Association of Procalcitonin With Acute Pyelonephritis
and Renal Scars in Pediatric UTI
WHAT’S KNOWN ON THIS SUBJECT: Prompt, high-quality
diagnosis of acute pyelonephritis and later identification of
children with scarring are important to prevent future
complications. Examination by dimercaptosuccinic acid scan is
the current clinical gold standard but is not routinely performed.
WHAT THIS STUDY ADDS: Procalcitonin demonstrated a more
robust predictive ability, compared with C-reactive protein or
white blood cell count, to selectively identify both children who
had acute pyelonephritis during the early stage of urinary tract
infections, as well as those with late scarring.
abstractBACKGROUND AND OBJECTIVE: Urinary tract infections (UTIs) are com-
mon childhood bacterial infections that may involve renal parenchymal
infection (acute pyelonephritis [APN]) followed by late scarring.
Prompt, high-quality diagnosis of APN and later identification of
children with scarring are important for preventing future complications.
Examination via dimercaptosuccinic acid scanning is the current clinical
gold standard but is not routinely performed. A more accessible assay
could therefore prove useful. Our goal was to study procalcitonin as
a predictor for both APN and scarring in children with UTI.
METHODS: A systematic review and meta-analysis of individual patient
data were performed; all data were gathered from children with UTIs who
had undergone both procalcitonin measurement and dimercaptosuccinic
acid scanning.
RESULTS: A total of 1011 patients (APN in 60.6%, late scarring in 25.7%)
were included from 18 studies. Procalcitonin as a continuous, class, and
binary variable was associated with APN and scarring (P , .001) and
demonstrated a significantly higher (P , .05) area under the receiver
operating characteristic curve than either C-reactive protein or white
blood cell count for both pathologies. Procalcitonin $0.5 ng/mL yielded
an adjusted odds ratio of 7.9 (95% confidence interval [CI]: 5.8–10.9)
with 71% sensitivity (95% CI: 67–74) and 72% specificity (95% CI: 67–76)
for APN. Procalcitonin $0.5 ng/mL was significantly associated with
late scarring (adjusted odds ratio: 3.4 [95% CI: 2.1–5.7]) with 79%
sensitivity (95% CI: 71–85) and 50% specificity (95% CI: 45–54).
CONCLUSIONS: Procalcitonin was a more robust predictor compared
with C-reactive protein or white blood cell count for selectively
identifying children who had APN during the early stages of UTI, as
well as those with late scarring. Pediatrics 2013;131:870–879
AUTHORS: Sandrine Leroy, MD, PhD,a,b Anna Fernandez-
Lopez, MD,c Roya Nikfar, MD,d Carla Romanello, MD,e
François Bouissou, MD,f Alain Gervaix, MD,g Metin K.
Gurgoze, MD,h Silvia Bressan, MD,i Vladislav Smolkin, MD,j
David Tuerlinckx, MD,k Constantinos J. Stefanidis, MD,l
Georgos Vaos, MD,m Pierre Leblond, MD,n Firat Gungor,
MD,o Dominique Gendrel, MD,p and Martin Chalumeau, MD,
PhDp,q
aCentre for Statistics in Medicine, University of Oxford, Oxford,
United Kingdom; bEpidemiology of Emerging Disease Unit, Institut
Pasteur, Paris, France; cDepartment of Pediatrics, Joan de Deu
Hospital, Barcelona, Spain; dDepartment of Pediatrics, Abuzar
Children Medical Center Hospital, Ahvaz, Iran; eDepartment of
Pediatrics, University of Udine, Udine, Italy; fDepartment of
Pediatrics, Children’s Hospital, Paul Sabathier University, CHU
Purpan, Toulouse, France; gDepartment of Pediatrics, University
Hospital of Geneva, Geneva, Switzerland; hFirat University
Medicine Faculty, Department of Pediatrics, Division of Pediatric
Nephrology, Elazı�g, Turkey; iDepartment of Pediatrics, University
of Padova, Padova, Italy; jDepartment of Pediatrics, Medical
Center, Afula, Israel; kDepartment of Pediatrics, Cliniques
Universitaires de Mont-Godinne, Université Catholique de Louvain,
Yvoir, Belgium; lDepartment of Pediatrics, ‘‘P. and A. Kyriakou’’
Children’s Hospital, Athens, Greece; mDepartment of Pediatric
Surgery, Alexandroupolis University Hospital, Democritus
University of Thrace School of Medicine, Thrace, Greece;nDepartment of Pediatrics, Jeanne de Flandre Hospital, Lille,
France; oAkdeniz University, School of Medicine, Department of
Nuclear Medicine, Antalya, Turkey; pDepartment of Pediatrics of
Necker Hospital, Paris-Descartes University, Paris, France; andqInserm U953 Unit, Paris, France
KEY WORDS
acute pyelonephritis, children, procalcitonin, renal scarring,
urinary tract infection
ABBREVIATIONS
APN—acute pyelonephritis
AUC—area under the curve
CI—confidence interval
CRP—C-reactive protein
DCA—decision curve analysis
DMSA—dimercaptosuccinic acid
LR—likelihood ratio
OR—odds ratio
PCT—procalcitonin
ROC—receiver operating characteristic
UTI—urinary tract infection
VUR—vesicoureteral reflux
WBC—white blood cell
(Continued on last page)
870 LEROY et al at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from
asurement and dimercaptosuccinic
acid scanning. that's a DMSA scan...
