Early Human Development 91 (2015) 77–85

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Size at birth by gestational age and hospital mortality in very preterm infants: Results of the area-based ACTION project M. Da Frè a,1, A. Polo b,2, D. Di Lallo b,3, S. Piga c,4, L. Gagliardi d,5, V. Carnielli e,6, S. Miniaci f,7, F. Macagno g,8, L. Ravà c,9, P. Ferrante c,10, M. Cuttini c,⁎ a

Unit of Epidemiology, Regional Agency For Health of Tuscany, Via Pietro Dazzi 1, 50141 Firenze, Italy Hospital Network Planning and Research Area, Lazio Regional Health Authority, Via R. Raimondi Garibaldi 7, 00145 Roma, Italy Medical Directory, Bambino Gesù Children's Hospital, Viale Ferdinando Baldelli 41, 00146 Roma, Italy d Division of Pediatrics and Neonatology, Ospedale Versilia, Via Aurelia 335, Lido di Camaiore, 55043 Lucca, Italy e Maternal and Child Health Institute, Marche University and Salesi Hospital, Via Toti 4, 60123 Ancona, Italy f Neonatal Intensive Care Unit, Pugliese-Ciaccio Hospital, Viale Pia X 83, 88100 Catanzaro, Italy g Neonatal Intensive Care Unit, S. Maria della Misericordia Hospital, Piazzale S. Maria della Misericordia 15, 33100 Udine, Italy b c

a r t i c l e

i n f o

Article history: Received 26 May 2014 Received in revised form 18 November 2014 Accepted 20 November 2014 Available online xxxx Keywords: Very preterm neonates Small for gestational age Anthropometric reference charts Hospital mortality

a b s t r a c t Background: Size at birth is an important predictor of neonatal outcomes, but there are inconsistencies on the definitions and optimal cut-offs. Aims: The aim of this study is to compute birth size percentiles for Italian very preterm singleton infants and assess relationship with hospital mortality. Study design: Prospective area-based cohort study. Subjects: All singleton Italian infants with gestational age 22–31 weeks admitted to neonatal care in 6 Italian regions (Friuli Venezia-Giulia, Lombardia, Marche, Tuscany, Lazio and Calabria) (n. 1605). Outcome measure: Hospital mortality. Methods: Anthropometric reference charts were derived, separately for males and females, using the lambda (λ) mu (μ) and sigma (σ) method (LMS). Logistic regression analysis was used to estimate mortality rates by gestational age and birth weight centile class, adjusting for sex, congenital anomalies and region. Results: At any gestational age, mortality decreased as birth weight centile increased, with lowest values observed between the 50th and the 89th centiles interval. Using the 75th–89th centile class as reference, adjusted mortality odds ratios were 7.94 (95% CI 4.18–15.08) below 10th centile; 3.04 (95% CI 1.63–5.65) between the 10th and 24th; 1.96 (95% CI 1.07–3.62) between the 25th and the 49th; 1.25 (95% CI 0.68–2.30) between the 50h and the 74th; and 2.07 (95% CI 1.01–4.25) at the 90th and above. Conclusions: Compared to the reference, we found significantly increasing adjusted risk of death up to the 49th centile, challenging the usual 10th centile criterion as risk indicator. Continuous measures such as the birthweight z-score may be more appropriate to explore the relationship between growth retardation and adverse perinatal outcomes. © 2014 Elsevier Ireland Ltd. All rights reserved.

⁎ Corresponding author. Tel.: +39 06 68592856; fax: +39 06 68592853. E-mail addresses: [email protected] (M. Da Frè), [email protected] (A. Polo), [email protected] (D. Di Lallo), [email protected] (S. Piga), [email protected] (L. Gagliardi), [email protected] (V. Carnielli), [email protected] (S. Miniaci), [email protected] (F. Macagno), [email protected] (L. Ravà), [email protected] (P. Ferrante), [email protected] (M. Cuttini). 1 Tel.: +39 055 4624 370; fax: +39 055 3841 470. 2 Tel.: +39 06 83060489; fax: +39 06 83060463. 3 Tel.: +39 06 5168 4895; fax: +39 06 5168 4665. 4 Tel.: +39 06 68593065; fax: +39 06 68592853. 5 Tel.: +39 0584 6059765; fax: +39 0584 6059764. 6 Tel.: +39 071 5962045; fax: +39 071 5962231. 7 Tel.: +39 0961 883428; fax: +39 0961 883430. 8 Tel./fax: +39 0432 552812. 9 Tel.: +39 06 68592741; fax: +39 06 68592853. 10 Present address of Pierpaolo Ferrante: Occupational Medicine Department, Research Area, National Workers Compensation Authority (INAIL), Via Stefano Gradi 55, Roma, 00143 Italy.

http://dx.doi.org/10.1016/j.earlhumdev.2014.11.007 0378-3782/© 2014 Elsevier Ireland Ltd. All rights reserved.

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M. Da Frè et al. / Early Human Development 91 (2015) 77–85

1. Introduction Intrauterine growth restriction (IUGR) is a powerful predictor of perinatal and infant mortality, and later health outcomes [1–3]. However, its diagnosis is not straightforward, and a combination of clinical, echographic and fetal Doppler velocimetry monitoring is recommended [4]. Being “small for gestational age” (SGA), defined on the basis of the tenth, the fifth or even the third centile is generally used as a proxy indicator of IUGR [5]. Whether or not growth standards derived from a given population may be applicable to different settings and ethnic groups is controversial [6,7]. Black infants are known to have lower mean birthweight than white infants [8,9], and Chinese and South Asian neonates are lighter compared to other ethnic categories [10]. As shown by Grafmans et al. in their analyses of birth registries from seven European countries, both modal birthweight and perinatal mortality vary between countries, and “optimal” birthweight (i.e. corresponding to lowest mortality) is lower in populations with lower average birthweight [11]. The origin of variations in anthropometric measurement in different ethnic groups is a matter of debate. Those who feel that the origin is genetic argue for population-specific standards [7,10]. Others believe that differences are attributable to environmental and living conditions, a concept at the base of the WHO international growth charts [12]. Whatever the origin, differences nevertheless exist, and variable casemix can unpredictably distort the calculation of the growth references. Therefore, many countries have produced population-specific anthropometric charts. In Italy, birthweight centiles were created for the regions of NorthEast, and for the central regions of Tuscany and Lazio [13–15]. National charts were based on births occurred in the Eighties [16]. More recently, anthropometric reference charts for singletons born at 23–42 weeks of gestational age were produced, based on the voluntary participation of 34 of the 125 Italian maternities associated with a neonatal intensive care unit [17]. None of the Italian percentile charts was validated against perinatal or neonatal mortality. We computed birthweight, length and head circumference centiles for very preterm neonates (i.e. below 32 weeks of gestational age), using the data collected within a population-based prospective study (ACTION) carried out in six regions in Italy. We then explored the association between birth weight centiles and hospital mortality by week of gestational age, aiming to identify the centile above which there is no significantly increased risk of death linked to size at birth. 2. Methods 2.1. Study population and data collection The ACTION Project (“Accesso alle Cure e Terapie Intensive Ostetrico-Neonatali per i parti e neonati pretermine”, Access to Intensive Obstetric and Neonatal care for very preterm births) included live births at 22+ 0 to 31+ 6 completed weeks of gestation that occurred between July 2003 and December 2004 in Lombardia, Lazio and Calabria, and up to the end of June 2005 in Friuli Venezia-Giulia, Tuscany and Marche (n. 3091). Overall, these regions cover about 38% of Italian total births. In each region all hospitals participated in data collection, except for Lombardia and Calabria where only those with a neonatal intensive care unit (NICU) were involved. Ethics committee approval and parental informed consent were obtained. A structured questionnaire was used to record the information on mothers' demographic characteristics, pregnancy and delivery. Neonatal data included sex, gestational age, plurality, anthropometric measures at birth, health status at birth and at NICU admission, morbidity, hospital mortality and cause of death [18,2]. Gestational age (GA) was measured, in completed weeks and days, as the best obstetrical estimate taking into account the last menstrual period and the ultrasound dating. Congenital

