3335wGA RSV Prediction

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Respiratory Research
Open AccessResearch
A predictive model for respiratory syncytial virus (RSV)
hospitalisation of premature infants born at 33–35 weeks of
gestational age, based on data from the Spanish FLIP study
Eric AF Simões*1, Xavier Carbonell-Estrany2, John R Fullarton3,
Johannes G Liese4, Jose Figueras-Aloy5, Gunther Doering6, Juana Guzman7
and European RSV Risk Factor Study Group
Address: 1Professor of Pediatrics, Department of Pediatrics, Section of Infectious Diseases, The University of Colorado School of Medicine and The
Children's Hospital, Denver, Colorado, USA, 2Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia,
Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona, Barcelona,Spain, 3Analyst, Strategen Limited, Basingstoke,
Hampshire, UK, 4Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany, 5Neonatology Service, Hospital Clínic,
Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona,
Barcelona, Spain, 6Munich University of Technology, Department of Pediatrics, Munich, Germany and 7Hospital Reina Sofía, Córdoba, Spain
Email: Eric AF Simões* - eric.simoes@uchsc.edu; Xavier Carbonell-Estrany - xcarbo@clinic.ub.es; John R Fullarton - JohnRFullarton@aol.com;
Johannes G Liese - Johannes.Liease@med.uni-muenchen.de; Jose Figueras-Aloy - Jfiguer@clinic.ub.es;
Gunther Doering - guntherdoring@web.de; Juana Guzman - juanaguzman@telefonica.net; European RSV Risk Factor Study
Group - eric.simoes@uchsc.edu
* Corresponding author
Abstract
Background: The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict
RSV-related hospitalisation in premature infants born 33–35 weeks' gestational age (GA).
Methods: The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control
study of 183 premature infants born between 33–35 weeks' GA who were hospitalised with RSV, and 371 age-matched
controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to
analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest
probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC)
curves were plotted.
Results: An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve
of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of
7 variables: birth ± 10 weeks of start of season, birth weight, breast feeding for ≤ 2 months, siblings ≥ 2 years, family
members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area
under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold
bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the
ROC curve of 0.785 (range: 0.768–0.790), indicating a good internal validation. The calculated NNT for intervention to
treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5–13.6).
Conclusion: A robust model based on seven risk factors was developed, which is able to predict which premature
infants born between 33–35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to
optimise prophylaxis with palivizumab across Europe.
Published: 8 December 2008
Respiratory Research 2008, 9:78 doi:10.1186/1465-9921-9-78
Received: 8 February 2008
Accepted: 8 December 2008
This article is available from: http://respiratory-research.com/content/9/1/78
© 2008 Simoes et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Respiratory Research 2008, 9:78 http://respiratory-research.com/content/9/1/78
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Background
Respiratory syncytial virus (RSV) causes a severe lower res-
piratory tract disease that results in substantial morbidity
in premature infants [1,2]. Infants born up to 35 weeks'
gestational age (wGA) lack the necessary pulmonary and
immunologic development and function essential to
combating infection [3-5]. It is estimated that 1–3% of
previously healthy infants are hospitalised because of RSV
infection [6], whereas the RSV-hospitalisation rate ranges
between 3.75% and 9.8% for infants born between 33–35
wGA [1,7,8]. Studies suggest that infants born between
33–35 wGA are at risk of developing severe RSV infection
that can result in morbidity and health care resource utili-
sation similar to infants born ≤ 32 wGA [9,10]. Addition-
ally, RSV-related hospitalisation in 32–35 wGA infants
causes significant morbidity and healthcare utilisation in
the subsequent years [11].
Palivizumab, a humanised monoclonal antibody, has
been proven a safe and efficacious option to significantly
reduce RSV disease in prematurely born infants up to and
including 35 wGA [12-14]. Based on the findings of the
pivotal Phase III trial (IMpact RSV Study) [12], palivizu-
mab received European approval in 1999 for use in
infants up to and including 35 wGA [15]. Despite the clin-
ical evidence, only a few countries in Europe make passive
immunoprophylaxis available to at-risk 33–35 wGA
infants, as reflected in current national guideline and
reimbursement policies [16-18]. Passive immunoprophy-
laxis for all infants born at 33–35 wGA is not financially
viable. However, based on risk profile and a higher rate of
RSV-related hospitalisation, a certain proportion of these
infants may be legitimate candidates for prophylaxis.