NCCPeds Journal Club - June 2013
scan this to watch
a video about PCT
and infections
Urinary tract infections (UTIs) are the
most common invasive bacterial in-
fections among young febrile chil-
dren.1 UTIs can occur as simple
bladder infections (lower UTI; bacte-
riuria only) but can also involve the
kidneys (acute pyelonephritis [APN],
in which bacteriuria is associated
with infectious renal parenchymal in-
volvement), leading to renal scarring.2
The belief that persisting APN effects
followed by late renal scarring, some-
times with recurrences, may lead to
future complications such as hyperten-
sion and/or end-stage renal failure has
been the major driving force behind the
aggressive investigation and treatment
of first-occurrence UTIs.3 The prompt
and high-quality diagnosis of APN and
differentiation from lower UTI is there-
fore of key importance. A dimercapto-
succinic acid (DMSA) scan is considered
the gold standard in imaging for both
renal parenchymal involvement during
acute infection and for late renal dam-
age left by the infection.4 However, DMSA
scans are not performed in most
children with UTI due to the limited
availability of nuclear medicine de-
partments compared with the high
number of children with UTIs. Thus,
a more practical and accessible tool
that could assist clinicians in de-
termining the presence of renal pa-
renchymal involvement and/or late
renal damage would be of great clin-
ical value.
Procalcitonin (PCT), a 116–amino acid
propeptide of calcitonin without hor-
monal activity, is an early, sensitive,
and specific marker of bacterial in-
fection.5,6 PCT is almost undetectable
under physiologic conditions or dur-
ing viral infections but rises in re-
sponse to bacterial endotoxins; the
extent of this increase seems to be
proportional to the severity of the in-
fection.6 However, its exact role, if any,
in the inflammatory response and in
the cytokine cascade remains unknown.6
In febrile UTI, the predictive ability of high
PCT concentrations for both APN and late
renal scarring has been previously in-
vestigated by several teams. A review7
and a recent systematic review and
meta-analysis8 showed that a serum
PCT .0.5 ng/mL predicts early renal
parenchymal involvement reasonably
well (diagnostic odds ratio [OR]: 14.3
[95% confidence interval (CI): 4.7–
43.2]); however, heterogeneity made
these results inconclusive.8 Moreover,
results concerning late renal scarring
were controversial, with no pooled
measurements provided.7 Most of this
heterogeneity and these discrepancies
may be due to threshold effects be-
cause the initial studies chose different
PCT cutoff values due to population
variation; unfortunately, any effects
from the latter could not be fully ex-
plored with only pooled data from the
studies. Under these circumstances,
the only way to analyze PCT as a con-
tinuous biomarker without a priori
threshold choice, simultaneously con-
trol for potential individual-level con-
founders, and then provide robust
conclusions concerning PCT as a pre-
dictor of APN and/or scarring would be
to obtain individual data unaltered by
thresholds.9
We thus aimed to perform an updated
systematic reviewandmeta-analysison
individual patient data to investigate
PCT as a predictor for both APN and
renal scarring in children with a febrile
UTI. The most appropriate threshold
values of PCT were simultaneously
studied.
METHODS
We performed a systematic review and
meta-analysis on individual patient
data, in accordance with international
standards (Centre for Reviews and
Dissemination guidelines,10 PRISMA,11
and STARD12). We electronically and
manually searched for all cohort
studies of children with UTI, a PCT
measurement, and a renal DMSA scin-
tigraphy published between January
1993 and September 2011. The search
methods are detailed in Fig 1. Ethics
committees from each participating
center approved the protocol for each
initial study from which data were
collected.
All cohort studies of consecutively in-
cluded children with a febrile UTI, a PCT
measurement, and an early (ie, within
14 days) and a late (ie, repeated at least
FIGURE 1Flow chart of the systematic review. The elec-
tronic search was conducted in Medline for all
studies of UTI with a PCT measurement in chil-
dren published from January 1993 (when PCT
was first described in relation to bacterial in-
fection5) through November 2009, and updated
in September 2011. The search strategy used
medical subject heading terms and text words,
including “procalcitonin” and “children.” The
electronic search was enhanced by hand-
searching reference lists of all included articles,
obtaining any identified articles, and also sup-
plemented by a manual review of abstracts from
the European Society of Pediatric Infectious
Diseases, the European Society for Pediatric
Nephrology, the International Pediatric Ne-
phrology Association, the American Academy of
Pediatrics, and the American Society of Ne-
phrology and by discussion with experts in the
field. The electronic search was then validated,
comparing the obtained list with the reference
list of previous reviews on PCT and UTI7,8 to
identify any potential systematic default. No
language restriction was used. The search
ended with 290 potentially eligible abstracts,
among which 19 were considered for inclusion.
One article was not included because of absence
of DMSA scan data. Finally, 18 articles were in-
cluded, representing 13 centers as follows: Afula
(Israel),28 Ahvaz (Iran),33 Antalya (Turkey),22
Athens (Greece),27 Barcelona (Spain),19,20 Elazig
(Turkey),26 Geneva (Switzerland),17,21,31 Lille
(France),25 Padova (Italy),32 Thrace (Greece),29,30
Toulouse (France),16 Udine (Italy),23 and Yvoir
(Belgium).24
ARTICLE
PEDIATRICS Volume 131, Number 5, May 2013 871
at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from
and ER docs
still refuse
to check
for it
future complications such as hyperten-
sion and/or end-stage renal failure hassion and/or end-stage renal failure hassion and/or end-stage renal failure has
been the major driving force behind the
aggressive investigation and treatment
of first-occurrence UTIs.rst-occurrence UTIs.3
the gold standard in imaging for both
Procalcitonin (PCT), a 116–amino acid
propeptide of calcitonin without hor-
monal activity, is an early, sensitive,
and specific marker of bacterial in-
fection.fection.5,6
lots of
lit on
using this
for SBI,
etc. but
never panned
out as
practical
usuallymade byC-cells ofthyroid,but ininfection,made bylung and GIcells
seems like this has already been done,so why do it here? read on...
