

tic p

eptid

e; B

OD, b

ody

mas

s in

dex,

obs

truct

ion,

dys

pnea

, ris

k pr

edic

tion

scor

e; B

ODE,

bod

y m

ass

inde

x, o

bstru

ctio

n,

dysp

nea,

exe

rcis

e ris

k pr

edic

tion

scor

e; C

AP, c

omm

unity

-acq

uire

d pn

eum

onia

; CI,

conf

iden

ce in

terv

al; C

OMIC

, Coh

ort o

f Mor

talit

y an

d In

flam

mat

ion

in C

OPD;

COP

D, ch

roni

c obs

truct

ive

pulm

o-na

ry d

isea

se; C

PET,

card

iopu

lmon

ary

exer

cise

test

ing;

ED,

em

erge

ncy

depa

rtmen

t; FE

V 1, fix

ed e

xpira

tory

vol

ume

in 1

s; G

OLD,

Glo

bal I

nitia

tive

for O

bstru

ctiv

e Lu

ng D

isea

se; H

R, h

azar

d ra

tio;

LRTI

, low

er re

spira

tory

trac

t inf

ectio

n; m

MRC

, mod

ified

Med

ical

Res

earc

h Co

unci

l (dy

spne

a qu

estio

nnai

re);

ProA

DM, p

road

reno

med

ullin

; PRO

MIS

E-CO

PD, P

redi

ctin

g Ou

tcom

e Us

ing

Syst

emic

M

arke

rs in

Sev

ere

Exac

erba

tions

of C

hron

ic O

bstru

ctiv

e Pu

lmon

ary

Dise

ase

Stud

y.

(Tab

le 1

Cont

inue

d)

median ProADM levels (Table 2). This observation aligned with findings in another study [72] that in 85 patients with lung diseases (46% of whom had COPD), ProADM values did not differ between patients with FEV1 /forced vital capac-ity < 0.7 vs. ≥ 0.7: median [IQR] concentration 0.62 [0.46–0.78] nmol/L (n = 40) vs. 0.54 [0.47–0.75] nmol/L (n = 45), p = 0.38.

Also in the Basel study [39], ProADM values did not dis-tinguish among Anthonisen exacerbation types (Table 2).

Fifth, ProADM levels are significantly higher in non-survivors than in survivors (Table 2). This finding pertained to all-cause death, the mortality end point in all published studies, and was consistent across follow-up periods, including the hospital stay [39], 1 year [42], 2 years [39, 42], respective medians of 29 and 35 months post-stable-state measurement and post-exacerbated-state measurement [40], and 5–7 years [41].

Sixth, as a single factor, ProADM is a statistically signif-icant, independent, and accurate long-term all-cause mor-tality predictor in patients with COPD. In the Basel study [73], multivariate Cox regression showed ProADM above the study exacerbated-state median, 0.84 nmol/L, to be the only independent 2-year mortality predictor, with a 2.368 (1.167–4.803) hazard ratio (HR) (95% confidence interval, CI), p = 0.017. None of the other 11 clinical, spirometric, or laboratory variables considered in the multivariate model was independently predictive; these variables included

– Two of four components of the body mass, airflow obstruction, dyspnea, and exercise capacity index (BODE) [2], a frequently used multidimensional assessment tool: body mass index (BMI) and fixed expiratory volume in 1-s percentage of the predicted value (FEV1% predicted)

– Four other blood biomarkers: white blood cell count (WBC), C-reactive protein (CRP), procalcitonin (PCT), and pro-endothelin 1 (ProET-1)

– Age-adjusted Charlson comorbidity score [74] – Age – Partial pressure of oxygen in arterial blood (PaO2) or

partial pressure of carbon dioxide in arterial blood – Presence of pulmonary arterial hypertension (PAH)

(n = 123)

Additionally, the Enschede investigators found that after adjustment for age, sex, BMI, and GOLD grade, stable-state ProADM elevated above their 0.71-nmol/L study median remained associated with long-term mortality: corrected HR (95% CI) 2.98 (1.51–5.90).

Further, in PROMISE-COPD, multivariate Cox regres-sion modeling involving ProADM together with the four BODE components, BMI, FEV1% predicted, modified Medical Research Council (mMRC) dyspnea score, and


via free access


Table 2 ProADM in risk stratification of COPD patients: key values.

Group/subgroup Values, nmol/L

Healthy adult volunteers Manual luminoimmunoassay [10] (n = 264; n = 117 men and 147 women) Mean ± SD 0.33 ± 0.07a

Minimum–maximum 0.10–0.64a

99th percentile 0.52a

Automated homogeneous sandwich fluoroimmunoassay [70] (n = 144) Mean ± SD 0.37 ± 0.09b

Minimum–maximum 0.10–0.72b

99th percentile 0.60b

Community-based sample of adults [71] (n = 2532; men and 2448 women) Median [IQR] 0.44 [0.38–0.52]a

Subgroup without apparent cardiovascular disease or risk factors, 50th–99th percentile

Men (n = 374) 0.41–0.67a

Women (n = 557) 0.41–0.68a

GOLD stage (during severe exacerbation), median [IQR] (Basel study) [39] I 0.76 [0.55–0.98]a

II 0.97 [0.65–1.22]a

III 0.85 [0.59–1.33]a

IV 0.74 [0.51–1.14]a

COPD patients with severe exacerbation Basel study [39], median [IQR] (n = 167) 0.84 [0.59–1.22]a

Enschede [40], median (n = 181) 0.79b,c

ProHOSP [41], median [IQR] Overall (n = 469) 1.09 [0.74–1.58]a

Pneumonic exacerbation (n = 252) 1.27 [0.86–2.06]a

Non-pneumonic exacerbation (n = 217) 0.90 [0.65–1.31]a

Potocki study [20], patients presenting with acute dyspnea due to severe COPD exacerbation (n = 57), median [IQR]

0.8 [0.6–1.1]a

COPD patients after recovery from exacerbation, median [IQR] Basel study (n = 156) 0.72 [0.55–0.98]a

COPD patients in stable state Basel study [39], median [IQR] (n = 144) 0.66 [0.49–0.95]a

Enschede study [40], median (n = 181) 0.71b,c

PROMISE-COPD [42], median [IQR] (n = 549) 0.60 [0.48–0.79]b

Patients with FEV1/FVC < 0.7 Maeder et al. [72], median [IQR] 0.62 [0.46–0.78]a

Non-survivors vs. survivors In-hospital, median [IQR] Basel study [39] (n = 5 vs. n = 162) 1.07 [0.95–2.80] vs. 0.82 [0.58–1.22], p = 0.049a

2 Years, median [IQR] Severely exacerbated (Basel study) [39] (n = 37 vs. n = 130) 1.14 [0.80–1.56] vs. 0.76 [0.55–1.05], p < 0.0001a

Stable state (PROMISE-COPD) [42] (n = 43 vs. n = 506) 0.78 [0.51–1.20] vs. 0.59 [0.48–0.78], p = 0.006b

2–3 Years,c mean ± SD [40] (n = 78 vs. n = 103) Stable state 0.94 ± 0.41 vs. 0.72 ± 0.25, p < 0.001b

Severely exacerbated 1.08 ± 0.50 vs. 0.78 ± 0.27, p < 0.001b

5–7 Years, median [IQR] [41] (n = 213 vs. n = 256) Initial 1.25 [0.85–1.87] vs. 0.92 [0.68–1.34], p < 0.001a

Discharge 0.97 [0.71–1.34] vs. 0.71 [0.59–0.93], p < 0.001a

Suggested cutoffs “CURB65-A” [32] Intermediate-risk vs. low-risk category ≥ 0.75a

High-risk vs. intermediate-risk category ≥ 1.5a


via free access


Group/subgroup Values, nmol/L

“High ProADM” Patients with mixed LRTI including COPD (n = 1303) [34] ≥ 1.5a

Patients with acute dyspnea due to COPD or other causes (n = 981) [23] ≥ 1.985b

COPD, chronic obstructive pulmonary disease; CURB65-A, new-onset confusion, urea > 7 mmol/L, respiratory rate ≥ 30 breaths per minute, systolic or diastolic blood pressure < 90 or ≤ 60 mmHg, respectively, age ≥ 65 years (pneumonia/LRTI risk scoring system), ProADM value; FEV1, fixed expiratory volume in 1 s; FVC, forced vital capacity; IQR, interquartile range (25th–75th percentiles); ProADM, proadrenomedul-lin; PROMISE-COPD, Predicting Outcome Using Systemic Markers in Severe Exacerbations of Chronic Obstructive Pulmonary Disease Study; SD, standard deviation. aMeasured using a manual luminoimmunoassay. bMeasured using an automated homogeneous sandwich fluoroim-munoassay, which had a Spearman correlation coefficient of r = 0.97 with the manual luminoassay [70]. cIn the Enschede study [40], median follow-up was 29 months post-stable-state measurement and 35 months post-exacerbated-state measurement, respectively. dValues at discharge from index hospitalization.

