International Journal of Clinical Medicine, 2013, 4, 69-77
http://dx.doi.org/10.4236/ijcm.2013.42014 Published Online February 2013 (http://www.scirp.org/journal/ijcm)
Validation of a Risk-Based Biomarker-Enhanced Scorin g
System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
—An Observational Survey
Richard X. Sousa Da Silva1*, Frank Dusemund1*, Christian Nickel2, Roland Bingisser2,
Andreas Huber3, Beat Müller1, Werner C. Albrich1
1Medical University Department of the University of Basel, Kantonsspital Aarau, Switzerland; 2Department of Emergency Medicine,
University Hospital of Basel, Basel, Switzerland; 3Department of Laboratory Medicine, Kantonsspital Aarau, Switzerland.
Received December 13th, 2012; revised January 15th, 2013; accepted January 22nd, 2013
ABSTRACT
Background: Despiteits recommendation in management guide lines for community-acquired pneumonia (CAP), the
CURB65-score is frequently not followed for disposition decisions in clinical routine. We therefore proposed an
improved CURB65-A-score, supplemented by proadre nome dull in (ProADM) levels for patients with CAP and other
lower respiratory tract infections (LRTIs). In this study, we vali dated this risk-based biomarker-enhanced disposition in
patients with LRTIs presenting to the emergency department of the University Hospital of Basel. Methods: In this pro-
spective observational cohort study of 85 patients presenting with LRTIs, site of care was decided by the physicians in
charge according to their judgement. Retro spectively the CURB65-A-score was calculated and a virtual disposition
assigned. This was compared with the existing disposition in order to identify efficacy of the novel risk-based bio-
marker-enhanced disposition. Results: The novel disposition criteria considered 14 patients suitable for outpatient
treatment compared to 11 in the current disposition (p = 0.5). It detected 7 patients to be best treated outside the hospital
for nursing reasons, while the current disposition detected only 1 patient requiring geriatric care (p = 0.09). Further, it
decreased regular hospitalizations considerably (32 vs. 64, p < 0.001). Conclusion: The novel risk-based bio-
marker-enhanced disposition is an objective, safe and probably more efficient disposition system to identify outpatient
treatment options than the current practice at the University Hospital of Basel.
Keywords: Lower Respiratory Tract Infection; Proadrenomedullin; Biomarker-Enhanced Disposition;
CURB65-A-Score; Outpatient Treatment
1. Introduction
Lower respiratory tract infections (LRTIs), in particular
community-acquired pneumonia (CAP), are potentially
serious and highly prevalent infectious diseases with a
high economic and social burden [1]. CAP is the main
cause for death from infectious diseases in the developed
world [2]. In the period from 1993 to 2005 the age- and
gender-adjusted mortality in CAP decreased from 8.9%
to 4.1% (p < 0.001), although comorbidities increased.
This reduction of in-hospital mortality of inpatients with
CAP indicates that pneumonia is a successful example
for improved treatment [3]. Still, treatment of CAP cre-
ates overwhelming costs. Mostly they are generated in
the hospital setting, since the average costs for inpatient
treatment of CAP are 8 to 20 times higher than for outpa-
tient treatment [4]. This observation and the fact that
Swiss hospitals regularly operate at or beyond maximal
bed capacity make improved disposition pathways a re-
search priority, in order to find effective ways for safe
outpatient management. The largest contribution to high
costs in the treatment of LRTIs is the initial visit on the
emergency department that includes diagnostic tests, and
the subsequent hospitalization, which by itself accounts
for 63% of overall costs [5]. Concerning the patients it is
crucial to realize thata prolonged hospitalization has a
direct negative influence on their health status. First,
there is a high risk of induction or deterioration of frailty
[6]. Second, the length of stay (LOS) is a substantial risk
factor for nosocomial complications and infections [6].
Also, previous hospitalization is an independent risk fac-
*Equally contributing first authors.

Validation of a Risk-Based Biomarker-Enhanced Scoring System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
70
tor for suffering from a CAP. Therefore, disposition tools
should both prevent unnecessary hospitalizations and
reduce LOS [7].
The two most commonly used scoring systems in CAP
are the pneumonia severity index (PSI) [8] and the
CURB65-score [9]. Low scores suggest the possibility of
outpatient treatment based on a very low mortality pre-
diction. Both do not take the host response into account
and are limited by moderate sensitivity and specificity
for adverse events. One of the limitations of the PSI in
particular is its complex 2-stage 20-item design [10]. The
CURB65-score has been shown to have a poor sensitivity
for adverse outcomes in young and previously healthy
patients [11].
To improve the validity of risk-scores, several prog-
nostic biomarkers have been identified that correlate with
the severity of LRTIs, such as ProADM, cortisol, urea
and Pro-Endothelin-1 (Pro-ET1) [12]. Cortisol for exam-
ple is as good in predicting severity and outcome of CAP
as the PSI [13]. Similar results are obtained by measuring
Pro-ET1, which correlates with the severity of CAP and
is an independent predictor for ICU admission and mor-
tality [12]. However, neither cortisol nor pro-ET1 pro-
vides an additional improvement of clinical scores.
ProADM is a precursor peptide of the endogenous va-
soactive hormone adrenomedullin (ADM). ProADM is
used for measurement of adrenomedullin levels, since
adrenomedullin is hard to measure in blood plasma and
the more stable ProADM is secreted equimolarly to
ADM [14]. Adrenomedullin is a potent vasodilatator, has
a natriuretic effect, and is elevated in congestive heart
failure patients [15]. It may even be part of a novel hor-
monal system controlling circulation [16,17]. The name
of this peptide derives from its discoveryin pheochro-
mocytoma tissue emerging from adrenal medulla, where
it is also present abundantly. Adrenomedullin has immu-
nomodulatory and bactericidal properties, which may
enable it to be a future prognostic biomarker for deter-
mining the severity of LRTIs [18]. Therefore, this study
shows great interest in ProADM as a diagnostic and
prognostic factor.
