Risk factors and predictive modeling for blood transfusion in extremely preterm infants: The role of perinatal factors and clinical outcomes

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Blood transfusion is crucial in their management, but its relationship withcomplications remains debated. This study aimed to identify transfusion risk factors in EPIs and develop a predictive model. Methods: We analyzed data from the Dryad database, focusing on EPIs with a gestational age (GA) of <33 weeks. We compared the clinical data between transfused and non-transfused groups, and developed a predictive model for blood transfusion. Results: A total of 578 EPIs were included, with an overall transfusion rate of 20.93%. The transfused group had lower GA, birth weight (BW), hematocrit at 2 hours (Hct2h), and Apgar scores at 1 and 5 minutes than in the non-transfused group ( p < .001). The transfused group also showed higher incidences of intubation, cardiac compression, chronic lung disease, death, length of stay, severe retinopathy of prematurity, necrotizing enterocolitis, any intraventricular hemorrhage (IVH), and severe IVH ( p < 0.01). GA (OR = 0.670, 95% CI: 0.548–0.819, p < 0.001), BW (OR = 0.998, 95% CI: 0.997–1.000, p = 0.007), and Hct2h (OR = 0.888, 95% CI: 0.847–0.930, p < 0.001) were independent risk factors for transfusion in EPIs. The combination of these factors predictedtransfusion needs with an AUC of 0.9145. Discussion: Blood transfusion in EPIs is associated with several complications. BW, GA, and Hct2h are independent risk factors for transfusion, and their combination can effectively predicttransfusion need in this population. extremely preterm infants blood transfusion risk factors hematocrit neonatal outcomes Figures Figure 1 Figure 2 Introduction The World Health Organization (WHO) defines preterm birth as any delivery occurring before 37 weeks of gestation. Globally, the average preterm birth rate is 10.6%; however, this figure varies significantly by region. In the United States, the rate ranges from 12% to 13%, while in developed countries such as Europe, it averages between 5% and 9%. In some African nations, the rate can reach 18% [1-4]. Of all preterm births, about 5% are classified as extremely preterm (gestational age [GA] <28 weeks), approximately 15% as very preterm (GA between 28 and 31 weeks), around 20% as moderately preterm (GA between 32 and 33 weeks), and 60–70% as late preterm (GA between 34 and 36 weeks) [5]. Preterm birth has profound effects on children, their families, and society at large, serving as a leading cause of childhood mortality, responsible for 18% of deaths in children under five and 35% of neonatal deaths [6]. Short-term complications of preterm birth include an increased risk of neonatal respiratory distress syndrome, bronchopulmonary dysplasia (BPD), necrotizing enterocolitis (NEC), sepsis, periventricular leukomalacia, seizures, intraventricular hemorrhage (IVH), cerebral palsy, hypoxic-ischemic encephalopathy, feeding challenges, and visual and auditory impairments [7-10]. Additionally, preterm birth is linked to poor neurodevelopmental outcomes, higher rates of hospitalization, and challenges in behavior, social-emotional development, and learning during childhood [11-14]. Furthermore, preterm birth results in significant long-term healthcare costs and imposes considerable psychological and financial burdens on the families of preterm infants [15,16]. Blood transfusion is a common treatment for preterm infants, with over one-third of patients requiring this therapy [17]. However, whether red blood cell (RBC) transfusion increases the risk of adverse clinical outcomes, such as BPD, retinopathy of prematurity (ROP), and NEC, remains a long-standing research question with no conclusive answers [18]. Data on blood transfusions in EPIs were obtained from the Dryad database. This study analyzed differences in baseline data and clinical outcomes between transfused and non-transfused groups, identified transfusion-related influencing factors, and developed a prediction model for blood transfusion in EPIs. The aim of this study was to provide a reference for clinical blood transfusion therapy. Materials and methods Data source This study constituted a retrospective observational analysis of de-identified data sourced from the Dryad database (https://datadryad.org/). The data were derived from two previously published datasets: one by Dongli Song (https://datadryad.org/stash/dataset/doi:10.5061/dryad.4q3d3 ) and another by Priya Jegatheesan (https://datadryad.org/stash/dataset/doi:10.5061/dryad.vt4b8gv2r) [19,20]. As the original studies were observational in nature and did not involve any prospective intervention, this secondary analysis was not considered a clinical trial and did not require trial registration. Study population The original studies were conducted at the Santa Clara Valley Medical Center (SCVMC) from January 2008 to April 2014 and at the American Academy of Pediatrics (AAP) level IV NICU in a California public safety net hospital from January 2014 to December 2019. According to the original study protocol, eligibility was limited to preterm neonates born at <33 weeks of gestation, excluding cases with 1) non-resuscitation orders, 2) delivery room deaths, or 3) incomplete medical records. Data collection and measurements Records with complete data on demographic variables, hematocrit levels, delivery room (DR) measures, neonatal outcomes, and transfusion data were selected from the dataset. The collected demographic characteristics included birth weight (BW), GA, and delivery mode (vaginal or cesarean section). Hematocrit measurements were obtained for each clinical indication. Only hematocrit values collected within the first 2 hours postnatally (median time: 0.8 hours) were included in this analysis. Post-transfusion hematocrit values were systematically excluded to avoid confounding effects. The DR indicators included Apgar scores at 1 and 5 minutes, endotracheal intubation, chest compressions, and temperature upon admission. Neonatal outcomes included chronic lung disease (CLD), length of stay (LOS), severe ROP (defined as grade 3 or threshold disease or those receiving anti-VEGF treatment/laser therapy for ROP), NEC, any IVH (grades 1-4), severe IVH (grades 3-4), composite survival outcomes (survival without severe IVH, severe ROP, late-onset sepsis, NEC, or CLD), and death. Statistical analysis In an original research study [20], gestational age was reported in 1-week intervals, and birth weight was reported in 0.050-kilogram intervals. For statistical convenience, we used the midpoint of each interval for analysis. For example, if the original birth weight was recorded as 1.051-1.100 kilograms, the value taken was 1.075 kilograms. For continuous variables, we first performed normality testing using the Shapiro-Wilk test. Normally distributed data are presented as mean±SD, while non-normally distributed data are expressed as median (interquartile range [IQR]). Categorical variables are expressed as frequencies (%). The t-test was used to compare normally distributed variables, the Mann-Whitney rank-sum test for non-normally distributed continuous variables, and the chi-square or Fisher’s exact test to compare categorical variables. Risk factors were analyzed using univariate logistic regression, with statistically significant variables ( p < 0.05) subsequently entered into a multivariate logistic regression model to identify independent predictors of transfusion in very preterm infants. The receiver operating characteristic (ROC) curve was used to analyze the predictive value of risk factors ( p < 0.05 in the multivariate logistic regression model for red blood cell transfusion). Key indices for this analysis include the area under the curve (AUC) and the cutoff value. The AUC represents predictive efficiency, with higher values indicating better performance, while the cutoff value represents the threshold for predicting the need for transfusion. All statistical analyses were performed using SPSS version 19.0 (IBM, Chicago, IL, USA) and GraphPad Prism version 9.4 (GraphPad Software, USA). Statistical significance was defined as a two-sided p < 0.05. Results Baseline characteristics of the participants A total of 578 extremely preterm infants (EPIs) were included, with 457 in the non-transfusion group and 121 in the transfusion group. The overall blood transfusion rate was 20.93%. The median GA (IQR) was 26.3 (25.2–28.5) weeks in the transfusion group and 31.4 (29.5–32.5) weeks in the non-transfusion group. The mean BW was 0.955 ± 0.336 kg in the transfusion group and 1.551 ± 0.406 kg in the non-transfusion group. Both GA and BW were significantly lower in the transfusion group than in the non-transfusion group ( p 0.05). Demographic characteristics of the transfusion and non-transfusion groups are shown in Table 1. Table 1 Comparison of baseline characteristics , DR indicators and neonatal outcomes of the participants between the non-transfusion and transfusion groups Non-transfusion (n=457) Transfusion (n=121) t/U/ χ² p- value Baseline characteristics GA(weeks), median (IQR) 31.4 (29.5-32.5) 26.3 (25.2-28.5) 6542.5 ＜0.001 BW(kg), mean (SD) 1.5514±0.406 0.955±0.336 16.598 ＜0.001 Delivery Type 1.570 0.210 Vag, n (%) 220 (48.14) 66 (54.55) Others, n (%) 237 (51.86) 55 (45.45) DR indicators Apgar 1, median (IQR) 7 (5-8) 6 (3-7) 17682.0 ＜0.001 Apgar 5, median (IQR) 8 (7-9) 7 (5-8) 16408.5 ＜0.001 Admit Temp-C, median (IQR) 36.9 (36.7-37.3) 36.9 (36.6-37.2) 26607.0 0.523 Intubation，n(%) 19 (4.16) 35 (28.93) 69.293 ＜0.001 Cardiac Compression，n(%) 5 (1.09) 6 (4.96) 7.602 0.006 Neonatal outcomes CLD，n(%) 23 (5.03) 54 (44.63) 129.894 ＜0.001 Death，n(%) 0 (0) 5 (4.13) -* ＜0.001 Late Onset Sepsis，n(%) 2 (0.44) 28 (23.14) 95.644 ＜0.001 Severe_ROP，n(%) 2 (0.44) 22 (18.18) 71.294 ＜0.001 NEC，n(%) 2 (0.44) 13 (10.74) 36.226 ＜0.001 IVH，n(%) 54 ( 11.82) 42 (34.71) 21.739 ＜0.001 IVH_Severe，n(%) 6 (1.31) 14 (11.57) 30.132 ＜0.001 Survival_wo_Major_Morbidity，n(%) 102 (22.32) 22 (18.18) 5.354 0.021 GA=gestational age in weeks at birth, BW=birth weight