(Table 2 Continued)

6MWD, showed ProADM to be one of three significant independent 2-year all-cause mortality predictors: HR (95% CI) per one-quartile concentration change 1.77 (1.30–2.42), p < 0.001; the other two independent predictors were BMI and 6MWD. Based on the likelihood ratio χ2 (13.0 vs. 8.5 and 7.5, respectively), ProADM was the strongest of these three prognostic factors.

Studies of potential adverse outcome predictors typically measure predictive accuracy using one or more among a number of variables. These variables, described in Appendix 1, include sensitivity, specificity, positive

predictive value, negative predictive value, area under the receiver operating characteristics curve (AUC of the ROC curve), or its equivalent, the c index or c statistic, or net reclassification improvement (NRI) or the integrated dis-crimination improvement index (IDI) [75]. Findings for ProADM as a single factor to foretell non-survival are sum-marized in Table 3; observational data thus far suggest that this blood biomarker alone has moderate mortality prediction accuracy, even beyond a half decade of follow-up. Unsurprisingly, ProADM mortality prediction accu-racy tends to decrease as follow-up time increases.

Table 3 ProADM as a single variable in long-term all-cause mortality prediction in patients with COPD.

Study

Follow-up period

1 Year 2 Years 2–3 Yearsa 5–7 Years

Enschede [40] (n = 181) Death rate, % Stable 10.0 22.7 43.1 AECOPD 6.5 19.8 Survivors/non-survivors Stable-state measurement 18/180 40/176 78/181 AECOPD measurement 11/170 32/162 AUC (95% CI) or c statistic, mortality prediction Stable-state measurement 0.83 (0.74–0.92) 0.76 AECOPD measurement 0.78 (0.66–0.91) 0.74 PROMISE-COPD [42] (n = 549) Death rate, % 4.7 7.8 Non-survivors/survivors 26/549 43/549 c statistic (stable-state measurement) 0.691 0.635 ProHOSP (n = 469) [41] Death rate, % 16% 35% 55% AUC (95% CI) value at recovery from AECOPD 0.709 (0.644–0.775) 0.663 (0.611–0.716) 0.685 (0.637–0.733)

Please refer to Appendix 1 for explanation of AUC and c statistic. AECOPD, acute exacerbation of chronic obstructive pulmonary disease; CI, confidence interval; ProADM, proadrenomedullin; PROMISE-COPD, Predicting Outcome Using Systemic Markers in Severe Exacerbations of Chronic Obstructive Pulmonary Disease Study. aMedian follow-up in the Enschede study was 35 months after the AECOPD measurement and 29 months after the stable-state measurement.


via free access


Seventh, combined with different groups of demo-graphic/clinical variables, ProADM provides significant incremental mortality prediction power (Table 4). In PROMISE-COPD, combining stable-state ProADM con-centration with BODE significantly improved predictive power for 1- or 2-year all-cause mortality compared with using BODE alone [42]. In the ProHOSP study’s COPD sub-group, adding ProADM levels at discharge after severe exacerbation to a demographic/clinical model provided significant incremental predictive power for 1-, 3-, and 5- to 7-year all-cause mortality [41]. The model included age, smoking status, BMI, New York Heart Association (NYHA) dyspnea class, presence/absence of important comorbidi-ties such as chronic renal failure, cancer, coronary artery disease, diabetes mellitus, or chronic heart failure, and exacerbation type (pneumonic vs. non-pneumonic).

Eighth, there are suggestions that ProADM may have a role in predicting adverse outcomes besides death in COPD patients. In the Basel study [39], ProADM levels sig-nificantly correlated with hospital length of stay (r = 0.274, p < 0.0001) (n = 167) and trended toward correlation with intensive care unit (ICU) length of stay (r = 0.151, p = 0.051) (n = 16). Additionally, median [IQR] ProADM levels were higher in patients needing vs. not needing ICU admission, a difference approaching significance: 1.23 [0.61–2.14] vs. 0.83 [0.59–1.14] nmol/L, p = 0.057.

Lastly, and unsurprisingly, based on literature regard-ing ADM or ProADM in cardiovascular and malignant disease [48, 76–80], ProADM levels appear to be associ-ated with certain comorbidities in COPD patients. In the Basel study [39], median values were significantly higher in patients with vs. without cancer: 1.225 [0.718–1.770] nmol/L (n = 24) vs. 0.810 [0.571–1.100] nmol/L (n = 143), p = 0.008. Median values also were significantly elevated in those with PAH vs. with normal pulmonary pressures: 1.06 [0.76–1.63] nmol/L (n = 28) vs. 0.82 [0.61–1.23] nmol/L (n = 95), p = 0.031. ProADM concentrations correlated sig-nificantly with left ventricular ejection fraction (r = –0.222, p = 0.014), but did not differentiate between the subgroups below vs. at or above a cutoff of < 40% for this variable. The Enschede investigators reported correlation between ProADM levels and heart failure or myocardial infarction in their COPD patients [40].

ProADM in the risk assessment of patients with other pulmonary diseases/disordersAs noted earlier, there also exists a substantial observa-tional literature regarding ProADM use as an all-cause

Table 4 ProADM adds significant incremental prognostic power to demographic/clinical variables in long-term all-cause mortality predic-tion in COPD patients.

Predictive factorStudy name

c statistic or AUC (95% CI) by follow-up

1 Year 2 Years 3 Years 6 Years

PROMISE-COPD [42] ProADM alonea 0.691 0.635 BODE alone 0.745 0.679 ProADM+BODE 0.818 0.750 p, ProADM+BODE vs. BODE alone < 0.001 < 0.001 NRI (95% CI), ProADM+BODE vs. BODE

alone, p 25.1% (2.0%–

48.2%), 0.003 12.3% (–4.1%–

28.8%), 0.14

ProHOSP [41] ProADM aloneb 0.709 (0.644–0.775) 0.663 (0.611–0.716) 0.685 (0.637–0.733) Demographic/clinical modelc 0.731 (0.676–0.786) 0.673 (0.623–0.722) 0.727 (0.681–0.772) ProADM+demographic/clinical modelc 0.771 (0.715–0.827) 0.709 (0.660–0.758) 0.745 (0.701–0.789) p, ProADM+demographic/clinical

modelc vs. modelc alone 0.032 0.022 0.043

p-Values in boldface are statistically significant at p < 0.05. Please refer to Appendix 1 for explanation of c statistic, AUC, and NRI. AUC, area under the receiver operating characteristics curve; BODE, body mass, airflow obstruction, dyspnea, and exercise capacity index; CI, con-fidence interval; NRI, net reclassification improvement; NYHA, New York Heart Association; ProADM, proadrenomedullin; PROMISE-COPD, Predicting Outcome Using Systemic Markers in Severe Exacerbations of Chronic Obstructive Pulmonary Disease Study. aProADM measured in COPD stable state. bProADM measured at discharge from hospitalization for severe (pneumonic or non-pneumonic) COPD exacerbation. cDemographic/clinical model included age, smoking status, BMI ( ≤ 21 vs. > 21 kg/m2), NYHA dyspnea class (I vs. II vs. III vs. IV), presence or absence of individual comorbidities (chronic renal failure, neoplastic disease, coronary artery disease, diabetes mellitus, or chronic heart failure), and exacerbation type (pneumonic vs. non-pneumonic).


