Development and Validation of a Multivariate Nomogram for Predicting Retinopathy of Prematurity in Infants with Gestational Age ≤ 34 Weeks

Development and Validation of a Multivariate Nomogram for Predicting Retinopathy of Prematurity in Infants with Gestational Age ≤ 34 Weeks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and Validation of a Multivariate Nomogram for Predicting Retinopathy of Prematurity in Infants with Gestational Age ≤ 34 Weeks Leilei Shen, Juan Zeng, Min Tao, Chenghuan Zhang, Sheng Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4791992/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To delineate risk factors and develop a predictive nomogram for retinopathy of prematurity (ROP) in infants with gestational age (GA) ≤ 34 weeks. Methods We conducted a comprehensive retrospective analysis of infants with GA ≤ 34 weeks, divided into ROP and non-ROP groups based on fundus screening results. Clinical and laboratory data were collected to identify risk factors associated with ROP. Multivariable logistic regression was performed to identify independent predictors, and a nomogram was developed to predict the occurrence of ROP in infants with GA ≤ 34 weeks. Results Our analysis identified five independent risk factors for ROP in infants with GA ≤ 34 weeks: hypertensive disorders of pregnancy (HDP), number of blood transfusions, oxygen therapy time (OTT), oxygen therapy concentration (OTC) > 50%, and blood glucose spikes in the first postnatal week. These predictors were incorporated into a nomogram to estimate individual ROP risk. The predictive model achieved a C-index of 0.923 (95% CI: 0.888–0.959), indicating high predictive accuracy. Internal validation of the nomogram demonstrated excellent calibration and practical utility for clinical decision-making. Conclusions The validated nomogram, based on five critical factors, provides clinicians with a reliable tool for assessing the risk of developing ROP in infants with GA ≤ 34 weeks. This tool has the potential to improve outcomes by facilitating timely and appropriate therapeutic interventions. Health sciences/Diseases/Eye diseases/Retinal diseases Health sciences/Medical research/Paediatric research Premature Infants Retinopathy of Prematurity Risk Factors Predictive Nomogram Neonatal Health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Retinopathy of prematurity (ROP) is a devastating neurovascular disease of the retina in newborn infants that can lead to visual defects or even blindness, accounting for approximately 6–8% of the causes of blindness in children [ 1 ] . With advances in perinatal medicine and neonatology and the widespread establishment of neonatal intensive care units (NICUs), survival rates for preterm and low-birth-weight infants have increased significantly. As a result, the number of infants at risk for ROP has increased. The incidence of ROP varies widely across different countries and is linked to the socioeconomic development as well as the quality and accessibility of health care facilities [ 2 ] . In low and middle-income countries an “epidemic” of ROP blindness is currently occurring. In 2010, ten countries including China accounted for nearly two- thirds of all cases of visual impairment due to ROP [ 3 ] . Recent data indicate that the incidence of ROP in low birth weight infants in China ranges from 8.2–17.8%, with the incidence in very premature infants as high as 65.1% [ 4 – 7 ] . Despite ongoing research, a comprehensive understanding of the risk factors for ROP remains elusive. To address this critical gap, we performed a retrospective analysis of 452 infants with gestational age (GA) ≤ 34 weeks. Our study aimed to delineate the relationships between clinical features, laboratory tests, and the occurrence of ROP. We developed and validated robust predictive models to improve early diagnosis and outcomes in this vulnerable population. Material and Methods We performed a retrospective analysis of clinical records of infants with GA ≤ 34 weeks who were admitted to our NICUs immediately after birth, from January 2018 to January 2024. Inclusion criteria: (1) Premature infants with GA ≤ 34 weeks; (2) Informed consent of the parents for fundus screening; (3) Complete clinical information was available. Referring to the diagnostic criteria of the “Guidelines for the prevention and treatment of oxygen and retinopathy in preterm infants” [ 8 ] , infants were divided into ROP group and non-ROP group based on the occurrence of ROP. Exclusion criteria: (1) Congenital eye diseases such as retinoblastoma, congenital cataract, glaucoma, etc.; (2) Hereditary metabolic diseases and severe congenital malformations; (3) Death during hospitalization, discontinuation of treatment, or incomplete clinical data; (4) Failure to complete the fundus examination. This study was approved by the Ethics Committee (KY2024171) and adhered to the tenets of the Declaration of Helsinki. All data were completely anonymized to ensure patient confidentiality. According to the "Chinese Guidelines for Screening Retinopathy of Prematurity (2014)" [ 9 ] , the initial screening for fundus lesions was performed at 4 to 6 weeks after birth or at 31 to 32 weeks of corrected GA. The scope of the screening included the peripheral retinal blood vessels to ensure comprehensive lesion detection. Relevant definitions: (1) Number of blood transfusions: The cumulative number of transfusions of blood products, including red cells, plasma, platelets, and other blood components. (2) oxygen therapy time (OTT): The total duration of oxygen therapy, calculated by adding the time of mechanical ventilation, hooded oxygen therapy, and nasal cannula oxygen therapy; (3) Oxygen therapy concentration (OTC) > 50%: The concentration of oxygen used during oxygen therapy when it exceeds 50%; (4) Hyperglycemia (< 1week): In the first postnatal week, when the blood glucose value is monitored ≥ 7.0 mmol/L, the peripheral blood glucose on the contralateral foot is re-measured. If the blood glucose value is still ≥ 7.0 mmol/L, hyperglycemia is diagnosed; (5) Blood sugar spikes (< 1week): The highest blood glucose value recorded during the first postpartum week; (6) Average blood sugar (< 1week): The sum of all blood glucose monitoring values during the first postnatal week divided by the total number of monitoring sessions. Basic neonatal demographic information was collected from the medical records, including gender, gestational age, birth weight, mode of delivery, maternal gestational comorbidities, and prenatal and intrapartum conditions. Additionally, data on comorbidities, blood glucose levels in the first postnatal week, and details of oxygen therapy were collected. Potential predictors were first identified by univariate analysis. Significant variables were then included in multivariate logistic regression analyses to develop a prediction model for ROP in infants with GA ≤ 34 weeks. Subsequently, internal verification was conducted to create a nomogram with excellent calibration and discrimination capabilities. In the multivariate analysis, variables with P < 0.05 were included in the nomogram. The foundation of the nomograph lies in scaling each regression coefficient in multiple logistic regression to a range of 0-100 points. The cumulative score, representing the predictive probability, can be obtained by summing up the scores assigned to each variable. The prediction accuracy and consistency of the model are assessed using the calibration curve, receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), consistency index (C index), and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) were also assessed. The net benefits of the model to neonates are reflected by decision curve analysis (DCA). By bootstrapping 1,000 resamples, identification and calibration are assessed. This study uses ROP as the dependent variable to construct a prediction model, takes the AUC value of the prediction model as the main index, and utilizes PASS 15 software (NCSS, Kaysville, Utah, USA) to calculate the Power of the current sample size. Finally, this study included 65 cases of ROP and 387 cases of non-ROP. The AUC value of the prediction model was 0.923. Under the condition of setting a two-sided test α = 0.05, we inputted data into PASS 15 software (NCSS, Kaysville, Utah, USA) for Curve Tests and