Muzzammil Muhammad, Owais Minhas Muhammad, Jamil Amna
Department of Orthopedics, Sindh Gov. Services Hospital, Karachi, Pakistan.
Department of Orthopedics, Jinnah Postgraduate Medical Center, Pakistan.
J Clin Orthop Trauma. 2025 Apr 19;65:103018. doi: 10.1016/j.jcot.2025.103018. eCollection 2025 Jun.
Osteoporotic fractures pose a significant public health burden, particularly in resource-constrained settings where diagnostic tools like DXA scans are unavailable. This study aimed to develop and validate a simple, community-based osteoporosis risk scoring system that incorporates demographic, clinical, and radiographic parameters to identify high-risk individuals for early intervention.
A cross-sectional study was conducted in Karachi, Pakistan, involving 750 participants aged 40 years and above. Data on demographic characteristics, clinical risk factors, and lifestyle habits were collected using a structured questionnaire. Radiographic assessments identified vertebral compression fractures, generalized osteopenia, and trabecular bone loss. Participants were stratified into four risk categories: low, moderate, high, and very high risk. The predictive validity of the scoring system was evaluated using logistic regression and receiver operating characteristic (ROC) curve analysis.
The developed tool classified participants into low (38 %), moderate (32 %), high (20 %), and very high (10 %) risk groups. Fracture incidence ranged from 11.29 % in the low-risk group to 28.23 % in the very high-risk group. The scoring system demonstrated strong predictive accuracy, with a sensitivity of 83 %, specificity of 75 %, and an area under the curve (AUC) of 0.82. Odds ratios for fractures progressively increased with higher risk categories, confirming the model's validity.
This Muzzammil's osteoporosis risk scoring system is a cost-effective and practical tool for early identification of high-risk individuals in low-resource settings. Its implementation could aid in targeted prevention strategies, reducing osteoporotic fracture incidence and improving public health outcomes. Further validation in diverse populations is recommended to optimize its utility.
骨质疏松性骨折给公共卫生带来了沉重负担，在像双能X线吸收测定扫描这类诊断工具无法获取的资源受限环境中尤为如此。本研究旨在开发并验证一种简单的、基于社区的骨质疏松风险评分系统，该系统纳入人口统计学、临床和影像学参数，以识别高危个体以便进行早期干预。
在巴基斯坦卡拉奇开展了一项横断面研究，纳入750名40岁及以上的参与者。使用结构化问卷收集了人口统计学特征、临床风险因素和生活方式习惯的数据。影像学评估确定了椎体压缩性骨折、全身性骨质减少和小梁骨丢失情况。参与者被分为四个风险类别：低、中、高和极高风险。使用逻辑回归和受试者工作特征（ROC）曲线分析评估了评分系统的预测效度。
所开发的工具将参与者分为低风险组（38%）、中风险组（32%）、高风险组（20%）和极高风险组（10%）。骨折发生率从低风险组的11.29%到极高风险组的28.23%不等。该评分系统显示出很强的预测准确性，灵敏度为83%，特异度为75%，曲线下面积（AUC）为0.82。骨折的比值比随着风险类别的升高而逐渐增加，证实了该模型的有效性。
这种穆扎米尔骨质疏松风险评分系统是一种在资源匮乏环境中早期识别高危个体的经济有效且实用的工具。其实施有助于制定有针对性的预防策略，降低骨质疏松性骨折的发生率并改善公共卫生结果。建议在不同人群中进一步验证以优化其效用。