A comprehensive review of the literature revealed environ-
mental and demographic risk factors that predispose
infants to developing severe RSV leading to hospitalisa-
tion [19]. Subsequent prospective studies in Spain [9],
Canada [7], and Germany [20] examined those risk fac-
tors in infants born 33–35 wGA. The risk factors identified
include: chronological age, number of siblings/contacts,
history of atopy, absence/duration of breast feeding, post-
natal cigarette smoke exposure, male sex, and day care
attendance [7,9,20]. Despite these data, no predictive tool
that can identify infants most at risk of RSV-hospitalisa-
tion has been developed. We have developed an objective,
evidence-based model to assist clinicians to predict the
likelihood of RSV hospitalisation in European infants
born 33–35 wGA. Such a model would facilitate the effec-
tive and responsible application of passive immuno-
prophylaxis in this population.
Methods
Population used for modelling
The predictive model was derived from the Spanish FLIP
dataset [9], a prospective, case-control study, which aimed
to identify those risk factors most likely to lead to the
development of RSV-related hospitalisation among pre-
mature infants born at 33–35 wGA. The dataset comprises
186 cases and 371 age-matched controls recruited from 50
centres across Spain during the 2002/2003 RSV season
(Oct. 2002-Apr. 2003). Criteria for inclusion as a case
included: GA between 33–35 weeks, discharge during the
RSV season (or age ≤ 6 months at the start of the RSV sea-
son), and proven RSV-related hospitalisation. Controls
were selected from premature infants born or discharged
from the same hospital, during the same time period, and
within the same GA limits as cases, but who had not been
previously hospitalised for any acute respiratory illness
during the RSV season. Additionally, although not a crite-
rion for study exclusion, no infant had chronic lung dis-
ease.
Statistical methodology
Discriminant function analysis [21] was used to build the
predictive model. Univariate analyses included the Stu-
dent's t test, the χ2 test, the Mann-Whitney's U test, and
the calculation of odds ratios (with 95% confidence inter-
vals). The model was internally validated using bootstrap-
ping methods [22]. All data were analysed by SPSS
software (version 10) [23]. Records with missing values
for one or more of the predictor variables were excluded
from the analyses.
Development of a model to predict RSV-related hospitalisation of
infants 33–35 wGA
All the available risk factors collected in the FLIP study
were included in the discriminant analysis. The discrimi-
nant analysis established how well the presence or
absence of certain risk factors was able to separate infants
in the hospitalised group from those in the non-hospital-
ised group (generating a discriminant function).
Following the development of an initial model, backward
selection was used to remove the variables that contrib-
uted least to the discriminant function. The elimination of
a variable from the analysis was based on a comparison of
the discriminant power of the function derived with and
without the variable. At each stage, the functions for each
reanalysis were compared to identify the most discrimina-
tory.
Receiver operator characteristic (ROC) curves were con-
structed by plotting the sensitivity against 1-the specifi-
city. The area under the curve was calculated for each ROC
plot, with areas closer to 1 representing better predictive
accuracy. To explore diagnostic accuracy, positive predic-
tive values (PPV), negative predictive values (NPV), and
likelihood ratios were generated [24,25]. Additionally,
example numbers needed to treat (NNT) were calculated.

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Validation of the predictive model
The FLIP dataset was subject to 100-fold bootstrapping
validation [22]. For each of the 100 samples, coefficients
for each predictor variable were calculated. The 100 coef-
ficient sets were then used to derive predictor functions on
100 replicates of the original data. The correct prediction
of RSV-related hospitalisation was calculated and ROC
curves were plotted for each of the 100 outputs. The dis-
tribution of correct prediction rates and areas under the
ROC curve were then assessed. To test for normality in the
distribution of correct prediction rates and areas under the
ROC curve, the Kolmogorov-Smirnov test was used [26].
The results were also tested for skewness.
