Patterns of femoral neck fracture and its treatment methods in patients with osteogenesis imperfecta.

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Abstract

[Indexed for MEDLINE] 14. Orphanet J Rare Dis. 2024 Nov 9;19(1):420. doi: 10.1186/s13023-024-03433-1. Height prediction of individuals with osteogenesis imperfecta by machine learning. Yang H(#)(1), Zhu W(#)(1), Li B(#)(1), Wang H(2), Xing C(1), Xiong Y(1), Ren X(3), Ning G(4). Author information: (1)Department of Orthopedics, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord Injury, Tianjin Medical University General Hospital, Tianjin, China. (2)Tianjin Medical University, Tianjin, China. (3)Department of orthopedics, Children's Hospital of Soochow University, Suzhou, China. renxiuzhi7320@suda.edu.cn. (4)Department of Orthopedics, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord Injury, Tianjin Medical University General Hospital, Tianjin, China. gzning@tmu.edu.cn. (#)Contributed equally BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significant gap in knowledge regarding the growth patterns across different types of OI, and the prediction of height in individuals with OI was not adequately addressed. In this study, we described the growth patterns and predicted the height of individuals with OI employing multiple machine learning (ML) models. Accurate height prediction enables effective monitoring and facilitates the development of personalized intervention plans for managing OI. METHOD: This study included cross-sectional data for 323 participants with OI, and the median height Z-score for OI types I, III and IV were - 0.62 (-5.93 ~ 3.24), -3.97 (-10.44 ~ -0.02) and - 1.64 (-6.67 ~ 2.44), respectively. Based on the cross-sectional data of participants, the height curves across different gender and OI types were plotted and compared. Subsequently, feature selection techniques, specifically the filter and wrapper methods, were employed to identify predictive factors for the height of participants. Finally, multiple machine learning (ML) models were constructed for height prediction, and the performance of each model was systematically evaluated. RESULTS: The analysis of height curves revealed that male with OI are significantly taller than female with OI from the age of 14 (p = 0.045), individuals with OI type III are statistically shorter than those with OI types I and IV starting from 3 years old (p = 0.006), and those with OI type IV are statistically shorter than those with OI type I from the age of 10 (p = 0.028). The application of filter and wrapper methods identified gender (p = 0.001), age (p