RESEARCH
Predicting treatment failu
u
e
Community-acquired pneumonia (CAP) is a major health 11% for late failure [2].
Martin-Loeches et al. Respiratory Research 2014, 15:75
http://respiratory-research.com/content/15/1/75interleukin 8 (IL-8) [5]. Some inflammation markers, suchBarcelona, Spain
Full list of author information is available at the end of the articleproblem worldwide. Clinically, CAP exhibits enormous
variety in the severity of presentation, from septic shock at
one end of the spectrum to almost asymptomatic disease
at the other [1]. Treatment failure is a matter of great con-
cern in the management of CAP. The reported incidence
of treatment failure among hospitalized patients with CAP
It has been recognized that an excessive systemic pro-
inflammatory response in patients with sepsis and severe
CAP is associated with deleterious effects and a worse prog-
nosis [3]. Conversely, an exaggerated anti-inflammatory re-
sponse conducted by some grade of impaired immune
response may have a negative effect on clinical resolution
[4]. Among the cytokines involved in the inflammatory re-
sponse, we can include interleukin 10 (IL-10) as a critical
anti-inflammatory cytokine that attenuates inflammatory
responses in macrophages and T cells, tumor necrosis fac-
tor (TNF-α), interleukin 1 (IL-1), interleukin 6 (IL-6) and
* Correspondence: ATORRES@clinic.ub.es
†Equal contributors
5Hospital Clinic, IDIBAPS, Universitat de Barcelona, CIBER enfermedades
respiratorias, Servei de Pneumologia, Institut del Torax, Villarroel 170, 08036IntroductionAbstract
Introduction: Treatment failure in community-acquired-pneumonia (CAP) patients is associated with a high mortality
rate, and therefore are a matter of great concern in clinical management. Those patients have increased mortality and
are a target population for randomized clinical trials.
Methods: A case–control study was performed in patients with CAP (non-failure cases vs. failure cases, discriminating
by late and early failure). CRP, PCT, interleukin 1, 6, 8 and 10 and TNF were determined at days 1 and 3 of
hospitalization.
Results: A total of 253 patients were included in this study where 83 patients presented treatment failure. Of these,
40 (48.2%) had early failure. A discriminative effect was found for a higher CURB-65 score among late failure patients
(p = 0.004). A significant increase on day 1 of hospitalization in CRP (p < 0.001), PCT (p = 0.004), IL-6 (p < 0.001) and IL-8
(p = 0.02), and a decrease in IL-1 (p = 0.06) in patients with failure was observed compared with patients without failure.
On day 3, only the increase in CRP (p < 0.001), PCT (p = 0.007) and IL-6 (p < 0.001) remained significant. Independent
predictors for early failure were higher IL-6 levels on day 1 (OR = 1.78, IC = 1.2-2.6) and pleural effusion (OR = 2.25,
IC = 1.0-5.3), and for late failure, higher PCT levels on day 3 (OR = 1.60, IC = 1.0-2.5), CURB-65 score≥ 3 (OR = 1.43,
IC = 1.0-2.0), and multilobar involvement (OR = 4.50, IC = 2.1-9.9).
Conclusions: There was a good correlation of IL-6 levels and CAP failure and IL-6 & PCT with late CAP failure. Pleural
effusion and multilobar involvement were simple clinical predictors of early and late failure, respectively.
Trial registration: IRB Register: 2009/5451.
Keywords: Treatment failure, Cytokines, Pneumonia, Community acquired pneumonia
ranges from 2.4% to 31% for early failure and from 3.9% tocommunity acquired pne
study
Ignacio Martin-Loeches1†, Xavier Valles2†, Rosario Menend
Antonio Artigas1 and Antoni Torres5*© 2014 Martin-Loeches et al.; licensee BioMed
Creative Commons Attribution License (http:/
distribution, and reproduction in any medium
Domain Dedication waiver (http://creativecom
article, unless otherwise stated.Open Access
re in patients with
monia: a case-control
z3, Oriol Sibila4, Beatriz Montull3, Catia Cilloniz1,Central Ltd. This is an Open Access article distributed under the terms of the
/creativecommons.org/licenses/by/4.0), which permits unrestricted use,
, provided the original work is properly credited. The Creative Commons Public
mons.org/publicdomain/zero/1.0/) applies to the data made available in this
Martin-Loeches et al. Respiratory Research 2014, 15:75 Page 2 of 8
http://respiratory-research.com/content/15/1/75as C-reactive protein (CRP) and procalcitonin (PCT), have
been recognized for some time in clinical management,
[6]. Therefore, besides clinical factors, cytokine levels can
predict CAP-failure and should be included to determine
those patients with CAP who are most at risk of develop-
ing treatment failure. Early prediction of CAP-failure is a
critical step toward better management and improvement
of survival. In this study, we aimed to determine clinical
and cytokine-level predictors of CAP-failure, including the
different types of failure (early and late). The aim of this
research is to ascertain the parameters, including cytokine
levels, for predicting CAP failure in general, and more
specifically, late and early failure.