Based on the finding that ProADMhas shown to in-
crease the prognostic accuracy of clinical scores for mor-
tality and severe adverse events in LRTI [19-21], we
identified two optimal ProADM cut-off values (0.75
nmol/l and 1.5 nmol/l) to separate patients into low-risk
and high-risk groups. We then combined the CURB65
classes with the ProADM cut-offs to a new risk score
termed CURB65-A-score.The new score provided a higher
accuracy for adverse events and mortality than the usual
CURB65-score [21]. The CURB65-A-score was gener-
ated as following: CURB65-A class I was defined as a
CURB65-score of 0 to 1 points and a ProADM ≤ 0.75
nmol/l (Figure 1). It represents the low-risk category and
is considered adequate for outpatient treatment. CURB65-
A class III results whenever the CURB65-score is of 3 or
more points, or whenever the ProADM is ≥1.5 nmol/l. It
represents the high-risk class and requires hospitalization
(inpatient treatment) (Figure 1). All other combinations of
CURB65-scores and ProADM values result in CURB65-
A class II (Figure 1), which represents an intermediate-
risk, for which short hospitalization for up to 48 hours
with subsequent reevaluation is recommended [22].
In a previously realized prospective observational study
named OPTIMA IAarau we analyzed the current disposi-
tion practice at the Kantonsspital Aaraufor patients with
LRTIs, and assessed the potential of CURB65-A-score
assisted disposition [22]. It showed that according to the
CURB65-A-score there was a large potential of increas-
ing out patient treatment, as in fact more then 90% of all
patients with LRTIs were hospitalized. Further, it identi-
fied a great potential to shorten hospitalization, as pa-
tients remained hospitalized for a mean of 3.6 days after
they had already reached medical stability [22].
In the current study named OPTIMA I Basel we
evaluate the potential of the novel biomarker-enhanced
CURB65-A-score in the University Hospital of Basel,
and compare it with the locally used rather subjective
disposition pathway. We also compare the current data
with data from the OPTIMA I Aarau study.
2. Methods
2.1. Research Object
This study is an observational survey to compare the
current disposition practice with a novel biomarker-en-
hanced CURB65-A-score on patients presenting with
LRTIs. From September 2010 to December 2011, we en-
rolled a convenience sample of 85 patients presenting
with LRTIs during office hours to the Emergency De-
partment of the University Hospital of Basel. There were
no exclusion criteria. Patients were triaged and treated by
the physician in charge, who determined the further
management without interference by the study team. Site
of care was decided according to local guidelines and bed
availability.
The local Institutional Review Board (Kantonale Ethik
kommissionbeider Basel) classified this study as obser-
vational quality surveillance and waived the need for
patient informed consent (EKBB 102/10).
2.2. Disposition of Patients
The three sites of care summarized as “ASG” disposition
pathway, which is currently used in the University Hos-
pital of Basel, are the following (Ta ble 1): Category “A”

Validation of a Risk-Based Biomarker-Enhanced Scoring System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
71
[22]
Figure 1. Disposition criteria.
stands for “acute”, as when needed hospitalization with
further diagnostics and medical surveillance. Category
“S” stands for “short”, e.g. when there is only the need of
a short hospitalization with an overnight stay and dis-
charge to home on the following day. Category “G”
stands for “geriatric”, e.g. when there is the need of in-
tensive nursing care on a dedicated acute geriatric ward,
despite medical stability. All other patients who did not
need hospitalization or an overnight stay received out pa-
tient treatment, which represents the fourth and last
treatment site. The decision of treatment site was taken
by the physician in charge according to his judgment,
local guidelines and in agreement with the patients and
their relatives, but without using any clinical score. The
CURB65-A-score [9] was not available for the treating
physicians, as it was calculated retrospectively by the
research team. The remainder of the phlebotomy speci-
men of each patient taken on admission day was then
sent to the Laboratory Department of Kantonsspital Aa-
rau for measurement of ProADM.
2.3. Measurement of ProADM
Measurement of ProADM was performed using a sand-
wich immunoassay with an analytical detection limit of
0.08 nmol/l [22,23]. The treating physicians had no ac-
cess to the ProADM values, since these were retrospec-
tively measured.
2.4. Inquiring Adverse Events
The CURB65-A-score was calculated as described pre-
viously (Figure 1) [21]. Each patient received a phone
interview on day 30 after presentation to the emergency
department, in order to identify adverse events (Table 2).
Missing information to calculate the “Selbstpflege index”
(SPI = self care index) [24] and the post-acute care dis-
charge score (PACD) [25] was completed at this point as
well. Finally, the novel biomarker-enhanced disposition
was virtually applied to establish the recommended dis-
position sites. Low-risk patients (CURB65-A class I)
were further categorized regarding the need of nursing
supply as determined by the SPI and PACD scores (Fig-
ure 1 and Table 3).
2.5. Endpoints
Primary endpoints were the comparison of treatment site
and adverse events of the actual disposition with the novel
risk-based biomarker-enhanced disposition criteria in the
University Hospital of Basel. Secondary endpoints were
the comparison of adverse events between the low risk
subgroups of the CURB65-score and the CURB65-
A-score. Finally, we compared our findings regarding

Validation of a Risk-Based Biomarker-Enhanced Scoring System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
72
Table 1. Baseline characteristics.
Baseline characteristics OPTIMA I Basel vs. OPTIMA I Aarau
Final diagnosis
OPTIMA I Basel vs.
