Background: Distal biceps tendon injuries typically occur in the dominant arm of men in their fourth decade of life. Surgical repair restores flexion and supination strength, resulting in good functional outcome. The complication profile of each surgical approach and fixation technique has not been widely studied in the literature. Purpose: To report the rate of complications after repair of complete distal biceps ruptures, to classify them according to surgical approach and fixation technique, and to analyze risk factors and outcomes of the individual complications. Study Design: Systematic review. Methods: Studies published in English on primary repair of the distal biceps between January 1998 and January 2019 were identified. Data on complications were extracted and classified as major and minor for analysis. A quantitative synthesis of data was done to compare the complication rates between (1) limited anterior incision, extensile anterior incision, and double incision and (2) 4 fixation methods. Results: Seventy-two articles including 3091 primary distal biceps repairs were identified. The overall complication rate was 25% (n = 774). The major complication rate was 4.6% (n = 144) and included a 1.6% (n = 51) rate of posterior interosseous nerve injury; 0.3% (n = 10), median nerve injury; 1.4% (n = 43), rerupture; and a 0.1% (n = 4), synostosis. Brachial artery injury, ulnar nerve injury, compartment syndrome, proximal radius fracture, and chronic regional pain syndrome occurred at a rate of <0.1% each. The majority of nerve injuries resolved with an expectant approach. The minor complication rate was 20.4% (n = 630). The most common complication was lateral cutaneous nerve injury (9.2%, n = 283). An extensile single incision was associated with a higher rate of superficial radial nerve injury when compared to limited single incision(6% vs 2.1%, P = .002). Limited anterior single incision technique had a higher rate of lateral antebrachial cutaneous nerve injury compared to extensile single incision. (9.7% vs 5.2%, P = .03). Synostosis occurred only with double incision. Fixation technique had no significant effect on rerupture rate and posterior interosseous nerve injury rate. Conclusion: This is the largest analysis of complications after distal biceps repair, indicating a major complication rate of 4.6%. This study provides valuable data with regard to the choice of technique, surgical approach, and rate of complications, which is essential for surgical planning and patient consent. Registration: CRD42017074066 (PROSPERO).

Background and aims: The Multidimensional Prognostic Index (MPI), an objective and quantifiable tool based on the Comprehensive Geriatric Assessment, has been shown to predict adverse outcomes in European cohorts. We conducted a validation study of the original MPI, and of adapted versions that accounted for the use of specific drugs and cultural diversity in the assessment of cognition, in older Australians. Methods: The capacity of the MPI to predict 12-month mortality was assessed in 697 patients (median age: 80 years; interquartile range: 72–86) admitted to a metropolitan teaching hospital between September 2015 and February 2017. Results: In simple logistic regression analysis, the MPI was associated with 12-month mortality (Low risk: OR reference group; moderate risk: OR 2.50, 95% CI: 1.67–3.75; high risk: OR 4.24, 95% CI: 2.28–7.88). The area under the receiver operating characteristic curve (AUC) for the unadjusted MPI was 0.61 (0.57–0.65) and 0.64 (95% CI: 0.59–0.68) with age and sex adjusted. The adapted versions of the MPI did not significantly change the AUC of the original MPI. Conclusion: The original and adapted MPI were strongly associated with 12-month mortality in an Australian cohort. However, the discriminatory performance was lower than that reported in European studies.

Background The Multidimensional Prognostic Index (MPI) is an aggregate, comprehensive, geriatric assessment scoring system derived from eight domains that predict adverse outcomes, including 12-month mortality. However, the prediction accuracy of using the three MPI categories (mild, moderate, and severe risk) was relatively poor in a study of older hospitalized Australian patients. Prediction modeling using the component domains of the MPI together with additional clinical features and machine learning (ML) algorithms might improve prediction accuracy. Objective This study aims to assess whether the accuracy of prediction for 12-month mortality using logistic regression with maximum likelihood estimation (LR-MLE) with the 3-category MPI together with age and gender (feature set 1) can be improved with the addition of 10 clinical features (sodium, hemoglobin, albumin, creatinine, urea, urea-to-creatinine ratio, estimated glomerular filtration rate, C-reactive protein, BMI, and anticholinergic risk score; feature set 2) and the replacement of the 3-category MPI in feature sets 1 and 2 with the eight separate MPI domains (feature sets 3 and 4, respectively), and to assess the prediction accuracy of the ML algorithms using the same feature sets. Methods MPI and clinical features were collected from patients aged 65 years and above who were admitted to either the general medical or acute care of the elderly wards of a South Australian hospital between September 2015 and February 2017. The diagnostic accuracy of LR-MLE was assessed together with nine ML algorithms: decision trees, random forests, extreme gradient boosting (XGBoost), support-vector machines, naïve Bayes, K-nearest neighbors, ridge regression, logistic regression without regularization, and neural networks. A 70:30 training set:test set split of the data and a grid search of hyper-parameters with 10-fold cross-validation—was used during model training. The area under the curve was used as the primary measure of accuracy. Results A total of 737 patients (female: 370/737, 50.2%; male: 367/737, 49.8%) with a median age of 80 (IQR 72-86) years had complete MPI data recorded on admission and had completed the 12-month follow-up. The area under the receiver operating curve for LR-MLE was 0.632, 0.688, 0.738, and 0.757 for feature sets 1 to 4, respectively. The best overall accuracy for the nine ML algorithms was obtained using the XGBoost algorithm (0.635, 0.706, 0.756, and 0.757 for feature sets 1 to 4, respectively). Conclusions The use of MPI domains with LR-MLE considerably improved the prediction accuracy compared with that obtained using the traditional 3-category MPI. The XGBoost ML algorithm slightly improved accuracy compared with LR-MLE, and adding clinical data improved accuracy. These results build on previous work on the MPI and suggest that implementing risk scores based on MPI domains and clinical data by using ML prediction models can support clinical decision-making with respect to risk stratification for the follow-up care of older hospitalized patients.

Background Four-dimensional computed tomography (4D CT) is rapidly emerging as a diagnostic tool for the investigation of dynamic upper limb disorders. Dynamic elbow pathologies are challenging to diagnose, and at present, limitations exist in current imaging modalities Objective We aimed to assess the clinical utility of 4D CT in detecting potential dynamic elbow disorders. Methods Twenty-eight elbow joints from 26 patients with symptoms of dynamic elbow pathology were included in this study. They were first assessed by a senior orthopedic surgeon with subsequent qualitative data obtained via a Siemens Force Dual Source CT scanner (Erlangen, Germany), producing two- and three-dimensional “static” images and 4D dynamic “movie” images for assessment in each clinical scenario. Clinical assessment before and after scan was compared. Results Use of 4D CT scan resulted in a change of diagnosis in 16 cases (57.14%). This included a change in primary diagnosis in 2 cases (7.14%) and secondary diagnosis in 14 cases (50%). In 25 cases (89.29%), the 4D CT scan allowed us to understand the pathological anatomy in greater detail which led to a change in the management plan of 15 cases (53.57%). Conclusion 4D CT is a promising diagnostic tool in the management of dynamic elbow disorders and may be considered in clinical practice. Future studies need to compare it with other diagnostic modalities such as three-dimensional CT.