BACKGROUND:Intertrochanteric femoral fractures are the main type of fragility fracture in the elderly,and proximal femoral nail antirotation is the preferred surgical option,but the factors associated with postoperative internal fixation failure are controversial.OBJECTIVE:A new"three-column"classification of intertrochanteric femoral fractures was proposed by evaluating patients'imaging data preoperatively and analyzing its interaction with postoperative internal fixation failure.A risk prediction model was developed and validated by using numerical algorithms,which facilitates clinicians to identify and intervene in high-risk patients preoperatively.METHODS:Patients with intertrochanteric femur fractures in Sanshui Branch of Foshan Hospital of Traditional Chinese Medicine between June 2012 and June 2022 were selected.The patients were divided into the internal fixation failure group and the internal fixation maintenance group according to whether they had internal fixation failure after surgery.According to the preoperative radiographs,the proximal femur was divided into three columns:the medial column,the lateral column,and the middle column.Each column had different subgroups.The relationship between the morphological characteristics of the"three columns"and the failure of proximal femoral nail antirotation internal fixation was analyzed,and the independent risk factors for internal fixation failure were screened out by single and then multifactorial logistic regression analyses.A risk prediction model was constructed according to the independent risk factors using R language software.The Bootstrap method was used to resample 1 000 times.The area under the curve,calibration curve,and clinical decision curve were used to evaluate the differentiation,calibration ability,and clinical application value of the model.The Youden index was used to determine the optimal risk cut-off value of the prediction model,according to which the patients were divided into high and low risk groups.The stability and extensibility of the model were evaluated according to the accuracy of its risk prediction ability.RESULTS AND CONCLUSION:(1)The four independent risk factors for postoperative internal fixation failure after surgery were predicted using the"three-column"typing system:medial column(comminuted fracture of the lesser trochanter and femoral talar)[odds ratio=5.385,95％CI(1.961,14.782),P=0.001],medial column(chimney type)[odds ratio=2.893,95％CI(1.167,7.173),P=0.022],lateral column(lateral wall thickness＜20.5 mm)[odds ratio=2.804,95％CI(1.078,7.297),P=0.035]and lateral column(lateral wall fracture)[odds ratio=4.278,95％CI(1.670,10.959),P=0.012].(2)The constructed risk prediction model showed good discrimination and accuracy[area under the receiver operating characteristic curve=0.852,95％CI(0.837,0.922)].The calibration curve showed good agreement between the model-predicted risk and the actual risk of occurrence.(3)The clinical decision curve suggested that the model had good clinical applicability when the risk threshold probability was in the range of 0.2-0.82.The risk probability of 28％was the optimal threshold for risk stratification of the model,and the predictive performance of the model was better in patients with different risk groups.(4)The"three-column"typing system constructs a predictive model to calculate the risk probability of postoperative internal fixation failure in patients with intertrochanteric femoral fractures.This method is accurate,simple,and easy to apply clinically,and can be used as a digital tool to guide personalized clinical treatment.

BACKGROUND:Secondary screw perforation of the articular surface is one of the major complications after locking plate internal fixation of proximal humerus fracture,and cut-out screws can damage shoulder function by abrading the glenoid and causing impingement of the acromion.Therefore,accurate risk prediction has positive clinical significance.OBJECTIVE:To screen risk factors for secondary screw perforation after proximal humerus fracture plating by machine learning methods,and to develop and validate a risk prediction model that facilitates clinicians to identify and intervene in high-risk patients at an early stage.METHODS:Clinical data of 214 patients with proximal humerus fractures who underwent locking plate internal fixation from June 2013 to June 2022 were collected as a training group to establish the model,and 61 similar patients from another hospital in the same period were included in the external validation group.The patients were divided into secondary screw perforation and screw maintenance groups according to whether they developed secondary screw perforation after surgery.The training group used three machine learning algorithms,namely,random forest,support vector machine,and logistic regression,to construct the prediction model.The recursive feature elimination method was used,and 10-fold cross-validation resampling was used as the screening method for the variables,and the intersection of the variables that were included when the accuracy of the three models was the highest was taken as the highly correlated with the secondary screw perforation reliable risk variables.The dynamic predictive model was constructed by R language software and presented as a web calculator,and the model was internally and externally validated.The internal test of the model was conducted by the Bootstrap method with 1 000 resamples,and the area under the receiver operating characteristic curve,the calibration curve,and the clinical decision curve were used to evaluate the differentiation,calibration ability,and clinical application value of the model.The Youden index was used to determine the optimal risk threshold of the prediction model,according to which the patients in the external validation group were divided into high-and low-risk groups,and the stability and extensibility of the model were evaluated according to the accuracy of its risk prediction ability.RESULTS AND CONCLUSION:(1)The machine learning algorithm identified four risk variables that were highly correlated with secondary screw perforation,namely cortical support of the proximal medial humeral column,deltoid tuberosity index,fracture type,and postoperative reduction.(2)The constructed risk prediction model showed good discrimination and accuracy[area under the curve=0.874,95％confidence interval(0.827,0.922)],and the calibration curve showed good agreement between the model predicted risk and the actual occurrence risk.(3)The clinical decision curve suggested that the model had good clinical applicability when the probability of the risk threshold was in the 0.1-0.75 range.(4)A risk probability of 26％was the optimal threshold for model risk stratification,and the external validation group used model risk stratification to predict secondary screw perforation with an overall accuracy rate of 84％.(5)The risk prediction model has good accuracy and extrapolation,and may provide a basis for guiding clinical treatment.