in grams, CLD=chronic lung disease, ROP=retinopathy of prematurity, NEC=necrotizing enterocolitis, IVH=intraventricular hemorrhage, Survival_wo_Major_Morbidity=survival without severe intraventricular hemorrhage, severe retinopathy of prematurity, late onset sepsis, necrotizing enterocolitis or chronic lung disease * Fisher’s exact test. For further analysis, we divided the GA into subgroups of <28 weeks and ≥28 weeks. The blood transfusion rates for these subgroups were 63.24% (86/136) in the GA <28 weeks group and 7.92% (35/442) in the GA ≥28 weeks group. For the BW subgroups, the transfusion rates were 34.90% (112/321) in the BW <1.5 kg group and 3.50% (9/257) in the BW ≥1.5 kg group. The results showed that in both the GA <28 weeks or ≥28 weeks groups, the BW of the transfusion group was significantly lower than that of the non-transfusion group. Additionally, a subgroup analysis was performed by categorizing BW into <1.5 kg and ≥1.5 kg groups. The results demonstrated that in the <1.5 kg subgroup, the GA of the transfusion group was significantly lower than that of the non-transfusion group, showing a statistically significant difference. However, no statistically significant difference was observed in the ≥1.5 kg subgroup. Regarding the mode of delivery, no statistically significant differences were found between the transfusion and non-transfusion groups in either subgroup. Demographic characteristics of the transfusion and non-transfusion groups across the BW and GA subgroups are shown in Table S1. Hematocrit in the first 2 hours of life Figure 1 shows the hematocrit at 2 hours (Hct2h) outcomes in the transfusion and non-transfusion groups. The transfusion group exhibited significantly lower Hct2h (%) levels (43.22±6.0) compared to the non-transfusion group (50.84±6.61), with a statistically significant difference (t = 11.486, p < 0.001). Subgroup analyses based on BW and GA consistently demonstrated that the transfusion group exhibited significantly lower Hct2h levels than the non-transfusion group across all subgroups ( p < 0.05). DR indicators The DR indicators for the EPIs are listed in Table 1 and Table S2. The transfusion group showed significantly lower Apgar scores at 1 minute (Apgar 1) and 5 minutes (Apgar 5) compared to the non-transfusion group, with statistically significant differences. Thirty-five (28.93%) and 6 (4.96%) patients in the transfused group received endotracheal intubation and chest compressions, respectively, which were significantly higher than 19 (4.16%) and 5 (1.09%) in the non-transfused group. However, no statistically significant difference was found in admission temperature (Admit Temp-C) between the two groups. Subgroup analysis based on GA and BW showed that in the GA < 28 weeks group, the 5-minute Apgar score in the transfused group was lower than that in the non-transfused group. In the transfused group, 31 (36.05%) preterm infants underwent intubation, which was significantly higher than 7 (14.00%) in the non-transfused group. In the subgroup with GA ≥28 weeks and BW <1.5 kg, the 1-minute Apgar score, 5-minute Apgar score, and the number of cases undergoing intubation in the transfused group were all lower than those in the non-transfused group. In the BW ≥1.5 kg subgroup, both the 1-minute Apgar score and the number of cases undergoing intubation were lower in the transfused group than in the non-transfused group. Neonatal outcomes Neonatal outcomes are shown in Table 1 and Table S3. In the blood transfusion group, the rates of CLD, late onset sepsis, severe ROP, NEC, IVH, severe IVH, as well as survival without major morbidity and death were all significantly higher compared to the non-transfusion group, with statistically significant differences. Subgroup analyses by GA and BW consistently demonstrated the same trends as described above. Logistic regression analysis of risk factors for blood transfusion We initially performed univariate logistic regression analysis to identify risk factors for blood transfusion. Variables with p < 0.05 were subsequently included in the multivariate logistic regression analysis. The results indicated that GA (OR = 0.670, 95% CI: 0.548–0.819, p < 0.001), BW (OR = 0.998, 95% CI: 0.997–1.000, p = 0.007), and Hct2h (OR = 0.888, 95% CI: 0.847–0.930, p < 0.001) were significant factors influencing the need for blood transfusion in EPIs (Table 2). Table 2 Multivariate logistic regression analysis of risk factors for blood transfusion B S.E Wals OR 95%CI p- value All participants GA -0.401 0.103 15.250 0.670 0.548-0.819 ＜0.001 BW -0.002 0.001 7.259 0.998 0.997-1.000 0.007 Hct2h -0.119 0.024 50.539 0.888 0.847-0.930 ＜0.001 GA ＜ 28w BW -0.005 0.001 19.174 0.995 0.992-0.997 ＜0.001 Hct2h -0.146 0.042 12.255 0.864 0.797-0.938 ＜0.001 GA ≥ 28w BW -0.002 0.001 8.555 0.998 0.997-0.999 0.003 Hct2h -0.136 0.031 18.913 0.873 0.821-0.928 ＜0.001 BW ＜ 1.5kg GA -0.630 0.094 44.913 0.533 0.443-0.640 ＜0.001 Hct2h -0.130 0.027 23.454 0.878 0.834-0.926 ＜0.001 GA=gestational age in weeks at birth, BW=birth weight in grams, Hct2h=hematocrit in the first 2 hours of life, OR=Odds ratio, CI= Confidence interval Subgroup analysis by gestational age showed that in the GA <28 weeks group, BW (OR = 0.995, 95% CI: 0.992–0.997, p < 0.001) and Hct2h (OR = 0.864, 95% CI: 0.797–0.938, p < 0.001) were significant influencing factors for blood transfusion. In the GA ≥28 weeks group, BW (OR = 0.998, 95% CI: 0.997–0.999, p = 0.003) and Hct2h (OR = 0.873, 95% CI: 0.821–0.928, p < 0.001) were also significant influencing factors. Subgroup analysis based on birth weight showed that in the BW <1.5 kg group, GA (OR = 0.533, 95% CI: 0.443–0.640, p < 0.001) and Hct2h (OR = 0.878, 95% CI: 0.834–0.926, p < 0.001) were significant risk factors for blood transfusion. However, none of the factors showed statistical significance in the BW ≥1.5 kg group (Table 2). Analysis of the ROC curve for the detection of transfusion We performed ROC curve analysis of transfusion-related factors ( p < 0.05 in the aforementioned multivariate logistic regression analysis) to evaluate their predictive value for transfusion. The results showed that the AUC for the individual prediction of transfusion in EPIs was 0.8817 for GA, 0.8726 for BW, and 0.8059 for Hct2h. The combined model demonstrated superior predictive performance, with an AUC of 0.9145, sensitivity of 81.8%, and specificity of 89.5%, outperforming all individual parameters. Stratified analyses were performed to assess the predictive performance of different parameter combinations for transfusion risk in EPIs. In infants with GA <28 weeks, the combined model of BW and Hct2h demonstrated an AUC of 0.8363, which was significantly higher than that of BW (AUC = 0.7879, p < 0.001) or Hct2h (AUC = 0.7364, p < 0.001) alone. Similarly, in infants with GA ≥28 weeks, the BW + Hct2h combination showed improved predictive accuracy (AUC = 0.8189) compared to individual parameters (both p < 0.05). For infants with BW <1.5 kg, the GA + Hct2h combined model achieved superior performance, with an AUC of 0.8829 (sensitivity = 86.6%, specificity = 75.6%), significantly outperforming either GA (AUC = 0.8464, p < 0.001) or Hct2h (AUC = 0.7908, p < 0.001) alone. These results consistently demonstrate that combined parameter models offer enhanced predictive capability for transfusion risk across different subgroups of EPIs (Table 3 and Figure 2). Table 3 Predictive value of GA, BW, and Hct2h for transfusion risk in extremely preterm infants AUC 95%CI Sensitivity (%) Specificity (%) Youden index All participants 0.9145 0.885-0.944 0.818 0.895 0.7132 GA＜28w 0.8363 0.762-0.910 0.721 0.900 0.6209 GA≥28w 0.8189 0.747-0.891 0.829 0.686 0.5141 BW＜1.5kg 0.8829 0.845-0.921 0.866 0.756 0.6221 AUC=area under the receiver operating characteristic curve; CI=confidence interval; GA=gestational age in weeks at birth, BW=birth weight in grams Discussion Anemia is one of the most common complications in EPIs. RBC transfusion is typically used to improve perfusion and oxygenation in preterm infants suffering from anemia of prematurity [21,22]. Previous studies have shown that up to 90% of extremely low birth weight infants and 58% of EPIs born at less than 32 weeks of gestation receive RBC transfusions during hospitalization [23-25]. In our study, the overall blood transfusion rate among EPIs was 20.93%. Subgroup analyses based on gestational age and birth weight showed that blood transfusion rates varied between 3.50% and 63.24%, which were somewhat lower than the rates reported in earlier studies. This discrepancy may be attributed to differences in the inclusion criteria and study periods, which could account for variations in the patient populations analyzed [26]. Hct levels are often used to assess the severity of anemia in EPIs and indirectly reflect tissue oxygenation levels [27]. Our study found that the Hct2h in the transfused group was significantly lower than that in the non-transfused group, consistent with the findings of Liao Z et al [28]. Preterm birth and low birth weight are well-established risk factors for blood transfusion in EPIs. In our study, we observed that both GA and BW in the transfused group were significantly lower than those in the non-transfused group. Subgroup analysis revealed a consistent trend across all subgroups except for the BW ≥1.5 kg group. This exception can likely be attributed to the small sample size (n = 9) in the transfused group within the BW ≥1.5 kg subgroup. Additionally, logistic regression analysis indicated that both GA and BW were independent risk factors for blood transfusion in EPIs. This finding is consistent with the results reported by Dos S A et al [29-31]. Our study found that both the Apgar 1 and Apgar 5 scores in the transfused group were significantly lower than those in the non-transfused group. This is consistent with the findings reported by Kim Y J et al.[26], whereas Jeon G W et al.