via free access


mortality predictor in patients with a variety of underly-ing diseases presenting to the ED with acute dyspnea [20–26] and, especially, in patients with CAP [13, 27–38]; key studies are summarized in Table 1. Many of the dyspnea and CAP studies [20–23, 25–27, 29–31] had cohorts com-prising > 25% of patients with COPD as a cormorbidity. Collectively, the published studies in these settings have had more ethnically and, particularly, geographically diverse samples than did the published ProADM studies focusing on COPD; whereas the latter have taken place overwhelmingly in Caucasian patients and exclusively in Europe, the dyspnea and CAP investigation has in aggre-gate included more non-white individuals, and in some cases, occurred partly or entirely in North America [21, 23, 24, 28], Asia [25], or Oceania [21, 23]. Vital status follow-up durations in these studies ranged from the hospital stay to 4 years. As with the COPD-focused studies, the CAP- and dyspnea-focused body of work has, albeit with limited exceptions [22, 36], shown ProADM to be a strong, independent mortality predictor in multivariate analysis. Where combining ProADM with clinical/demographic/other laboratory variables has been studied in dyspnea or CAP [23, 24, 32, 35], the biomarker has provided sig-nificant incremental prognostic power. Most published ProADM studies in dyspnea or CAP evaluated predic-tive accuracy of single measurements of this analyte; however, the two published studies to address the issue [23, 34] suggested that serial ProADM measurement may add predictive information.

ProADM and cardiopulmonary reserve/exercise capacity/physical activityThree papers from PROMISE-COPD [42–44] and one from a single-center study at University Hospital Basel [45] (summarized in Tables 1 and 5) collectively show strong links between ProADM and cardiopulmonary reserve/exercise capacity/physical activity in patients with COPD. This work also suggests that this analyte may be a global cardiopulmonary stress marker that is able to supplement or even substitute for CPET in assessing this health dimen-sion. In the COPD setting, replacing the 6MWT with a simple and almost universally applicable blood test could have two main benefits. First, doing so could increase the availability of exercise capacity-related data, which is hampered by both resource constraints and patient limi-tations. The resource constraints, particularly relevant to primary care, stem from the relatively complex, time-consuming nature of, e.g., the 6MWT, as well as that test’s requirements for a 30-m track, an examiner certified in cardiopulmonary resuscitation with at least one American Health Association-approved course in basic life support, and supplies and facilities for rapid medical emergency response [42, 81]. The patient limitations, which affect an appreciable portion of the population with more advanced COPD, include frailty, peripheral arterial disease, or mus-culoskeletal or neuromuscular impairments. Notably,

Table 5 ProADM as a global cardiopulmonary stress marker in 162 patients with pulmonary or cardiac conditions including COPD (n = 39, 24.1%) undergoing CPET [45]: correlation of resting ProADM concentration with key CPET variables.

Variable Description of variable Pearson correlation coefficient

p-Value

Peak oxygen consumption –0.57 < 0.001Peak oxygen pulse Absolute oxygen consumption/heart rate at peak exercise, a

composite measure of stroke volume and peripheral oxygen extraction that provides information on cardiac and muscular function

–0.20 0.01

Percent predicted heart rate –0.32 < 0.001Minute ventilation/carbon dioxide production at peak exercise

Measure of ventilatory efficiency indicating disease severity in patients with both pulmonary and cardiac disease

0.36 < 0.001

Breathing reserve Estimated maximum voluntary ventilation minus minute ventilation at peak exercise: a measure of ventilatory capacity; lower values indicate more severe ventilatory limitation

–0.17 0.04

Physiological dead space/tidal volume at peak exercise

Marker of dead space ventilation and thus ventilatory inefficiency; higher values are more abnormal

0.35 < 0.001

Alveolo-arterial oxygen gradient at peak exercise Measure of diffusion capacity based on blood gas results 0.24 0.003

Patients underwent symptom-limited upright cycle exercise tests using ramp protocols with continuous monitoring of the electrocardio-gram and non-invasive blood pressure measurement every second minute. Peak exercise was defined as peak ventilation-to-carbon dioxide production ratio.


via free access


even though these latter impairments were among the PROMISE-COPD exclusion criteria, 8% (51/638) of that study’s sample had unavailable 6MWD. Interestingly, absence of 6MWD data was associated with a statistically significant almost tripling of the 2-year death rate relative to that of patients with such data (n = 549): 21.6% vs. 7.8%, p = 0.003 [42].

A second potential benefit of replacing the 6MWT with a blood biomarker test might be cost savings: the cost of the former was estimated to approach 40 euros in Switzerland, whereas the cost of a ProADM measurement was estimated at 15–20 euros [42]. Importantly, however, any benefits of using a “ProADM instead of 6MWT or other CPET” strategy must be verified through prospective, ran-domized, controlled interventional study.

The apparent strong links between ProADM and car-diopulmonary stress/exercise capacity in patients with COPD involve walking distance, exertional hypoxia, and CPET variables. For example, a PROMISE-COPD substudy in. 105 patients with stable COPD from Uni-versity Hospital Basel examined the association with ProADM levels of daily walking activity measured over 6 consecutive days by accelometry [43]. In two separate stepwise multivariate regression analyses, each includ-ing one accelometry variable plus patient age, age-adjusted Charlson comorbidity score, mMRC dyspnea score, and St. George’s Respiratory Questionnaire score, total walking minutes/day or steps walked/day were the sole factors independently associated with ProADM concentration (regression coefficient –0.0004, 95% CI –0.0007 to –0.0002; regression coefficient –0.0004, 95% CI –0.0006 to –0.0001; both p < 0.0001). However, fast walk (min/day walking 5 km/h or 81–115 m/min) was not significantly associated with that dependent variable.

More notably, in 549 PROMISE-COPD participants with available ProADM and BODE data, Cox regression modeling demonstrated that ProADM plus “BOD”, i.e., the non-6MWD BODE components, namely, BMI, FEV1% predicted, and mMRC dyspnea score, had higher prog-nostic accuracy for 1- or 2-year all-cause mortality than did BODE: c statistics 0.800 vs. 0.745 for 1-year mortal-ity, 0.738 vs. 0.679 for 2-year mortality [42]. These obser-vations held up in post hoc sensitivity analyses adding in the 45 patients with ProADM and only “BOD” data, whether using Hotdeck imputation in this subgroup or assigning those patients the worst possible BODE score, 3 points, for 6MWD. Compared with using “BOD” alone, combining ProADM with “BOD” achieved an NRI of 41.2% (95% CI 15.6%–66.8%) of patients for 1-year mortality risk and of 8.8% (95% CI –10.6%–28.3%) for 2-year mortality

prediction. The NRI for 1-year mortality was significant (p = 0.0016), although that for 2-year mortality was not (p = 0.37).

Additionally, a multivariable linear logistic regres-sion analysis of 1233 6MWTs performed over 2 years by 574 PROMISE-COPD participants with stable COPD found that ProADM as well as post-bronchodilator FEV1% predicted each independently foretold exertional hypoxemia: respectively, HR 6.85 (95% CI 2.91–16.08) per logarithmic change and HR 0.76 (95% CI 0.72–0.88) per 10% increase, both p < 0.001 [44]. Exertional hypoxemia was defined as nadir arterial oxygen saturation < 88% on continuous transcutaneous pulse oximetry. As with that of FEV1% predicted, the significant independent asso-ciation with exertional hypoxemia of ProADM persisted even after inclusion into multivariate modeling of the annual COPD exacerbation rate plus the age-adjusted Charlson comorbidity score, or of any or all of conges-tive heart failure, coronary artery disease, or history of acute myocardial infarction. Moreover, adding ProADM to FEV1% predicted to foretell exertional desaturation provided a significant NRI of 7.4% (95% CI 1.3%–13.6%) (p = 0.0184) of patients relative to using the spirometric variable alone.

