Predictors of adverse events after percutaneous pedicle screws fixation in patients with single-segment thoracolumbar burst fractures

Predicting factors for extremity fracture among border-fall patients using machine learning computing

Background

The factors causing the injuries sustained from falls at US-Mexican border include falls from border wall or fence, fleeing from border patrols, ejecting from vehicle, and others. This study aimed to determine the factors leading to anatomical injuries and to identify the importance of factors leading to limb fracture and internal organ injuries.

Methods

A total of 178 patients who sustained musculoskeletal injuries or internal organ injuries and were admitted to our hospital were included in this retrospective study. Factors indexed for analysis included demographics, comorbidities, and falling mechanic factors. Correlations between anatomical injuries and mechanical injuries were analyzed. Multilayer perceptron neural network (MPNN) was used to identify predictive factors and to stratify the importance of these factors leading to injuries. The SPSS software was used for statistical analysis and predictive factor analysis.

Results

The extremity fracture was associated with border wall/fence fall (p = 0.001) and fleeing (p = 0.002). The spine fracture was correlated with bridge jump/fall (p = 0.007), fence jump/fall (p = 0.026). The vehicle ejecting/MVA was correlated with head injury (P < 0.001), chest injury (P < 0.001), and abdominal injury p < 0.001). MNPP stratify the importance of factor causing injury with multiple factor considered.

Conclusion

The various injury factors caused different anatomical injuries. Multifactorial assessment associated with these injuries can improve the accuracy of diagnosis and develop a predictive model for clinical applications.

Systematic review of machine-learning models in orthopaedic trauma

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

Methods

A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.

Results

A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures.

Conclusion

The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.

Analysis of factors associated with the failure of treatment in thoracolumbar burst fractures treated with short-segment posterior spinal fixation

BACKGROUND

The treatment of thoracolumbar burst fractures continues to pose challenges. Although short-segment posterior spinal fixation (SSPSF) has shown satisfactory clinical outcomes, it is accompanied by a relatively high rate of treatment failure. This study aimed to assess factors associated with treatment failure in thoracolumbar burst fractures treated with SSPSF.

METHODS

The clinical data of 241 consecutive patients with a traumatic thoracolumbar burst fracture who underwent SSPSF at our center between Apr 2016 and Apr 2021 were retrospectively reviewed. Patients were divided into two groups (failure of the treatment group and non-failure of the treatment group). We compared potential risk factors for the failure of treatment including age, gender, body mass index, smoking, diabetes, vertebral body compression rate, use of crosslinks, percentage of anterior height compression, presence of index level instrumentation, Cobb angle, interpedicular distance (IPD), canal compromise, Load Sharing Classification (LSC) score, use of posterolateral fusion, and pain intensity between the two groups.

RESULTS

A sum of 137 (56.8%) males and 104 (43.2%) females were enrolled where the mean age and follow-up of the participants were 48.34 ± 10.23 years and 18.67 ± 5.23 months, respectively. Treatment failure was observed in 34 cases (14.1%). The results of the binary logistic regression analysis revealed that the lack of index level instrumentation (OR 2.21; 95% CI 1.78-3.04; P = 0.014), LSC score (odds ratio [OR] 2.64; 95% confidence interval [95% CI], 1.34-3.77; P = 0.007), and IPD (OR 1.77; 95% CI 1.51-2.67; P = 0.023) were independently associated with a higher rate of failure of treatment.

CONCLUSIONS

The findings of this study revealed that increased rates of treatment failure in thoracolumbar burst fractures treated with SSPSF were associated with factors such as the absence of index level instrumentation, higher LSC scores, and larger IPD. These findings could be helpful in the proper management of patients with unstable thoracolumbar burst fractures.