Constructing a risk prediction model for failure after locking plate fixation for proximal humeral fractures in the elderly by combining the deltoid tuberosity index with preoperative factors
- VernacularTitle:三角肌结节指数联合术前因素构建老年肱骨近端骨折锁定钢板内固定失效的风险预测模型
- Author:
Daxing XU
1
,
2
;
Muqiang JI
;
Zesong TU
;
Weipeng XU
;
Weilong XU
;
Wei NIU
Author Information
1. 广州中医药大学,广东省广州市 510006
2. 佛山市中医院三水医院骨科,广东省佛山市 528100
- Keywords:
proximal humeral fracture;
elderly;
fracture internal fixation;
deltoid tuberosity index;
risk prediction model;
nomogram
- From:
Chinese Journal of Tissue Engineering Research
2024;28(21):3299-3305
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND:Proximal humeral fracture in older adults is one of the three major osteoporotic fractures.Anatomic locking plate fixation is the first choice for most scholars to treat difficult-to-reduce and complex fracture types.However,the probability of reduction failure after the operation is high,which seriously affects patients'quality of life. OBJECTIVE:To investigate the correlation between deltoid tuberosity index and postoperative reduction failure of proximal humeral fractures in the elderly,analyze and filter preoperative independent risk factors for reduction failure of proximal humeral fractures in the elderly,and construct and verify the effectiveness of a clinical prediction model. METHODS:The clinical data of 153 elderly patients with proximal humeral fractures who met the diagnosis and inclusion criteria and received open reduction and locking plate surgery in Foshan Hospital of TCM from June 2012 to June 2021 were collected.The patients were divided into the reduction failure subgroup and the reduction maintenance subgroup.The independent risk factors were selected by multivariate Logistic regression analysis,and the nomogram was constructed by R language.After 1000 times of resampling by Bootstrap method,the Hosmer-Lemeshow goodness of fit correlation test,receiver operating characteristic curve,calibration curve,clinical decision,and influence curve were plotted to evaluate its goodness of fit,discrimination,calibration ability,and clinical application value.Fifty-five elderly patients with proximal humeral fractures from June 2013 to August 2021 were selected as the model's external validation group to evaluate the prediction model's stability and accuracy. RESULTS AND CONCLUSION:(1)Of the 153 patients in the training group,44 patients met reduction failure after internal plate fixation.The prevalence of postoperative reduction failure was 28.8%.Multivariate Logistic regression analysis identified that deltoid tuberosity index[OR=9.782,95%CI(3.798,25.194)],varus displacement[OR=4.209,95%CI(1.472,12.031)],and medial metaphyseal comminution[OR=4.278,95%CI(1.670,10.959)]were independent risk factors for postoperative reduction failure of proximal humeral fractures in older adults(P<0.05).(2)A nomogram based on independent risk factors was then constructed.The Hosmer-Lemeshow test results for the model of the training group showed that χ2=0.812(P=0.976)and area under curve=0.830[95%CI(0.762,0.898)].The calibration plot results showed that the model's predicted risk was in good agreement with the actual risk.The decision and clinical influence curves showed good clinical applicability.(3)In the validation group,the accuracy rate in practical applications was 86%,area under curve=0.902[95%CI(0.819,0.985)].(4)It is concluded that deltoid tuberosity index<1.44,medial metaphyseal comminution,and varus displacement were independent risk factors for reduction failure.(5)The internal and external validation of the risk prediction model demonstrated high discrimination,accuracy,and clinical applicability could be used to individually predict and screen the high-risk population of postoperative reduction failure of proximal humeral fractures in the elderly.The predicted number of patients at high risk is highly matched to the actual number of patients who occur when the model's threshold risk probability is above 65%,and clinicians should use targeted treatment.