OPTIMA I Aarau
Allpatients
(n = 85) vs. (n = 253)
CAP
(n = 59) vs. (n = 151)
Bronchitis
(n = 6) vs. (n = 29)
AECOPD
(n = 11) vs. (n = 34)
Other diagnoses
(n = 9) vs. (n = 37)
Demographic characteristics
I Basel vs. OPTIMA I Aarau
Mean age (years) 70 vs. 65
p = 0.015
70 vs. 66
p = 0.04
56 vs. 57
p = 0.86
75 vs. 71
p = 0.29
70 vs. 59
p = 0.005
Sex (male), no. (%) 48 (56.5) vs. 144 (56.9)
p = 0.94
38 (64.4) vs. 87 (57.6)
p = 0.36
3 (50) vs. 14 (48.3)
p = 0.93
4 (36.4) vs. 20 (58.8)
p = 0.19
3 (33.3) vs. 22 (59.5)
p = 0.15
Coexistingillnesses, no. (%)
OPTIMA I Basel vs.
OPTIMA I Aarau
Coronary heart disease 19 (22.4) vs. 53 (20.9)
p = 0.78
13 (22) vs. 39 (25.8)
p = 0.56
1 (16.7) vs. 2 (6.9)
p = 0.43
2 (18.2) vs. 4 (11.8)
p = 0.58
3 (33.3) vs. 7 (18.9)
p = 0.34
Cerebrovascular disease 11 (12.9) vs. 23 (9.1)
p = 0.41
8 (13.6) vs. 19 (12.6)
p = 0.84
0 (0) vs. 1 (3.4)
p = 0.64
3 (27.3) vs. 2 (5.9)
p = 0.04
0 (0) vs. 1 (2.7)
p = 0.61
Renal dysfunction 24 (28.2) vs. 83 (32.8)
p = 0.43
15 (25.4) vs. 49 (32.5)
p = 0.32
2 (33.3) vs. 8 (27.6)
p = 0.77
3 (27.3) vs. 13 (38.2)
p = 0.50
4 (44.4) vs. 13 (35.1)
p = 0.60
Pneumopathy 31 (36.5) vs. 94 (37.2)
p = 0.91
15 (25.4) vs. 36 (23.8)
p = 0.81
2 (33.3) vs. 8 (27.6)
p = 0.77
11 (100) vs. 34 (100)
p = 0.9999999
3 (33.3) vs. 19 (51.3)
p = 0.33
Lung cancer 7 (8.2) vs. 13 (5.1)
p = 0.29
5 (8.5) vs. 9 (6.0)
p = 0.51
0 (0) vs. 2 (6.9)
p = 0.50
2 (18.2) vs. 0 (0)
p = 0.01
0 (0) vs. 2 (5.4)
p = 0.47
Other malignancy 16 (18.8) vs. 31 (12.3)
p = 0.12
13 (22) vs. 21 (13.9)
p = 0.15
1 (16.7) vs. 3 (10.3)
p = 0.65
1 (9.1) vs. 3 (8.8)
p = 0.97
1 (11.1) vs. 4 (10.8)
p = 0.97
Diabetes 18 (21.2) vs. 51 (20.2)
p = 0.84
14 (23.7) vs. 31 (20.5)
p = 0.61
0 (0) vs. 6 (20.7)
p = 0.22
2 (18.2) vs. 7 (20.6)
p = 0.86
2 (22.2) vs. 31 (83.8)
p = 0.009
Clinicalfindings
OPTIMA I Basel vs.
OPTIMA I Aarau
Confusion (%) 4.7 vs. 15.8
p = 0.008
6.8 vs. 16.6
p = 0.06
0 vs. 10.4
p = 0.41
0 vs. 8.8
p = 0.30
0 vs. 18.9
p = 0.15
Systolic blood pressure
(mmHg) mean (IQR*)
134 (117 - 153)
vs. 124 (110 - 139)
p = 0.07
133 (117 - 152.5)
vs. 120 (106 - 134)
p = 0.055
129 (112.5 - 146)
vs. 128 (118 - 140)
p = 0.94
131 (111.5 - 142)
vs. 130 (118 - 149)
p = 0.948
150 (130 - 171)
vs. 130 (111 - 147)
p = 0.328
Diastolic blood pressure
(mmHg) mean (IQR*)
72 (33 - 122) vs.
96 (80 - 111)
p = 0.781
71 (40 - 108) vs.
99 (80 - 113)
p = 0.407
74 (64 - 85) vs.
87 (79 - 100)
p = 0.638
73 (33 - 122) vs.
97 (78.5 - 110)
p = 0.302
74 (49 - 110) vs.
102 (84 - 111)
p = 0.384
Laboratory findings
OPTIMA I Basel vs.
OPTIMA I Aarau
ProADM (nmol/l), mean (IQR*)
1.09 (0.68 - 1.64)
vs. 1.12 (0.81 - 2.07)
p = 0.62
1.17 (0.79 - 1.73)
vs. 1.28 (0.88 - 2.49)
p = 0.665
0.70 (0.58 - 0.76)
vs. 0.84 (0.68 - 1.53)
p = 0.648
0.93 (0.59 - 1.23)
vs. 1.01 (0.87 - 1.54)
p = 0.479
0.96 (0.70 - 1.36)
vs. 1.03 (0.74 - 1.80)
p = 0.455
*IQR: Interquartile range.
adverse events and baseline characteristics with those of
the precursor OPTIMA I Aarau study [22].
2.6. Definitions
LRTI included community-acquired pneumonia (CAP),
acute exacerbation of chronic obstructive pulmonary
disease (AECOPD), acute bronchitis and a mixed sub-
group called “other diagnoses” including for example
pleurisy or exacerbation of asthma. Adverse events were
assessed on day 30 after admission day based on phone
interviews and medical documentation in the hospital.
Adverse events included admission to the intensive care
unit (ICU), mechanical ventilation, empyema, adverse
reaction to antibiotics, death from LRTI and relapse (Ta-
bles 2 and 3).