obtained a Power > 0.999. All statistical analyses were determined using SPSS software version 26.0 (IBM SPSS Statistics, Chicago, IL, USA) and R software (Version 4.1.2). Continuous variables are expressed as mean ± standard deviation. Non-parametric data are expressed as median (25–75% interquartile range). Categorical variables are presented as absolute numbers and percentages. Continuous data between two groups were compared by independent samples t test or Mann-Whitney U test. Categorical data between two groups were compared by Chi-square test. According to the results of logistic multivariate regression analysis, a predictive model was constructed, and receiver operator characteristic (ROC) curve analysis was used to evaluate the area under the curve (AUC) and its 95% CI, and the model was validated by random sampling 1000 times using the bootstrap method. Lastly, a calibration plot was constructed. The performance of the predictive model was evaluated by the Hosmer–Lemeshow test, AUC, and goodness-of-fit. Decision curve analysis (DCA) was used to validate the clinical net benefit rate of the predictive model. Results A total of 471 patients’ clinical information was obtained; 19 cases did not meet the inclusion criteria, of which 4 died during hospitalization, 7 did not complete fundus screening, and 8 had incomplete clinical data. Finally, 452 patients were enrolled in this study (Fig. 1 ). Preterm infants who met the diagnostic criteria for ROP in the “Guidelines for the prevention and treatment of oxygen and retinopathy in preterm infants” [ 8 ] were divided into the ROP group (n = 65), and the rest were placed into the non-ROP group (n = 387). The differences in all data between the ROP and non-ROP group were shown in Table 1 . ROP group had significant differences in GA, birth weight, hypertensive disorders of pregnancy (HDP), and fetal distress. In addition, comorbidities and treatments including bronchopulmonary dysplasia (BPD), sepsis, intracranial hemorrhage (ICH), respiratory distress syndrome (RDS), necrotizing enterocolitis (NEC), number of blood transfusions, OTT, OTC > 50%, parenteral nutrition (PN) > 14days(d), hyperglycemia (< 1week), blood sugar spikes (< 1week), and the average blood sugar (< 1week) showed a significant difference between ROP and non-ROP patients ( P < 0.05). Table 1 Comparison of baseline clinical characteristics comorbidities, and treatments between the non-ROP group and ROP group. non-ARDS group( n = 387) ARDS group( n = 65) P General data Gender, male, n(%) 187(48.3) 33(50.8) 0.715 Gestational age (weeks) 33.00(31.10,33.50) 30.2(28.80,32.25) < 0.001 Birth weight(kg) 1.72 ± 0.41 1.42 ± 0.40 < 0.001 Delivery mode, eutocia, n(%) 79(20.4) 20(30.8) 0.062 Pregnancy complication HDP, n(%) 67(17.3) 36(55.4) < 0.001 GDM, n(%) 121(31.3) 22(33.8) 0.679 ICP, n(%) 40(10.3) 6(9.2) 0.785 Perinatal condition ACT, n(%) 209(54.0) 36(55.4) 0.836 Fetal distress, n(%) 62(16.0) 26(40.0) < 0.001 Comorbidities BPD, n(%) 74(19.1) 39(60.0) < 0.001 Sepsis, n(%) 17(4.4) 15(23.1) < 0.001 ICH, n(%) 37(9.6) 16(24.6) < 0.001 PDA, n(%) 289(74.7) 50(76.9) 0.699 RDS, n(%) 170(43.9) 56(86.2) < 0.001 NEC, n(%) 20(5.2) 9(13.8) 0.018 Hyperglycemia (< 1week), n(%) 34(8.8) 37(56.9) < 0.001 Laboratory metrics Blood sugar spikes (< 1week)(mmol/L) 5.50(4.90,6.30) 8.20(5.60,8.55) < 0.001 Average blood sugar (< 1week)(mmol/L) 4.30(3.90,4.70) 5.10(4.50,6.10) < 0.001 Treatments Number of blood transfusions (n) 0.00(0.00,3.00) 6.00(2.5,12.50) < 0.001 OTT (days) 7.00(2.00,22.00) 37.00(7.5.00,54.50) 50%, n(%) 25(6.5) 35(53.8) 14d, n(%) 136(35.1) 49(75.4) < 0.001 HDP, hypertensive disorders of pregnancy; GDM, gestational diabetes mellitus;ICP, intrahepatic cholestasis of pregnancy༛ACT, antenatal corticosteroid therapy; BPD, bronchopulmonary dysplasia; ICH, intracranial hemorrhage; PDA, patent ductus arteriosus; RDS, respiratory distress syndrome; NEC, necrotizing enterocolitis; OTT, oxygen therapy time;OTC, oxygen therapy concentration༛ Multivariable logistic regression analysis showed that five factors were independent predictors of ROP in infants with GA ≤ 34 weeks, as follows: HDP ( P = 0.001, odds ratio[OR] 1.329, 95% confidence interval [CI] 1.766–8.077), number of blood transfusions ( P 50% ( P < 0.001, OR 1.714, 95% CI 2.413–12.766), and blood sugar spikes (< 1week) ( P = 0.025, OR 0.253, 95% CI 1.032–1.609) (Table 2 ). Table 2 Predictors of ROP in infants with GA ≤ 34 weeks. B SE Waldχ 2 P OR(95% CI) HDP 1.329 0.388 11.739 0.001 3.777(1.766 ~ 8.077) Blood transfusions 0.195 0.039 25.486 50% 1.714 0.425 16.262 < 0.001 5.550(2.413 ~ 12.766) Blood sugar spikes (< 1week) 0.253 0.113 4.998 0.025 1.288(1.032 ~ 1.609) Constants -5.865 0.766 58.583 < 0.001 - HDP, hypertensive disorders of pregnancy; OTT, oxygen therapy time;OTC, oxygen therapy concentration; The final regression model, as shown in Table 2 , can be represented by the formula Ln(P/1-P) = 1.329*HDP (yes = 1, no = 0) + 0.195*blood transfusions + 0.031*OTT + 1.714*OTC (> 50%=1, ≤ 50%=0) + 0.253*blood sugar spikes-5.865. According to the results of multivariable logistic regression analysis, the following factors were associated with ROP: HDP, number of blood transfusions, OTT, OTC > 50% and blood sugar spikes (< 1week). These five factors were included in the prediction model, and a nomogram was created to visualize the results of the regression analysis (Fig. 2 ). The ROC curves and corresponding AUC values generated by HDP, number of blood transfusions, OTT, OTC > 50%, blood sugar spikes ( 50%, blood sugar spikes (< 1week) and predictive modeling were obtained by using the maximum value of Jorden's index as the optimal threshold, and the associated sensitivity, specificity, positive predictive value and negative predictive value are shown in Table 3 . The difference in AUC values between the prediction model and each independent predictor is statistically significant ( P < 0.05). Table 3 Comparison of the prediction effect of each independent predictor and prediction model of ROP. AUC 95% CI P cutoff values Sensitivity Specificity PPV NPV HDP 0.690 0.614–0.766 < 0.001 - 55.4 82.7 35.0 91.7 Blood transfusions 0.799 0.741–0.857 < 0.001 1.5 86.2 62.0 27.6 96.4 OTT 0.776 0.708–0.844 50% 0.737 0.659–0.814 < 0.001 - 53.8 93.5 58.3 92.3 Blood sugar spikes (< 1week) 0.782 0.713–0.851 < 0.001 7.45 55.4 94.1 61.0 92.6 Predictive Model 0.923 0.888–0.959 < 0.001 0.11698 87.7 85.3 50.0 97.6 HDP, hypertensive disorders of pregnancy; OTT, oxygen therapy time;OTC, oxygen therapy concentration; By internally validating the accuracy of the prediction model using the Bootstrap resampling technique, and the Hosmer-Lemeshow test showed that χ 2 = 7.715, P = 0.462 > 0.05, the C-index was 0.923, with a strong fit between the original and corrected curves, demonstrating the effectiveness of the prediction model(Figure 4 ). Decision analysis (DCA) was performed on the data to evaluate the clinical utility of the prediction model. The analysis of the decision curve analysis that the model can significantly improve the clinical efficiency in predicting ROP in infants with GA ≤ 34 weeks, as shown in Fig. 5 . Discussion ROP, responsible for the majority of visual sequelae in premature infants, is one of the main preventable causes of childhood blindness [ 10 ] . Currently, the ROP screening guidelines for preterm infants vary between countries, especially between developed and developing countries [ 11 – 14 ] . We selected preterm infants with GA ≤ 34 weeks as study subjects in the hope that the resulting predictive model would ensure that infants who are likely to require treatment are not missed. This study showed that HDP, number of blood transfusions, OTT, OTC > 50% and blood sugar spikes (< 1week) were predictors of ROP in infants with GA ≤ 34 weeks. As we know, HDP was strongly associated with certain adverse outcomes in newborns (i.e., preterm birth, small for gestational age, restricted growth and development and intrauterine distress) [ 15 , 16 ] . Nawsherwan et al. [ 17 ] reported that HDP was associated with a higher risk of C-section, preterm birth, perinatal mortality, and low birth weight (LBW) in both singleton and twin pregnancies compared with the non-HDP. Neonates born to mothers with HDP had significantly lower GA, mean birth weight, and birth percentile, and the incidence of very premature