[25] did not observe a statistically significant difference between the two groups. In addition, the proportions of infants requiring endotracheal intubation and chest compressions in the transfused group were significantly higher than those in the non-transfused group. These findings suggest that infants with a younger gestational age, lower birth weight, and less mature respiratory systems are at a higher risk of intrapartum asphyxia. Consequently, these infants require higher hemoglobin levels to improve oxygen-carrying capacity, which likely contributes to the higher blood transfusion rates observed in this group. The role of RBC transfusion in improving anemia in EPIs is well-established [23]. However, the side effects of RBC transfusion in this population remain a significant concern and urgently need to be addressed. In addition to the risk of conventional transfusion-related adverse reactions, the association between blood transfusions and adverse outcomes in EPIs requires further investigation. Our study found that the incidences of CLD, death, LOS, severe ROP, NEC, IVH, and severe IVH were all significantly higher in the transfused group compared to the non-transfused group. NEC is one of the leading causes of mortality in preterm infants, with a fatality rate as high as 20%–30% [32]. However, the underlying mechanisms remain poorly understood. Several studies have identified red blood cell transfusion and anemia as major risk factors; however, evidence regarding the association between RBC transfusion and the development of NEC remains inconsistent [33]. Mohamed A et al. reported that RBC transfusion is a risk factor for NEC and increases its incidence in preterm infants [34]. In contrast, Sharma R et al. found no significant relationship between RBC transfusion and the onset of NEC [35, 36]. Interestingly, AlFaleh K et al. suggested that RBC transfusion might serve as a protective factor, potentially reducing the prevalence of NEC [37,38]. Given these conflicting results, the relationship between RBC transfusion and the development of NEC in preterm infants—especially extremely preterm infants—requires further investigation through multicenter, large-scale, and high-quality studies. IVH is a severe complication in preterm infants. Previous studies have shown that early RBC transfusion is an independent risk factor for IVH in preterm infants [39,40]. In our study, the incidence of IVH among EPIs in the transfused group was significantly higher than that in the non-transfused group, which is consistent with the findings of the previous studies. However, because IVH itself can lead to anemia and the subsequent need for RBC transfusion, the causal relationship between RBC transfusion and IVH remains controversial. Skubisz A et al. found that in most infants, IVH occurred before the first RBC transfusion [41]. Our study also demonstrated that the incidence of ROP in EPIs was significantly higher in the transfused group than in the non-transfused group. Studies have shown that RBC transfusion is an independent risk factor for ROP in EPIs [42,43]. Specifically, the younger the GA, the higher the risk of ROP development [44], and the risk of ROP is associated with the number and volume of RBC transfusions [45]. Two potential mechanisms by which RBC transfusion could induce severe ROP in EPIs have been identified. First, RBC transfusion can lead to iron overload in the body of EPIs, potentially causing retinal damage. Second, due to the lower oxygen affinity of adult RBCs, their transfusion into EPIs results in increased oxygen unloading in the retinas, thereby inducing retinal damage [44,46]. Our study found that the prevalence of CLD in the transfused group was significantly higher than that in the non-transfused group, which is consistent with the findings of Bahr T M et al [47]. Madhou A et al [48]. also reported that RBC transfusion was strongly associated with increased LOS in very low birth weight infants, with an OR of 9.22 (95% CI: 2.30–36.91), which is consistent with the results of our study. Although there were differences in multiple indicators between EPIs in the transfused and non-transfused groups, multivariate logistic regression analysis revealed that GA, BW, and Hct2h were independent risk factors for blood transfusion in EPIs. Furthermore, ROC curve analysis demonstrated that the AUC for the combined prediction of these three factors reached 0.9145 (95% CI: 0.885–0.944), indicating excellent predictive performance. Therefore, the combination of GA, BW, and Hct2h levels can effectively predict the need for blood transfusion in EPIs. Limitations This study has several limitations. First, the data were obtained from the Dryad database. Although the data were collected from multiple hospitals, all cases were from the United States, limiting the generalizability of the findings to other countries and regions. This geographic limitation may have introduced selection bias, and caution should be exercised when extrapolating the study results to other populations. Second, this study is a secondary analysis of data from a public database. Due to the limitations of the original dataset, it was not possible to analyze the impact of other indicators, such as hemoglobin levels, on blood transfusion in EPIs. Third, the study could not assess the temporal relationship between the timing of blood transfusions and the occurrence of various complications due to limitations in the original data. As such, the causal relationship between blood transfusion and complications in EPIs could not be further explored. Conclusion In conclusion, blood transfusion is associated with a variety of complications in EPIs. BW, GA, and Hct2h are independent risk factors for blood transfusion in EPIs. A combination of these three factors can effectively predict the need for blood transfusion in this population, offering valuable insights for clinical decision-making. Declarations EPIs Extremely preterm infants DR Delivery room GA Gestational age BW Birth weight IVH Intraventricular hemorrhage BPD Bronchopulmonary dysplasia NEC Necrotizing enterocolitis ROP Retinopathy of prematurity CLD Chronic lung disease LOS Length of stay ROC Receiver operating characteristic Hct2h Hematocrit at 2 hours Declarations Acknowledgments The authors gratefully thank Dryad database, Dongli Song, and Priya Jegatheesan for providing the original study data. This work was supported by the National Natural Science Foundation of China (81801581) and the Science and Technology Department of Gansu Province Natural Science Foundation (23JRRA1305). The authors thank Editage for English language editing. Authors’ contributions Junyi Chen: Conceptualization, Methodology, Research Execution and Drafting the Original Draft; Jie Shi and Wen Wang: Investigation and Data Management; Jian Shi and Qi Dang: Resources and Data Curation; Yanying Dong and Xiaojuan Li: Conceptualization, Writing the Review and Editing, Supervision, Project Management and Funding Support. All authors critically reviewed the manuscript and approved the final manuscript. Funding This work was financially supported by the National Natural Science Foundation of China (81801581) and the Science and the Technology Department of Gansu Province Natural Science Foundation (23JRRA1305). Data availability Data can be downloaded from https://datadryad.org/ Ethics approval and consent to participate This study was conducted using publicly available, de-identified data from the Dryad public database. Therefore, it does not involve human participants or identifiable personal privacy information, and ethical approval as well as informed consent are not required. The conduct of this study complies with the relevant principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors have disclosed no conflicts of interest. Author details 1 Department of Clinical Transfusion Medicine, The Second Hospital & Clinical Medical School, Lanzhou University,Lanzhou,Gansu,China 2 Department of Transfusion Medicine, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China References Chawanpaiboon S, Vogel JP, Moller AB, Lumbiganon P, Petzold M, Hogan D, et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health. 2019;7(1):e37-e46. Cao G, Liu J, Liu M. 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Arch Dis Child Fetal Neonatal Ed. 2015;100(4):F301-8. Arpi E, D'Amico R, Lucaccioni L, Bedetti L, Berardi A, Ferrari F. Worse global intellectual and worse neuropsychological functioning in preterm-born children at preschool age: a meta-analysis. Acta Paediatr. 2019;108(9):1567-79. Vogel JP, Chawanpaiboon S, Moller AB, Watananirun K, Bonet M, Lumbiganon P. The global epidemiology of preterm birth. Best Pract Res Clin Obstet Gynaecol. 2018;52:3-12. Petrou S, Abangma G, Johnson S, Wolke D, Marlow N. Costs and health utilities associated with extremely preterm birth: evidence from the EPICure study. Value Health. 2009;12(8):1124-34. Houben N, Fustolo-Gunnink S, Fijnvandraat K, Caram-Deelder C, Carrascosa MA, Beuchée A, et al. Red blood cell transfusion in european neonatal intensive care units, 2022 to 2023. JAMA Netw Open. 2024;7(9):e2434077. Bellach L, Eigenschink M, Hassanein A, Savran D, Salzer U, Müllner EW, et al. Packed red blood cell transfusion in preterm infants. 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Red blood cell transfusion in preterm infants: current evidence and controversies. Neonatology. 2018;114(1):7-16. Keir AK, Yang J, Harrison A, Pelausa E, Shah PS, Network CN. Temporal changes in blood product usage in preterm neonates born at less than 30 weeks' gestation in Canada. Transfusion. 2015;55(6):1340-6. Jeon GW, Sin JB. Risk factors of transfusion in anemia of very low birth weight infants. Yonsei Med J. 2013;54(2):366-73. Kim YJ, Yoon SA. Risk factors associated with anemia of prematurity requiring red blood cell transfusion in very low birth weight infants: a retrospective study. BMC Pediatr. 2024;24(1):623. Kong X, Wang H, Yang R, Zhang M, Li C, Zhang R, et al. Association between hematocrit in the first two hours of life and retinopathy during prematurity: a retrospective study from DRYAD. BMC Pediatr. 