43.2]); however, heterogeneity made
key statistic in a meta-analysis. Ifthe main results in a bunch of smallerstudies vary too much, they can't reallybe combined well.
cause the initial studies chose different
PCT cutoff values due to population
to obtain individual data unaltered by
thresholds.thresholds.9Wow - most meta-analysesdon't get access to theactual data, but the results
**think of the study population in a meta-analysis as individual studies. Just like you need to evaluate how patients are selectedin a RCT, you need to evaluate how studies are selected to include in a meta-analysis. Was their search complete? What was inclusionexclusion criteria? Classic rookie mistakes are just searching PubMed, or searching only using keywords, or just using English articles.
tronic search was conducted in Medline for all
medical subject heading terms and text words,
youneedtoknowwhata MESHis.Checkit outthenexttimeyousearchPubMedelectronic search was enhanced by hand-
searching reference lists of all included articles,
plemented by a manual review of abstracts from
phrology and by discussion with experts in the
identify any potential systematic default. No
language restriction was used. The search
of DMSA scan data. Finally, 18 articles were in-
cluded, representing 13 centers as follows: Afula
3 months later if available) renal DMSA
scintigraphy were included. Febrile UTI
was defined as fever ($38°C) with
a positive result on bacterial urine
culture (thresholds for collection
techniques are given in Table 1). We
asked the authors to send us their
study data files (duplicates were dis-
carded if any), from which we ex-
tracted clinical (gender, age), laboratory
(C-reactive protein [CRP], PCT, white
blood cell [WBC] count), and radio-
logic (DMSA scan results, vesicou-
reteral reflux [VUR] grade13 on
cystography) data. Information con-
cerning the standard operating pro-
cedures used for urine collection, PCT
measurement, DMSA scanning (and
timing), and cystography at each
center was also collected. Methodo-
logic study quality was assessed via
a checklist (Supplemental Appendix
Tables 1 and 2).14
We analyzed the relationships between
APN/renal damage and PCT, CRP, and
WBCcount, respectively, usingdifferent,
backward, stepwise multilevel logistic-
regression models for each biomarker
(center was a group-level variable),
with fractional polynomial transfor-
mation for continuous variables if the
model assumption of linearity was vi-
olated. The discriminative ability of
each biomarker for APN and then late
renal damage was evaluated by draw-
ing receiver operating characteristic
(ROC) curves, as well as by calculating
sensitivity, specificity, predictive values,
and likelihood ratios (LR) after dichot-
omization. In addition, we compared
biomarker models by using decision
curve analysis (DCA), a method for
evaluating the clinical net benefit of
predictionmodels in which the benefits
(true-positives) are addedand theharms
(false-positives) are subtracted.15
Due to collinearity, no attempt was
made to combine biomarkers. Sta-
tistical methods are detailed in the
Appendix. TABLE
1PopulationCharacteristicsAccordingto
Each
Center
Centera
UrineCollectionTechniques
(Thresholdofthe
Positive
Bacteriuria)b
TimingofLate
DMSAScan
No.Included
(forAPN)
APN,n(%
)No.Included
(forLRS)
LRS,n(%
)Male,n(%
)Age(m
o),
Median(IQR)
All-Grade
VUR,n(%
)
Grade$3
VUR,n(%
)
Centers
using
SAorUC
Afula28
SA(any),UC(103)
—64
23(36)
0—
22(35)
14.0(4.5–23.5)
17(27)
9(14)
Antalya22
UC(104),CVM(105)
3–6mo
33
21(64)
23
4(17)
2(6)
48.0(24.0–72.0)
2(33)
2(33)
Athens2
7SA(103),UC(104),CVM(105)
6mo
61
25(41)
59
10(17)
27(44)
0.6(0.2–2.0)
9(15)
7(12)
Barcelona19,20
SA(any),UC(5.104),CVM(105)
—174
92(53)
0—
83(48)
4.8(2.0–9.0)
27(23)
9(5)
Elazig26
UC(103),CVM(105)
6mo
76
34(45)
76
12(16)
28(37)
30.0(10.5–72)
3(4)
0(0)
Geneva
17,21,31
SA(103),UC(103),CVM(105)
3mo
80
77(96)
59
34(58)
32(40)
8.9(2.4–21.5)
21(28)
9(12)
Thrace
29,30
SA(any),UC(104),CVM(105)
6mo
57
27(47)
57
12(21)
13(23)
16.0(7.0–40.0)
15(29)
11(22)
Udine23
UC(105),CVM(105)
6mo
100
63(63)
79
18(23)
31(31)
8.0(4.0–17.5)
16(18)
9(10)
Yvoir24
SA(103),UC(5.104),CVM(105)
6mo
61
48(79)
52
18(35)
13(21)
37.2(12.5–78.4)
13(22)
6(10)
Centers
usingSB
Ahvaz33
SB(105),CVM(105)
—100
62(62)
0—
19(19)
8.0(17.5–57.0)
——
Lille25
SB(105)
—42
21(50)
0—
12(29)
13.5(6.0–48.0)
14(58)
3(13)
Padova
18,32
SB(105)
12mo
72
52(72)
61
14(23)
31(43)
4.5(1.1–9.8)
13(18)
3(4)
Toulouse
16
SB(105),CVM(105)
6–24moc
91
68(75)
59
13(22)
19(21)
20.0(8.8–64.9)
32(36)
12(14)
Between-center
variability(P)
,.001
,.001
.008
.001
,.001
,.001
Total
1011
613(61)
525
135(26)
332
(33)
10.0(4.0–30.0)
182(23)
80(10)
Referencesforthearticlescorrespondingtoeach
centerare
presentedintheSupplementalAppendix.CVM,clean-voidedmidstream;IQR,interquartilerange;LRS,laterenalscars;SA,suprapubicaspiration;SB,sterilebag;UC,urethralcatheterization.
aClassifiedaccordingto
theurinecollectiontechniqueinnon–toilet-trainedchildren.
bIncolony-form
ingunitspermilliliter.
cMedianof9months.
