Adulkasem Nath, Wongcharoenwatana Jidapa, Ariyawatkul Thanase, Chotigavanichaya Chatupon, Kaewpornsawan Kamolporn, Eamsobhana Perajit
Department of Orthopaedic Surgery, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
Children (Basel). 2021 Oct 6;8(10):890. doi: 10.3390/children8100890.
Early identification of pathological causes for pediatric genu varum (bowlegs) is crucial for preventing a progressive, irreversible knee deformity of the child. This study aims to develop and validate a diagnostic clinical prediction algorithm for assisting physicians in distinguishing an early stage of Blount's disease from the physiologic bowlegs to provide an early treatment that could prevent the progressive, irreversible deformity. The diagnostic prediction model for differentiating an early stage of Blount's disease from the physiologic bowlegs was developed under a retrospective case-control study from 2000 to 2017. Stepwise backward elimination of multivariable logistic regression modeling was used to derive a diagnostic model. A total of 158 limbs from 79 patients were included. Of those, 84 limbs (53.2%) were diagnosed as Blount's disease. The final model that included age, BMI, MDA, and MMB showed excellent performance (area under the receiver operating characteristic (AuROC) curve: 0.85, 95% confidence interval 0.79 to 0.91) with good calibration. The proposed diagnostic prediction model for discriminating an early stage of Blount's disease from physiologic bowlegs showed high discriminative ability with minimal optimism.
早期识别小儿膝内翻（弓形腿）的病理原因对于预防儿童膝关节进行性、不可逆畸形至关重要。本研究旨在开发并验证一种诊断临床预测算法，以协助医生区分早期布朗特病与生理性弓形腿，从而提供可预防进行性、不可逆畸形的早期治疗。在一项2000年至2017年的回顾性病例对照研究中，开发了用于区分早期布朗特病与生理性弓形腿的诊断预测模型。采用多变量逻辑回归建模的逐步向后消除法来推导诊断模型。共纳入了79例患者的158条肢体。其中，84条肢体（53.2%）被诊断为布朗特病。包含年龄、体重指数、内侧干骺端角和内侧半月板角的最终模型表现出色（受试者操作特征曲线下面积：0.85，95%置信区间0.79至0.91）且校准良好。所提出的用于区分早期布朗特病与生理性弓形腿的诊断预测模型显示出高辨别能力且乐观偏差最小。