2025;25(1):176. Liao Z, Zhao X, Rao H, Kang Y. Analysis of correlative risk factors for blood transfusion therapy for extremely low birth weight infants and extreme preterm infants. Am J Transl Res. 2021;13(7):8179-85. dos Santos AM, Guinsburg R, de Almeida MF, Procianoy RS, Marba ST, Ferri WA, et al. Factors associated with red blood cell transfusions in very-low-birth-weight preterm infants in Brazilian neonatal units. BMC Pediatr. 2015;15:113. Maier RF, Sonntag J, Walka MM, Liu G, Metze BC, Obladen M. Changing practices of red blood cell transfusions in infants with birth weights less than 1000 g. J Pediatr. 2000;136(2):220-4. Ekhaguere OA, Morriss FH Jr, Bell EF, Prakash N, Widness JA. Predictive factors and practice trends in red blood cell transfusions for very-low-birth-weight infants. Pediatr Res. 2016;79(5):736-41. Patel RM, Kandefer S, Walsh MC, Bell EF, Carlo WA, Laptook AR, et al. Causes and timing of death in extremely premature infants from 2000 through 2011. N Engl J Med. 2015;372(4):331-40. Patel RM, Knezevic A, Shenvi N, Hinkes M, Keene S, Roback JD, et al. Association of red blood cell transfusion, anemia, and necrotizing enterocolitis in very low-birth-weight infants. JAMA. 2016;315(9):889-97. Mohamed A, Shah PS. Transfusion associated necrotizing enterocolitis: a meta-analysis of observational data. Pediatrics. 2012;129(3):529-40. Sharma R, Kraemer DF, Torrazza RM, Mai V, Neu J, Shuster JJ, et al. Packed red blood cell transfusion is not associated with increased risk of necrotizing enterocolitis in premature infants. J Perinatol. 2014;34(11):858-62. Wallenstein MB, Arain YH, Birnie KL, Andrews J, Palma JP, Benitz WE, et al. Red blood cell transfusion is not associated with necrotizing enterocolitis: a review of consecutive transfusions in a tertiary neonatal intensive care unit. J Pediatr. 2014;165(4):678-82. AlFaleh K, Al-Jebreen A, Baqays A, Al-Hallali A, Bedaiwi K, Al-Balahi N, et al. Association of packed red blood cell transfusion and necrotizing enterocolitis in very low birth weight infants. J Neonatal Perinatal Med. 2014;7(3):193-8. Sood BG, Rambhatla A, Thomas R, Chen X. Decreased hazard of necrotizing enterocolitis in preterm neonates receiving red cell transfusions. J Matern Fetal Neonatal Med. 2016;29(5):737-44. Christensen RD, Baer VL, Lambert DK, Ilstrup SJ, Eggert LD, Henry E. Association, among very-low-birthweight neonates, between red blood cell transfusions in the week after birth and severe intraventricular hemorrhage. Transfusion. 2014;54(1):104-8. D'Amato G, Faienza MF, Palladino V, Bianchi FP, Natale MP, Christensen RD, et al. Red blood cell transfusions and potentially related morbidities in neonates under 32 weeks' gestation. Blood Transfus. 2021;19(2):113-9. Skubisz A, de Vries LS, Jansen SJ, van der Staaij H, Lopriore E, Steggerda SJ. Early red blood cell transfusion and the occurrence of intraventricular hemorrhage in very preterm infants. Early Hum Dev. 2024;189:105926. Lust C, Vesoulis Z, Jackups R Jr, Liao S, Rao R, Mathur AM. Early red cell transfusion is associated with development of severe retinopathy of prematurity. J Perinatol. 2019;39(3):393-400. Glaser K, Härtel C, Dammann O, Herting E, Andres O, Speer CP, et al. Erythrocyte transfusions are associated with retinopathy of prematurity in extremely low gestational age newborns. Acta Paediatr. 2023;112(12):2507-15. Zhu Z, Hua X, Yu Y, Zhu P, Hong K, Ke Y. Effect of red blood cell transfusion on the development of retinopathy of prematurity: A systematic review and meta-analysis. PLoS One. 2020;15(6):e0234266. Bas AY, Demirel N, Koc E, Ulubas Isik D, Hirfanoglu İM, Tunc T, et al. Incidence, risk factors and severity of retinopathy of prematurity in Turkey (TR-ROP study): a prospective, multicentre study in 69 neonatal intensive care units. Br J Ophthalmol. 2018;102(12):1711-6. Crawford TM, Andersen CC, Hodyl NA, Robertson SA, Stark MJ. The contribution of red blood cell transfusion to neonatal morbidity and mortality. J Paediatr Child Health. 2019;55(4):387-92. Bahr TM, Ohls RK, Henry E, Davenport P, Ilstrup SJ, Kelley WE, et al. The number of blood transfusions received and the incidence and severity of chronic lung disease among NICU patients born >31 weeks gestation. J Perinatol. 2025;45(2):218-23. Madhou A, Lloyd LG, Mundey N, Nell EM, van Wyk L. Adverse outcomes after red blood cell transfusion in very low birth weight infants in a resource-restricted hospital. Transfusion. 2025;65(5):897-908. Additional Declarations No competing interests reported. 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14:32:13","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139845,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7994284/v1/8cde286147ea240a93a80291.html"},{"id":97367657,"identity":"79f12658-54b6-40c3-9ca4-4923d1c798fa","added_by":"auto","created_at":"2025-12-03 16:20:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48663,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Hct2h(%) between the non-transfusion and transfusion groups. (A)Comparison of Hct2h of the all participants between the two groups. (B) Comparison of Hct2h of the GA＜28W between the two groups. (C) Comparison of Hct2h of the GA≥28W between the two groups. (D) Comparison of Hct2h of the BW＜1.5kg between the two groups. (E) Comparison of Hct2h of the BW≥1.5kg between the two groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7994284/v1/00af94888320e7cf76463593.png"},{"id":97266099,"identity":"dff9e20e-2839-4981-9c36-869d0032860e","added_by":"auto","created_at":"2025-12-02 14:32:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93525,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic (ROC) curve for predicting blood transfusion. (A) The ROC curve for all participants. (B) The ROC curve for GA＜28w. (C) The ROC curve for GA≥28w. (D) The ROC curve for BW＜1.5kg.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7994284/v1/1d6029a74ea035e37415f56c.png"},{"id":97664584,"identity":"70a7cb8f-595c-42be-bc2c-8ea404e8ac98","added_by":"auto","created_at":"2025-12-08 09:10:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":980370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7994284/v1/ed69e086-3885-4f94-bff1-59a56adc6c5d.pdf"},{"id":97266094,"identity":"8a177062-05a6-4a5f-8a6e-1a2be96beeb9","added_by":"auto","created_at":"2025-12-02 14:32:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25064,"visible":true,"origin":"","legend":"","description":"","filename":"SupplymentTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7994284/v1/c7bfa5c901d6bf5785db6578.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk factors and predictive modeling for blood transfusion in extremely preterm infants: The role of perinatal factors and clinical outcomes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe World Health Organization (WHO) defines preterm birth as any delivery occurring before 37 weeks of gestation. Globally, the average preterm birth rate is 10.6%; however, this figure varies significantly by region. In the United States, the rate ranges from 12% to 13%, while in developed countries such as Europe, it averages between 5% and 9%. In some African nations, the rate can reach 18% [1-4]. Of all preterm births, about 5% are classified as extremely preterm (gestational age [GA] \u0026lt;28 weeks), approximately 15% as very preterm (GA between 28 and 31 weeks), around 20% as moderately preterm (GA between 32 and 33 weeks), and 60\u0026ndash;70% as late preterm (GA between 34 and 36 weeks) [5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePreterm birth has profound effects on children, their families, and society at large, serving as a leading cause of childhood mortality, responsible for 18% of deaths in children under five and 35% of neonatal deaths [6].\u0026nbsp;Short-term complications of preterm birth include an increased risk of neonatal respiratory distress syndrome, bronchopulmonary dysplasia (BPD), necrotizing enterocolitis (NEC), sepsis, periventricular leukomalacia, seizures, intraventricular hemorrhage (IVH), cerebral palsy, hypoxic-ischemic encephalopathy, feeding challenges, and visual and auditory impairments [7-10]. Additionally, preterm birth is linked to poor neurodevelopmental outcomes, higher rates of hospitalization, and challenges in behavior, social-emotional development, and learning during childhood [11-14]. Furthermore, preterm birth results in significant long-term healthcare costs and imposes considerable psychological and financial burdens on the families of preterm infants [15,16].\u003c/p\u003e\n\u003cp\u003eBlood transfusion is a common treatment for preterm infants, with over one-third of patients requiring this therapy [17]. However, whether red blood cell (RBC) transfusion increases the risk of adverse clinical outcomes, such as BPD, retinopathy of prematurity (ROP), and NEC, remains a long-standing research question with no conclusive answers [18]. Data on blood transfusions in EPIs were obtained from the Dryad database. This study analyzed differences in baseline data and clinical outcomes between transfused and non-transfused groups, identified transfusion-related influencing factors, and developed a prediction model for blood transfusion in EPIs. The aim of this study was to provide a reference for clinical blood transfusion therapy.\u003c/p\u003e"},{"header":"Materials and methods ","content":"\u003cp\u003e\u003cstrong\u003eData source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study constituted a retrospective observational analysis of de-identified data sourced from the Dryad database (https://datadryad.org/). The data were derived from two previously published datasets: one by Dongli Song (https://datadryad.org/stash/dataset/doi:10.5061/dryad.4q3d3 ) and another by Priya Jegatheesan (https://datadryad.org/stash/dataset/doi:10.5061/dryad.vt4b8gv2r) [19,20]. As the original studies were observational in nature and did not involve any prospective intervention, this secondary analysis was not considered a clinical trial and did not require trial registration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original studies were conducted at the Santa Clara Valley Medical Center (SCVMC) from January 2008 to April 2014 and at the American Academy of Pediatrics (AAP) level IV NICU in a California public safety net hospital from January 2014 to December 2019.