复合炎症指标在鉴别单纯骨质疏松与椎体压缩性骨折中的价值

Value of Composite Inflammatory Indices in Differentiating Simple Osteoporosis from Osteoporotic Vertebral Compression Fractures

摘要: 目的：探究复合炎症指标在鉴别单纯骨质疏松与椎体压缩性骨折(OVCF)患者中的应用价值，为临床早期识别高危人群提供参考。方法：回顾性分析2025年3月至2026年3月青岛市市立医院东院骨质疏松以及OVCF患者的临床资料，共纳入170例患者，其中单纯骨质疏松组(OP组) 80例，骨质疏松性椎体压缩性骨折组(OVCF组) 90例。计算NLR、LMR、SII、SIRI及PIV，结合年龄、性别及BMI进行多因素Logistic回归分析，评估各指标与OVCF的独立关联，并通过校正曲线评估模型稳定性。采用受试者工作特征(ROC)曲线分析其鉴别能力，并通过Bootstrap检验评估诊断准确性。结果：OVCF组患者年龄较大。NLR、SII、SIRI、PIV显著升高，LMR显著降低。多因素分析显示，NLR、SII、SIRI、PIV为OVCF独立正相关因素，LMR为负相关因素。ROC分析显示LMR敏感度最高，PIV和SIRI特异度性相对较高，NLR和SII鉴别能力可接受。年龄在所有模型中均为独立正相关因素，性别和BMI无显著关联。结论：NLR、LMR、SII、SIRI及PIV均与单纯骨质疏松患者OVCF发生密切相关，并为独立相关因素。LMR适合作为筛查指标，SIRI和PIV适合作为提示性指标，NLR和SII可作为参考指标。结合年龄、性别及BMI构建的模型拟合度好，稳定性高，可为临床早期识别高危患者和制定干预措施提供参考。

Abstract: Objective: To investigate the value of composite inflammatory indices in differentiating patients with simple osteoporosis from those with osteoporotic vertebral compression fractures (OVCF), and to provide evidence for the early identification of high-risk populations in clinical practice. Methods: A retrospective analysis was conducted on the clinical data of patients with osteoporosis and OVCF treated at the East Campus of Qingdao Municipal Hospital between March 2025 and March 2026. A total of 170 patients were included, comprising 80 patients with simple osteoporosis (OP group) and 90 patients with osteoporotic vertebral compression fractures (OVCF group). Composite inflammatory indices, including the NLR, LMR, SII, SIRI, and PIV, were calculated. Multivariate logistic regression analyses adjusted for age, sex, and body mass index (BMI) were performed to evaluate the independent associations between these indices and OVCF. Model stability was assessed using calibration curves. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate discriminative performance, and bootstrap validation was used to assess diagnostic accuracy. Results: Patients in the OVCF group were older. NLR, SII, SIRI, and PIV were significantly elevated, whereas LMR was significantly reduced in the OVCF group. Multivariate analysis demonstrated that NLR, SII, SIRI, and PIV were independent positive predictors of OVCF, while LMR was an independent negative predictor. ROC analysis showed that LMR had the highest sensitivity, whereas PIV and SIRI exhibited relatively high specificity. NLR and SII showed acceptable discriminative ability. Age was an independent positive predictor in all models, whereas sex and BMI were not significantly associated with OVCF. Conclusion: NLR, LMR, SII, SIRI, and PIV are closely associated with the occurrence of OVCF in patients with osteoporosis and serve as independent predictive factors. LMR may be useful as a screening marker, whereas SIRI and PIV may function as warning indicators. NLR and SII can be considered supplementary reference markers. The predictive model incorporating age, sex, and BMI demonstrated good calibration and stability, providing a useful tool for the early identification of high-risk patients and the development of preventive strategies.