Development and Validation of a Nomogram-Based Risk Prediction Model for Pulmonary Hemorrhage in Preterm Infants Under 32 Weeks of Gestation

Development and Validation of a Nomogram-Based Risk Prediction Model for Pulmonary Hemorrhage in Preterm Infants Under 32 Weeks of Gestation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Nomogram-Based Risk Prediction Model for Pulmonary Hemorrhage in Preterm Infants Under 32 Weeks of Gestation Min Qi, Shibing Yang, Chunyan Shi, Xuandong Zhang, Zhou Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7390997/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Backround: Pulmonary hemorrhage (PH) is a serious lung disease that endangers the safety of premature infants, but its occurrence is difficult to predict.Our research aims to establish and validate a predictive model for pulmonary hemorrhage in extremely premature infants. Methods: This single-center retrospective cohort study included 402 preterm infants admitted to the NICU of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, between June 1, 2018, and October 31, 2023. Participants were randomly allocated into a training set (n=277) and a validation set (n=125).Univariate and multivariate logistic regression analyses identified independent risk factors. A nomogram prediction model was constructed and evaluated using ROCcurves, calibration plots, and decision curve analysis (DCA). Result:A mong 402 infants, 59 developed PH (incidence: 14.7%). Independent risk factors included hemodynamically significant patent ductus arteriosus (hsPDA; OR=2.62, P=0.019), invasive respiratory support on the first postnatal day (OR=3.86, P=0.013), sepsis (OR=4.56, P=0.004), and shock (OR=4.25, P=0.005). The model demonstrated excellent predictive performance in both training and validation sets (AUC: 0.858 vs. 0.879). Hosmer-Lemeshow tests (training set P=0.957; validation set P=0.738) and calibration curves indicated good consistency. DCA confirmed significant clinical net benefits. Conclusion: The developed nomogram provides a validated, visually accessible tool for early identification of high-risk preterm infants, enabling timely interventions to improve outcomes. Pulmonary hemorrhage preterm infants risk factors risk prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Preterm birth remains a major global health challenge. Infants born before 32 weeks of gestation, due to immature organ systems, face exceptionally high risks of severe complications and mortality [ 1 ] . Among this population, PH is a life-threatening complication. PH is a severe condition associated with acute respiratory deterioration, increased mortality, and long-term adverse outcomes, often occurring in critically ill neonates during the late stages of life. Studies indicate that infants with PH require prolonged mechanical ventilation, face higher risks of intraventricular hemorrhage, and experience extended oxygen dependency and hospitalization [ 2 – 5 ] . Reported mortality rates in neonates with PH range from 40–60%[6]. Epidemiology shows that PH incidences of 3%-32% in very low birth weight (VLBW) infants and 11%-18.8% in extremely low birth weight (ELBW) infants [ 3 , 7 – 9 ] . A Brazilian cohort study identified PH as an independent risk factor for mortality in neonates with gestational age (GA) < 32 weeks [ 10 ] . The incidence of PH in infants born before 32 weeks is significantly higher than in other neonates [ 2 ] . To date, both domestic and international studies have demonstrated associations between PH and hsPDA, early-onset sepsis, disseminated intravascular coagulation, delivery room intubation, lower gestational age, low birth weight, low Apgar scores, severe neonatal respiratory distress syndrome (NRDS), and repeated surfactant administration [7.8.11.12] . However, no existing models integrate dynamic clinical indicators to quantify PH risk, making its prediction particularly challenging—especially in extremely preterm infants, whose pathophysiology differs from other preterm populations. Early identification of high-risk infants remains a critical unmet need. According to the 2023 European Consensus Guidelines on the Management of Neonatal Respiratory Distress Syndrome, early warning of PH is essential for improving clinical outcomes [ 13 ] . This highlights the urgent need for reliable predictive tools to guide clinical decision-making. Therefore, this study aims to develop the first dedicated predictive model for PH in infants born before 32 weeks of gestation, providing evidence-based support for clinical practice. We seek to identify independent predictors of PH and translate these findings into a clinically visual nomogram. This tool will enable early warning of PH onset, facilitate timely diagnosis and treatment, and ultimately improve patient outcomes. Methods Study Design and Participants This single-center retrospective cohort study enrolled preterm infants with a GA < 32 weeks who were admitted to the Neonatal Intensive Care Unit (NICU) of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, between June 1, 2018, and October 31, 2023. The exclusion criteria were as follows: (1) Infants with severe congenital malformations (e.g., complex cardiac anomalies, chromosomal abnormalities); (2) death or treatment withdrawal within 24 hours; (3) Transfer to other pediatric hospitals within the first postnatal week for surgical conditions (e.g., gastrointestinal perforation, congenital heart disease requiring immediate intervention). (4) Incomplete clinical data.A total of 413 infants were initially screened. After excluding 11 cases (3 due to parental withdrawal of care, 2 deaths within 24 hours, 3 transfers for surgical management, 1 major congenital anomaly, and 4 with incomplete data), 402 infants were included in the final analysis. These infants were randomly allocated into a training set (277 cases, 70%) and a validation set (125 cases, 30%) using SPSS 25.0. The flowchart of the study design is shown in Fig. Figure 1 Data Collection Two blinded researchers extracted data from electronic medical records, with discrepancies resolved by a senior neonatologist. Variables included: Maternal factors :Primiparity, gestational diabetes mellitus (GDM), hypertension (PIH), antenatal steroids, in vitro fertilization (IVF), prolonged membrane rupture (> 24 hours), cesarean delivery, multiple gestation. Neonatal factors :GA, birth weight (BW), small for GA (SGA), birth asphyxia, delivery room intubation, surfactant doses, invasive ventilation on day 1(defined as endotracheal intubation with mechanical ventilation), hsPDA, Sepsis (culture-confirmed or clinical diagnosis with elevated inflammatory markers), Ureaplasma urealyticum (UU) infection,Thrombocytopenia (platelet count < 100×10⁹/L), Severe acidosis (pH 5 mmol/L), shock(defined as hypotension requiring vasopressors or fluid resuscitation), and blood product transfusion (platelets, fresh frozen plasma, or packed red blood cells), Occurrence of pulmonary hemorrhage. For patients with thrombocytopenia, severe acidosis, edema, high lactate, shock, and transfusion of blood products, data from the first 3 days before pulmonary hemorrhage were collected for the pulmonary hemorrhage group, and data from the first week after birth were collected for non pulmonary hemorrhage patients. Diagnostic Criteria Pulmonary Hemorrhage (PH) [ 11 ] :Clinical diagnosis required: a. Presence of bloody fluid aspirated from the endotracheal tube, accompanied by an acute need for escalated respiratory support (e.g., increased FiO₂, higher ventilation pressures) or red blood cell transfusion. b. Chest radiograph demonstrating new-onset diffuse infiltrates or opacities. HsPDA [ 14 ] : Having all of the following echocardiographic characteristics: a.Ductal diameter ≥ 1.5 mm;Left atrial-to-aortic root ratio (LA/Ao) ≥ 1.5 Diastolic retrograde flow in the descending aorta. Simultaneously present with one of the following clinical manifestations: systolic murmurs in the precordial area, increased pulsation, water pulse, increased pulse pressure difference, and increased demand for respiratory support. Statistical Analysis Data were analyzed using SPSS 25.0 and StataSE 15. Continuous variables were expressed as mean ± standard deviation (SD) if normally distributed or median (interquartile range, IQR) if non-normally distributed. Categorical variables were reported as frequencies (%). Between-group comparisons utilized Student’s t -test, Mann-Whitney U -test, χ² test, or Fisher’s exact test, as appropriate. Model Development : Univariate Analysis :Variables with P < 0.05 in univariate logistic regression were selected for multivariate analysis. Multivariate Analysis :Backward stepwise logistic regression (likelihood ratio method) was applied to identify independent predictors. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Model Validation : Discrimination :Assessed using the area under the receiver operating characteristic curve (AUC). Calibration :Evaluated via calibration plots and the Hosmer-Lemeshow goodness-of-fit test ( P > 0.05 indicates good fit). Clinical Utility :Decision curve analysis (DCA) quantified net benefits across threshold probabilities. Results General Characteristics of the Study Population During the study period, 413 preterm infants < 32 weeks GA were born and admitted to the NICU. After excluding 11 cases (3 due to parental withdrawal of care within 24 hours, 2 deaths within 24 hours, 3 transfers for surgical conditions, 1 major congenital anomaly, and 4 with incomplete data), 402 infants were included in the final analysis. The overall incidence of pulmonary hemorrhage (PH) was 14.7% (59/402), with a median onset at 2 postnatal days. Of these, 40 cases (67.8%) occurred within the first postnatal week. Infants were randomly divided into a training set (n = 277, 70%) and a validation set (n = 125, 30%). The training set had a median GA of 29.3 weeks (IQR: 27.8–30.9) and a mean birth weight of 1271.1 ± 327.3 g, with a PH incidence of 14.1% (39/277). The validation set had a median GA of 29.7 weeks (IQR: 27.6–31.0) and a mean birth weight of 1285.8 ± 364.3 g, with a PH incidence of 16.0% (20/125). Baseline characteristics were balanced between groups except for a higher prevalence of shock in the validation set (17.6% vs. 8.7%, P = 0.009). Detailed demographic and clinical comparisons are presented in Table 1 . Table 1 Demographic and Clinical Characteristics of Preterm Infants in the Training and Validation Sets Variables Training set (N=277) Validation set (N=125) P value Primiparity n (%) 203(73.3) 92(73.6) 0.947 GDM n (%) 41(14.8) 16(12.8) 0.594 PIH n (%) 29(10.5) 15(12) 0.649 Antenatal steroids n (%) 179(64.6) 76(60.8) 0.462 IVF n (%) 137(49.5) 61(48.8) 0.903 Premature rupture of membranes(> 24 h) n (%) 71(25.6) 32(25.6) 0.995 Cesarean delivery n (%) 245(88.4) 102(83.2) 0.150 Multiple gestation n (%) 133(48.0) 61(48.8) 0.884 Male sex n (%) 147(53.1) 56(44.8) 0.125 GA(weeks),median (IQR) 29.3(27.8,30.9) 29.7(27.6,31.0) 0.863 BW (g), mean ± SD 1271.1 ± 327.3 1285.8 ± 364.3 0.687 SGA n (%) 6(2.2) 4(3.2) 0.787 Birth asphyxia n (%) 119(43.0) 59(47.2) 0.428 Intubation in delivery room n (%) 96(34.7) 41(32.8) 0.716 Surfactant administration > 1 dose. n (%) 44(15.9) 21(16.8) 0.817 Sepsis n (%) 108(39) 56(44.8) 0.272 UU infection n (%) 48(17.3) 19(15.2) 0.596 Invasive ventilation on day1 n (%) 132(47.7) 58(46.4) 0.816 HsPDA n (%) 72(26.0) 35(28.0) 0.673 Thrombocytopenia n (%) 47(17.0) 20(16) 0.810 Severe acidosis n (%) 85(30.7) 45(36.0) 0.292 Edema n (%) 18(6.5) 4(3.2) 0.267 High lactate n (%) 49(17.7) 23(18.4) 0.863 Shock n (%) 24(8.7) 22(17.6) 0.009 Blood product transfusion n (%) 33(11.9) 19(15.2) 0.363 PH n (%) 39(14.1) 20(16.0) 0.614 Predictive Model Construction In the training set, univariate logistic regression identified 14 variables significantly associated with PH ( P < 0.05), including lower GA (OR = 0.71, 95% CI: 0.58–0.86), birth asphyxia (OR = 3.12, 95% CI: 1.53–6.37), delivery room intubation (OR = 4.20, 95% CI: 2.06–8.55), multiple surfactant doses (OR = 3.95, 95% CI: 1.91–8.20), sepsis (OR = 11.95, 95% CI: 4.80–29.75), thrombocytopenia (OR = 6.18, 95% CI: 2.95–12.94), severe acidosis (OR = 2.80, 95% CI: 1.41–5.58), edema (OR = 9.91, 95% CI: 3.62–27.13), hyperlactatemia (OR = 4.32, 95% CI: 2.07–9.03), shock (OR = 12.77, 95% CI: 5.14–31.74), and invasive ventilation on the first postnatal day (OR = 9.71, 95% CI: 3.67–25.72) (Table 2 ). Multivariate backward logistic regression analysis revealed four independent predictors of PH: hsPDA (OR = 2.62, 95% CI: 1.67–5.87, P = 0.019). invasive ventilation on the first postnatal day (OR = 3.86, 95% CI: 1.32–11.26, P = 0.013). sepsis (OR = 4.56, 95% CI: 1.64–12.64, P = 0.004). shock (OR = 4.25, 95% CI: 1.56–11.62, P = 0.005). (Table 2 ).These variables were integrated into a nomogram for PH risk stratification (Fig. 2 ). Table 2 Univariate and Multivariate Logistic Regression Analysis of Risk Factors for Pulmonary Hemorrhage in Preterm Infants Varialies Univariate regression Multivariate regression OR (95% CI) P value OR (95% CI) P value Primiparity 0.53(0.26,1.07) 0.077 GDM 1.93(0.84,4.44) 0.121 PIH 0.42(0.10,1.85) 0.253 Antenatal steroids 0.97(0.48,1.97) 0.942 IVF 1.09(0.55,2.14) 0.806 Premature rupture of membranes(> 24 h) 1.78(0.87,3.65) 0.117 Cesarean delivery 2.67(0.61,11.65) 0.192 Multiple gestation 0.92(0.47,1.81) 0.802 Male sex 1.70(0.84,3.43) 0.139 GA(weeks) 0.71(0.58,0.86) <0.001 BW (g) 0.998(0.997,0.999) 0.001 SGA 1.23(0.14,10.79) 0.854 Birth asphyxia 3.12(1.53,6.37) 0.002 Intubation in delivery room 4.20(2.06,8.55) 1 dose 3.95(1.91,8.20) <0.001 Sepsis 11.95 (4.80,29.75) <0.001 4.56(1.64,12.64) 0.004 UU infection 0.67(0.25,1.80) 0.425 Invasive ventilation on day1 9.71(3.67,25.72) <0.001 3.86(1.32,11.26) 0.013 HsPDA 3.32(1.65,6.67) 0.001 2.62(1.67,5.87) 0.019 Thrombocytopenia 6.18(2.95,12.94) <0.001 Severe acidosis 2.80(1.41,5.58) 0.003 Edema 9.91(3.62,27.13) <0.001 High lactate 4.32(2.07,9.03) <0.001 Shock 12.77(5.14,31.74) <0.001 4.25(1.56,11.62) 0.005 Blood product transfusion 2.68(1.14,6.30) 0.024 Predictive Model Validation The model demonstrated robust discrimination in both the training and validation sets, with AUC values of 0.858 (95% CI: 0.798–0.918) and 0.879 (95% CI: 0.810–0.948), respectively (Fig. 3 ). Calibration plots showed excellent agreement between predicted probabilities and observed outcomes, further supported by nonsignificant Hosmer-Lemeshow test results ( P = 0.957 for training set; P = 0.738 for validation set;Figure 4 ). Decision curve analysis (DCA) confirmed significant clinical net benefits across threshold probabilities of 10–50%, outperforming both "treat-all" and "treat-none" strategies (Fig. 5 ). Discussion In this study, we developed and validated a nomogram-based risk prediction model for PH in preterm infants with a GA < 32 weeks. The model identified four independent risk factors: hsPDA, invasive respiratory support on the first postnatal day, sepsis, and shock. The model demonstrated excellent predictive performance, with AUC values of 0.858 and 0.879 in the training and validation sets, respectively, and showed good calibration and clinical utility through decision curve analysis (DCA).There are very few studies with clinical applications, and one researcher developed a prediction model for respiratory distress syndrome with PH in very preterm infants [15], but this model type was not tested using independent samples, so the model's performance was poor and could not be widely generalized. This study is the first to develop a specific prediction tool for pulmonary hemorrhage in a group of very preterm infants, which has a certain degree of reliability and application value, and provides a quantitative assessment tool for the early identification of high-risk infants in clinical practice. In this study, we found that hsPDA increased the risk of pulmonary hemorrhage in preterm infants (OR = 2.62), which was consistent with the high predictive value of arterial catheterization insufficiency for massive pulmonary hemorrhage reported by LI et al [ 16 ] OR = 11.4 Many studies have shown that PDA is associated with pulmonary hemorrhage [ 8 , 17 ] Kluckow and Evans reported that PH in preterm infants is associated with a high number of catheter shunt and high pulmonary blood flow [ 18 ] . The HsPDA, which shunts from left to right through the arterial conduit, leads to overload of the pulmonary circulation. The limited responsiveness of the preterm left ventricle to changes in preload and afterload, increased pulmonary arterial blood flow and vascular pressures may also impair cardiac function and increase the risk of pulmonary hemorrhage, which may also be induced by increased hydrostatic pressure in the pulmonary capillaries [ 14 , 19 , 20 ] . Of course, some studies have shown that HsPDA is not an independent risk factor for pulmonary hemorrhage [ 7 , 11 ] , suggesting that further research is needed to elucidate the hemodynamic mechanisms linking hsPDA to PH. Notably, the need for invasive respiratory support on the first postnatal day (OR = 3.86) had a more prominent predictive validity, suggesting the key role of mechanical ventilation-related pneumatic pressure injury and oxygen toxicity in the pathogenesis of pulmonary hemorrhage in very preterm premature infants. This corroborates with Sweet et al. who suggested that the intensity of respiratory support is positively associated with the risk of lung injury [ 13 ] . Invasive ventilatory support on the first day may lead to alveolar hyperexpansion, causing pressure damage to alveolar capillaries and promoting PH formation. A single-center study found an association between invasive first day of life and the development of PH, with an increased incidence of PH in preterm infants receiving invasive ventilation on the first day [ 7 ] . Of course ventilator therapy is not necessarily the direct cause of pulmonary hemorrhage. The need for invasive ventilatory support early in life can also