872 LEROY et al at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from
similar
subjects,
similar
definitions,
similar
outcomes
backward, stepwise multilevel logistic-
regression models for each biomarker
with fractional polynomial transfor-
mation for continuous variables if the
model assumption of linearity was vi-
olated. The discriminative ability of
'logistic'means weshould begettingodds ratios
'stepwise'means eachvariablewas added orsubtractedfrom theregressionone at atime - sub-tractedsince it's'backward'
this is
fancy
talk for
'the data
wasn't
bell-shaped
and the
effect
non-linear
so we
changed it
for the
model &
then changed
it back
ing receiver operating characteristic
(ROC) curves, as well as by calculating
who says exciting new stats of interest to the average clinicians aren't being invented anymore?
Good
Table1!
Theindividualstudies
arethe
subjectsin
ameta-analysis.
You
canuse
this
Tableto
compare
each
study.
Try
answeringthesequestions:
-Nameastudy
thatused
abag-urine(generously&mistakenlycalleda'sterilebag')that
onlydid
earlyDMSAs?
-Nameastudy
thathad
the
highest%of
femalesAND
thehighest%of
severereflux?
Between-center
,.001
,.001
.008
.001
.001
.008
.001
.001
.008
.001
,.001
,.001
variability(P)PP
this
showswhetherthere
were
signif.
differencesbetweenstudies.
Werethere?
Due to collinearity, no attempt was
made to combine biomarkers. Sta-
blood cell [WBC] count), and radio-
(C-reactive protein [CRP], PCT, white(C-reactive protein [CRP], PCT, white
these are
the 3
things
they will
analyze
RESULTS
Study Characteristics
Following the aforementioned criteria,
weretrieved227abstractsbyelectronic
searching; 19 were potentially suitable
(Fig 1). After full text review, 1 study
was not included because of absence
of DMSA scan data, leaving 18 articles
to be included.16–33 The 13 corre-
sponding study authors were con-
tacted; all agreed to participate and
send data. A total of 1011 (97.9%)
patients fully met the inclusion criteria.
All studies had a high methodologic
quality (Supplemental Appendix Tables
1 and 2). Nine (69.2%) centers per-
formed both early and late DMSA
scans. All centers performed the early
DMSA scan within 7 days; 5 of 9 centers
performed the late scan at 6 months,
and the other centers varied between 3
and 24 months (Table 1). Among the 9
centers performing late DMSA scans,
late scanning was systematically
conducted regardless of early-scan
results in only 1 (11.1%) center (Elazig,
Turkey). Nine (69.2%) centers col-
lected urine samples following high-
quality standard operating proce-
dures (suprapubic aspiration, urethral
catheterization for non–toilet-trained
children, and clean-voided midstream
for the other patients). All centers
measured PCT by using the LUMItest
PCT immunoluminometric assay or
the BRAHMS PCT-Q semiquantitative
rapid test (BRAHMS, Hennigsdorf,
Germany). All centers included hos-
pitalized children with UTI. No ad-
verse events had been reported
in performing PCT measurement,
DMSA scanning, or cystography.
Table 1 provides details on the
characteristics of each center’s
population.
Analysis of APN and late renal scars
involved 1011 and 525 patients, re-
spectively. APNbygradewasanalyzed in
357 patients. PCT as a continuous vari-
able involved only 883 (87.3%) patients,
as PCT was measured by the PCT-Q
semiquantitative test for 128 patients.
Analysis of CRP and WBC count involved
959 (94.9%) and 962 (95.2%) patients,
respectively. VUR was examined in 772
(76.4%) patients.
Predicting APN
APN was demonstrated in 613 children
(60.6%) of the 1011 patients included.
The mean6 SD age of the children was
25.2 6 32.8 months (median: 10.5;
interquartile range: 4.3–32.3); 332
(32.8%) were boys. VUR was di-
agnosed in 182 (23.6%), and VUR $3
was found in 80 children (10.4%). PCT
as a continuous, class, or binary var-
iable was significantly associated with
APN (Table 2, Fig 2). The strength of the
association increased when the PCT
category (when ordinal variable) in-
creased (Table 2). PCT $0.5 ng/mL
(current threshold used in the litera-
ture) yielded an adjusted OR of 7.9
(95% CI: 5.8–10.9). CRP and WBC count
were also significantly related to APN,
with similar OR values for CRP as
previously described; however, lower
OR values were obtained for WBC
count (Table 2). PCT as a continuous
variable offered an area under the
ROC curve (AUC ROC) of 0.82 (95% CI:
0.79–0.84), after adjusting according
to the chosen model. The AUC ROCs for
CRP and WBC count were significantly
lower (P , .0001): 0.72 (95% CI: 0.69–
0.76) and 0.62 (95% CI: 0.57–0.65), re-
spectively, once adjusted by using the
model (Fig 3). The DCA demonstrated
that PCT provided a more statistically
robust test than CRP, WBC count, or ex-
treme systematic strategies (ie, DMSA
for all or no patients) for all threshold
probabilities. A PCT threshold $0.3 ng/
mL (median of nondiseased patient
distribution) demonstrated 88% sen-
sitivity (95% CI: 85–90), with 47%
specificity (95% CI: 42–52) (Table 2);
interestingly, PCT $0.5 ng/mL offered
a higher specificity of 72% (95% CI: 67–
76) with a 71% sensitivity (95% CI: 67–
74) (Table 3). PCT remained strongly
associated with APN when assessed
by clinical grade, as did CRP and WBC
count (Supplemental Appendix Ta-
ble 3).
Predicting Late Renal Scars
Late scars were demonstrated in 135
(25.7%) of the 525 children included.
The mean6 SD patient age was 26.66
33.8 months (median: 11.0; inter-
quartile range: 4–36); 162 (31%) were
male. VUR was present in 107 (22.0%)
of the 486 patients who underwent
cystography; VUR$3 was diagnosed in
51 (10.5%) children. PCT as a continu-
ous and binary variable was signifi-
cantly associated with renal scars
(Table 2, Fig 2). PCT$0.5 ng/mL yielded
an adjusted OR of 3.4 (95% CI: 2.1–5.7).
