Can we improve the targeting of respiratory syncytial virus (RSV) prophylaxis in infants born 32-35 weeks' gestational age with more informed use of risk factors?

http://informahealthcare.com/jmf ISSN: 1476-7058 (print), 1476-4954 (electronic) J Matern Fetal Neonatal Med, Early Online: 1–9 ! 2014 Informa UK Ltd. DOI: 10.3109/14767058.2014.947573

ORIGINAL ARTICLE

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Can we improve the targeting of respiratory syncytial virus (RSV) prophylaxis in infants born 32–35 weeks’ gestational age with more informed use of risk factors? Xavier Carbonell-Estrany1, John R. Fullarton2, Barry S. Rodgers-Gray2, Katherine L. Gooch3, Pamela G. Vo3, and Jose Figueras-Aloy4 1

Neonatology Service, Hospital Clinic, Institut d’Investigacios Biomediques August Pi Sun˜er (IDIBAPS), Barcelona, Spain, 2Strategen Limited, Basingstoke, UK, 3Neonatology, Virology and Respiratory Global Health Economics and Outcomes Research, AbbVie, North Chicago, IL, USA, and 4 Neonatology Service, Hospital Clıńic, Institut Clıńic de Ginecologia Obstetricia i Neonatologia, Barcelona, Spain Abstract

Keywords

Objective: To evaluate the key risk factors for respiratory syncytial virus (RSV) hospitalisation in 32–35 weeks’ gestational age (wGA) infants. Methods: Published risk factors were assessed for predictive accuracy (area under the receiver operating characteristic curve [ROC AUC]) and for number needed to treat (NNT). Results: Key risk factors included: proximity of birth to the RSV season; having siblings; crowding at home; day care; smoking; breast feeding; small for GA; male gender; and familial wheezing/ eczema. Proximity of birth to the RSV season appeared the most predictive. Risk factors models from Europe and Canada were found to have a high level of predictive accuracy (ROC AUC both 40.75; NNT for European model 9.5). A model optimised for three risk factors (birth ±10 weeks from start of RSV season, number of siblings 2 years and breast feeding for 2 months) had a similar level of prediction (ROC AUC: 0.776; NNT: 10.2). An example two-risk factor model (day care attendance and living with 2 siblings 55 years old) had a lower level of predictive accuracy (ROC AUC: 0.55; NNT: 26). Conclusions: An optimised combination of risk factors has the potential to improve the identification of 32–35 wGA infants at heightened risk of RSV hospitalisation.

Bronchiolitis, hospitalisation, NNT, palivizumab, premature infants

Introduction Respiratory syncytial virus lower respiratory tract infection (RSV-LRTI) remains a significant cause of acute hospitalisation and potential longer-term morbidity in premature infants and other vulnerable patient groups [1,2]. For moderatepreterm (32–35 weeks’ gestational age [wGA]) infants, estimates suggest that somewhere between 4% and 10% are hospitalised with RSV-LRTI in the first year of life [1,3,4]. RSV prophylaxis has been proven highly efficacious in preventing severe RSV infection in moderate-preterm infants [5]; however, identifying moderate preterms at most heightened risk is required in order to improve cost-effectiveness. Several large studies [4,6,7] have identified common risk factors that are significantly associated with the possibility of a moderate-preterm infant being hospitalised with severe

Address for correspondence: Dr. Xavier Carbonell-Estrany, Neonatology Service, Hospital Clinic, Institut d’Investigacios Biomediques August Pi Sun˜er (IDIBAPS), Barcelona, Spain. E-mail: [email protected]

History Received 28 March 2014 Revised 9 July 2014 Accepted 20 July 2014 Published online 13 August 2014

RSV-LRTI. The employment of these risk factors has the potential to both improve targeting of RSV prophylaxis in this wGA group and to increase its cost-effectiveness. A key question, about which there is still much debate, is how best to optimise use of these risk factors to aid the identification of high-risk infants. The Spanish FLIP study [6] and subsequent FLIP-2 study [7] and the Canadian PICNIC study [4] represent the largest datasets investigating the epidemiology of RSV and associated risk factors in moderate-preterms published to date. Commonly identified risk factors can relate to disease exposure (e.g. day care attendance) or be associated with biological (e.g. gender) or social (e.g. smoking around the infant) factors [4,6,7]. Predicated on the FLIP [6] and PICNIC [4] studies, respectively, risk assessment tools have been developed in Europe [8] and Canada [9], which have shown these documented risk factors to be reliable predictors of RSV hospitalisation. Several other studies [3,8,10–13] have also provided supporting information on risk factors for RSV hospitalisation in this wGA group. Based on the available evidence, many of these risk factors have been used to inform national guidance on the optimal use of RSV prophylaxis in

