Postnatal Weight Trajectories Drives Beter Head Circumference Growth in Preterm Infants ≤32 Weeks: A Multicenter Latin American Cohort Study

Postnatal Weight Trajectories Drives Beter Head Circumference Growth in Preterm Infants ≤32 Weeks: A Multicenter Latin American Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Postnatal Weight Trajectories Drives Beter Head Circumference Growth in Preterm Infants ≤32 Weeks: A Multicenter Latin American Cohort Study Angela Hoyos, Pablo Vasquez-Hoyos, Carlos Fajardo, Horacio Osiovich, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9647430/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Postnatal weight loss is nearly universal in preterm infants, and its relationship to head circumference (HC) growth remains incompletely characterized. Growth standards were recently updated with the Fenton 2025 charts, which modify Z-score parameters compared to the 2013 version. Objective To evaluate the association between postnatal weight trajectory and HC growth from birth to discharge in preterm infants ≤ 32 weeks of gestation across a large multicenter Latin American cohort, using Fenton 2025 growth standards. Methods This observational cohort study used secondary analysis of prospective data from EpicLatino, a quality improvement registry of 35 NICUs across Latin America and the Caribbean (2015–2024). Infants within 48 hours of birth were included. The exposure was the change in weight Z-score (Fenton 2025) from birth to discharge, categorized into 10 groups at 0.4-unit intervals. The primary outcome was the change in HC Z-score (Fenton 2025). A multilevel linear model with a random intercept by center was used, adjusting for gestational age, necrotizing enterocolitis, sepsis, SNAP-II score, length of stay, treated patent ductus arteriosus, Apgar score at 5 minutes, HC Z-score at birth, weight Z-score at birth, and outborn status. Results 3,926 infants from 35 centers were included (analytical sample n = 3,757). Median gestational age was 30 [IQR 29–32] weeks. The intraclass correlation coefficient was 18.0%, confirming relevant between-center variability. Adjusted HC Δ Z-score ranged from − 0.02 (95% CI − 0.07 to 0.02) in infants with weight Δ Z-score > 0, to − 2.58 (95% CI − 2.73 to − 2.43) in those with weight Δ Z-score ≤ − 3.2. All group comparisons were statistically significant except the reference group (p < 0.001). Conclusions Postnatal weight trajectory is independently and progressively associated with HC growth in preterm infants ≤ 32 weeks across Latin America. Supporting weight recovery toward birth Z-score is a key nutritional target to promote adequate HC growth in the NICU. Health sciences/Health care Health sciences/Health care/Paediatrics preterm infant head circumference postnatal growth weight trajectory Z-score Fenton 2025 Latin America neonatal intensive care Figures Figure 1 Introduction Adequate postnatal growth is a recognized priority in the care of preterm infants. While weight gain is the most widely monitored growth parameter in neonatal intensive care units (NICUs), head circumference (HC) growth may be a more direct reflection of brain development and is associated with long-term neurocognitive outcomes.1,2 In preterm infants, postnatal growth restriction — defined as a decline in weight Z-score between birth and discharge — is nearly universal and has been linked to reduced HC growth and adverse neurodevelopmental trajectories.3,4 The dose-response relationship between the magnitude of postnatal weight loss and HC growth has received limited attention, particularly in low- and middle-income countries where preterm birth rates are highest and nutritional management may differ substantially from high-income settings.5 Latin America is a region that remains underrepresented in neonatal nutrition research despite bearing a disproportionate burden of preterm birth.6 A methodological complexity has been introduced by the 2025 revision of the Fenton preterm growth charts, the most widely used reference for this population.7 This update revised Z-score parameters for weight and HC across gestational ages, so growth classifications based on the 2013 version are not directly equivalent. Prior associations between weight trajectory and HC outcomes derived from Fenton 2013 require re-examination under the updated standard. Using data from EpicLatino, a multicenter prospective registry of 35 NICUs across Latin America and the Caribbean spanning ten years (2015–2024), we evaluated the association between postnatal weight trajectory — categorized into 10 Δ Z-score groups using Fenton 2025 — and HC growth from birth to discharge, after adjustment for clinical confounders and center-level clustering. Methods Study design and setting This was an observational cohort study based on secondary analysis of a multicenter prospective cohort using data from EpicLatino, a quality improvement registry modeled after the Canadian Neonatal Network. The registry prospectively collects standardized clinical data from participating NICUs in Latin America and the Caribbean. Data from 8 countries and 35 units contributed to this analysis, covering admissions from January 2015 through December 2024. Each participating unit obtained ethics committee approval under the framework of the EpicLatino registry. Participants We included liveborn infants with gestational age ≤ 32 weeks admitted to a participating NICU within 48 hours of birth. Infants were excluded if they had severe intraventricular hemorrhage, major congenital malformations, implausible or missing weight or HC measurements at birth or discharge, or death before discharge. Readmissions were excluded; only the index admission was analyzed. Infants who died before discharge were excluded because their incomplete follow-up would have yielded truncated Z-score data; this exclusion introduces survivor bias, which is acknowledged as a limitation. Growth assessment and Z-score calculation Weight and HC were recorded at birth and at hospital discharge. Z-scores for both measurements were calculated using the Fenton 2025 preterm growth charts, which provide sex-specific, gestational-age-adjusted reference values from 22 to 50 weeks of corrected age.7 Fenton 2025 was selected to apply the most current reference standard available and to avoid misclassification associated with the Z-score recalibration introduced by this revision. The primary exposure was postnatal weight trajectory, defined as the change in weight Z-score from birth to discharge (Weight Δ Z-score = Z-score at discharge − Z-score at birth). The primary outcome was the analogous change in HC Z-score (HC Δ Z-score). Weight Δ Z-score was categorized into 10 groups using 0.4-unit intervals, with the reference category defined as Δ Z-score > 0. Covariates The following variables were included as confounders based on clinical plausibility: gestational age at birth (weeks, continuous); necrotizing