Methods
Ethical issues
This study was approved by the Ethics Committee of Hospital
Clinic, Barcelona, Spain (IRB Register: 2009/5451) and pa-
tients or their relatives signed informed consent.
Patients and methods
A case–control study in patients with CAP was conducted by
the Pneumology Department in Hospital Clínic, Barcelona,
Spain and Hospital de la Fe, Valencia, Spain. Cases were
considered to be patients with CAP who developed treat-
ment failure and controls were CAP patients who did not
develop failure after a follow-up until they were dis-
charged. Patients with CAP (failure and non-failure) were
enrolled between January 2002 and December 2003. For
each case, 1 to 2 controls were included and matched by
age and time of hospitalization (hospitalized during the
same week).
The diagnosis of pneumonia was based on the presence
of acute onset of signs and symptoms suggesting lower re-
spiratory tract infection on admission, and radiographic
evidence of a pulmonary infiltrate that had no other known
cause [7]. CAP and Health Care—Associated Pneumonia
(HCAP) patients were included in the present study. Ex-
clusion criteria were patients with tuberculosis, obstructive
pneumonia caused by neoplasia or pneumonia as a ter-
minal event of chronic and progressive disease, HIV infec-
tion, neoplasia, those taking cytotoxic drugs or long-term
oral steroid therapy, such as daily dose of 20 mg of pred-
nisolone or the equivalent for >2 weeks and patients with a
previous admission within the previous 15 days.
Data collection
Patients with CAP (failure and non-failure) were enrolled
over a 24 month period. All CAP-failure without exclusion
criteria were included, and 1 to 2 non-failure CAP hospi-
talized within the same period of admission (within the
same week) were included as controls.
The following data were recorded from CAP patients
using a standard questionnaire: age, gender, smoking history,alcohol abuse (> 80 g/day) and comorbidities (chronic ob-
structive pulmonary disease (COPD), cardiac, liver, renal,
digestive or central nervous system diseases). The initial
severity was assessed using the pneumonia severity index
(PSI) and CURB 65 (confusion, urea, respiratory rate, blood
pressure and age) score [8].
Early treatment failure was defined, as clinical deterior-
ation within 72 h of treatment, as indicated by develop-
ment of shock, need for invasive mechanical ventilation, or
death. Late treatment failure was defined as radiographic
progression (increase > 50% of pulmonary infiltrates com-
pared to baseline), persistence of severe respiratory failure
(PaO2/FiO2 < 200, with respiratory rate > 30 min-1 in non-
intubated patients), development of shock, need for inva-
sive mechanical ventilation, or death between 72 and 96 h
after start of treatment [9,10]. Severe sepsis and septic
shock were defined following the criteria of the American
College of Chest Physicians and the Society of Critical Care
Medicine [11]. The indications for mechanical ventilation
and/or inotropic support were left to the discretion of the
attending physicians.
Measurements of serum cytokines and laboratory
procedures
Blood samples were drawn from all patients on enrol-
ment in the study, immediately at the moment of pneu-
monia diagnosis, and at day three after admission. Blood
was centrifuged, coded, and serum aliquoted and frozen
at -80°C until subsequent analysis. IL-10, TNF-α, IL-1,
IL-8 and IL-6 protein levels were measured blinded to
the groups, using a commercial enzyme immunoassay
technique (BioSource, Nivelles, Belgium). The limit of de-
tection was 1 pg/mL. CRP and PCT were measured using
standard procedures. Cytokine measurements were car-
ried out on the day of hospitalization (day 1) and at day 3
of hospitalization. Microbiological diagnostic were carried
out using routine procedures.