CRP and WBC count were also signifi-
cantly related to renal scarring (Ta-
ble 2, Fig 2). PCT as a continuous
variable resulted in an AUC ROC of 0.75
(95% CI: 0.70–0.80) once adjusted
according to the model built and was
significantly higher (P = .02) than those
values observed for CRPandWBC count
(0.70 [95% CI: 0.65–0.76] and 0.66 [95%
CI: 0.60–0.72], respectively) (Fig 3).
According to DCA, PCT was better than
CRP, WBC count, and both extreme
systematic strategies (ie, DMSA for
everyone or no one) (Fig 3). PCT $0.5
ng/mL had a 79% of sensitivity (95% CI:
71–85), with a 50% specificity (95% CI:
45–54) (Table 3).
DISCUSSION
We demonstrated that the measure-
ment of serum PCT can provide con-
siderable predictive value for the
development of APN and renal scars,
and that this predictive capacity is
better than that provided by either CRP
or WBC count regardless of considered
thresholds. Because the related medi-
cal decision process is binary (to per-
form or not to perform a DMSA scan),
our goal was to provide an alternative
ARTICLE
PEDIATRICS Volume 131, Number 5, May 2013 873
at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from
tacted; all agreed to participate and
send data. A total of 1011 (97.9%)
unheard ofin U.S.wherepublish orperishmakes oneprotectiveof data
Turkey). Nine (69.2%) centers col-
lected urine samples following high-
quality standard operating proce-quality standard operating proce-quality standard operating proce-
dures (suprapubic aspiration, urethral
so 4 used
low-quality
procedures
like the
'sterile'
bag
as a continuous, class, or binary var-
iable was significantly associated with
APN (Table 2, Fig 2). The strength of the
just
diffways
to
categorize
creased (Table 2). PCT 0.5 ng/mL$0.5 ng/mL
ture) yielded an adjusted OR of 7.9 diff
(95% CI: 5.8–10.9). CRP and WBC count
count (Table 2). PCT as a continuous
variable offered an area under the
ROC curve (AUC ROC) of 0.82 (95% CI:
0.79–0.84), after adjusting according
model (Fig 3). The DCA demonstrated
that PCT provided a more statistically
robust test than CRP, WBC count, or ex-
ble 2, Fig 2). PCT as a continuous
variable resulted in an AUC ROC of 0.75
(95% CI: 0.70–0.80) once adjusted
(Table 2, Fig 2). PCT$0.5 ng/mL yielded
an adjusted OR of 3.4 (95% CI: 2.1
According to DCA, PCT was better than
CRP, WBC count, and both extreme
assay that could contribute to di-
agnosis and investigate the optimal
threshold by which these clinical de-
cisions could be made. PCT$0.5 ng/mL
seemed to offer the optimal compro-
mise of sensitivity and specificity for
both APN and late renal scars: 71%
sensitivity (95% CI: 67–74) with a 72%
specificity (95% CI: 67–76) for APN;
79% sensitivity (95% CI: 71–85) with
a 50% of specificity (95% CI: 45–54)
for late scarring. Furthermore, DCA
showed that PCT offered the best
benefit/harm balance irrespective of
the chosen threshold, compared with
CRP, WBC count, or systematic strat-
egies (DMSA for everyone or no one)
for the selective identification of
children who might benefit from
a DMSA scan.
Our findings add evidence to those of
Mantadakis et al.8 Together, they sug-
gest a reasonably strong predictive
value based on PCT levels; however,
data from studies using pooled esti-
mates lead to a more cautious in-
terpretation due to the significant
heterogeneity found within these
study pools. We avoided these issues
by working with individual data,
adjusting for intercenter variability
modeling with multilevel regressions,
as well as accounting for all covari-
ables of interest at the individual level.
Moreover, the study design (a meta-
analysis of individual patient data)
allowed us to study the impact of dif-
ferent threshold levels, to perform
DCA, and to draw conclusions without
the usual threshold effect that often
affects diagnostic accuracy assessed
by meta-analysis, thus confounding
results. Our approach was comple-
mentary to that of Zaffanello et al,7
who performed a nonsystematic re-
view of the potential of PCT to predict
late renal scarring, without comput-
ing pooled estimates, as they were
confronted with different studies and
cutoffs.With our systematicmeta-analysis,
TABLE 2 Relationship Between APN or Late Renal Scars and PCT, CRP, and WBC Count
Variables Crude OR (95% CI) P Adjusted OR (95% CI) P
APN
PCT (ng/mL)a
PCT as a continuous variable 2.6 (2.2–3.1) ,.0001 2.7 (2.3–3.1) ,.0001
PCT as a class variable
,0.13 1 1
0.13–0.3 2.5 (1.3–4.9) .006 2.3 (1.2–4.6) .01
0.3–0.6 3.1 (1.6–5.8) .001 3.0 (1.6–5.6) .001
.0.6 16.9 (9.3–30.6) ,.0001 17.0 (9.3–30.9) ,.0001
PCT as a class variable
,0.5 1 1
0.5–2 3.5 (2.5–5.0) ,.0001 3.7 (2.6–5.3) ,.0001
2–10 25.3 (14.8–43.2) ,.0001 26.3 (15.3–45.0) ,.0001
.10 132.5 (17.9–979) ,.0001 146.3 (19.7–1085) ,.0001
PCT as a binary variable ($0.3) 5.5 (3.8–7.8) ,.0001 5.8 (4.0–8.3) ,.0001
PCT as a binary variable ($0.5) 7.5 (5.5–10.3) ,.0001 7.9 (5.8–10.9) ,.0001
CRP (mg/L)b
CRP as a continuous variable 2.7 (2.3–3.3) ,.0001 2.7 (2.3–3.3) ,.0001
CRP as a class VARIABLE
,10 1 1
10–20 2.7 (1.4–5.5) .005 2.9 (1.4–5.9) .003
20–60 8.0 (4.2–15.2) ,.0001 8.2 (4.3–15.5) ,.0001
.60 26.0 (13.6–49.7) ,.0001 26.5 (13.8–50.8) ,.0001
CRP as a binary variable ($20) 8.6 (5.5–13.3) ,.0001 8.6 (5.5–13.3) ,.0001
CRP as a binary variable ($30) 6.3 (4.4–9.0) ,.0001 6.3 (4.4–9.1) ,.0001
WBC count (cell/mm3)c
WBC count as a continuous variable 1.0 (1.0–1.0) ,.0001 1.0 (1.0–1.0) ,.0001
WBC count as a class variable
,10 000 1 1
10 000–15 000 1.8 (1.2–3.1) .01 1.8 (1.1–3.0) .01
15 000–20 000 3.1 (1.9–5.1) ,.0001 3.0 (1.8–5.0) ,.0001