preterm birth increased by 4.7% [ 18 ] . A study in two southern provinces China [ 19 ] found that the incidence rates of LBW/small-for-gestational-age (SGA) in gestational hypertension and pre-eclampsia group increased by 1.47%/1.9% and 3.86%/4.93% respectively. Our study also indicated that HDP was an independent predictor of ROP and was included in the predictive model. This result is not difficult to understand, vascular endothelial growth factor (VEGF) is currently recognized as an important mechanism of pathological neovascular proliferation in ROP [ 20 ] , HDP can lead to decreased expression of VEGF antagonist receptors [ 21 ] , and elevated VEGF expression in the maternal environment ultimately interferes with fetal retinal vascular development, making them more susceptible to ROP after birth. Therefore, regular examinations during pregnancy to monitor the mother's blood pressure status, and active control of blood pressure during pregnancy are important measures to prevent the occurrence of ROP. A European multicenter study showed that unrestricted threshold blood transfusions lead to an increased incidence of ROP in very low birth weight infants [ 22 ] . According to a meta-analysis [ 23 ] , blood transfusion was identified as an independent risk factor for the development of ROP in preterm infants born with GA < 32 weeks, which is consistent with the results of this study. Blood transfusion gradually reduces fetal hemoglobin (HbF) levels in neonates while resolving anemia, and the reduction in HbF leads to compromised retinal perfusion and antioxidant capacity [ 24 ] . In addition, blood transfusion leads to an increase in free iron in plasma, which catalyzes the reaction of reactive oxygen species and the formation of oxygen free radicals [ 25 ] , which increases the risk of retinal damage. Therefore, preterm infants with a history of multiple blood transfusions should be closely monitored for retinal conditions. In our study, OTT and OTC > 50% were also independent predictors of ROP in infants with GA ≤ 34 weeks. The longer the oxygen inhalation time, the higher the points in the nomogram model, and the higher the probability of ROP. Retinal hyperoxygenation is a recognized factor in the development of ROP. Premature and low birth weight infants with immature lung development usually require various modalities of oxygen therapy. The high concentration of oxygen can be toxic to the immature retina, inhibiting the development of retinal vasculature, leading to endothelial damage in retinal blood vessels, causing ischemic retinopathy in the avascular zone, and promoting proliferation and constriction of fibro-neovascular membranes, which induce ROP [ 26 ] . Selection of higher oxygen saturation targets early in clinical care can result in preterm infants being exposed to hyperoxic risks such as fluctuating partial pressures of oxygen, high oxygen concentrations, etc., and is associated with an increased incidence of ROP [ 27 ] . The United Kingdom’s National Institute for Health and Care Excellence (NICE) guidelines recommend a target oxygen saturation of 91–95% in preterm infants born at less than 32 weeks of gestation [ 28 ] . Shukla et al. [ 29 ] found that compared with static oxygen standards, biphasic oxygen targets are associated with decreased incidence and severity of ROP without increasing mortality, but the set point for oxygen saturation is currently controversial, and there are no large-scale studies to clarify whether this strategy is helpful in reducing the incidence of ROP and the mortality rate of preterm infants. However, in clinical practice, the indications for oxygen inhalation in premature infants should be strictly controlled to reduce the unregulated use of oxygen. Additionally, close monitoring of oxygen partial pressure and oxygen saturation is necessary to minimize the risk of ROP. High peak blood glucose in the first postnatal week was also an independent predictor of ROP and was included in our predictive model. It has been found that very low birth weight infant (VLBWI) are prone to hyperglycemia in the first postnatal week [ 30 ] , and hyperglycemia is usually one of the clinical manifestations of a variety of acute stresses and serious illnesses, as well as early hyperglycemia has been associated with an increased incidence of a variety of complications in VLBWI, including ROP [ 31 ] . Hyperglycemia in preterm infants can lead to low levels of insulin-like growth factor 1, which is a cytokine necessary for neovascularization formation in the retina [ 32 ] . Our study showed that the ROP group had higher average blood glucose value in the first postnatal week compared with the non-ROP group. Although the average blood glucose value in the ROP group was within the normal range, the higher average blood glucose could laterally reflect the high number of hyperglycemia exposures or the high glucose level in that time period. In addition, multifactorial analysis showed that high peak blood glucose in the first postnatal week was an independent risk factor for ROP in preterm infants, This may be related to the effect of high glucose concentration on retinal development in preterm infants on the one hand [ 33 , 34 ] , and to the fact that the infants in this group had a younger gestational age, lower body weight, and were relatively sicker on the other hand. Therefore, attention should be paid to blood glucose management during the first week of life, and early hyperglycemia requires close monitoring and timely intervention to prevent exposure to higher glucose concentrations and reduce the occurrence of ROP. In this study, we evaluated the clinical and laboratory data of infants with GA ≤ 34 weeks, developed an early risk prediction model for ROP, and validated the model's good accuracy, consistency, and net benefit. The visual and personalized model, i.e. the nomogram, provides clinicians with a simple and intuitive tool for practical prediction. While our single-center study provides preliminary insights, the generalizability of its findings is inherently limited. To validate and potentially refine our predictive model, future research should include multicenter studies that assess these predictive factors in a broader range of neonatal populations. In addition, the current model has only been validated internally. It is important to confirm its predictive accuracy through external validation to ensure robustness and applicability in different clinical settings. Conclusion In conclusion, our study demonstrates that HDP, number of blood transfusions, OTT, OTC > 50%, and blood sugar spikes(< 1 week) are significant predictors of ROP in infants with GA ≤ 34 weeks. Our robust, internally validated predictive nomogram provides clinicians with a practical tool for early identification of neonates at risk, thereby facilitating timely and appropriate therapeutic interventions. Declarations Availability of data and materials The dataset generated or analyzed in this study may be obtained from the first author and corresponding author upon reasonable request. Declaration of interests None of the authors have any conflicts of interest to disclose. None of the authors have any financial relationships relevant to this article to disclose. Funding This research was funded by National Natural Science Foundation of China (Nos. 82170565). Special project to improve scientific and technological innovation capabilities of Army Medical University(Nos. 2019XQY09). Army Medical University Excellent Talent Pool Key Support Program Project (Nos. XZ-2019-505-030). Author Contribution Indication Conceptualization – Sheng Chen; Software – Min Tao, Chenghuan Zhang; Writing – original draft – Leilei Shen, Juan Zeng; Writing – review & editing – Sheng Chen. Waiver of Informed Consent Statement This study was a retrospective study using information and data obtained from previous clinical treatments, so informed consent was exempted, approved by the ethics committee (KY2024171). References Chiang MF, Quinn GE, Fielder AR, Ostmo SR, Paul Chan RV, Berrocal A, et al. International Classification of Retinopathy of Prematurity, Third Edition. Ophthalmology. 2021. 128(10): e51-e68. Gilbert C, Fielder A, Gordillo L, Quinn G, Semiglia R, Visintin P, Zin A. 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Arch Dis Child Educ Pract Ed. 2020. 