Another study from University Hospital Basel [45] found pre-exercise ProADM to be significantly associated with impaired peak oxygen consumption, defined as such consumption < 14 mL/min/kg – including after multivari-able adjustment for factors including age, BMI, and FEV1 (Table 5). Additionally, ProADM was consistently signifi-cantly associated with other variables reflecting impaired cardiac output reserve, ventilatory efficiency, and diffu-sion capacity (Table 5), and hence, global cardiopulmo-nary stress.

The investigators found these associations to be at least as strong as those of at-rest B-type natriuretic peptide (BNP), an established cardiac stress blood biomarker that they also measured. According to the authors, these observations suggested that ProADM was a more universal cardiopulmonary stress marker than was the more “cardiac-specific” BNP. Notably, unlike BNP, ProADM did not rise significantly from before to imme-diately after a maximal exercise test, which the authors noted “may be an advantage of [ProADM] as a robust clinical marker”. The study involved 162 consecutive eligible patients (mean age 56 ± 16 years, 58.6% [95/162] male) with a gamut of cardiac and pulmonary conditions who underwent CPET to assess subjective chronic exer-cise intolerance. Histories including COPD, other lung conditions, or cardiac disease respectively were present in 39 (24.1%), 70 (43.2%), and 51 (31.5%) patients. The


via free access


CPET comprised symptom-limited upright cycle exercise tests employing ramp protocols, with continuous electro-cardiogram monitoring and non-invasive blood pressure measurement every second minute. Blood biomarkers were quantified in samples obtained at rest and 1 min after peak exercise, defined as peak minute ventilation/carbon dioxide output ratio [45].

Clinical considerationsConfounding factors do not seem to be a major issue with ProADM measurement. Some potential factors have been identified in healthy volunteer, community-based, or general population studies [10, 58, 71, 82, 83], or COPD risk stratification studies, perhaps most notably age [10, 71, 83, 84], which correlates positively with this analyte. However, likely due to disparate study sample character-istics (e.g., age, comorbidity profile) [71], the literature conflicts regarding whether some factors, e.g., gender [58, 71, 83, 84], indeed are confounders. Moreover, multivari-ate analyses in some of the COPD-focused studies [39–42] have suggested that many of the potential confounders, namely, age, sex, age-adjusted Charlton comorbidity score, BMI, PaO2, mMRC dyspnea score, and levels of PCT, CRP, or ProET-1, indeed do not affect ProADM’s mortality prediction ability in that setting. Additionally, exclusion of patients with known cardiovascular disease or risk factors from a large population-based sample seemed not to markedly alter ProADM values (Table 2) [71]. Lastly, neither circadian variation nor prandial status appears to affect ProADM measurements [10].

The consensus favoring multidimensional assessment of COPD [1, 2, 85–87], experience with blood biomarkers and risk scoring systems in other pulmonary disease set-tings [32, 88, 89], and literature focused on ProADM in COPD [42, 90] appear to support three concepts in clini-cal application of ProADM (Supplemental Data, Panel 2). First, the use of this analyte in conjunction with a limited number of demographic and clinical, and possibly other laboratory, variables may be a practical and effective approach. This approach conforms to the widely accepted adage that biomarker values should be interpreted within the comprehensive context of each particular case rather than in isolation [91].

Second, to increase ease of application and mitigate any effects of confounding factors, risk scoring systems incorporating ProADM should use a very small number of relatively widely spaced cutoff values for this blood bio-marker. An example of ProADM use in a multidimensional

framework are Stolz et al.’s proposal of “BODE-A” or “BOD-A” risk scoring systems. These classification methods combine ProADM, BMI, FEV1% predicted, and mMRC dyspnea score, respectively with or without 6MWD [42]. In another example, the ProHOSP investigators evaluated a model combining ProADM with patient age, smoking status, BMI, NYHA dyspnea class, exacerbation type (pneumonic vs. non-pneumonic), and comorbidities (chronic renal failure, neoplastic disease, coronary artery disease, chronic heart failure, and diabetes mellitus) [41]. An embodiment of applying few, widely spaced ProADM cutoffs is the ProHOSP investigators’ proposed CURB65-A score for patients with LRTIs including COPD exacerba-tion [32] (Table 2). The CURB65-A score combines the five CURB65 criteria, developed for pneumonia severity/risk classification [92], with two ProADM cutoffs, 0.75 and 1.5 nmol/L, to create three risk categories, low, intermedi-ate, and high. The CURB65 criteria comprise new onset confusion, urea > 7 mmol/L, respiratory rate ≥ 30 breaths per minute, systolic or diastolic blood pressure < 90 or ≤ 60 mmHg, respectively, and age ≥ 65 years. Tripartite risk stratification is desirable if a biomarker or score seeks to identify both patients who should receive less intense intervention(s) and those who should receive more intense intervention(s) [23] rather than only one of these subgroups.

Third, as should be the case with cutoffs for all mul-tidimensional scoring system components [89], ProADM cutoffs should be calibrated to local conditions, e.g., EDs treating large numbers of COPD patients for acute dyspnea or pneumonic exacerbations probably need higher cutoffs than would outpatient clinics primarily seeing patients in the COPD stable state.

Future research directionsFuture research on ProADM in risk stratification of patients with COPD should take a number of different directions (Supplemental Data, Panel 3). First, additional obser-vational data should be gathered regarding non-white patients and never-smokers, two groups mostly absent from studies published to date focusing on ProADM in COPD [39–42].

Second, observational studies should further assess whether serial ProADM measurements offer additional prognostic power. Two published studies in the acute LRTI [34] or acute dyspnea [23] settings have suggested informativeness of moves between “low” and “high” ProADM categories every 2–4 days [34] or from


via free access


admission to discharge (median [IQR] interval: 7 [3–12] days) [23]. These studies each had approximately 30% or more patients with COPD as a comorbidity and used respective cutoffs of 1.5 [34] or 1.985 nmol/L [23] for the “high” ProADM classification (Table 2). Besides further evaluating this serial measurement strategy, future analyses should assess the incremental prognostic value of absolute or relative, i.e., percentage ProADM changes.

Third, future studies should compare the mortality prediction accuracy of ProADM vs. other blood biomark-ers as single factors or in various combinations. Published multivariate or ROC curve analyses have suggested that in the COPD setting, ProADM may provide superior dis-crimination of non-survival to that of CRP [39, 41], WBC [39, 41], ProET-1 [39], PCT [39–42], copeptin [42], or pro-atrial natriuretic peptide [42] as single factors. Addition-ally, PROMISE-COPD noted that combinations of ProADM and one or more of the latter three other analytes offered no incremental accuracy over that of ProADM alone [42]. However, these comparisons have been relatively few and studies have not always reported statistical significance data. Further, no comparisons yet have been published of ProADM vs. fibrinogen, interleukins 6 or 8, surfactant protein D, or neutrophil count, blood biomarkers that also have shown accuracy in long-term mortality predic-tion in patients with COPD [8, 87, 93].

Fourth, interventional studies should be undertaken to demonstrate whether ProADM use in risk-based guid-ance of monitoring/treatment decisions improves clini-cal, quality-of-life, or pharmacoeconomic outcomes in patients with COPD. Published research on such topics [68, 69] has been very preliminary. OPTIMA II, a pro-spective, multicenter, randomized, controlled proof-of-concept study [68] compared hospital length-of-stay in patients with acute LRTIs assigned 1:1 to interprofessional medical and nursing risk assessment with vs. without use of ProADM data. The study involved 313 enrollees, of whom 43 (13.7%) had COPD exacerbation. These patients were treated at any of one acute-care hospital or two post-acute-care centers in Switzerland. The interprofessional risk assessment included CURB65 scoring at admission, medical stability evaluation during hospitalization, and functional and biopsychosocial assessment, respec-tively using the Self-Care Index and Post-Acute Care Dis-charge Score at both times. In the ProADM arm, ProADM levels of 0.75 and 1.5 nmol/L helped delimit “low-risk”, “intermediate-risk”, and “high-risk” categories, which in the control arm were determined only by the inter-professional assessment. In both study arms, patient site-of-care assignments were based on an algorithm

incorporating the risk category at the given assessment time. However, for any individual case, treating physi-cians could override algorithm recommendations for a variety of pre-specified medical, psychosocial, or admin-istrative/logistical reasons.

