Association Between the C-Reactive Protein-to-Albumin Ratio and Treatment-Requiring Retinopathy of Prematurity 

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The C-reactive protein-to-albumin ratio (CAR) has emerged as a composite biomarker reflecting inflammatory burden and nutritional status. This study aimed to evaluate the association between CAR and both severity and treatment requirement ROP, and to assess its diagnostic performance alongside classical perinatal risk factors. Methods This retrospective study included 390 premature infants followed in a tertiary ROP unit between January 2021 and December 2025. Infants were categorized into three groups: with no ROP, with spontaneously regressed ROP, and with treated ROP. CAR was calculated as the ratio of C-reactive protein to serum albumin. Associations with ROP severity were analyzed using ordinal logistic regression, and independent predictors of treatment requirement were evaluated using multivariable logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to assess predictive performance. Results Gestational age (GA) and birth weight (BW) were significantly and inversely associated with ROP severity (p < 0.001). CAR was positively associated with both ROP severity (OR = 1.97; 95% CI: 1.67–2.32; p < 0.001) and treatment requirement (OR = 1.42; 95% CI: 1.26–1.61; p < 0.001). In multivariable analysis, GA and CAR remained independent predictors of treatment requirement. ROC analysis demonstrated good discriminative ability for GA (AUC: 0.799) and BW (AUC: 0.761), and acceptable performance for CAR (AUC: 0.712). Conclusions While GA remains the strongest predictor of treatment-requiring ROP, CAR represents an independent biomarker reflecting systemic inflammatory burden. The integration of CAR with classical perinatal risk factors may enhance risk prediction for ROP in premature infants. Retinopathy of prematurity C-reactive protein-to-albumin ratio Inflammation Treated ROP Premature Figures Figure 1 Introduction Retinopathy of prematurity (ROP) is defined as a microangiopathy characterized by abnormal retinal vascular development in premature infants and may lead to severe visual impairment [ 1 ]. The pathophysiology of ROP is multifactorial, involving oxidative stress, fluctuations between hypoxia and hyperoxia, and inflammatory processes. In this context, the relationship between inflammatory markers and ROP prognosis has been investigated [ 2 ]. These mechanisms play critical roles in angiogenesis and overall retinal development and are additionally influenced by nutritional and immune status [ 3 , 4 ]. Advances in neonatal care have increased survival rates among premature infants; however, this has also led to a higher prevalence of ROP in this vulnerable population [ 5 , 6 ]. The incidence and severity of ROP are closely associated with gestational age (GA) and birth weight (BW), with the highest risk observed in infants with extremely low BW and lower GA [ 7 – 9 ]. C-reactive protein (CRP) and serum albumin are well-established biomarkers that have long been used to assess systemic inflammation and nutritional status. C-reactive protein is widely recognized as a sensitive marker of acute inflammation, whereas serum albumin is used in clinical practice as an indicator of both nutritional status and chronic inflammatory burden. In recent decades, the combination of these two parameters, expressed as the C-reactive protein-to-albumin ratio (CAR), has emerged as a prognostic biomarker reflecting both the severity of the inflammatory response and nutritional status. The prognostic and diagnostic value of CAR was initially investigated in oncological studies and was found to be significantly associated with survival and complication risk in various malignancies. In clinical conditions where inflammation plays a central role—such as sepsis, malignancies, cardiac ischemia, acute appendicitis, and postoperative infectious complications—receiver operating characteristic (ROC) analyses have supported the clinical utility of CAR in terms of sensitivity and specificity [ 10 – 17 ]. The aim of this study was to evaluate the association between CAR and both severity and treatment requirement ROP—a topic that has not been comphrehensively addressed in the literature before—and to assess the diagnostic performance of CAR alongside classical perinatal risk factors such as GA and BW. Materials and Methods Study Design, Participants and Data Collection The study was approved by the Ondokuz Mayıs University Clinical Research Ethics Committee (approval number: 2026/149) and was conducted in accordance with the principles of the Declaration of Helsinki. This retrospective study was conducted at Ondokuz Mayıs University Faculty of Medicine Hospital. Premature infants followed in the ROP unit of the Ophthalmology Clinic between January 2021 and December 2025 were reviewed. The study included premature infants with a GA ≤ 32 weeks and/or a BW 32 weeks and/or a BW ≥ 1500 g who were considered at risk for ROP by a neonatologist and infants for whom serum albumin and CRP measurements were available. Only samples obtained at the time of the highest ROP severity or within a maximum of two weeks prior to diagnosis were analyzed, corresponding to different gestational periods across cases. Premature infants with a major congenital anomalies, chromosomal disorders, severe systemic diseases that could influence study outcomes, incomplete clinical or laboratory data, and who did not regularly attend ROP follow-up were excluded from ths study. Clinical data were obtained from medical records of patients. GA, BW, serum albumin, and CRP levels were recorded. The CAR was calculated using the following formula: CAR = CRP / Albumin Participants were categorized into three groups as follows: Group 1: Those without ROP Group 2: Those with spontaneously regressed ROP Group 3: Those with Treated ROP (severe ROP) Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics (version 26.0; IBM Corp., Armonk, NY, USA). The distribution of continuous variables was assessed using visual and analytical methods. Non-normally distributed variables were expressed as median (minimum–maximum), and categorical variables were presented as number and percentage (%). Continuous variables were compared using the Kruskal–Wallis test, and categorical variables were compared using the chi-square test. When significant differences were identified, pairwise comparisons were performed with appropriate post hoc analyses. Multivariable logistic regression analysis was conducted to identify independent predictors of treatment requirement. Results were reported as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Model fit was assessed using the Hosmer–Lemeshow test. The predictive performance of GA, BW, and CAR for treatment requirement was evaluated