Background Several risk assessments have been developed to evaluate fall risk in older adults, but it has not been conclusively established which of these tools is most effective for assessing fall risk in this vulnerable population. Recently, the U.S. Centers for Disease Control and Prevention (CDC) developed the self-rated Fall Risk Questionnaire (self-rated FRQ), a 12-item questionnaire designed to screen older adults who are at risk of falling and has been widely used in many centers. This study aimed to determine the validity and reliability of the self-rated FRQ in older adults with osteoporosis. Methods This prospective study was conducted at the Department of Orthopedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand from December 2019 to March 2020. Sixty-eight men or postmenopausal women aged > 65 years who were diagnosed with osteoporosis either by bone mineral density T-score or by occurrence of fragility fracture were evaluated with the self-rated FRQ, the Thai falls risk assessment test (Thai-FRAT), the timed get-up-and-go test (TUG test), the Berg Balance Scale (BBS), and the 5 times sit-to-stand test (5TSTS test). Validity of the self-rated FRQ was assessed by evaluating the correlations (r) between the self-rated FRQ score and the scores from the other four assessments. Reliability of the self-rated FRQ was evaluated by measuring test-retest reliability and internal consistency. Results The self-rated FRQ was moderately strongly correlated with the BBS, TUG test, and 5TSTS test (r = 0.535 to 0.690; p < 0.001), and fairly correlated with the Thai-FRAT (r = 0.487; p < 0.001). Test-retest reliability of the self-rated FRQ was high, with a Kappa of 1. Internal consistency of the self-rated FRQ was excellent (Cronbach’s alpha: 0.936). Conclusions The self-rated FRQ was found to be a valid and reliable tool for evaluating fall risk in older adults with osteoporosis. Since assessment of fall risk requires a multifaceted measurement tool, the self-rated FRQ is an appropriate tool that can be integrated into the fall risk assessment algorithm in older adults with osteoporosis.

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).

BackgroundDuring total hip arthroplasty (THA) using the direct anterior approach, orthopaedic surgeons can identify all anatomical landmarks required for pericapsular nerve group (PENG) blocks and carry out the latter under direct vision. This cadaveric study investigated the success of surgeon-performed PENG block. Success was defined as dye staining of the articular branches of the femoral and accessory obturator nerves.Methods11 cadavers (18 hip specimens) were included in the current study. To simulate THA in live patients, an orthopaedic surgeon inserted trial prostheses using the direct anterior approach. Subsequently, a block needle was advanced until contact with the bone (between the anterior inferior iliac spine and iliopubic eminence). 20 mL of 0.1% methylene blue was injected. Cadavers were then dissected to document the presence and dye staining of the femoral, lateral femoral cutaneous, obturator and accessory obturator nerves as well as the articular branches of the femoral, obturator and accessory obturator nerves.ResultsMethylene blue stained the articular branches of the femoral nerve and the articular branches of the accessory obturator nerve (when present) in all hip specimens. Therefore, surgical PENG block achieved a 100% success rate. Dye stained the femoral and obturator nerve in one (5.6%) and two (11.1%) hip specimens, respectively. No dye staining was observed over the accessory obturator nerve in the pelvis nor the lateral femoral cutaneous nerve.ConclusionSurgeon-performed PENG block during direct anterior THA reliably targets the articular branches of the femoral and accessory obturator nerves. Future trials are required to compare surgeon-performed PENG block with anaesthesiologist-performed, ultrasound-guided PENG block, and surgeon-performed periarticular local anaesthetic infiltration.