indicate neonatal hypoxia, severe lung disease, or critical condition, which also predicts a greater likelihood of pulmonary hemorrhage. The present study showed that sepsis has a high predictive value for pulmonary hemorrhage in preterm infants (OR = 4.56), which is consistent with the results of Wang et al.'s study [ 7 ] , whose study was conducted on preterm infants with a birth weight of less than 1,000 grams, and which showed that preterm sepsis is an independent risk factor for pulmonary hemorrhage in preterm infants. In sepsis, pathogens and related toxins, activate immune cells and release a large number of pro-inflammatory factors, which directly damage the endothelial cells of pulmonary blood vessels. These disrupt the vascular barrier function, leading to extravasation of plasma proteins and erythrocytes, resulting in pulmonary hemorrhage [ 21 , 22 ] . The high predictive value of sepsis for pulmonary hemorrhage stems not only from its direct pro-inflammatory effect, but may also be closely related to coagulation dysfunction. Sepsis may also induce disseminated intravascular coagulation, manifested by thrombocytopenia, coagulation factor depletion, and secondary hyperfibrinolysis. This process may exacerbate the risk of pulmonary microvascular thrombosis with localized bleeding [ 23 , 24 ] . In the present study, due to the small body weight of very early preterm infants, dynamic coagulation indices (e.g., D-dimer) were not included in this study due to blood sampling limitations. Although thrombocytopenia did not enter the final model, its strong association in univariate analysis (OR = 6.18) suggests that coagulation dysfunction may play a role in specific subgroups, warranting further investigation. The independent predictive effect of shock on pulmonary hemorrhage (OR = 4.25) reflects the complex interaction between circulatory failure and lung injury. Early in shock, the body maintains cardiac and cerebral perfusion by vasoconstriction, but the pulmonary circulation, as a low-pressure system, is susceptible to endothelial cell hypoxic injury due to hypoperfusion. In the late stage of shock, compensatory mechanisms collapse, and the decline in cardiac output exacerbates pulmonary stasis, increases capillary hydrostatic pressure, and promotes hemorrhage [ 21 , 25 ] . Hemodynamic instability in preterm infants makes circulatory management relatively difficult because the immaturity of the preterm myocardium makes it extremely sensitive to changes in afterload. A sudden decrease in left ventricular output during shock can lead to pulmonary venous stasis, elevating capillary hydrostatic pressure and directly inducing hemorrhage [ 19 , 24 ] . In addition, reperfusion injury during correction of shock may further disrupt pulmonary vascular endothelial connectivity and increase vascular permeability through mitochondrial reactive oxygen species bursts [ 26 , 27 ] . In clinical practice, fluid management in children with shock needs to be refined to avoid aggravation of pulmonary edema by over-expansion, and lactate levels need to be monitored dynamically to assess the recovery of tissue oxygenation [ 28 ] . Compared to previous studies, our model offers several innovations. First, it focuses exclusively on the high-risk population of preterm infants < 32 weeks GA, addressing a gap in existing models that often include mixed gestational age groups. Second, it incorporates dynamic early-life indicators, such as respiratory support intensity, which provide more timely risk assessment than static factors like birth weight. Finally, the use of a nomogram facilitates rapid, bedside risk stratification, enhancing clinical applicability. The model maintained a stable predictive efficacy (AUC 0.879) in the validation set, suggesting that it has good generalization ability. Despite its strengths, this study has limitations. As a single-center retrospective study, it may be subject to selection bias. Future multicenter prospective studies with larger cohorts are needed to validate and refine the model. Additionally, subgroup analyses, particularly for infants < 28 weeks GA, were not performed due to sample size constraints. Future research could integrate biomarkers, such as vascular endothelial growth factor, and advanced hemodynamic monitoring parameters to further optimize the model. Conclusion We successfully developed and validated a column-line graph-based predictive model for pulmonary hemorrhage in very preterm infants that integrates four independent risk factors: hemodynamically significant arterial ductus arteriosus, invasive respiratory support on the first day of life, sepsis, and shock. The model demonstrated strong discrimination, calibration, and clinical utility. Despite limitations, the model represents an important step toward personalizing care for very preterm infants in the neonatal intensive care unit. Abbreviations Table 3. Thumbnails Number Full name Abbreviation 1 Pulmonary hemorrhage PH 2 receiver operating characteristic ROC 3 decision curve analysis DCA 4 Area Under the Cure AUC 5 hemodynamically significant patent ductus arteriosus hsPDA 6 very low birth weight VLBW 7 extremely low birth weight ELBW 8 gestational age GA 9 neonatal respiratory distress syndrome NRDS 10 Neonatal Intensive Care Unit NICU 11 gestational diabetes mellitus GDM 12 hypertension PIH 13 in vitro fertilization IVF 14 birth weight BW 15 small for gestational age SGA 16 Ureaplasma urealyticum UU 17 standard deviation SD 18 interquartile range IQR 19 odds ratios OR 20 confidence intervals CIs Declarations Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Sir Run Run Shaw Hospital (Approval No.2025-0179). Written informed consent was obtained from all participants' legal guardians. Consent for publication Not applicable. Availability of data The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This study was supported by the Department of Research, Run Run Shaw Hospital, Zhejiang University School of Medicine. Authors' contributions MQ conceptualized and designed the study and was responsible for validation, methodology, data collection, data analysis, writing, and the first draft. ZJ was responsible for the methodology of the study, overseeing data collection, and critically reviewing the manuscript for important intellectual content. SY,CS, XZ collected data, was responsible for data interpretation, and conducted preliminary analysis. All authors approved the final manuscript as submitted and agreed to be responsible for all aspects of this work Acknowledgements We are particularly grateful to the study participants for their contributions to the study. 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Surviving Sepsis Campaign International Guidelines for the Management of Septic Shock and Sepsis-Associated Organ Dysfunction in Children[J]. Pediatr Crit Care Med,2020,21(2):e52-e106 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 09 Sep, 2025 Editor assigned by journal 27 Aug, 2025 First submitted to journal 25 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7390997","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512665383,"identity":"8c561276-4894-4101-8aaa-d78a67c72ada","order_by":0,"name":"Min Qi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACNmbGhgMf/9TwMLY3HyBOCx97c+PDmQ3H5Jh7jiUQp0WO53izMW8DszH7jBwDIh0mkdgmzbuDLbG358zHG28Y7OR0G4jQIjn3jEzizPbezZZzGJKNzQ4QoUXiDRtb4saes9ukeRgOJG4jSgsPG3Pi/hs5z4jUwnOw2ZC3jdmYcUYOG5Fa2BsbH844c0yOseeYseUcAyL8It/M/uDAhwpwVD688abCTo6gFhQgwUNk1CBrIVXHKBgFo2AUjAgAAA1RRlHc0nDtAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University School of Medicine Sir Run Run Shaw Hospital","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Qi","suffix":""},{"id":512665384,"identity":"2a7cab0d-4605-4d5e-82d5-4a35894673d5","order_by":1,"name":"Shibing Yang","email":"","orcid":"","institution":"Zhejiang University School of Medicine Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shibing","middleName":"","lastName":"Yang","suffix":""},{"id":512665385,"identity":"d5e22f3c-d8e9-4c58-b8bf-69627a71b3e0","order_by":2,"name":"Chunyan Shi","email":"","orcid":"","institution":"Zhejiang University School of Medicine Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Shi","suffix":""},{"id":512665386,"identity":"8ac72eec-90f0-4c9c-a683-f64577403d1b","order_by":3,"name":"Xuandong Zhang","email":"","orcid":"","institution":"Zhejiang University School of Medicine Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuandong","middleName":"","lastName":"Zhang","suffix":""},{"id":512665387,"identity":"b7fd121a-9033-483c-9202-d0ffeddbb482","order_by":4,"name":"Zhou Jiang","email":"","orcid":"https://orcid.org/0000-0002-0085-313X","institution":"Zhejiang University School of Medicine Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-08-17 07:36:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7390997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7390997/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91527236,"identity":"8eee24b0-dfe4-435b-b336-03107a04ee73","added_by":"auto","created_at":"2025-09-17 