A Nomogram model for predicting early hyperglycemia in premature infants

A Nomogram model for predicting early hyperglycemia in premature infants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Nomogram model for predicting early hyperglycemia in premature infants Yongming Wang, Fengzhi Xu, Huijuan Yin, Wang Xu, Jingxia Luo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4343491/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND/AIMS: Hyperglycemia in preterm infants is likely to lead to severe complications and higher mortality. Timely identification of hyperglycemia in preterm infants is vital for the prognosis of patients. We developed and validated predictive models for hyperglycemia in preterm infants < 32 weeks of gestational age to aid in the early detection of these patients. METHODS: A retrospective analysis was performed on 460 premature infants to examine the association of various clinical variables with hyperglycemia. We collected data from June 1, 2021, to May 31, 2023. clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis(backward method). We constructed a nomogram to assess the risk of hyperglycemia. The model's accuracy was validated using bootstrap resampling (n=500), and the POC curve was used for discrimination analysis to calibrate function and value. Calibration was evaluated via a calibration curve. The model's clinical utility was evaluated through decision curve analysis. RESULTS: Of the 29 potential predictors analyzed in 460 premature infants, the incidence of hyperglycemia was 24.1%. Multivariable logistic regression analysis identified birth weight, invasive ventilation, and Intraventricular hemorrhage as independent risk factors for premature infants with hyperglycemia. The resulting nomogram accurately predicted hyperglycemia risk with an area under the curve of 0.735(95%CI: 0.685-0.786). The bootstrap-validated area under the curve remained at 0.735(95%CI: 0.687-0.785). This model exhibited excellent calibration and demonstrated greater predictive efficacy and clinical utility for hyperglycemia. CONCLUSIONS: We have developed a prediction nomogram of hyperglycemia that can assist clinical treatment decision-making. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Risk factors Neonatal premature infants hyperglycemia Nomogram Predictive model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction With the development of perinatal medicine and neonatal critical management technology, the survival rate of premature infants has gradually increased [ 1 , 2 ]. The incidence of complications in surviving preterm infants is high, such as brain injury, bronchopulmonary dysplasia, retinopathy of prematurity, NEC, and so on. The disorder of glucose metabolism is one of the complications of premature infants. The younger the gestational age, the higher the incidence of hyperglycemia [ 3 , 4 ]. This is related to the imperfect function of islet β cells and immature glycogen decomposing enzymes leading to insulin resistance in premature infants. Newborns with asphyxia, infection, and hypothermia are also prone to hyperglycemia [ 5 ]. Iatrogenic hyperglycemia is common in intravenous glucose infusion rate ≥ 5.1mg/kg.min [ 6 ]. Related studies have found that hyperglycemia is not only associated with short-term complications such as mortality, intracranial hemorrhage, bronchopulmonary dysplasia, and ROP in premature infants but also with long-term complications such as impaired vision, hypertension, and neuro disability [ 4 , 7 – 12 ]. Therefore, to study the risk factors of hyperglycemia in premature infants and construct a Nomogram prediction model of hyperglycemia, it is convenient for clinicians to identify and deal with hyperglycemia in premature infants. In this study, the data of premature infants with gestational age less than 32 weeks in 3 tertiary hospitals and one second-grade hospital in the Ningxia Hui Autonomous region were retrospectively analyzed to analyze the effects of different factors on early hyperglycemia in premature infants. The selected influencing factors draw the risk diagram model of early hyperglycemia in very premature infants to provide a reference for clinical work. 1 Materials and methods This study was conducted in accordance with the Helsinki Declaration. The requirement for informed consent was waived due to the retrospective nature of the study by the Institutional Review Board (IRB) of Yinchuan Maternal and Children's Healthcare Hospital. The study has been approved by the IRB of Yinchuan Maternal and Children's Healthcare Hospital (approval number: 2024-51). All data generated or analysed during this study are included in this published article and its supplementary information files. 1.1 participants This was a retrospective, multicenter, case-control study. Electronic records were used to identify participants. All infants with gestational age < 32 weeks were hospitalized in the neonatal intensive care unit (NICU) of four Hospitals from June 1, 2021, to May 31, 2023, were selected as subjects. The inclusion criteria were as follows: (1) hospitalization within 24 hours after birth; (2) hospitalization time ≥ three days; (3) Blood glucose levels were monitored ≥ three times on the first day after birth and ≥ two times on the second and third days after birth. The exclusion criteria were as follows: (1) newborns suffered from genetic and metabolic diseases; (2) malformations, including chromosome malformations. Of the 529 preterm infants with gestational age < 32 weeks were admitted, 69 cases were excluded due to death, discharge, missing or integrated blood glucose value, inherited metabolic disorders, or major malformations, including chromosome malformations (loss rate 13.0%). The final participants included 460 people. A flow diagram of the study design is shown in (Fig. 1 ) . The study has been approved by the Medical Ethics Committee of Yinchuan Maternal and Children Health Hospital (approval number: 2024-51). The data are anonymous, and the requirement for informed consent was therefore waived. This study was conducted in the Helsinki Declaration. 1.2 Measurements and definition: Blood glucose levels were measured from capillary blood samples taken after the babies' heels were heated prefeed. Measured by Roche micro-blood glucose meter (ACCU-CHEKActive) and matching blood glucose test paper. Hyperglycemia was defined as a blood glucose level > 150 mg/dL on at least twice continuous measurements within three days after birth [ 13 ]. RDS was diagnosed as hard-working breathing and supplemental oxygen requirements (fraction of inspired oxygen of more than 30% to maintain oxygen saturation in the range of 88–94%)[ [ 14 ]. Intraventricular hemorrhage (IVH) was defined and graded according to et al. [ 15 ]. Hemodynamically significant patent ductus arteriosus (hs-PDA) is defined as follows: left atrium to aortic root diameter ratio(LA/Ao ≥ 1.4); left ventricular (LV enlargement, LVEF < 50%, ductal diameter ≥ 2mm or ductal diameter greater than mean pulmonary artery(MPA) diameter; PDA maximum velocity(PDA Vmax = 0.5m/s; highly elevated mean and diastolic PA flow; severe PA dilation; anterior cerebral artery resistance index (ACA RI) > = 0.9; Holodiastolic retrograde descending aorta (DAO) flow [ 16 ]. Feeding Intolerance(FI) is defined as the inability to digest enteral feedings presented as gastric residual volume of more than 50%, abdominal distension or emesis or both, and the disruption of the patient's feeding plan [ 17 ]. An apnea is defined as a cessation of breathing for 20 seconds or longer or a shorter pause accompanied by bradycardia (< 100 bpm), cyanosis, or pallor [ 18 ]. 