enterocolitis (NEC) (binary); positive blood or CSF culture, used as a proxy for sepsis (binary); Score for Neonatal Acute Physiology-II (SNAP-II, continuous); length of hospital stay (days, continuous); treatment of patent ductus arteriosus (PDA) with medication or surgical ligation (binary); Apgar score at 5 minutes (continuous); HC Z-score at birth (Fenton 2025, continuous); weight Z-score at birth (Fenton 2025, continuous); and outborn status (binary). Statistical analysis Baseline characteristics are presented as median [interquartile range] for continuous variables and as count (%) for categorical variables. Because patients were nested within 35 centers and the design is multicenter by nature, a multilevel linear model with a random intercept by center was used to account for between-center clustering, including differences attributable to altitude and institutional protocols. The degree of clustering was quantified by the intraclass correlation coefficient (ICC). Fixed effects were estimated by restricted maximum likelihood (REML). Adjusted average marginal predictions of HC Δ Z-score were estimated for each weight group by setting the group indicator to each value in turn while holding all other covariates at their observed values. The reference category was Weight Δ Z-score > 0. Observations with missing data in any covariate were excluded via listwise deletion (n = 169, 4.3%). Analyses were conducted in Python 3.12 using the statsmodels library. The study is reported following the STROBE guidelines for cohort studies. Results Study population A total of 3,926 infants from 35 centers met the inclusion criteria. Baseline characteristics are presented in Table 1 . Median gestational age was 30 weeks [IQR 29–32]; 49.6% of infants were born at 32 weeks. Male sex accounted for 53.8% of the cohort and 4.8% were outborn. Positive blood or CSF cultures were documented in 611 infants (15.6%), NEC stage ≥ II in 189 (4.8%), and treated PDA in 624 (15.9%). Median length of stay was 42 days [IQR 30–61]. Mean Weight Δ Z-score was − 0.86 (SD 1.02) and mean HC Δ Z-score was − 0.92 (SD 1.30). The distribution of infants across weight trajectory groups is shown in Table 2 ; the largest groups were those with moderate weight loss (Δ Z-score > − 0.8 to ≤ − 0.4, n = 658, and > − 1.2 to ≤ − 0.8, n = 665). Association between weight trajectory and HC growth The ICC was 18.0% (between-center variance 0.187, within-center variance 0.855), confirming substantial clustering by center and justifying the multilevel approach. After adjustment for all covariates and center-level random effects, adjusted HC Δ Z-score declined progressively with increasing postnatal weight loss across all 10 groups (Table 2 , Fig. 1 ). Infants with Weight Δ Z-score > 0 had a predicted HC Δ Z-score of − 0.02 (95% CI − 0.07 to 0.02), not significantly different from zero (p = 0.38). Predicted values declined to − 0.44 (group 2), − 0.74 (group 3), − 0.96 (group 4), − 1.21 (group 5), − 1.34 (group 6), − 1.73 (group 7), − 1.76 (group 8), − 2.19 (group 9), and − 2.58 (95% CI − 2.73 to − 2.43) in infants with the most severe weight loss (Weight Δ Z-score ≤ − 3.2). All group estimates were statistically significant (p < 0.001) except the reference group. Among the fixed-effect covariates, HC Z-score at birth had the strongest association with HC Δ Z-score (β=−0.70, 95% CI − 0.74 to − 0.66, p < 0.001). Weight Z-score at birth was independently and positively associated (β=+0.44, 95% CI 0.40 to 0.48, p < 0.001). Length of stay was inversely associated (β=−0.008 per day, 95% CI − 0.010 to − 0.006, p < 0.001). Gestational age showed a small positive association (β=+0.028 per week, 95% CI 0.003 to 0.052, p = 0.025). NEC, sepsis, SNAP-II, treated PDA, outborn status, and Apgar score at 5 minutes were not independently associated with HC Δ Z-score after full adjustment. Discussion In this cohort of 3,926 preterm infants ≤ 32 weeks from 35 NICUs across Latin America and the Caribbean, postnatal weight trajectory was independently and progressively associated with HC growth from birth to discharge. This association was present across the entire range of weight loss categories and persisted after adjustment for gestational age, illness severity, morbidities, and center-level clustering. The magnitude of the association is clinically relevant. The difference in predicted HC Δ Z-score between the reference group (Weight Δ Z-score > 0) and the most severe weight loss group ( ≤ − 3.2) was approximately 2.6 Z-score units. HC growth in infancy is strongly correlated with brain volume and subsequent neurodevelopmental outcomes,1,2 and a difference of this magnitude — exceeding two standard deviations — is likely to translate into meaningful differences in neurodevelopment. Even mild weight loss (Δ Z-score > − 0.4 to ≤ 0) was associated with lower HC growth compared to infants who maintained or bordered their birth weight Z-score, a finding that argues against the clinical assumption that small amounts of postnatal weight loss are inconsequential for brain growth. A key methodological decision was the use of a multilevel model with a random intercept by center. The ICC of 18% indicates that a substantial fraction of the variance in HC Δ Z-score is attributable to between-center differences — including altitude, nutritional protocols, and institutional resources — rather than to individual patient characteristics. Using a standard OLS regression in this setting would underestimate standard errors and produce overconfident inferences. The multilevel approach accounts for this clustering without requiring altitude to be measured and included as a fixed covariate. The negative coefficient for HC Z-score at birth (β=−0.70) reflects regression to the mean: infants born with a higher HC Z-score tend to have a greater decline, and vice versa. This is an expected statistical phenomenon in repeated measurements of Z-scores and does not imply that a high HC at birth is harmful. Controlling for this effect is essential to isolate the independent contribution of weight trajectory to HC change. The positive coefficient for weight Z-score at birth (β=+0.44) is also interpretable: infants who are better nourished at birth — independent of their HC — have greater reserve to sustain HC growth throughout hospitalization. The independent inverse association of length of stay with HC Δ Z-score warrants consideration. Longer NICU stays may reflect greater cumulative morbidity and nutritional challenges that are not fully captured by the covariates included in the model. Alternatively, extended hospitalization may represent prolonged exposure to the NICU environment — with its attendant nutritional and metabolic demands — beyond what can be attributed to illness severity alone. The directionality of this relationship should not be interpreted as evidence that early discharge promotes HC growth; rather, it likely reflects residual