.20 000 4.9 (2.9–8.3) ,.0001 4.9 (2.9–8.3) ,.0001
WBC count, binary variable ($15 000) 2.4 (1.8–3.3) ,.0001 2.4 (1.8–3.3) ,.0001
Late renal scars
PCT (ng/mL)d
PCT as a continuous variable 1.8 (1.6–2.1) ,.0001 1.7 (1.5–2.0) ,.0001
PCT as a binary variable ($0.5) 3.8 (2.3–6.1) ,.0001 3.4 (2.1–5.7) ,.0001
CRP (mg/L)e
CRP as a continuous variable 2.3 (1.8–2.9) ,.0001 2.2 (1.7–2.9) ,.0001
CRP as a binary variable ($20) 4.8 (2.5–9.4) ,.0001 4.6 (2.3–9.4) ,.0001
CRP as a binary variable ($30) 4.6 (2.5–8.4) ,.0001 4.2 (2.3–7.9) ,.0001
WBC count (cell/mm3)f
WBC count as a continuous variable 1.0 (1.0–1.0) ,.0001 1.0 (1.0–1.0) ,.0001
WBC count, binary variable ($15 000) 2.1 (1.4–3.3) ,.0001 2.1 (1.3–3.3) ,.0001
A model was separately derived for each biomarker (PCT, CRP, or WBC count). Univariate and multivariate analysis used
a multilevel regression model. The final multivariate model was built by using a backward stepwise reduction procedure.
Each multivariate model for each biomarker was significantly better than the reduced model based on the maximum
likelihood test (P , .05).a The analysis included 883 patients for PCTas a continuous or class variable, 937 patients for PCT dichotomized according to
the 0.3 ng/mL threshold, and 1011 patients for PCT dichotomized according to the 0.5 ng/mL threshold (because some
patients had a PCT-Q test [see text]). The final multivariate model was based on PCTand age (as a continuous variable, after
linear transformation); continuous PCTwas transformed as follows to assess the model linearity assumption: ln(PCT/100) +
3.33732173.b The analysis included 959 patients. The final multivariate model was based on CRP and age (as a continuous variable, after
linear transformation); CRP was transformed as follows to assess the model linearity assumption: ln(CRP/100) +
0.3965495066.c The analysis included 962 patients. The final multivariate model was based on WBC count and gender.d The analysis included 479 patients. The finalmultivariatemodel was based on PCTand high-grade VUR; PCTwas transformed
as follows to assess the model linearity assumption: ln(PCT/100) + 3.293186643.e The analysis included 478 patients. The final multivariate model was based on CRP and high-grade VUR; CRP was trans-
formed in the same purpose as above into: ln(CRP/100) + 0.439036841.f The analysis included 478 patients. The final multivariate model was based on WBC count and high-grade VUR.
874 LEROY et al at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from
CRP as a class VARIABLE
someone leftcaps lock on
2.6x incr for
each 1 ng/mL
PCT increase
(Reference)3.1x incr compared
to those w/ PCT
< 0.13 (the ref)
Late renal scars
APN
linear transformation); CRP was transformed as follows to assess the model linearity assumption: ln(CRP/100) +
0.3965495066. here's some specifics of transformation mentioned in methods
great caption footnotes. A table should
be able to stand on its own if plucked
out of an article and placed on the
front of USA Today.
nal multivariate model was based on WBC count and gender.
nal multivariate model was based on CRP and high-grade VUR; CRP was trans-
nal multivariate model was based on WBC count and high-grade VUR.
nalmultivariatemodel was based on PCTand high-grade VUR; PCTwas transformed
different
variables
used in the
different multivariable regressions
we offer further evidence to support
their results, updating the review in
a systematic manner and providing
pooled estimates of the predictive
ability of PCT, leading to a robust con-
clusion.
The use of imaging in this field has been
largely debated in the last decade.
However, the decision as to which tests,
if any, should be routinely conducted in
children with UTIs necessarily depends
on many factors. The “top-down” ap-
proach uses early DMSA scanning as
a screening test.34 Although children
with a negative acute-phase DMSA scan
are unlikely to develop scarring, DMSA
scans are expensive, invasive, and ex-
pose children to radiation. However,
the top-down strategy raises 2 concerns:
first, it requires DMSA scan availability
across countries and settings, which is
not currently the case, and second, the
identification of late renal scarring
results in only a more careful follow-up
of affected children. Therefore, PCT may
occupy an intermediate position useful
for identifying children at high risk for
APN and renal scarring, and for whom
a DMSA scan can be selectively pro-
posed to confirm parenchymal in-
volvement. The reported sensitivity and
specificity values may not appear very
convincing (∼70%). However, PCT is not
meant to replace DMSA scanning, which
remains the gold standard for assess-
ing parenchymal involvement (APN or
scarring). PCT could be used as an in-
termediate strategy, based on a single
biomarker, easier to set up than a nu-
clear imaging process, which can help
discriminate between lower UTI and
APN, even in settings in which DMSA
scans are not available. Interestingly,
PCT offered the best benefit/harm
balance irrespective of the chosen
threshold, compared with systematic
strategies (DMSA for everyone or no
one) for the selective identification of
children who might benefit from
a DMSA scan. Later in the imaging
evaluation, a cystography could be
proposed for children with a proven
APN, to diagnose or rule out VUR, and
treat it if necessary. Moreover, PCT
may also be helpful when choosing
between oral or intravenous antibi-
otic treatments during the early in-
fectious phase, depending on the
severity of the UTI (lower UTI or APN).