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the original study protocol, eligibility was limited to preterm neonates born at \u0026lt;33 weeks of gestation, excluding cases with 1) non-resuscitation orders, 2) delivery room deaths, or 3) incomplete medical records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecords with complete data on demographic variables, hematocrit levels, delivery room (DR) measures, neonatal outcomes, and transfusion data were selected from the dataset. The collected demographic characteristics included birth weight (BW), GA, and delivery mode (vaginal or cesarean section). Hematocrit measurements were obtained for each clinical indication. Only hematocrit values collected within the first 2 hours postnatally (median time: 0.8 hours) were included in this analysis. Post-transfusion hematocrit values were systematically excluded to avoid confounding effects. The DR indicators included Apgar scores at 1 and 5 minutes, endotracheal intubation, chest compressions, and temperature upon admission. Neonatal outcomes included chronic lung disease (CLD), length of stay (LOS), severe ROP (defined as grade 3 or threshold disease or those receiving anti-VEGF treatment/laser therapy for ROP), NEC, any IVH (grades 1-4), severe IVH (grades 3-4), composite survival outcomes (survival without severe IVH, severe ROP, late-onset sepsis, NEC, or CLD), and death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn an original research study [20], gestational age was reported in 1-week intervals, and birth weight was reported in 0.050-kilogram intervals. For statistical convenience, we used the midpoint of each interval for analysis. For example, if the original birth weight was recorded as 1.051-1.100 kilograms, the value taken was 1.075 kilograms.\u003c/p\u003e\n\u003cp\u003eFor continuous variables, we first performed normality testing using the Shapiro-Wilk test. Normally distributed data are presented as mean\u0026plusmn;SD, while non-normally distributed data are expressed as median (interquartile range [IQR]). Categorical variables are expressed as frequencies (%). The t-test was used to compare normally distributed variables, the Mann-Whitney rank-sum test for non-normally distributed continuous variables, and the chi-square or Fisher\u0026rsquo;s exact test to compare categorical variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRisk factors were analyzed using univariate logistic regression, with statistically significant variables (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05) subsequently entered into a multivariate logistic regression model to identify independent predictors of transfusion in very preterm infants. The receiver operating characteristic (ROC) curve was used to analyze the predictive value of risk factors (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 in the multivariate logistic regression model for red blood cell transfusion). Key indices for this analysis include the area under the curve (AUC) and the cutoff value. The AUC represents predictive efficiency, with higher values indicating better performance, while the cutoff value represents the threshold for predicting the need for transfusion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS version 19.0 (IBM, Chicago, IL, USA) and GraphPad Prism version 9.4 (GraphPad Software, USA). Statistical significance was defined as a two-sided \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 578 extremely preterm infants (EPIs) were included, with 457 in the non-transfusion group and 121 in the transfusion group. The overall blood transfusion rate was 20.93%. The median GA (IQR) was 26.3 (25.2\u0026ndash;28.5) weeks in the transfusion group and 31.4 (29.5\u0026ndash;32.5) weeks in the non-transfusion group. The mean BW was 0.955 \u0026plusmn; 0.336 kg in the transfusion group and 1.551 \u0026plusmn; 0.406 kg in the non-transfusion group. Both GA and BW were significantly lower in the transfusion group than in the non-transfusion group (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05); however, no statistically significant difference was observed in the mode of delivery between the two groups (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05). Demographic characteristics of the transfusion and non-transfusion groups are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Comparison of baseline characteristics , DR indicators and neonatal outcomes of the participants between the non-transfusion and transfusion groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-transfusion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=457)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransfusion\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=121)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et/U/\u003c/strong\u003e\u003cstrong\u003e\u0026chi;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eGA(weeks), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e31.4 (29.5-32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e26.3 (25.2-28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e6542.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eBW(kg), mean (SD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.5514\u0026plusmn;0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.955\u0026plusmn;0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e16.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eDelivery Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eVag, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e220 (48.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e66 (54.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eOthers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e237 (51.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e55 (45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDR indicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eApgar 1, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e7 (5-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (3-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e17682.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eApgar 5, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e8 (7-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e7 (5-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e16408.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eAdmit Temp-C, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e36.9 (36.7-37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e36.9 (36.6-37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e26607.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eIntubation，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e19 (4.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e35 (28.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e69.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eCardiac Compression，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e5 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (4.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e7.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeonatal outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eCLD，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e23 (5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e54 (44.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e129.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eDeath，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e5 (4.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eLate Onset Sepsis，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e28 (23.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e95.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eSevere_ROP，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e22 (18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e71.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eNEC，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e13 (10.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e36.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eIVH，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e54 ( 11.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e42 (34.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e21.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eIVH_Severe，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e6 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e14 (11.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e30.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 234px;\"\u003e\n \u003cp\u003eSurvival_wo_Major_Morbidity，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e102 (22.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e22 (18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e5.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGA=gestational age in weeks at birth, BW=birth weight in grams, CLD=chronic lung disease, ROP=retinopathy of prematurity, NEC=necrotizing enterocolitis, IVH=intraventricular hemorrhage, Survival_wo_Major_Morbidity=survival without severe intraventricular hemorrhage, severe retinopathy of prematurity, late onset sepsis, necrotizing enterocolitis or chronic lung disease\u003c/p\u003e\n\u003cp\u003e* Fisher\u0026rsquo;s exact test.