1.3 Data collection Demographic and perinatal characteristics of all infants and their mothers were collected from the charts. (1) Mother's information: maternal age, body mass index at delivery, total prenatal glucocorticoids, mode of delivery, multiple pregnancy, maternal hypertension, gestational diabetes mellitus(GDM), maternal hypothyroidism, assisted reproduction. (2) premature infant information: sex, gestational age(GA), birth weight(BW), age of admission (min), Apgar score 1 minute, Apgar score 5 minutes, premature rupture of membranes(PROM), fasting time, starting feeding time, PN start time, Breastfeeding, umbilical vein catheterization(UVC), umbilical artery catheterization(UAC), invasive ventilation, noninvasive ventilation, RDS, IVH, FI, apnea, hs-PDA. 1.4 Statistical analysis For missing values, the variables with missing values ≥ 20% are eliminated directly, the missing value is dealt with by a simple filling method, the classified data is filled by mode, and a median fills the dose data. The R software Rversion4.2.0 was used for statistical analysis. Baseline description and univariate analysis: The compare-groups package was used with automatic identification, the glm package was used for multifactor Logistic regression, and the pROC package was used for discrimination analysis to calibrate function and value. Prob function in rms package was used for calibration, riskrggression package was used for the calibration curve, rmda package was used for the DCA curve, and rms package was used for nomogram. Statistical significance was set at P < 0.05. Results 460 premature infants were enrolled to develop and validate our predictive nomogram model. We summarize the clinical characteristics of infants in Table 1 . Of the 460 patients, 258 (56.1%) were males and 202 (43.9%) were females, with the age of patients ranging from 3minutes to 2h (median: 13minutes), with gestation age ranging from 25 to 31 + 6 weeks (median:30 + 6 weeks), with birth weight ranging from 650-2310g(median:1400g). The hyperglycemia group comprised of 111 (24.1%) infants. Table 1 Clinical characteristics of patients Variable (ALL]) n = 460 Non-hyperglycemia group (n = 349) Hyperglycemia group (n = 111) P value Sex N (%) 0.713 female 202 (43.9) 155 (44.5) 47 (42.0) male 258 (56.1) 193 (55.5) 65 (58.0) Age, minutes 13.0 [11.0;19.2] 13.0 [11.0;20.0] 13.0 [11.0;19.0] 0.743 Apgar score1min ≤ 7, yes, N (%) 137 (29.8) 93 (26.6) 44 (39.6) 0.016 Apgar score 5min ≤ 7, yes, N (%) 17 (3.70) 9 (2.58) 8 (7.21) 0.040 Cesarean, yes, N (%) 336 (73.0) 250 (71.8) 86 (76.8) 0.366 Multiple pregnancy, yes, N (%) 83 (18.0) 54 (15.5) 29 (26.1) 0.019 Mother's age, year 30.0 [26.0;33.0] 30.0 [26.0;33.0] 30.0 [27.0;33.0] 0.512 MBMI 25.8 [24.2;28.1] 25.8 [24.2;27.9] 26.6 [24.6;28.7] 0.203 GDM, yes, N (%) 51 (11.1) 33 (9.48) 18 (16.1) 0.079 Maternal hypertension, yes, N (%) 145 (31.5) 102 (29.3) 43 (38.4) 0.092 Maternal hypothyroidism, yes, N (%) 24 (5.22) 18 (5.17) 6 (5.36) 1.000 Assisted reproduction, yes, N (%) 37 (8.04) 24 (6.88) 13 (11.7) 0.163 Fast time ≥ 24h, yes, N (%) 245 (53.3) 177 (50.9) 68 (60.7) 0.087 Start feeding time ≥ 24h, yes, N (%) 202 (43.9) 147 (42.2) 55 (49.1) 0.244 Breastfeeding, yes, N (%) 183 (39.8) 134 (38.5) 49 (43.8) 0.454 PN start time ≥ 24h, yes, N (%) 157 (34.1) 114 (32.8) 43 (38.4) 0.327 UVC, yes, N (%) 343 (74.6) 245 (70.4) 98 (87.5) < 0.001 UAC, yes, N (%) 54 (11.7) 31 (8.91) 23 (20.5) 0.002 RDS, yes, N (%) 288 (62.6) 201 (57.8) 87 (77.7) <0.001 IVH, yes, N (%) 119 (25.9) 72 (20.7) 47 (42.0) <0.001 FI, yes, N (%) 273 (59.3) 192 (55.2) 81 (72.3) 0.002 Apnea, yes, N (%) 188 (40.9) 133 (38.1) 55 (49.5) 0.054 Hs-PDA, yes, N (%) 42 (9.13) 22 (6.32) 20 (17.9) <0.001 Invasive ventilation, yes, N (%) 239 (52.0) 156 (44.8) 83 (74.1) <0.001 Noninvasive ventilation, yes, N (%) 425 (92.4) 316 (90.8) 109 (97.3) 0.040 Total prenatal glucocorticoids, yes, N (%) 350 (76.1) 269 (77.3) 81 (72.3) 0.344 BW <0.001 ≥1500g, yes, N (%) 174 (37.8) 152 (43.7) 22 (19.6) 1000g-1499g, yes, N (%) 241 (52.4) 170 (48.9) 71 (63.4) <1000g, yes, N (%) 45 (9.78) 26 (7.47) 19 (17.0) GA < 0.001 ≥30w, yes, N (%) 298 (64.8) 244 (70.1) 54 (48.2) <30w, yes, N (%) 162 (35.2) 104 (29.9) 58 (51.8) PROM ≥ 18h, yes, N (%) 78 (17.0) 62 (17.8%) 16 (14.3%) 0.471 Patients in this region use Dexamethasone, which promotes fetal lung maturation. The total prenatal glucocorticoid means 6mg dexamethasone intramuscular injection, q12h, for two days. MBMI maternal body mass index, GDM gestational diabetes mellitus.PN parenteral nutrition, UVC umbilical vein catheterization, UAC umbilical artery catheterization, RDS respiratory distress syndrome, IVH intraventricular hemorrhage, FI feeding Intolerance, hs-PDA Hemodynamically significant patent ductus arteriosus, BW birth weight, GA gestational age. Selected predictors for the model After univariate logistic analysis (Table 2 ), variables including Apgar score ≤ 7, Apgar score ≤ 7, multiple pregnancies, UVC, UAC, RDS, IVH, FI, hs-PDA, invasive ventilation, noninvasive ventilation, BW and GA were included in the multivariable logistic regression analysis. The multivariable logistic analysis based on the backward stepwise approach demonstrated that premature infant hyperglycemia was significantly related to BW(1000-1499g) (P = 0.038), IVH (P < 0.001), and invasive ventilation (P = 0.001) (Table 2 ). Compared with the non-hyperglycemia group, BW(1000-1499g), IVH, and invasive ventilation are at higher risk of hyperglycemia. Table 2 Univariable and multivariable logistic regression analysis of the predictor of hyperglycemia in premature infants Univariable analysis Multivariable analysis Variables OR (95%CI) P OR(95%CI) P GA < 30weeks 2.520(1.631–3.906) < 0.001 - - BW ≥1500g - - - - 1000-1499g 2.886(1.73–4.975) < 0.001 1.878(1.047–3.449) 0.038 < 1000g 5.049(2.405–10.66) < 0.001 2.184(0.932–5.089) 0.070 Total prenatal glucocorticoids, 0.767(0.476–1.257) 0.284 - - Hs-PDA 3.221(1.675–6.171) < 0.001 - - Apnea 1.560(1.015–2.397) 0.042 - - FI 2.123(1.346–3.417) 0.001 - - IVH 2.772(1.754–4.376) < 0.001 2.552(1.536–4.244) < 0.001 RDS 2.545(1.575–4.236) < 0.001 - - UAC 2.643(1.456–4.747) 0.001 - - UVC 2.943(1.654–5.598) < 0.001 - - PN start time ≥ 24h 1.279(0.819–1.984) 0.275 - - Breastfeeding 0.856(0.543–1.348) 0.501 - - Start feeding time ≥ 24h 1.319(0.860–2.024) 0.204 - - Fast time ≥ 24h 1.493(0.971–2.314) 0.070 - - Noninvasive ventilation 3.679(1.284–15.520) 0.034 - - Invasive ventilation 3.523(2.219–5.725) < 0.001 2.358(1.398–4.044) 0.001 Assisted reproduction 1.773(0.848–3.560) 0.115 - - Maternal hypothyroidism 1.038(0.368–2.547) 0.939 - - Maternal hypertention 1.503(0.959–2.341) 0.073 - - GDM 1.828(0.968–3.36) 0.056 - - PROM 0.769(0.412–1.366) 0.387 - - MBMI 1.027(0.969–1.089) 0.371 - - Mother's age, year 1.010(0.972–1.049) 0.608 - - multiple 1.902(1.130–3.160) 0.014 - - cesarean 1.297(0.798–2.162) 0.306 - - Apgar score 5min 2.897(1.063–7.765) 0.033 - - Apgar score 1min 1.774(1.130–2.771) 0.012 - - Age(minute) 0.996(0.989-1.000) 0.194 - - Sex 1.111(0.723–1.715) 0.633 - - Patients in this region use Dexamethasone, which promotes fetal lung maturation. The total prenatal glucocorticoids mean 6mg dexamethasone intramuscular injection, q12h, for two days. MBMI maternal body mass index, GDM gestational diabetes mellitus, PN parenteral nutrition, UVC umbilical vein catheterization, UAC umbilical artery catheterization, RDS respiratory distress syndrome, IVH intraventricular hemorrhage, FI feeding Intolerance, hs-PDA Hemodynamically significant patent ductus arteriosus, BW birth weight, GA gestational age. Predictive nomogram for the risk of hyperglycemia. Based on the final multivariate logistic regression, a nomogram was established that included three significant predictors for hyperglycemia prediction (Fig. 2 ). A total score was generated using BW, IVH, and invasive ventilation. This nomogram was used to quantitatively predict the risk probability of hyperglycemia in premature infants with GA < 32 weeks. Predictive Model Validation We use the ROC curve to evaluate the discriminatory capacity of the predictive model. For the predictive model, the pooled area under the ROC of the nomogram is 0.735(95%CI: 0.685–0.786 (DeLong))(Fig. 3), which indicates moderately good performance. We perform internal validation using the bootstrap method with 500 repetitions. The pooled area under the ROC of the nomogram is 0.735 (95%CI༚0.687–0.784) (Fig. 3) , demonstrating moderate discrimination in estimating the risk of hyperglycemia. We