Test of the predictive model against an external dataset
Despite extensive investigation, there were no suitable
European datasets available against which the model
could be fully externally validated. Therefore, to gain a
measure of the applicability of the model to other Euro-
pean populations, the model was tested against data from
the Munich RSV study [8]. The Munich RSV study, a pop-
ulation based cohort study, examined the incidence and
risk factors for RSV-related hospitalisation of premature
infants born ≤ 35 wGA. Questionnaires were sent to all
parents of infants discharged from primary neonatal care
to determine the event of rehospitalisation for acute respi-
ratory infections. A total of 717 infants were studied, 375
of whom were born between 33–35 wGA and were used
in the validation. There were 37 RSV-related hospitalisa-
tions (5.2%) overall and 20 amongst the 375 preterms of
33–35 wGA (5.3%). Of the 20 RSV-related hospitalisa-
tions, six had a confirmed diagnosis of RSV, with the
remaining 14 cases being classified as having a clinical
suspicion of RSV, although two had a negative RSV test on
one occasion. The two infants with a negative RSV test
were excluded from the analysis.
The predictive function derived from the FLIP dataset was
tested in two ways against data from the Munich RSV
study. Firstly, the predictive variables identified from the
FLIP dataset were used to generate a discriminant function
from the data of the Munich RSV study itself. Secondly,
the non-normalised coefficients (derived from unad-
justed variable data) generated from the FLIP dataset were
applied to the Munich data.
Prior to testing, the final model had to be adjusted to
account for differences in the data captured within the
FLIP study and that which were captured within the
Munich RSV study. The variable 'number of family mem-
bers with wheeze' had to be removed, as this was not
available in the Munich dataset, the variable 'breast fed for
≤ 2 months or not' had to be modified to 'breast fed Yes/
No', and the variable 'number of family members with
atopy' had to be changed to a categorical 'family member
with atopy Yes/No'.
Test of the predictive model against the Spanish Guidelines
recommendations for prophylaxis of 32–35 wGA infants
To put the clinical usefulness of the model into perspec-
tive, its predictive ability was compared to that based on
the Spanish Neonatal Society Guidelines [16] recommen-
dations for prophylaxis of infants born 32–35 wGA. The
Spanish Guidelines [16] recommend that premature
infants born 32–35 wGA who are ≤ 6 months old when
the RSV season starts and have two risk factors (less than
10 weeks when RSV season starts, tobacco smoke at home,
day care assistance, no breast feeding, family history of
wheezing, school age siblings, and crowded homes [≥ 4
residents and/or visitors at home, excluding school age
siblings and the subject him/herself]) receive prophylaxis
with palivizumab. Using these criteria, a discriminant
function was generated from the FLIP dataset, a ROC
curve plotted, and diagnostic accuracy tested. The results
from this analysis were then compared to the results for
the model.
Results
Development of the predictive model
The 15 variables in the FLIP study are compared in the
hospitalised and non-hospitalised infants in Table 1. In a
univariate analysis of the FLIP data, hospitalised infants
were significantly more likely to be born within 10 weeks
of the start of the RSV season, be heavier at birth, have
more family members with atopy or who wheezed, had
more carers at home, had mothers who smoked during
pregnancy, had more siblings ≥ 2 years of age, and were
breast fed for ≤ 2 months or not at all.
The initial analysis of the FLIP dataset produced a func-
tion based on 15 risk factors, which could discriminate
significantly between hospitalised and non-hospitalised
infants. This function could correctly classify whether a
child was hospitalised or not in 72% of cases (table 2).
Importantly, the correct classification of hospitalised
infants was 71%. The area under the ROC curve was 0.795
(Figure 1A).
The variable reduction exercise resulted in a final model of
seven variables (table 1 in italics), with an area under the
ROC curve very similar to that of the 15 variable model
(Figure 1B). Discrimination also remained similar at 71%,
with 76% of hospitalisations classified correctly (table 2).
