A predictive nomogram for the early diagnosis of neonatal necrotizing enterocolitis: Insights from a retrospective study

A predictive nomogram for the early diagnosis of neonatal necrotizing enterocolitis: Insights from a retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A predictive nomogram for the early diagnosis of neonatal necrotizing enterocolitis: Insights from a retrospective study Yu Lang, Juan Ma, Yuedi Hu, Leilei Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6518823/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To develop a predictive nomogram for early identification of neonatal necrotizing enterocolitis (NEC) in neonates presenting with nonspecific gastrointestinal or systemic symptoms prior to definitive diagnosis via Modified Bell’s. Methods Using retrospective data from 248 neonates at Southwest Hospital (2018–2024), we constructed a predictive model externally validated with 80 neonates from Children’s Hospital (2020–2024). Multivariate logistic regression identified independent predictors, with model performance assessed via discrimination (AUC/C-index), calibration curves, and bootstrapping (1,000 resamples). Results Apgar score at 5 minutes (odds ratio [OR] 0.539, 95% confidence interval [CI] 0.379–0.767), parenteral nutrition (OR 2.856, 95% CI 1.461–5.584), feeding rate (OR 0.202, 95% CI 0.102–0.400), neutrophil percentage (OR 1.027, 95% CI 1.007–1.048), and procalcitonin (OR 1.184, 95% CI 1.033–1.357) were identified as independent predictors of NEC. The nomogram demonstrated strong predictive performance with an AUC and C-index of 0.839. The Hosmer–Lemeshow test indicated good model calibration ( P = 0.720), with the calibration curve closely aligning with the ideal reference line. Decision curve analysis and clinical impact curves confirmed the nomogram's clinical utility. External validation yielded consistent results, supporting the model’s robustness. Conclusions This validated nomogram provides clinicians with an effective tool for NEC risk stratification, potentially improving clinical decisions and neonatal outcomes. Neonate Necrotizing Enterocolitis Predictive Nomogram Early Identification Risk Stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Necrotizing enterocolitis (NEC) is the most common and severe gastrointestinal emergency in preterm infants, typically characterized by insidious onset, rapid progression, and unpredictability [ 1 ] . It is one of the important diseases that causes death and disability in premature infants [ 2 ] . With the advancement of perinatal medicine, the survival rate of preterm infants has increased annually. However, the incidence and mortality rates of NEC remain high. In very low birth weight infants, the incidence of NEC is approximately 7.6%, with a mortality rate as high as 40.5% [ 3 , 4 ] . Survivors are at risk of complications such as short bowel syndrome and long-term neurodevelopmental impairments [ 4 , 5 ] , which significantly affect the quality of life of affected children and pose a significant challenge to clinicians. Current understanding of NEC remains incomplete. Early identification through risk stratification models, coupled with evidence-based interventions, is critical for reducing disease incidence, improving survival rates, and optimizing long-term outcomes. Early clinical manifestations of NEC are nonspecific, often overlapping with other conditions such as feeding intolerance, allergic enteritis, and late-onset sepsis, making early diagnosis particularly challenging [ 6 ] . Several studies have attempted to develop predictive models for early diagnosis and assessment of surgical risk by identifying clinical parameters associated with NEC [ 7 – 10 ] . However, most of these studies have been case-control in design, frequently involving gestational age–matched controls rather than neonates presenting with symptoms that overlap with those of NEC. To address this gap, the present study performs a retrospective analysis of 248 neonates, focusing on those with suspected or confirmed NEC symptoms, such as hematochezia, abdominal distention, vomiting, apnea, and hypotonia. This analysis aims to further elucidate the relationship between clinical features, laboratory test results, and the development of NEC. Ultimately, our goal is to develop and validate a predictive nomogram to improve early diagnostic capabilities, thereby enhancing treatment outcomes for this vulnerable population. Methods 1.1 Study Design and Population This study was designed as a retrospective analysis of neonates enrolled and managed at Southwest Hospital of Third Military Medical University and Children's Hospital of Chongqing Medical University, Chongqing, China. The study protocol was approved by the Ethics Committee of Southwest Hospital, Army Medical University (Approval No. [B] KY 2024234), the requirement for informed consent was waived due to the retrospective nature of the research and registered at the Chinese Clinical Trial Registry (MR-50-24-041238; registration date: October 14, 2024). These methods were carried out in accordance with the Declaration of Helsinki. The training cohort included 248 neonates admitted to Southwest Hospital in Chongqing, China, between January 1, 2018, and January 1, 2024. The validation cohort comprised 80 neonates admitted to Children’s Hospital of Chongqing Medical University between January 1, 2020, and January 1, 2024.Inclusion criteria were: (a) admission to the neonatal intensive care unit (NICU); (b) clinical presentation, gastrointestinal manifestations, and radiological findings consistent with Modified Bell’s Stage I, II, or III NEC [ 11 ] . Exclusion criteria were: (a) congenital malformations; (b) surgical emergencies unrelated to NEC; (c) hereditary endocrine or metabolic diseases; and (d) incomplete medical records. Eligible neonates were identified through a systematic review of electronic medical records using ICD-10 codes for NEC (P77.9) and symptom-specific codes (e.g., P92.8 for feeding intolerance). All cases were independently reviewed by two blinded neonatologists to confirm compliance with Modified Bell’s staging criteria. Discrepancies were resolved by a third senior clinician. Patients were divided into the NEC group and the non-NEC group based on whether they met Modified Bell's stage II or higher. Neonates who progressed from Modified Bell's stage I to stage II or higher with symptomatic treatment were classified into the NEC group; otherwise, they were assigned to the non-NEC group. The final diagnosis was determined by two associate professors in neonatology. This classification was used for diagnostic predictive analysis. 1.2 Data Collection The data collected from the enrolled neonates included general information (gestational age [GA], birth weight, sex, 5-minute Apgar score [Apgar 5min], mode of delivery, and postnatal age at disease onset), associated risk factors at delivery (premature rupture of membranes, asphyxia), pregnancy complications (gestational diabetes mellitus [GDM], intrahepatic cholestasis of pregnancy [ICP], hypertensive disorders of pregnancy [HDP]), feeding strategies (initiation time of enteral feeding, formula feeding, parenteral nutrition [PN], and rate of feeding [Feeding rate]), therapeutic interventions (use of antibiotics [< 7 days after birth], antibiotic use or red blood cell transfusion within 72 hours prior to disease onset), and laboratory parameters (white blood cell count [WBC], platelet count [PLT], absolute neutrophil count [ANC], neutrophil percentage [NE%], hemoglobin [HGB], C-reactive protein [CRP], procalcitonin [PCT], and ionized calcium [iCa]). 1.3 Definitions The diagnosis of neonatal NEC was reached according to Modified Bell's stage II or higher criteria [ 11 ] . The initiation time of enteral feeding was categorized as less than 48 hour after birth (< 48h), never initiated/initiated after 48h of birth (≥ 48h). Feeding rate was measured based on the daily increase in milk volume and was divided into two categories: an increase of ≤ 20 ml/kg per day and an increase of > 20 ml/kg per day. The postnatal age at disease onset was defined as the age at which systemic manifestations, gastrointestinal symptoms, and/or radiological findings consistent with Modified Bell's stage I/II/III first appeared. All laboratory data were collected at the initial presentation of the corresponding clinical symptoms and/or signs. 1.4 Development and Assessment of the Model A multivariate logistic regression model was utilized to construct a nomogram for predicting the occurrence of neonatal NEC. Independent predictors ( P < 0.05) were identified via multivariate logistic regression and subsequently incorporated into the nomogram development using the dataset designated for predicting neonatal NEC. To apply the nomogram, predictor lines were drawn upward to obtain the corresponding points, which were then summed and located on the “Total Points” axis. A vertical line was subsequently drawn downward from this total to the bottom scale to determine the predicted probability of neonatal NEC. The nomogram was then subjected to external validation to assess its performance. The Hosmer–Lemeshow test was employed to evaluate the goodness of fit of the model. The predictive accuracy and calibration of the model were assessed using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), concordance index (C-index), and calibration curve. Decision curve analysis (DCA) and clinical impact curve (CIC) analysis were performed to estimate the clinical utility of the nomogram. Both discrimination and calibration were assessed using bootstrapping with 1000 resamples. 1.5 Statistical Analysis This study uses NEC as the dependent variable to construct a prediction model, takes the AUC value of the prediction model as the main index, and utilizes PASS 15 software (NCSS, Kaysville, Utah, USA) to calculate the Power of the current sample size. Finally, this study included 77 cases of NEC and 171 cases of non-NEC. The AUC value of the prediction model was 0.839. Under the condition of setting two-sided test α = 0.05, we inputted data into PASS 15 software (NCSS, Kaysville, Utah, USA) for Curve Tests and obtained a Power > 0.999. Statistical analyses were performed using SPSS 26.0 and R 4.3.1. For parameters with continuous data, the normal distribution was expressed as mean ± standard deviation, and the skewed distribution was expressed as medians (interquartile ranges). Comparisons between groups were conducted using the Mann–Whitney U test. Categorical data were expressed as counts (%), and comparisons between groups were performed using the chi-square (χ²) test. All statistical tests were two-sided and P value < 0.05 was considered significant. Results 2.1 General Characteristics In the training cohort, the clinical information a total of 269 patients were obtained from Southwest Hospital, Third Military Medical University; 21 cases did not meet the inclusion criteria. Finally, 248 patients were enrolled in this study, and their data were utilized to construct the nomogram (Supplementary Fig. 1). The median Apgar 5min was 10. PN was used in 45.2% of patients, and the feeding rate was ≤ 20 ml/kg·d in 31.9%. The NE% was 51.07 ± 17.46, and the median level of PCT was 0.36 (Table 1). In the validation cohort, 80 neonates from Children's Hospital, Chongqing Medical University, were included for external validation (a flow chart of patient selection is shown in Supplementary Fig. 2). According to the Modified Bell's Staging Criteria of neonatal NEC, 77 patients (77/248, 31.0%) who were hospitalised at Southwest Hospital were included in the NEC group, and 171 patients (171/248, 69.0%) were included in the non-NEC group. Patients in the non-NEC group were diagnosed with feeding intolerance, allergic enteritis, or sepsis, and did not meet the Modified Bell's stage II or higher criteria, thus not fulfilling the diagnostic criteria for neonatal NEC. There were no significant differences between the two groups in terms of GA, birth weight, or sex. A total of 26 factors were listed (Table 2). 