\u003c/p\u003e\n\u003cp\u003eFor further analysis, we divided the GA into subgroups of \u0026lt;28 weeks and \u0026ge;28 weeks. The blood transfusion rates for these subgroups were 63.24% (86/136) in the GA \u0026lt;28 weeks group and 7.92% (35/442) in the GA \u0026ge;28 weeks group. For the BW subgroups, the transfusion rates were 34.90% (112/321) in the BW \u0026lt;1.5 kg group and 3.50% (9/257) in the BW \u0026ge;1.5 kg group. The results showed that in both the GA \u0026lt;28 weeks or \u0026ge;28 weeks groups, the BW of the transfusion group was significantly lower than that of the non-transfusion group. Additionally, a subgroup analysis was performed by categorizing BW into \u0026lt;1.5 kg and \u0026ge;1.5 kg groups. The results demonstrated that in the \u0026lt;1.5 kg subgroup, the GA of the transfusion group was significantly lower than that of the non-transfusion group, showing a statistically significant difference. However, no statistically significant difference was observed in the \u0026ge;1.5 kg subgroup. Regarding the mode of delivery, no statistically significant differences were found between the transfusion and non-transfusion groups in either subgroup. Demographic characteristics of the transfusion and non-transfusion groups across the BW and GA subgroups are shown in\u0026nbsp;Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHematocrit in the first 2 hours of life\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the hematocrit at 2 hours (Hct2h) outcomes in the transfusion and non-transfusion groups. The transfusion group exhibited significantly lower Hct2h (%) levels (43.22\u0026plusmn;6.0) compared to the non-transfusion group (50.84\u0026plusmn;6.61), with a statistically significant difference (t = 11.486, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eSubgroup analyses based on BW and GA consistently demonstrated that the transfusion group exhibited significantly lower Hct2h levels than the non-transfusion group across all subgroups (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDR indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DR indicators for the EPIs are listed in Table 1 and Table S2. The transfusion group showed significantly lower Apgar scores at 1 minute (Apgar 1) and 5 minutes (Apgar 5) compared to the non-transfusion group, with statistically significant differences. Thirty-five (28.93%) and 6 (4.96%) patients in the transfused group received endotracheal intubation and chest compressions, respectively, which were significantly higher than 19 (4.16%) and 5 (1.09%) in the non-transfused group. However, no statistically significant difference was found in admission temperature (Admit Temp-C) between the two groups.\u003c/p\u003e\n\u003cp\u003eSubgroup analysis based on GA and BW showed that in the GA \u0026lt; 28 weeks group, the 5-minute Apgar score in the transfused group was lower than that in the non-transfused group. In the transfused group, 31 (36.05%) preterm infants underwent intubation, which was significantly higher than 7 (14.00%) in the non-transfused group. In the subgroup with GA \u0026ge;28 weeks and BW \u0026lt;1.5 kg, the 1-minute Apgar score, 5-minute Apgar score, and the number of cases undergoing intubation in the transfused group were all lower than those in the non-transfused group. In the BW \u0026ge;1.5 kg subgroup, both the 1-minute Apgar score and the number of cases undergoing intubation were lower in the transfused group than in the non-transfused group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeonatal outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeonatal outcomes are shown in Table 1 and Table S3. In the blood transfusion group, the rates of CLD, late onset sepsis, severe ROP, NEC, IVH, severe IVH, as well as survival without major morbidity and death were all significantly higher compared to the non-transfusion group, with statistically significant differences. Subgroup analyses by GA and BW consistently demonstrated the same trends as described above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic regression analysis of risk factors for blood transfusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe initially performed univariate logistic regression analysis to identify risk factors for blood transfusion. Variables with \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 were subsequently included in the multivariate logistic regression analysis. The results indicated that GA (OR = 0.670, 95% CI: 0.548\u0026ndash;0.819, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), BW (OR = 0.998, 95% CI: 0.997\u0026ndash;1.000, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.007), and Hct2h (OR = 0.888, 95% CI: 0.847\u0026ndash;0.930, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) were significant factors influencing the need for blood transfusion in EPIs (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2\u003c/strong\u003e Multivariate logistic regression analysis of risk factors for blood transfusion\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll participants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e15.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.548-0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e7.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.997-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eHct2h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e50.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.847-0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGA\u003c/strong\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e28w\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e19.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.992-0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eHct2h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e12.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.797-0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGA\u003c/strong\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e28w\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e8.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.997-0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eHct2h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e18.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.821-0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBW\u003c/strong\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e1.5kg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e44.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.443-0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eHct2h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e23.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.834-0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGA=gestational age in weeks at birth, BW=birth weight in grams, Hct2h=hematocrit in the first 2 hours of life, OR=Odds ratio, CI= Confidence interval\u003c/p\u003e\n\u003cp\u003eSubgroup analysis by gestational age showed that in the GA \u0026lt;28 weeks group, BW (OR = 0.995, 95% CI: 0.992\u0026ndash;0.997, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) and Hct2h (OR = 0.864, 95% CI: 0.797\u0026ndash;0.938, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) were significant influencing factors for blood transfusion. In the GA \u0026ge;28 weeks group, BW (OR = 0.998, 95% CI: 0.997\u0026ndash;0.999, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.003) and Hct2h (OR = 0.873, 95% CI: 0.821\u0026ndash;0.928, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) were also significant influencing factors. Subgroup analysis based on birth weight showed that in the BW \u0026lt;1.5 kg group, GA (OR = 0.533, 95% CI: 0.443\u0026ndash;0.640,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) and Hct2h (OR = 0.878, 95% CI: 0.834\u0026ndash;0.926, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) were\u0026nbsp;significant risk\u0026nbsp;factors for blood transfusion. However, none of the factors showed statistical significance in the BW \u0026ge;1.5 kg group (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of the ROC curve for the detection of transfusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed ROC curve analysis of transfusion-related factors (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 in the aforementioned multivariate logistic regression analysis) to evaluate their predictive value for transfusion. The results showed that the AUC for the individual prediction of transfusion in EPIs was 0.8817 for GA, 0.8726 for BW, and 0.8059 for Hct2h. The combined model demonstrated superior predictive performance, with an AUC of 0.9145, sensitivity of 81.8%, and specificity of 89.5%, outperforming all individual parameters.\u003c/p\u003e\n\u003cp\u003eStratified analyses were performed to assess the predictive performance of different parameter combinations for transfusion risk in EPIs. In infants with GA \u0026lt;28 weeks, the combined model of BW and Hct2h demonstrated an AUC of 0.8363, which was significantly higher than that of BW (AUC = 0.7879, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) or Hct2h (AUC = 0.7364, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) alone. Similarly, in infants with GA \u0026ge;28 weeks, the BW + Hct2h combination showed improved predictive accuracy (AUC = 0.8189) compared to individual parameters (both \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). For infants with BW \u0026lt;1.5 kg, the GA + Hct2h combined model achieved superior performance, with an AUC of 0.8829 (sensitivity = 86.6%, specificity = 75.6%), significantly outperforming either GA (AUC = 0.8464,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) or Hct2h (AUC = 0.7908, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) alone. These results consistently demonstrate that combined parameter models offer\u0026nbsp;enhanced predictive capability for transfusion risk across different subgroups of EPIs (Table 3 and Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Predictive value of GA, BW, and Hct2h for transfusion risk in extremely preterm infants\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"597\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYouden index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eAll participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.9145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.885-0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.7132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGA＜28w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.8363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.762-0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.6209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGA\u0026ge;28w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.8189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.747-0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.5141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eBW＜1.5kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.8829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.845-0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.6221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC=area under the receiver operating characteristic curve; CI=confidence interval; GA=gestational age in weeks at birth, BW=birth weight in grams\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAnemia is one of the most common complications in EPIs. RBC transfusion is typically used to improve perfusion and oxygenation in preterm infants suffering from anemia of prematurity [21,22]. Previous studies have shown that up to 90% of extremely low birth weight infants and 58% of EPIs born at less than 32 weeks of gestation receive RBC transfusions during hospitalization [23-25].