2.7. Overruling Criteria
By meeting at least one of the following medical over-
ruling criteria, inpatient treatment was considered ap-
ropriate, even in the setting of a low CURB65-A-score: p

Validation of a Risk-Based Biomarker-Enhanced Scoring System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
73
Table 2. Adverse events in CURB65-score and CURB65-A-score.
Adverse events stratified for CURB65-score and CURB65-A-score on patients with LRTIs
CURB65-score
OPTIMA I Basel vs.
OPTIMA I Aaraup-value
CURB65 0-1
(n = 37) vs. (n = 63)
CURB65 2
(n = 29) vs. (n = 33)
CURB65 3-5
(n = 19) vs. (n = 40)
Overall
(n = 85) vs. (n = 136)*
ICU admission 0 (0%) vs. 3 (4.8%)
p = 0.17
1 (3.4%) vs. 7 (21.2%)
p = 0.03
2 (10.5%) vs. 6 (15.0%)
p = 0.63
3 (3.5%) vs. 16 (11.8%)
p = 0.03
Mechanical ventilation 1 (2.7%) vs. 3 (4.8%)
p = 0.61
0 (0%) vs. 5 (15.2%)
p = 0.028
0 (0%) vs. 5 (12.5%)
p = 0.10
1 (1.8%) vs. 13 (9.6%)
p = 0.01
Empyema 0 (0%) vs. 0 (0%)
p = 1.0
0 (0%) vs. 0 (0%)
p = 1.0
1 (5.3%) vs. 1 (2.5%)
p = 0.58
1 (1.2%) vs. 1 (0.7%)
p = 0.73
Adverse reaction to antibiotics 0 (0%) vs. 1 (1.6%)
p = 0.44
1 (3.4%) vs. 0 (0%)
p = 0.28
0 (0%) vs. (0%)
p = 1.0
1 (1.2%) vs. 1 (0.7%)
p = 0.73
Death from LRTI 0 (0%) vs. 1 (1.6%)
p = 44
0 (0%) vs. 0 (0%)
p = 1.0
0 (0%) vs. 4 (10.0%)
p = 0.15
0 (0%) vs. 5 (3.7%)
p = 0.07
Relapse 1 (2.7%) vs. 2 (3.2%)
p = 0.89
0 (0%) vs. 0 (0%)
p = 1.0
1 (5.3%) vs. 5 (12.5%)
p = 0.39
2 (2.4%) vs. 7 (5.1%)
p = 0.30
CURB65-A-score
OPTIMA I Basel vs. OPTIMA I
Aaraup-value
CURB65-A I
(n = 21) vs. (n = 24)
CURB65-A II
(n = 32) vs. (n = 47)
CURB65-A III
(n = 32) vs. (n = 67)
Overall
(n = 85) vs. (n = 138)*
ICU admission 0 (0%) vs. 0 (0%)
p = 1.0
0 (0%) vs. 4 (8.5%)
p = 0.09
3 (9.4%) vs. 12 (17.7%)
p = 0.26
3 (3.5%) vs. 16 (11.8%)
p = 0.03
Mechanical ventilation 0 (0%) vs. 0 (0%)
p = 1.0
1 (3.1%) vs. 4 (8.5%)
p = 0.33
0 (0%) vs. 9 (13.4%)
p = 0.02
1 (1.8%) vs. 13 (9.6%)
p = 0.01
Empyema 0 (0%) vs. 0 (0%)
p = 1.0
0 (0%) vs. 0 (0%)
p = 1.0
1 (3.1%) vs. 1 (1.5%)
p = 0.58
1 (1.2%) vs. 1 (0.7%)
p = 0.72
Adverse reaction to antibiotics 0 (0%) vs. 0 (0%)
p = 1.0
1 (3.1%) vs. 1 (2.1%)
p = 0.78
0 (0%) vs. 0 (0%)
p = 1.0
1 (1.2%) vs. 1 (0.7%)
p = 0.72
Death from LRTI 0 (0%) vs. 0 (0%)
p = 1.0
0 (0%) vs. 1 (2.1%)
p = 0.40
0 (0%) vs. 4 (6.0%)
p = 0.15
0 (0%) vs. 5 (3.7%)
p = 0.07
Relapse 0 (0%) vs. 1 (4.2%)
p = 0.34
0 (0.0%) vs. 1 (2.1%)
p = 0.40
2 (6.3%) vs. 5 (7.5%)
p = 0.82
2 (2.4%) vs. 7 (5.1%)
p = 0.31
*OPTIMA I Aarau; there are two more patients in CURB65-A (138) than in CURB65 (136) because of the patients who had no CURB65-score but a
ProADM>1.5 nmol/l, which classified them directly into CURB65-A III subgroup.
1) Admission to ICU for a) respiratory instability (respi-
ratory rate ≥ 30/min and/or O2-saturation < 90% with 6l
O2/min) or b) hemodynamic instability (systolic blood
pressure < 90 mmHg for ≥1 hour, despite adequate vol-
ume resuscitationor vasopressor requirement; 2) Immi-
nent death; 3) Complications (abscess, empyema); 4)
COPD GOLD class III or IV with O2-saturation < 90%,
despite 30 minutes of intensive treatment; 5) Acute ill-
ness requiring hospitalization independent from LRTI; 6)
Comorbidity, e.g. immunodeficiency (neutrophils < 500/
μl; if HIV+: CD4 < 350/μl, leukemia, lymphoma, mye-
loma, cytotoxic medications, hemodialysis), pneumonia
within last 6 weeks, antibiotics or hospitalization (inde-
pendent of indication) within 7 days, other significant
lung disease (cancer, fibrosis, bronchiectasis, tuberculo-
sis, pulmonary embolism, cavitarylung disease) and 7)
Confusion, delirium or intravenous drug use [22]. Fur-
thermore, by meeting any of the following nursing and
organizational overruling criteria the patient was also con
ganizational overruling criteria the patient was also con-
sidered inappropriate for outpatient treatment: 1) SPI-
Index < 32 points; 2) Criteria requiring intensive nursing
care, e.g. dementia, recurrent falls, decubitus ulcer, in-
ability to reliably take medications; 3) Waiting for non-
acute medical care, e.g. holiday bed, rehabilitation, nurs-
ing home, home health care; 4) Deficit of mobility or
self-care requiring treatment; 5) Other reasons, such as
inconvenient timing (weekend, night) and 6) Patients’
and relatives’ preferences: a) concern about safety at
home; b) lack of supporting social network; c) other rea-
sons [22].