confounding by disease burden. The use of Fenton 2025 as the reference standard is a key methodological feature of this analysis. The 2025 revision introduced updated parameters that modify Z-score assignments relative to the 2013 version. As a consequence, the numerical results reported here are not directly comparable to prior studies using Fenton 2013, including an earlier version of this work presented in PAS 2026 poster form. Investigators applying these findings in clinical or research contexts should verify which version of the Fenton charts is in use. EpicLatino provides a large, prospectively collected, standardized dataset from a region systematically underrepresented in neonatal nutrition literature. Most available evidence comes from high-income country registries in which nutritional protocols, population characteristics, and institutional resources may differ substantially from Latin American NICUs. Our results provide region-specific estimates that can inform nutritional guidelines in this context. Several limitations should be acknowledged. Listwise deletion was used for missing covariate data (4.3%), which may introduce bias if data are not missing at random. Positive blood or CSF culture was used as a proxy for sepsis, and clinical sepsis episodes without bacteremia would be misclassified. Nutritional intake data — including parenteral nutrition volumes, enteral feeding advancement protocols, and breast milk availability — were not available in the registry and represent important unmeasured confounders. HC at discharge may not be the most clinically meaningful endpoint; outcomes at 18–24 months corrected age would better capture the neurodevelopmental implications of HC growth. Survivor bias is present, as only patients who survived to discharge were included; deceased patients were excluded because their shorter follow-up would have yielded incomplete Z-score data, potentially underestimating growth faltering in the full cohort. Finally, participating units are not a random sample of NICUs in the region, and generalizability to facilities without quality improvement infrastructure is uncertain. In summary, postnatal weight trajectory is strongly and independently associated with HC growth across all severity categories in preterm infants ≤ 32 weeks across Latin America. These findings support the clinical goal of minimizing postnatal weight Z-score decline as a nutritional target to promote brain growth in the NICU. Declarations Ethics considerations: The EpicLatino Neonatal Network registry has Institutional Review Board approval at each participating site. Data are anonymized before submission to the central database. This secondary analysis of de-identified data was considered exempt from individual informed consent, in accordance with international guidelines (Declaration of Helsinki, CIOMS) and national regulations on human subject research. Data handling complied with applicable confidentiality and data protection standards. Competing interests: The authors declare no competing interests. Funding: No funding. Data availability: The data supporting the findings of this study are not publicly available because they are subject to confidentiality agreement with the Latin America and the Caribbean units but are available from the corresponding author upon request. Author contributions: Conceptualization ABH, PVH, CF, HO, MB, CV. Data curation ABH, PVH. Formal analysis ABH, PVH. Investigation ABH, PVH. Methodology ABH, PVH. Project administration ABH, PVH, HO, CF, MB, CV. Validation ABH, PVH, HO, CF, MB, CV. Visualization ABH, PVH, HO, CF, MB, CV. Writing – original draft ABH, PVH. Writing – review & editing ABH, PVH, HO, CF, MB, CV. Statement on Artificial Intelligence use: Artificial intelligence tools (Claude, Anthropic) were used to assist with statistical analysis, language editing, and organization of sections. No content was generated by AI without author supervision. All authors reviewed and revised the manuscript, and take full responsibility for its content. References Raghuram K, Yang J, Church PT, et al. Head circumference growth trajectory in preterm infants and neurodevelopmental outcomes at 18 months corrected age. J Pediatr. 2017;190:84–92.e2. Leviton A, Fichorova RN, O'Shea TM, et al. Two-hit model of brain damage in the very preterm newborn: small for gestational age and postnatal systemic inflammation. Pediatr Res. 2013;73(3):362–370. Embleton NE, Pang N, Cooke RJ. Postnatal malnutrition and growth retardation: an inevitable consequence of current recommendations in preterm infants? Pediatrics. 2001;107(2):270–273. Ehrenkranz RA, Dusick AM, Vohr BR, et al. Growth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics. 2006;117(4):1253–1261. Blencowe H, Cousens S, Oestergaard MZ, et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis. Lancet. 2012;379(9832):2162–2172. González Garello T, Barbeito-Andrés J, Pérez A, Cueto G, Nuñez P, Bonfili N, Gonzalez P. Head circumference at birth and postnatal growth trajectory in vulnerable groups from Argentina. Am J Biol Anthropol. 2024;184(2):e24921. doi: 10.1002/ajpa.24921 . Fenton TR, Elmrayed S, Alshaikh BN. Fenton Third-Generation Growth Charts of Preterm Infants Without Abnormal Fetal Growth: A Systematic Review and Meta-Analysis. Paediatr Perinat Epidemiol. 2025;39(6):543–555. doi: 10.1111/ppe.70035 . Tables Table 1. Baseline Characteristics of the Study Population Characteristic Value Missing, n (%) Total infants, n 3,926 — Gestational age, weeks — median [IQR] 30 [29–32] 0 23–24 weeks, n (%) 25 (0.6%) 25–27 weeks, n (%) 198 (5.0%) 28–29 weeks, n (%) 657 (16.7%) 30–31 weeks, n (%) 1,098 (28.0%) 32 weeks, n (%) 1,948 (49.6%) Male sex, n (%) 2,111 (53.8%) 8 (0.2%) Outborn, n (%) 190 (4.8%) 0 Birth weight, g — median [IQR] 1390 [1120–1670] 0 Weight Z-score at birth (Fenton 2025) — mean (SD) −0.43 (1.16) 0 Head circumference Z-score at birth (Fenton 2025) — mean (SD) 0.00 (1.11) 0 Small for gestational age, n (%) (< 10 th percentile) 849 (21.6%) 0 Apgar score at 5 min — median [IQR] 8 [7–9] 87 (2.2%) SNAP-II score — median [IQR] 5 [0–17] 70 (1.8%) Necrotizing enterocolitis stage ≥II, n (%) 189 (4.8%) 15 (0.4%) Positive blood/CSF culture, n (%) 611 (15.6%) 15 (0.4%) Treated patent ductus arteriosus, n (%) 624 (15.9%) 0 Length of stay, days — median [IQR] 42 [30–61] 0 Weight Δ Z-score (birth to discharge) — mean (SD) −0.86 (1.02) 0 HC Δ Z-score (birth to discharge) — mean (SD) −0.92 (1.30) 0 HC, head circumference; IQR, interquartile range; SD, standard deviation; SNAP-II, Score for Neonatal Acute Physiology-II; NEC, necrotizing enterocolitis; PDA, patent ductus arteriosus; SGA, small for gestational age. Z-scores calculated with Fenton 2025 preterm growth charts. Table 2. Adjusted Average Marginal Predictions of HC Δ Z-score by Postnatal Weight Trajectory Group Weight Δ Z-score Group N Adjusted HC Δ Z-score SE 95% CI p >0 646 −0.02 0.024 −0.07 to 0.02 0.38 >−0.4 to ≤0 529 −0.44 0.027 −0.49 to −0.39 −0.8 to ≤−0.4 658 −0.74 0.024 −0.78 to −0.69 −1.2 to ≤−0.8 665 −0.96 0.024 −1.01 to −0.92 −1.6 to ≤−1.2 517 −1.21 0.027 −1.26 to −1.16 −2.0 to ≤−1.6 334 −1.34 0.034 −1.40 to −1.27 −2.4 to ≤−2.0 179 −1.73 0.046 −1.82 to −1.64 −2.8 to ≤−2.4 103 −1.76 0.061 −1.88 to −1.64 −3.2 to ≤−2.8 61 −2.19 0.079 −2.35 to −2.04 <0.001 ≤−3.2 65 −2.58 0.077 −2.73 to −2.43 0): reference group. Adjusted for gestational age, necrotizing enterocolitis, sepsis (positive blood/CSF culture), SNAP-II score, length of stay, treated patent ductus arteriosus, Apgar score at 5 minutes, HC Z-score at birth, weight Z-score at birth (Fenton 2025), and outborn status. Multilevel linear model with random intercept by center. N=3,757. ICC=18.0%. Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9647430","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":638961950,"identity":"80b33993-8904-42fd-95ed-563ab448b4c4","order_by":0,"name":"Angela Hoyos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYLCCBwwHQBTjYzCPmbmBsJYEiBZmYwYGAyDFSLwWNmmwFgYCWvhnt198kFBzR87gePuz6oKKP9H87UAtPyq24dQicedMsUHCsWfGBmfOmN2eccYgd8ZhxgbGnjO3cVtzIydNIoHtcOKGGzlst3nbDHIbgFqYGdtwa5G/kZP+I+EfSEv6s2KQlvmEtBjcSD/GkNgG0pJgxgzSsoGQFsMbOcwSiX2HjSXPnDGW5jljnLsRqOUgPr/I3Uh/+OHDt8NyfMfbH37mqZDLnXf+8MEHPyrweJ+BxwBMKRxAEjuATSECsD8AU/IN+JWNglEwCkbBCAYAdE9jlRBk+MkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5403-3268","institution":"Universidad El Bosque","correspondingAuthor":true,"prefix":"","firstName":"Angela","middleName":"","lastName":"Hoyos","suffix":""},{"id":638961951,"identity":"8e8ca665-5a7c-418e-8636-ef44342baf1c","order_by":1,"name":"Pablo Vasquez-Hoyos","email":"","orcid":"https://orcid.org/0000-0002-4892-5032","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Vasquez-Hoyos","suffix":""},{"id":638961952,"identity":"f7c0dd71-3188-43bb-b6f6-a1dd12f8363e","order_by":2,"name":"Carlos Fajardo","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Fajardo","suffix":""},{"id":638961953,"identity":"8d19ed89-62f0-466f-8ac0-0f78f1236223","order_by":3,"name":"Horacio Osiovich","email":"","orcid":"","institution":"Children's \u0026 Women's Health Centre of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Horacio","middleName":"","lastName":"Osiovich","suffix":""},{"id":638961954,"identity":"9f13d932-70dd-433a-9b4f-204d102b816a","order_by":4,"name":"Martha Baez","email":"","orcid":"","institution":"Clinica del Country","correspondingAuthor":false,"prefix":"","firstName":"Martha","middleName":"","lastName":"Baez","suffix":""},{"id":638961955,"identity":"8397ef59-f9b2-4369-973c-aa1618750844","order_by":5,"name":"Carolina Villegas-Alvarez","email":"","orcid":"https://orcid.org/0000-0002-3930-8745","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Villegas-Alvarez","suffix":""}],"badges":[],"createdAt":"2026-05-08 01:20:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9647430/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9647430/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109269493,"identity":"61baf7b0-158a-491a-99fe-64f49b4ffbdd","added_by":"auto","created_at":"2026-05-14 13:29:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49073,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted average marginal predictions of HC Δ Z-score (birth to discharge) across 10 postnatal weight trajectory groups (Fenton 2025). Each point represents the adjusted marginal prediction with 95% confidence interval (shaded area). The reference group is Weight ΔZ-score \u0026gt;0. Derived from a multilevel linear model with random intercept by center, adjusted for gestational age, necrotizing enterocolitis, sepsis (positive blood/CSF culture), SNAP-II score, length of stay, treated patent ductus arteriosus, Apgar score at 5 minutes, HC Z-score at birth, weight Z-score at birth, and outborn status. N=3,757. ICC=18.0%. HC, head circumference; Δ Z-score, change in Z-score from birth to discharge; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9647430/v1/ed3f3f446adb5d682610f959.jpg"},{"id":109269495,"identity":"8e2a47eb-e43c-407e-ae44-f6bc0ef03a73","added_by":"auto","created_at":"2026-05-14 13:29:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":234619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9647430/v1/bdd71734-45c5-4463-88a5-022d2e3434a5.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Postnatal Weight Trajectories Drives Beter Head Circumference Growth in Preterm Infants ≤32 Weeks: A Multicenter Latin American Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdequate postnatal growth is a recognized priority in the care of preterm infants. While weight gain is the most widely monitored growth parameter in neonatal intensive care units (NICUs), head circumference (HC) growth may be a more direct reflection of brain development and is associated with long-term neurocognitive outcomes.1,2 In preterm infants, postnatal growth restriction \u0026mdash; defined as a decline in weight Z-score between birth and discharge \u0026mdash; is nearly universal and has been linked to reduced HC growth and adverse neurodevelopmental trajectories.3,4\u003c/p\u003e \u003cp\u003eThe dose-response relationship between the magnitude of postnatal weight loss and HC growth has received limited attention, particularly in low- and middle-income countries where preterm birth rates are highest and nutritional management may differ substantially from high-income settings.5 Latin America is a region that remains underrepresented in neonatal nutrition research despite bearing a disproportionate burden of preterm birth.6\u003c/p\u003e \u003cp\u003eA methodological complexity has been introduced by the 2025 revision of the Fenton preterm growth charts, the most widely used reference for this population.7 This update revised Z-score parameters for weight and HC across gestational ages, so growth classifications based on the 2013 version are not directly equivalent. Prior associations between weight trajectory and HC outcomes derived from Fenton 2013 require re-examination under the updated standard.\u003c/p\u003e \u003cp\u003eUsing data from EpicLatino, a multicenter prospective registry of 35 NICUs across Latin America and the Caribbean spanning ten years (2015\u0026ndash;2024), we evaluated the association between postnatal weight trajectory \u0026mdash; categorized into 10 Δ Z-score groups using Fenton 2025 \u0026mdash; and HC growth from birth to discharge, after adjustment for clinical confounders and center-level clustering.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis was an observational cohort study based on secondary analysis of a multicenter prospective cohort using data from EpicLatino, a quality improvement registry modeled after the Canadian Neonatal Network. The registry prospectively collects standardized clinical data from participating NICUs in Latin America and the Caribbean. Data from 8 countries and 35 units contributed to this analysis, covering admissions from January 2015 through December 2024. Each participating unit obtained ethics committee approval under the framework of the EpicLatino registry.