At the 0.75 sensitivity intercept, the specificity was 0.67,
with the false positive fraction (FPF) being 0.33. The NNT
to prevent hospitalisation of 75% of at risk infants was
calculated to be 11.7, assuming a 5% hospitalisation rate
(consensus of European RSV Risk Factor Study Group
based on a review of the available data [1,7,8]) and 80%

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Table 1: A comparison of the risk factors for RSV hospitalised and non hospitalised infants in the FLIP and Munich studies†
FLIP [9] Munich [8]
Hospitalised
(n = 186)
Non-
hospitalised
(n = 367)
Odds Ratio
(CI 95%)
P-value* Hospitalised
(n = 20)
Non-
hospitalised
(n = 357)
Odds Ratio
(CI 95%)
P-value*
Birth ± 10 weeks of
start of season
136 (73.1%) 145 (39.5%) 4.16
(2.78–6.23)
<0.0001 12 (60.0%) 148 (41.5%) 2.12
(0.77–6.12)
0.1101
Birth weight, kga 2.20 (0.38) 2.12 (0.42) - 0.0419 2.14 (0.38) 2.11 (0.39) - 0.7526
Breast fed 2
months or not§
146 (78.5%) 206 (56.1%) 2.85
(1.87–4.40)
<0.0001 18 (90.0%) 286 (80.1%) 2.23
(0.51–20.3)
0.3887
Number of siblings
2 years
1 (0–1) 0 (0–1) - <0.0001 1 (0–2) 0 (0–1) - 0.0172
Number of family
with atopy§
0 (0-0) 0 (0-0) - 0.0117 12 (60.0%) 175 (49.0%) 1.56
(0.57–4.51)
0.3671
Male gender 117 (62.9%) 199 (54.2%) 1.43
(0.98–2.09)
0.0513 18 (90.0%) 177 (49.6%) 9.15
(2.13–82.14)
0.0003
Number of family
with wheeze
0 (0–1) 0 (0-0) - 0.0004 - - - -
Gestational age
33 weeks 49 (26.3%) 77 (21.0%) 1.34
(0.87–2.07)
0.1554 4 (20.0%) 119 (33.3%) 0.50
(0.12–1.60)
0.3265
34 weeks 60 (32.3%) 139 (37.9%) 0.78
(0.53–1.15)
0.1935 11 (55.0%) 172 (48.2%) 1.31
(0.48–3.68)
0.648
35 weeks 77 (41.4%) 151 (41.1%) 1.01
(0.69–1.47)
0.9544 5 (25.0%) 66 (18.5%) 1.47
(0.40–4.44)
0.5544
Number of regular
carers
2 (1–2) 2 (1–2) - 0.0377 - - - -
Furred pets at
home
46 (24.7%) 68 (18.5%) 1.44
(0.92–2.25)
0.0885 - - - -
Educational level
of parents
No school 7 (3.8%) 4 (1.1%) 3.54
(0.89–16.71)
0.0711 - - - -
Primary 53 (28.5%) 84 (22.9%) 1.34
(0.88–2.04)
0.1491 - - - -
High school 78 (41.9%) 156 (42.5%) 0.98
(0.67–1.42)
0.8978 - - - -
University 48 (25.8%) 123 (33.5%) 0.69
(0.45–1.04)
0.0639 - - - -
Number of births
in delivery
1 (1–2) 1 (1–2) - 0.531 1 (1-1) 1 (1–2) - 0.1675
Smoking during
pregnancyb
56 (30.3%) 79 (21.5%) 1.58
(1.03–2.40)
0.0241 - - - -

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[12] treatment efficacy (table 3). At the point of maximum
sensitivity/specificity the NNT was 10.7, again assuming a
5% hospitalisation rate and 80% treatment efficacy (Fig-
ure 1). The likelihood ratio for this model was 2.45 and
the PPV and NPV were 55% and 85%, respectively.
Contribution of individual variables
A variable reduction exercise on the 7 variable model
showed that, although some variables were more impor-
tant than others, removing any variable produces a
decrease in discrimination and/or area under the ROC
curve. For example, removing 'sex' reduced the area under
the ROC curve to 0.789 (Figure 1D). On this basis, no
clear case could be made for removing any of the constit-
uent seven variables. Thus, the final seven variable model
includes: birth within 10 weeks of the start of season, birth
weight, breast fed for ≤ 2 months or not, number of sib-
lings ≥ 2 years, number of family members with atopy,
male sex, and number of family members with wheeze.