anomalies were coded according to the International Classification of Diseases, 9th revision, Clinical Modification, and grouped into inevitably lethal, acutely life-threatening, and non acutely life-threatening as required by the Clinical Risk Index (CRIB) score calculation [19]. 2.2. Statistical analysis For the purposes of this paper, only singletons born to Italian mothers (i.e. born in Italy and with Italian citizenship) were considered (n. 1605, 866 males). Two infants were excluded because of missing information on sex, and 3 (2 males and 1 female) because birthweight was missing. Body length and head circumference were not recorded for cases born in Lazio in 2003 (n. 400, males 219). Additionally, length was missing for 120 cases (74 males) and head circumference for 112 (66 males). Thus, the analyses were performed on 1600 infants (864 males, 736 females) for birthweight; 1080 (571 males, 509 females) for body length; and 1088 (579 males, 509 females) for head circumference. Anthropometric centiles were computed separately for males and females. GA was analyzed in completed weeks, according to WHO recommendations and previous studies [12,20]. Outliers were identified according to Tukey [21] and excluded from the analysis (6 cases for birthweight, 11 for length and 4 for cranial circumference). Centiles were computed using the LMS method proposed by Cole [22]. L (lambda, λ), M (mu, μ) and S (sigma, σ) parameters were determined by maximum penalized likelihood [23]. Birthweight, length and head circumference centiles (C) were estimated from those values using the formula 1=L

C ¼ M½1 þ LSZ 

where L, M and S are the values estimated for each gestational age week and Z is the z score corresponding to the desired centile [23]. Q tests showed adequate fit to the data. The relation between size at birth, as measured by the birth weight centile class, and hospital mortality was explored by means of a multivariable logistic regression analysis. For this analysis, two infants with lethal anomalies (Patau's and Edward's syndrome respectively) were excluded. The following variables were considered as predictors: birth weight centiles (coded as b10th, 10–24th, 25–49th, 50–74th, 75–89th, and 90th and over), GA in completed weeks, gender, and presence of non lethal congenital anomaly (coded as none, non acutely lifethreatening, and acutely life-threatening). Region of birth was also adjusted for. No significant interaction was detected between GA and birth weight centiles (test for interaction: chi2 with 38 d.f. 35.66, p = 0.578). The model showed a good fit (Hosmer–Lemeshow chi2 with 8d.f. 7.49, p = 0.48) and satisfactory discriminative power (area under ROC curve 0.887, 95% CI 0.87–0.91). Adjusted risks of mortality and 95% confidence intervals (CI) by week of gestation and centile class were derived from the multivariable model. The LMS ChartMaker software was used to calculate centiles. Other analyses were carried out with the STATA software, version 10 (StataCorp 2007. Stata Statistical Software: Release 10. College Station, Tx: StataCorp). 3. Results The characteristics of the study population are shown in Table 1. Most neonates (864, 54%) were males. One hundred and seven babies had at least one congenital anomaly; 24 cases were classified as acutely life-threatening, and 2 as lethal. About 84% percent of infants were discharged alive from hospital. For 4 babies the outcome was unknown, as they were transferred to hospitals outside the participating regions. Table 2 shows, separately for males and females, the smoothed anthropometric centiles values for birth weight, length and head

M. Da Frè et al. / Early Human Development 91 (2015) 77–85 Table 1 Characteristics of the study population (n. 1600 singletons)a. n Gender Male Female Gestational age (weeks) 22–25 26–27 28–29 30–31 Birth weight (g) b1000 1000–1499 ≥1500 Congenital anomalies No Non life threatening Life threatening Lethal Unknown Disharged alive from hospital No Yes Unknown a

% 864 736

54.0 46.0

206 287 414 693

12.9 17.9 25.9 43.3

616 714 270

38.5 44.6 16.9

1422 81 24 2 45

90.3 5.2 1.5 0.1 2.9

260 1336 4

16.3 83.5 0.2

2 cases with unknown gender and 3 with missing birth weight excluded.

circumference. For length and head circumference, the lowest gestational age classes (22 and 23) were considered together because of small number of observations. L and S values are also reported, while M (median) corresponds to the 50th centile. As expected, median values are always larger for males than females. L represents the power needed to achieve normality of data distribution. A value of 1 indicates no transformation required, less than 1 adjusting for positive skewness, and more than 1 for negative skewness. As shown in Table 2, departures from 1 were small for birth weight, and larger for head circumference and especially body length. Table 3 presents the comparison between our birthweight centile estimates for selected gestational ages (25, 28 and 31 weeks) and those from other Italian references. Our estimates tend to yield lower birthweight values for both females and males, particularly when compared with the older references. This is not true for the most recent charts [17], whose estimates are close to ours. Table 4 shows the estimated risk of death before discharge by week of gestation and birthweight centile class. As the risks were obtained from the multivariable logistic model, they are adjusted for sex, region of birth and presence of non-lethal congenital anomalies. As expected, mortality increased with decreasing gestational age. At each GA week, mortality decreased with increasing birth weight percentile up to the 75th or 89th, raising again at the 90th centile and over. Fig. 1 shows the adjusted odds ratios and 95% CI for mortality in each birth weight centile category, using as reference the 75th–89th class. Compared to the reference, mortality odds ratios were 7.94 (95% CI 4.18–15.08) below 10th centile; 3.04 (95% CI 1.63–5.65) between the 10th and 24th; 1.96 (95% CI 1.07–3.62) between the 25th and the 49th; 1.25 (95% CI 0.68–2.30) between the 50h and the 74th; and 2.07 (95% CI 1.01–4.25) at the 90th and above. As indicated by the confidence intervals, differences from the reference class were statistically significant below the 50th centile but only marginally beyond the 89th (p = 0.048), although the interval was largely skewed towards higher values. 4. Discussion This study explored the relationship between birth weight centiles and hospital mortality by week of gestational age in a large area-based sample of very preterm singletons. As expected, mortality decreased with increasing gestational age. At any gestational age, mortality decreased as birth weight centile increased, with lowest values observed