Statistical analysis and data management
Statistical analysis was performed using the SPSS 17.0 and
STATA 10.0 software programs. Results are presented as
mean, standard deviation (SD), median, frequency or per-
centage, as required. Continuous variables were analyzed
using the t test, whereas categorical variables were analyzed
using the χ2 test. Non-parametric tests (Mann–Whitney U,
Kruskal-Wallis or Fisher test) were used when necessary.
The association was adjusted using multivariate logistic re-
gression and multinomial (polytomous) logistic regression
when stratifying cases by early and late failure [12]. ROC
analysis was performed for each cytokine level regarding
CAP failure, early CAP failure and late CAP failure. CRP
levels were used as a baseline for comparisons. Cut-offs of
maximum sensitivity/specificity were determined for each
cytokine level and different outcomes (failure, early failure
and late failure), at day 1 and day 3 of hospitalization.
Cytokine levels were categorized and analyzed in equiva-
lent quartiles. Effects were calculated as odds ratios (OR)
with their corresponding 95% confidence intervals (CI).
A p-value of 0.05 was considered significant for the
univariate analysis, and variables with p < 0.1 were con-
sidered in the logistic regression model. Early failure cases
were dropped when considering cytokine levels at day 3
of admission.
Results
General description
A total of 253 patients with CAP were included in this
study (mean [SD] age, 69.1 [16.1] years; range, 19–91 years):
Briefly, the distribution of subjects of CURB 65 classes
among CAP patients was: 0-I, n = 102 (40.3%); II-III, n =
117 (46.1%); IV-V, n = 34 (13.4%). Septic shock was
present in 31 patients (12.8%). The main demographic
characteristics, comorbid conditions and severity are
shown in Table 1. Eighty-three patients (32.9%) developed
treatment failure, of which 40 (48.2%) had early failure.
Table 1 shows the general results and univariate analysis
of CAP and CAP failure.
Microbiological documentation of CAP was achieved in
106 patients (41.9%). Streptococcus pneumoniae was the
most common pathogen isolated (17.8%), followed by
Legionella pneumophila (5.1%) and Staphylococcus aureus
(3.9%). A total of 46.9% of the patients received third gen-
eration cephalosporin and a macrolide, 23.2% quinolone
alone and 8.7% third generation cephalosporin and quin-
olone as first line treatment. Significant differences were
found between patients with and without treatment fail-
ure. Patients with treatment failure presented higher se-
verity index or PSI scores (mean [SD], 109.6 [42.2] vs. 97.8
[36.6]; p = 0.02), higher prevalence of pleural effusion
(26.5% vs. 14.1%; p = 0.006), and ICU admission (36% vs.
3%; p < 0.001) with multilobar involvement (52.1% vs.
47.2%; p < 0.001) than those without treatment failure.
Length of hospital stay was higher in patients who pre-
sented treatment failure (mean [SD], 17.1 [14.1] days vs.
Table 1 General description and univariate analysis of variables under study
Variable (N, %) CAP non-failure N = 170 Early failure N = 40 p value** Late failure N = 43 p value***
Age, mean (SD) 67.2 (17.6) 67.4 (18.5) 0.8 69.0 (15.8) 0.8
Gender, male 116 (68.6) 26 (65) 0.6 24 (55.8) 0.1
Smoking habit 31 (18.3) 8 (20) 0.8 13 (30.2) 0.08
Alcohol intake 17 (10.1) 4 (10) 0.9 5 (11.6) 0.7
Pulmonary diseases 66 (39.1) 18 (45) 0.4 23 (53.3) 0.08
COPD 31 (18.3) 6 (15) 0.6 7 (16.3) 0.8
(4
17
20
(3
22
2.5
10
2.5
17
18
(5
15
(5
(3
(3
(3
.5 (
20
Martin-Loeches et al. Respiratory Research 2014, 15:75 Page 3 of 8
http://respiratory-research.com/content/15/1/75Previous Antibiotic therapy 61 (36.1) 19
Previous hospitalization 33 (19.5) 7 (
Previous CAP 48 (28.4) 8 (