105(6): 355-357. Shukla A, Sonnie C, Worley S, Sharma A, Howard D, Moore J, et al. Comparison of Biphasic vs Static Oxygen Saturation Targets Among Infants With Retinopathy of Prematurity. JAMA Ophthalmol. 2019. 137(4): 417-423. Fernández-Martínez MDM, Gómez-Llorente JL, Momblán-Cabo J, Martin-González M, Calvo-Bonachera M, Olvera-Porcel M, et al. Monitoring the incidence, duration and distribution of hyperglycaemia in very-low-birth-weight newborns and identifying associated factors. J Perinat Med. 2020. 48(6): 631-637. Slidsborg C, Jensen LB, Rasmussen SC, Fledelius HC, Greisen G, Cour M. Early postnatal hyperglycaemia is a risk factor for treatment-demanding retinopathy of prematurity. Br J Ophthalmol. 2018. 102(1): 14-18. Cakir B, Hellström W, Tomita Y, Fu Z, Liegl R, Winberg A, et al. IGF1, serum glucose, and retinopathy of prematurity in extremely preterm infants. JCI Insight. 2020. 5(19): e140363. Kermorvant-Duchemin E, Le Meur G, Plaisant F, Marchand-Martin L, Flamant C, Porcher R, et al. Thresholds of glycemia, insulin therapy, and risk for severe retinopathy in premature infants: A cohort study. PLoS Med. 2020. 17(12): e1003477. Nicolaeva GV, Sidorenko EI, Iosifovna AL. Influence of the blood glucose level on the development of retinopathy of prematurity in extremely premature children. Arq Bras Oftalmol. 2015. 78(4): 232-5. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4791992","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":345798621,"identity":"55f73652-203f-476b-a83f-47cb4436e3e2","order_by":0,"name":"Leilei Shen","email":"","orcid":"","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Leilei","middleName":"","lastName":"Shen","suffix":""},{"id":345798622,"identity":"6ecf78ec-6dbe-4b22-8f8a-0ee0af6c0387","order_by":1,"name":"Juan Zeng","email":"","orcid":"","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Zeng","suffix":""},{"id":345798623,"identity":"e1435e27-3fec-4111-9990-19d9c30dd20b","order_by":2,"name":"Min Tao","email":"","orcid":"","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Tao","suffix":""},{"id":345798624,"identity":"6d330ba8-f7a9-4203-8c05-0d6d4b890dba","order_by":3,"name":"Chenghuan Zhang","email":"","orcid":"","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chenghuan","middleName":"","lastName":"Zhang","suffix":""},{"id":345798625,"identity":"3bcdd7b3-997d-4853-b373-edb16cd8367c","order_by":4,"name":"Sheng Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDACCSBmbJCQ42dvgIocIE6LhbFkz2HStFQkbriRTKQW/tnNxx7+3CHBuOHm+6ObbrYxyPHdSGD8XIDPkjvH0o15z0gwS95OZrud28ZgLHkjgVl6Bh4tBhI5ZtKMbRJsfFAtQBcmsDHz4NWS/03yZ5sED8PNw2At9URoyWGT4G2TkBC4wQzWkmBASIvEjTQzaaAWA8meZLPbOeckDGeeedgsjU8L/4zkZ0CH1dX3sx98djunzEae73jywc/4tGDYygCKJhI0jIJRMApGwSjABgA7uUr3aEZ6rgAAAABJRU5ErkJggg==","orcid":"","institution":"Third Military Medical University Southwest Hospital","correspondingAuthor":true,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-07-24 03:17:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4791992/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4791992/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64006773,"identity":"b51e12da-ba81-4053-bbe4-0cc869d6d2e3","added_by":"auto","created_at":"2024-09-04 21:52:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228437,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for patient selection. GA = gestational age; ROP = retinopathy of prematurity.\u003c/p\u003e","description":"","filename":"fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4791992/v1/ff039698628d90ab6e0b4a0e.png"},{"id":64007551,"identity":"0c650c1f-0675-4993-8724-cd2a7beb677c","added_by":"auto","created_at":"2024-09-04 22:00:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1404123,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of ROP in infants with GA ≤34 weeks. HDP, hypertensive disorders of pregnancy; OTT, oxygen therapy time; OTC, oxygen therapy concentration.\u003c/p\u003e","description":"","filename":"fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4791992/v1/692e37bde626ceeb26f85488.png"},{"id":64007649,"identity":"7481bf52-aec2-41d0-9d9c-6c3208e16ecb","added_by":"auto","created_at":"2024-09-04 22:08:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1046284,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ROC curves for each predictor and prediction model. HDP, hypertensive disorders of pregnancy; OTT, oxygen therapy time;OTC, oxygen therapy concentration.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4791992/v1/80f6a432afd1aeb6a21a63b4.png"},{"id":64006774,"identity":"8d622554-6f38-494f-8d55-4afdff30cfb7","added_by":"auto","created_at":"2024-09-04 21:52:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":373514,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve for predicting the probability of ROP in infants with GA ≤34 weeks.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4791992/v1/62572fdf69d93210d5d10cc2.png"},{"id":64006776,"identity":"5494abe9-4324-4e7d-aace-e5828bd0d597","added_by":"auto","created_at":"2024-09-04 21:52:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":260837,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram decision curve for ROP in infants with GA ≤34 weeks.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4791992/v1/8bcc7fa5ddb75df8d71183c7.png"},{"id":64814104,"identity":"7743f36a-4e69-4f73-a15d-bc2e43e72c6f","added_by":"auto","created_at":"2024-09-19 06:23:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4124396,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4791992/v1/7dc98ce6-b65c-484e-8991-db1fb5bdd323.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Multivariate Nomogram for Predicting Retinopathy of Prematurity in Infants with Gestational Age ≤ 34 Weeks","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRetinopathy of prematurity (ROP) is a devastating neurovascular disease of the retina in newborn infants that can lead to visual defects or even blindness, accounting for approximately 6\u0026ndash;8% of the causes of blindness in children\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. With advances in perinatal medicine and neonatology and the widespread establishment of neonatal intensive care units (NICUs), survival rates for preterm and low-birth-weight infants have increased significantly. As a result, the number of infants at risk for ROP has increased.\u003c/p\u003e \u003cp\u003eThe incidence of ROP varies widely across different countries and is linked to the socioeconomic development as well as the quality and accessibility of health care facilities\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In low and middle-income countries an \u0026ldquo;epidemic\u0026rdquo; of ROP blindness is currently occurring. In 2010, ten countries including China accounted for nearly two- thirds of all cases of visual impairment due to ROP\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Recent data indicate that the incidence of ROP in low birth weight infants in China ranges from 8.2\u0026ndash;17.8%, with the incidence in very premature infants as high as 65.1%\u003csup\u003e[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite ongoing research, a comprehensive understanding of the risk factors for ROP remains elusive. To address this critical gap, we performed a retrospective analysis of 452 infants with gestational age (GA)\u0026thinsp;\u0026le;\u0026thinsp;34 weeks. Our study aimed to delineate the relationships between clinical features, laboratory tests, and the occurrence of ROP. We developed and validated robust predictive models to improve early diagnosis and outcomes in this vulnerable population.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eWe performed a retrospective analysis of clinical records of infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks who were admitted to our NICUs immediately after birth, from January 2018 to January 2024. Inclusion criteria: (1) Premature infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks; (2) Informed consent of the parents for fundus screening; (3) Complete clinical information was available. Referring to the diagnostic criteria of the \u0026ldquo;Guidelines for the prevention and treatment of oxygen and retinopathy in preterm infants\u0026rdquo;\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, infants were divided into ROP group and non-ROP group based on the occurrence of ROP. Exclusion criteria: (1) Congenital eye diseases such as retinoblastoma, congenital cataract, glaucoma, etc.; (2) Hereditary metabolic diseases and severe congenital malformations; (3) Death during hospitalization, discontinuation of treatment, or incomplete clinical data; (4) Failure to complete the fundus examination.