79

between the 50th and the 89th centiles interval. Risk of death raised again beyond the 89th centile, with a 95% CI largely skewed towards higher values. The steepest decline in mortality occurred between the b10th and the 10–24th birth weight centile classes, with adjusted ORs decreasing from 7.9 to 3. However, in the 25th–49th centile risk of death was still about two times higher than the reference category (aOR 1.96, CI 1.07–3.62), thus challenging the identification of the 10th centile as the appropriate cut-off to identify high-risk neonatal size. Similar results were reported by previous studies. Seeds and Peng found significantly elevated fetal mortality for birth weights through the 15th centile [24]. International European results from the MOSAIC study [25] showed a significantly increased risk of neonatal death up to the 24th birth weight centile (crude OR 1.68, 95% CI 1.29–2.18), while risk of bronchopulmonary dysplasia increased up to the 49th. In this study, the reference category was the 50th–74th class. In search for a “viability centile”, other Authors focussed on the lower end of the birth weight centile distribuition. Kamoji et al. [26] in the UK found a markedly decreased survival in infants born ≤ 28 completed weeks of gestation with birth weights ≤ 2th centile. This pattern however was not observed in the growth retarded babies born at 29 weeks and over, who in the vast majority of cases did very well. Different results were reported by Xu et al. [27], who studied the birth weight percentiles cut-offs in a very large USA population of non malformed singleton live births. Using the 25th–75th centile reference group, they found that the 15th centile cut-off consistently predicted mortality risk ratios N 2 for all gestational ages group, except for those below 28 weeks. In these extremely preterm babies there was a continuous increased risk of death over decreasing birth weight centile, and no meaningful cut-off could be identified. Different treatment policies for extremely preterm and growth retarded babies in the UK and US might partially explain the discrepancy between these studies. More recently, Cole et al. developed a logistic regression model based on gestation, birth weight for gestation, and also base deficit from umbilical cord to predict survival of very preterm infants to 40 weeks gestational age [28]. They found that weight for gestation had a powerful but not linear effect on survival, with values between the median and the 85th centile predicting the highest survival. Most published growth charts are derived from vital statistics. While this approach has the advantage of very large sample sizes, there may be problems in the accuracy of the available information. Assessment of gestational age is particularly important. In our study it was measured as best obstetrical estimate, using both the information from the last menstrual period (LMP) and the results of early ultrasound examination. The respective merits and problems of menstrual versus ultrasound dating have been widely discussed in the literature [29–31]. LMP dating tends to overestimate gestational age, mainly because of the possibility of delayed ovulation [30]. Poor recall, in contrast, can bias gestational age in any direction. Ultrasound estimation of gestational age rests on the assumption that in early gestation fetuses of equal size have the same gestational age. Although ultrasound estimates have been shown to better predict date of birth compared to LMP, the method has been criticized for failure to take into account physiological variability [29], and for being ultimately based on the LMP dates originally used as gold standard to develop the reference formulae [30,31]. For this study, we did not have the information on the exact proportion of ultrasound dating. However at the time of data collection early ultrasound examination was already common in Italy, and obstetricians were routinely using it to correct gestational age estimates in case of non reliable LMP date. In agreement with most authors, we developed percentile charts separately for girls and boys, because of their well recognized different growth patterns. Whether or not additional factors beyond ethnicity and sex should be taken into account when computing birth size charts is debated. Gardosi et al. developed a system to calculate a fetus' “term optimal weight” that takes into account factors able to influence

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M. Da Frè et al. / Early Human Development 91 (2015) 77–85

Table 2 Smoothed estimated centiles for birth weight, length and head circumference by gestational age and sex. Singletons only. N Birth weight (gr.) Gestational age: 22 w 23 w 24 w 25 w 26 w 27 w 28 w 29 w 30 w 31 w 22 w 23 w 24 w 25 w 26 w 27 w 28 w 29 w 30 w 31 w Length (cm) Gestational age: 22–23 w 24 w 25 w 26 w 27 w 28 w 29 w 30 w 31 w 22 w 23 w 24 w 25 w 26 w 27 w 28 w 29 w 30 w 31 w

Males 9 24 27 56 54 92 100 121 178 203 Females 5 20 32 33 68 73 83 110 157 155

Males 18 17 39 27 56 62 91 133 128 Females 12 11 23 24 44 51 51 76 114 114

Head circumference (cm) Gestational age: Males 22–23 w 19 24 w 16 25 w 39 26 w 29 27 w 58 28 w 63 29 w 90 30 w 134 31 w 131 Females 22–23 w 11 24 w 23 25 w 24 26 w 44 27 w 50 28 w 51 29 w 76 30 w 116 31 w 114