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWe included liveborn infants with gestational age\u0026thinsp;\u0026le;\u0026thinsp;32 weeks admitted to a participating NICU within 48 hours of birth. Infants were excluded if they had severe intraventricular hemorrhage, major congenital malformations, implausible or missing weight or HC measurements at birth or discharge, or death before discharge. Readmissions were excluded; only the index admission was analyzed. Infants who died before discharge were excluded because their incomplete follow-up would have yielded truncated Z-score data; this exclusion introduces survivor bias, which is acknowledged as a limitation.\u003c/p\u003e\n\u003ch3\u003eGrowth assessment and Z-score calculation\u003c/h3\u003e\n\u003cp\u003eWeight and HC were recorded at birth and at hospital discharge. Z-scores for both measurements were calculated using the Fenton 2025 preterm growth charts, which provide sex-specific, gestational-age-adjusted reference values from 22 to 50 weeks of corrected age.7 Fenton 2025 was selected to apply the most current reference standard available and to avoid misclassification associated with the Z-score recalibration introduced by this revision.\u003c/p\u003e \u003cp\u003eThe primary exposure was postnatal weight trajectory, defined as the change in weight Z-score from birth to discharge (Weight Δ Z-score\u0026thinsp;=\u0026thinsp;Z-score at discharge\u0026thinsp;\u0026minus;\u0026thinsp;Z-score at birth). The primary outcome was the analogous change in HC Z-score (HC Δ Z-score). Weight Δ Z-score was categorized into 10 groups using 0.4-unit intervals, with the reference category defined as Δ Z-score\u0026thinsp;\u0026gt;\u0026thinsp;0.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe following variables were included as confounders based on clinical plausibility: gestational age at birth (weeks, continuous); necrotizing enterocolitis (NEC) (binary); positive blood or CSF culture, used as a proxy for sepsis (binary); Score for Neonatal Acute Physiology-II (SNAP-II, continuous); length of hospital stay (days, continuous); treatment of patent ductus arteriosus (PDA) with medication or surgical ligation (binary); Apgar score at 5 minutes (continuous); HC Z-score at birth (Fenton 2025, continuous); weight Z-score at birth (Fenton 2025, continuous); and outborn status (binary).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics are presented as median [interquartile range] for continuous variables and as count (%) for categorical variables. Because patients were nested within 35 centers and the design is multicenter by nature, a multilevel linear model with a random intercept by center was used to account for between-center clustering, including differences attributable to altitude and institutional protocols. The degree of clustering was quantified by the intraclass correlation coefficient (ICC). Fixed effects were estimated by restricted maximum likelihood (REML). Adjusted average marginal predictions of HC Δ Z-score were estimated for each weight group by setting the group indicator to each value in turn while holding all other covariates at their observed values. The reference category was Weight Δ Z-score\u0026thinsp;\u0026gt;\u0026thinsp;0. Observations with missing data in any covariate were excluded via listwise deletion (n\u0026thinsp;=\u0026thinsp;169, 4.3%). Analyses were conducted in Python 3.12 using the statsmodels library. The study is reported following the STROBE guidelines for cohort studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003e A total of 3,926 infants from 35 centers met the inclusion criteria. Baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Median gestational age was 30 weeks [IQR 29\u0026ndash;32]; 49.6% of infants were born at 32 weeks. Male sex accounted for 53.8% of the cohort and 4.8% were outborn. Positive blood or CSF cultures were documented in 611 infants (15.6%), NEC stage\u0026thinsp;\u0026ge;\u0026thinsp;II in 189 (4.8%), and treated PDA in 624 (15.9%). Median length of stay was 42 days [IQR 30\u0026ndash;61]. Mean Weight Δ Z-score was \u0026minus;\u0026thinsp;0.86 (SD 1.02) and mean HC Δ Z-score was \u0026minus;\u0026thinsp;0.92 (SD 1.30). The distribution of infants across weight trajectory groups is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; the largest groups were those with moderate weight loss (Δ Z-score\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;0.8 to \u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.4, n\u0026thinsp;=\u0026thinsp;658, and \u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;1.2 to \u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.8, n\u0026thinsp;=\u0026thinsp;665).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between weight trajectory and HC growth\u003c/h3\u003e\n\u003cp\u003eThe ICC was 18.0% (between-center variance 0.187, within-center variance 0.855), confirming substantial clustering by center and justifying the multilevel approach. After adjustment for all covariates and center-level random effects, adjusted HC Δ Z-score declined progressively with increasing postnatal weight loss across all 10 groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Infants with Weight Δ Z-score\u0026thinsp;\u0026gt;\u0026thinsp;0 had a predicted HC Δ Z-score of \u0026minus;\u0026thinsp;0.02 (95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.07 to 0.02), not significantly different from zero (p\u0026thinsp;=\u0026thinsp;0.38). Predicted values declined to \u0026minus;\u0026thinsp;0.44 (group 2), \u0026minus;\u0026thinsp;0.74 (group 3), \u0026minus;\u0026thinsp;0.96 (group 4), \u0026minus;\u0026thinsp;1.21 (group 5), \u0026minus;\u0026thinsp;1.34 (group 6), \u0026minus;\u0026thinsp;1.73 (group 7), \u0026minus;\u0026thinsp;1.76 (group 8), \u0026minus;\u0026thinsp;2.19 (group 9), and \u0026minus;\u0026thinsp;2.58 (95% CI\u0026thinsp;\u0026minus;\u0026thinsp;2.73 to \u0026minus;\u0026thinsp;2.43) in infants with the most severe weight loss (Weight Δ Z-score\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;3.2). All group estimates were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) except the reference group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the fixed-effect covariates, HC Z-score at birth had the strongest association with HC Δ Z-score (β=\u0026minus;0.70, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.74 to \u0026minus;\u0026thinsp;0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Weight Z-score at birth was independently and positively associated (β=+0.44, 95% CI 0.40 to 0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Length of stay was inversely associated (β=\u0026minus;0.008 per day, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.010 to \u0026minus;\u0026thinsp;0.006, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Gestational age showed a small positive association (β=+0.028 per week, 95% CI 0.003 to 0.052, p\u0026thinsp;=\u0026thinsp;0.025). NEC, sepsis, SNAP-II, treated PDA, outborn status, and Apgar score at 5 minutes were not independently associated with HC Δ Z-score after full adjustment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cohort of 3,926 preterm infants\u0026thinsp;\u0026le;\u0026thinsp;32 weeks from 35 NICUs across Latin America and the Caribbean, postnatal weight trajectory was independently and progressively associated with HC growth from birth to discharge. This association was present across the entire range of weight loss categories and persisted after adjustment for gestational age, illness severity, morbidities, and center-level clustering.