Readmission Rate of Outpatient Distal Radius Fixation Surgery with Brachial Plexus Block and Midline Pronator Quadratus Approach in the COVID-19 Era: A Retrospective Case Series Report in a Secondary Care Hospital in Thailand

Objective: To demonstrate the readmission rate after distal radius fixation surgery performed with a brachial plexus block and the midline pronator quadratus approach in an ambulatory manner in a secondary care hospital. Materials and Methods: This retrospective study analyzed data on distal radius fracture patients treated with ambulatory surgery. The patients were enrolled from 1 January 2020 to 28 February 2022, which was during the COVID-19 pandemic. The primary outcome was the readmission rate within 30 days after the surgery. The secondary outcomes were complications, postoperative pain, radiographic outcome, and functional score. All patients were followed up for at least 1 year after the surgery. Results: Thirty-one patients were enrolled in this study. Their mean age was 58.5 years, and the fractures were mainly caused by low-energy trauma. No postoperative complications were reported, and no readmission after surgery was observed. Overall radiographic parameters were in the acceptable range (radial inclination = 21.9, radial height = 10.26, volar tilt = 2.65, and ulna variance = 1.33). All patients returned to their preinjury statuses within 5 months. Conclusion: Distal radius fixation surgery can be managed in an ambulatory manner with a low readmission rate, even in secondary care hospitals. This repair technique provides adequate soft tissue coverage of the volar radius plate while decreasing the risk of iatrogenic radial artery injuries

Risk Factor of Proximal Lag Screw Cut-Out After Cephalomedullary Nail Fixation in Trochanteric Femoral Fractures: A Retrospective Analytic Study

Objective: A cephalomedullary nail is the treatment of choice for trochanteric fractures; however, a lag screw cutout is one of the most devastating complications. The lag screw cut-out rate was reported to be around 2.5%–8.3%. This study aimed to evaluate the prevalence of lag screw cut-outs and identify the associated risk factors. Materials and Methods: A retrospective review of 267 trochanteric fracture patients treated with cephalomedullary nail fixation from January 2007 to December 2017 was conducted. The demographic variables were documented, comprising age, gender, fracture pattern, and AO/OTA classification. Immediate postoperative radiographs were assessed for quality of reduction and implant position. Lag screw cut-outs or radiographic union were determined using the final follow-up radiograph. Prognostic factors associated with lag screw cut-out were determined using univariate and multivariate logistic regression analyses. Results: Of the 175 patients, 154 were successfully treated, and 21 had a lag screw cut-out. There were no significant differences in mean ages or genders of the union and cut-out groups. No lag screw cut-outs were observed in patients with AO/OTA 31-A1. Patients with AO/OTA 31-B2.1 had a higher rate of screw cut-out (OR 10.5, [3.22, 34.25] p < .001). The disintegration of basicervical fragments was significantly associated with lag screw cut-out (OR 5.51, [2.01, 15.12] p = .001). The highest cut-out rate was found in the superoanterior and superoposterior positions of the lag screw. However, the screw position did not reach the significance level in a multivariate analysis (p = .094). Conclusion: The prevalence of lag screw cut-out after cephalomedullary nail fixation for trochanteric fractures was 12%. A simple, two-part, basicervical trochanteric fracture hads a significantly higher risk of lag screw cut-out

Torsional stability of fixation methods in basicervical femoral neck fractures: a biomechanical study

Abstract Background Basicervical femoral neck fracture is a rare proximal femur fracture with a high implant failure rate. Biomechanical comparisons between cephalomedullary nails (CMNs) and dynamic hip screws (DHSs) under torsion loading are lacking. This study compared the biomechanical performance of three fixations for basicervical femoral neck fractures under torsion load during early ambulation. Methods The biomechanical study models used three fixations: a DHS, a DHS with an anti-rotation screw, and a short CMN. Finite element analysis was used to simulate hip rotation with muscle forces related to leg swing applied to the femur. The equivalent von Mises stress (EQV) on fixation, fragment displacement, and strain energy density at the proximal cancellous bone were monitored for fixation stability. Results The EQV of the short CMN construct (304.63 MPa) was comparable to that of the titanium DHS construct (293.39 MPa) and greater than that of the titanium DHS with an anti-rotation screw construct (200.94 MPa). The proximal fragment displacement in the short CMN construct was approximately 0.13 mm, the greatest among the constructs. The risk of screw cutout for the lag screw in short CMNs was 3.1–5.8 times greater than that for DHSs and DHSs with anti-rotation screw constructs. Conclusions Titanium DHS combined with an anti-rotation screw provided lower fragment displacement, stress, and strain energy density in the femoral head than the other fixations under torsion load. Basicervical femoral neck fracture treated with CMNs may increase the risk of lag screw cutout. Graphical abstrac

Exploring the osteoporosis treatment gap after fragility hip fracture at a Tertiary University Medical Center in Thailand

Abstract Background (1) To evaluate the prescription rate of anti-osteoporosis medication, and (2) to identify factors associated with patients not receiving anti-osteoporosis medication or, when prescribed, not persisting with medication 1 year after hip fracture treatment. Methods We retrospectively reviewed the medical records of all fragility hip fracture patients admitted to the orthopedic unit of the Faculty of Medicine Siriraj Hospital, Mahidol University, between July 1, 2016, and December 31, 2019. We identified patients who did not receive anti-osteoporosis medication both 6 months and 1 year after fracture treatment. Patients who did not receive the medication 1 year after their treatment were enrolled and interviewed using a no-treatment questionnaire. Results In total, 530 patients with fragility hip fractures were eligible (mean age, 79.0 years), and most (74.5%) were women. Only 148 patients (31.6%) received anti-osteoporosis medication 1 year after hip fracture. Logistic regression analysis identified predictors for not receiving the medication: male sex (OR 1.8; 95% CI 1.1–3.0), Charlson comorbidity index score ≥ 5 (OR 1.5; 95% CI 1.0–2.3), and secondary school education or below (OR 2.0; 95% CI 1.2–3.3). The main reason for not receiving the medication was that healthcare providers neither discussed nor initiated pharmacological treatment for osteoporosis (48.2%). When the medication was prescribed, non-persistence primarily stemmed from transportation difficulties that resulted in patients missing follow-ups (50.0%). Conclusions Improved physician attitudes toward anti-osteoporosis medications might enhance the treatment rate. Developing a follow-up team and facilitating access to medications (eg, courier delivery to patients) would promote therapy compliance. Trial registrations The protocol for the first phase and second phase was approved by the Siriraj Institutional Review Board of the Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand (COA no. Si 180/2021) and for the second phase, patients-informed consent forms used in the cross-sectional component were approved by the Siriraj Institutional Review Board of the Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand (COA no. Si 180/2021). The research was registered with the Thai Clinical Trials Registry (TCTR number: 20210824002). The study was conducted in accordance with the Declaration of Helsinki. Each patient (or a relative/caregiver) provided informed consent in writing or by telephone to participate in this second study phase. </jats:sec

Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture

Abstract Background Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. Methods This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). Results For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Conclusions Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com. External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Trial registration Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003). </jats:sec

Additional file 1 of Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture

Additional file 1