2.2 Screening for Predictive Factors Multivariate logistic regression analysis identified five independent predictors of neonatal NEC, as follows: Apgar 5min ( P = 0·001, odds ratio [OR] 0.539, 95% confidence interval [CI] 0.379–0.767), PN ( P = 0·002, OR 2.856, 95% CI 1.461–5.584), feeding rate ( P < 0·001, OR 0.202, 95% CI 0.102–0.4), NE% ( P = 0·009, OR 1.027, 95% CI 1.007–1.048), and PCT ( P = 0·015, OR 1.184, 95% CI 1.033–1.357) (Table 3). 2.3 Risk Prediction Nomogram Development According to the results of multivariable logistic regression analysis, the following factors were associated with NEC: Apgar 5min, PN, feeding rate, NE% and PCT. These five factors were included in the prediction model, and a nomogram was created to visualize the results of the regression analysis (Fig. 1). For each patient, higher total points indicated a higher risk of neonatal NEC. For instance, if an infant with an Apgar 5min score is 9, who received PN after birth, had a feeding advancement rate of less than 20 ml/kg/d, and presented with NE% of 80% and PCT level of 2 when symptoms first appeared (systemic manifestations, gastrointestinal symptoms, and/or radiological findings consistent with Modified Bell's stage I/II/III), the corresponding scores will be approximately 18, 30, 47.5, 55, and 10, respectively. The total score is approximately 160.5, indicating an estimated probability of NEC of 84% for this case. In addition, The Hosmer–Lemeshow test demonstrated that the model was a good fit ( P = 0.720). 2.4 Predictive Accuracy, Net Benefit, and Clinical Impact of the Nomogram In the training cohort, the AUC was 0.839 (Fig. 2A), and the calibration curve was close to the ideal diagonal line (Fig. 3A). Furthermore, the DCA showed significantly better net benefit in the predictive model (Fig. 4A), and CIC visually indicated that nomogram conferred high clinical net benefit and confirmed the clinical value of the nomogram (Fig. 5A). For external validation, 80 patients from Children's Hospital, Chongqing Medical University, Chongqing, China, were included. The AUC for the validation cohort was 0.819 (Fig. 2B), confirming the strong accuracy of the nomogram. The calibration curve for the validation cohort also closely aligned with the ideal diagonal line, indicating consistent performance (Fig. 3B). DCA again demonstrated a substantial net benefit of the model, with similar results in the validation cohort (Fig. 4B). The CIC further highlighted the model's strong differential diagnostic ability (Fig. 5B). These results collectively demonstrate that the nomogram offers significant potential to support clinical decision-making. Discussion NEC is a common gastrointestinal disorder in neonates, and diagnosis often relies on the modified Bell-NEC staging criteria [ 6 ] . Stage II is considered a definitive diagnosis, whereas Stage III is associated with critical illness and an extremely high mortality rate [ 12 , 13 ] . In contrast, Stage I is typically indicative of suspected NEC and is frequently confused with common conditions in preterm infants, such as feeding intolerance, gastrointestinal dysfunction, cow's milk protein allergy, and other infectious diseases [ 14 , 15 ] . Our study is the first to focus on neonates with modified Bell-NEC Stage I/II/III, and to develop a model that integrates perinatal factors, feeding practices, and laboratory indicators to predict the occurrence of neonatal NEC. This study revealed that Apgar 5min, PN, feeding rate, NE% and PCT as predictive factors for NEC in this cohort of neonates. The Apgar score is one of the most convenient and widely accepted methods for assessing the condition of newborns immediately after birth. While the 1-minute Apgar score cannot predict individual outcomes, a low Apgar 5min is associated with neonatal mortality and organ dysfunction [ 16 – 18 ] . Therefore, we included the Apgar 5min as a screening variable for predicting neonatal NEC. Our results showed a negative correlation between the Apgar 5min and the nomogram score, indicating that a lower Apgar score is associated with a higher risk of NEC. A low Apgar score suggests that the neonate may have hypoxemia, hypercapnia, and organ dysfunction [ 19 , 20 ] . To ensure that blood flow is directed to vital organs such as the heart and brain, the body reallocates blood supply, resulting in vasoconstriction and reduced blood flow in the vascular systems of non-vital organs such as the intestines and skin [ 12 ] . The intestines, being one of the most sensitive organs to ischemia and hypoxia in the human body, are more susceptible to damage when blood perfusion is insufficient due to their high metabolic demands and relatively low oxygen reserves [ 21 ] . This can lead to impaired intestinal barrier function, thereby increasing the risk of NEC. This study found that PN is significantly associated with the risk of NEC, consistent with previous research [ 22 , 23 ] . Preterm infants, especially those with very low or extremely low birth weight, often face difficulties in early enteral feeding, PN can provide additional energy and nutrients, serving as a life-saving therapeutic measure. However, long-term dependence on PN may delay the initiation of enteral feeding, leading to intestinal mucosal atrophy and impaired barrier function due to lack of stimulation [ 24 ] . Evidence from research indicates that the duration of PN use is significantly and positively associated with the risk of NEC, with each additional day of PN use resulting in a 19% increase in the risk of developing NEC [ 25 ] . Moreover, PN may alter the composition of the gut microbiota, inhibiting the colonization of beneficial bacteria and promoting the overgrowth of pathogenic bacteria, thereby inducing intestinal inflammation and bacterial translocation [ 26 – 28 ] . Additionally, metabolic issues related to PN, such as hyperglycemia [ 27 ] and cholestasis, may indirectly exacerbate the development of NEC through mechanisms like oxidative stress and immune suppression [ 29 , 30 ] . Our study also indicated that feeding rate is an independent predictor of neonatal NEC and was incorporated into the predictive model. The NEC group generally had a slower feeding rate, suggesting that neonates who developed NEC might experience multiple episodes of feeding intolerance during hospitalization. Studies have shown that slow advancement of feeding does not reduce the incidence of NEC, while early progressive feeding can shorten hospital stay and reduce the occurrence of late-onset sepsis [ 31 ] . Therefore, the clinical necessity and potential risks of PN should be balanced, with priority given to a progressive enteral feeding strategy. When neonates present with suspected symptoms of NEC, routine examinations including complete blood count, CRP, PCT and other inflammatory indicators are typically conducted. In our study, the NE% and PCT were identified as independent predictors of neonatal NEC. Elevated levels of NE% and PCT indicate a systemic inflammatory response, which is closely related to the pathophysiological process of NEC [ 32 ] . Neutrophil infiltration is a hallmark of intestinal wall damage in NEC [ 12 ] , neutrophils are the first-line immune cells in the body's response to infections, particularly bacterial infections, with their percentage rapidly increasing in the early stages of inflammation [ 33 ] . In patients with community-acquired pneumonia, the dynamic changes in the NE% at admission and during the early course of illness (72–120 hours) are significantly correlated with the intensity of the inflammatory response, and these changes occur earlier than those of other indicators, such as CRP [ 34 ] . While PCT, as an infection biomarker, is significantly elevated in bacterial infections but typically remains unchanged in viral infections [ 35 , 36 ] . In recent years, PCT, as a biomarker of inflammation and infection, has played a significant role in the diagnosis, risk assessment, and therapeutic monitoring of various inflammatory diseases [ 37 – 39 ] . The NE% changes rapidly and can promptly reflect the inflammatory response of the body, whereas PCT has higher sensitivity and specificity, allowing for a more accurate assessment of the severity of inflammation. For the first time, our study integrates these two markers into a predictive model, addressing the previous models' insufficient attention to inflammatory indicators. Their combined application can provide a more comprehensive evaluation of the risk of developing NEC, thereby enhancing the accuracy and timeliness of diagnosis. In this study, we assessed the predictors of NEC and established a risk prediction model for early predictions and intervention for children with gastrointestinal or/and systemic symptoms after birth. Our external validation confirmed the good accuracy and conformity of the model, alongside its net benefit. The visual and personalized model, that is the nomogram, provides clinicians with a simple and intuitive tool for practical prediction. However, there are several limitations to our study. Firstly, certain variables (such as microbiome data and dynamic inflammatory markers) were not included in the analysis due to missing data, which may have led to the omission of potential predictors. Secondly, stratified analysis based on the severity of NEC (e.g., Bell's stage II vs. III) was not performed; future research could explore specific predictive factors for different subtypes. Lastly, the study data were derived from a single region (Southwest China) within a Han Chinese population, which may be influenced by regional medical practices and genetic backgrounds. The generalizability of the model should be validated through multicenter studies in the future. Conclusion In conclusion, our study identified five independent predictors of neonatal NEC—Apgar 5min, PN, feeding rate, NE%, and PCT—and used them to develop a predictive nomogram for early identification of NEC. External validation confirmed the model’s accuracy and clinical relevance. This nomogram, offering a simple and intuitive tool for clinicians, has the potential to improve early detection and intervention, ultimately reducing NEC-associated morbidity and mortality. Declarations 5 Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. 6 Conflict of Interest statement No conflict of interest declared. 