BACKGROUND:Secondary screw perforation of the articular surface is one of the major complications after locking plate internal fixation of proximal humerus fracture,and cut-out screws can damage shoulder function by abrading the glenoid and causing impingement of the acromion.Therefore,accurate risk prediction has positive clinical significance.OBJECTIVE:To screen risk factors for secondary screw perforation after proximal humerus fracture plating by machine learning methods,and to develop and validate a risk prediction model that facilitates clinicians to identify and intervene in high-risk patients at an early stage.METHODS:Clinical data of 214 patients with proximal humerus fractures who underwent locking plate internal fixation from June 2013 to June 2022 were collected as a training group to establish the model,and 61 similar patients from another hospital in the same period were included in the external validation group.The patients were divided into secondary screw perforation and screw maintenance groups according to whether they developed secondary screw perforation after surgery.The training group used three machine learning algorithms,namely,random forest,support vector machine,and logistic regression,to construct the prediction model.The recursive feature elimination method was used,and 10-fold cross-validation resampling was used as the screening method for the variables,and the intersection of the variables that were included when the accuracy of the three models was the highest was taken as the highly correlated with the secondary screw perforation reliable risk variables.The dynamic predictive model was constructed by R language software and presented as a web calculator,and the model was internally and externally validated.The internal test of the model was conducted by the Bootstrap method with 1 000 resamples,and the area under the receiver operating characteristic curve,the calibration curve,and the clinical decision curve were used to evaluate the differentiation,calibration ability,and clinical application value of the model.The Youden index was used to determine the optimal risk threshold of the prediction model,according to which the patients in the external validation group were divided into high-and low-risk groups,and the stability and extensibility of the model were evaluated according to the accuracy of its risk prediction ability.RESULTS AND CONCLUSION:(1)The machine learning algorithm identified four risk variables that were highly correlated with secondary screw perforation,namely cortical support of the proximal medial humeral column,deltoid tuberosity index,fracture type,and postoperative reduction.(2)The constructed risk prediction model showed good discrimination and accuracy[area under the curve=0.874,95％confidence interval(0.827,0.922)],and the calibration curve showed good agreement between the model predicted risk and the actual occurrence risk.(3)The clinical decision curve suggested that the model had good clinical applicability when the probability of the risk threshold was in the 0.1-0.75 range.(4)A risk probability of 26％was the optimal threshold for model risk stratification,and the external validation group used model risk stratification to predict secondary screw perforation with an overall accuracy rate of 84％.(5)The risk prediction model has good accuracy and extrapolation,and may provide a basis for guiding clinical treatment.

BACKGROUND:Intertrochanteric femoral fractures are the main type of fragility fracture in the elderly,and proximal femoral nail antirotation is the preferred surgical option,but the factors associated with postoperative internal fixation failure are controversial.OBJECTIVE:A new"three-column"classification of intertrochanteric femoral fractures was proposed by evaluating patients'imaging data preoperatively and analyzing its interaction with postoperative internal fixation failure.A risk prediction model was developed and validated by using numerical algorithms,which facilitates clinicians to identify and intervene in high-risk patients preoperatively.METHODS:Patients with intertrochanteric femur fractures in Sanshui Branch of Foshan Hospital of Traditional Chinese Medicine between June 2012 and June 2022 were selected.The patients were divided into the internal fixation failure group and the internal fixation maintenance group according to whether they had internal fixation failure after surgery.According to the preoperative radiographs,the proximal femur was divided into three columns:the medial column,the lateral column,and the middle column.Each column had different subgroups.The relationship between the morphological characteristics of the"three columns"and the failure of proximal femoral nail antirotation internal fixation was analyzed,and the independent risk factors for internal fixation failure were screened out by single and then multifactorial logistic regression analyses.A risk prediction model was constructed according to the independent risk factors using R language software.The Bootstrap method was used to resample 1 000 times.The area under the curve,calibration curve,and clinical decision curve were used to evaluate the differentiation,calibration ability,and clinical application value of the model.The Youden index was used to determine the optimal risk cut-off value of the prediction model,according to which the patients were divided into high and low risk groups.The stability and extensibility of the model were evaluated according to the accuracy of its risk prediction ability.RESULTS AND CONCLUSION:(1)The four independent risk factors for postoperative internal fixation failure after surgery were predicted using the"three-column"typing system:medial column(comminuted fracture of the lesser trochanter and femoral talar)[odds ratio=5.385,95％CI(1.961,14.782),P=0.001],medial column(chimney type)[odds ratio=2.893,95％CI(1.167,7.173),P=0.022],lateral column(lateral wall thickness＜20.5 mm)[odds ratio=2.804,95％CI(1.078,7.297),P=0.035]and lateral column(lateral wall fracture)[odds ratio=4.278,95％CI(1.670,10.959),P=0.012].(2)The constructed risk prediction model showed good discrimination and accuracy[area under the receiver operating characteristic curve=0.852,95％CI(0.837,0.922)].The calibration curve showed good agreement between the model-predicted risk and the actual risk of occurrence.(3)The clinical decision curve suggested that the model had good clinical applicability when the risk threshold probability was in the range of 0.2-0.82.The risk probability of 28％was the optimal threshold for risk stratification of the model,and the predictive performance of the model was better in patients with different risk groups.(4)The"three-column"typing system constructs a predictive model to calculate the risk probability of postoperative internal fixation failure in patients with intertrochanteric femoral fractures.This method is accurate,simple,and easy to apply clinically,and can be used as a digital tool to guide personalized clinical treatment.