L d.f. = 3

S d.f. = 3

3th

10th

25th

50th

75th

90th

97th

1.4 1.4 1.3 1.3 1.2 1.1 1.0 0.9 0.8 0.7

0.154 0.171 0.187 0.202 0.216 0.225 0.231 0.234 0.237 0.240

349.6 383.0 415.0 440.9 468.8 510.7 573.2 647.7 729.3 816.6

411.3 462.0 513.6 558.5 605.3 664.1 741.3 828.1 920.9 1017.1

469.4 536.3 606.4 670.0 735.6 813.0 908.0 1011.3 1120.1 1231.2

524.6 607.0 695.0 776.8 861.4 958.4 1073.7 1196.8 1326.2 1458.1

577.5 674.7 780.2 879.9 983.5 1101.1 1238.5 1384.3 1538.3 1697.0

628.4 740.1 862.6 980.0 1102.6 1241.4 1402.5 1573.6 1756.0 1947.3

677.7 803.4 942.5 1077.5 1219.1 1379.7 1565.9 1764.4 1978.9 2208.4

1.0 1.1 1.3 1.4 1.5 1.4 1.3 1.2 0.9 0.7

0.139 0.159 0.178 0.196 0.211 0.221 0.224 0.223 0.219 0.215

345.9 380.0 404.8 416.2 428.4 457.0 514.3 604.6 718.6 847.1

390.3 442.1 490.4 530.5 574.1 627.7 696.8 788.1 898.6 1026.1

434.7 503.1 571.9 635.7 704.6 780.0 864.0 964.4 1080.5 1215.0

479.2 563.1 650.3 734.4 824.9 920.3 1020.8 1135.2 1264.2 1413.1

523.7 622.2 726.2 828.1 937.7 1051.8 1169.7 1301.7 1449.4 1620.0

568.2 680.6 799.9 917.8 1044.6 1176.4 1312.4 1464.6 1636.0 1835.1

612.7 738.3 871.7 1004.2 1146.7 1295.5 1450.0 1624.4 1823.7 2058.1

1.7 2.3 2.9 3.3 3.6 3.5 3.1 2.6 1.9

0.068 0.068 0.068 0.069 0.070 0.071 0.073 0.075 0.078

26.0 26.9 27.5 28.1 28.9 29.9 31.1 32.3 33.4

27.5 28.6 29.4 30.3 31.3 32.4 33.6 34.8 35.7

28.9 30.2 31.1 32.1 33.3 34.5 35.8 37.0 38.0

30.3 31.6 32.7 33.8 35.0 36.3 37.7 39.1 40.1

31.7 33.0 34.1 35.2 36.6 38.0 39.4 40.9 42.1

33.0 34.3 35.4 36.6 38.0 39.4 41.0 42.7 44.1

34.3 35.6 36.6 37.8 39.2 40.8 42.5 44.3 45.9

2.4 2.5 2.6 2.6 2.7 2.7 2.9 3.1 3.4 2.4

0.074 0.076 0.077 0.078 0.078 0.076 0.072 0.069 0.067 0.074

23.9 25.2 26.2 27.2 28.3 29.5 30.7 32.0 33.3 23.9

25.7 27.2 28.3 29.4 30.7 31.9 33.1 34.3 35.8 25.7

27.3 28.9 30.2 31.4 32.8 34.0 35.1 36.4 38.0 27.3

28.8 30.5 31.9 33.2 34.6 35.9 37.0 38.2 39.9 28.8

30.1 32.0 33.5 34.9 36.4 37.7 38.7 39.9 41.6 30.1

31.4 33.4 35.0 36.4 38.0 39.3 40.3 41.4 43.1 31.4

32.7 34.7 36.3 37.9 39.5 40.8 41.7 42.9 44.6 32.7

−0.2 0.5 1.1 1.6 1.9 2.2 2.3 2.3 2.0

0.067 0.065 0.064 0.063 0.063 0.063 0.061 0.060 0.249

18.9 19.4 19.9 20.5 21.3 22.3 23.2 24.0 24.9

19.7 20.3 20.9 21.5 22.4 23.5 24.5 25.3 26.1

20.6 21.2 21.8 22.6 23.5 24.7 25.6 26.5 27.3

21.5 22.2 22.8 23.6 24.6 25.8 26.8 27.6 28.4

22.5 23.2 23.8 24.5 25.6 26.8 27.8 28.7 29.5

23.5 24.2 24.7 25.5 26.6 27.8 28.9 29.7 30.5

24.6 25.2 25.7 26.4 27.5 28.8 29.8 30.7 31.5

1.4 1.5 1.6 1.7 1.7 1.8 2.1 2.1 2.1

0.060 0.063 0.065 0.066 0.067 0.065 0.061 0.056 0.051

17.9 18.8 19.6 20.3 21.0 21.7 22.6 23.8 25.0

18.7 19.8 20.6 21.4 22.2 22.9 23.8 24.9 26.0

19.6 20.7 21.7 22.5 23.3 24.1 25.0 26.0 27.1

20.4 21.6 22.7 23.6 24.4 25.2 26.1 27.0 28.0

21.2 22.5 23.6 24.6 25.5 26.3 27.1 28.0 29.0

22.0 23.4 24.6 25.6 26.5 27.3 28.1 29.0 29.9

22.8 24.2 25.5 26.6 27.5 28.3 29.1 29.9 30.7

physiological weight variability, usually maternal variables (parity, ethnicity, height and weight) [32]. These “customized” charts aim at discriminating between the “small but normal” baby and the pathologically growth retarded, and have been shown to better predict perinatal

mortality and morbidity [33]. However, the real contribution of adjustment for maternal characteristics over the use of the intrauterine standards inherent in the Gardosi customized system has been questioned [34].

M. Da Frè et al. / Early Human Development 91 (2015) 77–85

81

Table 3 Comparison between ACTION centiles and other Italian charts.

Males ACTION study (2003–2005)a Bertino et al. [17] (2005–2007) b Polo et al. [15] (2000–2003)c Festini et al. [14] (1991–2002)d Parazzini et al. [16] (1984–1985)e Gagliardi et al. [13] 1979–1992)f Females ACTION study (2003–2005)a Bertino et al. [17] (2005–2007) b Polo et al. [15] (2000–2003)c Festini et al. [14] (1991–2002)d Parazzini et al. [16] (1984–1985)e Gagliardi et al. [13] 1979–1992)f a b c d e f

Gestational age range

25 weeks

(weeks)

10th

50th

90th

10th

50th

90th

10th

50th

90th

22–31 23–42 27–43 24–43 28–43 26–43

559 517 – 610

777 697 – 787

980 877 – 963

–

–

–

741 736 743 813 812 787

1074 1022 1088 1157 1222 1085

1403 1307 1522 1423 1782 1383

1017 1103 1179 1120 1173 1228

1459 1495 1640 1620 1777 1644

1947 1887 2179 2050 2810 2060

22–31

523 502 – 573

721 668 – 740

889 834 – 947

–

–

–

737 717 688 683 757 709

1027 979 1008 1057 1153 1007

1286 1240 1409 1340 1771 1304

1059 1072 1104 1067 1093 1133

1440 1433 1536 1510 1695 1550

1791 1794 2040 1960 2864 1966

27–43 24–43 28–43 26–43

28 weeks

31 weeks

Area-based, six Italian regions: Lombardia and Friuli Venezia-Giulia (North), Tuscany, Lazio and Marche (Center), and Calabria (South). Singletons. Hospital-based, 34 Italian NICUs. Singletons. Area-based, Lazio region. Singletons. Area-based, Tuscany region. All. Area-based, Italy. All. For 31 week gestation, also stillbirths included. Area-based, Friuli Venezia–Giulia region and Trento area. Singletons.

Consistently with other studies concerning preterm neonates [35, 36], we found that our birthweight curves tend to yield lower values compared to the older Italian published references, and the effect was evident particularly at older gestational ages. These differences may be partly explained by the different gestational age ranges and methods used to estimate centiles. More importantly, a cohort effect might be present because of growing use of ultrasound dating, increased frequency of indicated delivery in case of poor fetal growth, and improved survival of growth retarded fetuses due to better perinatal care. This study has limitations. Despite the large sample size, we could not develop separate birth size charts for multiples. These are generally considered to have a risk of SGA larger than singletons [37], although the gestational age at which this effect becomes evident is debated [38]. As it is the case with most currently adopted standards, our birth size curves were estimated from cross-sectional anthropometric measurements performed after birth, rather than by repeated ultrasound assessments carried out in utero. Preterm birth is known to be associated with intrauterine growth retardation [39], and birthweight at a given gestational age is not necessarily representative of the weight of the fetuses still in utero at the same time of gestation. Therefore, the use of postnatal charts involves a certain degree of underestimation of

intrauterine growth retardation. However, ultrasound derived growth charts are difficult to obtain at population level [39,40]. Most published standards are based on small and selected samples, or data pooled over several years [39]. As pointed out by Paneth [40], ultimately neonatologists are bound to deal with liveborn preterm babies rather than fetuses, and to them “the growth of these infants compared with their more fortunate peers remaining in utero is not really relevant”. This study confirms that the usual cut-off at 10th centile adopted to identify small for gestational age is an inadequate criterion to predict actual risk of death. Although the steepest decline in mortality occurred in our population between the b10th and the 10–24th class, mortality continued to decrease up to the 75th centile. Neonatologists should be aware of these trends, and should not overlook the risks associated with a 10th–24th or even 25th–49th birthweight centiles in very preterm neonates. Continuous measures of growth retardation such as the birthweight z-score may be more appropriate than the usual “small for gestational age” definition to explore the relationship with adverse perinatal outcomes [28,40]. Competing interests None.

Table 4 Hospital mortality and 95% confidence interval (CI) by gestational age and birth weight centile category, as estimated from multivariable logistic modela. GA

Birth weight centile category b10th

22 23 24 25 26 27 28 29 30 31 Total

10th–24th

25th–49th

50th–74th

≥90th

75th–89th

Total

n.

%

(95% CI)

n.

%

(95% CI)

n.

%

(95% CI)

n.

%

(95% CI)

n.

%

(95% CI)

n.

%

(95% CI)

n.