also calibrate the predictive model with a calibration plot and the Hosmer–Lemeshow test. The calibration curves show a good fit for the predictive model and the bootstrap(500 repetitions). As the Hosmer–Lemeshow test demonstrated, the predicted and bootstrap probability were highly consistent (χ 2 = 1.485, P = 0.997) ( Fig. 4 ) . The DCA showed that if the threshold probability of an individual is between 6% and 52%, using this model to predict hyperglycemia adds more benefit than either the treat-all or treat-none tactics. ( Fig. 5 ) . Discussion We developed a simple, intuitive statistical predictive model to quantify hyperglycemia among premature infants with gestational age < 32 weeks. The model focuses on demographic and clinical variables routinely available to diagnose hyperglycemia. According to the predictive model, birth weight, invasive ventilation, and Intraventricular hemorrhage are independent risk factors for premature infants with hyperglycemia. We use the bootstrap method for internal verification. The final model showed good discrimination and calibration, with the ROC of 0.735 (0.735 via bootstrapping validation). The calibration curves show a good fit for the predictive model, and the bootstrap and the Hosmer–Lemeshow test showed no significant statistic ( P = 0.997). We constructed DCA curves to show that if the threshold probability of an individual is between 6 and 50%, using this model to predict hyperglycemia adds more benefits. Our study showed that BW was an independent risk in premature infants with GW < 32 weeks. The lower the birth weight, the higher the incidence of hyperglycemia [ 4 ]. A prospective cohort study of 188 premature infants with birth weight less than 1500g showed that birth weight was an independent risk factor for hyperglycemia in early preterm infants (< 48 hours). Another study showed that BW is related to hyperglycemia in extremely premature infants( OR = 0.995, 95% CI 0.993 ~ 0.997, P < 0.05). [ 19 ] The incidence of hyperglycemia increased by about 1.6 times for every standard deviation of weight loss [ 20 ]. These studies are consistent with our results. Animal experimental studies show that hyperglycemia is associated with intracranial hemorrhage, inducing brain damage, neuronal cell death, or apoptosis [ 21 , 22 ]. Our results showed that hyperglycemia is associated with any grade IVH, Which was similar to the results of a meta-analysis study ( OR = 2.60) [ 7 ]. Two prospective studies found that hyperglycemia is associated with intracranial hemorrhage. [ 23 , 24 ]. High blood glucose concentrations increase the risk of early death and grade 3 or 4 intraventricular bleeding [ 25 ]. In other studies, there is no statistically significant between hyperglycemia and any grade IVH [ 26 – 28 ]. This inconsistency may be related to the selection of subjects and sample size, the timing, and the method of detecting intracranial hemorrhage. One study showed that mechanical ventilation increases the chance of developing hyperglycemia three times( P = 0.032) [ 4 ]. A study showed that hyperglycemia was significantly associated with a shorter duration of mechanical ventilation ( P = 0.006) [ 24 ]. This may be related to stress hyperglycemia in premature infants induced by mechanical ventilation. A nomogram is a visual statistical model that calculates a risk score based on the proportion of selected predictors in the prediction model and calculates the probability of occurrence of related clinical events so clinicians can identify and treat the disease [ 29 – 32 ]. The model can accurately screen relevant variables and indicators and identify the most appropriate risk factors. There are several limitations to the nomogram presented here. First, we constructed the prediction nomogram based on the retrospective review of medical records; the database did not include other risk factors for hyperglycemia, such as sepsis early on sepsis and glucose infusion rate, which may introduce selection bias. Second, we don't unify the model of the blood glucose meter and blood glucose test strips and the specific time interval for monitoring blood glucose in each center, which may cause selection and measurement bias. Third, with only three factors (BW, invasive ventilation, and IVH), the nomogram cannot predict hyperglycemia beyond three days. A multicenter, prospective trial is required to confirm the model's accuracy. In conclusion, our study presents a nomogram model to calculate a risk score and identify premature infants with GA < 32 weeks with an increased likelihood of hyperglycemia. Applying this model as a convenient and specific tool may prove advantageous for clinical decision-making. Declarations Author Contribution Yongming Wang, Fengzhi Xu participated in study concept, design, and drafting of the manuscript; Huijuan Yin participated in the statistical analysis and manuscript revision; Wang Xu Jiangping He Shasha Wu participated in acquisition, analysis and interpretation of data; Jingxia Luo participated in critical revision of the manuscript for important intellectual content. 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Diabetes therapy: research, treatment and education of diabetes and related disorders 2020, 11(9):2057–2073. Mei Z, Chen J, Chen P, Luo S, Jin L, Zhou L: A nomogram to predict hyperkalemia in patients with hemodialysis: a retrospective cohort study. BMC Nephrol 2022, 23(1):351. Zhang J, Weng X: Development of a Nomogram to Predict the Risk for Acute Necrotizing Pancreatitis. Gut Liver 2024. Additional Declarations No competing interests reported. Supplementary Files rawdataofhyperglycemia.csv 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. 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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-4343491","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":301411480,"identity":"359a1ee9-9eb0-4ad0-ab78-9080f0c1203d","order_by":0,"name":"Yongming Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYLCCBAYGOTb29gOkaTHm4zmTQJpFifMkHAyIU2pwIzvtwYOaw+ltEkDLflRsI0ZL7naDhGOHc9ukGw8w9py5TViL2Y3cbRKJDUAtMgcSmBnbSNCSziaRYECalgTitdifebtNIuFYumEbMJAPEuUXyfbcbZI/aqzl5dvbDz74UUGEFgaBBAT7ABHqgYCfSHWjYBSMglEwggEApAE+niYqW2QAAAAASUVORK5CYII=","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yongming","middleName":"","lastName":"Wang","suffix":""},{"id":301411482,"identity":"210f6bb5-b5e8-4922-ab00-8e755d148460","order_by":1,"name":"Fengzhi Xu","email":"","orcid":"","institution":"Ningxia Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fengzhi","middleName":"","lastName":"Xu","suffix":""},{"id":301411483,"identity":"2ac0dd88-3f7d-428e-9267-565a2a7958ad","order_by":2,"name":"Huijuan Yin","email":"","orcid":"","institution":"Ningxia Hui Autonomous Region Peoples Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huijuan","middleName":"","lastName":"Yin","suffix":""},{"id":301411484,"identity":"65b2f91c-3eec-4824-a2af-97f06215f5e2","order_by":3,"name":"Wang Xu","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Xu","suffix":""},{"id":301411485,"identity":"e0e626b7-b9c9-4a9f-a9ac-86aaf467cfc3","order_by":4,"name":"Jingxia Luo","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingxia","middleName":"","lastName":"Luo","suffix":""},{"id":301411486,"identity":"ad2a1722-4b38-459b-9b1f-eec3f73a41fa","order_by":5,"name":"Jianping He","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"He","suffix":""},{"id":301411487,"identity":"58991a1e-dbb7-48e0-907b-1258a8ca3b2b","order_by":6,"name":"Shasha Wu","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shasha","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-04-29 14:21:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4343491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4343491/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56479051,"identity":"24b8a593-34c0-4d4b-885a-f08f2e3630e7","added_by":"auto","created_at":"2024-05-14 17:58:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194016,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study design\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4343491/v1/857c10141294f78fd50bac11.png"},{"id":56479050,"identity":"29645448-c6b6-4919-a7d2-8bbf5a794aa9","added_by":"auto","created_at":"2024-05-14 17:58:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37696,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram developed with BW, IVH and Invasive ventilation incorporated.