using ROC curve analysis. The area under the curve (AUC) with 95% CI was calculated, and optimal cut-off values were determined using the Youden index. Since lower GA and BW are associated with increased risk, variable direction was evaluated in accordance with clinical interpretation. A p-value < 0.05 was considered statistically significant. After Bonferroni correction, a p-value < 0.0167 was considered statistically significant. Results A total of 390 premature infants (167 females, 223 males) were included in the study. There were 209 cases in Group 1, 102 in Group 2, and 79 in Group 3. The demographic characteristics and laboratory parameters of the patients are summarized in Table 1 . Table 1 Demographic characteristics and laboratory values ​​in patient groups Variables Group 1 (n = 209) Group 2 (n = 102) Group 3 (n = 79) P 1 P 2 Gestational age (weeks) 32 (25–37) 30 (25–35) 27 (22–34) ,001* Pa = 0.001* Pb = 0.001* Pc = 0.001* Female/Male, n (%) 86(% 41) / 123( %59) 40 (%39) / 62(%61) 41(%51) / 38 (%49) ,180 Pa = 0.745 Pb = 0.102 Pc = 0.090 Birth weight (g) 1725 (670–3280) 1290 (680–3270) 1040 (520–2480) ,001* Pa = 0.001* Pb = 0.001* Pc = 0.001* Serum albumin (g/dL) 3,71 (2,80 − 4,82) 3.20 (2,43 − 4,30) 3.03 (2.12–4.26) ,001* Pa = 0.001* Pb = 0.001* Pc = 0.029 CRP (mg/dL) 2,15 (0,01–37,90) 6,69 (0,60 − 22,04) 16,51 (0,00-224) ,001* Pa = 0.001* Pb = 0.001* Pc = 0.005* CAR 0,55 (0,00–8,61) 2,05 (0,19 − 5,82) 5,87 (0,00–80) ,001* Pa = 0.001* Pb = 0.001* Pc = 0.007* CRP: C-reactive protein; CAR:CRP/Albumin Ratio; P 1 : Kruskal-Wallis test or chi-square test (p < 0,05); P 2 :Bonferonni adjusted Mann-Whitney U test (p < 0,0167); Pa: The difference between groups 1 and 2; Pb: The difference between groups 1 and 3, Pc: The difference between groups 2 and 3; *:Statistically significant. Significant differences were observed among the groups in terms of demographic and clinical variables. GA and BW differed significantly across groups (p < 0.05). Post hoc analyses revealed that infants in the group 3 had significantly lower GA and BW compared with the other groups. CAR levels also differed significantly between groups (p < 0.05), with higher values observed in the group 3. Table 2 presents the association between GA, BW, and CAR with ROP outcomes. In ordinal logistic regression analysis, GA(OR = 0.66; 95% CI: 0.61–0.72; p < 0.001) and BW (OR = 0.66; 95% CI: 0.61–0.72; p < 0,001). CAR was positively and significantly associated with ROP severity (OR = 1.97; 95% CI: 1.67–2.32; p < 0.001). Table 2 Association of Clinical Variables and CAR with ROP Outcomes Outcome Variable B p OR 95% CI ROP severity¹ Gestational age (weeks) -0.419 < 0.001* 0.66 0.61–0.72 ROP severity¹ Birth weight (g) -0.002 < 0.001* 0.998 0.997–0.998 ROP severity¹ CAR 0.677 < 0.001* 1.97 1.67–2.32 Treatment requirement² CAR 0.353 < 0.001* 1.42 1.26–1.61 ROP:Retinopathy of prematurity;B: beta coefficient; OR:Odds ratio; CI:Confidence interval, *:Statistically significant; CAR:C-reactive protein/albumin ratio. ¹Ordinal logistic regression model (logit link). ²Binary logistic regression model (logit link). In binary logistic regression analysis, CAR was significantly associated with treatment requirement of ROP. Each one-unit increase in CAR increased the odds of treatment requirement by 42% (OR = 1.42; 95% CI: 1.26–1.61; p < 0.001). In multivariable logistic regression analysis, GA and CAR remained independent predictors of treatment requirement of ROP. While each additional week of gestation significantly reduced treatment risk, higher CAR levels increased treatment risk. But BW did not retain independent significance in the model (Table 3 ). Table 3 Multivariable Logistic Regression Analysis Variable Adjusted OR 95% CI p-value Gestational Age (weeks) 0.53 0.41–0.69 < 0.001* CAR 1.46 1.29–1.66 < 0.001* Birth Weight (g) 1.00 0.999–1.002 0.911 OR:Odds ratio; CI:Confidence interval; CAR:C-reactive protein-to-albumin ratio, *:Statistically significant ROC curve analysis is shown in Fig. 1. GA, BW, and CAR were all significant predictors of treatment requirement of ROP ( p < 0.001) (Table 4 ). Table 4 ROC Analysis of Predictors for Treatment Requirement Variable AUC (95% CI) p Cut-off Sensitivity (%) Specificity (%) Gestational age (weeks) 0.799 (0.743–0.855) < 0.001* ≤ 28.5 63.3 81.7 Birth weight (g) 0.761 (0.701–0.821) < 0.001* ≤ 1172.5 64.6 76.8 CAR 0.712 (0.628–0.795) < 0.001* ≥ 1.018 65.8 69.1 AUC:Area Under the Curve; *:Statistical significance; CAR:C-reactive protein-to-albumin ratio. GA demonstrated good discriminative ability with an AUC of 0.799 (95% CI: 0.743–0.855). At a cut-off value of ≤ 28.5 weeks, sensitivity was 63.3% and specificity was 81.7%. BW also showed good predictive performance with an (AUC of: 0.761; 95% CI: 0.701–0.821). At a cut-off value of ≤ 1172.5 g, sensitivity was 64.6% and specificity was 76.8%. CAR demonstrated acceptable discriminative ability with an AUC of 0.712 (95% CI: 0.628–0.795). At a cut-off value of ≥ 1.018, sensitivity was 65.8% and specificity was 69.1% . Discussion The pathophysiology of ROP is characterized by a biphasic disturbance of vascular development. In the first phase, hyperoxia suppresses normal vascular growth, whereas in the second phase, hypoxia and inflammation-driven mechanisms promote pathological neovascularization. The impact of inflammation on retinal vascular development is mediated through cytokine release, endothelial dysfunction, and alterations in vascular endothelial growth factor regulation [ 18 – 20 ]. In this context, it is biologically plausible that biomarkers reflecting systemic inflammatory burden may be associated with ROP progression. The Glasgow Prognostic Score has been shown to reflect an individual’s immune and nutritional status and is based on CRP and albumin, which are acute-phase proteins synthesized in the liver. C-reactive protein levels are regulated by various proinflammatory cytokines, including interleukin-1, tumor necrosis factor-α, transforming growth factor-β, interferon-γ, and interleukin-6. Serum albumin is widely used as a biomarker of hepatic function and nutritional status. Hypoalbuminemia has been associated with systemic inflammatory response, cancer recurrence, and metastasis. While CRP reflects acute inflammatory activity, albumin is a negative acute-phase reactant that decreases during inflammation [ 21 – 24 ]. The CAR, which combines these two parameters, provides a more comprehensive assessment by simultaneously reflecting both positive and negative components of the inflammatory response. The prognostic value of CAR was first described in oncological studies, where it was associated with survival in colorectal and pancreatic cancers [ 21 – 25 ]. Considering the role of neonatal inflammation in retinal vascular development, the association between CAR and ROP is consistent