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X. Carbonell-Estrany et al.

different countries, including Spain [14], Italy [15], Germany [16], Canada [17] and the United States [18]. The recommendations within these guidelines, however, differ due to unique epidemiology, geography, practice settings and health care and drug costs, with recommendations for 32–35 wGA infants being the most divergent [19]. An updated review of the published data on risk factors for RSV hospitalisation in 32–35 wGA infants was undertaken to evaluate their consistency and predictive accuracy. It was subsequently investigated how the use of risk factors to identify high-risk, moderate-preterm infants could be optimised for the most cost-effective use of RSV prophylaxis.


Methods The primary aim of this study was to identify the key risk factors associated with RSV hospitalisation in moderatepreterm (320–356 wGA) infants and to see how these key factors could be optimised in the identification of high-risk infants. This was accomplished by undertaking the following steps. Review of published studies on risk factors in moderate-preterm infants A comprehensive review of the published literature was undertaken in PubMed to identify environmental, social and biological risk factors, which have been found to predispose moderate-preterm (32–35 wGA) infants to RSV hospitalisation. The search results were supplemented by manual searching of current journals, reference lists in key articles, other relevant documentation and expert input. Identification of the key risk factors in moderate-preterm infants The European [8] model combined and optimised key risk factors from the FLIP [6] study, whilst the Canadian [9] model was predicated on the PICNIC [4] study. The level of concordance in the risk factors used in the two models was assessed as well as the individual weighting (contribution) of each risk factor towards RSV hospitalisation. The FLIP study [6], from which the European model [8] was developed, was a prospective, case–controlled study primarily aimed at identifying the risk factors most likely to predispose infants born at 330–356 wGA to require hospitalisation for RSV infection during the first year of life. FLIP comprised 186 cases and 371 age-matched controls recruited during the RSV season between October 2002 and April 2003 [6]. The European model consisted of seven variables: birth within 10 weeks of the start of RSV season, birth weight, breast feeding for 2 months, number of siblings 2 years, family members with atopy, family members with wheeze and male gender [8]. Whilst the model was developed using Spanish (FLIP) data [8], its applicability across Europe was demonstrated when validated against data from Germany [8], Denmark [20], Italy [11] and France [13]. The PICNIC study [4], from which the Canadian [9] model was developed, enrolled 1860 infants (330–356 wGA) over two RSV seasons (2001–2002 and 2002–2003). Of the 1832 infants who completed at least one month of follow-up,

J Matern Fetal Neonatal Med, Early Online: 1–9

66 (3.6%) were hospitalised with confirmed RSV infection [4]. The final model consisted of seven variables: small for gestational age (510th percentile); gender; born during RSV season; family history of eczema; subject or siblings attending day care; 45 individuals in the home; and 41 smoker in the household [9]. The Canadian model was subsequently validated using data from the FLIP study [9]. Assessment of predictive accuracy when using risk factors The predictive accuracy of the European [8] and Canadian [9] models was compared alongside that of an example two-risk factor prediction rule (day care attendance and living with 2 siblings55 years old), to illustrate the importance of selecting an optimal combination of risk factors. The two-risk factor prediction rule was developed using the raw data from the FLIP-2 study [7]. FLIP-2 [7] is a prospective, 2-cohort study, undertaken during the October 2005 to April 2006 and October 2006 to April 2007 RSV seasons, to validate the risk factors for RSV hospitalisation identified in the earlier, case–controlled FLIP study [6] in premature infants born at 321–350 wGA. During the study period, 5441 children from 37 Spanish hospitals were included in the risk factor analysis, 202 of whom were hospitalised with RSV. After excluding those infants who had received palivizumab from the FLIP-2 dataset, a total of 190 RSV-hospitalised infants and 4566 age-matched, nonhospitalised infants (4.0% hospitalisation rate) remained for analysis. Predictive accuracy was assessed by examination of the area under the receiver operating characteristic (ROC) curve and calculation of number needed to treat (NNT) for each assessment tool. ROC curves are constructed by plotting the sensitivity (true positives; number of RSV hospitalised infants predicted to be hospitalised) against the specificity (false positives ¼ number of non-hospitalised infants predicted to be RSV hospitalised), with areas closer to one representing better predictive accuracy. The NNT represents the number of patients who need to be prophylaxed to prevent one additional RSV hospitalisation and was calculated as follows: True positive fraction ðTPFÞ þ false positive fraction ðFPFÞ=TPF The NNT was evaluated for a RSV hospitalisation rate of 4.0% for unprophylaxed infants derived from FLIP-2 [7] and was adjusted for a RSV prophylaxis efficacy rate of 80% for 32–35 wGA infants from the IMpact study [5]. ROC curve, AUCs and TPFs and FPFs were extracted from the published papers for the European [8] and Canadian [9] models and were calculated for the two-risk factor prediction rule. Optimisation of risk factors Two strategies were employed in an attempt to optimise the use of risk factors for identifying moderate-preterm infants at increased risk of RSV hospitalisation. First, the sensitivity and specificity for the European [8] risk factor model were calculated at the point of maximum separation. In the article

