An Optimization Approach for Creating Application-specific Ultrasound Speckle Tracking Algorithms.

OBJECTIVE: Ultrasound speckle tracking enables in vivo measurement of soft tissue deformation or strain, providing a non-invasive diagnostic tool to quantify tissue health. However, adoption into new fields is challenging since algorithms need to be tuned with gold-standard reference data that are expensive or impractical to acquire. Here, we present a novel optimization approach that only requires repeated measurements, which can be acquired for new applications where reference data might not be readily available or difficult to get hold of. METHODS: Soft tissue motion was captured using ultrasound for the medial collateral ligament (MCL) of three quasi-statically loaded porcine stifle joints, and medial ligamentous structures of a dynamically loaded human cadaveric knee joint. Using a training subset, custom speckle tracking algorithms were created for the porcine and human ligaments using surrogate optimization, which aimed to maximize repeatability by minimizing the normalized standard deviation of calculated strain maps for repeat measurements. An unseen test subset was then used to validate the tuned algorithms by comparing the ultrasound strains to digital image correlation (DIC) surface strains (porcine specimens) and length change values of the optically tracked ligament attachments (human specimens). RESULTS: After 1500 iterations, the optimization routine based on the porcine and human training data converged to similar values of normalized standard deviations of repeat strain maps (porcine: 0.19, human: 0.26). Ultrasound strains calculated for the independent test sets using the tuned algorithms closely matched the DIC measurements for the porcine quasi-static measurements (R > 0.99, RMSE < 0.59%) and the length change between the tracked ligament attachments for the dynamic human dataset (RMSE < 6.28%). Furthermore, strains in the medial ligamentous structures of the human specimen during flexion showed a strong correlation with anterior/posterior position on the ligaments (R > 0.91). CONCLUSION: Adjusting ultrasound speckle tracking algorithms using an optimization routine based on repeatability led to robust and reliable results with low RMSE for the medial ligamentous structures of the knee. This tool may be equally beneficial in other soft-tissue displacement or strain measurement applications and can assist in the development of novel ultrasonic diagnostic tools to assess soft tissue biomechanics.

Predicting hip-knee-ankle and femorotibial angles from knee radiographs with deep learning.

BACKGROUND: Knee alignment affects the development and surgical treatment of knee osteoarthritis. Automating femorotibial angle (FTA) and hip-knee-ankle angle (HKA) measurement from radiographs could improve reliability and save time. Further, if HKA could be predicted from knee-only radiographs then radiation exposure could be reduced and the need for specialist equipment and personnel avoided. The aim of this research was to assess if deep learning methods could predict FTA and HKA angle from posteroanterior (PA) knee radiographs. METHODS: Convolutional neural networks with densely connected final layers were trained to analyse PA knee radiographs from the Osteoarthritis Initiative (OAI) database. The FTA dataset with 6149 radiographs and HKA dataset with 2351 radiographs were split into training, validation, and test datasets in a 70:15:15 ratio. Separate models were developed for the prediction of FTA and HKA and their accuracy was quantified using mean squared error as loss function. Heat maps were used to identify the anatomical features within each image that most contributed to the predicted angles. RESULTS: High accuracy was achieved for both FTA (mean absolute error 0.8°) and HKA (mean absolute error 1.7°). Heat maps for both models were concentrated on the knee anatomy and could prove a valuable tool for assessing prediction reliability in clinical application. CONCLUSION: Deep learning techniques enable fast, reliable and accurate predictions of both FTA and HKA from plain knee radiographs and could lead to cost savings for healthcare providers and reduced radiation exposure for patients.

An automated, web-based triage tool may optimise referral pathways in elective orthopaedic surgery: A proof-of-concept study.

Introduction: Knee pain is caused by various pathologies, making evaluation in primary-care challenging. Subsequently, an over-reliance on imaging, such as radiographs and MRI exists. Electronic-triage tools represent an innovative solution to this problem. The aims of this study were to establish the magnitude of unnecessary knee imaging prior to orthopaedic surgeon referral, and ascertain whether an e-triage tool outperforms existing clinical pathways to recommend correct imaging. Methods: Patients ≥18 years presenting with knee pain treated with arthroscopy or arthroplasty at a single academic hospital between 2015 and 2020 were retrospectively identified. The timing and appropriateness of imaging were assessed according to national guidelines, and classified as 'necessary', 'unnecessary' or 'required MRI'. Based on an eDelphi consensus study, a symptom-based e-triage tool was developed and piloted to preliminarily diagnose five common knee pathologies and suggest appropriate imaging. Results: 1462 patients were identified. 17.2% (n = 132) of arthroplasty patients received an 'unnecessary MRI', 27.6% (n = 192) of arthroscopy patients did not have a 'necessary MRI', requiring follow-up. Forty-one patients trialled the e-triage pilot (mean age: 58.4 years, 58.5% female). Preliminary diagnoses were available for 33 patients. The e-triage tool correctly identified three of the four knee pathologies (one pathology did not present). 79.2% (n = 19) of participants would use the tool again. Conclusion: A substantial number of knee pain patients receive incorrect imaging, incurring delays and unnecessary costs. A symptom-based e-triage tool was developed, with promising performance and user feedback. With refinement using larger datasets, this tool has the potential to improve wait-times, referral quality and reduce cost.