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee (KY2024171) and adhered to the tenets of the Declaration of Helsinki. All data were completely anonymized to ensure patient confidentiality.\u003c/p\u003e \u003cp\u003eAccording to the \"Chinese Guidelines for Screening Retinopathy of Prematurity (2014)\"\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, the initial screening for fundus lesions was performed at 4 to 6 weeks after birth or at 31 to 32 weeks of corrected GA. The scope of the screening included the peripheral retinal blood vessels to ensure comprehensive lesion detection.\u003c/p\u003e \u003cp\u003eRelevant definitions: (1) Number of blood transfusions: The cumulative number of transfusions of blood products, including red cells, plasma, platelets, and other blood components. (2) oxygen therapy time (OTT): The total duration of oxygen therapy, calculated by adding the time of mechanical ventilation, hooded oxygen therapy, and nasal cannula oxygen therapy; (3) Oxygen therapy concentration (OTC)\u0026thinsp;\u0026gt;\u0026thinsp;50%: The concentration of oxygen used during oxygen therapy when it exceeds 50%; (4) Hyperglycemia (\u0026lt;\u0026thinsp;1week): In the first postnatal week, when the blood glucose value is monitored\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, the peripheral blood glucose on the contralateral foot is re-measured. If the blood glucose value is still\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, hyperglycemia is diagnosed; (5) Blood sugar spikes (\u0026lt;\u0026thinsp;1week): The highest blood glucose value recorded during the first postpartum week; (6) Average blood sugar (\u0026lt;\u0026thinsp;1week): The sum of all blood glucose monitoring values during the first postnatal week divided by the total number of monitoring sessions.\u003c/p\u003e \u003cp\u003eBasic neonatal demographic information was collected from the medical records, including gender, gestational age, birth weight, mode of delivery, maternal gestational comorbidities, and prenatal and intrapartum conditions. Additionally, data on comorbidities, blood glucose levels in the first postnatal week, and details of oxygen therapy were collected.\u003c/p\u003e \u003cp\u003ePotential predictors were first identified by univariate analysis. Significant variables were then included in multivariate logistic regression analyses to develop a prediction model for ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks. Subsequently, internal verification was conducted to create a nomogram with excellent calibration and discrimination capabilities. In the multivariate analysis, variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in the nomogram. The foundation of the nomograph lies in scaling each regression coefficient in multiple logistic regression to a range of 0-100 points. The cumulative score, representing the predictive probability, can be obtained by summing up the scores assigned to each variable. The prediction accuracy and consistency of the model are assessed using the calibration curve, receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), consistency index (C index), and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) were also assessed. The net benefits of the model to neonates are reflected by decision curve analysis (DCA). By bootstrapping 1,000 resamples, identification and calibration are assessed.\u003c/p\u003e \u003cp\u003eThis study uses ROP as the dependent variable to construct a prediction model, takes the AUC value of the prediction model as the main index, and utilizes PASS 15 software (NCSS, Kaysville, Utah, USA) to calculate the Power of the current sample size. Finally, this study included 65 cases of ROP and 387 cases of non-ROP. The AUC value of the prediction model was 0.923. Under the condition of setting a two-sided test α\u0026thinsp;=\u0026thinsp;0.05, we inputted data into PASS 15 software (NCSS, Kaysville, Utah, USA) for Curve Tests and obtained a Power\u0026thinsp;\u0026gt;\u0026thinsp;0.999.\u003c/p\u003e \u003cp\u003eAll statistical analyses were determined using SPSS software version 26.0 (IBM SPSS Statistics, Chicago, IL, USA) and R software (Version 4.1.2). Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Non-parametric data are expressed as median (25\u0026ndash;75% interquartile range). Categorical variables are presented as absolute numbers and percentages. Continuous data between two groups were compared by independent samples t test or Mann-Whitney U test. Categorical data between two groups were compared by Chi-square test. According to the results of logistic multivariate regression analysis, a predictive model was constructed, and receiver operator characteristic (ROC) curve analysis was used to evaluate the area under the curve (AUC) and its 95% CI, and the model was validated by random sampling 1000 times using the bootstrap method. Lastly, a calibration plot was constructed. The performance of the predictive model was evaluated by the Hosmer\u0026ndash;Lemeshow test, AUC, and goodness-of-fit. Decision curve analysis (DCA) was used to validate the clinical net benefit rate of the predictive model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 471 patients\u0026rsquo; clinical information was obtained; 19 cases did not meet the inclusion criteria, of which 4 died during hospitalization, 7 did not complete fundus screening, and 8 had incomplete clinical data. Finally, 452 patients were enrolled in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Preterm infants who met the diagnostic criteria for ROP in the \u0026ldquo;Guidelines for the prevention and treatment of oxygen and retinopathy in preterm infants\u0026rdquo;\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e were divided into the ROP group (n\u0026thinsp;=\u0026thinsp;65), and the rest were placed into the non-ROP group (n\u0026thinsp;=\u0026thinsp;387). The differences in all data between the ROP and non-ROP group were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. ROP group had significant differences in GA, birth weight, hypertensive disorders of pregnancy (HDP), and fetal distress. In addition, comorbidities and treatments including bronchopulmonary dysplasia (BPD), sepsis, intracranial hemorrhage (ICH), respiratory distress syndrome (RDS), necrotizing enterocolitis (NEC), number of blood transfusions, OTT, OTC\u0026thinsp;\u0026gt;\u0026thinsp;50%, parenteral nutrition (PN)\u0026thinsp;\u0026gt;\u0026thinsp;14days(d), hyperglycemia (\u0026lt;\u0026thinsp;1week), blood sugar spikes (\u0026lt;\u0026thinsp;1week), and the average blood sugar (\u0026lt;\u0026thinsp;1week) showed a significant difference between ROP and non-ROP patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\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\u003eComparison of baseline clinical characteristics comorbidities, and treatments between the non-ROP group and ROP group.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enon-ARDS group(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;387)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eARDS group(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeneral data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, male, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187(48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.00(31.10,33.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.2(28.80,32.