Targeting of RSV prophylaxis in 32–35 wGA infants


DOI: 10.3109/14767058.2014.947573

describing the model [8], the sensitivity and specificity were expressed as the shortest tangent on the ROC curve to allow easier comparison with other published models. It is noteworthy that this might not necessarily represent the optimal balance between sensitivity and specificity, as the ROC curve is biased by the greater proportion of non-hospitalised infants in the FLIP [6] dataset. Second, a systematic variable reduction exercise was undertaken on the European [8] predictive model to determine whether a potentially more practical tool could be developed, which uses fewer risk factors (i.e. 57 risk factors) but maintains a high degree of predictive accuracy. This was achieved by using backward selection to remove the variables that contributed least to the predictive accuracy of the model. The elimination of a variable from the analysis was based on a comparison of the predictive accuracy derived with and without the variable. Statistical methodology Discriminant function analysis [21] was used to develop the two-risk factor prediction rule and to optimise the sensitivity/ specificity and number of variables in the European [8] risk factor model. The techniques employed are the same as those used to develop the European risk factor model, which are described in more detail in the paper by Simo˜es et al. [8]. All data were analysed by SPSS software (version 15) [22].

Results Review of published studies on risk factors in moderate-preterm infants A total of six studies were identified that specifically report risk factors for RSV hospitalisation in 32–35 wGA infants (Table 1). The results of the literature search confirm that

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FLIP [6], FLIP-2 [7] and PICNIC [4] remain the most rigorous studies published to date investigating risk factors for RSV hospitalisation in these infants. A number of other studies were also identified that report risk factors for RSV hospitalisation in 33–35 wGA infants from Germany and Austria [3,8], France [12,13] and Italy [10,11]. There were a number of common, independently significant, risk factors for RSV hospitalisation across the studies [3,4,6–8,10–13], including: presence/number of siblings (6/6 studies); proximity of birth to the RSV season (3/6 studies); and smoking around infants/during pregnancy (3/6 studies). Multivariate analyses of the FLIP studies [6,7] and PICNIC [4] reveal 10 apparently key risk factors for RSV hospitalisation in this wGA group, covering exposure (proximity of birth to the RSV season, living with school-age siblings, crowding at home and day care attendance), social factors (smoking around infants and breast feeding) and biological factors (small for GA, male gender, familial wheezing and familial eczema; Table 2). All of the risk factors were associated with an increased risk of RSV hospitalisation, except breast feeding and familial eczema, which were protective [4,6,7]. Two other studies [23,24] have conducted multivariate analyses of risk factors for RSV hospitalisation covering the 33–35 wGA group. An analysis [23] of the combined Munich [3] and Austrian cohort studies (n ¼ 1236) reported that neurological problems (odds ratio [OR] 3.6; 95% confidence interval [CI]: 1.3–9.9; p ¼ 0.01), male gender (OR: 2.8; 95% CI: 1.6–5.5; p50.01), presence of an older sibling (age limit for older sibling not stated; OR: 1.7; 95% CI: 1.0–3.2; p ¼ 0.07) and discharge from the hospital maternity unit between October and December (OR: 1.7; 95% CI: 0.9–3.1; p ¼ 0.09) were significant risk factors for RSV hospitalisation in 29–35 wGA infants. Preliminary results of a large cohort study [24] undertaken in Italy, which enrolled 1064 infants