%

(95% CI)

2 3 3 9 19 22 21 22 25 35 161

99 95 86 76 62 43 29 20 12 5 25

(89–99.9) (88–98) (74–93) (62–85) (48–74) (30–56) (19–43) (11–32) (6–22) (3–11) (17–33)

1 12 7 14 16 31 31 36 65 59 272

99 93 50 55 36 24 17 11 4 2 12

(94–99.9) (84–97) (33–66) (44–68) (24–50) (16–35) (10–27) (6–18) (2–8) (1–5) (8–17)

2 9 18 19 18 28 38 50 74 74 330

95 89 43 50 29 20 11 6 3 1 8

(69–99) (76–96) (29–59) (36–63) (19–42) (13–30) (6–18) (4–11) (1–5) (0.6–3) (5–11)

4 11 13 32 33 43 40 55 82 82 395

89 86 36 35 22 11 7 4 2 1 5

(51–99) (70–94) (23–52) (24–48) (14–33) (7–17) (4–12) (2–8) (0.8–3) (0.4–2) (3–7)

4 5 9 8 27 26 36 50 69 74 308

93 79 43 39 17 10 6 3 2 1 4

(60–99) (57–91) (26–61) (25–56) (10–27) (5–16) (3–12) (1–6) (0.7–3) (0.3–2) (3–7)

1 4 9 7 9 15 17 18 19 33 132

97 87 51 47 31 20 16 7 3 1 9

(76–99.6) (70–95) (34–68) (30–64) (19–47) (12–33) (9–27) (4–14) (1–6) (0.8–4) (5–15)

14 44 59 89 122 165 183 231 334 357 1598

95 89 47 46 29 17 11 6 3 1 16

(72–99) (76–95) (34–61) (35–58) (21–38) (12–23) (7–17) (4–10) (1–5) (0.7–3) (14–18)

n. indicates the number of infants per cell. a Risks are adjusted for sex, presence of congenital anomalies and region of birth.

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M. Da Frè et al. / Early Human Development 91 (2015) 77–85

Fig. 1. Adjusted odds ratios (Adj OR) and 95% confidence intervals for mortality by birth weight centile category (reference: 75th–89th). ORs are adjusted for sex, gestational age, presence of non lethal congenital anomalies and region of birth.

Funding The project was funded by the Italian Ministry of Health (Programma di Ricerca Finalizzata 2002–2004) and coordinated by the Unit of Epidemiology of the Regional Agency for Health of Tuscany. The sponsor had no part in the design of the study, data analysis, and preparation of the paper. Acknowledgments The results presented in this paper are part of the project on “Accesso alle Cure e Terapie Intensive Ostetrico-Neonatali” (ACTION). We thank E. Coianiz, R. Da Riol and M. Montico (Friuli Venezia-Giulia), R. Bellù, M. Bellasio and M. Fusi (Lombardia), F. Sidoti, M. Giannelli and F. Rusconi (Tuscany), R. Freddara and L. Pellegrini (Marche), M. Barbolini, R Aduna, M. Di Renzi, and S. Santoni (Lazio) and S. Dodaro (Calabria), who monitored data collection at regional level. S. Alberico and G. Mello participated in the preparation of the data collection instrument, prenatal section. We are very grateful to all the parents who agreed to participate in the project, and to the physicians and midwives who collected the data. The list of participating hospitals and local project coordinators is presented in Appendix A. Appendix A. List of participating hospitals A.1. Friuli Venezia-Giulia ▪ IRCCS “Burlo Garofolo”, Trieste (S. Alberico, S. Demarini, S. Guaschino, I. Redaelli, V. Soini, F. Uxa) ▪ Ospedale di Gorizia (D. Faraguna, C. Gigli) ▪ Ospedale di Monfalcone, Gorizia (M.T. Calipa, D. Dragovich, D. Faraguna, C. Varagnolo) ▪ Ospedale di Gemona del Friuli, Udine (D. Bassini, M. Bottega, L. Cattarossi, E. D'Ambrosi, R. Pinzano)

▪ Ospedale di Tolmezzo, Udine (D. Bassini, M. Bottega, L. Cattarossi, R. Pinzano) ▪ Ospedale di San Daniele del Friuli, Udine (L. Battistella, G. Del Frate, B. Sacher) ▪ Azienda Ospedaliera Santa Maria della Misericorda, Udine (R. Da Riol, F. Macagno, D. Marchesoni, A. Pontrelli, A. Rossi) ▪ Ospedale di Palmanova, Udine (M. Casco, A. Gabbiotti, F. Patamia, C. Zompicchiatti) ▪ Ospedale di Latisana, Udine (A. Comuzzi, R. Perini, S. Puglisi Allegra, A. Scarpa) ▪ Ospedale di San Vito al Tagliamento, Pordenone (F. Colonna, M. Gamper) ▪ Azienda Ospedialiera Santa Maria degli Angeli, Pordenone (V. Adamo, A. Bordugo, M. Fadalti, L. Peratoner) ▪ Clinica San Giorgio, Pordenone (A. Rosadini, G. Scozzari, G. Vazzoler) A.2. Lombardia ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪

Ospedali Riuniti, Bergamo (G. Burgio, A. Colombo) Spedali Civili, Brescia (A. Cavazza, G. Chirico, R. Marzollo) Ospedale Valduce, Como (F. Frisone, M. Maccabruni) Ospedale S.Anna, Como (L. Caccamo, P. Faldini, D. Merazzi, L. Rossi) Ospedale A. Manzoni, Lecco (R. Bellù, S. De Poli, R. Zanini) Ospedale Fornaroli, Magenta Mi (A. Avanzino, S. Barp, R. Crossignani, S. Santucci) Ospedale San Raffaele, Milano (G. Barera, D. Cella, L. Moiraghi) Ospedale Buzzi, Milano (M. Battaglioli, G. Compagnoni) Clinica Mangiagalli, Milano (P. Bastrenta, F. Mosca) Ospedale Niguarda Ca' Granda, Milano (A. Brunelli, S. Martinelli) Presidio Ospedaliero M. Melloni, Milano (M. Franco, G. Moro) Ospedale S. Gerardo, Monza (P. Tagliabue, M.L. Ventura) Ospedale Salvini, Rho, Milano (R. Germani, L. Magni, M. Pelti) Policlinico San Matteo, Pavia (G.F. Perotti, E. Polito)