2.8. Statistical Analyses
Discrete variables were reported as counts (percentages),
continuous variables as means. Chi-square-test and
Mood’s-Median-test respectively Mann-Whitney-U-test
were applied as appropriate. ults were considered Test res

Validation of a Risk-Based Biomarker-Enhanced Scoring System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
74
Table 3. Adverse events in ASG disposition pathway and risk-based biomarker-enhanced disposition.
Adverse events stratified for ASG disposition pathway and risk-based biomarker-enhanced disposition on patients with LRTIs
ASG disposition
pathway Outpatient (n = 11)
G (=geriatric)
(n = 1)
S (= short)
(n = 9)
A (= acute)
(n = 64)
Overall
(n = 85)
ICU admission 0 (0%) 0 (0%) 0 (0%) 3 (4.7%) 3 (3.5%)
Mechanical ventilation 0 (0%) 0 (0%) 0 (0%) 1 (1.5%) 1 (1.2%)
Empyema 0 (0%) 0 (0%) 0 (0%) 1 (1.5%) 1 (1.2%)
Adverse reaction
to antibiotics 0 (0%) 0 (0%) 0 (0%) 1 (1.5%) 1 (1.2%)
Death from LRTI 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
Relapse 0 (0%) 0 (0%) 0 (0%) 2 (3.1%) 2 (2.4%)
Sum of adverse events 0 (0%) 0 (0%) 0 (0%) 8 (12.5%) 8 (9.4%)
Non-acute medical institution* (n = 21)
(= CURB65-A class I)
Risk-based
biomarker-
enhanced disposition Outpatient
(n = 14)
Home health care, health resort,
holiday bed (n = 7)
Nurse-led unit
(n = 0)
Short hospitalization
(n = 32)
(=CURB65-A class II)
Hospitalization
(n = 32)
(=CURB65-A class III)
Overall
(n = 85)
ICU admission 0 (0%) 0 (0%) - 0 (0%) 3 (9.4%) 3 (3.5%)
Mechanical ventilation 0 (0%) 0 (0%) - 1 (3.1%) 0 (0%) 1 (1.2%)
Empyema 0 (0%)0 (0%) - 0 (0%) 1 (3.1%) 1 (1.2%)
Adverse reaction
to antibiotics 0 (0%) 0 (0%) - 1 (3.1%) 0 (0%) 1 (1.2%)
Death from LRTI 0 (0%) 0 (0%) - 0 (0%) 0 (0%) 0 (0%)
Relapse 0 (0%) 0 (0%) - 0 (0.0%) 2 (6.3%) 2 (2.4%)
Sum of adverse events 0 (0%) 0 (0%) 2 (6.3%) 6 (18.8%) 8 (9.4%)
*Non-acute medical institution: PACD < 8 (outpatient), PACD 8 - 15 (home health care, health resort, holiday bed), PACD > 15 (nurse-led unit).
statistically significant if p-values were <0.05. All tests
were performed with Microsoft Excel statistical analysis
tools and OpenEpi.
3. Results
3.1. Baseline Characteristics
Of the 85 included patients with LRTIs 59 (69.4%) had a
CAP, 11 (12.9%) an AECOPD and 6 (7.1%) an acute
bronchitis. The remaining 9 (10.6%) patients were diag-
nosed other diseases (e.g. pleurisy). Mean age of all pa-
tients was 70 years, and 56.5% were males (Table 1).
The highest mean value of ProADM was observed in the
CAP subgroup (1.17 nmol/l), followed by other diagnosis
(0.96 nmol/l), AECOPD (0.93 nmol/l) and bronchitis
(0.70nmol/l) (Table 1). Comparing the ProADM levels
of this study with the precursor OPTIMA I Aarau study
shows similar values and similar distribution. In general,
baseline characteristics, including distribution of coex-
isting illnesses, was similar in both studies, only age an-
drate of confusionwere significantly higher in the OP-
TIMA I Aarau study (Table 1). One reason could be the
more consequent inclusion of sicker patients, possibly
due to the fact that the principle investigators of both
observational studies were located in Aarau.
3.2. Adverse Events
The comparison of adverse events within each risk cate-
gory showed no difference between the current study
with the precursor OPTIMA I Aarau study (Table 2).
Although not statistically significant there is a visible
increase in predictability of adverse events in the new
CURB65-A-score compared to the CURB65-score when
looking at the equivalent low-risk groups, such as
CURB65 class 0 - 1 with 2 adverse events (5.4%) and the
CURB65-A class I with no adverse event (0%) (Table
2).
In the current disposition at the University Hospital of

Validation of a Risk-Based Biomarker-Enhanced Scoring System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
75
Basel none of theoutpatients had any adverse event (0%)
(Table 3). Neither the single patient of category G
(=geriatric) nor the patients of category S (=short) ha-
dany adverse event (0%) (Table 3). Among the patients
of category A (=acute) 3 patients were admitted to the
ICU (4.7%), 1 required mechanical ventilation (1.5%), 1
had an empyema (1.5%), 1 had an adverse reaction to
antibiotics (1.5%) and 2 developed a relapse (3.1%) (Ta-
ble 3).