\u003c/p\u003e \u003cp\u003eThe magnitude of the association is clinically relevant. The difference in predicted HC Δ Z-score between the reference group (Weight Δ Z-score\u0026thinsp;\u0026gt;\u0026thinsp;0) and the most severe weight loss group (\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;3.2) was approximately 2.6 Z-score units. HC growth in infancy is strongly correlated with brain volume and subsequent neurodevelopmental outcomes,1,2 and a difference of this magnitude \u0026mdash; exceeding two standard deviations \u0026mdash; is likely to translate into meaningful differences in neurodevelopment. Even mild weight loss (Δ Z-score\u0026thinsp;\u0026gt;\u0026thinsp;\u0026minus;\u0026thinsp;0.4 to \u0026le;\u0026thinsp;0) was associated with lower HC growth compared to infants who maintained or bordered their birth weight Z-score, a finding that argues against the clinical assumption that small amounts of postnatal weight loss are inconsequential for brain growth.\u003c/p\u003e \u003cp\u003eA key methodological decision was the use of a multilevel model with a random intercept by center. The ICC of 18% indicates that a substantial fraction of the variance in HC Δ Z-score is attributable to between-center differences \u0026mdash; including altitude, nutritional protocols, and institutional resources \u0026mdash; rather than to individual patient characteristics. Using a standard OLS regression in this setting would underestimate standard errors and produce overconfident inferences. The multilevel approach accounts for this clustering without requiring altitude to be measured and included as a fixed covariate.\u003c/p\u003e \u003cp\u003eThe negative coefficient for HC Z-score at birth (β=\u0026minus;0.70) reflects regression to the mean: infants born with a higher HC Z-score tend to have a greater decline, and vice versa. This is an expected statistical phenomenon in repeated measurements of Z-scores and does not imply that a high HC at birth is harmful. Controlling for this effect is essential to isolate the independent contribution of weight trajectory to HC change. The positive coefficient for weight Z-score at birth (β=+0.44) is also interpretable: infants who are better nourished at birth \u0026mdash; independent of their HC \u0026mdash; have greater reserve to sustain HC growth throughout hospitalization.\u003c/p\u003e \u003cp\u003eThe independent inverse association of length of stay with HC Δ Z-score warrants consideration. Longer NICU stays may reflect greater cumulative morbidity and nutritional challenges that are not fully captured by the covariates included in the model. Alternatively, extended hospitalization may represent prolonged exposure to the NICU environment \u0026mdash; with its attendant nutritional and metabolic demands \u0026mdash; beyond what can be attributed to illness severity alone. The directionality of this relationship should not be interpreted as evidence that early discharge promotes HC growth; rather, it likely reflects residual confounding by disease burden.\u003c/p\u003e \u003cp\u003eThe use of Fenton 2025 as the reference standard is a key methodological feature of this analysis. The 2025 revision introduced updated parameters that modify Z-score assignments relative to the 2013 version. As a consequence, the numerical results reported here are not directly comparable to prior studies using Fenton 2013, including an earlier version of this work presented in PAS 2026 poster form. Investigators applying these findings in clinical or research contexts should verify which version of the Fenton charts is in use.\u003c/p\u003e \u003cp\u003eEpicLatino provides a large, prospectively collected, standardized dataset from a region systematically underrepresented in neonatal nutrition literature. Most available evidence comes from high-income country registries in which nutritional protocols, population characteristics, and institutional resources may differ substantially from Latin American NICUs. Our results provide region-specific estimates that can inform nutritional guidelines in this context.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. Listwise deletion was used for missing covariate data (4.3%), which may introduce bias if data are not missing at random. Positive blood or CSF culture was used as a proxy for sepsis, and clinical sepsis episodes without bacteremia would be misclassified. Nutritional intake data \u0026mdash; including parenteral nutrition volumes, enteral feeding advancement protocols, and breast milk availability \u0026mdash; were not available in the registry and represent important unmeasured confounders. HC at discharge may not be the most clinically meaningful endpoint; outcomes at 18\u0026ndash;24 months corrected age would better capture the neurodevelopmental implications of HC growth. Survivor bias is present, as only patients who survived to discharge were included; deceased patients were excluded because their shorter follow-up would have yielded incomplete Z-score data, potentially underestimating growth faltering in the full cohort. Finally, participating units are not a random sample of NICUs in the region, and generalizability to facilities without quality improvement infrastructure is uncertain.\u003c/p\u003e \u003cp\u003eIn summary, postnatal weight trajectory is strongly and independently associated with HC growth across all severity categories in preterm infants\u0026thinsp;\u0026le;\u0026thinsp;32 weeks across Latin America. These findings support the clinical goal of minimizing postnatal weight Z-score decline as a nutritional target to promote brain growth in the NICU.