7 Funding This research was funded by the National Natural Science Foundation of China (Nos. 82170565) and the Chongqing Medical Research Fund (Grant No. 2024NSCQ-KJFZZDX0018). 8 Author Contribution Indication Formal Analysis – Yuedi Hu; Writing – original draft – Yu Lang, Juan Ma; Writing – review & editing – Leilei Shen. References Caplan MS, Underwood MA, Modi N, et al. Necrotizing Enterocolitis: Using Regulatory Science and Drug Development to Improve Outcomes. J Pediatr. 2019. 212: 208-215.e1. Thänert R, Keen EC, Dantas G, Warner BB, Tarr PI. Necrotizing Enterocolitis and the Microbiome: Current Status and Future Directions. J Infect Dis. 2021. 223(12 Suppl 2): S257-S263. Han SM, Hong CR, Knell J, et al. Trends in incidence and outcomes of necrotizing enterocolitis over the last 12 years: A multicenter cohort analysis. J Pediatr Surg. 2020. 55(6): 998-1001. Jones IH, Hall NJ. Contemporary Outcomes for Infants with Necrotizing Enterocolitis-A Systematic Review. J Pediatr. 2020. 220: 86-92.e3. Berken JA, Chang J. Neurologic Consequences of Neonatal Necrotizing Enterocolitis. Dev Neurosci. 2022. 44(4-5): 295-308. Kim JH, Sampath V, Canvasser J. Challenges in diagnosing necrotizing enterocolitis. Pediatr Res. 2020. 88(Suppl 1): 16-20. Feng B, Zhang Z, Wei Q, et al. A prediction model for neonatal necrotizing enterocolitis in preterm and very low birth weight infants. Front Pediatr. 2023. 11: 1242978. Wang YP, Zheng MY, Xiao YY, Qu YM, Wu H. Risk factors for necrotizing enterocolitis and establishment of prediction model of necrotizing enterocolitis in preterm infants. Zhongguo Dang Dai Er Ke Za Zhi. 2022. 24(1): 41-48. Li J, Zhou J, Weng J, et al. Rapidly progressive necrotizing enterocolitis: Risk factors and a predictive model. Pediatr Res. 2024 . El Manouni El Hassani S, Niemarkt HJ, Derikx J, et al. Predictive factors for surgical treatment in preterm neonates with necrotizing enterocolitis: a multicenter case-control study. Eur J Pediatr. 2021. 180(2): 617-625. Walsh MC, Kliegman RM. Necrotizing enterocolitis: treatment based on staging criteria. Pediatr Clin North Am. 1986. 33(1): 179-201. Roberts AG, Younge N, Greenberg RG. Neonatal Necrotizing Enterocolitis: An Update on Pathophysiology, Treatment, and Prevention. Paediatr Drugs. 2024. 26(3): 259-275. Rausch LA, Hanna DN, Patel A, Blakely ML. Review of Necrotizing Enterocolitis and Spontaneous Intestinal Perforation Clinical Presentation, Treatment, and Outcomes. Clin Perinatol. 2022. 49(4): 955-964. Ergenekon E, Tayman C, Özkan H. Turkish Neonatal Society Necrotizing Enterocolitis Diagnosis, Treatment and Prevention Guidelines. Turk Arch Pediatr. 2021. 56(5): 513-524. Duess JW, Sampah ME, Lopez CM, et al. Necrotizing enterocolitis, gut microbes, and sepsis. Gut Microbes. 2023. 15(1): 2221470. The Apgar Score. Pediatrics. 2015. 136(4): 819-22. Cnattingius S, Norman M, Granath F, Petersson G, Stephansson O, Frisell T. Apgar Score Components at 5 Minutes: Risks and Prediction of Neonatal Mortality. Paediatr Perinat Epidemiol. 2017. 31(4): 328-337. Cnattingius S, Johansson S, Razaz N. Apgar Score and Risk of Neonatal Death among Preterm Infants. N Engl J Med. 2020. 383(1): 49-57. Shah PS, Norman M, Rusconi F, et al. Five-minute Apgar score and outcomes in neonates of 24-28 weeks' gestation. Arch Dis Child Fetal Neonatal Ed. 2022. 107(4): 437-446. Zaigham M, Maršál K. Apgar score in premature infants associated with neonatal death prediction. J Pediatr. 2020. 226: 309-313. Kaplina A, Kononova S, Zaikova E, Pervunina T, Petrova N, Sitkin S. Necrotizing Enterocolitis: The Role of Hypoxia, Gut Microbiome, and Microbial Metabolites. Int J Mol Sci. 2023. 24(3): 2471. Barr PA, Mally PV, Caprio MC. Standardized Nutrition Protocol for Very Low-Birth-Weight Infants Resulted in Less Use of Parenteral Nutrition and Associated Complications, Better Growth, and Lower Rates of Necrotizing Enterocolitis. JPEN J Parenter Enteral Nutr. 2019. 43(4): 540-549. Hong KY, Zhu Y, Wu F, et al. The role of nutrition in analysis of risk factors and short-term outcomes for late-onset necrotizing enterocolitis among very preterm infants: a nationwide, multicenter study in China. BMC Pediatr. 2024. 24(1): 172. Cahova M, Bratova M, Wohl P. Parenteral Nutrition-Associated Liver Disease: The Role of the Gut Microbiota. Nutrients. 2017. 9(9): 987. Berkhout D, Klaassen P, Niemarkt HJ, et al. Risk Factors for Necrotizing Enterocolitis: A Prospective Multicenter Case-Control Study. Neonatology. 2018. 114(3): 277-284. Campos-Martinez AM, Expósito-Herrera J, Gonzalez-Bolívar M, Fernández-Marin E, Uberos J. Evaluation of Risk and Preventive Factors for Necrotizing Enterocolitis in Premature Newborns. A Systematic Review of the Literature. Front Pediatr. 2022. 10: 874976. Wang P, Sun H, Maitiabula G, et al. Total parenteral nutrition impairs glucose metabolism by modifying the gut microbiome. Nat Metab. 2023. 5(2): 331-348. Covello C, Becherucci G, Di Vincenzo F, et al. Parenteral Nutrition, Inflammatory Bowel Disease, and Gut Barrier: An Intricate Plot. Nutrients. 2024. 16(14): 2288. Lavoie JC, Chessex P. Parenteral nutrition and oxidant stress in the newborn: A narrative review. Free Radic Biol Med. 2019. 142: 155-167. Pierre JF. Gastrointestinal immune and microbiome changes during parenteral nutrition. Am J Physiol Gastrointest Liver Physiol. 2017. 312(3): G246-G256. Oddie SJ, Young L, McGuire W. Slow advancement of enteral feed volumes to prevent necrotising enterocolitis in very low birth weight infants. Cochrane Database Syst Rev. 2021. 8(8): CD001241. Kim W, Seo JM. Necrotizing Enterocolitis. N Engl J Med. 2020. 383(25): 2461. Maier-Begandt D, Alonso-Gonzalez N, Klotz L, et al. Neutrophils-biology and diversity. Nephrol Dial Transplant. 2024. 39(10): 1551-1564. Curbelo J, Luquero Bueno S, Galván-Román JM, et al. Inflammation biomarkers in blood as mortality predictors in community-acquired pneumonia admitted patients: Importance of comparison with neutrophil count percentage or neutrophil-lymphocyte ratio. PLoS One. 2017. 12(3): e0173947. Pierrakos C, Velissaris D, Bisdorff M, Marshall JC, Vincent JL. Biomarkers of sepsis: time for a reappraisal. Crit Care. 2020. 24(1): 287. Schuetz P. How to best use procalcitonin to diagnose infections and manage antibiotic treatment. Clin Chem Lab Med. 2023. 61(5): 822-828. Sullivan RC, Schmidt EP. Procalcitonin: A Mediator of Microvascular Dysfunction during Critical Illness. Am J Respir Crit Care Med. 2022. 206(4): 375-376. Xu HG, Tian M, Pan SY. Clinical utility of procalcitonin and its association with pathogenic microorganisms. Crit Rev Clin Lab Sci. 2022. 59(2): 93-111. Norman-Bruce H, Umana E, Mills C, et al. Diagnostic test accuracy of procalcitonin and C-reactive protein for predicting invasive and serious bacterial infections in young febrile infants: a systematic review and meta-analysis. Lancet Child Adolesc Health. 2024. 8(5): 358-368. Tables Table 1. Baseline characteristics of all patients in the training cohort and validation cohort. Variables Training cohort (n = 248) Mean±SD/M (P25, P75)/N (%) Validation cohort (n = 80) Mean±SD/M (P25, P75)/N (%) P- value Sex 0.836 Male 130(52.4) 43(53.8) Female 118(47.6) 37(46.3) GA (weeks) 35.7±3.3 36.5±3.9 0.057 Birth weight (gram) 2382.43±761.85 2458±805.56 0.447 Caesarean 161(64.9) 48(60.0) 0.426 GDM 63(25.4) 16(20.0) 0.326 HDP 33(13.3) 7(8.8) 0.279 ICP 19(7.7) 2(2.5) 0.101 PROM 45(18.1) 18(22.5) 0.390 Asphyxia 14(5.6) 7(8.8) 0.324 Apgar 5min 10.00(10.00,10.00) 10.00(9.00,10.00) 0.236 Initiation time of enteral feeding 0.392 ＜48h 217(87.5) 67(83.8) ≥48h 31(12.5) 13(16.3) PN 112(45.2) 35(43.8) 0.825 Feeding rate 0.602 ≤20 ml/kg.d 79(31.9) 28(35.0) ＞20 ml/kg.d 169(68.1) 52(65.0) Formula feeding 111(44.8) 32(40.0) 0.456 Use of Antibiotics (<7 days after birth) 86(34.7) 32(40.0) 0.388 Use of Antibiotics (<72 hours prior to disease onset) 57(23.0) 17(21.3) 0.747 Red blood cell transfusion (<72 hours prior to disease onset) 27(10.9) 9(11.3) 0.928 Postnatal age at disease onset 9.00(5.00,16.00) 9.50(8.00,14.75) 0.061 WBC 10.13(7.18,13.11) 9.60(6.93,13.79) 0.785 PLT 304.88±122.67 315.56±136.43 0.511 ANC 4.64(3.07,7.12) 4.39(2.77,6.73) 0.374 NE% 51.07±17.46 48.24±16.3 0.200 HGB 144.68±34.03 145.85±33.12 0.788 CRP 5.00(2.41,9.05) 8.00(0.80,8.00) 0.926 PCT 0.36(0.09,1.61) 0.51(0.11,1.71) 0.603 iCa 1.20±0.20 1.22±0.19 0.484 GA = gestational age; GDM = gestational diabetes mellitus; HDP = hypertensive disorders of pregnancy; ICP = intrahepatic cholestasis of pregnancy; PROM = premature rupture of membrane; PN = parenteral nutrition; WBC = white blood cell count; PLT = platelet count; ANC = absolute neutrophil count; NE% = neutrophil percentage; HGB = hemoglobin; CRP = C-reactive protein; PCT = procalcitonin; iCa = ionized calcium. Table 2. General characteristics of the patients and univariate logistic regression analyses for screening predictors. Variables Non- NEC(n = 171) Mean±SD/M (P25, P75)/N (%) NEC(n = 77) Mean±SD/M (P25, P75)/N (%) P- value OR(95% CI) Sex 0.469 0.819(0.477~1.406) Male 87(50.9) 43(55.8) Female 84(49.1) 34(44.2) GA (weeks) 35.61±3.13 35.76±3.60 0.754 1.014(0.934~1.101) Birth weight (gram) 2383.87±761.3 2379.22±768.07 0.965 0.992(0.696~1.413) Caesarean 108(63.2) 53(68.8) 0.386 1.288(0.726~2.286) GDM 41(24.0) 22(28.6) 0.442 1.268(0.692~2.326) HDP 22(12.9) 11(14.3) 0.761 1.129(0.518~2.462) ICP 12(7.0) 7(9.1) 0.570 1.325(0.500~3.508) PROM 27(15.8) 18(23.4) 0.151 1.627(0.833~3.176) Asphyxia 8(4.7) 6(7.8) 0.493 1.722(0.576~5.145) Apgar 5min 10.00(10.00,10.00) 10.00(8.00,10.00) <0.001 0.474(0.350~0.640) Initiation time of enteral feeding 0.001 3.708(1.711~8.037) < 48h 158(92.4) 59(76.6) ≥48h 13(7.6) 18(23.4) PN 61(35.7) 51(66.2) <0.001 3.537(2.007~6.233) Feeding rate 20 ml/kg.d 137(80.1) 32(41.6) Formula feeding 68(39.8) 43(55.8) 0.018 1.916(1.112~3.301) Use of Antibiotics (<7 days after birth) 45(26.3) 41(53.2) <0.001 3.189(1.817~5.596) Use of Antibiotics (<72 hours prior to disease onset) 35(20.5) 22(28.6) 0.160 1.554(0.837~2.885) Red blood cell transfusion (<72 hours prior to disease onset) 17(9.9) 10(13.0) 0.476 1.352(0.588~3.107) Postnatal age at disease onset 8.00(5.00,16.00) 11.00(5.50,19.00) 0.169 1.015(0.987~1.044) WBC 10.15(7.18,13.11) 9.95(7.21,13.55) 0.818 1.012(0.962~1.066) PLT 306.84±130.35 300.55±104.28 0.686 1.000(0.997~1.002) ANC 4.53(2.95,6.65) 5.36(3.36,9.2) 0.048 1.092(1.027~1.161) NE% 48.57±17.04 56.63±17.20 0.001 1.028(1.011~1.044) HGB 146.67±33.29 140.26±35.45 0.170 0.994(0.986~1.002) CRP 5.00(2.79,5.00) 5.00(1.88,10.85) 0.105 1.020(0.981~1.062) PCT 0.22(0.07,0.89) 1.08(0.26,3.10) <0.001 1.286(1.137~1.455) iCa 1.23±0.17 1.15±0.23 0.008 0.123(0.030~0.512) GA = gestational age; GDM = gestational diabetes mellitus; HDP = hypertensive disorders of pregnancy; ICP = intrahepatic cholestasis of pregnancy; PROM = premature rupture of membrane; PN = parenteral nutrition; WBC = white blood cell count; PLT = platelet count; ANC = absolute neutrophil count; NE% = neutrophil percentage; HGB = hemoglobin; CRP = C-reactive protein; PCT = procalcitonin; iCa = ionized calcium. Table 3. Multivariate logistic regression analyses for screening predictors. Variables B SE Waldχ 2 P OR(95% CI) Apgar 5min -0.617 0.180 11.811 0.001 0.539(0.379~0.767) PN 1.049 0.342 9.415 0.002 2.856(1.461~5.584) Feeding rate ≤20 ml/kg.d - - - - 1.000 >20 ml/kg.d -1.601 0.349 21.009 <0.001 0.202(0.102~0.4) NE% 0.027 0.010 6.840 0.009 1.027(1.007~1.048) PCT 0.169 0.070 5.894 0.015 1.184(1.033~1.357) Constants 5.465 1.741 9.854 0.002 - PN = parenteral nutrition; NE% = neutrophil percentage; PCT = procalcitonin. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6518823","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452816664,"identity":"eb2f334b-2e80-4a1a-be07-40ec269449f1","order_by":0,"name":"Yu Lang","email":"","orcid":"","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Lang","suffix":""},{"id":452816665,"identity":"5d0ba89d-ab65-49f7-b319-addc89173b89","order_by":1,"name":"Juan Ma","email":"","orcid":"","institution":"Children’s Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Ma","suffix":""},{"id":452816666,"identity":"6bfe1ebb-fb05-4689-a070-81ee903f7445","order_by":2,"name":"Yuedi Hu","email":"","orcid":"","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuedi","middleName":"","lastName":"Hu","suffix":""},{"id":452816667,"identity":"f10f1f7b-72f4-472f-8833-c169ea0d76db","order_by":3,"name":"Leilei Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYLCCBwU2PPz8DaRoSTBIk5GccYA0LYdtDBoSiFTN336A+UOCwXkeA4YDjB8+5hChReJMAptEgsFtHnPmBmbJmduIseZAAhsDSItlwwE2Zl5itMiffwBy2DkeA6Be4rQY3EhgADrsAAlaDG88APklmUdyxsFm4vwidz6B+cOHCjt7fv7mgx8+EuV9Bv4PUAZjA1HqR8EoGAWjYBQQAQBy7DNri3NAlgAAAABJRU5ErkJggg==","orcid":"","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":true,"prefix":"","firstName":"Leilei","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2025-04-24 08:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6518823/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6518823/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82584607,"identity":"20d0740e-c2dd-4174-9e73-013f323ed3f6","added_by":"auto","created_at":"2025-05-13 06:54:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for the prediction of neonatal NEC. \u003c/strong\u003ePN = parenteral nutrition; NE% = neutrophil percentage; PCT = procalcitonin; NEC = necrotizing enterocolitis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6518823/v1/20d7b4681b53ba9f1e12b3d1.png"},{"id":82582630,"identity":"25c98681-0ec6-4bc1-be31-675e4436ade4","added_by":"auto","created_at":"2025-05-13 06:46:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves.\u003c/strong\u003e (A) Training cohort. (B) Validation cohort. ROC = receiver operating characteristic; AUC = area under the ROC curve.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6518823/v1/5500178aaa8d11ef380f2f94.png"},{"id":82582632,"identity":"96d8fff3-ee3c-4de9-938e-da6913e05b2d","added_by":"auto","created_at":"2025-05-13 06:46:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve for predicting probability of neonatal NEC. \u003c/strong\u003e(A) Training cohort. (B) Validation cohort. NEC = necrotizing enterocolitis.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6518823/v1/49415dd8379acdb7fad15a44.png"},{"id":82585062,"identity":"4fcd7328-5e45-4a65-82c8-0c74973b1b4d","added_by":"auto","created_at":"2025-05-13 07:02:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis in prediction of neonatal NEC. \u003c/strong\u003e(A) Training cohort. (B) Validation cohort. NEC = necrotizing enterocolitis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6518823/v1/e2929bf95689aadf41a38bf8.png"},{"id":82582639,"identity":"18cd6821-e33f-4918-810c-e2c248b955ad","added_by":"auto","created_at":"2025-05-13 06:46:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical impact curve in prediction of neonatal NEC. \u003c/strong\u003e(A) Training cohort. (B) Validation cohort. NEC = necrotizing enterocolitis.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6518823/v1/98d9aaeb84ff69ea147d4624.png"},{"id":83888024,"identity":"8451fc01-0c5a-4554-9ddd-2b18ea976b67","added_by":"auto","created_at":"2025-06-04 07:17:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1697860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6518823/v1/d2ee861f-260e-44a3-ba6c-a4ff1b9f1872.pdf"},{"id":82582637,"identity":"2291692f-29cf-4942-9a14-72a5cae10702","added_by":"auto","created_at":"2025-05-13 06:46:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10667906,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6518823/v1/400035b630b5e44ffc84b957.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A predictive nomogram for the early diagnosis of neonatal necrotizing enterocolitis: Insights from a retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNecrotizing enterocolitis (NEC) is the most common and severe gastrointestinal emergency in preterm infants, typically characterized by insidious onset, rapid progression, and unpredictability\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. It is one of the important diseases that causes death and disability in premature infants\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. With the advancement of perinatal medicine, the survival rate of preterm infants has increased annually. However, the incidence and mortality rates of NEC remain high. In very low birth weight infants, the incidence of NEC is approximately 7.6%, with a mortality rate as high as 40.5%\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Survivors are at risk of complications such as short bowel syndrome and long-term neurodevelopmental impairments\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, which significantly affect the quality of life of affected children and pose a significant challenge to clinicians. Current understanding of NEC remains incomplete. Early identification through risk stratification models, coupled with evidence-based interventions, is critical for reducing disease incidence, improving survival rates, and optimizing long-term outcomes.\u003c/p\u003e\u003cp\u003eEarly clinical manifestations of NEC are nonspecific, often overlapping with other conditions such as feeding intolerance, allergic enteritis, and late-onset sepsis, making early diagnosis particularly challenging\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Several studies have attempted to develop predictive models for early diagnosis and assessment of surgical risk by identifying clinical parameters associated with NEC\u003csup\u003e[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, most of these studies have been case-control in design, frequently involving gestational age–matched controls rather than neonates presenting with symptoms that overlap with those of NEC.\u003c/p\u003e\u003cp\u003eTo address this gap, the present study performs a retrospective analysis of 248 neonates, focusing on those with suspected or confirmed NEC symptoms, such as hematochezia, abdominal distention, vomiting, apnea, and hypotonia. This analysis aims to further elucidate the relationship between clinical features, laboratory test results, and the development of NEC. Ultimately, our goal is to develop and validate a predictive nomogram to improve early diagnostic capabilities, thereby enhancing treatment outcomes for this vulnerable population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003e1.1 Study Design and Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study was designed as a retrospective analysis of neonates enrolled and managed at Southwest Hospital of Third Military Medical University and Children's Hospital of Chongqing Medical University, Chongqing, China. The study protocol was approved by the Ethics Committee of Southwest Hospital, Army Medical University (Approval No. [B] KY 2024234), the requirement for informed consent was waived due to the retrospective nature of the research and registered at the Chinese Clinical Trial Registry (MR-50-24-041238; registration date: October 14, 2024). These methods were carried out in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eThe training cohort included 248 neonates admitted to Southwest Hospital in Chongqing, China, between January 1, 2018, and January 1, 2024. The validation cohort comprised 80 neonates admitted to Children’s Hospital of Chongqing Medical University between January 1, 2020, and January 1, 2024.Inclusion criteria were: (a) admission to the neonatal intensive care unit (NICU); (b) clinical presentation, gastrointestinal manifestations, and radiological findings consistent with Modified Bell’s Stage I, II, or III NEC\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Exclusion criteria were: (a) congenital malformations; (b) surgical emergencies unrelated to NEC; (c) hereditary endocrine or metabolic diseases; and (d) incomplete medical records.\u003c/p\u003e\u003cp\u003eEligible neonates were identified through a systematic review of electronic medical records using ICD-10 codes for NEC (P77.9) and symptom-specific codes (e.g., P92.8 for feeding intolerance). All cases were independently reviewed by two blinded neonatologists to confirm compliance with Modified Bell’s staging criteria. Discrepancies were resolved by a third senior clinician. Patients were divided into the NEC group and the non-NEC group based on whether they met Modified Bell's stage II or higher. Neonates who progressed from Modified Bell's stage I to stage II or higher with symptomatic treatment were classified into the NEC group; otherwise, they were assigned to the non-NEC group. The final diagnosis was determined by two associate professors in neonatology. This classification was used for diagnostic predictive analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.2 Data Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data collected from the enrolled neonates included general information (gestational age [GA], birth weight, sex, 5-minute Apgar score [Apgar 5min], mode of delivery, and postnatal age at disease onset), associated risk factors at delivery (premature rupture of membranes, asphyxia), pregnancy complications (gestational diabetes mellitus [GDM], intrahepatic cholestasis of pregnancy [ICP], hypertensive disorders of pregnancy [HDP]), feeding strategies (initiation time of enteral feeding, formula feeding, parenteral nutrition [PN], and rate of feeding [Feeding rate]), therapeutic interventions (use of antibiotics [\u0026lt; 7 days after birth], antibiotic use or red blood cell transfusion within 72 hours prior to disease onset), and laboratory parameters (white blood cell count [WBC], platelet count [PLT], absolute neutrophil count [ANC], neutrophil percentage [NE%], hemoglobin [HGB], C-reactive protein [CRP], procalcitonin [PCT], and ionized calcium [iCa]).\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.3 Definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe diagnosis of neonatal NEC was reached according to Modified Bell's stage II or higher criteria\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The initiation time of enteral feeding was categorized as less than 48 hour after birth (\u0026lt; 48h), never initiated/initiated after 48h of birth (≥ 48h). Feeding rate was measured based on the daily increase in milk volume and was divided into two categories: an increase of ≤ 20 ml/kg per day and an increase of \u0026gt; 20 ml/kg per day. The postnatal age at disease onset was defined as the age at which systemic manifestations, gastrointestinal symptoms, and/or radiological findings consistent with Modified Bell's stage I/II/III first appeared. All laboratory data were collected at the initial presentation of the corresponding clinical symptoms and/or signs.