The distribution of adverse events in the new risk
categories created by the application of the new risk-
based biomarker-enhanced CURB65-A-score would have
been as following: none of the patients in the three sub-
groups of disposition site “non-acute medical institution”
had any adverse event (0%) (Table 3). In the intermedi-
ate-risk group, where short hospitalization would be re-
commended, 1 patient would have required mechanical
ventilation (3.1%) and 1 would have developed an ad-
verse reaction to antibiotics (3.1%) (Table 3). Finally, in
the high-risk group for which hospitalization is recom-
mended there would be 3 cases of ICU admission (9.4%),
1 case of empyema (3.1%) and 2 cases of relapse (6.3%)
(Table 3).
3.3. Disposition Site
The current disposition at the University Hospital of
Basel (ASG disposition pathway) led to the following
distribution: 11 (12.9%) patients were treated as outpa-
tients, 1 (1.2%) patient belonged to category G (=geriat-
ric) and needed treatment in a geriatric hospital, 9 (10.6%)
patients had a short hospitalization (category S) and 64
(75.3%) patients were hospitalized (category A) (Table
3). The disposition sites obtained by the new risk-based
biomarker-enhanced disposition would have been the
following: 21 (24.7%) patients would end up in a non-
acute medical institution, while14 (16.5%) out of them
would be treated as outpatients and 7 (8.2%) in either a
home health care, a health resort or in a holiday bed (Ta-
ble 3). 32 (37.6%) patients would need only a short hos-
pitalization, whereas the other 32 (37.6%) patients would
need hospitalization. Further, the new risk-based bio-
marker-enhanced disposition criteria considered 14 (16.5%)
patients suitable for outpatient treatment compared to 11
(12.9%) in the current disposition (p = 0.5) (Table 3). It
detected 7 (8.2%) patients to be best treated outside the
hospital for nursing reasons, while the current disposition
detected only 1 (1.2%) patient requiring geriatric care (p
= 0.09) (Table 3). Most importantly, it shows with only
32 hospitalized patients a significant (p < 0.001) decrease
in the number of regular hospitalizations compared to
the64hospitalizedpatients in the current ASG disposition
pathway, represented in category A (=acute) (Table 3).
3.4. Limitations
Convenience sample of patients in working hours might
have led to selection bias. The low number of enrolled
patients is the main limitation of our study. Therefore, it
has to be considered a proof-of-concept study for the
transfer of a novel triage pathway from one Swiss hospi-
tal to another. As a purely observational study, we cannot
claim the safety or efficacy, but only the potential for an
improved triage, which is being tested in an interven-
tional randomized controlled study at the Medical Uni-
versity Department of the Kantonsspital Aarau at the
moment.
4. Discussion
This study shows that using the new risk-based bio-
marker-enhanced disposition would have led to signifi-
cantly fewer hospitalizations than the current disposition
system. Both disposition systems are safe, as no adverse
event was identified in the “non-acute medical institu-
tion” group (=CURB65-A classI) or in the non-hospital
groups of the current disposition system(=outpatients and
group G) (Table 3). The new risk-based ProADM-en-
hanced disposition indicates that several patients could
be shifted from the acute hospital setting to nursing fa-
cilities resulting in a reduction of costs and nosocomial
complications. This suggests that the current more sub-
jective disposition systems may provide less confidence
for the treating physicians to select outpatient manage-
ment, even when the medical risk for the patient is low.
This suggestionis consistent with our prior observation
that unnecessary fear of potential complications even in
low-risk patients is one of the major drivers for hospi-
talization in Switzerland [26]. This is where we see the
opportunity of home health care, health resorts or holiday
beds thatwould reduce unnecessary and costly hospitali-
zations in an acute care facility due to LRTIs. In addition
to likely economic benefits, a reduction of unnecessary
or unnecessarily long hospitalizations has further benefits,
especially considering thatprolonged hospitalization leads
to increased frailty [6] and to a higher risk for nosoco-
mial infections [6]. Since the use of ProADM increases
the prognostic accuracy of clinical scores for mortality
and severe adverse events [19-21], it enables the CUR
B65-A-score to be superior to the common CURB65-
score as previously shown [21] and indicated in this
study) (Table 2). An advantage of this novel risk-based
biomarker-enhanced disposition compared to clinical
scores alone, as for example the pneumonia severity in-
dex (PSI), is that it also takes into consideration func-
tional and biopsychosocial factors by using appropriate
scores (PACD and SPI) in addition to comorbid illnesses
and a set of predefined overruling criteria. This is of

Validation of a Risk-Based Biomarker-Enhanced Scoring System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
76
great importance, given that the most common reason for
still hospitalizing low-risk patients is the presence of a
comorbid illness [10].
The broad implementation of objective scoring sys-
tems like PSI and CURB65-score into daily clinical rou-
tine has until now been constricted by either complexity
(PSI) or only moderate sensitivity and specificity for
adverse reactions (CURB65-score) and by their neglect
of comorbid illness, biopsychosocial and organizational
factors [10]. These handicaps of previous scores are ad-
dressed by the novel risk-based biomarker-enhanced dis-
position that includesthe new ProADM-enhanced CURB
65-A-score.
5. Conclusion
In conclusion, our interdisciplinary biomarker-enhanced
risk-based dispositionis an objective tool and might be
more efficient in detecting patients for outpatient treat-
ment or treatment in a nursing care facility than the cur-
rent triage practice at the University Hospital of Basel.
Further, his study supports the external validity of Pro
ADM-enhanced disposition pathway.