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics considerations:\u0026nbsp;\u003c/strong\u003eThe EpicLatino Neonatal Network registry has Institutional Review Board approval at each participating site. Data are anonymized before submission to the central database. This secondary analysis of de-identified data was considered exempt from individual informed consent, in accordance with international guidelines (Declaration of Helsinki, CIOMS) and national regulations on human subject research. Data handling complied with applicable confidentiality and data protection standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data supporting the findings of this study are not publicly available because they are subject to confidentiality agreement with the Latin America and the Caribbean units but are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eConceptualization ABH, PVH, CF, HO, MB, CV. Data curation ABH, PVH. Formal analysis ABH, PVH. Investigation ABH, PVH. Methodology ABH, PVH. Project administration ABH, PVH, HO, CF, MB, CV. Validation ABH, PVH, HO, CF, MB, CV. Visualization ABH, PVH, HO, CF, MB, CV. Writing – original draft ABH, PVH. Writing – review \u0026amp; editing ABH, PVH, HO, CF, MB, CV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement on Artificial Intelligence use:\u0026nbsp;\u003c/strong\u003eArtificial intelligence tools (Claude, Anthropic) were used to assist with statistical analysis, language editing, and organization of sections. No content was generated by AI without author supervision. All authors reviewed and revised the manuscript, and take full responsibility for its content.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRaghuram K, Yang J, Church PT, et al. Head circumference growth trajectory in preterm infants and neurodevelopmental outcomes at 18 months corrected age. J Pediatr. 2017;190:84\u0026ndash;92.e2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeviton A, Fichorova RN, O'Shea TM, et al. Two-hit model of brain damage in the very preterm newborn: small for gestational age and postnatal systemic inflammation. Pediatr Res. 2013;73(3):362\u0026ndash;370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmbleton NE, Pang N, Cooke RJ. Postnatal malnutrition and growth retardation: an inevitable consequence of current recommendations in preterm infants? Pediatrics. 2001;107(2):270\u0026ndash;273.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhrenkranz RA, Dusick AM, Vohr BR, et al. Growth in the neonatal intensive care unit influences neurodevelopmental and growth outcomes of extremely low birth weight infants. Pediatrics. 2006;117(4):1253\u0026ndash;1261.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlencowe H, Cousens S, Oestergaard MZ, et al. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis. Lancet. 2012;379(9832):2162\u0026ndash;2172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez Garello T, Barbeito-Andr\u0026eacute;s J, P\u0026eacute;rez A, Cueto G, Nu\u0026ntilde;ez P, Bonfili N, Gonzalez P. Head circumference at birth and postnatal growth trajectory in vulnerable groups from Argentina. Am J Biol Anthropol. 2024;184(2):e24921. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajpa.24921\u003c/span\u003e\u003cspan address=\"10.1002/ajpa.24921\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFenton TR, Elmrayed S, Alshaikh BN. Fenton Third-Generation Growth Charts of Preterm Infants Without Abnormal Fetal Growth: A Systematic Review and Meta-Analysis. Paediatr Perinat Epidemiol. 2025;39(6):543\u0026ndash;555. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ppe.70035\u003c/span\u003e\u003cspan address=\"10.1111/ppe.70035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline Characteristics of the Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissing, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eTotal infants, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e3,926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eGestational age, weeks \u0026mdash; median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e30 [29\u0026ndash;32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;23\u0026ndash;24 weeks, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e25 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;25\u0026ndash;27 weeks, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e198 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;28\u0026ndash;29 weeks, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e657 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;30\u0026ndash;31 weeks, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e1,098 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;32 weeks, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e1,948 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eMale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e2,111 (53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e8 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eOutborn, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e190 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eBirth weight, g \u0026mdash; median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e1390 [1120\u0026ndash;1670]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eWeight Z-score at birth (Fenton 2025) \u0026mdash; mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026minus;0.43 (1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eHead circumference Z-score at birth (Fenton 2025) \u0026mdash; mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e0.00 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eSmall for gestational age, n (%) (\u0026lt; 10\u003csup\u003eth\u003c/sup\u003e percentile)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e849 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eApgar score at 5 min \u0026mdash; median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e8 [7\u0026ndash;9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e87 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eSNAP-II score \u0026mdash; median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e5 [0\u0026ndash;17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e70 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eNecrotizing enterocolitis stage \u0026ge;II, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e189 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e15 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003ePositive blood/CSF culture, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e611 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e15 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eTreated patent ductus arteriosus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e624 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eLength of stay, days \u0026mdash; median [IQR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e42 [30\u0026ndash;61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eWeight \u0026Delta; Z-score (birth to discharge) \u0026mdash; mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026minus;0.86 (1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eHC \u0026Delta; Z-score (birth to discharge) \u0026mdash; mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026minus;0.92 (1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHC, head circumference; IQR, interquartile