\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.4 Development and Assessment of the Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA multivariate logistic regression model was utilized to construct a nomogram for predicting the occurrence of neonatal NEC. Independent predictors (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) were identified via multivariate logistic regression and subsequently incorporated into the nomogram development using the dataset designated for predicting neonatal NEC. To apply the nomogram, predictor lines were drawn upward to obtain the corresponding points, which were then summed and located on the “Total Points” axis. A vertical line was subsequently drawn downward from this total to the bottom scale to determine the predicted probability of neonatal NEC. The nomogram was then subjected to external validation to assess its performance. The Hosmer–Lemeshow test was employed to evaluate the goodness of fit of the model. The predictive accuracy and calibration of the model were assessed using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), concordance index (C-index), and calibration curve. Decision curve analysis (DCA) and clinical impact curve (CIC) analysis were performed to estimate the clinical utility of the nomogram. Both discrimination and calibration were assessed using bootstrapping with 1000 resamples.\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.5 Statistical Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study uses NEC as the dependent variable to construct a prediction model, takes the AUC value of the prediction model as the main index, and utilizes PASS 15 software (NCSS, Kaysville, Utah, USA) to calculate the Power of the current sample size. Finally, this study included 77 cases of NEC and 171 cases of non-NEC. The AUC value of the prediction model was 0.839. Under the condition of setting two-sided test α = 0.05, we inputted data into PASS 15 software (NCSS, Kaysville, Utah, USA) for Curve Tests and obtained a Power \u0026gt; 0.999.\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using SPSS 26.0 and R 4.3.1. For parameters with continuous data, the normal distribution was expressed as mean ± standard deviation, and the skewed distribution was expressed as medians (interquartile ranges). Comparisons between groups were conducted using the Mann–Whitney U test. Categorical data were expressed as counts (%), and comparisons between groups were performed using the chi-square (χ²) test. All statistical tests were two-sided and \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 was considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003e2.1 General Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the training cohort, the clinical information a total of 269 patients were obtained from Southwest Hospital, Third Military Medical University; 21 cases did not meet the inclusion criteria. Finally, 248 patients were enrolled in this study, and their data were utilized to construct the nomogram (Supplementary Fig.\u0026nbsp;1). The median Apgar 5min was 10. PN was used in 45.2% of patients, and the feeding rate was ≤ 20 ml/kg·d in 31.9%. The NE% was 51.07 ± 17.46, and the median level of PCT was 0.36 (Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eIn the validation cohort, 80 neonates from Children's Hospital, Chongqing Medical University, were included for external validation (a flow chart of patient selection is shown in Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eAccording to the Modified Bell's Staging Criteria of neonatal NEC, 77 patients (77/248, 31.0%) who were hospitalised at Southwest Hospital were included in the NEC group, and 171 patients (171/248, 69.0%) were included in the non-NEC group. Patients in the non-NEC group were diagnosed with feeding intolerance, allergic enteritis, or sepsis, and did not meet the Modified Bell's stage II or higher criteria, thus not fulfilling the diagnostic criteria for neonatal NEC. There were no significant differences between the two groups in terms of GA, birth weight, or sex. A total of 26 factors were listed (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.2 Screening for Predictive Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMultivariate logistic regression analysis identified five independent predictors of neonatal NEC, as follows: Apgar 5min (\u003cem\u003eP\u003c/em\u003e = 0·001, odds ratio [OR] 0.539, 95% confidence interval [CI] 0.379–0.767), PN (\u003cem\u003eP\u003c/em\u003e = 0·002, OR 2.856, 95% CI 1.461–5.584), feeding rate (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0·001, OR 0.202, 95% CI 0.102–0.4), NE% (\u003cem\u003eP\u003c/em\u003e = 0·009, OR 1.027, 95% CI 1.007–1.048), and PCT (\u003cem\u003eP\u003c/em\u003e = 0·015, OR 1.184, 95% CI 1.033–1.357) (Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3 Risk Prediction Nomogram Development\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAccording to the results of multivariable logistic regression analysis, the following factors were associated with NEC: Apgar 5min, PN, feeding rate, NE% and PCT. These five factors were included in the prediction model, and a nomogram was created to visualize the results of the regression analysis (Fig.\u0026nbsp;1). For each patient, higher total points indicated a higher risk of neonatal NEC. For instance, if an infant with an Apgar 5min score is 9, who received PN after birth, had a feeding advancement rate of less than 20 ml/kg/d, and presented with NE% of 80% and PCT level of 2 when symptoms first appeared (systemic manifestations, gastrointestinal symptoms, and/or radiological findings consistent with Modified Bell's stage I/II/III), the corresponding scores will be approximately 18, 30, 47.5, 55, and 10, respectively. The total score is approximately 160.5, indicating an estimated probability of NEC of 84% for this case. In addition, The Hosmer–Lemeshow test demonstrated that the model was a good fit (\u003cem\u003eP\u003c/em\u003e = 0.720).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4 Predictive Accuracy, Net Benefit, and Clinical Impact of the Nomogram\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the training cohort, the AUC was 0.839 (Fig.\u0026nbsp;2A), and the calibration curve was close to the ideal diagonal line (Fig.\u0026nbsp;3A). Furthermore, the DCA showed significantly better net benefit in the predictive model (Fig.\u0026nbsp;4A), and CIC visually indicated that nomogram conferred high clinical net benefit and confirmed the clinical value of the nomogram (Fig.\u0026nbsp;5A).\u003c/p\u003e\u003cp\u003eFor external validation, 80 patients from Children's Hospital, Chongqing Medical University, Chongqing, China, were included. The AUC for the validation cohort was 0.819 (Fig.\u0026nbsp;2B), confirming the strong accuracy of the nomogram. The calibration curve for the validation cohort also closely aligned with the ideal diagonal line, indicating consistent performance (Fig.\u0026nbsp;3B). DCA again demonstrated a substantial net benefit of the model, with similar results in the validation cohort (Fig.\u0026nbsp;4B). The CIC further highlighted the model's strong differential diagnostic ability (Fig.\u0026nbsp;5B). These results collectively demonstrate that the nomogram offers significant potential to support clinical decision-making.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNEC is a common gastrointestinal disorder in neonates, and diagnosis often relies on the modified Bell-NEC staging criteria\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Stage II is considered a definitive diagnosis, whereas Stage III is associated with critical illness and an extremely high mortality rate\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In contrast, Stage I is typically indicative of suspected NEC and is frequently confused with common conditions in preterm infants, such as feeding intolerance, gastrointestinal dysfunction, cow's milk protein allergy, and other infectious diseases\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Our study is the first to focus on neonates with modified Bell-NEC Stage I/II/III, and to develop a model that integrates perinatal factors, feeding practices, and laboratory indicators to predict the occurrence of neonatal NEC. This study revealed that Apgar 5min, PN, feeding rate, NE% and PCT as predictive factors for NEC in this cohort of neonates.\u003c/p\u003e\u003cp\u003eThe Apgar score is one of the most convenient and widely accepted methods for assessing the condition of newborns immediately after birth. While the 1-minute Apgar score cannot predict individual outcomes, a low Apgar 5min is associated with neonatal mortality and organ dysfunction\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Therefore, we included the Apgar 5min as a screening variable for predicting neonatal NEC. Our results showed a negative correlation between the Apgar 5min and the nomogram score, indicating that a lower Apgar score is associated with a higher risk of NEC. A low Apgar score suggests that the neonate may have hypoxemia, hypercapnia, and organ dysfunction\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. To ensure that blood flow is directed to vital organs such as the heart and brain, the body reallocates blood supply, resulting in vasoconstriction and reduced blood flow in the vascular systems of non-vital organs such as the intestines and skin\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The intestines, being one of the most sensitive organs to ischemia and hypoxia in the human body, are more susceptible to damage when blood perfusion is insufficient due to their high metabolic demands and relatively low oxygen reserves\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. This can lead to impaired intestinal barrier function, thereby increasing the risk of NEC.\u003c/p\u003e\u003cp\u003eThis study found that PN is significantly associated with the risk of NEC, consistent with previous research\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Preterm infants, especially those with very low or extremely low birth weight, often face difficulties in early enteral feeding, PN can provide additional energy and nutrients, serving as a life-saving therapeutic measure. However, long-term dependence on PN may delay the initiation of enteral feeding, leading to intestinal mucosal atrophy and impaired barrier function due to lack of stimulation\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Evidence from research indicates that the duration of PN use is significantly and positively associated with the risk of NEC, with each additional day of PN use resulting in a 19% increase in the risk of developing NEC\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Moreover, PN may alter the composition of the gut microbiota, inhibiting the colonization of beneficial bacteria and promoting the overgrowth of pathogenic bacteria, thereby inducing intestinal inflammation and bacterial translocation\u003csup\u003e[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Additionally, metabolic issues related to PN, such as hyperglycemia\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e and cholestasis, may indirectly exacerbate the development of NEC through mechanisms like oxidative stress and immune suppression\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Our study also indicated that feeding rate is an independent predictor of neonatal NEC and was incorporated into the predictive model. The NEC group generally had a slower feeding rate, suggesting that neonates who developed NEC might experience multiple episodes of feeding intolerance during hospitalization. Studies have shown that slow advancement of feeding does not reduce the incidence of NEC, while early progressive feeding can shorten hospital stay and reduce the occurrence of late-onset sepsis\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Therefore, the clinical necessity and potential risks of PN should be balanced, with priority given to a progressive enteral feeding strategy.