with current pathophysiological understanding. In the present study, CAR was positively associated with ROP severity and independently predicted treatment requirement of ROP, supporting the role of inflammatory burden in disease progression. The stronger association observed with ROP severity suggests that inflammatory load may exert a more pronounced effect in advanced stages of the disease. Nevertheless, the confirmation of these findings in multivariable models is essential to ensure that the observed associations are not confounded by established perinatal risk factors. From a clinical perspective, CAR is an easily calculated, cost-effective, and readily available biomarker derived from routine laboratory parameters. It may contribute to risk assessment, particularly in infants with borderline GA or uncertain clinical course. However, CAR appears to function more appropriately as a complementary parameter rather than a standalone predictor, supporting classical perinatal risk factors. In the ROC analysis, GA demonstrated superior discriminative ability compared with CAR, reaffirming prematurity as the primary determinant of ROP risk. Previous studies have consistently reported that lower GA and lower BW are the strongest predictors of ROP incidence and treatment requirement [ 8 , 26 – 28 ]. Our findings are in line with these reports. The loss of independent significance of BW in the multivariable model may be explained by its strong correlation with GA. Since GA more directly reflects retinal vascular maturity, it may emerge as the dominant predictor in statistical modeling. Importantly, CAR retained independent significance after adjustment for GA, suggesting that inflammatory processes contribute to ROP risk beyond the degree of prematurity alone. A recent study reported that CAR levels measured during the first postnatal month were significantly associated with the development of more severe ROP [ 29 ]. This finding supports the potential clinical relevance of CAR in neonatal vascular diseases. In our study, the evaluation of CAR in relation to both ROP severity and treatment requirement using ordinal and binary regression analyses provides methodological robustness. Furthermore, the identification of independent predictors through multivariable analysis enhances the clinical interpretability of the results. The determination of cut-off values via ROC analysis further supports potential clinical applicability. Given the limited number of studies investigating CAR in the context of ROP, our findings contribute novel data to the existing literature. Several limitations should be acknowledged. The retrospective design precludes causal inference. Inflammatory markers were assessed at a single time point, and serial measurements were not available. Prospective, multicenter studies with longitudinal biomarker assesment would be beneficial to clarify the clinical utility of CAR in ROP risk stratification. Conclusion In conclusion, GA remains the strongest predictor of treatment-requiring ROP. However, the CAR represents an independent biomarker reflecting systemic inflammatory burden and contributes to risk assessment. The integration of CAR with classical perinatal risk factors may facilitate earlier identification of high-risk infants and improve clinical decision-making. Declarations Author Contributions Conceptualization and Study Design: Ahmet ÖZDEMİR, Mustafa Subaşı Data Collection and Management: Ahmet ÖZDEMİR, Mertcan Esenkaya,, Canan Seren, Zeynep İmamoğlu Statistical Analysis: Mustafa Subaşı, Begüm Kalyoncu Manuscript Writing – Original Draft Preparation: Mustafa Subaşı, Ahmet ÖZDEMİR Manuscript Review and Editing: Ozlem Eski Yucel Supervision: Ozlem Eski Yucel All authors read and approved the final manuscript. Ethics Approval and Consent to Participate This study was conducted in accordance with the tenets of the Declaration of Helsinki and received approval from the Ondokuz Mayıs University Faculty of Medicine Ethics Committee (Approval number: 2026/149). Written informed consent was obtained from the parents. Acknowledgements The authors would like to express their gratitude to the clinical staff at the Department of Ophthalmology, Ondokuz Mayıs University, for their invaluable assistance in data collection and patient care. 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A novel marker for predicting type 1 retinopathy of prematurity: C-reactive protein/albumin ratio. Int Ophthalmol. 2023;43(9):3345–53. Additional Declarations No competing interests reported. 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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-9169566","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628002705,"identity":"08d1f571-34b4-421d-9edb-ee071139caa1","order_by":0,"name":"Mustafa Subaşı","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie3PMUvDQBTA8QcHmVKyyUmG+wpPhFIw+EVcLhx0ypWCSwfnOAh1bfBLBAKdLwQ6VbOmZBHcJS5ygoi9VJzSmNHh/nDwOPhx7wBstn+Zuz/4My8g4O3A54MIAdjClINjCP5F4JcUBwI9hK1k/qbncIVlmTfqppx57C5snhGYd6I6CVYz4bsIMq0EoWpTX5/Go4zuFztLHng3oRH60BICfuPUYboZpYZwrLsJW0XnH9qQsiBafT0Zkuk+AlU0pu1iSjg0j5Uh695XcPs6vXCRyqQS40m+FGESy/WEIz36F3Yri53+DOSyzF8q9X4Z3pPHbKcXAfP8I4sdogNubDabzTa8bzdIYxFCfUevAAAAAElFTkSuQmCC","orcid":"","institution":"Merzifon Kara Mustafa Pasa State Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Subaşı","suffix":""},{"id":628002707,"identity":"a9381d4a-c487-4282-a9a9-645108caf0e4","order_by":1,"name":"Ahmet Ozdemir","email":"","orcid":"","institution":"Cukurca State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"Ozdemir","suffix":""},{"id":628002711,"identity":"debe4403-32b2-412e-aa67-d5d6c88ccc2d","order_by":2,"name":"Mertcan Esenkaya","email":"","orcid":"","institution":"Bartın State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mertcan","middleName":"","lastName":"Esenkaya","suffix":""},{"id":628002712,"identity":"3842eb69-b8ab-4fce-a075-0753f72fbfc5","order_by":3,"name":"Begüm Kalyoncu","email":"","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":false,"prefix":"","firstName":"Begüm","middleName":"","lastName":"Kalyoncu","suffix":""},{"id":628002713,"identity":"5214e88b-ea58-4a1d-9132-f643e880452a","order_by":4,"name":"Canan Seren","email":"","orcid":"","institution":"Ondokuz Mayis University","correspondingAuthor":false,"prefix":"","firstName":"Canan","middleName":"","lastName":"Seren","suffix":""},{"id":628002720,"identity":"7099c1bd-150a-4705-ac3f-bb23a05b09e4","order_by":5,"name":"Ozlem Eski Yucel","email":"","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":false,"prefix":"","firstName":"Ozlem","middleName":"Eski","lastName":"Yucel","suffix":""},{"id":628002722,"identity":"937c5653-1612-45eb-9205-f28a8a19a2dc","order_by":6,"name":"Zeynep İmamoğlu","email":"","orcid":"","institution":"Ondokuz Mayıs