PCT could find a place in the debated
process of UTI imaging and treatment,
as a key point test in the decisional
flowchart.
There are several potential limitations
to our study that should be addressed.
First, despite the extensive electronic
and hand searches performed, a pub-
lication bias is possible, especially be-
cause test accuracy studies are more
FIGURE 2Distribution of PCT, CRP, and WBC count values according to the presence of APN or late renal scars. (A–C) Plots of PCT, CRP, and WBC count for APN. (D–F)
Plots of PCT, CRP, and WBC count for late renal scars. In each graphic, the horizontal line is the proposed threshold for dichotomizing classification via the
biomarker.
ARTICLE
PEDIATRICS Volume 131, Number 5, May 2013 875
at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from
Look at Graph A. What would you tell your cousin if she asked what's the PCT cutoff fordetermining whether her kid has pyleo? There's so much overlap, right? That's wherea Receiver-Operating Curve can help
There are several potential limitations
to our study that should be addressed.
good
studies
discuss
their
limit-
ations.
How many
did
they
list?
First, despite the extensive electronic
easily conducted and abandoned than
randomized controlled trials, and are
then particularly susceptible to publi-
cation bias.35 However, our current
knowledge about the precise effects of
publication bias on meta-analytic esti-
mates, as well as how to assess the
extents of these possible limitations,
are limited.36 Therefore, due to the
complexity of accurately assessing
this issue, we can provide no esti-
mates of the effect of a probable
publication bias. Secondly, a partici-
pation bias related to the response
and voluntary participation of the
centers also might be possible but
seems unlikely because all authors
contacted responded positively to our
FIGURE 3ROC curve and DCA for PCT, CRP, and WBC count. (A) ROC curves of PCT, CRP, and WBC count for APN. (B) ROC curves of PCT, CRP, and WBC count for late renal
scars. (C) The DCA of PCT, CRP, andWBC count for APN. One line represents 1 biomarker (PCT, CRP, andWBC count); the line “all” represents the benefit/harm
curve if everyone is investigated with a DMSA scan, whereas the line “none” represents the corresponding curve if no one undergoes examination.
(D) The DCA of PCT, CRP, and WBC count for late renal scars. One line represents 1 biomarker (PCT, CRP, and WBC count); the line “all” represents the
benefit/harm curve if everyone is investigated with a DMSA scan, whereas the line “none” represents the corresponding curve if no one undergoes
examination.
TABLE 3 Diagnostic Accuracy of PCT, CRP, and WBC Count for APN and Late Renal Scars
Variable Sensitivity Specificity Positive Predictive Value Negative Predictive Value Positive LR Negative LR
For APN
PCT$0.3 ng/mL 88 (85–90) 47 (42–52) 74 (71–77) 69 (63–74) 1.6 (1.5–1.8) 0.3 (0.2–0.3)
PCT$0.5 ng/mL 71 (67–74) 72 (67–76) 79 (76–83) 61 (57–66) 2.5 (2.1–3.0) 0.4 (0.4–0.5)
PCT$1 ng/mL 65 (61–69) 87 (83–90) 90 (86–92) 60 (55–64) 4.9 (3.7–6.5) 0.4 (0.4–0.5)
CRP $20 mg/L 87 (84–89) 41 (37–47) 70 (67–74) 66 (59–71) 1.5 (1.3–1.6) 0.3 (0.3–0.4)
CRP $30 mg/L 74 (70–77) 54 (49–59) 72 (69–76) 56 (51–61) 1.6 (1.4–1.8) 0.5 (0.4–0.6)
WBC count $15 000/mm3
63 (59–67) 55 (50–60) 71 (67–74) 46 (42–51) 1.4 (1.2–1.6) 0.7 (0.6–0.8)
For late renal scars
PCT$0.3 ng/mL 91 (85–95) 30 (26–35) 31 (27–36) 91 (84–95) 1.3 (1.2–1.4) 0.3(0.2–0.5)
PCT$0.5 ng/mL 88 (83–91) 50 (45–54) 51 (46–56) 87 (82–91) 1.6 (1.4–1.8) 0.4 (0.3–0.6)
PCT$1.0 ng/mL 74 (66–81) 67 (62–71) 43 (37–50) 88 (84–92) 2.3 (1.9–2.6) 0.4 (0.3–0.5)
CRP $20 mg/L 85 (78–90) 36 (32–41) 32(27–37) 87 (81–92) 1.3 (1.2–1.5) 0.4 (0.3–0.6)
CRP $30 mg/L 78 (71–85) 47 (41–52) 34 (29–39) 86 (81–90) 1.5 (1.3–1.7) 0.5 (0.3–0.7)
WBC count $15 000/mm3
68 (60–75) 51 (46–56) 33 (27–38) 82 (77–86) 1.4 (1.2–1.6) 0.6 (0.5–0.8)
Data are presented as % (95% CI).