\u003c/p\u003e\u003cp\u003eWhen neonates present with suspected symptoms of NEC, routine examinations including complete blood count, CRP, PCT and other inflammatory indicators are typically conducted. In our study, the NE% and PCT were identified as independent predictors of neonatal NEC. Elevated levels of NE% and PCT indicate a systemic inflammatory response, which is closely related to the pathophysiological process of NEC\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Neutrophil infiltration is a hallmark of intestinal wall damage in NEC\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, neutrophils are the first-line immune cells in the body's response to infections, particularly bacterial infections, with their percentage rapidly increasing in the early stages of inflammation\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In patients with community-acquired pneumonia, the dynamic changes in the NE% at admission and during the early course of illness (72–120 hours) are significantly correlated with the intensity of the inflammatory response, and these changes occur earlier than those of other indicators, such as CRP\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. While PCT, as an infection biomarker, is significantly elevated in bacterial infections but typically remains unchanged in viral infections\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In recent years, PCT, as a biomarker of inflammation and infection, has played a significant role in the diagnosis, risk assessment, and therapeutic monitoring of various inflammatory diseases\u003csup\u003e[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e–\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. The NE% changes rapidly and can promptly reflect the inflammatory response of the body, whereas PCT has higher sensitivity and specificity, allowing for a more accurate assessment of the severity of inflammation. For the first time, our study integrates these two markers into a predictive model, addressing the previous models' insufficient attention to inflammatory indicators. Their combined application can provide a more comprehensive evaluation of the risk of developing NEC, thereby enhancing the accuracy and timeliness of diagnosis.\u003c/p\u003e\u003cp\u003eIn this study, we assessed the predictors of NEC and established a risk prediction model for early predictions and intervention for children with gastrointestinal or/and systemic symptoms after birth. Our external validation confirmed the good accuracy and conformity of the model, alongside its net benefit. The visual and personalized model, that is the nomogram, provides clinicians with a simple and intuitive tool for practical prediction. However, there are several limitations to our study. Firstly, certain variables (such as microbiome data and dynamic inflammatory markers) were not included in the analysis due to missing data, which may have led to the omission of potential predictors. Secondly, stratified analysis based on the severity of NEC (e.g., Bell's stage II vs. III) was not performed; future research could explore specific predictive factors for different subtypes. Lastly, the study data were derived from a single region (Southwest China) within a Han Chinese population, which may be influenced by regional medical practices and genetic backgrounds. The generalizability of the model should be validated through multicenter studies in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study identified five independent predictors of neonatal NEC—Apgar 5min, PN, feeding rate, NE%, and PCT—and used them to develop a predictive nomogram for early identification of NEC. External validation confirmed the model’s accuracy and clinical relevance. This nomogram, offering a simple and intuitive tool for clinicians, has the potential to improve early detection and intervention, ultimately reducing NEC-associated morbidity and mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5 Data Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6 Conflict of Interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7 Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (Nos. 82170565) and the Chongqing Medical Research Fund (Grant No. 2024NSCQ-KJFZZDX0018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8 Author Contribution Indication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormal Analysis \u0026ndash; Yuedi Hu; Writing \u0026ndash; original draft \u0026ndash; Yu Lang, Juan Ma; Writing \u0026ndash; review \u0026amp; editing \u0026ndash; Leilei Shen.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCaplan MS, Underwood MA, Modi N, et al. Necrotizing Enterocolitis: Using Regulatory Science and Drug Development to Improve Outcomes. J Pediatr. 2019. 212: 208-215.e1.\u003c/li\u003e\n\u003cli\u003eTh\u0026auml;nert R, Keen EC, Dantas G, Warner BB, Tarr PI. Necrotizing Enterocolitis and the Microbiome: Current Status and Future Directions. J Infect Dis. 2021. 223(12 Suppl 2): S257-S263.\u003c/li\u003e\n\u003cli\u003eHan SM, Hong CR, Knell J, et al. Trends in incidence and outcomes of necrotizing enterocolitis over the last 12 years: A multicenter cohort analysis. J Pediatr Surg. 2020. 55(6): 998-1001.\u003c/li\u003e\n\u003cli\u003eJones IH, Hall NJ. Contemporary Outcomes for Infants with Necrotizing Enterocolitis-A Systematic Review. J Pediatr. 2020. 220: 86-92.e3.\u003c/li\u003e\n\u003cli\u003eBerken JA, Chang J. Neurologic Consequences of Neonatal Necrotizing Enterocolitis. Dev Neurosci. 2022. 44(4-5): 295-308.\u003c/li\u003e\n\u003cli\u003eKim JH, Sampath V, Canvasser J. Challenges in diagnosing necrotizing enterocolitis. Pediatr Res. 2020. 88(Suppl 1): 16-20.\u003c/li\u003e\n\u003cli\u003eFeng B, Zhang Z, Wei Q, et al. A prediction model for neonatal necrotizing enterocolitis in preterm and very low birth weight infants. Front Pediatr. 2023. 11: 1242978.\u003c/li\u003e\n\u003cli\u003eWang YP, Zheng MY, Xiao YY, Qu YM, Wu H. Risk factors for necrotizing enterocolitis and establishment of prediction model of necrotizing enterocolitis in preterm infants. Zhongguo Dang Dai Er Ke Za Zhi. 2022. 24(1): 41-48.\u003c/li\u003e\n\u003cli\u003eLi J, Zhou J, Weng J, et al. Rapidly progressive necrotizing enterocolitis: Risk factors and a predictive model. Pediatr Res. 2024 .\u003c/li\u003e\n\u003cli\u003eEl Manouni El Hassani S, Niemarkt HJ, Derikx J, et al. Predictive factors for surgical treatment in preterm neonates with necrotizing enterocolitis: a multicenter case-control study. Eur J Pediatr. 2021. 180(2): 617-625.\u003c/li\u003e\n\u003cli\u003eWalsh MC, Kliegman RM. Necrotizing enterocolitis: treatment based on staging criteria. Pediatr Clin North Am. 1986. 33(1): 179-201.\u003c/li\u003e\n\u003cli\u003eRoberts AG, Younge N, Greenberg RG. Neonatal Necrotizing Enterocolitis: An Update on Pathophysiology, Treatment, and Prevention. Paediatr Drugs. 2024. 26(3): 259-275.\u003c/li\u003e\n\u003cli\u003eRausch LA, Hanna DN, Patel A, Blakely ML. Review of Necrotizing Enterocolitis and Spontaneous Intestinal Perforation Clinical Presentation, Treatment, and Outcomes. Clin Perinatol. 2022. 49(4): 955-964.\u003c/li\u003e\n\u003cli\u003eErgenekon E, Tayman C, \u0026Ouml;zkan H. Turkish Neonatal Society Necrotizing Enterocolitis Diagnosis, Treatment and Prevention Guidelines. Turk Arch Pediatr. 2021. 56(5): 513-524.\u003c/li\u003e\n\u003cli\u003eDuess JW, Sampah ME, Lopez CM, et al. Necrotizing enterocolitis, gut microbes, and sepsis. Gut Microbes. 2023. 15(1): 2221470.\u003c/li\u003e\n\u003cli\u003eThe Apgar Score. Pediatrics. 2015. 136(4): 819-22.\u003c/li\u003e\n\u003cli\u003eCnattingius S, Norman M, Granath F, Petersson G, Stephansson O, Frisell T. Apgar Score Components at 5 Minutes: Risks and Prediction of Neonatal Mortality. Paediatr Perinat Epidemiol. 2017. 31(4): 328-337.\u003c/li\u003e\n\u003cli\u003eCnattingius S, Johansson S, Razaz N. Apgar Score and Risk of Neonatal Death among Preterm Infants. N Engl J Med. 2020. 383(1): 49-57.\u003c/li\u003e\n\u003cli\u003eShah PS, Norman M, Rusconi F, et al. Five-minute Apgar score and outcomes in neonates of 24-28 weeks\u0026apos; gestation. Arch Dis Child Fetal Neonatal Ed. 2022. 107(4): 437-446.\u003c/li\u003e\n\u003cli\u003eZaigham M, Mar\u0026scaron;\u0026aacute;l K. Apgar score in premature infants associated with neonatal death prediction. J Pediatr. 2020. 226: 309-313.\u003c/li\u003e\n\u003cli\u003eKaplina A, Kononova S, Zaikova E, Pervunina T, Petrova N, Sitkin S. Necrotizing Enterocolitis: The Role of Hypoxia, Gut Microbiome, and Microbial Metabolites. Int J Mol Sci. 2023. 24(3): 2471.\u003c/li\u003e\n\u003cli\u003eBarr PA, Mally PV, Caprio MC. Standardized Nutrition Protocol for Very Low-Birth-Weight Infants Resulted in Less Use of Parenteral Nutrition and Associated Complications, Better Growth, and Lower Rates of Necrotizing Enterocolitis. JPEN J Parenter Enteral Nutr. 2019. 43(4): 540-549.\u003c/li\u003e\n\u003cli\u003eHong KY, Zhu Y, Wu F, et al. The role of nutrition in analysis of risk factors and short-term outcomes for late-onset necrotizing enterocolitis among very preterm infants: a nationwide, multicenter study in China. BMC Pediatr. 2024. 24(1): 172.\u003c/li\u003e\n\u003cli\u003eCahova M, Bratova M, Wohl P. Parenteral Nutrition-Associated Liver Disease: The Role of the Gut Microbiota. Nutrients. 2017. 9(9): 987.\u003c/li\u003e\n\u003cli\u003eBerkhout D, Klaassen P, Niemarkt HJ, et al. Risk Factors for Necrotizing Enterocolitis: A Prospective Multicenter Case-Control Study. Neonatology. 2018. 114(3): 277-284.\u003c/li\u003e\n\u003cli\u003eCampos-Martinez AM, Exp\u0026oacute;sito-Herrera J, Gonzalez-Bol\u0026iacute;var M, Fern\u0026aacute;ndez-Marin E, Uberos J. Evaluation of Risk and Preventive Factors for Necrotizing Enterocolitis in Premature Newborns. A Systematic Review of the Literature. Front Pediatr. 2022. 10: 874976.\u003c/li\u003e\n\u003cli\u003eWang P, Sun H, Maitiabula G, et al. Total parenteral nutrition impairs glucose metabolism by modifying the gut microbiome. Nat Metab. 2023. 5(2): 331-348.\u003c/li\u003e\n\u003cli\u003eCovello C, Becherucci G, Di Vincenzo F, et al. Parenteral Nutrition, Inflammatory Bowel Disease, and Gut Barrier: An Intricate Plot. Nutrients. 2024. 