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.

BACKGROUND:Internal fixation and open reduction with locking plate is the main treatment for proximal humeral fractures with medial column instability.However,reduction failure is one of the main postoperative complications,and accurate risk factor assessment is beneficial for screening high-risk patients and clinical decision selection. OBJECTIVE:To construct four types of prediction models by different machine learning algorithms,compare the optimal model to analyze and sort the risk variables according to their weight scores on the impact of outcome,and explore their significance in guiding clinical diagnosis and treatment. METHODS:262 patients with proximal humeral fractures with medial column instability,aged(60.6±10.2)years,admitted to Foshan Hospital of Traditional Chinese Medicine between June 2012 and June 2022 were included.All patients underwent open reduction with locking plate surgery.According to the occurrence of reduction failure at 5-month follow-up,the patients were divided into a reduction failure group(n=64)and a reduction maintenance group(n=198).Clinical data of patients were collected,and model variables and their classification were determined.The data set was randomly divided into a training set and a test set according to a 7:3 ratio,and the optimal hyperparameters were obtained in the training set according to a 5-fold cross-over test.Four machine learning prediction models of logistic regression,random forest,support vector machine,and XGBoost were constructed,and the performance of different algorithms was observed in the test set using AUC,correctness,sensitivity,specificity,and F1 scores,so as to comprehensively evaluate the prediction performance of the models.The best-performing model was evaluated using SHAP to assess important risk variables and to evaluate its clinical guidance implications. RESULTS AND CONCLUSION:(1)There were significant differences between the two groups in deltoid tuberosity index,fracture type,fracture end with varus deformity before operation,fragment length of inferior metaphyseal of humerus,postoperative reduction,cortical support of medial column of proximal humerus,and insertion of calcar screw(P<0.05).(2)The best-combined performance of the four machine models was XGBoost.The AUC,accuracy,and F1 scores were 0.885,0.885,and 0.743,respectively;followed by random forest and support vector machine,with both models performing at approximately equal levels.Logistic regression had the worst combined performance.The SHAP interpretation tool was used in the optimal model and results showed that deltoid tuberosity index,medial humeral column cortical support,fracture type,fracture reduction quality,and the status of the calcar screw were important influencing fators for postoperative fracture reduction failure.(3)The accuracy of using machine learning to analyze clinical problems is superior to that of traditional logistic regression analysis methods.When dealing with high-dimensional data,the machine learning approach can solve multivariate interaction and covariance problems well.The SHAP interpretation tool can not only clarify the importance of individual variables but also obtain detailed information on the impact of dummy variables in each variable on the outcome.

BACKGROUND:Problems can occur at the donor site where the autologous tendon graft is taken. Alogenic tissue has become an important graft option for the reconstruction of the anterior cruciate ligament.
OBJECTIVE: To study the clinical curative effect and immune rejection of arthroscopic reconstruction of the anterior cruciate ligament by alogeneic tendon graft.
METHODS:The 120 included patients with anterior cruciate ligament injury were randomized into autologous tendon graft and alogeneic tendon graft groups, respectively, with 60 patients in each group. The autologous semitendinosus-gracilis graft at the affected side or the alogeneic anterior cruciate ligament was implanted into the knee joint in autologous tendon graft group or alogeneic tendon graft group, respectively. Lachman test results, Lysholm and Larson scores of joint function, and postoperative immune rejection were observed and assessed during 2-year folow-up.
RESULTS AND CONCLUSION: Lysholm and Larson scores in autologous tendon graft and alogeneic tendon graft groups were significantly increased after the reconstruction compared with before the reconstruction; however, there was no difference in the scores between both two groups at 2 years after the reconstruction (P > 0.05). At the end of the folow-up, the normal function of the knee joint was restored, and no lag and flexion constraint of the knee joint were observed. No significant differences were observed in Lachman test results between both groups (P> 0.05). Compared with the autologous tendon graft group, immune rejection rates were higher in the alogeneic tendon graft group; however, no significant differences were observed between both groups (P > 0.05). In summary, the alogeneic tendon graft may be a candidate for the reconstruction of the anterior cruciate ligament due to its similar clinical curative effect to the autologous tendon graft and low immune rejection rates.