876 LEROY et al at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from
Here are the basics of a ROC. The point closest to the upper left corner is the best cut-off for
an imperfect test. It also means the highest area under the curve (AUC)
The curve that
is highest is
best. But it
may not be
best in every
situation.
publication bias. Secondly, a partici-
requests for patient data sets. Thirdly,
the possibility of a classification bias
seems unlikely because PCT was
measured by using validated techni-
ques (immunoluminometric assay or
semiquantitative PCT-Q assay), while
blinded to the outcome. Fourthly, we
assumed that patients who had a nor-
mal DMSA had no late lesions even if
late DMSA was not performed. How-
ever, this assumption is commonplace
in the literature,37 and we verified this
assumption in the only center (Elazig,
Turkey) in which all patients system-
atically underwent both late and early
DMSA: none of the negative early DMSA
cases were followed by a positive late
DMSA. This outcome gives an in-
dication on the robustness of our
assumption. Fifth, we addressed het-
erogeneity issues due to data pooling
from different centers (including dif-
ferent time frames for the late DMSA
scan) by analyzing them as hierar-
chical data and using multilevel mod-
eling. We chose to analyze the data set
as a meta-analysis of individual pa-
tient data because this method pro-
vides the least biased and most
reliable means of addressing the
questions at hand.9 Sixth, technical
concerns, such as the collection of
urine from non–toilet-trained children
in sterile bags at 4 of the selected
centers (not a recommended method)
could have led to selection bias.38,39
This procedure could have increased
the number of false-positive results
for UTI but without consequences to
the relationship between APN or late
scarring and PCTor other biomarkers.
In addition, the inclusion of only hos-
pitalized children by the centers might
have led to another selection bias, due
to the inclusion of only the sickest
children. However, all children with
febrile UTI were systematically hospi-
talized in the included centers. Sev-
enth, the absence of previous negative
DMSA scintigraphy results might also
have introduced a selection bias. Even
if all the centers confirmed having
included exclusively or mostly chil-
dren with a first febrile UTI, it could
not guarantee that previous UTI with
persistent scarring had not occurred
in the included patients, as correct
diagnosis depends on the clinical
evaluation performed during pre-
vious febrile episodes. Moreover, not
all UTIs in children are accompanied
by fever, which is the main clinical
reason for obtaining urine cultures in
infants; therefore, previous UTIs
unaccompanied by fever may not have
been clearly identified. Lastly, the
delay between the first indications
of infection and PCT level measures
was not taken into account, and this
might have introduced a bias in the
results but only by underestimating
the relationship between APN or late
scarring and PCT, because this
marker increases as early as 6 hours
postinfection and also decreases just
as quickly at the end of infection.
CONCLUSIONS
We demonstrated that PCT has a robust
predictive ability to selectively identify
children who had APN in the early
stages of UTI and those that developed
later renal scarring. The use of serum
PCT measurements has the potential
to aid the clinical decision-making
process regarding the appropriate
acute management of children with
UTI. In particular, due to limited re-
sources and technical availability, it
may be helpful to use such an assay to
selectively identify children who may
benefit from a DMSA scan at the early
and late stages of infection. The impact
of PCT measurements on the currently
debated practice of UTI examinations
needs to be evaluated by a well-
designed impact study and may lead
to possible refinement of the de-
cisional process.
ACKNOWLEDGMENTS
The authors thank Dr Gardikis and
Dr Deftereos (Department of Pediatric
Surgery andRadiology, Alexandroupolous
University Hospital, Democritus Uni-
versity of Thrace School of Medicine,
Thrace, Greece), Dr Galetto-Lacour (De-
partment of Pediatrics, University Hos-
pital of Geneva, Geneva, Switzerland),
Dr Ellero (Department of Pediatrics,
University of Udine, Udine, Italy), Profes-
sor Da Dalt (Department of Pediatrics,
University of Padova, Italy) for par-
ticipation in data collection/sharing,
Dr Bacchetta (Department of Pediatric
Nephrology–Reference Centre for Rare
Renal Diseases, Femme Mère Enfant
Hospital, University of Lyon, Lyon, France)
for helpful discussions, and Melissa
Laird (Inserm U818, Institut Pasteur,
Paris, France) for pertinent comments
and corrections.
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(Continued from first page)
Dr Leroy designed the study, helped in collecting the data, analyzed the data, interpreted results, drafted the manuscript, and approved the article version to be
published; Drs Fernandez-Lopez, Nikfar, Romanello, Bouissou, Gurgoze, Bressan, Smolkin, Leblond, Mr Vaos, and Mr Gervaix contributed to the study design,
acquired all the data in their individual centers, revised the paper for important intellectual content, and approved the article version to be published; Drs
Tuerlinckx and Gungor contributed to the study design, acquired all the data in their centers, and revised the paper for important intellectual content; Mr
Stefanidis had great input in the conception of the study, realized the data collection in his center, performed in-depth revision of the manuscript, and approved
the article version to be published; and Ms Gendrel and Mr Chalumeau contributed to the study design and interpretation of results, critically revised the
manuscript, and approved the article version to be published.
www.pediatrics.org/cgi/doi/10.1542/peds.2012-2408
doi:10.1542/peds.2012-2408
Accepted for publication Jan 24, 2013
Address correspondence to Sandrine Leroy, MD, PhD, Centre for Statistics in Medicine, University of Oxford, Wolfson College Annexe, Linton Rd, Oxford OX2 6UD,
United Kingdom. E-mail: [email protected]
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2013 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
FUNDING: Dr Leroy was funded by a postdoctoral fellowship grant from the French Society of Nephrology and from the Fondation Bettencourt.
ARTICLE
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at Inova Fairfax Hosp on May 27, 2013pediatrics.aappublications.orgDownloaded from
X-Axis is 'Threshold Probability' - abbreviated Pt
It's the probability of disease derived from a test that a clinician would act
i.e. cut out a tumor, do a bronch, admit for IV antibiotics, etc.
Ranges from 0 to 100%
y-axis
is 'Net benefit'
True Positives
minus
(False Positives
* Pt/(1-Pt)
0if you did nothing no matter what the test result is, the net benefit is 0 no matter what
you can translatethe net benefit 0.Yas 'I can identifyY patients w/ a diseaseby testing 100 ptsand not treating anyoneunnecessarily
treating
everyone no
matter what the
test is looks like the
pink line.
Here are the basic components of a DCA curve: the x and y axis, and the 2 standard curves
(treat everyone, treat noone). The results of the test are plotted with these.
0.3
0.2
0.1
-0.1
-0.2
-0.3
The authors have indicated they have no financial relationships relevant to this article to disclose.