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4343491/v1/f8f11d79b61b3a9a36ddfdc8.png"},{"id":56479669,"identity":"d802da71-ad69-4aef-a6b0-91b8af459328","added_by":"auto","created_at":"2024-05-14 18:06:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38763,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve (ROC) validation of the hypweglycemia risk nomogram prediction. The y-axis represents the true positive rate of the risk prediction, the x-axis represents the false positive rate of the risk prediction. The thick red line represents the performance of the nomogram in the entire set (a) and bootstrap(500 repetitions).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4343491/v1/c43e95101760ea6c5c2862c3.png"},{"id":56479670,"identity":"76cf1207-7151-4478-b3ab-231875da5a79","added_by":"auto","created_at":"2024-05-14 18:06:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22402,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the predictive hyperglycemia risk nomogram. The y-axis represents actual diagnosed cases of hyperglycemia, the x-axis represents the predicted risk of hyperglycemia. The dotted line represents a perfect prediction by an ideal model, the blue line represents the performance of the entire cases, and red line the performace of bias-correction by bootstrapping(500 repetitions). The calibration curves show a good fit for the predictive model and the bootstrap.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4343491/v1/f7bd2c9e8f7eae08aef5e6df.png"},{"id":56479055,"identity":"7f05bb36-3296-4df8-8314-4713924a266f","added_by":"auto","created_at":"2024-05-14 17:58:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24792,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the hyperglycemia risk nomogram. The y-axis represents the net benefit, The x-axis shows the threshold probability. The red line represents the nomogram.The thin grey solid line represents the assumption that all patients have hyperglycemia, the thick black horizontal solid line that all patients have no heyperglycemia\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4343491/v1/3f08641adb72433857beef35.png"},{"id":64722420,"identity":"edac80a1-b79a-43c9-b76b-8f2fe99c4ec4","added_by":"auto","created_at":"2024-09-18 04:40:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":906883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4343491/v1/9740c123-b6fc-47a9-96a8-818e66ad10ad.pdf"},{"id":56479053,"identity":"e3807a6a-95b7-46db-acd6-7570e5bc0fe5","added_by":"auto","created_at":"2024-05-14 17:58:25","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34658,"visible":true,"origin":"","legend":"","description":"","filename":"rawdataofhyperglycemia.csv","url":"https://assets-eu.researchsquare.com/files/rs-4343491/v1/a3d3d369c8366dd8e39e60a5.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Nomogram model for predicting early hyperglycemia in premature infants","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the development of perinatal medicine and neonatal critical management technology, the survival rate of premature infants has gradually increased [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The incidence of complications in surviving preterm infants is high, such as brain injury, bronchopulmonary dysplasia, retinopathy of prematurity, NEC, and so on. The disorder of glucose metabolism is one of the complications of premature infants. The younger the gestational age, the higher the incidence of hyperglycemia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This is related to the imperfect function of islet β cells and immature glycogen decomposing enzymes leading to insulin resistance in premature infants. Newborns with asphyxia, infection, and hypothermia are also prone to hyperglycemia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Iatrogenic hyperglycemia is common in intravenous glucose infusion rate\u0026thinsp;\u0026ge;\u0026thinsp;5.1mg/kg.min [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Related studies have found that hyperglycemia is not only associated with short-term complications such as mortality, intracranial hemorrhage, bronchopulmonary dysplasia, and ROP in premature infants but also with long-term complications such as impaired vision, hypertension, and neuro disability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Therefore, to study the risk factors of hyperglycemia in premature infants and construct a Nomogram prediction model of hyperglycemia, it is convenient for clinicians to identify and deal with hyperglycemia in premature infants. In this study, the data of premature infants with gestational age less than 32 weeks in 3 tertiary hospitals and one second-grade hospital in the Ningxia Hui Autonomous region were retrospectively analyzed to analyze the effects of different factors on early hyperglycemia in premature infants. The selected influencing factors draw the risk diagram model of early hyperglycemia in very premature infants to provide a reference for clinical work.\u003c/p\u003e"},{"header":"1 Materials and methods","content":"\u003cp\u003e This study was conducted in accordance with the Helsinki Declaration. The requirement for informed consent was waived due to the retrospective nature of the study by the Institutional Review Board (IRB) of Yinchuan Maternal and Children's Healthcare Hospital. The study has been approved by the IRB of Yinchuan Maternal and Children's Healthcare Hospital (approval number: 2024-51).\u003c/p\u003e \u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\u003cp\u003e1.1 participants\u003c/p\u003e \u003cp\u003eThis was a retrospective, multicenter, case-control study. Electronic records were used to identify participants. All infants with gestational age\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks were hospitalized in the neonatal intensive care unit (NICU) of four Hospitals from June 1, 2021, to May 31, 2023, were selected as subjects. The inclusion criteria were as follows: (1) hospitalization within 24 hours after birth; (2) hospitalization time\u0026thinsp;\u0026ge;\u0026thinsp;three days; (3) Blood glucose levels were monitored\u0026thinsp;\u0026ge;\u0026thinsp;three times on the first day after birth and \u0026ge;\u0026thinsp;two times on the second and third days after birth. The exclusion criteria were as follows: (1) newborns suffered from genetic and metabolic diseases; (2) malformations, including chromosome malformations. Of the 529 preterm infants with gestational age\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks were admitted, 69 cases were excluded due to death, discharge, missing or integrated blood glucose value, inherited metabolic disorders, or major malformations, including chromosome malformations (loss rate 13.0%). The final participants included 460 people. A flow diagram of the study design is shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e The study has been approved by the Medical Ethics Committee of Yinchuan Maternal and Children Health Hospital (approval number: 2024-51). The data are anonymous, and the requirement for informed consent was therefore waived. This study was conducted in the Helsinki Declaration.