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelivery mode, eutocia, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy complication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDP, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121(31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22(33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICP, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerinatal condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACT, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e209(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFetal distress, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62(16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPD, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74(19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICH, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDA, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e289(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50(76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDS, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170(43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56(86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEC, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperglycemia (\u0026lt;\u0026thinsp;1week), n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37(56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory metrics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u0026nbsp;sugar\u0026nbsp;spikes (\u0026lt;\u0026thinsp;1week)(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.50(4.90,6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.20(5.60,8.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage blood sugar (\u0026lt;\u0026thinsp;1week)(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.30(3.90,4.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.10(4.50,6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of blood transfusions (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00(0.00,3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00(2.5,12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTT (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.00(2.00,22.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.00(7.5.00,54.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTC\u0026thinsp;\u0026gt;\u0026thinsp;50%, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35(53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePN\u0026thinsp;\u0026gt;\u0026thinsp;14d, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49(75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eHDP, hypertensive disorders of pregnancy; GDM, gestational diabetes mellitus;ICP, intrahepatic cholestasis of pregnancy༛ACT, antenatal corticosteroid therapy; BPD, bronchopulmonary dysplasia; ICH, intracranial hemorrhage; PDA, patent ductus arteriosus; RDS, respiratory distress syndrome; NEC, necrotizing enterocolitis; OTT, oxygen therapy time;OTC, oxygen therapy concentration༛\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariable logistic regression analysis showed that five factors were independent predictors of ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks, as follows: HDP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, odds ratio[OR] 1.329, 95% confidence interval [CI] 1.766\u0026ndash;8.077), number of blood transfusions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR 0.195, 95% CI 1.127\u0026ndash;1.311), OTT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, OR 0.031, 95% CI 1.012\u0026ndash;1.051), OTC\u0026thinsp;\u0026gt;\u0026thinsp;50% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR 1.714, 95% CI 2.413\u0026ndash;12.766), and blood sugar spikes (\u0026lt;\u0026thinsp;1week) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025, OR 0.253, 95% CI 1.032\u0026ndash;1.609) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" 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\u003ePredictors of ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWaldχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOR(95% CI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.777(1.766\u0026thinsp;~\u0026thinsp;8.077)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.215(1.127\u0026thinsp;~\u0026thinsp;1.311)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.031(1.012\u0026thinsp;~\u0026thinsp;1.051)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTC\u0026thinsp;\u0026gt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.550(2.413\u0026thinsp;~\u0026thinsp;12.766)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u0026nbsp;sugar\u0026nbsp;spikes (\u0026lt;\u0026thinsp;1week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.288(1.032\u0026thinsp;~\u0026thinsp;1.609)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eHDP, hypertensive disorders of pregnancy; OTT, oxygen therapy time;OTC, oxygen therapy concentration;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe final regression model, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, can be represented by the formula Ln(P/1-P)\u0026thinsp;=\u0026thinsp;1.329*HDP (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0)\u0026thinsp;+\u0026thinsp;0.195*blood transfusions\u0026thinsp;+\u0026thinsp;0.031*OTT\u0026thinsp;+\u0026thinsp;1.714*OTC (\u0026gt;\u0026thinsp;50%=1, \u0026le;\u0026thinsp;50%=0)\u0026thinsp;+\u0026thinsp;0.253*blood sugar spikes-5.865.\u003c/p\u003e \u003cp\u003eAccording to the results of multivariable logistic regression analysis, the following factors were associated with ROP: HDP, number of blood transfusions, OTT, OTC\u0026thinsp;\u0026gt;\u0026thinsp;50% and blood sugar spikes (\u0026lt;\u0026thinsp;1week). These five factors were included in the prediction model, and a nomogram was created to visualize the results of the regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ROC curves and corresponding AUC values generated by HDP, number of blood transfusions, OTT, OTC\u0026thinsp;\u0026gt;\u0026thinsp;50%, blood sugar spikes (\u0026lt;\u0026thinsp;1week) and prediction model are 0.690, 0.799, 0.776, 0.737, 0.782 and 0.923 respectively, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e/Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The cut-off values for HDP, number of blood transfusions, OTT, OTC\u0026thinsp;\u0026gt;\u0026thinsp;50%, blood sugar spikes (\u0026lt;\u0026thinsp;1week) and predictive modeling were obtained by using the maximum value of Jorden's index as the optimal threshold, and the associated sensitivity, specificity, positive predictive value and negative predictive value are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The difference in AUC values between the prediction model and each independent predictor is statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the prediction effect of each independent predictor and prediction model of ROP.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecutoff values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.614\u0026ndash;0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e82.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.741\u0026ndash;0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.708\u0026ndash;0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTC\u0026thinsp;\u0026gt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.659\u0026ndash;0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u0026nbsp;sugar\u0026nbsp;spikes (\u0026lt;\u0026thinsp;1week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.713\u0026ndash;0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictive Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.888\u0026ndash;0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e85.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eHDP, hypertensive disorders of pregnancy; OTT, oxygen therapy time;OTC, oxygen therapy concentration;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy internally validating the accuracy of the prediction model using the Bootstrap resampling technique, and the Hosmer-Lemeshow test showed that \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.715, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.462\u0026thinsp;\u0026gt;\u0026thinsp;0.05, the C-index was 0.923, with a strong fit between the original and corrected curves, demonstrating the effectiveness of the prediction model(Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDecision analysis (DCA) was performed on the data to evaluate the clinical utility of the prediction model. The analysis of the decision curve analysis that the model can significantly improve the clinical efficiency in predicting ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eROP, responsible for the majority of visual sequelae in premature infants, is one of the main preventable causes of childhood blindness\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Currently, the ROP screening guidelines for preterm infants vary between countries, especially between developed and developing countries\u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. We selected preterm infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks as study subjects in the hope that the resulting predictive model would ensure that infants who are likely to require treatment are not missed. This study showed that HDP, number of blood transfusions, OTT, OTC\u0026thinsp;\u0026gt;\u0026thinsp;50% and blood sugar spikes (\u0026lt;\u0026thinsp;1week) were predictors of ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks.\u003c/p\u003e \u003cp\u003eAs we know, HDP was strongly associated with certain adverse outcomes in newborns (i.e., preterm birth, small for gestational age, restricted growth and development and intrauterine distress)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Nawsherwan et al.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e reported that HDP was associated with a higher risk of C-section, preterm birth, perinatal mortality, and low birth weight (LBW) in both singleton and twin pregnancies compared with the non-HDP. Neonates born to mothers with HDP had significantly lower GA, mean birth weight, and birth percentile, and the incidence of very premature preterm birth increased by 4.7%\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. A study in two southern provinces China\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e found that the incidence rates of LBW/small-for-gestational-age (SGA) in gestational hypertension and pre-eclampsia group increased by 1.47%/1.9% and 3.86%/4.93% respectively. Our study also indicated that HDP was an independent predictor of ROP and was included in the predictive model. This result is not difficult to understand, vascular endothelial growth factor (VEGF) is currently recognized as an important mechanism of pathological neovascular proliferation in ROP\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, HDP can lead to decreased expression of VEGF antagonist receptors\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, and elevated VEGF expression in the maternal environment ultimately interferes with fetal retinal vascular development, making them more susceptible to ROP after birth. Therefore, regular examinations during pregnancy to monitor the mother's blood pressure status, and active control of blood pressure during pregnancy are important measures to prevent the occurrence of ROP.\u003c/p\u003e \u003cp\u003eA European multicenter study showed that unrestricted threshold blood transfusions lead to an increased incidence of ROP in very low birth weight infants\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. According to a meta-analysis\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, blood transfusion was identified as an independent risk factor for the development of ROP in preterm infants born with GA\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks, which is consistent with the results of this study. Blood transfusion gradually reduces fetal hemoglobin (HbF) levels in neonates while resolving anemia, and the reduction in HbF leads to compromised retinal perfusion and antioxidant capacity\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In addition, blood transfusion leads to an increase in free iron in plasma, which catalyzes the reaction of reactive oxygen species and the formation of oxygen free radicals\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, which increases the risk of retinal damage. Therefore, preterm infants with a history of multiple blood transfusions should be closely monitored for retinal conditions.\u003c/p\u003e \u003cp\u003eIn our study, OTT and OTC\u0026thinsp;\u0026gt;\u0026thinsp;50% were also independent predictors of ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks. The longer the oxygen inhalation time, the higher the points in the nomogram model, and the higher the probability of ROP. Retinal hyperoxygenation is a recognized factor in the development of ROP. Premature and low birth weight infants with immature lung development usually require various modalities of oxygen therapy. The high concentration of oxygen can be toxic to the immature retina, inhibiting the development of retinal vasculature, leading to endothelial damage in retinal blood vessels, causing ischemic retinopathy in the avascular zone, and promoting proliferation and constriction of fibro-neovascular membranes, which induce ROP\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Selection of higher oxygen saturation targets early in clinical care can result in preterm infants being exposed to hyperoxic risks such as fluctuating partial pressures of oxygen, high oxygen concentrations, etc., and is associated with an increased incidence of ROP\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The United Kingdom\u0026rsquo;s National Institute for Health and Care Excellence (NICE) guidelines recommend a target oxygen saturation of 91\u0026ndash;95% in preterm infants born at less than 32 weeks of gestation\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Shukla et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e found that compared with static oxygen standards, biphasic oxygen targets are associated with decreased incidence and severity of ROP without increasing mortality, but the set point for oxygen saturation is currently controversial, and there are no large-scale studies to clarify whether this strategy is helpful in reducing the incidence of ROP and the mortality rate of preterm infants. However, in clinical practice, the indications for oxygen inhalation in premature infants should be strictly controlled to reduce the unregulated use of oxygen. Additionally, close monitoring of oxygen partial pressure and oxygen saturation is necessary to minimize the risk of ROP.\u003c/p\u003e \u003cp\u003eHigh peak blood glucose in the first postnatal week was also an independent predictor of ROP and was included in our predictive model. It has been found that very low birth weight infant (VLBWI) are prone to hyperglycemia in the first postnatal week\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, and hyperglycemia is usually one of the clinical manifestations of a variety of acute stresses and serious illnesses, as well as early hyperglycemia has been associated with an increased incidence of a variety of complications in VLBWI, including ROP\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Hyperglycemia in preterm infants can lead to low levels of insulin-like growth factor 1, which is a cytokine necessary for neovascularization formation in the retina\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Our study showed that the ROP group had higher average blood glucose value in the first postnatal week compared with the non-ROP group. Although the average blood glucose value in the ROP group was within the normal range, the higher average blood glucose could laterally reflect the high number of hyperglycemia exposures or the high glucose level in that time period. In addition, multifactorial analysis showed that high peak blood glucose in the first postnatal week was an independent risk factor for ROP in preterm infants, This may be related to the effect of high glucose concentration on retinal development in preterm infants on the one hand\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, and to the fact that the infants in this group had a younger gestational age, lower body weight, and were relatively sicker on the other hand. Therefore, attention should be paid to blood glucose management during the first week of life, and early hyperglycemia requires close monitoring and timely intervention to prevent exposure to higher glucose concentrations and reduce the occurrence of ROP.