Validation
The bootstrapping analysis resulted in a tight symmetrical
distribution of results for the 100 calculations of percent-
age correctly predicted and area under the ROC curve
(table 4). The mean percentage of cases predicted correctly
was 72% (standard deviation [SD]: 2.18) and the median
area under the ROC curve was 0.785 (range 0.768–0.790).
The Kolmogorov-Smirnov test indicated that the distribu-
tion of results for the correct prediction of outcomes
(asymptotic significance: P = 0.910) and for the ROC
curves (asymptotic significance: P = 0.101) is assumed to
be normal for the purposes of calculation. Calculation of
the skewness statistic found no indication of skewness in
the distribution of results for the correct prediction of out-
comes (0.19, two standard errors of skewness [SES]: 0.48),
Number of
smokers around
infantc
1 (0–2) 1 (0–2) - 0.062 0 (0–1) 0 (0–1) - 0.9479
Number of family
with asthma
0 (0-0) 0 (0-0) - 0.1114 - - - -
The 8 variables used in the final model are shown in italics. All variables were used in the initial 15 variable model
† Mean (standard deviation), median (P25-P75), number (%)
* Student's t test, Mann-Whitney U test, χ2 test
§Recorded as breast fed yes/no and atopy yes/no for Munich
a 2 missing values for FLIP, 5 missing values for Munich
b 1 missing value for FLIP
c 2 missing values for Munich
Table 1: A comparison of the risk factors for RSV hospitalised and non hospitalised infants in the FLIP and Munich studies† (Continued)
Table 2: Analyses of the predictive accuracy of the various models
True
Positive
False
Positive
False
Negative
True
Negative
Sensitivity Specificity PPV
%
NPV
%
LR Diagnostic
Accuracy %
FLIP 15 vari-
able model§
130 102 53 265 0.71 0.72 56 83 2.56 72
FLIP Final 7
variable
model¤
139 113 45 254 0.76 0.69 55 85 2.45 71
Munich 6
variable
model†
14 106 4 247 0.78 0.70 12 98 2.59 70
§ Records for 550 infants were included within the analysis. Seven records were dropped from the analysis due to missing data for one or more of
the predictor variables
¤ Records for 549 infants were included within the analysis. 8 records were dropped from the analysis due to missing data for one or more of the
predictor variables
† Records for 370 infants were included within the analysis. Three records were dropped from the analysis due to missing data for one or more of
the predictor variables. Two records for hospitalised cases were removed from the analysis, as they each had one negative RSV test
PPV = positive predictive value
NPV = negative predictive value
LR = likelihood ratio of a positive test; for information about likelihood ratios see reference 25
Standardised canonical discriminant function coefficients for the FLIP final 7 variable model: birth ± 10 weeks of start of season = 0.678, birth
weight, kg = 0.184, breast fed ≤ 2 months or not = 0.511, number of siblings ≥ 2 years = 0.489, number of family with atopy = 0.151, female sex = -
0.113, number of family with wheeze = 0.125

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Receiver operating characteristic (ROC) curves for 15 variable model (A), final 7 variable model (B), 6 variable model forMunich test (C), and 6 variable model with sex removed (D)Figure 1
Receiver operating characteristic (ROC) curves for 15 variable model (A), final 7 variable model (B), 6 variable
model for Munich test (C), and 6 variable model with sex removed (D). The number needed to treat (NNT) at the
point of maximum sensitivity/specificity is based on a hospitalisation rate of 5% and a treatment efficacy of 80%. Each point on
the ROC curve represents a case being either a true positive or a false positive, based on their discriminant score. CI = confi-
dence interval; TPF = true positive fraction; FPF = false positive fraction.