M. Da Frè et al. / Early Human Development 91 (2015) 77–85

▪ Ospedale Bolognini, Seriate, Bergamo (M. Felice, S. Ferrari, M. Somaschini) ▪ Ospedale di Circolo, Varese (M. Agosti, G. Citterio, P. Guidali) A.3. Toscana ▪ Ospedale S. Antonio Abate, Pontremoli, Massa Carrara (C. Lorenzini, G. Memmini, P. Migliorini, G. Ricci) ▪ Ospedale S. Antonio Abate, Fivizzano, Massa Carrara(M.R. Cappè, A. Giovannoni, G. Memmini, D. Milano) ▪ Ospedale Civile, Carrara, Massa Carrara (A. Kemeny, F. Oberti, G. Memmini, D. Milano) ▪ Ospedale SS. Giacomo e Cristoforo, Massa, Massa Carrara(P. Bai, P. Manfredi, G. Memmini, P. Migliorini) ▪ Ospedale S. Francesco, Barga, Lucca (P. Baldera, R. Domenici, R. Gualtierotti, O. Pieroni, A. Romano) ▪ Ospedale Generale Provinciale, Lucca (R. Domenici, L. Luti, A. Melani, L. Vecoli) ▪ Ospedale S. Maria Maddalena, Volterra, Pisa (A. Bertini, G. Biagini, A. Celandroni, M. Funaioli) ▪ Azienda Ospedaliero-Universitaria Pisana, Ospedali Riuniti S. Chiara, Pisa (A. Boldrini, P. Bottone, A. Carmignani, V. Facchini, A.R. Genazzani, L. Giardina, C. Maggi, E. Sigali, F. Strigini) ▪ Casa di Cura S. Rossore, Pisa (W. Francesconi, G. Gravina) ▪ Ospedale F. Lotti, Pontedera, Pisa (S. Amato, A. Celandroni, G. Gelato, M. Srebot) ▪ Ospedali Riuniti, Livorno (D. Anastassopulos, R. Danieli, E. Micheletti, M. Nuzzi) ▪ Ospedale Civile di Cecina, Livorno (C. Tantini, T.A. Balzini, G.S. Gragnani) ▪ Ospedale Civile di Piombino, Livorno (N. Calonaci, S. Denaro, R. Gabiglieri, L. Rizzo) ▪ Ospedale Civile Elbano, Portoferraio, Livorno (N. Calonaci, L. Malandra, F. Rosi, L. Rizzo) ▪ Ospedale Unico della Versilia, Lido di Camaiore, Lucca (L. Gagliardi, A. Marchetti, I. Merusi, G. Ternelli) ▪ Ospedale SS. Cosimo Damiano, Pescia, Pistoia (R. Agostiniani, F. Bray, M.L. Demuru, L. Niccoli) ▪ Ospedale del Ceppo, Pistoia (L. Capuzzo, M. Magni, S. Sani, L. Savino) ▪ Ospedale Misericordia e Dolce, Prato (U. Bottone, P. Ciolini, P. Dal Poggetto, L. Golin, E. Martelli) ▪ Nuovo Ospedale S.G. di Dio Torregalli, Firenze (L. Berti, C. Guerri, M. Pezzati, M. Strano, S. Tofani) ▪ Azienda Ospedaliero-Universitaria Careggi, Firenze (G. Bertini, M. Marchionni, G. Mello, S. Perugi, S. Rossi, F. Rubaltelli, G. Scarselli, F. Tondi) ▪ Azienda Ospedaliero-Universitaria Meyer, Firenze (G. Donzelli, G. Indolfi, M.S. Pignotti) ▪ Ospedale S.M. Annunziata, Bagno a Ripoli, Firenze (G. Assenza, F. Barciulli, C. Campatelli, D. Pettini, F. Romoli) ▪ Ospedale del Mugello, Borgo San Lorenzo, Firenze (A. Cecconi, C. Dettori, A. Fedi, M. Fusi, R. Martini) ▪ Casa di Cura S. Chiara, Firenze (D. Baronci, C. Lotti) ▪ Casa di Cura Villa Donatello, Firenze (A.M. Celesti, G. Fallai) ▪ Ospedale S. Giuseppe, Empoli, Firenze (L. Coccoli, M. Filippeschi, M. Turini, M. Zani) ▪ Ospedale degli Infermi, San Miniato, Pisa (M. Cicione, L. Coccoli, M. Filippeschi, M. Turini) ▪ Ospedali Riuniti Val D'Elsa, Poggibonsi, Siena (C. Buffi, B. D'Amato, A. Muccioli, L. Vispi) ▪ Ospedale Le Scotte Azienda Ospedaliero-Universitaria, Siena (A. Annesanti, F. Bagnoli, G. Bonocore, P. Marenzoni, F. Pietraglia, E. Piccolini) ▪ Ospedali Riuniti Val di Chiana, Montepulciano, Siena (M. Carlini, M.L. Di Palma, I. Giani, P. Grandioso)

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▪ Ospedale S. Maria alla Gruccia, Montevarchi, Arezzo (V. Barbagli, R. Caporali, G. Cariti, A. De Marco) ▪ Ospedale Civile, Bibbiena, Arezzo (G. D'Ascola, F. Lelli, C. Magni) ▪ Ospedale Val Tiberina, Sansepolcro, Arezzo (G. Cerulli, G. D'Ascola, G. Ientile, M.A. Venturelli) ▪ Ospedale S. Donato, Arezzo (L. Burroni, F. Catania, G. Cristini, G. D'Ascola, M. Paccariè) ▪ Ospedale S. Andrea, Massa Marittima, Grosseto (A. Benelli, G. Benetti, A. Campagna, G. Malagnino) ▪ Ospedale della Misericordia, Grosseto (E. Barlocco, S. Dell'Acqua, R. D'Alfonso, M. Milianti) ▪ Ospedale S.G. di Dio, Orbetello, Grosseto(F. Berti, A. Cancemi, G. Pappalettere, S. Salce) A.4. Marche ▪ Ospedale San Salvatore, Pesaro, Pesaro e Urbino (C.M. Bertoni, L. Felici, A. Marabini) ▪ Ospedale di Urbino, Pesaro e Urbino (A.M. Caporaletti, D. Carboni, N. Filomeni, L. Griffoni, N. Sanchi) ▪ Ospedale Santa Croce, Fano, Pesaro e Urbino (G. Franchi, Olivi, G. Quintini, R. Seclì) ▪ Presidio Ospedaliero, Novafeltria, Pesaro e Urbino (M. Andreotti, G. Cometa, Nesius) ▪ Ospedale di Senigallia, Senigallia, Ancona (N. Cester, M. Massacesi, L. Migliozzi, P. Radicioni) ▪ Ospedale di Fabriano, Fabriano, Ancona (P. Bolzonetti, P. La Manna, M. Zinnai) ▪ Ospedale di Osimo, Ancona (P. Coltroneo, N. Liberatori, M. Tiriduzzi, A. Zoppi) ▪ Ospedale di Jesi, Ancona (A. Curatola, T. Gaetti, M. Massaccesi, C. Moroncini) ▪ Ospedale di Macerata (E. Garbati, A. Mercuri, L. Palombi, P. Perri) ▪ Presidio Ospedaliero, Civitanova Marche, Macerata (E. Bartolotta, V. Bonifazi, A. Ruffini) ▪ Ospedale di Recanati, Macerata (E. Bartolotta, L. Bonazza, Z. Jacopini, L. Mattei) ▪ Ospedale di San Severino Marche, Macerata (A. Doga, A. Mancini, A. Mantovani, A. Rosini, L. Tubaldi) ▪ Ospedale di Ascoli Piceno (C. Amadio, A. Carlucci, P. Manieri, R. Rossi, M.R. Sabatini) ▪ Ospedale di Fermo (R. Brancaccio, A. Caucci, Poloni, A. Tacchetti) ▪ Ospedale di San Benedetto del Tronto, Ascoli Piceno (Galante, G. Infriccioli, M. Mattucci, A. Papi) ▪ Casa di Cura Stella Maris, San Benedetto del Tronto, Ascoli Piceno (O. Di Giulio, G. Micucci, F. Paielli) ▪ Case di Cura Villa Anna, San Benedetto del Tronto, Ascoli Piceno (M.P. Cicconi, O. Di Giulio) ▪ Azienda Ospedaliera Salesi, Ancona (V. Bazzeccheri, R. Buglia, V. Carnielli, C. Civitella, F. Del Savio, R. Freddara, L. Pellegrini, A.L. Tranquilli) ▪ Villa Igea, Ancona (S. Carletti, R. Cesarini, Polenta) A.5. Lazio ▪ Casa di Cura Quisisana, Roma (G. Giorgi) ▪ Ospedale San Giacomo, Roma (L. Ascani, M.G. Innocenti, E. Penvin, G. Sabino) ▪ Casa di Cura Villa Mafalda, Roma (E. Antonini) ▪ Clinica Villa Margherita, Roma (R. Foci) ▪ Clinica Villa Salaria, Roma (A. Lorenzetti) ▪ Clinica Mater Dei, Roma (G. Fazi, G. Grisci, R. Pedicino) ▪ Ospedale S. Giovanni Calibita-Fatebenefratelli, Roma (A. De Santis, E. Pontesilli, A. Sacco) ▪ Casa di Cura Nuova Itor, Roma (P. Abbatelli, M. Prezioso, F. Ricci) ▪ Policlinico Casilino, Roma (M.C. De Marco, C. Leo)

