| Objective:The most frequent type of humeral fracture in children is the supracondylar fracture.Surgery is the procedure of choice for treating significantly displaced humeral supracondylar fractures.Surgery for supracondylar humeral fractures has always raised questions about how well the elbow may recover postoperatively.Although pertinent research have suggested pertinent indicators as fracture risk factors,their clinical usability and efficacy are still far from optimal.Predictive models may successfully identify matching "high-risk" groups for the occurrence of endpoint events,aiding physicians in the early identification of illnesses and intervention in associated lifestyles.They also have a wide range of possible uses in therapeutic settings.The postoperative consequences of supracondylar humeral fracture do not yet have a risk factor analysis,nor is it frequently included in the prediction model for visualization.This article’s objectives are to develop a model to predict children’s recovery of elbow function following supracondylar fracture,analyze the risk factors affecting those children’s elbow function after surgery,establish an individual nomogram,identify and take early intervention steps,and propose a "one person,one policy" treatment strategy for elbow function in various children.Lastly,it can lessen the likelihood of postoperative elbow function restriction and help the elbow recover from injury.Methods:From September 2015 to June 2020,we gathered clinical data and follow-up information on kids who had been diagnosed with supracondylar humeral fracture.Good elbow function and restricted cubital function were the two categories for elbow function scores at the most recent follow-up.A modeling set(279 patients)and a validation set(131 patients)of kids in the included studies were arbitrarily split into two groups on a 7:3 basis.During hospitalization,medical history data,postoperative cast fixing time after discharge,daily light time after surgery,and elbow joint mobility at the most recent follow-up were all included.To identify statistically significant risk factors,univariate logistic regression analysis was used.Then,for further analysis,multivariate logistic regression was used with the above risk factors,and the best logistic regression model was chosen based on sensitivity and accuracy to create a nomogram.The model differentiation and clinical applicability were verified and assessed using the area under the curve(AUC)and decision curve analysis(DCA)under the receiver operating characteristic curve(ROC).Results:1.A total of 410 children were included in the study according to the inclusion criteria.Among them,there were 248 males and 162 females,and the fracture type: 147 cases of type Ⅱb and 263 cases of type Ⅲ.There were no significant changes in the afflicted limb’s lateral difference(OR=1.004,95% CI: 0.607-1.660,P=0.989),surgical method(OR=1.243,95% CI:0.237-6.532,P=0.797),onset season(P=0.554),or number of Kirschners(P=0.147),according to a univariate logistic regression analysis.Age(P<0.001),weight(P<0.001),height(P<0.001),preoperative elbow soft tissue injury(OR=1.724,95% CI: 1.040-2.859,P=0.035),sex(OR=2.220,95% CI: 1.299-3.794,P=0.004),fracture classification(Gartland Ⅱ)(OR=0.252,95% CI: 0.149-0.426,P <0.001),no nerve injury before surgery(OR=0.304,95%CI: 0.155-0.596,P=0.001),prying technique(OR=0.464,95%CI: 0.234-0.920,P=0.028),postoperative daily light time >2h(OR=0.488,95%CI: 0.249-0.955,P=0.036)has a significant difference in univariate analysis.2.Multivariate regression analysis yielded independent risk factors:fracture classification(OR=0.159,95% CI: 0.076-0.332,P<0.001);No nerve injury before surgery(OR=0.365,95% CI: 0.147-0.910,P=0.031);The daily light duration after surgery was > 2 hours(OR=0.463,95% CI: 0.195-1.102,P=0.082);soft tissue injury(OR=1.797,95% CI: 0.904-3.573,P=0.094);Age(P<0.001),postoperative cast fixation time(OR=3.541,95% CI:1.028-12.198,P=0.032).3.Based on the risk factors determined by multivariate logistics regression analysis,a predictive model was established and verified by a validation set,and the AUC value of the ROC curve of the modeling set was0.8366(95%CI: 0.81～0.90).The AUC of the verification set ROC curve is0.8035(95%CI: 0.79～0.93).The Hosmer-Lemeshow test showed X~2=6.29,P=0.837.The DCA and calibration curves show that the prediction performance of the modeling and validation groups is good.Conclusions:1.In this study,a predictive model for the recovery of elbow function after surgery in children with supracondylar fracture of the humerus was constructed,and the risk factors included(1)preoperative nerve injury,(2)fracture type: Gartland type Ⅲ,(3)older children,(4)daily light duration less than 2 h(5)accompanied by soft tissue injury,and(6)plaster fixation time greater than 4 weeks.2.For pediatric orthopedic physicians,the development of a prediction model is important because it will support the routine diagnosis and treatment of the humeral supracondylar fracture.3.Can the rational application of predictive models help clinicians identify the occurrence of elbow dysfunction? Stiff? For patients at higher risk,earlier postoperative rehabilitation interventions should be promoted to better promote the recovery of elbow function after surgery for supracondylar humeral fractures. |
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