\u0026nbsp;In our study, the overall blood transfusion rate among EPIs was 20.93%. Subgroup analyses based on gestational age and birth weight showed that blood transfusion rates varied between 3.50% and 63.24%, which were somewhat lower than the rates reported in earlier studies. This discrepancy may be attributed to differences in the inclusion criteria\u0026nbsp;and\u0026nbsp;study periods, which could account for variations in the patient populations analyzed [26]. Hct levels\u0026nbsp;are often used to assess the severity of anemia in EPIs and indirectly reflect tissue oxygenation levels [27].\u0026nbsp;Our study found that the Hct2h in the transfused group was significantly lower than that in the non-transfused group, consistent with the findings of Liao Z et al [28].\u003c/p\u003e\n\u003cp\u003ePreterm birth and low birth weight are well-established risk factors for blood transfusion in EPIs. In our study, we observed that both GA and BW in the transfused group were significantly lower than those in the non-transfused group. Subgroup analysis revealed a consistent trend across all subgroups except for the BW \u0026ge;1.5 kg group. This exception can likely be attributed to the small sample size (n = 9) in the transfused group within the BW \u0026ge;1.5 kg subgroup. Additionally, logistic regression analysis indicated that both GA and BW were independent risk factors for blood transfusion in EPIs. This finding is consistent with the results reported by\u0026nbsp;Dos S A\u0026nbsp;et al [29-31].\u003c/p\u003e\n\u003cp\u003eOur study found that both the Apgar 1 and Apgar 5 scores in the transfused group were significantly lower than those in the non-transfused group. This is consistent with the findings reported by Kim Y J et al.[26], whereas Jeon G W et al.[25] did not observe a statistically significant difference between the two groups. In addition, the proportions of infants requiring endotracheal intubation and chest compressions in the transfused group were significantly higher than those in the non-transfused group. These findings suggest that infants with a younger gestational age, lower birth weight, and less mature respiratory systems are at a higher risk of intrapartum asphyxia. Consequently, these infants require higher hemoglobin levels to improve oxygen-carrying capacity, which likely contributes to the higher blood transfusion rates observed in this group.\u003c/p\u003e\n\u003cp\u003eThe role of RBC transfusion in improving anemia in EPIs is well-established [23]. However, the side effects of RBC transfusion in this population remain a significant concern and urgently need to be addressed. In addition to the risk of conventional transfusion-related adverse reactions, the association between blood transfusions and adverse outcomes in EPIs requires further investigation. Our study found that the incidences of CLD, death, LOS, severe ROP, NEC, IVH, and severe IVH were all significantly higher in the transfused group compared to the non-transfused group.\u003c/p\u003e\n\u003cp\u003eNEC is one of the leading causes of mortality in preterm infants, with a fatality rate as high as 20%\u0026ndash;30% [32]. However, the underlying mechanisms remain poorly understood. Several studies have identified red blood cell transfusion and anemia as major risk factors; however, evidence regarding the association between RBC transfusion and the development of NEC remains inconsistent [33]. Mohamed A et al. reported that RBC transfusion is a risk factor for NEC and increases its incidence in preterm infants [34]. In contrast, Sharma R et al. found no significant relationship between RBC transfusion and the onset of NEC [35, 36]. Interestingly, AlFaleh K et al. suggested that RBC transfusion might serve as a protective factor, potentially reducing the prevalence of NEC [37,38]. Given these conflicting results, the relationship between RBC transfusion and the development of NEC in preterm infants\u0026mdash;especially extremely preterm infants\u0026mdash;requires further investigation through multicenter, large-scale, and high-quality studies.\u003c/p\u003e\n\u003cp\u003eIVH is a severe complication in preterm infants. Previous studies have shown that early RBC transfusion is an independent risk factor for IVH in preterm infants [39,40]. In our study, the incidence of IVH among EPIs in the transfused group was significantly higher than that in the non-transfused group, which is consistent with the findings of the previous studies. However, because IVH itself can lead to anemia and the subsequent need for RBC transfusion, the causal relationship between RBC transfusion and IVH remains controversial. Skubisz A et al. found that in most infants, IVH occurred before the first RBC transfusion [41].\u003c/p\u003e\n\u003cp\u003eOur study also demonstrated that the incidence of ROP in EPIs was significantly higher in the transfused group than in the non-transfused group. Studies have shown that RBC transfusion is an independent risk factor for ROP in EPIs [42,43]. Specifically, the younger the GA, the higher\u0026nbsp;the risk of ROP development [44],\u0026nbsp;and the risk of ROP is associated with the number and volume of RBC transfusions [45]. Two\u0026nbsp;potential mechanisms\u0026nbsp;by which RBC transfusion could induce severe ROP in EPIs have been identified. First, RBC transfusion can lead to iron overload in the body of EPIs, potentially causing retinal damage. Second, due to the lower oxygen affinity of adult RBCs, their transfusion into EPIs results in increased oxygen unloading in the retinas, thereby inducing retinal damage [44,46].\u0026nbsp;Our study found that the prevalence of CLD in the transfused group was significantly higher than that in the non-transfused group, which is consistent with the findings of Bahr T M et al [47]. Madhou A et al [48]. also reported that RBC transfusion was strongly associated with increased LOS in very low birth weight infants, with an OR of 9.22 (95% CI: 2.30\u0026ndash;36.91), which is consistent with the results of our study.\u003c/p\u003e\n\u003cp\u003eAlthough there were differences in multiple indicators between EPIs in the transfused and non-transfused groups, multivariate logistic regression analysis revealed that GA, BW, and Hct2h were independent risk factors for blood transfusion in EPIs. Furthermore, ROC curve analysis demonstrated that the AUC for the combined prediction of these three factors reached 0.9145 (95% CI: 0.885\u0026ndash;0.944), indicating excellent predictive performance. Therefore, the combination of GA, BW, and Hct2h levels can effectively predict the need for blood transfusion in EPIs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the data were obtained from the Dryad database. Although the data were collected from multiple hospitals, all cases were from the United States, limiting the generalizability of the findings to other countries and regions. This geographic limitation may have introduced selection bias, and caution should be exercised when extrapolating the study results to other populations. Second, this study is a secondary analysis of data from a public database. Due to the limitations of the original dataset, it was not possible to analyze the impact of other indicators, such as hemoglobin levels, on blood transfusion in EPIs. Third, the study could not assess the temporal relationship between the timing of blood transfusions and the occurrence of various complications due to limitations in the original data. As such, the causal relationship between blood transfusion and complications in EPIs could not be further explored.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, blood transfusion is associated with a variety of complications in EPIs. BW, GA, and Hct2h are independent risk factors for blood transfusion in EPIs. A combination of these three factors can effectively predict the need for blood transfusion in this population, offering valuable insights for clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEPIs \u0026nbsp; Extremely preterm infants\u003c/p\u003e\n\u003cp\u003eDR \u0026nbsp; \u0026nbsp;Delivery room\u003c/p\u003e\n\u003cp\u003eGA \u0026nbsp; \u0026nbsp;Gestational age\u003c/p\u003e\n\u003cp\u003eBW \u0026nbsp; \u0026nbsp;Birth weight\u003c/p\u003e\n\u003cp\u003eIVH \u0026nbsp; \u0026nbsp;Intraventricular hemorrhage\u003c/p\u003e\n\u003cp\u003eBPD \u0026nbsp; Bronchopulmonary dysplasia\u003c/p\u003e\n\u003cp\u003eNEC \u0026nbsp; Necrotizing enterocolitis\u003c/p\u003e\n\u003cp\u003eROP \u0026nbsp; Retinopathy of prematurity\u003c/p\u003e\n\u003cp\u003eCLD \u0026nbsp; Chronic lung disease\u003c/p\u003e\n\u003cp\u003eLOS \u0026nbsp; Length of stay\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eHct2h \u0026nbsp;Hematocrit at 2 hours\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully thank Dryad database, Dongli Song, and Priya Jegatheesan for providing the original study data. This work was supported by the National Natural Science Foundation of China (81801581) and the Science and Technology Department of Gansu Province Natural Science Foundation (23JRRA1305). The authors thank Editage for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; \u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJunyi Chen: Conceptualization, Methodology, Research Execution and Drafting the Original Draft; Jie Shi and Wen Wang: Investigation and Data Management; Jian Shi and Qi Dang: Resources and Data Curation; Yanying Dong and Xiaojuan Li: Conceptualization, Writing the Review and Editing, Supervision, Project Management and Funding Support. All authors critically reviewed the manuscript and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Natural Science Foundation of China (81801581) and the Science and the Technology Department of Gansu Province Natural Science Foundation (23JRRA1305).