16(14): 2288.\u003c/li\u003e\n\u003cli\u003eLavoie JC, Chessex P. Parenteral nutrition and oxidant stress in the newborn: A narrative review. Free Radic Biol Med. 2019. 142: 155-167.\u003c/li\u003e\n\u003cli\u003ePierre JF. Gastrointestinal immune and microbiome changes during parenteral nutrition. Am J Physiol Gastrointest Liver Physiol. 2017. 312(3): G246-G256.\u003c/li\u003e\n\u003cli\u003eOddie SJ, Young L, McGuire W. Slow advancement of enteral feed volumes to prevent necrotising enterocolitis in very low birth weight infants. Cochrane Database Syst Rev. 2021. 8(8): CD001241.\u003c/li\u003e\n\u003cli\u003eKim W, Seo JM. Necrotizing Enterocolitis. N Engl J Med. 2020. 383(25): 2461.\u003c/li\u003e\n\u003cli\u003eMaier-Begandt D, Alonso-Gonzalez N, Klotz L, et al. Neutrophils-biology and diversity. Nephrol Dial Transplant. 2024. 39(10): 1551-1564.\u003c/li\u003e\n\u003cli\u003eCurbelo J, Luquero Bueno S, Galv\u0026aacute;n-Rom\u0026aacute;n JM, et al. Inflammation biomarkers in blood as mortality predictors in community-acquired pneumonia admitted patients: Importance of comparison with neutrophil count percentage or neutrophil-lymphocyte ratio. PLoS One. 2017. 12(3): e0173947.\u003c/li\u003e\n\u003cli\u003ePierrakos C, Velissaris D, Bisdorff M, Marshall JC, Vincent JL. Biomarkers of sepsis: time for a reappraisal. Crit Care. 2020. 24(1): 287.\u003c/li\u003e\n\u003cli\u003eSchuetz P. How to best use procalcitonin to diagnose infections and manage antibiotic treatment. Clin Chem Lab Med. 2023. 61(5): 822-828.\u003c/li\u003e\n\u003cli\u003eSullivan RC, Schmidt EP. Procalcitonin: A Mediator of Microvascular Dysfunction during Critical Illness. Am J Respir Crit Care Med. 2022. 206(4): 375-376.\u003c/li\u003e\n\u003cli\u003eXu HG, Tian M, Pan SY. Clinical utility of procalcitonin and its association with pathogenic microorganisms. Crit Rev Clin Lab Sci. 2022. 59(2): 93-111.\u003c/li\u003e\n\u003cli\u003eNorman-Bruce H, Umana E, Mills C, et al. Diagnostic test accuracy of procalcitonin and C-reactive protein for predicting invasive and serious bacterial infections in young febrile infants: a systematic review and meta-analysis. Lancet Child Adolesc Health. 2024. 8(5): 358-368.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of all patients in the training cohort and validation cohort.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"88%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 248)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD/M (P25, P75)/N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026nbsp;=\u0026nbsp;80)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD/M (P25, P75)/N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e130(52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e43(53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e118(47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e37(46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eGA (weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e35.7\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e36.5\u0026plusmn;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eBirth weight (gram)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e2382.43\u0026plusmn;761.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e2458\u0026plusmn;805.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eCaesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e161(64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e48(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eGDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e63(25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e16(20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eHDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e33(13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e7(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eICP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e19(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e2(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003ePROM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e45(18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e18(22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eAsphyxia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e14(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e7(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eApgar 5min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e10.00(10.00,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e10.00(9.00,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eInitiation time of enteral feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e＜48h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e217(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e67(83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e\u0026ge;48h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e31(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e13(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003ePN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e112(45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e35(43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eFeeding rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e\u0026le;20 ml/kg.d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e79(31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e28(35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003e＞20 ml/kg.d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e169(68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e52(65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eFormula feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e111(44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e32(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eUse of Antibiotics\u003c/p\u003e\n \u003cp\u003e(\u0026lt;7 days after birth)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e86(34.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e32(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eUse of Antibiotics\u003c/p\u003e\n \u003cp\u003e(\u0026lt;72 hours prior to disease onset)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e57(23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e17(21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eRed blood cell transfusion\u003c/p\u003e\n \u003cp\u003e(\u0026lt;72 hours prior to disease onset)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e27(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e9(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003ePostnatal age at disease onset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e9.00(5.00,16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e9.50(8.00,14.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e10.13(7.18,13.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e9.60(6.93,13.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e304.88\u0026plusmn;122.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e315.56\u0026plusmn;136.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eANC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e4.64(3.07,7.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e4.39(2.77,6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eNE%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e51.07\u0026plusmn;17.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e48.24\u0026plusmn;16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eHGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e144.68\u0026plusmn;34.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e145.85\u0026plusmn;33.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e5.00(2.41,9.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e8.00(0.80,8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e0.36(0.09,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e0.51(0.11,1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.5918%;\"\u003e\n \u003cp\u003eiCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.6122%;\"\u003e\n \u003cp\u003e1.20\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.5714%;\"\u003e\n \u003cp\u003e1.22\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2245%;\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGA\u0026nbsp;=\u0026nbsp;gestational age; GDM = gestational diabetes mellitus; HDP = hypertensive disorders of pregnancy; ICP = intrahepatic cholestasis of pregnancy; PROM = premature rupture of membrane; PN = parenteral nutrition; WBC = white blood cell count; PLT = platelet count; ANC = absolute neutrophil count; NE% = neutrophil percentage; HGB = hemoglobin; CRP = C-reactive protein; PCT = procalcitonin; iCa = ionized calcium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. General characteristics of the patients and univariate logistic regression analyses for screening predictors.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-\u003c/strong\u003e\u003cstrong\u003eNEC(n\u0026nbsp;=\u0026nbsp;171)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD/M\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(P25, P75)/N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEC(n\u0026nbsp;=\u0026nbsp;77)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026plusmn;SD/M (P25, P75)/N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003evalue\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR(95% CI)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e0.819(0.477~1.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e87(50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e43(55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e84(49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e34(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eGA (weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e35.61\u0026plusmn;3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e35.76\u0026plusmn;3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.014(0.934~1.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eBirth weight (gram)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e2383.87\u0026plusmn;761.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e2379.22\u0026plusmn;768.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e0.992(0.696~1.413)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eCaesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e108(63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e53(68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.288(0.726~2.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eGDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e41(24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e22(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.268(0.692~2.326)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eHDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e22(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e11(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.129(0.518~2.462)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eICP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e12(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e7(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.325(0.500~3.