University","correspondingAuthor":false,"prefix":"","firstName":"Zeynep","middleName":"","lastName":"İmamoğlu","suffix":""}],"badges":[],"createdAt":"2026-03-19 12:39:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9169566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9169566/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107838925,"identity":"a30045d3-0b35-470b-b6e7-f321908211de","added_by":"auto","created_at":"2026-04-26 17:14:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142775,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve\u0026nbsp;\u003c/p\u003e","description":"","filename":"figure1600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-9169566/v1/5ee2f9783d557c1303b75730.png"},{"id":107870783,"identity":"9d0bbfea-fc05-47dc-9cfd-0c578f4a7127","added_by":"auto","created_at":"2026-04-27 07:40:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":627799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9169566/v1/e447e6b7-2f43-48a3-9edc-bf602a32eaa3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between the C-Reactive Protein-to-Albumin Ratio and Treatment-Requiring Retinopathy of Prematurity ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRetinopathy of prematurity (ROP) is defined as a microangiopathy characterized by abnormal retinal vascular development in premature infants and may lead to severe visual impairment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The pathophysiology of ROP is multifactorial, involving oxidative stress, fluctuations between hypoxia and hyperoxia, and inflammatory processes. In this context, the relationship between inflammatory markers and ROP prognosis has been investigated [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These mechanisms play critical roles in angiogenesis and overall retinal development and are additionally influenced by nutritional and immune status [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdvances in neonatal care have increased survival rates among premature infants; however, this has also led to a higher prevalence of ROP in this vulnerable population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The incidence and severity of ROP are closely associated with gestational age (GA) and birth weight (BW), with the highest risk observed in infants with extremely low BW and lower GA [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eC-reactive protein (CRP) and serum albumin are well-established biomarkers that have long been used to assess systemic inflammation and nutritional status. C-reactive protein is widely recognized as a sensitive marker of acute inflammation, whereas serum albumin is used in clinical practice as an indicator of both nutritional status and chronic inflammatory burden. In recent decades, the combination of these two parameters, expressed as the C-reactive protein-to-albumin ratio (CAR), has emerged as a prognostic biomarker reflecting both the severity of the inflammatory response and nutritional status. The prognostic and diagnostic value of CAR was initially investigated in oncological studies and was found to be significantly associated with survival and complication risk in various malignancies. In clinical conditions where inflammation plays a central role\u0026mdash;such as sepsis, malignancies, cardiac ischemia, acute appendicitis, and postoperative infectious complications\u0026mdash;receiver operating characteristic (ROC) analyses have supported the clinical utility of CAR in terms of sensitivity and specificity [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study was to evaluate the association between CAR and both severity and treatment requirement ROP\u0026mdash;a topic that has not been comphrehensively addressed in the literature before\u0026mdash;and to assess the diagnostic performance of CAR alongside classical perinatal risk factors such as GA and BW.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design, Participants and Data Collection\u003c/h2\u003e \u003cp\u003e The study was approved by the Ondokuz Mayıs University Clinical Research Ethics Committee (approval number: 2026/149) and was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eThis retrospective study was conducted at Ondokuz Mayıs University Faculty of Medicine Hospital. Premature infants followed in the ROP unit of the Ophthalmology Clinic between January 2021 and December 2025 were reviewed.\u003c/p\u003e \u003cp\u003eThe study included premature infants with a GA\u0026thinsp;\u0026le;\u0026thinsp;32 weeks and/or a BW\u0026thinsp;\u0026lt;\u0026thinsp;1500 g, infants with a GA\u0026thinsp;\u0026gt;\u0026thinsp;32 weeks and/or a BW\u0026thinsp;\u0026ge;\u0026thinsp;1500 g who were considered at risk for ROP by a neonatologist and infants for whom serum albumin and CRP measurements were available. Only samples obtained at the time of the highest ROP severity or within a maximum of two weeks prior to diagnosis were analyzed, corresponding to different gestational periods across cases.\u003c/p\u003e \u003cp\u003ePremature infants with a major congenital anomalies, chromosomal disorders, severe systemic diseases that could influence study outcomes, incomplete clinical or laboratory data, and who did not regularly attend ROP follow-up were excluded from ths study.\u003c/p\u003e \u003cp\u003eClinical data were obtained from medical records of patients. GA, BW, serum albumin, and CRP levels were recorded. The CAR was calculated using the following formula:\u003c/p\u003e \u003cp\u003eCAR\u0026thinsp;=\u0026thinsp;CRP / Albumin\u003c/p\u003e \u003cp\u003eParticipants were categorized into three groups as follows:\u003c/p\u003e \u003cp\u003eGroup 1: Those without ROP\u003c/p\u003e \u003cp\u003eGroup 2: Those with spontaneously regressed ROP\u003c/p\u003e \u003cp\u003eGroup 3: Those with Treated ROP (severe ROP)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics (version 26.0; IBM Corp., Armonk, NY, USA). The distribution of continuous variables was assessed using visual and analytical methods. Non-normally distributed variables were expressed as median (minimum\u0026ndash;maximum), and categorical variables were presented as number and percentage (%).\u003c/p\u003e \u003cp\u003eContinuous variables were compared using the Kruskal\u0026ndash;Wallis test, and categorical variables were compared using the chi-square test. When significant differences were identified, pairwise comparisons were performed with appropriate post hoc analyses.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression analysis was conducted to identify independent predictors of treatment requirement. Results were reported as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Model fit was assessed using the Hosmer\u0026ndash;Lemeshow test.