Table 1. Studies reporting risk factors for RSV hospitalisation in the first year of life for infants born at 33–35 wGA. Country: study

Design

Hospitalised

Non-hospitalised

Independently significant (p50.05) risk factors* Age 10 weeks at start of RSV season; 2 smokers at home; smoking during pregnancy; and living with school-age siblings Born in November–January; male gender; small for gestational age (510th percentile); father has high school education; family history of eczema; not currently fed breast milk; has any siblings; living with preschool-age siblings attending day care; 45 individuals in household (counting subject); 2 smokers in household; and day care attendance Age 10 weeks at start of RSV season; smoking during pregnancy; history of wheezing; history of eczema; living with 1 school-age siblings; breast feeding 2 months; and 4 people at home (excluding school-age siblings and subject) Number siblings 42 years and male gender

Spain: FLIP-2 [7]

Cohort

202

5239

Canada: PICNIC [4]

Cohort

66

1766

Spain: FLIP [6]

Case–control

186

371

Germany: Munich RSV study [3,8]y France: Extension of Burgundy study [12,13] Italy: Osservatorio study [10,11]z

Cohort

20

357

Case–control

77

154

Cohort

34

30

*Bivariate/univariate analyses. yEnrolled infants 35 wGA, n ¼ 717 total study population. zEnrolled infants 4 years of age born at any wGA. n ¼ 2110 total study population.

Number siblings 2 years; number of children at school; and day care attendance Number siblings 2 years

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Table 2. Multivariate analysis of risk factors associated with RSV hospitalisation in the first year of life for infants born at 33–35 wGA. Odds ratio (95% confidence interval) FLIP study (n ¼ 557) [6]

FLIP-2 study [7] (n ¼ 5441)

PICNIC study [4] (n ¼ 1758)*

3.95y (2.65–5.90) p 0.001 2.85y (1.88–4.33) p 0.001 1.91y (1.91–3.07) p ¼ 0.0074 –

2.99y (2.23–4.01) p 0.001 2.04z (1.53–2.74) p 0.001 –

4.88z (2.57–9.29) p50.001 2.76x (1.51–5.03) p ¼ 0.001 1.69z (0.93–3.10) p ¼ 0.088 12.32 (2.56–59.34) p ¼ 0.002

–

–

Smoking during pregnancy

–

Breast feeding 2 months

3.26 (1.96–5.42) p 0.001

1.61 (1.16–2.25) p ¼ 0.0044 –

Risk factors Exposure Age at start of RSV season (y10 weeks, zbirth November–January) Siblings (yschool age, zschool-age siblings or day care attendance and xpre-school-age) Crowding at home (y4 without subject and school age siblings and z45 counting subject) Day care attendance


Social factors Exposure to 2 smokers

Biological factors Small for gestational age (510th percentile)

–

–

–

–

1.90 (1.91–3.01) p ¼ 0.0068 –

–

Male gender Family history of wheezing Family history of eczema in first-degree relative

–

1.71 (0.97–3.00) p ¼ 0.064 – –

2.19 (1.14–4.22) p ¼ 0.019 1.91 (1.10–3.31) p ¼ 0.02 –

–

0.42 (0.18–0.996) p ¼ 0.049

*66 infants RSV hospitalised and 1692 controls (74 infants with RSV negative LRTI or not tested for RSV excluded).

born at 33 wGA, reported that breast feeding reduced the risk of RSV hospitalisation by 60% (hazard ratio 0.40; 95% CI: 0.2–0.9; p ¼ 0.043). Identification of the key risk factors in moderate-preterm infants The European [8] and Canadian [9] seven-variable models share many of the same/similar risk factors, including: proximity of birth to the RSV season; number in household; birth weight; familial atopy/eczema; and gender (Table 3). The most predictive risk factor in both models was proximity of birth to the RSV season, contributing to around 30% of the predictive capability of the European model and 25% of the Canadian model. Breast feeding was the second most predictive variable in the European model (contributing 23% of total predictive capability); however, it was not of sufficient predictive power to include in the Canadian model. Likewise, the second most predictive variable in the Canadian model, day care attendance (contributing 17% of the total predictive capability) [9], was insufficiently predictive to warrant inclusion in the European model [8]. Breast feeding was indeed captured in the PICNIC [4] study and day care attendance in the FLIP [6] study, although neither was significantly associated with RSV hospitalisation in the respective multivariate analyses. There were, however, limited data available for day care attendance in the FLIP dataset (36 of 500 [7%] infants attended day care; unpublished data). Even so, when day care attendance was added as an eighth variable in the European model, it did marginally increase the overall predictive accuracy (ROC AUC:

Table 3. Comparison of risk factors included in European [8] and Canadian [9] predictive models. Ranking: 1 ¼ most predictive (weighting*) Risk factor

European

Canadian

Birth ±10 weeks of season start/ born during season Breast feeding 2 months Number of siblings 2 years/ 45 individuals in the home Birth weight/small GA (510 percentile) Family members with atopy/eczema Family members with wheeze Gender Day care attendance 41 smoker in the household

1 (0.678)

1 (0.249)

2 (0.511) 3 (0.489)

Not included 3 (0.124)

4 (0.184)

3 (0.124)

5 (0.151)

3 (0.124)

6 (0.125) 7 (0.113) [8 (0.071)]y Not included

Not reported 6 (0.109) 2 (0.166) 7 (0.102)

*Relative weight/contribution to RSV hospitalisation. yNot included in published model.

seven-variable, 0.791; eight-variable: 0.795; previously unpublished data). A potentially important consideration for breast feeding as a risk factor is how the variable is defined. In the European model [8], it was defined as ‘‘breast feeding for 2 months’’, whereas the PICNIC dataset [4] captured whether infants were ‘‘currently fed breast milk’’. When the data for breast feeding in the FLIP dataset [6] were recast to ‘‘breast fed yes/no’’, the power of this risk factor was reduced warranting its substitution in the model (data not shown). It is potentially noteworthy that whilst breast feeding was more common in cases than controls in the FLIP (85.5% versus



DOI: 10.3109/14767058.2014.947573

67.6%, respectively) and FLIP-2 studies (75.6% versus 71.4%), ‘‘breast feeding for 2 months’’ was independently significant only in the FLIP study [6,7]. Whilst a family history of atopy/eczema was included as a risk factor in both models [8,9], the PICNIC study [4] found it to be protective, whereas the FLIP study [6] found it to be associated with RSV hospitalisation. Intuitively, it perhaps makes more clinical sense for a family history of atopy/eczema to increase the risk of RSV hospitalisation. However, Law et al. speculate that resultant behavioural changes in such families might reduce exposure to RSV (e.g. more protective handling of the infant) or there may be immunogenetic factors related to familial eczema influence the severity of RSV disease [4]. Other than FLIP [6] and PICNIC [4], no other studies were identified that found a significant association between RSV hospitalisation and atopic factors in moderate-preterm infants, including in FLIP-2 [7].

Table 4. Comparison of predictive accuracy in risk factor models.

Sensitivity/ specificity ROC AUC NNT*

European seven-variable model [8]

Canadian seven-variable model [9]

Two-risk factor model (day care attendance and living with 2 siblings 55 years old)

0.72/0.71

0.68/0.72

0.48/0.40

0.791 13.3

0.762 13.6

0.546 26.9

*Calculated based on RSV hospitalisation rate of 4.0% for unprophylaxed infants derived from FLIP-2 [7] and a RSV prophylaxis efficacy rate of 80% for 32–35 wGA infants from the IMpact study [5]. ROC AUC: area under the receiver operating characteristic curve and NNT: number needed to treat.

Assessment of predictive accuracy when using risk factors The European [8] and Canadian [9] risk factor models display a similar level of predictive accuracy, with both ROC AUCs being between 0.76 and 0.80 (Table 4). This contrasts markedly with the results from the two-risk factor model (day care attendance and living with 2 siblings 55 years old), where the ROC AUC was 0.546 (0.5 corresponding to random chance). The NNTs were also commensurately superior for the European and Canadian models over the two-risk factor prediction rule (13.3 and 13.6 versus 26.9, respectively [assumes a 4.0% RSV hospitalisation rate [7] and 80% treatment efficacy [5]]). Optimisation of risk factors Statistical optimisation to find the best balance between the sensitivity (true positives) and specificity (true negatives) of the European model [8] resulted in the NNT falling from 13.3 to 9.5 (Table 5). Although the optimised sensitivity was lower than when calculated directly from the ROC plot (0.55 versus 0.72), there was an increase in specificity (0.85 versus 0.71), reflecting that the vast majority of infants are nonhospitalised (RSV hospitalisation rate 4%). When the tworisk factor model was optimised, the resultant NNT was 26.3. Systematic variable reduction on the European model [8] demonstrated that predictive accuracy could be retained with three- and four-risk factor models (Table 5). A model containing only three risk factors (birth ±10 weeks of the start of the RSV season, number of siblings 2 years and breast feeding for 2 months) produced a ROC curve (0.776 versus 0.791, respectively) and resultant NNT (10.2 versus 9.5) not much less predictive than that of the original sevenrisk factor model. Variable reduction was carried out while ensuring that risk factors common in all countries were