Comorbidity (all) 93 (54.7) 15
Neurological 47 (27.7) 9 (
Cirrhosis 5 (2.9) 1 (
Cardiac insufficiency 33 (19.4) 4 (
Chronic Renal failure 14 (8.3) 1 (
Diabetes Mellitus 39 (22.9) 7 (
Digestive 34 (20.0) 8 (
Multilobar involvement 34 (20.0) 23
ICU admission 5 (3) 6 (
PSI IV-V 108 (63.9) 22
CURB 65 ≥ 3 50 (29.4) 14
Pleural effusion 24 (14.1) 13
Mortality 3 (1.8)* 13
Length of hospital stay, mean (SD) 8.1 (5.7) 17
Pneumococcal pneumonia 28 (16.5) 8 (*Patients included in this category; mortality was not directly attributed to the pneu
**p value comparing early failure cases with control group (non-failure CAP patient
***p value comparing late failure cases with control group (non-failure CAP patients7.5) 0.1 11 (25.6) 0.2
.5) 0.8 7 (16.3) 0.6
) 0.3 9 (20.9) 0.3
7.5) 0.05 24 (55.8) 0.9
.5) 0.5 13 (30.2) 0.7
) 0.8 2 (4.7) 0.6
.0) 0.2 11 (25.6) 0.4
) 0.2 1 (2.3) 0.2
.5) 0.5 7 (16.3) 0.3
.6) 0.8 7 (17.5) 0.7
3.5) < 0.001 14 (35.0) 0.04
) 0.002 24 (55.8) < 0.001
5) 0.3 34 (79.1) 0.06
5.0) 0.6 22 (51.2) 0.004
2.5) 0.006 9 (20.9) 0.3
2.5) < 0.001 14 (32.6) < 0.001
10.5) < 0.001 17.1 (17.1) < 0.001
.0) 0.6 8 (18.6) 0.7monia and occurred > 96 h, after admission.
s).
).
8.1 [5.7] days; p < 0.001). Thirty-one patients died (12.3%):
28 of 83 with treatment failure (33%) and 3 of 170 without
treatment failure (1.8%) (p < 0.001). Data are summarized
in Table 1.
Cytokine levels and CAP failure
With regard to cytokine levels, when comparing CAP vs.
CAP failure, we found a significant increase on day 1 of
hospitalization in CRP (p < 0.001), PCT (p = 0.004), IL-6
(p < 0.001) and IL-8 (p = 0.02). When stratifying CAP-
failure cases by early failure or late failure, we found a dis-
criminating effect of PCT for late failure on day 1 (p =
0.005), while it remained non-significant among patients
with early failure (p = 0.3). This was also observed, though
less marked, in IL-8 on day 1 (p = 0.06) and a protective
effect of IL-1 against late failure was found only on day 1
(p = 0.04). On day 3, CRP levels (p = 0.05), IL-6 levels
(p = 0.007) and PCT levels (p = 0.001), were significantly
higher among patients that developed late failure compared
to non-failure cases. No significant increases or decreases
addition, we found a discriminating effect of PCT levels
compared to CRP in the CAP late-failure group, with a
1.74 (1.2-2.5)          1.55 (1.1-2.2)1OR, (IC)
1.46 (1-2.1)1OR, (IC)
Martin-Loeches et al. Respiratory Research 2014, 15:75 Page 4 of 8
http://respiratory-research.com/content/15/1/75were observed in IL-10 or TNF. Figures 1, 2, 3 and 4 show
the box-plots and OR comparing CAP vs. CAP failure
and CRP, Il-6, IL-8 and PCT on day 1 of hospitalization.
Supplementary material (Additional file 1: Figure S1–S10)
shows the box-plots and OR for trend for each outcome
and cytokine level and day of hospitalization. No measur-
able predicting effect was observed when considering the
coefficient of variation between day 1 and day 3 in cases
with treatment failure.
To determine the usefulness of those parameters for
predicting CAP failure, we performed ROC (area under
1.93 (1.4-2.8)1OR, (IC)
Figure 1 Box-plot and OR for trend for treatment failure and
CRP at day 1 of hospitalization.the curve, AUC) analysis of significant cytokine levels
(CRP, PCT, IL-1, IL-6 and IL-8) (Tables 2, 3 and 4). Taking
CRP AUC analysis as a reference, we found that IL-6
showed better performance at predicting CAP failure
on day 1. This difference was also observed when stratify-
ing the analysis into early and late failure (Table 4). In
Figure 2 Box-plot and OR for trend for treatment failure and
IL-6 at day 1 of hospitalization.Figure 3 Box-plot and OR for trend for treatment failure and
IL-8 at day 1 of hospitalization.