11:16:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86449,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the Study Design\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7390997/v1/1baef7f538a606d23c803a9c.jpg"},{"id":91527235,"identity":"00d5cda9-9ccf-4b1a-9725-7db2b1b1e79f","added_by":"auto","created_at":"2025-09-17 11:16:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52161,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for Predicting the Risk of Pulmonary Hemorrhage in Preterm Infants with Gestational Age \u0026lt;32 Weeks\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7390997/v1/fc4284c9453879f5cdffbf33.jpg"},{"id":91527240,"identity":"0a245640-a54c-4e56-bd25-de8794e50472","added_by":"auto","created_at":"2025-09-17 11:16:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) Curves and Area Under the Curve (AUC) for the Prediction Model in the Training set (a)and Validation Set(b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7390997/v1/f4ddb28ec2170318d3bf661a.jpg"},{"id":91528359,"identity":"2371d534-3362-4adc-9999-582ecfa42e32","added_by":"auto","created_at":"2025-09-17 11:24:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70516,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration Curves of the Pulmonary Hemorrhage Risk Prediction Model in the Training set (a) and Validation Set (b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7390997/v1/793155006be518fae6de9e66.jpg"},{"id":91528360,"identity":"029be24d-1017-4901-8554-84c586d0ea5d","added_by":"auto","created_at":"2025-09-17 11:24:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50289,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision Curve Analysis (DCA) of the Pulmonary Hemorrhage Risk Prediction Model in the Training set (a) and Validation Set (b)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7390997/v1/83551b4e1588fc9f39e00d7c.jpg"},{"id":91529518,"identity":"26ff1a9a-b068-46ae-b044-2b123275969d","added_by":"auto","created_at":"2025-09-17 11:40:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1494589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7390997/v1/ddeffee6-aae5-484a-a57f-078821c02f18.pdf"}],"financialInterests":"","formattedTitle":"Development and Validation of a Nomogram-Based Risk Prediction Model for Pulmonary Hemorrhage in Preterm Infants Under 32 Weeks of Gestation","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreterm birth remains a major global health challenge. Infants born before 32 weeks of gestation, due to immature organ systems, face exceptionally high risks of severe complications and mortality\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Among this population, PH is a life-threatening complication. PH is a severe condition associated with acute respiratory deterioration, increased mortality, and long-term adverse outcomes, often occurring in critically ill neonates during the late stages of life. Studies indicate that infants with PH require prolonged mechanical ventilation, face higher risks of intraventricular hemorrhage, and experience extended oxygen dependency and hospitalization\u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Reported mortality rates in neonates with PH range from 40\u0026ndash;60%[6]. Epidemiology shows that PH incidences of 3%-32% in very low birth weight (VLBW) infants and 11%-18.8% in extremely low birth weight (ELBW) infants\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. A Brazilian cohort study identified PH as an independent risk factor for mortality in neonates with gestational age (GA)\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The incidence of PH in infants born before 32 weeks is significantly higher than in other neonates\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eTo date, both domestic and international studies have demonstrated associations between PH and hsPDA, early-onset sepsis, disseminated intravascular coagulation, delivery room intubation, lower gestational age, low birth weight, low Apgar scores, severe neonatal respiratory distress syndrome (NRDS), and repeated surfactant administration\u003csup\u003e[7.8.11.12]\u003c/sup\u003e. However, no existing models integrate dynamic clinical indicators to quantify PH risk, making its prediction particularly challenging\u0026mdash;especially in extremely preterm infants, whose pathophysiology differs from other preterm populations. Early identification of high-risk infants remains a critical unmet need. According to the 2023 European Consensus Guidelines on the Management of Neonatal Respiratory Distress Syndrome, early warning of PH is essential for improving clinical outcomes\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. This highlights the urgent need for reliable predictive tools to guide clinical decision-making.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to develop the first dedicated predictive model for PH in infants born before 32 weeks of gestation, providing evidence-based support for clinical practice. We seek to identify independent predictors of PH and translate these findings into a clinically visual nomogram. This tool will enable early warning of PH onset, facilitate timely diagnosis and treatment, and ultimately improve patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eThis single-center retrospective cohort study enrolled preterm infants with a GA\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks who were admitted to the Neonatal Intensive Care Unit (NICU) of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, between June 1, 2018, and October 31, 2023. The exclusion criteria were as follows: (1) Infants with severe congenital malformations (e.g., complex cardiac anomalies, chromosomal abnormalities); (2) death or treatment withdrawal within 24 hours; (3) Transfer to other pediatric hospitals within the first postnatal week for surgical conditions (e.g., gastrointestinal perforation, congenital heart disease requiring immediate intervention). (4) Incomplete clinical data.A total of 413 infants were initially screened. After excluding 11 cases (3 due to parental withdrawal of care, 2 deaths within 24 hours, 3 transfers for surgical management, 1 major congenital anomaly, and 4 with incomplete data), 402 infants were included in the final analysis. These infants were randomly allocated into a training set (277 cases, 70%) and a validation set (125 cases, 30%) using SPSS 25.0. The flowchart of the study design is shown in Fig. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eTwo blinded researchers extracted data from electronic medical records, with discrepancies resolved by a senior neonatologist. Variables included:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMaternal factors\u003c/b\u003e:Primiparity, gestational diabetes mellitus (GDM), hypertension (PIH), antenatal steroids, in vitro fertilization (IVF), prolonged membrane rupture (\u0026gt;\u0026thinsp;24 hours), cesarean delivery, multiple gestation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eNeonatal factors\u003c/b\u003e:GA, birth weight (BW), small for GA (SGA), birth asphyxia, delivery room intubation, surfactant doses, invasive ventilation on day 1(defined as endotracheal intubation with mechanical ventilation), hsPDA, Sepsis (culture-confirmed or clinical diagnosis with elevated inflammatory markers), Ureaplasma urealyticum (UU) infection,Thrombocytopenia (platelet count\u0026thinsp;\u0026lt;\u0026thinsp;100\u0026times;10⁹/L), Severe acidosis (pH\u0026thinsp;\u0026lt;\u0026thinsp;7.2 on arterial blood gas), edema(generalized or pulmonary edema documented clinically or radiologically), High lactate (serum lactate\u0026thinsp;\u0026gt;\u0026thinsp;5 mmol/L), shock(defined as hypotension requiring vasopressors or fluid resuscitation), and blood product transfusion (platelets, fresh frozen plasma, or packed red blood cells), Occurrence of pulmonary hemorrhage.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eFor patients with thrombocytopenia, severe acidosis, edema, high lactate, shock, and transfusion of blood products, data from the first 3 days before pulmonary hemorrhage were collected for the pulmonary hemorrhage group, and data from the first week after birth were collected for non pulmonary hemorrhage patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic Criteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePulmonary Hemorrhage (PH)\u003c/b\u003e \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e:Clinical diagnosis required:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003ea. Presence of bloody fluid aspirated from the endotracheal tube, accompanied by an acute need for escalated respiratory support (e.g., increased FiO₂, higher ventilation pressures) or red blood cell transfusion.\u003c/p\u003e\u003cp\u003eb. Chest radiograph demonstrating new-onset diffuse infiltrates or opacities.