range; SD, standard deviation; SNAP-II, Score for Neonatal Acute Physiology-II; NEC, necrotizing enterocolitis; PDA, patent ductus arteriosus; SGA, small for gestational age. Z-scores calculated with Fenton 2025 preterm growth charts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Adjusted Average Marginal Predictions of HC \u0026Delta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Z-score by Postnatal Weight Trajectory Group\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026Delta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Z-score Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted HC\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026Delta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Z-score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e646\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026minus;0.07 to \u0026nbsp;0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;\u0026minus;0.4 to \u0026le;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;0.49 to \u0026minus;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;\u0026minus;0.8 to \u0026le;\u0026minus;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;0.78 to \u0026minus;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;\u0026minus;1.2 to \u0026le;\u0026minus;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;1.01 to \u0026minus;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;\u0026minus;1.6 to \u0026le;\u0026minus;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;1.26 to \u0026minus;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;\u0026minus;2.0 to \u0026le;\u0026minus;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;1.40 to \u0026minus;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;\u0026minus;2.4 to \u0026le;\u0026minus;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;1.82 to \u0026minus;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;\u0026minus;2.8 to \u0026le;\u0026minus;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;1.88 to \u0026minus;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026gt;\u0026minus;3.2 to \u0026le;\u0026minus;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;2.35 to \u0026minus;2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026le;\u0026minus;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026minus;2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026minus;2.73 to \u0026minus;2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHC, head circumference; \u0026Delta; Z-score, change in Z-score from birth to discharge; SE, standard error; CI, confidence interval. Bold row (\u0026gt;0): reference group. Adjusted for gestational age, necrotizing enterocolitis, sepsis (positive blood/CSF culture), SNAP-II score, length of stay, treated patent ductus arteriosus, Apgar score at 5 minutes, HC Z-score at birth, weight Z-score at birth (Fenton 2025), and outborn status. Multilevel linear model with random intercept by center. N=3,757. ICC=18.0%.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"preterm infant, head circumference, postnatal growth, weight trajectory, Z-score, Fenton 2025, Latin America, neonatal intensive care","lastPublishedDoi":"10.21203/rs.3.rs-9647430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9647430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePostnatal weight loss is nearly universal in preterm infants, and its relationship to head circumference (HC) growth remains incompletely characterized. Growth standards were recently updated with the Fenton 2025 charts, which modify Z-score parameters compared to the 2013 version.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo evaluate the association between postnatal weight trajectory and HC growth from birth to discharge in preterm infants\u0026thinsp;\u0026le;\u0026thinsp;32 weeks of gestation across a large multicenter Latin American cohort, using Fenton 2025 growth standards.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis observational cohort study used secondary analysis of prospective data from EpicLatino, a quality improvement registry of 35 NICUs across Latin America and the Caribbean (2015\u0026ndash;2024). Infants within 48 hours of birth were included. The exposure was the change in weight Z-score (Fenton 2025) from birth to discharge, categorized into 10 groups at 0.4-unit intervals. The primary outcome was the change in HC Z-score (Fenton 2025). A multilevel linear model with a random intercept by center was used, adjusting for gestational age, necrotizing enterocolitis, sepsis, SNAP-II score, length of stay, treated patent ductus arteriosus, Apgar score at 5 minutes, HC Z-score at birth, weight Z-score at birth, and outborn status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e3,926 infants from 35 centers were included (analytical sample n\u0026thinsp;=\u0026thinsp;3,757). Median gestational age was 30 [IQR 29\u0026ndash;32] weeks. The intraclass correlation coefficient was 18.0%, confirming relevant between-center variability. Adjusted HC Δ Z-score ranged from \u0026minus;\u0026thinsp;0.02 (95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.07 to 0.02) in infants with weight Δ Z-score\u0026thinsp;\u0026gt;\u0026thinsp;0, to \u0026minus;\u0026thinsp;2.58 (95% CI\u0026thinsp;\u0026minus;\u0026thinsp;2.73 to \u0026minus;\u0026thinsp;2.43) in those with weight Δ Z-score\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;3.2. All group comparisons were statistically significant except the reference group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePostnatal weight trajectory is independently and progressively associated with HC growth in preterm infants\u0026thinsp;\u0026le;\u0026thinsp;32 weeks across Latin America. Supporting weight recovery toward birth Z-score is a key nutritional target to promote adequate HC growth in the NICU.\u003c/p\u003e","manuscriptTitle":"Postnatal Weight Trajectories Drives Beter Head Circumference Growth in Preterm Infants ≤32 Weeks: A Multicenter Latin American Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 13:29:11","doi":"10.21203/rs.3.rs-9647430/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7d5c501-e237-47aa-9547-41fca8482ad0","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"checksComplete","content":"","date":"2026-05-12T11:19:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-08T01:18:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Perinatology","date":"2026-05-08T01:17:59+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":68007111,"name":"Health sciences/Health care"},{"id":68007112,"name":"Health sciences/Health care/Paediatrics"}],"tags":[],"updatedAt":"2026-05-14T13:29:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 13:29:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9647430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9647430","identity":"rs-9647430","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}