\u003c/p\u003e \u003cp\u003eIn this study, we evaluated the clinical and laboratory data of infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks, developed an early risk prediction model for ROP, and validated the model's good accuracy, consistency, and net benefit. The visual and personalized model, i.e. the nomogram, provides clinicians with a simple and intuitive tool for practical prediction. While our single-center study provides preliminary insights, the generalizability of its findings is inherently limited. To validate and potentially refine our predictive model, future research should include multicenter studies that assess these predictive factors in a broader range of neonatal populations. In addition, the current model has only been validated internally. It is important to confirm its predictive accuracy through external validation to ensure robustness and applicability in different clinical settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study demonstrates that HDP, number of blood transfusions, OTT, OTC\u0026thinsp;\u0026gt;\u0026thinsp;50%, and blood sugar spikes(\u0026lt;\u0026thinsp;1 week) are significant predictors of ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks. Our robust, internally validated predictive nomogram provides clinicians with a practical tool for early identification of neonates at risk, thereby facilitating timely and appropriate therapeutic interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated or analyzed in this study may be obtained from the first author and corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors have any conflicts of interest to disclose. None of the authors have any financial relationships relevant to this article to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by National Natural Science Foundation of China (Nos. 82170565). Special project to improve scientific and technological innovation capabilities of Army Medical University(Nos. 2019XQY09). Army Medical University Excellent Talent Pool Key Support Program Project (Nos. XZ-2019-505-030).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Indication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization \u0026ndash; Sheng Chen; Software \u0026ndash;\u0026nbsp;Min Tao, Chenghuan Zhang; Writing \u0026ndash; original draft \u0026ndash; Leilei Shen,\u0026nbsp;Juan Zeng; Writing \u0026ndash; review \u0026amp; editing \u0026ndash; Sheng Chen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWaiver of Informed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a retrospective study using information and data obtained from previous clinical treatments, so informed consent was exempted, approved by the ethics committee (KY2024171).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChiang MF, Quinn GE, Fielder AR, Ostmo SR, Paul Chan RV, Berrocal A, et al. International Classification of Retinopathy of Prematurity, Third Edition. 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Effect of red blood cell transfusion on the development of retinopathy of prematurity: A systematic review and meta-analysis. PLoS One. 2020. 15(6): e0234266.\u003c/li\u003e\n\u003cli\u003eSimons M, Gretton S, Silkstone GGA, Rajagopal BS, Allen-Baume V, Syrett N, et al. Comparison of the oxidative reactivity of recombinant fetal and adult human hemoglobin: implications for the design of hemoglobin-based oxygen carriers. Biosci Rep. 2018. 38(4): BSR20180370 [pii].\u003c/li\u003e\n\u003cli\u003eRatanasopa K, Strader MB, Alayash AI, Bulow L. Dissection of the radical reactions linked to fetal hemoglobin reveals enhanced pseudoperoxidase activity. Front Physiol. 2015. 6: 39.\u003c/li\u003e\n\u003cli\u003eZhang W, Zhang DG, Liang X, Zhang WL, Ma JX. Effects of apelin on retinal microglial cells in a rat model of oxygen-induced retinopathy of prematurity. J Cell Biochem. 2018. 119(3): 2900-2910.\u003c/li\u003e\n\u003cli\u003eAskie LM, Darlow BA, Davis PG, Finer N, Stenson B, Vento M, et al. Effects of targeting lower versus higher arterial oxygen saturations on death or disability in preterm infants. Cochrane Database Syst Rev. 2017. 4(4): CD011190.\u003c/li\u003e\n\u003cli\u003eRodgers A, Singh C. Specialist neonatal respiratory care for babies born preterm (NICE guideline 124): a review. Arch Dis Child Educ Pract Ed. 2020. 105(6): 355-357.\u003c/li\u003e\n\u003cli\u003eShukla A, Sonnie C, Worley S, Sharma A, Howard D, Moore J, et al. Comparison of Biphasic vs Static Oxygen Saturation Targets Among Infants With Retinopathy of Prematurity. JAMA Ophthalmol. 2019. 137(4): 417-423.\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-Mart\u0026iacute;nez MDM, G\u0026oacute;mez-Llorente JL, Mombl\u0026aacute;n-Cabo J, Martin-Gonz\u0026aacute;lez M, Calvo-Bonachera M, Olvera-Porcel M, et al. Monitoring the incidence, duration and distribution of hyperglycaemia in very-low-birth-weight newborns and identifying associated factors. J Perinat Med. 2020. 48(6): 631-637.\u003c/li\u003e\n\u003cli\u003eSlidsborg C, Jensen LB, Rasmussen SC, Fledelius HC, Greisen G, Cour M. Early postnatal hyperglycaemia is a risk factor for treatment-demanding retinopathy of prematurity. Br J Ophthalmol. 2018. 102(1): 14-18.\u003c/li\u003e\n\u003cli\u003eCakir B, Hellstr\u0026ouml;m W, Tomita Y, Fu Z, Liegl R, Winberg A, et al. IGF1, serum glucose, and retinopathy of prematurity in extremely preterm infants. JCI Insight. 2020. 5(19): e140363.\u003c/li\u003e\n\u003cli\u003eKermorvant-Duchemin E, Le Meur G, Plaisant F, Marchand-Martin L, Flamant C, Porcher R, et al. Thresholds of glycemia, insulin therapy, and risk for severe retinopathy in premature infants: A cohort study. PLoS Med. 2020. 17(12): e1003477.\u003c/li\u003e\n\u003cli\u003eNicolaeva GV, Sidorenko EI, Iosifovna AL. Influence of the blood glucose level on the development of retinopathy of prematurity in extremely premature children. Arq Bras Oftalmol. 2015. 78(4): 232-5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Premature Infants, Retinopathy of Prematurity, Risk Factors, Predictive Nomogram, Neonatal Health","lastPublishedDoi":"10.21203/rs.3.rs-4791992/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4791992/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo delineate risk factors and develop a predictive nomogram for retinopathy of prematurity (ROP) in infants with gestational age (GA)\u0026thinsp;\u0026le;\u0026thinsp;34 weeks.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive retrospective analysis of infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks, divided into ROP and non-ROP groups based on fundus screening results. Clinical and laboratory data were collected to identify risk factors associated with ROP. Multivariable logistic regression was performed to identify independent predictors, and a nomogram was developed to predict the occurrence of ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur analysis identified five independent risk factors for ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks: hypertensive disorders of pregnancy (HDP), number of blood transfusions, oxygen therapy time (OTT), oxygen therapy concentration (OTC)\u0026thinsp;\u0026gt;\u0026thinsp;50%, and blood glucose spikes in the first postnatal week. These predictors were incorporated into a nomogram to estimate individual ROP risk. The predictive model achieved a C-index of 0.923 (95% CI: 0.888\u0026ndash;0.959), indicating high predictive accuracy. Internal validation of the nomogram demonstrated excellent calibration and practical utility for clinical decision-making.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe validated nomogram, based on five critical factors, provides clinicians with a reliable tool for assessing the risk of developing ROP in infants with GA\u0026thinsp;\u0026le;\u0026thinsp;34 weeks. This tool has the potential to improve outcomes by facilitating timely and appropriate therapeutic interventions.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Multivariate Nomogram for Predicting Retinopathy of Prematurity in Infants with Gestational Age ≤ 34 Weeks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-04 21:52:35","doi":"10.21203/rs.3.rs-4791992/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8813dd1f-3043-4394-9df6-4a6369fb7bbf","owner":[],"postedDate":"September 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36659942,"name":"Health sciences/Diseases/Eye diseases/Retinal diseases"},{"id":36659943,"name":"Health sciences/Medical research/Paediatric research"}],"tags":[],"updatedAt":"2024-09-19T06:14:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-04 21:52:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4791992","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4791992","identity":"rs-4791992","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}