\u003c/p\u003e\u003cp\u003e\u003col start=\"2\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHsPDA\u003c/b\u003e\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eHaving all of the following echocardiographic characteristics: a.Ductal diameter\u0026thinsp;\u0026ge;\u0026thinsp;1.5 mm;Left atrial-to-aortic root ratio (LA/Ao)\u0026thinsp;\u0026ge;\u0026thinsp;1.5 Diastolic retrograde flow in the descending aorta.\u003c/p\u003e\u003cp\u003eSimultaneously present with one of the following clinical manifestations: systolic murmurs in the precordial area, increased pulsation, water pulse, increased pulse pressure difference, and increased demand for respiratory support.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS 25.0 and StataSE 15. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) if normally distributed or median (interquartile range, IQR) if non-normally distributed. Categorical variables were reported as frequencies (%). Between-group comparisons utilized Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, Mann-Whitney \u003cem\u003eU\u003c/em\u003e-test, χ\u0026sup2; test, or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Development\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eUnivariate Analysis\u003c/b\u003e:Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate logistic regression were selected for multivariate analysis.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMultivariate Analysis\u003c/b\u003e:Backward stepwise logistic regression (likelihood ratio method) was applied to identify independent predictors. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Validation\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDiscrimination\u003c/b\u003e:Assessed using the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCalibration\u003c/b\u003e:Evaluated via calibration plots and the Hosmer-Lemeshow goodness-of-fit test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates good fit).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eClinical Utility\u003c/b\u003e:Decision curve analysis (DCA) quantified net benefits across threshold probabilities.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eGeneral Characteristics of the Study Population\u003c/h2\u003e\u003cp\u003eDuring the study period, 413 preterm infants\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks GA were born and admitted to the NICU. After excluding 11 cases (3 due to parental withdrawal of care within 24 hours, 2 deaths within 24 hours, 3 transfers for surgical conditions, 1 major congenital anomaly, and 4 with incomplete data), 402 infants were included in the final analysis. The overall incidence of pulmonary hemorrhage (PH) was 14.7% (59/402), with a median onset at 2 postnatal days. Of these, 40 cases (67.8%) occurred within the first postnatal week.\u003c/p\u003e\u003cp\u003eInfants were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;277, 70%) and a validation set (n\u0026thinsp;=\u0026thinsp;125, 30%). The training set had a median GA of 29.3 weeks (IQR: 27.8\u0026ndash;30.9) and a mean birth weight of 1271.1\u0026thinsp;\u0026plusmn;\u0026thinsp;327.3 g, with a PH incidence of 14.1% (39/277). The validation set had a median GA of 29.7 weeks (IQR: 27.6\u0026ndash;31.0) and a mean birth weight of 1285.8\u0026thinsp;\u0026plusmn;\u0026thinsp;364.3 g, with a PH incidence of 16.0% (20/125). Baseline characteristics were balanced between groups except for a higher prevalence of shock in the validation set (17.6% vs. 8.7%,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Detailed demographic and clinical comparisons are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003e\u003cb\u003eDemographic and Clinical Characteristics of Preterm Infants in the Training and Validation Sets\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraining set\u003c/p\u003e\u003cp\u003e(N=277)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValidation set\u003c/p\u003e\u003cp\u003e(N=125)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimiparity n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203(73.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92(73.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDM n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41(14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16(12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIH n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29(10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15(12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.649\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntenatal steroids n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179(64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76(60.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVF n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137(49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61(48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremature rupture of membranes(\u0026gt;\u0026thinsp;24 h) n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71(25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32(25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarean delivery n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245(88.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102(83.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple gestation n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133(48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61(48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e147(53.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGA(weeks),median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.3(27.8,30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.7(27.6,31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBW (g), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1271.1\u0026thinsp;\u0026plusmn;\u0026thinsp;327.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1285.8\u0026thinsp;\u0026plusmn;\u0026thinsp;364.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGA n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6(2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth asphyxia n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119(43.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59(47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntubation in delivery room n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96(34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41(32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurfactant administration\u0026thinsp;\u0026gt;\u0026thinsp;1 dose. n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44(15.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21(16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108(39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUU infection n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48(17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvasive ventilation on day1 n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132(47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58(46.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHsPDA n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72(26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35(28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThrombocytopenia n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47(17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere acidosis n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85(30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45(36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEdema n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18(6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(3.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh lactate n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49(17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShock n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24(8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood product transfusion n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33(11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39(14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePredictive Model Construction\u003c/h2\u003e\u003cp\u003eIn the training set, univariate logistic regression identified 14 variables significantly associated with PH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including lower GA (OR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.58\u0026ndash;0.86), birth asphyxia (OR\u0026thinsp;=\u0026thinsp;3.12, 95% CI: 1.53\u0026ndash;6.37), delivery room intubation (OR\u0026thinsp;=\u0026thinsp;4.20, 95% CI: 2.06\u0026ndash;8.55), multiple surfactant doses (OR\u0026thinsp;=\u0026thinsp;3.95, 95% CI: 1.91\u0026ndash;8.20), sepsis (OR\u0026thinsp;=\u0026thinsp;11.95, 95% CI: 4.80\u0026ndash;29.75), thrombocytopenia (OR\u0026thinsp;=\u0026thinsp;6.18, 95% CI: 2.95\u0026ndash;12.94), severe acidosis (OR\u0026thinsp;=\u0026thinsp;2.80, 95% CI: 1.41\u0026ndash;5.58), edema (OR\u0026thinsp;=\u0026thinsp;9.91, 95% CI: 3.62\u0026ndash;27.13), hyperlactatemia (OR\u0026thinsp;=\u0026thinsp;4.32, 95% CI: 2.07\u0026ndash;9.03), shock (OR\u0026thinsp;=\u0026thinsp;12.77, 95% CI: 5.14\u0026ndash;31.74), and invasive ventilation on the first postnatal day (OR\u0026thinsp;=\u0026thinsp;9.71, 95% CI: 3.67\u0026ndash;25.72) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMultivariate backward logistic regression analysis revealed four independent predictors of PH:\u003cb\u003ehsPDA\u003c/b\u003e(OR\u0026thinsp;=\u0026thinsp;2.62, 95% CI: 1.67\u0026ndash;5.87,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019).