\u003c/p\u003e\n\u003ch3\u003e1.2 Measurements and definition:\u003c/h3\u003e\n\u003cp\u003eBlood glucose levels were measured from capillary blood samples taken after the babies' heels were heated prefeed. Measured by Roche micro-blood glucose meter (ACCU-CHEKActive) and matching blood glucose test paper. Hyperglycemia was defined as a blood glucose level\u0026thinsp;\u0026gt;\u0026thinsp;150 mg/dL on at least twice continuous measurements within three days after birth [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. RDS was diagnosed as hard-working breathing and supplemental oxygen requirements (fraction of inspired oxygen of more than 30% to maintain oxygen saturation in the range of 88\u0026ndash;94%)[ [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Intraventricular hemorrhage (IVH) was defined and graded according to et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hemodynamically significant patent ductus arteriosus (hs-PDA) is defined as follows: left atrium to aortic root diameter ratio(LA/Ao\u0026thinsp;\u0026ge;\u0026thinsp;1.4); left ventricular (LV enlargement, LVEF\u0026thinsp;\u0026lt;\u0026thinsp;50%, ductal diameter\u0026thinsp;\u0026ge;\u0026thinsp;2mm or ductal diameter greater than mean pulmonary artery(MPA) diameter; PDA maximum velocity(PDA Vmax\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;2m/s); ductal left to right diastolic flow\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.5m/s; highly elevated mean and diastolic PA flow; severe PA dilation; anterior cerebral artery resistance index (ACA RI)\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.9; Holodiastolic retrograde descending aorta (DAO) flow [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Feeding Intolerance(FI) is defined as the inability to digest enteral feedings presented as gastric residual volume of more than 50%, abdominal distension or emesis or both, and the disruption of the patient's feeding plan [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. An apnea is defined as a cessation of breathing for 20 seconds or longer or a shorter pause accompanied by bradycardia (\u0026lt;\u0026thinsp;100 bpm), cyanosis, or pallor [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Data collection\u003c/h2\u003e \u003cp\u003eDemographic and perinatal characteristics of all infants and their mothers were collected from the charts. (1) Mother's information: maternal age, body mass index at delivery, total prenatal glucocorticoids, mode of delivery, multiple pregnancy, maternal hypertension, gestational diabetes mellitus(GDM), maternal hypothyroidism, assisted reproduction. (2) premature infant information: sex, gestational age(GA), birth weight(BW), age of admission (min), Apgar score 1 minute, Apgar score 5 minutes, premature rupture of membranes(PROM), fasting time, starting feeding time, PN start time, Breastfeeding, umbilical vein catheterization(UVC), umbilical artery catheterization(UAC), invasive ventilation, noninvasive ventilation, RDS, IVH, FI, apnea, hs-PDA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eFor missing values, the variables with missing values\u0026thinsp;\u0026ge;\u0026thinsp;20% are eliminated directly, the missing value is dealt with by a simple filling method, the classified data is filled by mode, and a median fills the dose data. The R software Rversion4.2.0 was used for statistical analysis. Baseline description and univariate analysis: The compare-groups package was used with automatic identification, the glm package was used for multifactor Logistic regression, and the pROC package was used for discrimination analysis to calibrate function and value. Prob function in rms package was used for calibration, riskrggression package was used for the calibration curve, rmda package was used for the DCA curve, and rms package was used for nomogram. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e460 premature infants were enrolled to develop and validate our predictive nomogram model. We summarize the clinical characteristics of infants in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the 460 patients, 258 (56.1%) were males and 202 (43.9%) were females, with the age of patients ranging from 3minutes to 2h (median: 13minutes), with gestation age ranging from 25 to 31\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003eweeks (median:30\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003eweeks), with birth weight ranging from 650-2310g(median:1400g). The hyperglycemia group comprised of 111 (24.1%) infants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(ALL]) n\u0026thinsp;=\u0026thinsp;460\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-hyperglycemia group (n\u0026thinsp;=\u0026thinsp;349)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHyperglycemia group (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.0 [11.0;19.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0 [11.0;20.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.0 [11.0;19.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score1min\u0026thinsp;\u0026le;\u0026thinsp;7, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score 5min\u0026thinsp;\u0026le;\u0026thinsp;7, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (7.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCesarean, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336 (73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250 (71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple pregnancy, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother's age, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.0 [26.0;33.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.0 [26.0;33.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.0 [27.0;33.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.8 [24.2;28.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.8 [24.2;27.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.6 [24.6;28.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (9.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal hypertension, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal hypothyroidism, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (5.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssisted reproduction, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (8.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (6.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFast time\u0026thinsp;\u0026ge;\u0026thinsp;24h, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart feeding time\u0026thinsp;\u0026ge;\u0026thinsp;24h, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePN start time\u0026thinsp;\u0026ge;\u0026thinsp;24h, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUVC, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343 (74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUAC, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (8.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDS, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e288 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVH, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFI, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e273 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApnea, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-PDA, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (6.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive ventilation, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (74.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoninvasive ventilation, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e425 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316 (90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal prenatal glucocorticoids, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350 (76.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1500g, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000g-1499g, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1000g, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (9.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;30w, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244 (70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;30w, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROM\u0026thinsp;\u0026ge;\u0026thinsp;18h, yes, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePatients in this region use Dexamethasone, which promotes fetal lung maturation. The total prenatal glucocorticoid means 6mg dexamethasone intramuscular injection, q12h, for two days. MBMI maternal body mass index, GDM gestational diabetes mellitus.PN parenteral nutrition, UVC umbilical vein catheterization, UAC umbilical artery catheterization, RDS respiratory distress syndrome, IVH intraventricular hemorrhage, FI feeding Intolerance, hs-PDA Hemodynamically significant patent ductus arteriosus, BW birth weight, GA gestational age.