OPTIMA II found that the ProADM group had a shorter mean length of stay did the controls, 6.3 (95% CI 5.4–7.2) days vs. 6.8 (95% CI 5.7–7.9) days. The difference favoring the ProADM group held true after adjustment for age, gender, LRTI type, and CURB65 score. Moreover, the differences consistently favored the ProADM arm in sub-group analyses, including those involving inpatients with a ≥ 1-day stay, men and women, patients with or without CAP, older or younger patients, patients with greater or lesser comorbidity burden, or CURB65 classes I, II, or III. However, in no case did the difference attain statisti-cal significance; the authors attributed this observation to the relatively small sample size, the great influence of logistical/administrative factors on site-of-care deci-sions, and the fact that the study centers had for years prior to OPTIMA II strongly emphasized minimizing length of stay [94].

The Biomarkers in Acute Heart Failure study (BACH) investigators conducted separate subanalyses of their American patients (n = 831) and European patients (n = 726) with acute dyspnea – stemming from COPD in just over 11% of each subgroup – comparing the actual distribution of site-of-care assignments vs. a hypotheti-cal distribution guided by ProADM values [69]. This analysis should be regarded as hypothesis-generating because it used ProADM values in isolation. For the US analysis, sites of care were divided into four levels, dis-charge, general ward, cardiac care or monitoring unit, and ICU, whereas for the European analysis, sites of care were divided into three levels because the cardiac care unit and ICU were considered as a single level. For both analyses, baseline ProADM ≥ 5.0 nmol/L would have led to the site of care being stepped up by one level. Values ≤ 0.50 nmol/L would have led to the site of care being stepped down by one level, except that in the European analysis, stepping down from the cardiac care unit/ICU to the general ward could occur if the ProADM was < 1.0 nmol/L. The investigators found that using such ProADM-guided assignment theoretically would have increased the number of discharged patients by 16.7% in the US (384 vs. 329) and 15.9% in Europe (219 vs. 189); notably, the very small number of discharged 90-day decedents (4 for both analyses) would not have increased. Overall, the theoretical NRI was 12.0% (95% CI 5.7%–18.4%) for the US and 16% (95% CI 8.2%–23.9%) for Europe, and 9.7% (81/831) of US patients and 9.9%


via free access


(72/726) of European patients would have changed site of care.

ConclusionsObservational studies to date show that plasma ProADM, the stable surrogate blood biomarker for the pluripotent regulatory peptide, ADM, is, as a single factor, a strong independent predictor of in-hospital to long-term all-cause mortality in patients with COPD. Additionally, the litera-ture suggests that when combined with demographic and clinical factors, this analyte provides significant incre-mental prognostic accuracy regarding this end point. This ability may derive from ProADM being a multidimensional marker of cardiopulmonary stress, exercise capacity, and physical activity. ProADM appears to merit further clinical investigation in COPD. This research should take the form of observational studies examining the analyte’s mortal-ity prediction power in never-smokers and non-white patients, its ability to foretell adverse outcomes other than mortality, and its comparison and combination with other biomarkers such as fibrinogen and interleukins 6 and 8, which have shown survival prediction accuracy. In addi-tion, interventional trials should test whether ProADM can improve clinical outcomes by helping guide interven-tion and monitoring and can save costs by substituting for the 6MWT and other CPET in patients with COPD.

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.Financial support: P.S. and B.M. have received support to fulfill speaking engagements and travel to medical meet-ings from Thermo Scientific Biomarkers, Hennigsdorf, Germany, the developer/manufacturer of the ProADM assay; Thermo Scientific Biomarkers also has provided monetary support and reagents to the Kantonsspital Aarau research fund. R.J.M. received support to fulfill speaking engagements and travel to medical meetings from Thermo Scientific Biomarkers.Employment or leadership: B.M. has served as a consultant to Thermo Scientific Biomarkers. R.J.M. has served/is serving as a paid consultant to Thermo Scientific Biomarkers and holds shares in Thermo Fisher Scientific, Inc., the publicly traded parent company of Thermo Scientific Biomarkers.Honorarium: None declared.Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

Appendix 1

Common measures of discriminative ( predictive) performance in risk stratification studies

Sensitivity: Number of patients that a potential predictor forecasts will have an adverse outcome/number patients actually having the outcome.

Specificity: Number of patients that a potential predictor forecasts will not have an adverse outcome/patients actu-ally not having the outcome.

Positive predictive value: Number of patients that a poten-tial predictor correctly forecasts will have an outcome/number of patients that the potential predictor correctly or incorrectly forecasts will have the outcome.

Negative predictive value: Number of patients that a poten-tial predictor correctly forecasts will not have an outcome/number of patients that the potential predictor correctly or incorrectly forecasts will not have the outcome.

AUC of the ROC: A potential predictor’s probability of cor-rectly categorizing an individual regarding outcome, e.g., as a non-survivor. Higher AUC reflects greater accuracy: 0.5, the null value, indicates “coin-toss accuracy”; 1.0, the maximum value, indicates infallibility. AUC is deter-mined by plotting the potential predictor’s true-positive rate (sensitivity) against its false-positive rate (1–specific-ity). The c statistic or c index is equivalent to the AUC, but adapted for censored data.

NRI [75, 95]: Percentage of patients correctly moving to lower- or higher-risk categories minus the percentage incor-rectly changing risk categories, when a potential predictor vs. an existing predictor is used. IDI is a similar measure to NRI, except that probability is used rather than categories.

References1. Global Initiative for Chronic Obstructive Pulmonary. Global Strat-

egy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. 2014. Available at: http://www.goldcopd.org/guidelines-global-strategy-for-diagnosis-manage-ment.html. Accessed on July 18, 2014.

2. Celli BR, Cote CG, Marin JM, Casanova C, Montes de Oca M, Mendez RA, et al. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 2004;350:1005–12.


via free access

http://www.goldcopd.org/guidelines-global-strategy-for-diagnosis-management.html




3. Puhan MA, Garcia-Aymerich J, Frey M, ter Riet G, Anto JM, Agusti AG, et al. Expansion of the prognostic assessment of patients with chronic obstructive pulmonary disease: the updated BODE index and the ADO index. Lancet 2009;374: 704–11.

4. Steer J, Gibson J, Bourke SC. The DECAF Score: predicting hospi-tal mortality in exacerbations of chronic obstructive pulmonary disease. Thorax 2012;67:970–6.

5. Jones PW, Harding G, Berry P, Wiklund I, Chen WH, Kline Leidy N. Development and first validation of the COPD Assessment Test. Eur Respir J 2009;34:648–54.

6. Koutsokera A, Stolz D, Loukides S, Kostikas K. Systemic biomarkers in exacerbations of COPD: the evolving clinical challenge. Chest 2012;141:396–405.

7. Kostikas K, Bakakos P, Papiris S, Stolz D, Celli BR. Systemic bio-markers in the evaluation and management of COPD patients: are we getting closer to clinical application? Curr Drug Targets 2013;14:177–91.

8. Faner R, Tal-Singer R, Riley JH, Celli B, Vestbo J, Macnee W, et al. Lessons from ECLIPSE: a review of COPD biomarkers. Thorax 2014;69:666–72.

9. Casaburi R, Celli B, Crapo J, Criner G, Croxton T, Gaw A, et al. The COPD Biomarker Qualification Consortium (CBQC). COPD 2013;10:367–77.

10. Morgenthaler NG, Struck J, Alonso C, Bergmann A. Measurement of midregional proadrenomedullin in plasma with an immunolu-minometric assay. Clin Chem 2005;51:1823–9.

11. Christ-Crain M, Morgenthaler NG, Struck J, Harbarth S, Bergmann A, Muller B. Mid-regional pro-adrenomedullin as a prognostic marker in sepsis: an observational study. Crit Care 2005;9:R816–24.