\u003cb\u003einvasive ventilation on the first postnatal day\u003c/b\u003e(OR\u0026thinsp;=\u0026thinsp;3.86, 95% CI: 1.32\u0026ndash;11.26,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013).\u003cb\u003esepsis\u003c/b\u003e(OR\u0026thinsp;=\u0026thinsp;4.56, 95% CI: 1.64\u0026ndash;12.64,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003cb\u003eshock\u003c/b\u003e(OR\u0026thinsp;=\u0026thinsp;4.25, 95% CI: 1.56\u0026ndash;11.62,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).These variables were integrated into a nomogram for PH risk stratification (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eUnivariate and Multivariate Logistic Regression Analysis of Risk Factors for Pulmonary Hemorrhage in Preterm Infants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVarialies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMultivariate regression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimiparity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53(0.26,1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.93(0.84,4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePIH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.42(0.10,1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntenatal steroids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97(0.48,1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.09(0.55,2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremature rupture of membranes(\u0026gt;\u0026thinsp;24 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.78(0.87,3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarean delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.67(0.61,11.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple gestation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92(0.47,1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70(0.84,3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGA(weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.71(0.58,0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBW (g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.998(0.997,0.999)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.23(0.14,10.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth asphyxia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.12(1.53,6.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntubation in delivery room\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.20(2.06,8.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurfactant administration\u0026thinsp;\u0026gt;\u0026thinsp;1 dose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.95(1.91,8.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.95 (4.80,29.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.56(1.64,12.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUU infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.67(0.25,1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvasive ventilation on day1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.71(3.67,25.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.86(1.32,11.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHsPDA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.32(1.65,6.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.62(1.67,5.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThrombocytopenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.18(2.95,12.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere acidosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.80(1.41,5.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEdema\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.91(3.62,27.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh lactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.32(2.07,9.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.77(5.14,31.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.25(1.56,11.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood product transfusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.68(1.14,6.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003ePredictive Model Validation\u003c/h3\u003e\n\u003cp\u003eThe model demonstrated robust discrimination in both the training and validation sets, with AUC values of 0.858 (95% CI: 0.798\u0026ndash;0.918) and 0.879 (95% CI: 0.810\u0026ndash;0.948), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Calibration plots showed excellent agreement between predicted probabilities and observed outcomes, further supported by nonsignificant Hosmer-Lemeshow test results (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.957 for training set;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.738 for validation set;Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Decision curve analysis (DCA) confirmed significant clinical net benefits across threshold probabilities of 10\u0026ndash;50%, outperforming both \"treat-all\" and \"treat-none\" strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and validated a nomogram-based risk prediction model for PH in preterm infants with a GA\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks. The model identified four independent risk factors: hsPDA, invasive respiratory support on the first postnatal day, sepsis, and shock. The model demonstrated excellent predictive performance, with AUC values of 0.858 and 0.879 in the training and validation sets, respectively, and showed good calibration and clinical utility through decision curve analysis (DCA).There are very few studies with clinical applications, and one researcher developed a prediction model for respiratory distress syndrome with PH in very preterm infants [15], but this model type was not tested using independent samples, so the model's performance was poor and could not be widely generalized. This study is the first to develop a specific prediction tool for pulmonary hemorrhage in a group of very preterm infants, which has a certain degree of reliability and application value, and provides a quantitative assessment tool for the early identification of high-risk infants in clinical practice.\u003c/p\u003e\u003cp\u003eIn this study, we found that hsPDA increased the risk of pulmonary hemorrhage in preterm infants (OR\u0026thinsp;=\u0026thinsp;2.62), which was consistent with the high predictive value of arterial catheterization insufficiency for massive pulmonary hemorrhage reported by LI et al \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e OR\u0026thinsp;=\u0026thinsp;11.4 Many studies have shown that PDA is associated with pulmonary hemorrhage \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e Kluckow and Evans reported that PH in preterm infants is associated with a high number of catheter shunt and high pulmonary blood flow \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The HsPDA, which shunts from left to right through the arterial conduit, leads to overload of the pulmonary circulation. The limited responsiveness of the preterm left ventricle to changes in preload and afterload, increased pulmonary arterial blood flow and vascular pressures may also impair cardiac function and increase the risk of pulmonary hemorrhage, which may also be induced by increased hydrostatic pressure in the pulmonary capillaries \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Of course, some studies have shown that HsPDA is not an independent risk factor for pulmonary hemorrhage \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, suggesting that further research is needed to elucidate the hemodynamic mechanisms linking hsPDA to PH.\u003c/p\u003e\u003cp\u003eNotably, the need for invasive respiratory support on the first postnatal day (OR\u0026thinsp;=\u0026thinsp;3.86) had a more prominent predictive validity, suggesting the key role of mechanical ventilation-related pneumatic pressure injury and oxygen toxicity in the pathogenesis of pulmonary hemorrhage in very preterm premature infants. This corroborates with Sweet et al. who suggested that the intensity of respiratory support is positively associated with the risk of lung injury \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Invasive ventilatory support on the first day may lead to alveolar hyperexpansion, causing pressure damage to alveolar capillaries and promoting PH formation. A single-center study found an association between invasive first day of life and the development of PH, with an increased incidence of PH in preterm infants receiving invasive ventilation on the first day \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Of course ventilator therapy is not necessarily the direct cause of pulmonary hemorrhage. The need for invasive ventilatory support early in life can also indicate neonatal hypoxia, severe lung disease, or critical condition, which also predicts a greater likelihood of pulmonary hemorrhage.