\u003c/p\u003e \u003cp\u003eThe predictive performance of GA, BW, and CAR for treatment requirement was evaluated using ROC curve analysis. The area under the curve (AUC) with 95% CI was calculated, and optimal cut-off values were determined using the Youden index. Since lower GA and BW are associated with increased risk, variable direction was evaluated in accordance with clinical interpretation.\u003c/p\u003e \u003cp\u003eA p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. After Bonferroni correction, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0167 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 390 premature infants (167 females, 223 males) were included in the study. There were 209 cases in Group 1, 102 in Group 2, and 79 in Group 3. The demographic characteristics and laboratory parameters of the patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics and laboratory values ​​in patient groups\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;209)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (25\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (25\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (22\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePa\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePb\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePc\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale/Male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86(% 41) /\u003c/p\u003e \u003cp\u003e123( %59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (%39) /\u003c/p\u003e \u003cp\u003e62(%61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(%51) /\u003c/p\u003e \u003cp\u003e38 (%49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePa\u0026thinsp;=\u0026thinsp;0.745\u003c/p\u003e \u003cp\u003ePb\u0026thinsp;=\u0026thinsp;0.102\u003c/p\u003e \u003cp\u003ePc\u0026thinsp;=\u0026thinsp;0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1725 (670\u0026ndash;3280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1290 (680\u0026ndash;3270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1040 (520\u0026ndash;2480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePa\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePb\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePc\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,71 (2,80\u0026thinsp;\u0026minus;\u0026thinsp;4,82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.20 (2,43\u0026thinsp;\u0026minus;\u0026thinsp;4,30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.03 (2.12\u0026ndash;4.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePa\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePb\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePc\u0026thinsp;=\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,15 (0,01\u0026ndash;37,90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,69 (0,60\u0026thinsp;\u0026minus;\u0026thinsp;22,04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,51 (0,00-224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePa\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePb\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePc\u0026thinsp;=\u0026thinsp;0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,55 (0,00\u0026ndash;8,61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,05 (0,19\u0026thinsp;\u0026minus;\u0026thinsp;5,82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,87 (0,00\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePa\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePb\u0026thinsp;=\u0026thinsp;0.001*\u003c/p\u003e \u003cp\u003ePc\u0026thinsp;=\u0026thinsp;0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCRP: C-reactive protein; CAR:CRP/Albumin Ratio; P\u003csup\u003e1\u003c/sup\u003e: Kruskal-Wallis test or chi-square test (p\u0026thinsp;\u0026lt;\u0026thinsp;0,05); P\u003csup\u003e2\u003c/sup\u003e:Bonferonni adjusted Mann-Whitney U test (p\u0026thinsp;\u0026lt;\u0026thinsp;0,0167); Pa: The difference between groups 1 and 2; Pb: The difference between groups 1 and 3, Pc: The difference between groups 2 and 3; *:Statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant differences were observed among the groups in terms of demographic and clinical variables. GA and BW differed significantly across groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Post hoc analyses revealed that infants in the group 3 had significantly lower GA and BW compared with the other groups. CAR levels also differed significantly between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with higher values observed in the group 3.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the association between GA, BW, and CAR with ROP outcomes. In ordinal logistic regression analysis, GA(OR\u0026thinsp;=\u0026thinsp;0.66; 95% CI: 0.61\u0026ndash;0.72; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BW (OR\u0026thinsp;=\u0026thinsp;0.66; 95% CI: 0.61\u0026ndash;0.72; p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). CAR was positively and significantly associated with ROP severity (OR\u0026thinsp;=\u0026thinsp;1.97; 95% CI: 1.67\u0026ndash;2.32; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eAssociation of Clinical Variables and CAR with ROP Outcomes\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROP severity\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGestational age (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61\u0026ndash;0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROP severity\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirth weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.997\u0026ndash;0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROP severity\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67\u0026ndash;2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment requirement\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u0026ndash;1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eROP:Retinopathy of prematurity;B: beta coefficient; OR:Odds ratio; CI:Confidence interval, *:Statistically significant; CAR:C-reactive protein/albumin ratio.\u003c/p\u003e \u003cp\u003e\u0026sup1;Ordinal logistic regression model (logit link).\u003c/p\u003e \u003cp\u003e\u0026sup2;Binary logistic regression model (logit link).