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSelected predictors for the model\u003c/h2\u003e \u003cp\u003eAfter univariate logistic analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), variables including Apgar score\u0026thinsp;\u0026le;\u0026thinsp;7, Apgar score\u0026thinsp;\u0026le;\u0026thinsp;7, multiple pregnancies, UVC, UAC, RDS, IVH, FI, hs-PDA, invasive ventilation, noninvasive ventilation, BW and GA were included in the multivariable logistic regression analysis. The multivariable logistic analysis based on the backward stepwise approach demonstrated that premature infant hyperglycemia was significantly related to BW(1000-1499g) (P\u0026thinsp;=\u0026thinsp;0.038), IVH (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and invasive ventilation (P\u0026thinsp;=\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with the non-hyperglycemia group, BW(1000-1499g), IVH, and invasive ventilation are at higher risk of hyperglycemia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable logistic regression analysis of the predictor of hyperglycemia in premature infants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;\u0026lt;\u0026thinsp;30weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.520(1.631\u0026ndash;3.906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;1500g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000-1499g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.886(1.73\u0026ndash;4.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.878(1.047\u0026ndash;3.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.049(2.405\u0026ndash;10.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.184(0.932\u0026ndash;5.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal prenatal glucocorticoids,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.767(0.476\u0026ndash;1.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-PDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.221(1.675\u0026ndash;6.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.560(1.015\u0026ndash;2.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.123(1.346\u0026ndash;3.417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.772(1.754\u0026ndash;4.376)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.552(1.536\u0026ndash;4.244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.545(1.575\u0026ndash;4.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.643(1.456\u0026ndash;4.747)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.943(1.654\u0026ndash;5.598)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePN start time\u0026thinsp;\u0026ge;\u0026thinsp;24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.279(0.819\u0026ndash;1.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.856(0.543\u0026ndash;1.348)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStart feeding time\u0026thinsp;\u0026ge;\u0026thinsp;24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.319(0.860\u0026ndash;2.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFast time\u0026thinsp;\u0026ge;\u0026thinsp;24h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.493(0.971\u0026ndash;2.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoninvasive ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.679(1.284\u0026ndash;15.520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.523(2.219\u0026ndash;5.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.358(1.398\u0026ndash;4.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssisted reproduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.773(0.848\u0026ndash;3.560)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal hypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.038(0.368\u0026ndash;2.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal hypertention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.503(0.959\u0026ndash;2.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.828(0.968\u0026ndash;3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.769(0.412\u0026ndash;1.366)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.027(0.969\u0026ndash;1.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother's age, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.010(0.972\u0026ndash;1.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.902(1.130\u0026ndash;3.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecesarean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.297(0.798\u0026ndash;2.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score 5min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.897(1.063\u0026ndash;7.765)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score 1min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.774(1.130\u0026ndash;2.771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.996(0.989-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.111(0.723\u0026ndash;1.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePatients in this region use Dexamethasone, which promotes fetal lung maturation. The total prenatal glucocorticoids mean 6mg dexamethasone intramuscular injection, q12h, for two days. MBMI maternal body mass index, GDM gestational diabetes mellitus, PN parenteral nutrition, UVC umbilical vein catheterization, UAC umbilical artery catheterization, RDS respiratory distress syndrome, IVH intraventricular hemorrhage, FI feeding Intolerance, hs-PDA Hemodynamically significant patent ductus arteriosus, BW birth weight, GA gestational age.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePredictive nomogram for the risk of hyperglycemia.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the final multivariate logistic regression, a nomogram was established that included three significant predictors for hyperglycemia prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total score was generated using BW, IVH, and invasive ventilation. This nomogram was used to quantitatively predict the risk probability of hyperglycemia in premature infants with GA\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Model Validation\u003c/h2\u003e \u003cp\u003eWe use the ROC curve to evaluate the discriminatory capacity of the predictive model. For the predictive model, the pooled area under the ROC of the nomogram is 0.735(95%CI: 0.685\u0026ndash;0.786 (DeLong))(Fig.\u0026nbsp;3), which indicates moderately good performance. We perform internal validation using the bootstrap method with 500 repetitions. The pooled area under the ROC of the nomogram is 0.735 (95%CI༚0.687\u0026ndash;0.784)\u003cb\u003e(Fig.\u0026nbsp;3)\u003c/b\u003e, demonstrating moderate discrimination in estimating the risk of hyperglycemia.\u003c/p\u003e \u003cp\u003eWe also calibrate the predictive model with a calibration plot and the Hosmer\u0026ndash;Lemeshow test. The calibration curves show a good fit for the predictive model and the bootstrap(500 repetitions). As the Hosmer\u0026ndash;Lemeshow test demonstrated, the predicted and bootstrap probability were highly consistent (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.485, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.997) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe DCA showed that if the threshold probability of an individual is between 6% and 52%, using this model to predict hyperglycemia adds more benefit than either the treat-all or treat-none tactics. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe developed a simple, intuitive statistical predictive model to quantify hyperglycemia among premature infants with gestational age\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks. The model focuses on demographic and clinical variables routinely available to diagnose hyperglycemia. According to the predictive model, birth weight, invasive ventilation, and Intraventricular hemorrhage are independent risk factors for premature infants with hyperglycemia. We use the bootstrap method for internal verification. The final model showed good discrimination and calibration, with the ROC of 0.735 (0.735 via bootstrapping validation). The calibration curves show a good fit for the predictive model, and the bootstrap and the Hosmer\u0026ndash;Lemeshow test showed no significant statistic (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.997). We constructed DCA curves to show that if the threshold probability of an individual is between 6 and 50%, using this model to predict hyperglycemia adds more benefits.\u003c/p\u003e \u003cp\u003eOur study showed that BW was an independent risk in premature infants with GW\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks. The lower the birth weight, the higher the incidence of hyperglycemia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A prospective cohort study of 188 premature infants with birth weight less than 1500g showed that birth weight was an independent risk factor for hyperglycemia in early preterm infants (\u0026lt;\u0026thinsp;48 hours). Another study showed that BW is related to hyperglycemia in extremely premature infants(\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.995, 95%\u003cem\u003eCI\u003c/em\u003e 0.993\u0026thinsp;~\u0026thinsp;0.997, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] The incidence of hyperglycemia increased by about 1.6 times for every standard deviation of weight loss [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These studies are consistent with our results.\u003c/p\u003e \u003cp\u003eAnimal experimental studies show that hyperglycemia is associated with intracranial hemorrhage, inducing brain damage, neuronal cell death, or apoptosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our results showed that hyperglycemia is associated with any grade IVH, Which was similar to the results of a meta-analysis study (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.60) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Two prospective studies found that hyperglycemia is associated with intracranial hemorrhage. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. High blood glucose concentrations increase the risk of early death and grade 3 or 4 intraventricular bleeding [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In other studies, there is no statistically significant between hyperglycemia and any grade IVH [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This inconsistency may be related to the selection of subjects and sample size, the timing, and the method of detecting intracranial hemorrhage.\u003c/p\u003e \u003cp\u003eOne study showed that mechanical ventilation increases the chance of developing hyperglycemia three times(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A study showed that hyperglycemia was significantly associated with a shorter duration of mechanical ventilation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This may be related to stress hyperglycemia in premature infants induced by mechanical ventilation.\u003c/p\u003e \u003cp\u003eA nomogram is a visual statistical model that calculates a risk score based on the proportion of selected predictors in the prediction model and calculates the probability of occurrence of related clinical events so clinicians can identify and treat the disease [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The model can accurately screen relevant variables and indicators and identify the most appropriate risk factors.\u003c/p\u003e \u003cp\u003eThere are several limitations to the nomogram presented here. First, we constructed the prediction nomogram based on the retrospective review of medical records; the database did not include other risk factors for hyperglycemia, such as sepsis early on sepsis and glucose infusion rate, which may introduce selection bias. Second, we don't unify the model of the blood glucose meter and blood glucose test strips and the specific time interval for monitoring blood glucose in each center, which may cause selection and measurement bias. Third, with only three factors (BW, invasive ventilation, and IVH), the nomogram cannot predict hyperglycemia beyond three days. A multicenter, prospective trial is required to confirm the model's accuracy.\u003c/p\u003e \u003cp\u003eIn conclusion, our study presents a nomogram model to calculate a risk score and identify premature infants with GA\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks with an increased likelihood of hyperglycemia. Applying this model as a convenient and specific tool may prove advantageous for clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYongming Wang, Fengzhi Xu participated in study concept, design, and drafting of the manuscript; Huijuan Yin participated in the statistical analysis and manuscript revision; Wang Xu Jiangping He Shasha Wu participated in acquisition, analysis and interpretation of data; Jingxia Luo participated in critical revision of the manuscript for important intellectual content.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript and supplementary information files\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eQiao J, Wang Y, Li X, Jiang F, Zhang Y, Ma J, Song Y, Ma J, Fu W, Pang R \u003cem\u003eet al\u003c/em\u003e: A Lancet Commission on 70 years of women's reproductive, maternal, newborn, child, and adolescent health in China. Lancet (London, England) 2021, 397(10293):2497\u0026ndash;2536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHug L, Alexander M, You D, Alkema L: National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis. The Lancet Global health 2019, 7(6):e710-e720.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZamir I, Tornevi A, Abrahamsson T, Ahlsson F, Engstr\u0026ouml;m E, Hallberg B, Hansen-Pupp I, Sj\u0026ouml;str\u0026ouml;m ES, Domell\u0026ouml;f M: Hyperglycemia in Extremely Preterm Infants-Insulin Treatment, Mortality and Nutrient Intakes. The Journal of pediatrics 2018, 200:104\u0026ndash;110.e101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eButorac Ahel I, Lah Tomulić K, Vlašić Cicvarić I, Žuvić M, Baraba Dekanić K, Šegulja S, Bilić Čače I: Incidence and Risk Factors for Glucose Disturbances in Premature Infants. Medicina (Kaunas, Lithuania) 2022, 58(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeniji EO, Kuti BP, JB EE: Prevalence, risk factors, and outcome of hospitalization of neonatal hyperglycemia at a Nigerian health facility. Nigerian journal of clinical practice 2020, 23(1):71\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStensvold HJ, Lang AM, Strommen K, Abrahamsen TG, Ogland B, Pripp AH, Ronnestad AE: Strictly controlled glucose infusion rates are associated with a reduced risk of hyperglycaemia in extremely low birth weight preterm infants. \u003cem\u003eActa paediatrica (Oslo, Norway\u003c/em\u003e: 1992) 2018, 107(3):442\u0026ndash;449.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRath CP, Shivamallappa M, Muthusamy S, Rao SC, Patole S: Outcomes of very preterm infants with neonatal hyperglycaemia: a systematic review and meta-analysis. Archives of disease in childhood Fetal and neonatal edition 2022, 107(3):269\u0026ndash;280.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei C, Duan J, Ge G, Zhang M: Association between neonatal hyperglycemia and retinopathy of prematurity: a meta-analysis. European journal of pediatrics 2021, 180(12):3433\u0026ndash;3442.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmeida AC, Silva GA, Santini G, Br\u0026iacute;zido M, Correia M, Coelho C, Borrego LM: Correlation between hyperglycemia and glycated albumin with retinopathy of prematurity. Scientific reports 2021, 11(1):22321.