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData can be downloaded from https://datadryad.org/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using publicly available, de-identified data from the Dryad public database. Therefore, it does not involve human participants or identifiable personal privacy information, and ethical approval as well as informed consent are not required. The conduct of this study complies with the relevant principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have disclosed no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Clinical Transfusion Medicine, The Second Hospital \u0026amp; Clinical Medical School, Lanzhou University,Lanzhou,Gansu,China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Department of Transfusion Medicine, The Second Affiliated Hospital of Xi\u0026rsquo;an Jiaotong University, Xi\u0026rsquo;an, Shaanxi, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChawanpaiboon S, Vogel JP, Moller AB, Lumbiganon P, Petzold M, Hogan D, et al. 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JAMA Netw Open. 2023;6(9):e2334889.\u003c/li\u003e\n\u003cli\u003eHowarth C, Banerjee J, Aladangady N. Red blood cell transfusion in preterm infants: current evidence and controversies. Neonatology. 2018;114(1):7-16.\u003c/li\u003e\n\u003cli\u003eKeir AK, Yang J, Harrison A, Pelausa E, Shah PS, Network CN. Temporal changes in blood product usage in preterm neonates born at less than 30 weeks\u0026apos; gestation in Canada. Transfusion. 2015;55(6):1340-6.\u003c/li\u003e\n\u003cli\u003eJeon GW, Sin JB. Risk factors of transfusion in anemia of very low birth weight infants. Yonsei Med J. 2013;54(2):366-73.\u003c/li\u003e\n\u003cli\u003eKim YJ, Yoon SA. Risk factors associated with anemia of prematurity requiring red blood cell transfusion in very low birth weight infants: a retrospective study. BMC Pediatr. 2024;24(1):623.\u003c/li\u003e\n\u003cli\u003eKong X, Wang H, Yang R, Zhang M, Li C, Zhang R, et al. 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Predictive factors and practice trends in red blood cell transfusions for very-low-birth-weight infants. Pediatr Res. 2016;79(5):736-41.\u003c/li\u003e\n\u003cli\u003ePatel RM, Kandefer S, Walsh MC, Bell EF, Carlo WA, Laptook AR, et al. Causes and timing of death in extremely premature infants from 2000 through 2011. N Engl J Med. 2015;372(4):331-40.\u003c/li\u003e\n\u003cli\u003ePatel RM, Knezevic A, Shenvi N, Hinkes M, Keene S, Roback JD, et al. Association of red blood cell transfusion, anemia, and necrotizing enterocolitis in very low-birth-weight infants. JAMA. 2016;315(9):889-97.\u003c/li\u003e\n\u003cli\u003eMohamed A, Shah PS. Transfusion associated necrotizing enterocolitis: a meta-analysis of observational data. Pediatrics. 2012;129(3):529-40.\u003c/li\u003e\n\u003cli\u003eSharma R, Kraemer DF, Torrazza RM, Mai V, Neu J, Shuster JJ, et al. Packed red blood cell transfusion is not associated with increased risk of necrotizing enterocolitis in premature infants. J Perinatol. 2014;34(11):858-62.\u003c/li\u003e\n\u003cli\u003eWallenstein MB, Arain YH, Birnie KL, Andrews J, Palma JP, Benitz WE, et al. Red blood cell transfusion is not associated with necrotizing enterocolitis: a review of consecutive transfusions in a tertiary neonatal intensive care unit. J Pediatr. 2014;165(4):678-82.\u003c/li\u003e\n\u003cli\u003eAlFaleh K, Al-Jebreen A, Baqays A, Al-Hallali A, Bedaiwi K, Al-Balahi N, et al. Association of packed red blood cell transfusion and necrotizing enterocolitis in very low birth weight infants. J Neonatal Perinatal Med. 2014;7(3):193-8.\u003c/li\u003e\n\u003cli\u003eSood BG, Rambhatla A, Thomas R, Chen X. Decreased hazard of necrotizing enterocolitis in preterm neonates receiving red cell transfusions. J Matern Fetal Neonatal Med. 2016;29(5):737-44.\u003c/li\u003e\n\u003cli\u003eChristensen RD, Baer VL, Lambert DK, Ilstrup SJ, Eggert LD, Henry E. Association, among very-low-birthweight neonates, between red blood cell transfusions in the week after birth and severe intraventricular hemorrhage. Transfusion. 2014;54(1):104-8.\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Amato G, Faienza MF, Palladino V, Bianchi FP, Natale MP, Christensen RD, et al. Red blood cell transfusions and potentially related morbidities in neonates under 32 weeks\u0026apos; gestation. Blood Transfus. 2021;19(2):113-9.\u003c/li\u003e\n\u003cli\u003eSkubisz A, de Vries LS, Jansen SJ, van der Staaij H, Lopriore E, Steggerda SJ. Early red blood cell transfusion and the occurrence of intraventricular hemorrhage in very preterm infants. Early Hum Dev. 2024;189:105926.\u003c/li\u003e\n\u003cli\u003eLust C, Vesoulis Z, Jackups R Jr, Liao S, Rao R, Mathur AM. Early red cell transfusion is associated with development of severe retinopathy of prematurity. J Perinatol. 2019;39(3):393-400.\u003c/li\u003e\n\u003cli\u003eGlaser K, H\u0026auml;rtel C, Dammann O, Herting E, Andres O, Speer CP, et al. Erythrocyte transfusions are associated with retinopathy of prematurity in extremely low gestational age newborns. Acta Paediatr. 2023;112(12):2507-15.\u003c/li\u003e\n\u003cli\u003eZhu Z, Hua X, Yu Y, Zhu P, Hong K, Ke Y. Effect of red blood cell transfusion on the development of retinopathy of prematurity: A systematic review and meta-analysis. PLoS One. 2020;15(6):e0234266.\u003c/li\u003e\n\u003cli\u003eBas AY, Demirel N, Koc E, Ulubas Isik D, Hirfanoglu İM, Tunc T, et al. Incidence, risk factors and severity of retinopathy of prematurity in Turkey (TR-ROP study): a prospective, multicentre study in 69 neonatal intensive care units. Br J Ophthalmol. 2018;102(12):1711-6.\u003c/li\u003e\n\u003cli\u003eCrawford TM, Andersen CC, Hodyl NA, Robertson SA, Stark MJ. The contribution of red blood cell transfusion to neonatal morbidity and mortality. J Paediatr Child Health. 2019;55(4):387-92.\u003c/li\u003e\n\u003cli\u003eBahr TM, Ohls RK, Henry E, Davenport P, Ilstrup SJ, Kelley WE, et al. The number of blood transfusions received and the incidence and severity of chronic lung disease among NICU patients born \u0026gt;31 weeks gestation. J Perinatol. 2025;45(2):218-23.\u003c/li\u003e\n\u003cli\u003eMadhou A, Lloyd LG, Mundey N, Nell EM, van Wyk L. Adverse outcomes after red blood cell transfusion in very low birth weight infants in a resource-restricted hospital. Transfusion. 2025;65(5):897-908.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"extremely preterm infants, blood transfusion, risk factors, hematocrit, neonatal outcomes","lastPublishedDoi":"10.21203/rs.3.rs-7994284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7994284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eExtremely preterm infants (EPIs) are at high risk for severe complications, contributing to neonatal mortality. Blood transfusion is crucial in their management, but its relationship withcomplications remains debated. This study aimed to identify transfusion risk factors in EPIs and develop a predictive model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe analyzed data from the Dryad database, focusing on EPIs with a gestational age (GA) of \u0026lt;33 weeks. We compared the clinical data between transfused and non-transfused groups, and developed a predictive model for blood transfusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 578 EPIs were included, with an overall transfusion rate of 20.93%. The transfused group had lower GA, birth weight (BW), hematocrit at 2 hours (Hct2h), and Apgar scores at 1 and 5 minutes than in the non-transfused group (\u003cem\u003ep\u003c/em\u003e\u0026lt; .001). The transfused group also showed higher incidences of intubation, cardiac compression, chronic lung disease, death, length of stay, severe retinopathy of prematurity, necrotizing enterocolitis, any intraventricular hemorrhage (IVH), and severe IVH (\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01). GA (OR = 0.670, 95% CI: 0.548–0.819, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001), BW (OR = 0.998, 95% CI: 0.997–1.000, \u003cem\u003ep \u003c/em\u003e= 0.007), and Hct2h (OR = 0.888, 95% CI: 0.847–0.930, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) were independent risk factors for transfusion in EPIs. The combination of these factors predictedtransfusion needs with an AUC of 0.9145.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion: \u003c/strong\u003eBlood transfusion in EPIs is associated with several complications. BW, GA, and Hct2h are independent risk factors for transfusion, and their combination can effectively predicttransfusion need in this population.\u003c/p\u003e","manuscriptTitle":"Risk factors and predictive modeling for blood transfusion in extremely preterm infants: The role of perinatal factors and clinical outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:32:09","doi":"10.21203/rs.3.rs-7994284/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-28T10:11:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-04T08:58:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-04T07:36:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-04T07:34:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-10-31T04:08:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3db591f-3cc9-4544-8db0-dfe18fd2d1b0","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T14:32:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 14:32:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7994284","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7994284","identity":"rs-7994284","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}