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▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪ ▪

M. Da Frè et al. / Early Human Development 91 (2015) 77–85

Ospedale Sandro Pertini, Roma (A. Andreuzzi, E. Giorgi) Clinica Annunziatella, Roma (M. Genova, R. Mazzei, G. Zeolla) Clinica Fabia Mater, Roma (A. Ceci, P. Guidoni, G. Riti) Ospedale Madre G. Vannini, Istituto Figlie di San Camillo, Roma (R. Gallo) Casa di Cura Villa Europa all'Eur, Roma (M. Bianco, I. Cozzolini, D. Julia) Casa di Cura Città di Roma, Roma (L. Barracco, L. Maragni, G. Melpignano) Ospedale Giovanni Battista Grassi, Roma (M. Bonci, C. Trivellini) Casa di Cura Salvator Mundi, Roma (N. Attolico) Casa di Cura Villa Pia, Roma (P. Cimmino) Casa di Cura Aurelia Hospital, Roma (M. Caramia, H. Zanetti) Ospedale Cristo Re, Roma (L. Cocconi, D. Parenti, C. Piscicelli) Ospedale S. Pietro-Fatebenefratelli, Roma (M. Barresi, R. Maruccio, P. Paesano, E. Scapillati) Ospedale Santo Spirito, Roma (A. Cicerone) Casa di Cura Villa Flaminia, Roma (M. Magliocco) Casa di Cura Santa Famiglia, Roma (A. Cappucci, F. Santoro) Casa di Cura Santa Maria di Leuca, Roma (M. Figliolini, Finocchi) Ospedale San Camillo-Forlanini, Roma (P. Favata, G. Pellegrini, F. Pierucci, G. Scassellati, F. Signore, Ottaviano, Carla) Ospedale San Filippo Neri, Roma (S. Anania, R. Balestrieri, S. Cucuzzoli, M. Matone, F. Valentini) Ospedale San Giovanni, Roma (I. Bezzi, F. Marchetti) IRCCS Bambino Gesù, Roma (C. Auriti, S. Lozzi, S. Palamides, N. Pirozzi) Policlinico Gemelli, Roma (S. Costa, C. Dell'Aquila, E. Zecca) Policlinico Umberto I, Roma (R. Aufieri, P. Ciolli, R. Lucchini, R. Paesano, A. Panero, M.G. Villani) Sant'Eugenio, Roma (L. Cristini, A. Di Paolo, P. Ndenga, C. Ticconi) Ospedale Civile di Bracciano, Roma (L. Bacchion) Ospedale San Paolo, Civitavecchia, Roma (G. Bragaglia, E. Proli) Ospedale L. Parodi Delfino, Colleferro, Roma (A. Felici) Ospedale Santissimo Gonfalone, Monterotondo, Roma (M.G. Angelini) Ospedale Coniugi Bernardini, Palestrina, Roma (L. Panepuccia, A. Porrà) Ospedale Angelucci, Subiaco, Roma (R. Di Pasquali, M. Tozzi) Ospedale San Giovanni Evangelista, Tivoli, Roma (A. Leodori, M. Marceca, D. Marini) Ospedale Generale Provinciale di Anzio, Roma (V. Ambrogi, A. Faiola, R. la Rocca) Ospedale Ercole De Santis, Genzano, Roma (L. Boccuzzi, A. Guglielmotti) Ospedale San Giuseppe, Marino, Roma (E. Mucchino, F. Ratto) Ospedale Civile di Velletri, Roma (M. Corigliano, M. Ferraro) Ospedale San Benedetto, Alatri, Frosinone (E. Baldaccini, A. Noce) Ospedale Civile di Anagni, Frosinone (P. Gueci) Ospedale Gemma de Bosis, Cassino, Frosinone (E. Cataldi, D. De Quattro) Casa di Cura Sant'Anna di Cassino, Frosinone (S. Adnan Abu) Ospedale Umberto I, Frosinone (G. Palermo, M.R. Pecci, S. Tambucci) Ospedale Santissima Trinità, Sora, Frosinone (M. Calcagni, C. Testani) Casa di Cura Città di Aprilia, Aprilia, Latina (F. Mascolo, M.A. Rubessa) Ospedale San Giovanni di Dio, Fondi, Latina (C. Mancini, A. Percoco, A. Roma) Ospedale Civile di Gaeta, Latina (C. Magliozzi, T. Vecchio) Ospedale Santa Maria Goretti, Latina (M. Coluzzi, P.C. Morosillo, A. Mosillo, N. Nardacci) Ospedale Regina Elena, Priverno, Latina (V. Cecchetti, A. Fantozzi) Ospedale San Camillo de Lellis, Rieti (B. Campanelli, M. Pizzoli, A. Rinaldi) Ospedale Andosilla, Civita Castellana, Viterbo (M.A. Ciappici) Ospedale Civile di Tarquinia, Viterbo (L. Felli, A. Perugini)