Martin-Loeches et al. Respiratory Research 2014, 15:75 Page 5 of 8
http://respiratory-research.com/content/15/1/75better sensitivity/specificity ratio compared to CRP levels
on day 1 and day 3 (Table 3).
Predicting CAP failure
A logistic regression model was applied to all clinical
and cytokine levels included in this study to determine
1.65 (1.2-2.3)1OR, (IC)
Figure 4 Box-plot and OR for trend for treatment failure and
PCT at day 1 of hospitalization.independent predictive factors for CAP failure, including
early and late failure. A final model to predict CAP failure
includes higher IL-6 levels on day 1, CURB 65 score ≥ 3,
pleural effusion and multilobar involvement. Regarding
early failure, significant and independent predictors were
higher IL-6 levels on day 1 and pleural effusion, and for
late failure, higher PCT levels on day 3, CURB 65 score ≥ 3,
and multilobar involvement. Table 5 shows raw and ad-
justed analysis of the variables included in the final logistic
regression model.
Discussion
We determined a set of clinical and biological factors that
can be used to predict CAP failure and to discriminate
Table 2 AUC analysis and sensitivity and specificity of
Cytokine levels and CAP vs CAP failure
Day 1
AUC 95% IC p Cut-off Sensitivity Specificity
CRP 0.66 (0.43-0.59) 19.7 62.2 61.7
PCT 0.62 (0.54-0.69) 0.4 1.21 56.8 56.8
Interleukin 1 0.41 (0.33-0.50) < 0.001 159 43.2 42.0
Interleukin 6 0.70 (0.63-0.77) 0.2 124 64.9 62.7
Interleukin 8 0.61 (0.53-0.70) 0.8 11 56.8 61.7those patients at risk of developing early (IL-6) and late
(IL-6 and PCT) failure to improve prevention measures
and clinical management. It is important to remark that,
in some conditions, clinical symptoms are not accurate
enough and clinicians must look for biological markers. In
consequence, proper follow-up using specific biological
markers should help to discriminate early and late failure
and better monitoring of patients at risk.
Early failure has been commonly used as a surrogate
for progressive pneumonia with clinical deterioration
within the first 72 h after hospital admission [13]. It is in
this phase that the patient is more vulnerable to serious
deterioration and often requires advance support treat-
ment based on vasopressors and/or mechanical ventila-
tion. In our study, IL-6 showed the best correlation in
predicting early CAP failure (better than the widely used
CRP). It is important to remark that blood levels of CRP
are primarily regulated by the production of IL-6 [14]. In
clinical practice the use of CRP can be seen as a surro-
gate marker for levels of IL-6 [15]. Furthermore, the
presence of elevated levels of IL-6 has a good correlation
with not only early but also late failure on day 1 and 3
Unfortunately, IL-6 is not used in clinical practice and
merits further discussion. IL-6 is a pleiotropic cytokine
and its function is yet to be fully determined [16]. IL-6 is
a marker of severity and prognosis of severe infections,
although their causal relationship remains unclear. IL-6
can be secreted in response to specific microbial mole-
cules that induce intracellular signaling cascades that
give rise to inflammatory cytokine production [17]. We
Table 3 AUC analysis and sensitivity and specificity of
Cytokine levels and CAP vs Early failure
Day 1
AUC 95% IC p Cut-off Sensitivity Specificity
CRP 0.67 (0.58-0.76) 20.2 61.1 63.0
PCT 0.56 (0.47-0.65) 0.02 2.69 55.6 55.6
Interleukin 1 0.43 (0.32-0.54) 0.002 102.0 44.4 45.0
Interleukin 6 0.72 (0.64-0.81) 0.3 142.0 69.4 65.8
Interleukin 8 0.60 (0.50-0.71) 0.3 11.0 55.6 55.9also found that IL-6 is a better marker for detecting
treatment than PCT. However, PCT may be more valu-
able for discriminating late failure at an earlier phase
than IL-6 and CRP and, therefore, defining a subset of
patients who deserve close monitoring. In addition to
these results, we show the AUC analysis of the different
cytokines considered and the cut-off for sensitivity and
sensibility, to explore their usefulness as treatment fail-
ure predictors in future prospective studies. No relevant
association was found for IL-10 and TNF levels and
CAP failure.