12. Wang RL, Kang FX. Prediction about severity and outcome of sepsis by pro-atrial natriuretic peptide and pro-adrenomedullin. Chin J Traumatol 2010;13:152–7.

13. Suberviola B, Castellanos-Ortega A, Llorca J, Ortiz F, Iglesias D, Prieto B. Prognostic value of proadrenomedullin in severe sepsis and septic shock patients with community-acquired pneumonia. Swiss Med Wkly 2012;142:w13542.

14. Guignant C, Voirin N, Venet F, Poitevin F, Malcus C, Bohe J, et al. Assessment of pro-vasopressin and pro-adrenomedullin as pre-dictors of 28-day mortality in septic shock patients. Intensive Care Med 2009;35:1859–67.

15. Roch A. Increased levels of pro-AVP and pro-ADM in septic shock patients: what could it mean? Intensive Care Med 2009;35:1827–9.

16. Hagag AA, Elmahdy HS, Ezzat AA. Prognostic value of plasma pro-adrenomedullin and antithrombin levels in neonatal sepsis. Indian Pediatr 2011;48:471–3.

17. Oncel MY, Dilmen U, Erdeve O, Ozdemir R, Calisici E, Yurttutan S, et al. Proadrenomedullin as a prognostic marker in neonatal sepsis. Pediatr Res 2012;72:507–12.

18. Pezzilli R, Barassi A, Pigna A, Morselli-Labate AM, Imbrogno A, Fabbri D, et al. Time course of proadrenomedullin in the early phase of septic shock. A comparative study with other proin-flammatory proteins. Panminerva Med 2012;54:211–7.

19. Akpinar S, Rollas K, Alagoz A, Segmen F, Sipit T. Performance evaluation of MR-proadrenomedullin and other scoring systems in severe sepsis with pneumonia. J Thorac Dis 2014;6:921–9.

20. Potocki M, Breidthardt T, Reichlin T, Morgenthaler NG, Bergmann A, Noveanu M, et al. Midregional pro-adrenomedullin in addition to b-type natriuretic peptides in the risk stratifica-tion of patients with acute dyspnea: an observational study. Crit Care 2009;13:R122.

21. Maisel A, Mueller C, Nowak R, Peacock WF, Landsberg JW, Ponikowski P, et al. Mid-region pro-hormone markers for diagnosis and prognosis in acute dyspnea: results from the BACH (Biomarkers in Acute Heart Failure) trial. J Am Coll Cardiol 2010;55:2062–76.

22. Dieplinger B, Gegenhuber A, Kaar G, Poelz W, Haltmayer M, Mueller T. Prognostic value of established and novel biomarkers in patients with shortness of breath attending an emergency department. Clin Biochem 2010;43:714–9.

23. Maisel A, Mueller C, Nowak RM, Peacock WF, Ponikowski P, Mockel M, et al. Midregion prohormone adrenomedullin and prognosis in patients presenting with acute dyspnea: results from the BACH (Biomarkers in Acute Heart Failure) trial. J Am Coll Cardiol 2011;58:1057–67.

24. Shah RV, Truong QA, Gaggin HK, Pfannkuche J, Hartmann O, Januzzi JL Jr. Mid-regional pro-atrial natriuretic peptide and pro-adrenomedullin testing for the diagnostic and prognos-tic evaluation of patients with acute dyspnoea. Eur Heart J 2012;33:2197–205.

25. Cinar O, Cevik E, Acar A, Kaya C, Ardic S, Comert B, et al. Evalua-tion of mid-regional pro-atrial natriuretic peptide, procalcitonin, and mid-regional pro-adrenomedullin for the diagnosis and risk stratification of dyspneic ED patients. Am J Emerg Med 2012;30:1915–20.

26. Travaglino F, Russo V, De Berardinis B, Numeroso F, Catania P, Cervellin G, et al. Thirty and ninety days mortality predictive value of admission and in-hospital procalcitonin and mid-regional pro-adrenomedullin testing in patients with dyspnea. Results from the VERyfing DYspnea trial. Am J Emerg Med 2014;32:334–41. [Epub 25 Feb 2014].

27. Christ-Crain M, Morgenthaler NG, Stolz D, Muller C, Bingisser R, Harbarth S, et al. Pro-adrenomedullin to predict severity and outcome in community-acquired pneumonia [ISRCTN04176397]. Crit Care 2006;10:R96.

28. Huang DT, Angus DC, Kellum JA, Pugh NA, Weissfeld LA, Struck J, et al. Midregional proadrenomedullin as a prognostic tool in community-acquired pneumonia. Chest 2009;136:823–31.

29. Kruger S, Ewig S, Giersdorf S, Hartmann O, Suttorp N, Welte T. Cardiovascular and inflammatory biomarkers to predict short- and long-term survival in community-acquired pneumonia: results from the German Competence Network, CAPNETZ. Am J Respir Crit Care Med 2010;182:1426–34.

30. Schuetz P, Wolbers M, Christ-Crain M, Thomann R, Falconnier C, Widmer I, et al. Prohormones for prediction of adverse medical outcome in community-acquired pneumonia and lower respira-tory tract infections. Crit Care 2010;14:R106.

31. Guertler C, Wirz B, Christ-Crain M, Zimmerli W, Mueller B, Schuetz P. Inflammatory responses predict long-term mortality risk in community-acquired pneumonia. Eur Respir J 2011;37:1439–46.

32. Albrich WC, Dusemund F, Ruegger K, Christ-Crain M, Zimmerli W, Bregenzer T, et al. Enhancement of CURB65 score with proadre-nomedullin (CURB65-A) for outcome prediction in lower respira-tory tract infections: derivation of a clinical algorithm. BMC Infect Dis 2011;11:112.


via free access


33. Bello S, Lasierra AB, Minchole E, Fandos S, Ruiz MA, Vera E, et al. Prognostic power of proadrenomedullin in community-acquired pneumonia is independent of aetiology. Eur Respir J 2012;39:1144–55.

34. Hartmann O, Schuetz P, Albrich WC, Anker SD, Mueller B, Schmidt T. Time-dependent Cox regression: serial measurement of the cardiovascular biomarker proadrenomedullin improves survival prediction in patients with lower respiratory tract infec-tion. Int J Cardiol 2012;161:166–73.

35. Renaud B, Schuetz P, Claessens YE, Labarere J, Albrich W, Mueller B. Proadrenomedullin improves Risk of Early Admission to ICU score for predicting early severe community-acquired pneumonia. Chest 2012;142:1447–54.

36. Kolditz M, Halank M, Schulte-Hubbert B, Bergmann S, Albrecht S, Hoffken G. Copeptin predicts clinical deterioration and persistent instability in community-acquired pneumonia. Respir Med 2012;106:1320–8.

37. Sarda Sanchez M, Hernandez JC, Hernandez-Bou S, Teruel GC, Rodriguez JV, Cubells CL. Pro-adrenomedullin usefulness in the management of children with community-acquired pneumonia, a preliminary prospective observational study. BMC Res Notes 2012;5:363.

38. Courtais C, Kuster N, Dupuy AM, Folschveiller M, Jreige R, Bargnoux AS, et al. Proadrenomedullin, a useful tool for risk stratification in high Pneumonia Severity Index score community acquired pneumonia. Am J Emerg Med 2013; 31:215–21.

39. Stolz D, Christ-Crain M, Morgenthaler NG, Miedinger D, Leuppi J, Muller C, et al. Plasma pro-adrenomedullin but not plasma pro-endothelin predicts survival in exacerbations of COPD. Chest 2008;134:263–72.

40. Zuur-Telgen MC, Brusse-Keizer MG, VanderValk PD, van der Palen J, Kerstjens HA, Hendrix MG. Stable-state midrange-proadrenomedullin level is a strong predictor of mortality in COPD patients. Chest 2014;145:534–51.

41. Grolimund E, Kutz A, Marlowe RJ, Vögeli A, Alan M, Christ-Crain M, et al. Long-term prognosis in COPD exacerbation: role of bio-markers, clinical variables and exacerbation type COPD. J Chron Obstruct Pulmon Dis 2014 (in press).