▪ Ospedale Belcolle, Viterbo (S. Bracaloni, F. Ciripicchia, K. Colella, L. Dattis, R. Navas) A.6. Calabria ▪ Azienda Ospedaliera Pugliese-Ciaccio, Catanzaro (S. Miniaci, V. Pascale) ▪ Azienda Ospedaliera dell'Annunziata, Cosenza (A. Contaldo, C. Corchia) ▪ Presidio Ospedaliero San Giovanni di Dio, Crotone (U. Corapi, C. Crugliano) ▪ Presidio Ospedaliero di Lamezia Terme, Lamezia Terme Cz (S.A. Canepa, G. Scozia) ▪ Azienda Ospedaliera Bianchi-Melacrino-Morelli, Reggio Calabria (R. Cimellaro, G. Fontanelli, A. Nicolò) References [1] Bukowski R. Stillbirth and fetal growth restriction. Clin Obstet Gynecol 2010;53: 673–80. [2] Corchia C, Ferrante P, Da Frè M, Di Lallo D, Gagliardi L, Carnielli V, et al. Cause-specific mortality of very preterm infants and antenatal events. J Pediatr 2013;162:1125–32. [3] Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med 2008;359:61–73. [4] Imdad A, Yakoob MY, Siddiqui S, Bhutta ZA. Screening and triage of intrauterine growth restriction (IUGR) in general population and high risk pregnancies: a systematic review with a focus on reduction of IUGR related stillbirths. BMC Public Health 2011;11(Suppl. 3):S1. http://dx.doi.org/10.1186/1471-2458-11-S3-S1. [5] Zhang J, Merialdi M, Platt LD, Kramer MS. Defining normal and abnormal fetal growth: promises and challenges. Am J Obstet Gynecol 2010;202:522–8. [6] Keirse MJ. International variations in intrauterine growth. Eur J Obstet Gynecol Reprod Biol 2000;92:21–8. [7] Reeves S, Bernstein IM. Optimal growth modelling. Semin Perinatol 2008;32: 148–53. [8] Goldenberg RL, Cliver SP, Cutter GR, Hoffman HJ, Cassady G, Davis RO, et al. Black– white differences in newborn anthropometric measurements. Obstet Gynecol 1991;78(5 Pt 1):782–8. [9] Alexander GR, Kogan MD, Himes JH, Mor JM, Goldenberg R. Racial differences in birthweight for gestational age and infant mortality in extremely-low-risk US populations. Paediatr Perinat Epidemiol 1999;13:205–17. [10] Kierans WJ, Joseph KS, Luo ZC, Platt R, Wilkins R, Kramer MS. Does one size fit all? The case for ethnic-specific standards of fetal growth. BMC Pregnancy Childbirth 2008;8:1. http://dx.doi.org/10.1186/1471-2393-8-1. [11] Graafmans WC, Richardus JH, Borsboom GJ, Bakketeig L, Langhoff-Roos J, Bergsjø P, et al. Birth weight and perinatal mortality: a comparison of “optimal” birth weight in seven Western European countries. Epidemiology 2002;13:569–74. [12] WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development. Geneva: World Health Organization; 2006. [13] Gagliardi L, Macagno F, Pedrotti D, Coraiola M, Furlan R, Agostinis L, et al. Weight, length and head circumference at birth of a North-Eastern Italian population. Report of the ad hoc committee of the Italian Society of Neonatology. Riv Ital Pediatr (Ital J Pediatr) 1999;25:159–69. [14] Festini F, Procopio E, Taccetti G, Repetto T, Cioni ML, Campana S, et al. Birth weight for gestational age centiles for Italian neonates. J Matern Fetal Neonatal Med 2004; 15:411–7. [15] Polo A, Pezzotti P, Spinelli A, Di Lallo D. Comparison of two methods for constructing birth weight charts in an Italian region. Years 2000–2003. Epidemiol Prev 2007;31: 261–9. [16] Parazzini F, Cortinovis I, Bortolus R, Fedele L, Decarli A. Weight at birth by gestational age in Italy. Hum Reprod 1995;10:1862–3. [17] Bertino E, Spada E, Occhi L, Coscia A, Giuliani F, Gagliardi L, et al. Neonatal anthropometric charts: the Italian Neonatal Study compared with other European Studies. JPGN 2010;51:353–61. [18] Gagliardi L, Rusconi F, Da Frè M, Mello G, Carnielli V, Di Lallo D, et al. Pregnancy disorders leading to very preterm birth influence neonatal outcomes: results of the population-based ACTION cohort study. Pediatr Res 2013;73:794–801. [19] The International Neonatal Network. The CRIB (clinical risk index for babies) score: a tool for assessing initial neonatal risk and comparing performance of neonatal intensive care units. Lancet 1993;342:193–8. Erratum in: Lancet 1993;342:626. [20] Kramer MS, Platt RW, Wen SW, Joseph KS, Allen A, Abrahamowicz M, et al. A new and improved population-based Canadian reference for birth weight for gestational age. Pediatrics 2001;108:E35. [21] Tukey JW. Exploratory data analysis. Don Mills, Ontario: Addison-Wesley; 1977. [22] Cole TJ. Fitting smoothed centile curves to reference data. J R Stat Soc 1988;151: 385–418. [23] Cole TJ, Green PJ. Smoothing reference centile curves: the LMS method and penalized likelihood. Stat Med 1992;11:1305–9.

M. Da Frè et al. / Early Human Development 91 (2015) 77–85 [24] Seeds JW, Peng T. Impaired growth and risk of fetal death: is the tenth percentile the appropriate standard? Am J Obstet Gynecol 1998;178:658–9. [25] Zeitlin J, El Ayoubi M, Jarreau PH, Draper E, Blondel B, Künzel W, et al. Impact of fetal growth restriction on mortality and morbidity in a very preterm birth cohort. J Pediatr 2010;157:733–9. [26] Kamoji VM, Dorling JS, Manktelow BN, Draper ES, Field DJ. Extremely growthretarded infants: is there a viability centile? Pediatrics 2006;118:758–63. [27] Xu H, Simonet F, Luo ZC. Optimal birth weight percentile cut-offs in defining small-or large-for-gestational age. Acta Paediatr 2010;99:550–5. [28] Cole TJ, Hey E, Richmond S. The PREM score: a graphical tool for predicting survival in very preterm births. Arch Dis Child Fetal Neonatal Ed 2010;95:F14–9. [29] Zhang J, Bowes WA. Birth-weight-for-gestational-age patterns by race, sex, and parity in the United States population. Obstet Gynecol 1995;86:200–8. [30] Lynch CD, Zhang J. The research implications of the selection of a gestational age estimation method. Paediatr Perinat Epidemiol 2007;21(Suppl. 2): 86–96. [31] Dietz PM, England LJ, Callaghan WM, Pearl M, Wier ML, Kharrazi M. A comparison of LMP-based and ultrasound-based estimates of gestational age using the linked California livebirth and prenatal screening records. Paediatr Perinat Epidemiol 2007;21(Suppl. 2):62–71. [32] Gardosi J, Chang A, Kalyan B, Symonds EM. Customised antenatal growth charts. Lancet 1992;339:283–7.

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[33] Zhang X, Platt RW, Cnattingius S, Joseph KS, Kramer MS. The use of customized versus population-based birthweight standards in predicting perinatal mortality. BJOG 2007;114:474–7. [34] Hutcheon JA, Zhang X, Cnattingius S, Kramer MS, Platt RW. Customised birthweight percentiles: does adjusting for maternal characteristics matter? BJOG 2008;115: 1397–404. [35] Bonellie S, Chalmers J, Gray R, Greer I, Jarvis S, Williams C. Centile charts for birthweight for gestational age for Scottish singleton births. BMC Pregnancy Childbirth 2008;8:5. http://dx.doi.org/10.1186/1471-2393-8-5. [36] Salomon LJ, Bernard JP, de Stavola B, Kenward M, Ville Y. Birth weight and size: charts and equations. J Gynecol Obstet Biol Reprod 2007;36:50–6. [37] Muhlhausler BS, Hancock SN, Bloomfield FH, Harding R. Are twins growth restricted? Pediatr Res 2011;70:117–22. [38] Gielen M, Lindsey PJ, Derom C, Loos RJF, Souren NY, Paulussen ADC, et al. Twin-specific intrauterine 'growth' charts based on cross-sectional birthweight data. Twin Res Hum Genet 2008;11:224–35. [39] Cooke RW. Conventional birth weight standards obscure fetal growth restriction in preterm infants. Arch Dis Child Fetal Neonatal Ed 2007;92:F189–92. [40] Paneth N. The hidden population in perinatal epidemiology. Am J Epidemiol 2008; 167:793–6.