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整合炎症标志物的评分模型用于预测早产儿视网膜病变的研究进展

Development of a scoring model integrating inflammatory markers for predicting ROP in preterm neonates.

作者信息

Hamilçıkan Şahin, Erolur Hatice, Can Ceren, Karakurt Yakup, Can Emrah

机构信息

Division of NICU, Department of Pediatrics, Bagcilar Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.

Department of Ophthalmology, Bagcilar Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.

出版信息

Int Ophthalmol. 2025 May 21;45(1):206. doi: 10.1007/s10792-025-03572-9.

Abstract

PURPOSE

ROP is a leading cause of blindness in preterm infants worldwide. ROP diagnosis is made through a detailed eye examination and supportive methods are required, especially in limited centers. ROP diagnosis primarily relies on frequent ophthalmological examinations; however, complementary diagnostic tools may significantly aid clinicians, particularly in centers with limited resources. This study introduces an innovative scoring model integrating systemic inflammatory markers for its early prediction of ROP.

METHODS

A retrospective case-control study involving 120 preterm infants (≤ 32 weeks, ≤ 1500 g) was conducted. Hemogram-based inflammatory indices, including the Systemic Inflammatory Response Index (SIRI), Platelet-to- Lymphocyte Ratio (PLR), and Pan-Immun Inflammatory Value (PIV), were calculated from blood samples obtained within the first 24 h of life and before ROP diagnosis. Logistic regression and ROC analyses informed the scoring model.

RESULTS

Infants with ROP showed lower gestational ages and birth weights (p < 0.001, p = 0.02). SIRI-2 displayed the highest diagnostic accuracy (AUC = 0.704). A combined model using SIRI-2, PLR-2, and PIV-2 achieved superior performance (AUC = 0.802). Logistic regression identified SIRI-2 and PLR-2 as independent predictors, enhancing risk stratification.

CONCLUSION

This scoring model not only enhances early risk stratification but also holds potential for widespread implementation in neonatal intensive care units, particularly in resource-limited settings. Future multicenter trials will further establish its role in optimizing neonatal outcomes globally.

摘要

目的

视网膜病变是全球早产儿失明的主要原因。视网膜病变的诊断需通过详细的眼部检查,且需要辅助方法,尤其是在资源有限的中心。视网膜病变的诊断主要依赖频繁的眼科检查;然而,辅助诊断工具可能会显著帮助临床医生,特别是在资源有限的中心。本研究引入了一种整合全身炎症标志物的创新评分模型,用于早期预测视网膜病变。

方法

进行了一项回顾性病例对照研究,纳入120例早产儿(≤32周,≤1500克)。从出生后24小时内及视网膜病变诊断前采集的血样中计算基于血常规的炎症指标,包括全身炎症反应指数(SIRI)、血小板与淋巴细胞比值(PLR)和全免疫炎症值(PIV)。逻辑回归和ROC分析为评分模型提供依据。

结果

患有视网膜病变的婴儿胎龄和出生体重较低(p<0.001,p=0.02)。SIRI-2显示出最高的诊断准确性(AUC=0.704)。使用SIRI-2、PLR-2和PIV-2的联合模型表现更优(AUC=0.802)。逻辑回归确定SIRI-2和PLR-2为独立预测因素,增强了风险分层。

结论

该评分模型不仅增强了早期风险分层,还具有在新生儿重症监护病房广泛应用的潜力,特别是在资源有限的环境中。未来的多中心试验将进一步确定其在全球优化新生儿结局中的作用。

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