In terms of the clinical factors under study, we found that
multilobar involvement associated not only with general
with CAP failure, but also specifically with late treatment
failure. Other authors have investigated independent
factors associated with early deaths in CAP and have
shown that age, abnormal mental status, multilobar pneu-
monia, shock, bacteremia, and inadequate empiric anti-
biotic therapy were predictors of death within 48 hours
[18]. Knowing predictors of treatment failure is import-
ant because this outcome parameter may assist further
studies in understanding the benefits of new antibacterial
or co-adjuvant therapies without including a large popula-
tion, which would be needed if mortality was the outcome
chosen [19].
Table 4 AUC analysis and sensitivity and specificity of Cytokine levels and CAP vs Late failure
CRP PCT Interleukin 1 Interleukin 6 Interleukin 8
Day 1 AUC 0.64 0.67 0.40 0.68 0.62
95% IC (0.53-0.75) (0.57-0.77) (0.29-0.51) (0.58-0.79) (0.51-0.74)
p 0.4 < 0.001 0.2 0.4
Cut-off 18.6 1.4 148.0 93.0 11.0
Sensitivity 60.6 60.5 39.5 57.9 57.9
Specificity 59.3 60.5 42.0 57.1 55.9
Day3 AUC 0.60 0.69 0.41 0.66 0.52
95% IC (0.47-0.73) (0.59-0.79) (0.28-0.54) (0.54-0.78) (0.39-0.65)
p 0.1 < 0.001 0.5 0.008
Cut-off 5.9 0.47 112.0 36.0 10.0
Sensitivity 53.6 80.7 46.4 64.3 50.0
Specificity 53.1 61.9 47.6 61.9 52.1
Table 5 Univariate (crude) and multivariate (adjusted) analysis of the variables included in the final predictor model
for CAP failure, early failure and late failure
iva
Martin-Loeches et al. Respiratory Research 2014, 15:75 Page 6 of 8
http://respiratory-research.com/content/15/1/75Un
OR
CURB 65 score≥ 3 CAP failure 1.31
Early 1.08
Late 1.59
Multilobar involvement CAP failure 3.21Early 2.15
Late 4.60
Pleural effusion CAP failure 2.19
Early 2.93
Late 1.61
Interleukin 6 level day 1 CAP failure 1.78
Early 1.85
Late 1.72
PCT level day 1 CAP failure 1.38
Early 1.19
Late 1.61
Interleukin level day 3 CAP failure ––––
Early –––
Late 1.71
PCT level day 3 CAP failure –––
Early –––
Late 2.01riate analysis Multivariate (Adjusted)
95% IC OR 95% IC
(1.1-1.6) 1.16 (0.9-1.5)
(0.8-1.4) 0.96 (0.7-1.3)
(1.2-2.1) 1.43 (1.0-2.0)
(1.8-5.7) 2.91 (1.5-5.5)(1.0-4.6) 1.79 (0.8-4.1)
(2.3-9.3) 4.50 (2.1-9.9)
(1.1-4.2) 1.85 (0.9-3.8)
(1.3-6.5) 2.25 (1.0-5.3)
(0.7-3.8) 1.40 (0.5-3.7)
(1.4-2.3) 1.60 (1.2-2.1)
(1.3-2.6) 1.78 (1.2-2.6)
(1.2-2.4) 1.42 (1.0-2.1)
(1.1-1.8) 1.10 (0.8-1.5)
(0.9-1.6) 0.98 (0.7-1.4)
(1.2-2.3) 1.22 (0.8-1.8)
–––– –––– –––
––– –––– –––
(1.3-3.0) 1.37 (0.9-2.1)
––– ––– –––
––– ––– –––
(1.2-2.5) 1.60 (1.0-2.5)
Marrie TJ, Kapoor WN: A prediction rule to identify low-risk patients with
Martin-Loeches et al. Respiratory Research 2014, 15:75 Page 7 of 8
http://respiratory-research.com/content/15/1/75To our knowledge, this is the first study in which the
kinetics of biomarkers have been investigated to detect
treatment failure using a case–control design. In this
sense, it is remarkably different than previous observa-
tional studies. Our findings reinforce results from previ-
ous studies [10], [1]. Nevertheless, this study has a more
suitable design, which may sustain implementation of
scientific results in clinical practice. In the case of treat-
ment failure in CAP, we have followed recommendations:
1-Starting with cross sectional studies, 2-Confirming these
results with a case–control and 3-The final step would be
to carry out proper cohort studies.