\u003c/p\u003e\u003cp\u003eThe present study showed that sepsis has a high predictive value for pulmonary hemorrhage in preterm infants (OR\u0026thinsp;=\u0026thinsp;4.56), which is consistent with the results of Wang et al.'s study \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, whose study was conducted on preterm infants with a birth weight of less than 1,000 grams, and which showed that preterm sepsis is an independent risk factor for pulmonary hemorrhage in preterm infants. In sepsis, pathogens and related toxins, activate immune cells and release a large number of pro-inflammatory factors, which directly damage the endothelial cells of pulmonary blood vessels. These disrupt the vascular barrier function, leading to extravasation of plasma proteins and erythrocytes, resulting in pulmonary hemorrhage \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The high predictive value of sepsis for pulmonary hemorrhage stems not only from its direct pro-inflammatory effect, but may also be closely related to coagulation dysfunction. Sepsis may also induce disseminated intravascular coagulation, manifested by thrombocytopenia, coagulation factor depletion, and secondary hyperfibrinolysis. This process may exacerbate the risk of pulmonary microvascular thrombosis with localized bleeding \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In the present study, due to the small body weight of very early preterm infants, dynamic coagulation indices (e.g., D-dimer) were not included in this study due to blood sampling limitations. Although thrombocytopenia did not enter the final model, its strong association in univariate analysis (OR\u0026thinsp;=\u0026thinsp;6.18) suggests that coagulation dysfunction may play a role in specific subgroups, warranting further investigation.\u003c/p\u003e\u003cp\u003eThe independent predictive effect of shock on pulmonary hemorrhage (OR\u0026thinsp;=\u0026thinsp;4.25) reflects the complex interaction between circulatory failure and lung injury. Early in shock, the body maintains cardiac and cerebral perfusion by vasoconstriction, but the pulmonary circulation, as a low-pressure system, is susceptible to endothelial cell hypoxic injury due to hypoperfusion. In the late stage of shock, compensatory mechanisms collapse, and the decline in cardiac output exacerbates pulmonary stasis, increases capillary hydrostatic pressure, and promotes hemorrhage\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Hemodynamic instability in preterm infants makes circulatory management relatively difficult because the immaturity of the preterm myocardium makes it extremely sensitive to changes in afterload. A sudden decrease in left ventricular output during shock can lead to pulmonary venous stasis, elevating capillary hydrostatic pressure and directly inducing hemorrhage\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In addition, reperfusion injury during correction of shock may further disrupt pulmonary vascular endothelial connectivity and increase vascular permeability through mitochondrial reactive oxygen species bursts \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In clinical practice, fluid management in children with shock needs to be refined to avoid aggravation of pulmonary edema by over-expansion, and lactate levels need to be monitored dynamically to assess the recovery of tissue oxygenation \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCompared to previous studies, our model offers several innovations. First, it focuses exclusively on the high-risk population of preterm infants\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks GA, addressing a gap in existing models that often include mixed gestational age groups. Second, it incorporates dynamic early-life indicators, such as respiratory support intensity, which provide more timely risk assessment than static factors like birth weight. Finally, the use of a nomogram facilitates rapid, bedside risk stratification, enhancing clinical applicability. The model maintained a stable predictive efficacy (AUC 0.879) in the validation set, suggesting that it has good generalization ability.\u003c/p\u003e\u003cp\u003eDespite its strengths, this study has limitations. As a single-center retrospective study, it may be subject to selection bias. Future multicenter prospective studies with larger cohorts are needed to validate and refine the model. Additionally, subgroup analyses, particularly for infants\u0026thinsp;\u0026lt;\u0026thinsp;28 weeks GA, were not performed due to sample size constraints. Future research could integrate biomarkers, such as vascular endothelial growth factor, and advanced hemodynamic monitoring parameters to further optimize the model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe successfully developed and validated a column-line graph-based predictive model for pulmonary hemorrhage in very preterm infants that integrates four independent risk factors: hemodynamically significant arterial ductus arteriosus, invasive respiratory support on the first day of life, sepsis, and shock. The model demonstrated strong discrimination, calibration, and clinical utility. Despite limitations, the model represents an important step toward personalizing care for very preterm infants in the neonatal intensive care unit.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eTable 3. Thumbnails\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eFull name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003ePulmonary hemorrhage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003ereceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003edecision curve analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eArea Under the Cure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003ehemodynamically significant patent ductus arteriosus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ehsPDA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003every low birth weight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eVLBW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eextremely low birth weight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eELBW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003egestational age\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eneonatal respiratory distress syndrome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eNRDS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eNeonatal Intensive Care Unit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eNICU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003egestational diabetes mellitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eGDM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003ehypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003ePIH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003ein vitro fertilization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eIVF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003ebirth weight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eBW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003esmall for gestational age\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eSGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eUreaplasma urealyticum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eUU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003estandard deviation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003einterquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eodds ratios\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003econfidence intervals\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eCIs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Sir Run Run Shaw Hospital (Approval No.2025-0179). Written informed consent was obtained from all participants\u0026apos; legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Department of Research, Run Run Shaw Hospital, Zhejiang University School of Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMQ conceptualized and designed the study and was responsible for validation, methodology, data collection, data analysis, writing, and the first draft.\u003c/p\u003e\n\u003cp\u003eZJ was responsible for the methodology of the study, overseeing data collection, and critically\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ereviewing the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eSY,CS, XZ collected data, was responsible for data interpretation, and conducted preliminary\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eanalysis.