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn binary logistic regression analysis, CAR was significantly associated with treatment requirement of ROP. Each one-unit increase in CAR increased the odds of treatment requirement by 42% (OR\u0026thinsp;=\u0026thinsp;1.42; 95% CI: 1.26\u0026ndash;1.61; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn multivariable logistic regression analysis, GA and CAR remained independent predictors of treatment requirement of ROP. While each additional week of gestation significantly reduced treatment risk, higher CAR levels increased treatment risk. But BW did not retain independent significance in the model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eMultivariable Logistic Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\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\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational Age (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026ndash;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026ndash;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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 (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999\u0026ndash;1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eOR:Odds ratio; CI:Confidence interval; CAR:C-reactive protein-to-albumin ratio, *:Statistically significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eROC curve analysis is shown in Fig.\u0026nbsp;1. GA, BW, and CAR were all significant predictors of treatment requirement of ROP ( p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC Analysis of Predictors for Treatment Requirement\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.799 (0.743\u0026ndash;0.855)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.761 (0.701\u0026ndash;0.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1172.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.712 (0.628\u0026ndash;0.795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAUC:Area Under the Curve; *:Statistical significance; CAR:C-reactive protein-to-albumin ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGA demonstrated good discriminative ability with an AUC of 0.799 (95% CI: 0.743\u0026ndash;0.855). At a cut-off value of \u0026le;\u0026thinsp;28.5 weeks, sensitivity was 63.3% and specificity was 81.7%. BW also showed good predictive performance with an (AUC of: 0.761; 95% CI: 0.701\u0026ndash;0.821). At a cut-off value of \u0026le;\u0026thinsp;1172.5 g, sensitivity was 64.6% and specificity was 76.8%. CAR demonstrated acceptable discriminative ability with an AUC of 0.712 (95% CI: 0.628\u0026ndash;0.795). At a cut-off value of \u0026ge;\u0026thinsp;1.018, sensitivity was 65.8% and specificity was 69.1% .\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe pathophysiology of ROP is characterized by a biphasic disturbance of vascular development. In the first phase, hyperoxia suppresses normal vascular growth, whereas in the second phase, hypoxia and inflammation-driven mechanisms promote pathological neovascularization. The impact of inflammation on retinal vascular development is mediated through cytokine release, endothelial dysfunction, and alterations in vascular endothelial growth factor regulation [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this context, it is biologically plausible that biomarkers reflecting systemic inflammatory burden may be associated with ROP progression.\u003c/p\u003e \u003cp\u003eThe Glasgow Prognostic Score has been shown to reflect an individual\u0026rsquo;s immune and nutritional status and is based on CRP and albumin, which are acute-phase proteins synthesized in the liver. C-reactive protein levels are regulated by various proinflammatory cytokines, including interleukin-1, tumor necrosis factor-α, transforming growth factor-β, interferon-γ, and interleukin-6. Serum albumin is widely used as a biomarker of hepatic function and nutritional status. Hypoalbuminemia has been associated with systemic inflammatory response, cancer recurrence, and metastasis. While CRP reflects acute inflammatory activity, albumin is a negative acute-phase reactant that decreases during inflammation [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe CAR, which combines these two parameters, provides a more comprehensive assessment by simultaneously reflecting both positive and negative components of the inflammatory response. The prognostic value of CAR was first described in oncological studies, where it was associated with survival in colorectal and pancreatic cancers [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Considering the role of neonatal inflammation in retinal vascular development, the association between CAR and ROP is consistent with current pathophysiological understanding.\u003c/p\u003e \u003cp\u003eIn the present study, CAR was positively associated with ROP severity and independently predicted treatment requirement of ROP, supporting the role of inflammatory burden in disease progression. The stronger association observed with ROP severity suggests that inflammatory load may exert a more pronounced effect in advanced stages of the disease. Nevertheless, the confirmation of these findings in multivariable models is essential to ensure that the observed associations are not confounded by established perinatal risk factors.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, CAR is an easily calculated, cost-effective, and readily available biomarker derived from routine laboratory parameters. It may contribute to risk assessment, particularly in infants with borderline GA or uncertain clinical course. However, CAR appears to function more appropriately as a complementary parameter rather than a standalone predictor, supporting classical perinatal risk factors.\u003c/p\u003e \u003cp\u003eIn the ROC analysis, GA demonstrated superior discriminative ability compared with CAR, reaffirming prematurity as the primary determinant of ROP risk. Previous studies have consistently reported that lower GA and lower BW are the strongest predictors of ROP incidence and treatment requirement [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our findings are in line with these reports.\u003c/p\u003e \u003cp\u003eThe loss of independent significance of BW in the multivariable model may be explained by its strong correlation with GA. Since GA more directly reflects retinal vascular maturity, it may emerge as the dominant predictor in statistical modeling. Importantly, CAR retained independent significance after adjustment for GA, suggesting that inflammatory processes contribute to ROP risk beyond the degree of prematurity alone.\u003c/p\u003e \u003cp\u003eA recent study reported that CAR levels measured during the first postnatal month were significantly associated with the development of more severe ROP [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This finding supports the potential clinical relevance of CAR in neonatal vascular diseases. In our study, the evaluation of CAR in relation to both ROP severity and treatment requirement using ordinal and binary regression analyses provides methodological robustness. Furthermore, the identification of independent predictors through multivariable analysis enhances the clinical interpretability of the results. The determination of cut-off values via ROC analysis further supports potential clinical applicability. Given the limited number of studies investigating CAR in the context of ROP, our findings contribute novel data to the existing literature.