Table 5. Refinement of the European [8] risk factor model. Model Seven-variable

Four-variable (version I)

Four-variable (version II)

Three-variable (optimal) Three-variable

5

Risk factors i. Birth ±10 weeks of RSV season ii. Birth weight iii. Breast feeding for 2 months iv. Number of siblings 2 years v. Family members with atopy vi. Family members with wheeze vii. Gender i. Birth ±10 weeks of RSV season ii. Breast feeding for 2 months iii. Gender iv. Birth weight i. Birth ±10 weeks of RSV season ii. Number of siblings 2 years iii. Gender iv. Birth weight i. Birth ±10 weeks of RSV season ii. Number of siblings 2 years iii. Breast feeding for 2 months i. Birth ±10 weeks of RSV season ii. Number of siblings 2 years iii. Gender

ROC AUC

NNT*

start

0.791

9.5

start

0.748

11.6

start

0.747

11.8

start

0.776

10.2

start

0.748

12.8

*Calculated based on RSV hospitalisation rate of 4.0% for unprophylaxed infants derived from FLIP-2 [7] and a RSV prophylaxis efficacy rate of 80% for 32–35 wGA infants from the IMpact study [5]. ROC AUC: area under the receiver operating characteristic curve and NNT: number needed to treat. NNTs calculated using statistically optimised sensitivity and specificity.

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retained, thereby minimising the likelihood of over-fitting the model to a specific data environment.

Discussion


There are a number of key risk factors but choice is critical A review of the literature and comparison of the European [8] and Canadian [9] predictive models reveal a number of apparently key risk factors for RSV hospitalisation in infants born at 32–35 wGA. Proximity of birth to the RSV season appeared to be the most significant variable when combined with other important risk factors [8,9]. The European [8] and Canadian [9] models have a high degree of predictive accuracy [8,9], which can be maintained in the European model with even fewer risk factors, if optimisation is undertaken appropriately (e.g. via backward selection to remove the variables that contribute least to the predictive accuracy of the model). The choice of which risk factors to include and how they are defined has critical implications for risk factor-based recommendations for RSV prophylaxis in this wGA group. What are the common risk factors? A number of common risk factors for RSV hospitalisation in infants born at 32–35 wGA have been identified in separate studies from Spain (FLIP [6] and FLIP-2 [7]), Canada (PICNIC [4]), Germany [3,8,23], France [12,13] and Italy [10,11,24]. Independently, significant risk factors include those related to exposure (e.g. proximity of birth to the RSV season, presence/number of siblings, crowding at home and day care attendance), social factors (e.g. smoking around infants and breast feeding) and biological factors (e.g. small for GA, male gender, familial wheezing and familial eczema). The real power of these risk factors is manifest when they are employed in combination, as evidenced by the European [8] and Canadian [9] risk factor models. There was a high level of concordance in the risk factors used in the two models, despite utilising different datasets (European: FLIP; Canadian: PICNIC) and methodologies (European: discriminant function analysis; Canadian: logistic regression) [8,9]. The external validations of both models [8,9,11,13,20] are further confirmation of the universal relevance of the included risk factors. Thus, all of the risk factors employed in these models should be considered to be salient risk factors for RSV hospitalisation. Potential caveats with birth in relation to the RSV season Proximity of birth to the RSV season appeared to be the single most significant risk factor, making up around a quarter of the predictive power in both models [8,9]. Whilst intuitively it makes sense that younger infants are more vulnerable to severe RSV-LRTI, this should not be interpreted to mean that only those infants born close to or during the RSV season are at exclusively increased risk of hospitalisation. A recent study by Carbonell et al. [25] reported an analysis of the FLIP-2 dataset [7], wherein the median age at RSV hospitalisation for infants born outside the RSV season was found to be 148 days