\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript as submitted and agreed to be responsible for all aspects of this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are particularly grateful to the study participants for their contributions to the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/em\u003eNeonatal Intensive Care Unit, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou 310016, China;\u003cem\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOhuma E O, Moller A B, Bradley E, et al. National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis[J]. Lancet,2023,402(10409):1261-1271.\u003c/li\u003e\n\u003cli\u003eAhmad K A, Bennett M M, Ahmad S F, et al. Morbidity and mortality with early pulmonary haemorrhage in preterm neonates[J]. Arch Dis Child Fetal Neonatal Ed,2019,104(1):F63-F68.\u003c/li\u003e\n\u003cli\u003eFerreira C H, Carmona F, Martinez F E. Prevalence, risk factors and outcomes associated with pulmonary hemorrhage in newborns[J]. J Pediatr (Rio J),2014,90(3):316-322.\u003c/li\u003e\n\u003cli\u003eBaier R J, Loggins J, Kruger T E. Increased interleukin-8 and monocyte chemoattractant protein-1 concentrations in mechanically ventilated preterm infants with pulmonary hemorrhage[J]. Pediatr Pulmonol,2002,34(2):131-137.\u003c/li\u003e\n\u003cli\u003eScholl J E, Yanowitz T D. Pulmonary hemorrhage in very low birth weight infants: a case-control analysis[J]. J Pediatr,2015,166(4):1083-1084.\u003c/li\u003e\n\u003cli\u003eHadzic D, Zulic E, Salkanovic-Delibegovic S, et al. SHORT-TERM OUTCOME OF MASSIVE PULMONARY HEMORRHAGE IN PRETERM INFANTS IN TUZLA CANTON[J]. Acta Clin Croat,2021,60(1):82-88.\u003c/li\u003e\n\u003cli\u003eWang T T, Zhou M, Hu X F, et al. Perinatal risk factors for pulmonary hemorrhage in extremely low-birth-weight infants[J]. World J Pediatr,2020,16(3):299-304.\u003c/li\u003e\n\u003cli\u003eJung J K, Kim E Y, Heo J S, et al. Analysis of perinatal risk factors for massive pulmonary hemorrhage in very low birth weight infant: A nationwide large cohort database[J]. Early Hum Dev,2024,191:105977.\u003c/li\u003e\n\u003cli\u003eYen T A, Wang C C, Hsieh W S, et al. Short-term outcome of pulmonary hemorrhage in very-low-birth-weight preterm infants[J]. Pediatr Neonatol,2013,54(5):330-334.\u003c/li\u003e\n\u003cli\u003ede Castro M P, Rugolo L M, Margotto P R. [Survival and morbidity of premature babies with less than 32 weeks of gestation in the central region of Brazil][J]. Rev Bras Ginecol Obstet,2012,34(5):235-242.\u003c/li\u003e\n\u003cli\u003eChen Y Y, Wang H P, Lin S M, et al. Pulmonary hemorrhage in very low-birthweight infants: risk factors and management[J]. Pediatr Int,2012,54(6):743-747.\u003c/li\u003e\n\u003cli\u003eFerreira C H, Carmona F, Martinez F E. Prevalence, risk factors and outcomes associated with pulmonary hemorrhage in newborns[J]. J Pediatr (Rio J),2014,90(3):316-322.\u003c/li\u003e\n\u003cli\u003eSweet D G, Carnielli V P, Greisen G, et al. European Consensus Guidelines on the Management of Respiratory Distress Syndrome: 2022 Update[J]. Neonatology,2023,120(1):3-23.\u003c/li\u003e\n\u003cli\u003eHamrick S, Sallmon H, Rose A T, et al. Patent Ductus Arteriosus of the Preterm Infant[J]. Pediatrics,2020,146(5).\u003c/li\u003e\n\u003cli\u003eLiu Y Q, Tao Y, Cai T N, et al. Development of a simplified model and nomogram for the prediction of pulmonary hemorrhage in respiratory distress syndrome in extremely preterm infants[J]. BMC Pediatr,2024,24(1):760.\u003c/li\u003e\n\u003cli\u003eLi J, Xia H, Ye L, et al. Exploring prediction model and survival strategies for pulmonary hemorrhage in premature infants: a single-center, retrospective study[J]. Transl Pediatr,2021,10(5):1324-1332.\u003c/li\u003e\n\u003cli\u003eAlfaleh K, Smyth J A, Roberts R S, et al. Prevention and 18-month outcomes of serious pulmonary hemorrhage in extremely low birth weight infants: results from the trial of indomethacin prophylaxis in preterms[J]. Pediatrics,2008,121(2):e233-e238.\u003c/li\u003e\n\u003cli\u003eKluckow M, Evans N. Ductal shunting, high pulmonary blood flow, and pulmonary hemorrhage[J]. J Pediatr,2000,137(1):68-72.\u003c/li\u003e\n\u003cli\u003eBattin M R, Knight D B, Kuschel C A, et al. Improvement in mortality of very low birthweight infants and the changing pattern of neonatal mortality: the 50-year experience of one perinatal centre[J]. J Paediatr Child Health,2012,48(7):596-599.\u003c/li\u003e\n\u003cli\u003eSu B H, Lin H Y, Huang F K, et al. Circulatory Management Focusing on Preventing Intraventricular Hemorrhage and Pulmonary Hemorrhage in Preterm Infants[J]. Pediatr Neonatol,2016,57(6):453-462.\u003c/li\u003e\n\u003cli\u003eAngus D C, van der Poll T. Severe sepsis and septic shock[J]. N Engl J Med,2013,369(9):840-851.\u003c/li\u003e\n\u003cli\u003eHotchkiss R S, Karl I E. The pathophysiology and treatment of sepsis[J]. N Engl J Med,2003,348(2):138-150.\u003c/li\u003e\n\u003cli\u003eSemeraro N, Ammollo C T, Semeraro F, et al. Sepsis-associated disseminated intravascular coagulation and thromboembolic disease[J]. Mediterr J Hematol Infect Dis,2010,2(3):e2010024.\u003c/li\u003e\n\u003cli\u003eGando S, Levi M, Toh C H. Disseminated intravascular coagulation[J]. Nat Rev Dis Primers,2016,2:16037.\u003c/li\u003e\n\u003cli\u003eVincent J L, De Backer D. Circulatory shock[J]. N Engl J Med,2013,369(18):1726-1734.\u003c/li\u003e\n\u003cli\u003eGranger D N, Kvietys P R. Reperfusion injury and reactive oxygen species: The evolution of a concept[J]. Redox Biol,2015,6:524-551.\u003c/li\u003e\n\u003cli\u003ePerrone S, Negro S, Tataranno M L, et al. Oxidative stress and antioxidant strategies in newborns[J]. J Matern Fetal Neonatal Med,2010,23 Suppl 3:63-65.\u003c/li\u003e\n\u003cli\u003eWeiss S L, Peters M J, Alhazzani W, et al. Surviving Sepsis Campaign International Guidelines for the Management of Septic Shock and Sepsis-Associated Organ Dysfunction in Children[J]. Pediatr Crit Care Med,2020,21(2):e52-e106\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary hemorrhage, preterm infants, risk factors, risk prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7390997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7390997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackround:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePulmonary hemorrhage (PH) is a serious lung disease that endangers the safety of premature infants, but its occurrence is difficult to predict.Our research aims to establish and validate a predictive model for pulmonary hemorrhage in extremely premature infants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis single-center retrospective cohort study included 402 preterm infants admitted to the NICU of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, between June 1, 2018, and October 31, 2023. Participants were randomly allocated into a training set (n=277) and a validation set (n=125).Univariate and multivariate logistic regression analyses identified independent risk factors. A nomogram prediction model was constructed and evaluated using ROCcurves, calibration plots, and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:A\u003c/strong\u003emong 402 infants, 59 developed PH (incidence: 14.7%). Independent risk factors included hemodynamically significant patent ductus arteriosus (hsPDA; OR=2.62, P=0.019), invasive respiratory support on the first postnatal day (OR=3.86, P=0.013), sepsis (OR=4.56, P=0.004), and shock (OR=4.25, P=0.005). The model demonstrated excellent predictive performance in both training and validation sets (AUC: 0.858 vs. 0.879). Hosmer-Lemeshow tests (training set P=0.957; validation set P=0.738) and calibration curves indicated good consistency. DCA confirmed significant clinical net benefits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThe developed nomogram provides a validated, visually accessible tool for early identification of high-risk preterm infants, enabling timely interventions to improve outcomes.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram-Based Risk Prediction Model for Pulmonary Hemorrhage in Preterm Infants Under 32 Weeks of Gestation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 11:16:33","doi":"10.21203/rs.3.rs-7390997/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-09-10T13:46:56+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-09T17:09:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-27T12:26:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Italian Journal of Pediatrics","date":"2025-08-26T00:14:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5f0601d-5961-4e80-b8e0-5151f8d9ea85","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-17T11:16:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 11:16:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7390997","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7390997","identity":"rs-7390997","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}