6. Acknowledgements
The authors are deeply grateful for the help of the fol-
lowing people that made this survey study possible:
 Ursula Schild for support with data collection;
 Antoinette Conca for support with statistics;
 Josefine Putbrese for patient screening;
 Dr. Natascha Woy, Dr. Anna Messmer and the staff
in the Emergency Department of the University Hos-
pital of Basel;
 Renate Hunziker and the staff in the central laborato-
ries of the Kantonsspital Aarau;
 The patients, family members, and caregivers who
participated in the study.
7. Competing Interests
The OPTIMA project series was supported in part by
grants from the Swiss National Science Foundation (32
003B_135222), the scientific council of the Kantonsspi-
talAarau AG and the Ministry of Health of the Canton of
Aargau. Werner Albrich and Beat Müller received sup-
port from BRAHMS Thermo Fisher and from bioMéri-
eux to attend meetings and fulfill speaking engagements
and served as consultants for BRAHMS Thermo Fisher.
Beat Müller received research support from BRAHMS
Thermo Fisher. All other authors report no conflict of
interest. No commercial sponsor had any involvement in
design and conduction of this study, namely, the collec-
tion, management, analysis, and inter- pretation of the
data; and preparation, decision to submit, review, or ap-
proval of the manuscript.
8. Contributors
WCA and BM had the idea, wrote the protocol and initi-
ated the study, RXSDS and FD managed the trial and
collected data, RXSDS and WCA performed the statisti-
cal analyses, RXSDS, FD and WCA drafted the manu-
script. All other authors amended and commented on the
manuscript. All authors approved the final version.
REFERENCES
[1] T. M. File Jr., “Case Studies of Lower Respiratory Tract
Infections: Community-Acquired Pneumonia,” The Ame-
rican Journal of Medicine, Vol. 123, No. 4, 2010, pp. S4-
S15. doi:10.1016/j.amjmed.2010.02.002
[2] D. Mertz and J. Johnstone, “Modern Management of
Community-Acquired Pneumonia: Is It Cost-Effective
and Are Outcomes Acceptable?” Current Infectious Dis-
ease Reports, Vol. 13, No. 3, 2011, pp. 269-277.
doi:10.1007/s11908-011-0178-8
[3] G. W. Ruhnke, M. Coca-Perraillon, B. T. Kitch and D. M.
Cutler, “Trends in Mortality and Medical Spending in Pa-
tients Hospitalized for Community-Acquired Pneumonia:
1993-2005,” Medical Care, Vol. 48, No. 12, 2010, pp.
1111-1116. doi:10.1097/MLR.0b013e3181f38006
[4] M. S. Niederman, J. S. McCombs, A. N. Unger, A. Kumar
and R. Popovian, “The Cost of Treating Community-
Acquired Pneumonia,” Clinical Therapeutics, Vol. 20,
No. 4, 1998, pp. 820-837.
doi:10.1016/S0149-2918(98)80144-6
[5] S. V. Monte, N. M. Paolini, E. M. Slazak, J. J. Schentag
and J. A. Paladino, “Costs of Treating Lower Respiratory
Tract Infections,” The American Journal of Managed
Care, Vol. 14, No. 4, 2008, pp. 190-196.
[6] K. E. Covinsky, E. Pierluissi and C. B. Johnston, “Hospi-
talization-Associated Disability: ‘She Was Probably Able
to Ambulate, but I’m Not Sure’,” The Journal of the
American Medical Association, Vol. 306, No. 16, 2011,
pp. 1782-1793. doi:10.1001/jama.2011.1556
[7] Reyes, S. R. Martinez, J. M. Vallés, E. Cases and R. Me-
nendez, “Determinants of Hospital Costs in Community-
Acquired Pneumonia,” The European Respiratory Jour-
nal: Official Journal of the European Society for Clinical
Respiratory Physiology, Vol. 31, No. 5, 2008, pp. 1061-
1067.
[8] M. J. Fine, T. E. Auble, D. M. Yealy, B. H. Hanusa, L. A.
Weissfeld, D. E. Singer, C. M. Coley, T. J. Marrie and W.
N. Kapoor, “A Prediction Rule to Identify Low-Risk Pa-
tients with Community-Acquired Pneumonia,” The New
England Journal of Medicine, Vol. 336, No. 4, 1997, pp.
243-250. doi:10.1056/NEJM199701233360402
[9] W. S. Lim, M. M. van der Eerden, R. Laing, W. G. Boer-
sma, N. Karalus, G. I. Town, S. A. Lewis and J. T. Mac-
farlane, “Defining Community Acquired Pneumonia Se-
verity on Presentation to Hospital: An International Deri-
vation and Validation Study,” Thorax, Vol. 58, No. 5,

Validation of a Risk-Based Biomarker-Enhanced Scoring System for Lower Respiratory Tract Infections
(OPTIMA I Basel)
77
2003, pp. 377-382. doi:10.1136/thorax.58.5.377
[10] D. Aujesky, J. B. McCausland, J. Whittle, D. S. Obrosky,
D. M. Yealy and M. J. Fine, “Reasons Why Emergency
Department Providers Do Not Rely on the Pneumonia
Severity Index to Determine the Initial Site of Treatment
for Patients with Pneumonia,” Clinical Infectious Dis-
eases, Vol. 49, No. 10, 2009, pp. e100-e108.
doi:10.1086/644741
[11] A. R. Akram, J. D. Chalmers and A. T. Hill, “Predicting
Mortality with Severity Assessment Tools in Out-Patients
with Community-Acquired Pneumonia,” QJM: Monthly
Journal of the Association of Physicians, Vol. 104, No.
10, 2011, pp. 871-879.
[12] P. Schuetz, D. Stolz, B. Mueller, N. G. Morgenthaler, J.