Table 3: Final seven variable model number needed to treat analyses*
ROC AUC plus
confidence limits
True Positive
Fraction
True positives
treated
False Positive
Fraction
False positives
treated
NNT NNT
(80% efficacy)
0.791
(mid point)
0.75 75 0.33 627 9.4 11.7
0.751
(lower limit)
0.75 75 0.39 741 10.9 13.6
0.830
(upper limit)
0.75 75 0.26 494 7.6 9.5
*Number needed to treat (NNT) to prevent hospitalisation of 75% of at risk infants, assuming a 5% hospitalisation rate and 80% treatment efficacy
(n = 2,000)

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but did find significant skewness in the area under the
ROC curve results (-1.20, 2 × SES: 0.48). However, a Q-Q
plot for the areas under the ROC curve suggests that the
deviation from normality was symmetrical (figure not
shown). In summary, this means that two SDs for the cor-
rect prediction of hospitalisation (2 × 2.18 = 4.36) can be
take as the 95% CI for the results i.e. 72% ± 4.36.
External Test
The Munich dataset did not include numbers of family
members with wheeze, so coefficients obtained for the
remaining six variables of the seven variable model were
used. The recalculated six variable model was somewhat
weaker than the seven variable model defined earlier.
However, its power, derived by running the model on the
FLIP data, was adequate for running the validation tests
(correct classification: 68%; area under ROC curve 0.753
(Figure 1C).
When we used the six variables identified in the FLIP
study to derive coefficients from the Munich dataset, the
function derived solely from the Munich data was compa-
rable to that obtained with the FLIP dataset (correct clas-
sification: 70% [table 2]; area under ROC curve 0.812,
95% CI 0.737–0.887). Applying the FLIP derived coeffi-
cients (from the seven variable model) to the Munich data
produced a function that could correctly classify 64% of
cases, with an area under the ROC curve of 0.677 (95% CI
0.551–0.804).
Spanish Guidelines Test
The discriminant function based on the guidelines recom-
mendations could correctly classify 38% of cases – which
is no better then chance – and had an area under the ROC
curve of 0.520 (95% CI 0.468–0.573). The PPV was 36%,
the NPV 100%, and the likelihood ratio 1.04. (It is worth
remembering that a completely non-discriminatory test
that selects all patients for treatment except one, would
have a NPV of 100% if this patient were truly negative.)
Based on a 5% hospitalisation rate and 80% efficacy, the
NNT to prevent hospitalisation of 75% of at risk infants
was calculated to be 24.7.
Discussion
We have developed and validated a robust European pre-
dictive model to identify the risk of RSV-related hospitali-
sation in infants born between 33–35 wGA. The FLIP 7-
variable model correctly classifies over 70% of cases,
which, to put into context, compares to a figure of 38%
when using the Spanish Guidelines [16] for prophylaxis.
The predictive ability of the model was confirmed
through validation. The tight symmetrical distributions
for both the correct predictions of hospitalisation and
area under the ROC curve results and the mostly convex
nature of the ROC curve demonstrate that the model is
not skewed by 'outliers' in the FLIP dataset and is, there-
fore, highly reproducible however the data may be sam-
pled. This lends a high degree of confidence to the model
derived from the FLIP dataset.
The seven variables used in the final model were 'birth
within 10 weeks of the start of season', 'birth weight',
'breast fed for ≤ 2 months or not', 'number of siblings ≥ 2
years', 'number of family members with atopy', 'male sex',
and 'number of family members with wheeze'. All of these
variables have been documented as risk factors for RSV-
related hospitalisation [7,19,20,27]. Indeed, a critical
evaluation of the literature concluded that 'male sex' and
'crowding/siblings' were significant risk factors for severe
RSV lower respiratory tract infection [19]. However, the
Table 4: 100-fold bootstrap statistics on the FLIP dataset
Percentages correctly predicted Areas under ROC curves (AUC)
Mean 72.00 0.784
Median 72.20 0.785
Standard deviation 2.18 0.004
Minimum 66.20 0.768
Maximum 77.40 0.790
Kolmogorov-Smirnov Z 0.56 (P = 0.910†) 1.22 (P = 0.101†)
Skewness statistic 0.19 (0.48§) -1.20 (0.48§)
n = valid: 100, missing: 0
† Asymptotic significance (2-tailed)
§ 2 standard error of skewness

Page 8 of 10
same review also reported that a lack of breast feeding did
not appear to increase the risk of severe RSV lower respira-
tory tract infection or RSV-related hospitalisation [19]. A
recently published nested case-control study supports that
familial atopy and wheezing are strong determinants of
RSV-related hospitalisation [27].
