Objective To explore the risk factors for developmental dysplasia of the hip (DDH) in children and construct a relevant prediction model, in order to provide more information for clinical screening, prevention, and treatment of DDH. Methods A retrospective study was conducted involving 62 children with DDH diagnosed and treated at the Affiliated Hospital of Yan'an University from January 2018 to December 2023. Additionally, 62 healthy children undergoing physical examinations during the same period were matched at a 1∶1 ratio as the control group. The occurrence of DDH in children was analyzed, and the risk factors were explored. Results Among the 62 children with DDH included in this study, 30 had the affected hip on the right side, and 32 on the left side. According to the Graf classification, there were 15 cases of type Ⅱa(+), 22 cases of type Ⅱa(-), 7 cases of type Ⅱb, 6 cases of type Ⅱc, 5 cases of type D, 6 cases of type Ⅲ, and 1 case of type Ⅳ. Univariate analysis revealed that gender, fetal position during delivery, prematurity status, leg wrapping and leggings habits, and family history of DDH may be associated with the occurrence of DDH in children (P<0.05). Logistic multivariate analysis confirmed that female, breech delivery, non-prematurity, leg wrapping and leggings habits, and family history of DDH were independent risk factors for DDH in children (P<0.05). Using the independent influencing factors and P-value prediction probabilities from the regression model to predict the risk of DDH in children, the Youden indices were 37.50%, 14.83%, 81.42%, 25.00%, 8.69%, and 82.42%, respectively. Conclusions Children who are female, delivered in the breech position, non-premature, having leg wrapping and leggings habits and family history of DDH are more prone to developing DDH. The data model constructed using these five factors demonstrates good performance in predicting the occurrence of DDH in patients and is worthy of further analysis in clinical practice.
Key words
developmental dysplasia of the hip /
prediction model /
children
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