(5 months) compared with a median of 58 d for those born in the season. The study [25] also suggested that some risk factors, including male sex, smoking whilst pregnant, birth month, breast feeding duration, number of siblings in school and number of smokers in household, can have an impact on RSV hospitalisation for infants born at 32–35 wGA for up to around six months of age. Similarly, an additional analysis using the two-risk factor model, as defined as day care attendance and living with 2 siblings 55 years old, showed that when applied to the FLIP-2 dataset [7], 52% of hospitalised infants were identified as high-risk of which 20% would have been hospitalised with RSV at 4 months of age. In total, 70% (133/190) of RSV hospitalisations in FLIP-2 occurred in infants older than three months. Whilst proximity of birth to the RSV season should be considered one of the most important risk factors for RSV hospitalisation in 33–35 wGA infants, its significance requires interpretation within the context of an infant’s overall vulnerability to severe RSV-LRTI and the potential influence of other risk factors. Selection of risk factors is dependent on local circumstances Careful selection, handling and interpretation of other risk factors are also required. The choice of which risk factors to use in any predictive model must take into account their applicability to the country or region in question. This is illustrated by the fact that day care attendance is infrequent in Spain, and, therefore, does not feature in the European model [8]; yet, being much more widespread in Canada, it is the second most important risk factor in that model (contributing 17% of the total power). Such differences in social habit require careful consideration and are a particular challenge when attempting to generalise the optimisation of prophylaxis. Similarly, since neurological problems were identified as a significant risk factor for RSV hospitalisation of 29–35 wGA infants in the combined Munich/Austrian dataset [23], it is included in the German RSV prophylaxis guidelines [16]. It may be that the use of a composite risk factor, in some cases, is the optimal approach to cover regional variability. For instance, in the FLIP-2 study [7], the importance of day care attendance as a risk factor for RSV hospitalisation was recognised by its combination with school-age siblings to form a very highly significant variable (p ¼ 1.47 E6). How risk factors are defined How the risk factors are defined appears to be another important consideration. For example, breast feeding was the second most predictive variable in the European model [8] (accounting for 23% of the total power), but was not included in the Canadian assessment [9], despite data for this variable being available in PICNIC [4]. Although there is evidence that breast feeding is associated with a significant reduction in RSV hospitalisation in several independent studies [4,6,24], though not in the FLIP-2 study [7], the protective effects of breast feeding makes its strongest contribution to the European model [8] when it is defined as ‘‘breast feeding for 2 months’’, not when ‘‘whether breast fed’’ is used. Subtle distinctions in definitions or format such as this can determine whether certain risk factors are worthwhile

DOI: 10.3109/14767058.2014.947573


including in any predictive model and/or risk assessment guidelines. How any risk factor is defined should also, of course, take into consideration the practicalities of its utilisation within any model/guidelines; ‘‘number of days breast fed’’ constitutes the most predictive derivative of the breast feeding data in FLIP [6] (data not shown), though the practical difficulties of accurately capturing this in a clinical setting far outweigh any marginal diminishment in power by using a categorical variable, such as ‘‘breast feeding for 2 months’’. Future studies investigating risk factors for RSV hospitalisation should give forethought to how best to capture any given risk factor to maximise later flexibility when it comes to developing any predictive models and/or risk assessment guidelines. High levels of predictive accuracy are possible When an optimal combination of risk factors is used, a high level of predictive accuracy can be achieved. The European and Canadian predictive models have ROC AUCs of 40.75, which is comparable to several published clinical decision rules; for example, the use of CD4 counts to determine virologic failure on HAART therapy for HIV patients (ROC AUC: 0.78) [26]; the use of Logistic Organ Dysfunction or Sequential Organ Failure Assessment scores to predict mortality in critically ill patients (ROC AUC: 0.720–0.766) [27]; or a four-variable clinical prediction rule for identifying children with difficult intravenous access (ROC AUC: 0.72) [28]. For diagnostic tests, a ROC AUC of 0.50–0.75 is generally considered as ‘‘fair’’, 0.75–0.92 as ‘‘good’’, 0.92–0.97 as ‘‘very good’’ and 0.97–1.00 as ‘‘excellent’’ predictive accuracy [29]. Despite the required accuracy for a diagnostic test being necessarily more stringent than for a clinical decision rule, the European and Canadian models are both rated as ‘‘good’’ on this scale, reinforcing their high levels of predictive accuracy. It is important to recognise that combinations of risk factors, if optimised appropriately, may provide more predictive power than the sum of their individual parts. This is perhaps best explained by the fact that most, if not all, risk factors are not truly independent (e.g. wheezing and smoking; gestational age and weight), and a statistically optimised combination might be better help to explain the variance and differentiate the RSV hospitalised from non-hospitalised infants. It is suggested that further research be undertaken to explore such combinations. Maintaining predictive accuracy with fewer risk factors Whilst the European and Canadian models include seven variables, predictive accuracy can still be maintained with fewer risk factors. This was shown in a three-variable model (birth ±10 weeks of RSV season start, number of siblings 2 years and breast feeding for 2 months) that was almost as accurately predictive as the original European [8] one (ROC AUC: 0.776 versus 0.791, respectively). The practical utility of this three-variable model requires further investigation, particularly as breast feeding has not universally been found to be a significantly protective factor. In contrast, the two-variable model (day care attendance and living with