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003ePROM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e27(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e18(23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.627(0.833~3.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eAsphyxia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e8(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e6(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.722(0.576~5.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eApgar 5min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e10.00(10.00,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e10.00(8.00,10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e0.474(0.350~0.640)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eInitiation time of enteral feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e3.708(1.711~8.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026lt; 48h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e158(92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e59(76.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026ge;48h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e13(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e18(23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003ePN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e61(35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e51(66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e3.537(2.007~6.233)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eFeeding rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e0.176(0.098~0.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026le;20 ml/kg.d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e34(19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e45(58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003e\u0026gt;20 ml/kg.d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e137(80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e32(41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eFormula feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e68(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e43(55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.916(1.112~3.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eUse of Antibiotics\u003c/p\u003e\n \u003cp\u003e(\u0026lt;7 days after birth)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e45(26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e41(53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e3.189(1.817~5.596)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eUse of Antibiotics\u003c/p\u003e\n \u003cp\u003e(\u0026lt;72 hours prior to disease onset)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e35(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e22(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.554(0.837~2.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eRed blood cell transfusion\u003c/p\u003e\n \u003cp\u003e(\u0026lt;72 hours prior to disease onset)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e17(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e10(13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.352(0.588~3.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003ePostnatal age at disease onset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e8.00(5.00,16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e11.00(5.50,19.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.169\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.015(0.987~1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e10.15(7.18,13.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e9.95(7.21,13.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.818\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.012(0.962~1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e306.84\u0026plusmn;130.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e300.55\u0026plusmn;104.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.000(0.997~1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eANC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e4.53(2.95,6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e5.36(3.36,9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.048\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.092(1.027~1.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eNE%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e48.57\u0026plusmn;17.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e56.63\u0026plusmn;17.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.028(1.011~1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eHGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e146.67\u0026plusmn;33.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e140.26\u0026plusmn;35.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e0.994(0.986~1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e5.00(2.79,5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e5.00(1.88,10.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.105\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.020(0.981~1.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e0.22(0.07,0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e1.08(0.26,3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.286(1.137~1.455)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7113%;\"\u003e\n \u003cp\u003eiCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e1.23\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.6495%;\"\u003e\n \u003cp\u003e1.15\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.27835%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6804%;\"\u003e\n \u003cp\u003e0.123(0.030~0.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGA = gestational age; GDM = gestational diabetes mellitus; HDP = hypertensive disorders of pregnancy; ICP = intrahepatic cholestasis of pregnancy; PROM = premature rupture of membrane; PN = parenteral nutrition; WBC = white blood cell count; PLT = platelet count; ANC = absolute neutrophil count; NE% = neutrophil percentage; HGB = hemoglobin; CRP = C-reactive protein; PCT = procalcitonin; iCa = ionized calcium.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 3. Multivariate logistic regression analyses for screening predictors.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e\u003cem\u003eWald\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR(95% CI)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003eApgar 5min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e-0.617\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.180\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e11.811\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e0.539(0.379~0.767)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003ePN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e1.049\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.342\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e9.415\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e2.856(1.461~5.584)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003eFeeding rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003e\u0026le;20 ml/kg.d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003e\u0026gt;20 ml/kg.d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e-1.601\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.349\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e21.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e0.202(0.102~0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003eNE%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.027\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e6.840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e1.027(1.007~1.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.169\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.070\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e5.894\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e1.184(1.033~1.357)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.9583%;\"\u003e\n \u003cp\u003eConstants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e5.465\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e1.741\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e9.854\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.4583%;\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.2083%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePN = parenteral nutrition; NE% = neutrophil percentage; PCT = procalcitonin.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonate, Necrotizing Enterocolitis, Predictive Nomogram, Early Identification, Risk Stratification","lastPublishedDoi":"10.21203/rs.3.rs-6518823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6518823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo develop a predictive nomogram for early identification of neonatal necrotizing enterocolitis (NEC) in neonates presenting with nonspecific gastrointestinal or systemic symptoms prior to definitive diagnosis via Modified Bell\u0026rsquo;s.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing retrospective data from 248 neonates at Southwest Hospital (2018\u0026ndash;2024), we constructed a predictive model externally validated with 80 neonates from Children\u0026rsquo;s Hospital (2020\u0026ndash;2024). Multivariate logistic regression identified independent predictors, with model performance assessed via discrimination (AUC/C-index), calibration curves, and bootstrapping (1,000 resamples).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eApgar score at 5 minutes (odds ratio [OR] 0.539, 95% confidence interval [CI] 0.379\u0026ndash;0.767), parenteral nutrition (OR 2.856, 95% CI 1.461\u0026ndash;5.584), feeding rate (OR 0.202, 95% CI 0.102\u0026ndash;0.400), neutrophil percentage (OR 1.027, 95% CI 1.007\u0026ndash;1.048), and procalcitonin (OR 1.184, 95% CI 1.033\u0026ndash;1.357) were identified as independent predictors of NEC. The nomogram demonstrated strong predictive performance with an AUC and C-index of 0.839. The Hosmer\u0026ndash;Lemeshow test indicated good model calibration (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.720), with the calibration curve closely aligning with the ideal reference line. Decision curve analysis and clinical impact curves confirmed the nomogram's clinical utility. External validation yielded consistent results, supporting the model\u0026rsquo;s robustness.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis validated nomogram provides clinicians with an effective tool for NEC risk stratification, potentially improving clinical decisions and neonatal outcomes.\u003c/p\u003e","manuscriptTitle":"A predictive nomogram for the early diagnosis of neonatal necrotizing enterocolitis: Insights from a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 06:46:22","doi":"10.21203/rs.3.rs-6518823/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59baf31e-f4b0-41a0-a937-f5bd14805bfa","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 06:46:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6518823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6518823","identity":"rs-6518823","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}