The strength and utility of the FLIP 7-variable model was
highlighted by an examination of NNT. Assuming a 5%
hospitalisation rate and 80% treatment efficacy, the calcu-
lated NNT to prevent hospitalisation of 75% of at-risk
patients was 11 (range 10–14). A NNT of 11 is better than
half the result if infants are prophylaxed based on the
Spanish Guidelines recommendations [16] (25) and is
considerably lower than the 17 obtained from using the
raw numbers of the IMpact-RSV trial [12].
Although various analytical approaches were considered,
it was decided to develop the model using discriminant
function analysis. This approach produced similar results
to logistic regression, but was arguably more applicable in
the manipulation involved in validation, such as handling
missing values and continuous data. Further, models
derived from discriminant function analysis can benefit
from the inclusion of variables that are not independently
significant, but which contribute to the overall predictive
ability of the model. Indeed, the discriminatory power of
such models is always greater than that afforded by the
simple sum of its component parts. To exemplify this, one
of the seven variables in the final model was not inde-
pendently significant (male sex), but is a well known risk
factor [19]. The model also has good flexibility, as the sen-
sitivity and specificity along the ROC curve can easily be
varied such that different cut-off points can be selected
and NNTs calculated according to the needs of the indi-
vidual European country.
As is the case whenever developing such a model, limita-
tions were imposed by what and how data were captured
within the base dataset. Although the FLIP study [9] con-
tained a great deal of information on risk factors and hos-
pitalisation rates for children born between 33–35 weeks'
GA, it was limited by being a case-control study. Since RSV
infection had to be proven and these were likely to have
been the most severe cases, this might have lead to selec-
tion and, therefore, bias in the dataset. Further, allowance
had to be made for the variability in admission criteria for
the various hospitals across Spain. Finally, since day care
attendance is not commonly practised in Spain, there
were limited data on this variable and it was not included
in the final analyses.
External validation of the model presented a challenge as
there were no suitable databases in Europe that were avail-
able for such a purpose. As a surrogate, the model was
tested against data from the Munich RSV study. Allow-
ances have to be made for the differences in how the study
was conducted and what data were captured compared
with FLIP. For example, no data were captured on wheeze
in the Munich study. Perhaps most significantly, data
were available for only 20 hospitalised infants within the
Munich study. Further, only six of the hospitalised infants
had a confirmed diagnosis of RSV, as testing is not routine
in Germany. Taking these differences into consideration,
the test can be considered a worse case scenario, as it
would be not be expected for the model to validate partic-
ularly well against the Munich data. However, despite
these significant limitations, the FLIP model tested very
well against the Munich data. Nevertheless, rigorous exter-
nal validations of the model are planned when suitable
prospective data become available within Europe over the
next couple of years.
A recently published Dutch model [28], which estimated
the monthly risk of hospitalisation, reported that gender,
GA, birth weight, presence of bronchopulmonary dyspla-
sia, age, and seasonal monthly RSV pattern were signifi-
cant predictors and could potentially be used to
discriminate between high and low risk children. The
Dutch model included only risk factors that were reported
as independently significant in the published literature. In
comparison, all risk factors available within the FLIP data-
set were included within our modelling, regardless of their
individual significance. In addition, the Dutch model
does not specifically address the group we are trying to
predict RSV-related hospitalisation within, namely, those
infants born 33–35 wGA without CLD. Finally, the Dutch
model imputed missing values, whereas in the develop-
ment of the FLIP model, patients with incomplete records
were excluded from the analyses. Several other studies
have proposed using identified risk factors to predict RSV
hospitalisation in premature infants [7,20,29]; however,
as far as the authors are aware, no other models or scoring
systems have been formally published.