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. The retrospective design precludes causal inference. Inflammatory markers were assessed at a single time point, and serial measurements were not available. Prospective, multicenter studies with longitudinal biomarker assesment would be beneficial to clarify the clinical utility of CAR in ROP risk stratification.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, GA remains the strongest predictor of treatment-requiring ROP. However, the CAR represents an independent biomarker reflecting systemic inflammatory burden and contributes to risk assessment. The integration of CAR with classical perinatal risk factors may facilitate earlier identification of high-risk infants and improve clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eConceptualization and Study Design:\u003c/strong\u003e Ahmet \u0026Ouml;ZDEMİR, Mustafa Subaşı\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection and Management:\u003c/strong\u003e Ahmet \u0026Ouml;ZDEMİR, Mertcan Esenkaya,, Canan Seren, Zeynep İmamoğlu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis:\u003c/strong\u003e Mustafa Subaşı, Beg\u0026uuml;m Kalyoncu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManuscript Writing \u0026ndash; Original Draft Preparation:\u003c/strong\u003e Mustafa Subaşı, \u0026nbsp;Ahmet \u0026Ouml;ZDEMİR\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManuscript Review and Editing:\u003c/strong\u003e Ozlem Eski Yucel\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupervision:\u003c/strong\u003e Ozlem Eski Yucel\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the tenets of the Declaration of Helsinki and received approval from the Ondokuz Mayıs University Faculty of Medicine Ethics Committee (Approval number: 2026/149). Written informed consent was obtained from the parents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the clinical staff at the Department of Ophthalmology, Ondokuz Mayıs University, for their invaluable assistance in data collection and patient care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study are presented in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGilbert C, Foster A. Childhood blindness in the context of VISION 2020\u0026ndash;the right to sight. Bull World Health Organ. 2001;79(3):227\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang D, Liu Z, Deng Y. Retinopathy of Prematurity (ROP): An Overview of Biomarkers in Various Samples for Prediction, Diagnosis, and Prognosis. Clin Ophthalmol. 2025;19:1515\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith LE. \u003cem\u003ePathogenesis of retinopathy of prematurity.\u003c/em\u003e Growth Horm IGF Res, 2004. 14 Suppl A: pp. S140-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn DA et al. Higher versus lower amino acid intake in parenteral nutrition for newborn infants. Cochrane Database Syst Rev, 2018. 3(3): p. Cd005949.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHellstr\u0026ouml;m A, Smith LE, Dammann O. Retinopathy Prematur Lancet. 2013;382(9902):1445\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiang MF et al. \u003cem\u003eInternational Classification of Retinopathy of Prematurity, Third Edition.\u003c/em\u003e Ophthalmology, 2021. 128(10): pp. e51-e68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYing GS, et al. Predictors for the development of referral-warranted retinopathy of prematurity in the telemedicine approaches to evaluating acute-phase retinopathy of prematurity (e-ROP) study. JAMA Ophthalmol. 2015;133(3):304\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFierson WM. Screening Examination of Premature Infants for Retinopathy of Prematurity. Pediatrics, 2018. 142(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYucel OE, et al. Incidence and risk factors for retinopathy of prematurity in premature, extremely low birth weight and extremely low gestational age infants. BMC Ophthalmol. 2022;22(1):367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeyhanli Z, et al. The Efficacy of C-Reactive Protein (CRP) to Albumin Ratio (CAR) and Fibrinogen to CRP Ratio (FCR) in Predicting the Latent Period of Preterm Labor. Am J Reprod Immunol. 2024;92(1):e13899.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIshizuka M, et al. Clinical Significance of the C-Reactive Protein to Albumin Ratio for Survival After Surgery for Colorectal Cancer. Ann Surg Oncol. 2016;23(3):900\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarlidag T et al. C-Reactive Protein to Albumin Ratio and Prognostic Nutrition Index as a Predictor of Periprosthetic Joint Infection and Early Postoperative Wound Complications in Patients Undergoing Primary Total Hip and Knee Arthroplasty. Diagnostics (Basel), 2025. 15(17).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArefnia M, et al. The predictive value of CRP/albumin ratio (CAR) in the diagnosis of ischemia in myocardial perfusion scintigraphy. Hipertens Riesgo Vasc. 2025;42(3):172\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplangoray M, et al. High CRP-albumin ratio is associated high thrombus burden in patients with newly diagnosed STEMI. Med (Baltim). 2023;102(41):e35363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCumaoglu MO, et al. Delta neutrophil index, CRP/albumin ratio, procalcitonin, immature granulocytes, and HALP score in acute appendicitis: Best performing biomarker? Open Med (Wars). 2025;20(1):20251308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang N et al. \u003cem\u003eReview of the Predictive Value of Biomarkers in Sepsis Mortality.\u003c/em\u003e Emerg Med Int, 2024. 2024: p. 2715606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirakawa T, et al. C-reactive protein/albumin ratio is the most significant inflammatory marker in unresectable pancreatic cancer treated with FOLFIRINOX or gemcitabine plus nab-paclitaxel. Sci Rep. 2023;13(1):8815.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavallaro G, et al. The pathophysiology of retinopathy of prematurity: an update of previous and recent knowledge. Acta Ophthalmol. 2014;92(1):2\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartnett ME. Retinopathy of Prematurity: Evolving Treatment With Anti-Vascular Endothelial Growth Factor. Am J Ophthalmol. 2020;218:208\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowe LW, et al. Vascular imaging findings in retinopathy of prematurity. Acta Ophthalmol. 2024;102(4):e452\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe L, et al. Prognostic Value of the Glasgow Prognostic Score or Modified Glasgow Prognostic Score for Patients with Colorectal Cancer Receiving Various Treatments: a Systematic Review and Meta-Analysis. Cell Physiol Biochem. 