42. Stolz D, Kostikas K, Blasi F, Boersma W, Milenkovic B, Lacoma A, et al. Adrenomedullin refines mortality prediction by the BODE index in COPD: the “BODE-A” index. Eur Respir J 2014;43:397–408.

43. Jehn M, Schindler C, Meyer A, Tamm M, Koehler F, Witt C, et al. Associations of daily walking activity with biomarkers related to cardiac distress in patients with chronic obstructive pulmonary disease. Respiration 2013;85:195–202.

44. Stolz D, Boersma W, Blasi F, Louis R, Milenkovic B, Kostikas K, et al. Exertional hypoxemia in stable COPD is common and pre-dicted by circulating proadrenomedullin. Chest 2014;146:328–38.

45. Zurek M, Maeder MT, Brutsche MH, Luthi A, Twerenbold R, Freese M, et al. Midregional pro-adrenomedullin and copeptin: exercise kinetics and association with the cardiopulmonary exercise response in comparison to B-type natriuretic peptide. Eur J Appl Physiol 2014;114:815–24.

46. Kitamura K, Kangawa K, Kawamoto M, Ichiki Y, Nakamura S, Matsuo H, et al. Adrenomedullin: a novel hypotensive peptide isolated from human pheochromocytoma. Biochem Biophys Res Commun 1993;192:553–60.

47. Kitamura K, Sakata J, Kangawa K, Kojima M, Matsuo H, Eto T. Cloning and characterization of cDNA encoding a precursor for human adrenomedullin. Biochem Biophys Res Commun 1993;194:720–5.

48. Kitamura K, Kangawa K, Eto T. Adrenomedullin and PAMP: discovery, structures, and cardiovascular functions. Microsc Res Tech 2002;57:3–13.

49. Kitamura K, Kangawa K, Kojima M, Ichiki Y, Matsuo H, Eto T. Complete amino acid sequence of porcine adrenomedul-lin and cloning of cDNA encoding its precursor. FEBS Lett 1994;338:306–10.

50. Temmesfeld-Wollbruck B, Hocke AC, Suttorp N, Hippenstiel S. Adrenomedullin and endothelial barrier function. Thromb Hae-most 2007;98:944–51.

51. Hao SL, Yu ZH, Qi BS, Luo JZ, Wang WP. The antifibrosis effect of adrenomedullin in human lung fibroblasts. Exp Lung Res 2011;37:615–26.

52. Marinoni E, Pacioni K, Sambuchini A, Moscarini M, Letizia C, R DII. Regulation by hypoxia of adrenomedullin output and expression in human trophoblast cells. Eur J Obstet Gynecol Reprod Biol 2011;154:146–50.

53. Martinez A, Miller MJ, Unsworth EJ, Siegfried JM, Cuttitta F. Expression of adrenomedullin in normal human lung and in pulmonary tumors. Endocrinology 1995;136:4099–105.

54. Elsasser TH, Kahl S. Adrenomedullin has multiple roles in disease stress: development and remission of the inflammatory response. Microsc Res Tech 2002;57:120–9.

55. Xue Y, Taub P, Iqbal N, Fard A, Clopton P, Maisel A. Mid-region pro-adrenomedullin adds predictive value to clinical predictors and Framingham risk score for long-term mortality in stable outpatients with heart failure. Eur J Heart Fail 2013;15:1343–9.

56. Allaker RP, Grosvenor PW, McAnerney DC, Sheehan BE, Srikanta BH, Pell K, et al. Mechanisms of adrenomedullin antimicrobial action. Peptides 2006;27:661–6.

57. Matsui H, Shimosawa T, Itakura K, Guanqun X, Ando K, Fujita T. Adrenomedullin can protect against pulmonary vascular remodeling induced by hypoxia. Circulation 2004;109:2246–51.

58. Smith JG, Newton-Cheh C, Hedblad B, Struck J, Morgenthaler NG, Bergmann A, et al. Distribution and correlates of midregional proadrenomedullin in the general population. Clin Chem 2009;55:1593–5.

59. Pfeil U, Aslam M, Paddenberg R, Quanz K, Chang CL, Park JI, et al. Intermedin/adrenomedullin-2 is a hypoxia-induced endothelial peptide that stabilizes pulmonary microvas-cular permeability. Am J Physiol Lung Cell Mol Physiol 2009;297:L837–45.

60. Di Paola R, Talero E, Galuppo M, Mazzon E, Bramanti P, Motilva V, et al. Adrenomedullin in inflammatory process associated with experimental pulmonary fibrosis. Respir Res 2011;12:41.

61. Qi JG, Ding YG, Tang CS, Du JB. Chronic administration of adre-nomedullin attenuates hypoxic pulmonary vascular structural remodeling and inhibits proadrenomedullin N-terminal 20-pep-tide production in rats. Peptides 2007;28:910–9.

62. Li W, Kong QY, Zhao CF, Zhao F, Li FH, Xia W, et al. Adrenomedul-lin and adrenotensin regulate collagen synthesis and prolifera-tion in pulmonary arterial smooth muscle cells. Braz J Medi Biological Res 2013;46:1047–55.


via free access


63. Vizza CD, Letizia C, Sciomer S, Naeije R, Della Rocca G, Di Roma A, et al. Increased plasma levels of adrenomedullin, a vasoactive peptide, in patients with end-stage pulmonary disease. Regul Pept 2005;124:187–93.

64. Lewis LK, Smith MW, Yandle TG, Richards AM, Nicholls MG. Adrenomedullin(1-52) measured in human plasma by radioim-munoassay: plasma concentration, adsorption, and storage. Clin Chem 1998;44:571–7.

65. Goode KM, Nicholls R, Pellicori P, Clark AL, Cleland JG. The in vitro stability of novel cardiovascular and sepsis biomarkers at ambient temperature. Clin Chem Lab Med 2014;52:911–8.

66. Stolz D, Christ-Crain M, Bingisser R, Leuppi J, Miedinger D, Muller C, et al. Antibiotic treatment of exacerbations of COPD: a randomized, controlled trial comparing procalcitonin-guidance with standard therapy. Chest 2007;131:9–19.

67. Schuetz P, Christ-Crain M, Thomann R, Falconnier C, Wolbers M, Widmer I, et al. Effect of procalcitonin-based guidelines vs standard guidelines on antibiotic use in lower respiratory tract infections: the ProHOSP randomized controlled trial. J Am Med Assoc 2009;302:1059–66.

68. Albrich WC, Ruegger K, Dusemund F, Schuetz P, Arici B, Litke A, et al. Biomarker-enhanced triage in respiratory infections: a proof-of-concept feasibility trial. Eur Respir J 2013;42:1064–75.

69. Mockel M, Searle J, Hartmann O, Anker SD, Peacock WF, Wu AH, et al. Mid-regional pro-adrenomedullin improves disposition strategies for patients with acute dyspnoea: results from the BACH trial. Emerg Med J 2013;30:633–7.

70. Caruhel P, Mazier C, Kunde J, Morgenthaler NG, Darbouret B. Homogeneous time-resolved fluoroimmunoassay for the measurement of midregional proadrenomedullin in plasma on the fully automated system B.R.A.H.M.S KRYPTOR. Clin Biochem 2009;42:725–8.

71. Neumann JT, Tzikas S, Funke-Kaiser A, Wilde S, Appelbaum S, Keller T, et al. Association of MR-proadrenomedullin with car-diovascular risk factors and subclinical cardiovascular disease. Atherosclerosis 2013;228:451–9.

72. Maeder MT, Brutsche MH, Arenja N, Socrates T, Reiter M, Meissner J, et al. Biomarkers and peak oxygen uptake in patients with chronic lung disease. Respiration 2010;80: 543–52.

73. Chen ZH, Kim HP, Sciurba FC, Lee SJ, Feghali-Bostwick C, Stolz DB, et al. Egr-1 regulates autophagy in cigarette smoke-induced chronic obstructive pulmonary disease. PLoS One 2008;3:e3316.

74. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol 1994;47:1245–51.

75. Pencina MJ, D’Agostino RB, Sr., Demler OV. Novel metrics for evaluating improvement in discrimination: net reclassification and integrated discrimination improvement for normal variables and nested models. Stat Med 2012;31:101–13.

76. Zudaire E, Martinez A, Cuttitta F. Adrenomedullin and cancer. Regul Pept 2003;112:175–83.

77. Khan SQ, O’Brien RJ, Struck J, Quinn P, Morgenthaler N, Squire I, et al. Prognostic value of midregional pro-adrenomedullin in patients with acute myocardial infarction: the LAMP (Leicester Acute Myocardial Infarction Peptide) study. J Am Coll Cardiol 2007;49:1525–32.

78. Masson S, Latini R, Carbonieri E, Moretti L, Rossi MG, Ciricugno S, et al. The predictive value of stable precursor fragments of vasoactive peptides in patients with chronic heart failure: data from the GISSI-heart failure (GISSI-HF) trial. Eur J Heart Fail 2010;12:338–47.

79. Wild PS, Schnabel RB, Lubos E, Zeller T, Sinning CR, Keller T, et al. Midregional proadrenomedullin for prediction of cardio-vascular events in coronary artery disease: results from the AtheroGene study. Clin Chem 2012;58:226–36.

80. Tzikas S, Keller T, Ojeda FM, Zeller T, Wild PS, Lubos E, et al. MR-proANP and MR-proADM for risk stratification of patients with acute chest pain. Heart 2013;99:388–95.

81. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med 2002;166:111–7.

82. Eggers KM, Venge P, Lindahl B, Lind L. Associations of mid-regional pro-adrenomedullin levels to cardiovascular and metabolic abnormalities, and mortality in an elderly population from the community. Int J Cardiol 2013;168:3537–42.

83. Bhandari SS, Davies JE, Struck J, Ng LL. Influence of confound-ing factors on plasma mid-regional pro-adrenomedullin and mid-regional pro-A-type natriuretic peptide concentrations in healthy individuals. Biomarkers 2011;16:281–7.

84. Brouwers FP, de Boer RA, van der Harst P, Struck J, de Jong PE, de Zeeuw D, et al. Influence of age on the prognostic value of mid-regional pro-adrenomedullin in the general population. Heart 2012;98:1348–53.

85. Celli BR, Cote CG, Lareau SC, Meek PM. Predictors of survival in COPD: more than just the FEV1. Respir Med 2008;102 Suppl 1:S27–35.

86. Divo M, Cote C, de Torres JP, Casanova C, Marin JM, Pinto-Plata V, et al. Comorbidities and risk of mortality in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012;186:155–61.

87. Celli BR, Locantore N, Yates J, Tal-Singer R, Miller BE, Bakke P, et al. Inflammatory biomarkers improve clinical prediction of mortality in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012;185:1065–72.

88. Schuetz P, Litke A, Albrich WC, Mueller B. Blood biomarkers for personalized treatment and patient management deci-sions in community-acquired pneumonia. Curr Opin Infect Dis 2013;26:159–67.

89. Schuetz P, Koller M, Christ-Crain M, Steyerberg E, Stolz D, Muller C, et al. Predicting mortality with pneumonia severity scores: importance of model recalibration to local settings. Epidemiol Infect 2008;136:1628–37.

90. Albrich WC, Dusemund F, Bucher B, Meyer S, Thomann R, Kuhn F, et al. Effectiveness and safety of procalcitonin-guided antibiotic therapy in lower respiratory tract infections in “real life”: an international, multicenter poststudy survey (ProREAL). Arch Intern Med 2012;172:715–22.

91. Schuetz P, Albrich W, Christ-Crain M, Chastre J, Mueller B. Procalcitonin for guidance of antibiotic therapy. Expert Rev Anti Infect Ther 2010;8:575–87.

92. Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, et al. Defining community acquired pneumonia sever-ity on presentation to hospital: an international derivation and validation study. Thorax 2003;58:377–82.

93. Duvoix A, Dickens J, Haq I, Mannino D, Miller B, Tal-Singer R, et al. Blood fibrinogen as a biomarker of chronic obstructive pulmonary disease. Thorax 2013;68:670–6.


via free access


94. Albrich WC, Ruegger K, Dusemund F, Bossart R, Regez K, Schild U, et al. Optimised patient transfer using an innovative multidisciplinary assessment in Kanton Aargau (OPTIMA I): an observational survey in lower respiratory tract infections. Swiss Med Wkly 2011;141:w13237.

95. Pencina MJ, D’Agostino RB, Sr., D’Agostino RB, Jr., Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157–72; discussion 207–12.

Supplemental Material: The online version of this article (DOI: 10.1515/cclm-2014-0748) offers supplementary material, available to authorized users.

BionotesProf. Dr. med. Philipp SchuetzUniversity Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001 Aarau, Switzerland, Phone: +41 62 838 4141, Fax: +41 62 838 4100; and Division of General Internal and Emergency Medicine, Medical University Department, Kantonsspital Aarau, Aarau, [email protected]

Philipp Schuetz is Professor of Endocrinology and Medicine at the University of Basel in Switzerland and works as an endocrinologist and internist at the University Department of the Kantonsspital Aarau. There, a substantial part of his practice involves patients with underlying COPD. He has broad clinical and research inter-ests, focusing on applying new developments in critical illness, infectious diseases, endocrinology, and general and personalized medicine. Prof. Schuetz has done extensive research on hormones and other biomarkers, among them procalcitonin and proadre-nomedullin, for better diagnostic and prognostic workup of patients with lower respiratory tract illness, including acute non-pneumonic and pneumonic exacerbations of COPD. As part of this work, he served as the principal investigator of the ProHOSP randomized, controlled, clinical trial of procalcitonin for antibiotic stewardship and of a variety of substudies and secondary analyses involving procalcitonin, proadrenomedullin, and other novel blood biomark-ers. Additionally, Prof. Schuetz earned a master of public health (MPH) degree at the Harvard School of Public Health in Boston, where he trained for 2 years at the Beth Israel Deaconess Medical Center (BIDMC). He has applied this training to perform and co-author several individual patient data and other meta-analyses regarding blood biomarkers and has co-authored eight published original reports regarding proadrenomedullin.

Robert J. MarloweSpencer-Fontayne Corporation, Jersey City, NJ, USA

Robert J. Marlowe has been an independent medical writer and editor working with academic, industry, clinical, and basic science researchers worldwide since 1986. During that time, he has co-authored nearly 20 papers published in peer-reviewed medical jour-nals. Topics of these papers have included blood biomarkers, risk stratification in pulmonary, cardiac, and malignant disease, person-alized medicine, and COPD. Mr. Marlowe also has edited some 150 other published scientific papers regarding these and other medical topics. Additionally, he has delivered scientific presentations before members of the COPD Biomarkers Qualification Consortium and at the 2010 Annual Congress of the Society of Nuclear Medicine. Mr. Marlowe holds an AB degree from Columbia College, Columbia University, New York, NY, USA.

Beat MuellerDivision of General Internal and Emergency Medicine, Medical University Department, Kantonsspital Aarau, Aarau, Switzerland

Beat Müller is Medical Director of the University Department, Kantonsspital, Aarau AG, Switzerland, and Full Professor of Internal Medicine and Endocrinology of the Medical Faculty of the Univer-sity of Basel. He studied Medicine in Berne, Switzerland, and in South Africa and did his postdoctoral at the Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. His broad clini-cal and research interests focus on pragmatic outcome and quality control studies using hormonal biomarkers in general medicine, endocrinology, infectious diseases, critical illness, and pulmonol-ogy. He masterminded several intervention trials enrolling > 4000 patients to validate his concept of a safe and more targeted anti-biotic stewardship using procalcitonin as biomarker in respiratory tract infections. He identified proadrenomedullin as a prognostic hormokine, unraveled its physiopathological regulation, and evalu-ated its clinical use to guide risk-adapted length of hospitalization.


via free access

mailto:[email protected]

schuetz proadm review cclm 2015

Documents