Importantly, although the significance of the individual
risk factors may vary between countries, the validation
and testing process indicates that the model may be appli-
cable for widespread use across Europe. Moreover, the
model appears flexible yet robust enough that, if neces-
sary, individual variable parameters can be modified to
suit the needs of individual countries. Further, although
the model is suitable for adoption as it stands, countries
could use their own data, either existing or prospectively
collected, to refine a predictive tool. When considering
intervention levels within a predictive tool, variation in
hospitalisation rates for RSV across different countries
would not affect the performance of the model in terms of
prediction, as this is not factored into the analysis.

Page 9 of 10
The model could be realised as a working tool in a variety
of formats to optimise its applicability to an individual
country, or, indeed, an individual unit. Formats could
potentially include a bespoke software application, a web-
site, a simple spreadsheet, or even a paper-based form or
nomogram. The big advantage of a software application
or website is that either could prospectively capture risk
factors and outcomes data, which could be used to further
refine and validate the model and justify its continuing
use. The tool itself would be used in daily practice to pre-
dict the risk of RSV-related hospitalisation for individual
infants. Chronic conditions such as CLD, congenital heart
disease, and severe neurological diseases may further
increase the risk of RSV-related hospitalisation, and, there-
fore, should always be taken into consideration when
using the tool.
Conclusion
By using data from the Spanish FLIP study [9] and carry-
ing out validation, we have produced an evidenced-based
model which is applicable for adaptation and use in dif-
ferent countries across Europe. The model has the poten-
tial to improve standards of care by better identifying high
risk infants and, thus, optimising prophylaxis. It may also
be used to inform guidance and to help clarify the justifi-
cation of funding and reimbursement for palivizumab
within health services. Finally, this study has led to a bet-
ter understanding of the risk factors and their interrela-
tionships for infants born between 33–35 weeks' GA.
Competing interests
JF has received fees from Abbott Laboratories for work on
various projects. XCE, ES, GD and JL have acted as expert
advisors and speakers for Abbott Laboratories and have
received honoraria in this regard.
Authors' contributions
XCE, ES, JL, and JF contributed to the concept and design
of the model. JF carried out the statistical modelling with
input from XCE, ES, and JL. XCE, ES, JL undertook the
clinical interpretation of the data. All authors contributed
to the manuscript.
Acknowledgements
European RSV Risk Factor Study Group
Xavier Carbonell-Estrany (co-Chair), Neonatology Service, Hospital Clínic,
Institut Clínic de Ginecologia Obstetricia i Neonatologia, Barcelona, Spain;
Eric AF Simoes (co-Chair), Department of Pediatrics, Section of Infectious
Diseases, The University of Colorado School of Medicine and The Chil-
dren's Hospital, Denver, Colorado, USA; Ignazio Barberi, Neonatal Inten-
sive Care Unit, Department of Pediatrics, University of Messina, Italy;
Angelika Berger, Department of Neonatology and Pediatric Intensive Care,
University Children's Hospital, Vienna, Austria; Louis Bont, Wilhelmina
Children's Hospital, University Medical Center, Utrecht, The Netherlands;
Jean Bottu, Department of Neonatology of Luxembourg, Luxembourg;
Karina Butler, Our Lady's Hospital for Sick Children, Dublin, Ireland; Veerle
Cossey, Neonatal Intensive Care Unit, University Hospital Gasthuisberg,
Leuven, Belgium; Gunther Doering, Munich University of Technology,
Department of Pediatrics, Munich, Germany; Bernard Guillois, Laboratory
of Human and Molecular Virology, Caen, France; E Farri-Kostopoulou, St
Andrew Hospital, Patras, Greece; Marcello Lanari, Pediatrics and Neona-
tology Unit, Hospital of Imola, Italy; Johannes Liese, Dr. von Hauner Chil-
dren's Hospital, Ludwig-Maximilians-University, Munich, Germany; Patrice
Morville, Pediatric Cardiology, American Memorial Hospital, Reims, France;
Bernhard Resch, Division of Neonatology, Department of Paediatrics, Uni-
versity Hospital Graz, Austria; Kate Sauer, Pediatrics, University Hospital,
Leuven, Belgium; Richard Thwaites, Paediatric Department, St Mary's Hos-
pital, Portsmouth, UK.
This study was funded by a grant from Abbott Laboratories, Abbott Park,
IL.
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