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung M, Black J, Bloomfield FH, Gamble GD, Harding JE, Jiang Y, Poppe T, Thompson B, Tottman AC, Wouldes TA \u003cem\u003eet al\u003c/em\u003e: Effects of Neonatal Hyperglycemia on Retinopathy of Prematurity and Visual Outcomes at 7 Years of Age: A Matched Cohort Study. The Journal of pediatrics 2020, 223:42\u0026ndash;50.e42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZamir I, Stoltz Sj\u0026ouml;str\u0026ouml;m E, Edstedt Bonamy AK, Mohlkert LA, Norman M, Domell\u0026ouml;f M: Postnatal nutritional intakes and hyperglycemia as determinants of blood pressure at 6.5 years of age in children born extremely preterm. Pediatric research 2019, 86(1):115\u0026ndash;121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuzone S, Diplomatico M, Caredda E, Maietta A, Miraglia Del Giudice E, Montaldo P: Hypoglycaemia and hyperglycaemia in neonatal encephalopathy: a systematic review and meta-analysis. Archives of disease in childhood Fetal and neonatal edition 2023, 109(1):18\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaulsen ME, Brown SJ, Satrom KM, Scheurer JM, Ramel SE, Rao RB: Long-Term Outcomes after Early Neonatal Hyperglycemia in VLBW Infants: A Systematic Review. Neonatology 2021, 118(5):509\u0026ndash;521.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSweet DG, Carnielli VP, Greisen G, Hallman M, Klebermass-Schrehof K, Ozek E, Te Pas A, Plavka R, Roehr CC, Saugstad OD \u003cem\u003eet al\u003c/em\u003e: European Consensus Guidelines on the Management of Respiratory Distress Syndrome: 2022 Update. Neonatology 2023, 120(1):3\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapile LA, Burstein J, Burstein R, Koffler H: Incidence and evolution of subependymal and intraventricular hemorrhage: a study of infants with birth weights less than 1,500 gm. The Journal of pediatrics 1978, 92(4):529\u0026ndash;534.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamrick SEG, Sallmon H, Rose AT, Porras D, Shelton EL, Reese J, Hansmann G: Patent Ductus Arteriosus of the Preterm Infant. Pediatrics 2020, 146(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtigoza EB: Feeding intolerance. Early human development 2022, 171:105601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEichenwald EC: Apnea of Prematurity. Pediatrics 2016, 137(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Xiaofan GN, Han Shuping, Chen Xiaohui, Wu Qi, Cheng Jia: The incidence and risk factors of early hyperglycemia in extremely preterm infants. Chin J Neonatol, 2023, 38(1):18\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeardsall K, Vanhaesebrouck S, Ogilvy-Stuart AL, Vanhole C, Palmer CR, Ong K, vanWeissenbruch M, Midgley P, Thompson M, Thio M \u003cem\u003eet al\u003c/em\u003e: Prevalence and determinants of hyperglycemia in very low birth weight infants: cohort analyses of the NIRTURE study. \u003cem\u003eThe Journal of pediatrics\u003c/em\u003e 2010, 157(5):715\u0026ndash;719.e711-713.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosa AP, Mescka CP, Catarino FM, de Castro AL, Teixeira RB, Campos C, Baldo G, Graf DD, de Mattos-Dutra A, Dutra-Filho CS \u003cem\u003eet al\u003c/em\u003e: Neonatal hyperglycemia induces cell death in the rat brain. Metab Brain Dis 2018, 33(1):333\u0026ndash;342.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTayman C, Yis U, Hirfanoglu I, Oztekin O, G\u0026ouml;ktaş G, Bilgin BC: Effects of hyperglycemia on the developing brain in newborns. Pediatric neurology 2014, 51(2):239\u0026ndash;245.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheurer JM, Gray HL, Demerath EW, Rao R, Ramel SE: Diminished growth and lower adiposity in hyperglycemic very low birth weight neonates at 4 months corrected age. Journal of perinatology: official journal of the California Perinatal Association 2016, 36(2):145\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimovic A, Kuc A, Jevtic E, Kocovic A, Markovic S, Stanojevic M, Jakovcevski M, Jeremic D: Can early hyperglycemia affect the morbidity/mortality of very low birth weight premature infants? 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Pediatrics 2010, 125(3):e584-591.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Lugt NM, Smits-Wintjens VE, van Zwieten PH, Walther FJ: Short and long term outcome of neonatal hyperglycemia in very preterm infants: a retrospective follow-up study. BMC pediatrics 2010, 10:52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng LH, Su T, Bu KP, Ren S, Yang Z, Deng CE, Li BX, Wei WY: A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes. Scientific reports 2020, 10(1):14359.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Shi R, Yu L, Ji L, Li M, Hu F: Establishment of a Risk Prediction Model for Non-alcoholic Fatty Liver Disease in Type 2 Diabetes. Diabetes therapy: research, treatment and education of diabetes and related disorders 2020, 11(9):2057\u0026ndash;2073.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMei Z, Chen J, Chen P, Luo S, Jin L, Zhou L: A nomogram to predict hyperkalemia in patients with hemodialysis: a retrospective cohort study. BMC Nephrol 2022, 23(1):351.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Weng X: Development of a Nomogram to Predict the Risk for Acute Necrotizing Pancreatitis. \u003cem\u003eGut Liver\u003c/em\u003e 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"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":"Neonatal, premature infants, hyperglycemia, Nomogram, Predictive model ","lastPublishedDoi":"10.21203/rs.3.rs-4343491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4343491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND/AIMS:\u003c/strong\u003e Hyperglycemia in preterm infants is likely to lead to severe complications and higher mortality. Timely identification of hyperglycemia in preterm infants is vital for the prognosis of patients. We developed and validated predictive models for hyperglycemia in preterm infants \u0026lt; 32 weeks of gestational age to aid in the early detection of these patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS:\u003c/strong\u003eA retrospective analysis was performed on 460 premature infants to examine the association of various clinical variables with hyperglycemia. We collected data from June 1, 2021, to May 31, 2023. clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis(backward method). We constructed a nomogram to assess the risk of hyperglycemia. The model's accuracy was validated using bootstrap resampling (n=500), and the POC curve was used for discrimination analysis to calibrate function and value. Calibration was evaluated via a calibration curve. The model's clinical utility was evaluated through decision curve analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS: \u003c/strong\u003eOf the 29 potential predictors analyzed in 460 premature infants, the incidence of hyperglycemia was 24.1%. Multivariable logistic regression analysis identified birth weight, invasive ventilation, and Intraventricular hemorrhage as independent risk factors for premature infants with hyperglycemia. The resulting nomogram accurately predicted hyperglycemia risk with an area under the curve of 0.735(95%CI: 0.685-0.786). The bootstrap-validated area under the curve remained at 0.735(95%CI: 0.687-0.785). This model exhibited excellent calibration and demonstrated greater predictive efficacy and clinical utility for hyperglycemia. CONCLUSIONS: We have developed a prediction nomogram of hyperglycemia that can assist clinical treatment decision-making.\u003c/p\u003e","manuscriptTitle":"A Nomogram model for predicting early hyperglycemia in premature infants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 17:58:20","doi":"10.21203/rs.3.rs-4343491/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":"a1fa7ba8-ed17-4244-834e-a6954e0854f0","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31803971,"name":"Health sciences/Diseases"},{"id":31803972,"name":"Health sciences/Endocrinology"},{"id":31803973,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-09-18T04:32:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-14 17:58:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4343491","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4343491","identity":"rs-4343491","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}