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2 siblings 55 years old) produced a ROC AUC of 0.546; again, highlighting the importance of choosing the right combination of risk factors. This result also calls into question the use of just two risk factors such as these in any guidelines for RSV prophylaxis in moderate-preterm infants, as well as their generalizability across different regions. Further studies have shown that the risk of RSV hospitalisation appears to persist to 5–6 months postpartum in 32–35 wGA infants [25], suggesting that those moderate-preterm infants considered to be at a high risk of severe RSV infection are potentially vulnerable throughout the RSV season, which should be reflected in guidelines for RSV prophylaxis. Implications of numbers needed to treat The NNT analysis provides insight into the clinical as well as the statistical significance [30] of using risk factor-based models or prediction rules. Although calculation of NNT is increasingly being used as a tool to aid clinical decision making [30,31], there are no set limits for NNTs to be regarded as clinically effective – it varies between therapies and disease areas, ultimately down to the individual decision maker. NNTs as high as 20–40 have been regarded as clinically effective in some disease areas (e.g. adding aspirin to streptokinase to reduce five-week vascular mortality rates after myocardial infarction) [32]. When the sensitivity and specificity of the European model [8] were optimised statistically, a NNT of 9.5 was produced, with the threevariable version producing a similar NNT of 10.2. Published analyses [31,33] for RSV prophylaxis programmes in infants with chronic lung disease (CLD) or congenital heart disease (CHD) have produced NNTs varying from 12 for infants with CLD to 26 for those with CHD. It is likely that the majority of infants with CLD are born younger than 32 wGA and might be considered to be at a comparatively increased risk of severe RSV-LRTI compared to those born at 32–35 wGA (in the FLIP studies [6,7] infants with CLD except bronchopulmonary dysplasia were excluded). The two-risk factor model described herein produced a NNT of 26, even when statistically optimised. In contrast, the NNT can be reduced to 510 with the European risk factor model [8]. Any improvements in NNT achieved are likely to result in improvements in the cost-effectiveness of RSV prophylaxis programmes. Possible limitations of this study This study was limited to the investigation of risk factors captured routinely in the published work reported in this study. It is conceivable that further confounding/risk factors, including clinical history, prenatal history, co-morbidities and co-infection with other respiratory viruses (i.e. parainfluenza and influenza) or perinatal infection (i.e. CMV), may also contribute to the prediction of RSV hospitalisation risk in moderate-preterm infants. The 4.0% hospitalisation rate for moderate-preterm infants, taken from the FLIP-2 data, is near the mid-point of the range of values (2–9.8%) reported in other studies [3,4,34–42]. Although absolute estimates of NNT, for a particular level of intervention, decrease inversely with hospitalisation rate, relative improvements in NNT, compared with 100% intervention, will remain of a similar order.

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Conclusion The results from this study suggest that appropriate selected and optimised combinations of risk factors can greatly aid the identification of infants born at 32–35 wGA at heightened risk of hospitalisation due to severe RSV.


Declaration of interest X. C-E. and J. F-A. have acted as expert advisors and speakers for AbbVie and have received honoraria in this regard. K. L. G. and P. G. V. are employees of AbbVie. J. R. F. has received fees from AbbVie for work on various projects. AbbVie participated in the interpretation of data, writing, reviewing and approving the publication. This study was funded by AbbVie, North Chicago, IL.

Authors’ contributions X. C-E., K. L. G., P. G. V., B. S. R-G. and J. R. F. contributed to the concept and design of the study. J. R. F. carried out the analytical modelling with input from X. C-E., K. L. G., P. G. V. and B. S. R-G., X. C-E. and J. F-A. undertook the clinical interpretation of the data. All authors contributed to the manuscript.

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