This study has some limitations that should be men-
tioned. The number of critically ill patients admitted is
low and our results need to be validated in a cohort of
patients admitted to ICU in order to minimize selection
bias. One of the limitations of this study as well as sev-
eral other studies of biomarkers of severity or failure is
that although the mean values of certain cytokines are
statistically different from patients with failure vs patients
without failure, the wide range of values for particular cy-
tokines make it difficult to use the value of a single patient
to predict clinical outcomes. Although samples were not
taken from patients on a daily basis, we chose to obtain
samples at day 1 and 3 in order to implement our study
design in current clinical practice, and some patients with-
out treatment failure were discharged before day three
(n = 24). However, when excluding those patients from
the general analysis, results and conclusions showed no
significant changes.
In summary, patients with treatment failure presented
higher levels of IL-6 on admission to hospital and a
CURB 65 score of 3 or more. Interestingly, high levels of
IL-6 had a high predictive value for early and late failure
on days 1 and for late failure on day 3, showing that IL-
6 is a good marker for progression to treatment failure,
and PCT may have a discriminative effect for predicting
late failure. In conclusion, a combination of IL-6, PCT
and CURB 65 score could provide a new tool for pre-
dicting failure and early and late failure. Pleural effusion
and multilobar involvement were simple clinical predic-
tors of early and late failure, respectively.
Additional file
Additional file 1: Figure S1. Box-plot and OR for trend for early and
late treatment failure and CRP at day 1 of hospitalization. Figure S2.
Box-plot and OR for trend for late treatment failure and CRP at day 3 of
hospitalization. Figure S3. Box-plot and OR for trend for early and late
treatment failure and PCT at day 1 of hospitalization. Figure S4. Box-plot
and OR for trend for late treatment failure and PCT at day 3 of hospitalization.
Figure S5. Box-plot and OR for trend for treatment failure and IL-1 at
day 1 of hospitalization. Figure S6. Box-plot and OR for trend for early
and late treatment failure and IL-1 at day 1 of hospitalization.
Figure S7. Box-plot and OR for trend for late treatment failure and
IL-1 at day 3 of hospitalization. Figure S8. Box-plot and OR for trendfor early and late treatment failure and IL-6 at day 1 of hospitalization.
Figure S9. Box-plot and OR for trend for late treatment failure and IL-6 at
day 3 of hospitalization. Figure S10. Box-plot and OR for trend for early and
late treatment failure and IL-8 at day 1 of hospitalization.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
IM-L, XV and AT assisted in the design of the study, coordinated patient
recruitment, analyzed and interpreted the data, and assisted in writing the
paper. CE, OS, CC, RM and BM made important contributions to the acquisition
and analysis of data. IML, XV, AA and AT were involved in revising the
manuscript critically for important intellectual content. AT, XV and IML made
substantial contributions to the concept, design, analysis and interpretation of
data and revised the final manuscript version. All authors read and approved
the final manuscript. AT acted as guarantor of/person responsible for the
entire manuscript.
Funding sources
CIBER enfermedades respiratorias, PII de Infecciones respiratorias de la SEPAR.
Author details
1Critical Care Center, Sabadell Hospital, CIBER Enfermedades Respiratorias,
Parc Tauli University Institute, Sabadell, Spain. 2Hospital Clinic, IDIBAPS,
Universitat de Barcelona, Servei de Pneumologia, Institut del Torax,
Barcelona, Spain. 3Hospital Universitari i Politècnic La Fe, Servicio de
Neumología, Valencia, Spain. 4Department of Pneumology, Hospital de la
Santa Creu i Sant Pau, Barcelona, Spain. 5Hospital Clinic, IDIBAPS, Universitat
de Barcelona, CIBER enfermedades respiratorias, Servei de Pneumologia,
Institut del Torax, Villarroel 170, 08036 Barcelona, Spain.
Received: 1 March 2014 Accepted: 4 June 2014
Published: 5 July 2014
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doi:10.1186/1465-9921-15-75
Cite this article as: Martin-Loeches et al.: Predicting treatment failure in
patients with community acquired pneumonia: a case-control study.
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