Struck, C. Mueller, R. Bingisser, M. Tamm and M. Christ-
Crain, “Endothelin-1 Precursor Peptides Correlate with
Severity of Disease and Outcome in Patients with Com-
munity Acquired Pneumonia,” BMC Infectious Diseases,
Vol. 8, 2008, p. 22. doi:10.1186/1471-2334-8-22
[13] M. Christ-Crain, D. Stolz, S. Jutla, O. Couppis, C. Mül-
ler, R. Bingisser and P. Schuetz, “Free and Total Cortisol
Levels as Predictors of Severity and Outcome in Com-
munity-Acquired Pneumonia,” American Journal of Res-
piratory and Critical Care Medicine, Vol. 176, No. 9,
2007, pp. 913-920.
[14] S. Q. Khan, R. J. O’Brien, J. Struck, P. Quinn, N. Mor-
genthaler, I. Squire, J. Davies, A. Bergmann and L. L. Ng,
“Prognostic Value of Midregional Pro-Adrenomedullin in
Patients with Acute Myocardial Infarction. The LAMP
(Leicester Acute Myocardial Infarction Peptide) Study,”
Journal of the American College of Cardiology, Vol. 49,
No. 14, 2007, pp. 1525-1532.
[15] M. Jougasaki, R. J. Rodeheffer, M. M. Redfield, K. Ya-
mamoto, C. M. Wei, L. J. McKinley and J. C. Burnett Jr.,
“Cardiac Secretion of Adrenomedullin in Human Heart
Failure,” The Journal of Clinical Investigation, Vol. 97,
No. 10, 1996, pp. 2370-2376. doi:10.1172/JCI118680
[16] K. Kitamura, K. Kangawa, M. Kawamoto, Y. Ichiki, S.
Nakamura, H. Matsuo and T. Eto, “Adrenomedullin: A
Novel Hypotensive Peptide Isolated from Human Pheo-
chromocytoma,” Biochemical and Biophysical Research
Communications, Vol. 192, No. 2, 1993, pp. 553-560.
doi:10.1006/bbrc.1993.1451
[17] R. Q. Wu, W. F. Dong, X. L. Qiang, Y. X. Ji, T. P. Cui, J.
T. Yang and M. Zhou, “Human Vasoactive Hormone
Adrenomedullin and Its Binding Protein Rescue Experi-
mental Animals from Shock,” Peptides, Vol. 29, No. 7,
200, pp. 1223-1230. doi:10.1016/j.peptides.2008.02.021
[18] D. Bell and B. J. McDermott, “Intermedin (Adrenome-
dullin-2): A Novel Counter-Regulatory Peptide in the
Cardiovascular and Renal Systems,” British Journal of
Pharmacology, Vol. 153, No. 1, 2008, pp. S247-S262.
doi:10.1038/sj.bjp.0707494
[19] P. Schuetz, M. Wolbers, M. Christ-Crain, R. Thomann, C.
Falconnier, I. Widmer and S. Neidert, “Prohormones for
Prediction of Adverse Medical Outcome in Community-
Acquired Pneumonia and Lower Respiratory Tract Infec-
tions,” Critical Care, Vol. 14, No. 3, 2010, p. R106.
[20] M. Christ-Crain, N. G. Morgenthaler, D. Stolz, C. Müller,
R. Bingisser, S. Harbarth, M. Tamm, J. Struck, A. Berg-
mann and B. Müller, “Pro-Adrenomedullin to Predict Se-
verity and Outcome in Community-Acquired Pneumo-
nia,” Critical Care, Vol. 10, No. 3, 2006, p. R96.
[21] W. C. Albrich, F. Dusemund, K. Rüegger, M. Christ-
Crain, W. Zimmerli, T. Bregenzer, S. Irani, et al., “En-
hancement of CURB65 Score with Proadrenomedullin
(CURB65-A) for Outcome Prediction in Lower Respira-
tory Tract Infections: Derivation of a Clinical Algorithm,”
BMC Infectious Diseases, Vol. 11, 2011, p. 112.
doi:10.1186/1471-2334-11-112
[22] W. C. Albrich, K. Rüegger, F. Dusemund, R. Bossart, K.
Regez, U. Schild and A. Conca, “Optimised Patient Trans-
fer Using an Innovative Multidisciplinary Assessment in
Kanton Aargau (OPTIMA IAarau): An Observational Sur-
vey in Lower Respiratory Tract Infections,” Swiss Me-
dical Wweekly, Vol. 141, 2011, Article ID: w13237.
[23] N. G. Morgenthaler, J. Struck, C. Alonso and A. Berg-
mann, “Measurement of Midregional Proadrenomedullin
in Plasma with an Immunoluminometric Assay,” Clinical
Chemistry, Vol. 51, No. 10, 2005, pp. 1823-1829.
doi:10.1373/clinchem.2005.051110
[24] großeSchlarmann J: Der CMS© imePA©. Verschiedene
Qualitätsdimensioneneines Instruments. Eineempirische
Analyse. Gelsenkirchen: Private Universität Witten/Her-
deckegGmbH, 2007.
[25] S. M. Louis, M. P. Kossovsky, P. Chopard, P. Sigaud, T.
V. Perneger and J. M. Gaspoz, “A Predictive Score to
Identify Hospitalized Patients’ Risk of Discharge to a
Post-Acute Care Facility,” BMC Health Services Re-
search, Vol. 8, 2008, p. 154.
doi:10.1186/1472-6963-8-154
[26] C. Baehni, S. Meier, P. Spreiter, U. Schild, K. Regez, R.
Bossart, R. Thomann, et al., “Which Patients with Lower
Respiratory Tract Infections Need Inpatient Treatment?
Perceptions of Physicians, Nurses, Patients and Rela-
tives,” BMC Pulmonary Medicine, Vol. 10, 2010, p. 12.
doi:10.1186/1471-2466-10-12