2018;51(3):1237\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris-Stiff G, Gomez D, Prasad KR. C-reactive protein in liver cancer surgery. Eur J Surg Oncol. 2008;34(7):727\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKomrokji RS, et al. Hypoalbuminemia is an independent prognostic factor for overall survival in myelodysplastic syndromes. Am J Hematol. 2012;87(11):1006\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaskins IN, et al. Preoperative hypoalbuminemia is associated with worse outcomes in colon cancer patients. Clin Nutr. 2017;36(5):1333\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinoshita A, et al. The C-reactive protein/albumin ratio, a novel inflammation-based prognostic score, predicts outcomes in patients with hepatocellular carcinoma. Ann Surg Oncol. 2015;22(3):803\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRevised indications for the. treatment of retinopathy of prematurity: \u003cem\u003eresults of the early treatment for retinopathy of prematurity randomized trial\u003c/em\u003e. Arch Ophthalmol. 2003;121(12):1684\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilbert C. Retinopathy of prematurity: a global perspective of the epidemics, population of babies at risk and implications for control. Early Hum Dev. 2008;84(2):77\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SJ, et al. Retinopathy of prematurity: a review of risk factors and their clinical significance. Surv Ophthalmol. 2018;63(5):618\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkinci DY, et al. A novel marker for predicting type 1 retinopathy of prematurity: C-reactive protein/albumin ratio. Int Ophthalmol. 2023;43(9):3345\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-ophthalmology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"boph","sideBox":"Learn more about [BMC Ophthalmology](http://bmcophthalmol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/boph","title":"BMC Ophthalmology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Retinopathy of prematurity, C-reactive protein-to-albumin ratio, Inflammation, Treated ROP, Premature","lastPublishedDoi":"10.21203/rs.3.rs-9169566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9169566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSystemic inflammation has been implicated in the pathogenesis of retinopathy of prematurity (ROP). The C-reactive protein-to-albumin ratio (CAR) has emerged as a composite biomarker reflecting inflammatory burden and nutritional status. This study aimed to evaluate the association between CAR and both severity and treatment requirement ROP, and to assess its diagnostic performance alongside classical perinatal risk factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e This retrospective study included 390 premature infants followed in a tertiary ROP unit between January 2021 and December 2025. Infants were categorized into three groups: with no ROP, with spontaneously regressed ROP, and with treated ROP. CAR was calculated as the ratio of C-reactive protein to serum albumin. Associations with ROP severity were analyzed using ordinal logistic regression, and independent predictors of treatment requirement were evaluated using multivariable logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to assess predictive performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGestational age (GA) and birth weight (BW) were significantly and inversely associated with ROP severity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). CAR was positively associated with both ROP severity (OR\u0026thinsp;=\u0026thinsp;1.97; 95% CI: 1.67\u0026ndash;2.32; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and treatment requirement (OR\u0026thinsp;=\u0026thinsp;1.42; 95% CI: 1.26\u0026ndash;1.61; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariable analysis, GA and CAR remained independent predictors of treatment requirement. ROC analysis demonstrated good discriminative ability for GA (AUC: 0.799) and BW (AUC: 0.761), and acceptable performance for CAR (AUC: 0.712).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWhile GA remains the strongest predictor of treatment-requiring ROP, CAR represents an independent biomarker reflecting systemic inflammatory burden. The integration of CAR with classical perinatal risk factors may enhance risk prediction for ROP in premature infants.\u003c/p\u003e","manuscriptTitle":"Association Between the C-Reactive Protein-to-Albumin Ratio and Treatment-Requiring Retinopathy of Prematurity ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 17:14:02","doi":"10.21203/rs.3.rs-9169566/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-28T23:53:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T05:05:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T07:15:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T06:53:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T21:40:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T02:16:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163485488577339071361878326919509993680","date":"2026-04-19T18:09:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210744876717893547663321842935174297343","date":"2026-04-19T15:33:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214603693052813463013787884637089395318","date":"2026-04-19T14:28:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T12:15:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256700239891615585691708327039575665076","date":"2026-04-17T22:35:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275061786861844711036673597530373679149","date":"2026-04-17T22:30:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295291526367151368550282021532078309964","date":"2026-04-17T18:31:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6784777405011376044288325656756057811","date":"2026-04-17T14:06:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T13:34:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T07:22:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-21T02:16:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-21T02:15:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Ophthalmology","date":"2026-03-19T12:29:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-ophthalmology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"boph","sideBox":"Learn more about [BMC Ophthalmology](http://bmcophthalmol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/boph","title":"BMC Ophthalmology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8a2ff039-d3f8-49be-918e-32b4dd02af88","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T17:14:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 17:14:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9169566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9169566","identity":"rs-9169566","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}