The accuracy of statistical shape models in predicting bone shape: A systematic review.

BACKGROUND: This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling. METHODS: A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible. RESULTS: 2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error). CONCLUSION: Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate.

A computational tool for automatic selection of total knee replacement implant size using X-ray images.

Purpose: The aim of this study was to outline a fully automatic tool capable of reliably predicting the most suitable total knee replacement implant sizes for patients, using bi-planar X-ray images. By eliminating the need for manual templating or guiding software tools via the adoption of convolutional neural networks, time and resource requirements for pre-operative assessment and surgery could be reduced, the risk of human error minimized, and patients could see improved outcomes. Methods: The tool utilizes a machine learning-based 2D-3D pipeline to generate accurate predictions of subjects' distal femur and proximal tibia bones from X-ray images. It then virtually fits different implant models and sizes to the 3D predictions, calculates the implant to bone root-mean-squared error and maximum over/under hang for each, and advises the best option for the patient. The tool was tested on 78, predominantly White subjects (45 female/33 male), using generic femur component and tibia plate designs scaled to sizes obtained for five commercially available products. The predictions were then compared to the ground truth best options, determined using subjects' MRI data. Results: The tool achieved average femur component size prediction accuracies across the five implant models of 77.95% in terms of global fit (root-mean-squared error), and 71.79% for minimizing over/underhang. These increased to 99.74% and 99.49% with ±1 size permitted. For tibia plates, the average prediction accuracies were 80.51% and 72.82% respectively. These increased to 99.74% and 98.98% for ±1 size. Better prediction accuracies were obtained for implant models with fewer size options, however such models more frequently resulted in a poor fit. Conclusion: A fully automatic tool was developed and found to enable higher prediction accuracies than generally reported for manual templating techniques, as well as similar computational methods.

Predicting Depression in Patients With Knee Osteoarthritis Using Machine Learning: Model Development and Validation Study.

BACKGROUND: Knee osteoarthritis (OA) is the most common form of OA and a leading cause of disability worldwide. Chronic pain and functional loss secondary to knee OA put patients at risk of developing depression, which can also impair their treatment response. However, no tools exist to assist clinicians in identifying patients at risk. Machine learning (ML) predictive models may offer a solution. We investigated whether ML models could predict the development of depression in patients with knee OA and examined which features are the most predictive. OBJECTIVE: The primary aim of this study was to develop and test an ML model to predict depression in patients with knee OA at 2 years and to validate the models using an external data set. The secondary aim was to identify the most important predictive features used by the ML algorithms. METHODS: Osteoarthritis Initiative Study (OAI) data were used for model development and external validation was performed using Multicenter Osteoarthritis Study (MOST) data. Forty-two features were selected, which denoted routinely collected demographic and clinical data such as patient demographics, past medical history, knee OA history, baseline examination findings, and patient-reported outcome measures. Six different ML classification models were trained (logistic regression, least absolute shrinkage and selection operator [LASSO], ridge regression, decision tree, random forest, and gradient boosting machine). The primary outcome was to predict depression at 2 years following study enrollment. The presence of depression was defined using the Center for Epidemiological Studies Depression Scale. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score. The most important features were extracted from the best-performing model on external validation. RESULTS: A total of 5947 patients were included in this study, with 2969 in the training set, 742 in the test set, and 2236 in the external validation set. For the test set, the AUC ranged from 0.673 (95% CI 0.604-0.742) to 0.869 (95% CI 0.824-0.913), with an F1 score of 0.435 to 0.490. On external validation, the AUC varied from 0.720 (95% CI 0.685-0.755) to 0.876 (95% CI 0.853-0.899), with an F1 score of 0.456 to 0.563. LASSO modeling offered the highest predictive performance. Blood pressure, baseline depression score, knee pain and stiffness, and quality of life were the most predictive features. CONCLUSIONS: To our knowledge, this is the first study to apply ML classification models to predict depression in patients with knee OA. Our study showed that ML models can deliver a clinically acceptable level of performance (AUC>0.7) in predicting the development of depression using routinely available demographic and clinical data. Further work is required to address the class imbalance in the training data and to evaluate the clinical utility of the models in facilitating early intervention and improved outcomes.

Artificial intelligence in orthopaedics: A scoping review.

There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946-2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.

The revision partial knee classification system: understanding the causative pathology and magnitude of further surgery following partial knee arthroplasty.

AIMS: Joint registries classify all further arthroplasty procedures to a knee with an existing partial arthroplasty as revision surgery, regardless of the actual procedure performed. Relatively minor procedures, including bearing exchanges, are classified in the same way as major operations requiring augments and stems. A new classification system is proposed to acknowledge and describe the detail of these procedures, which has implications for risk, recovery, and health economics. METHODS: Classification categories were proposed by a surgical consensus group, then ranked by patients, according to perceived invasiveness and implications for recovery. In round one, 26 revision cases were classified by the consensus group. Results were tested for inter-rater reliability. In round two, four additional cases were added for clarity. Round three repeated the survey one month later, subject to inter- and intrarater reliability testing. In round four, five additional expert partial knee arthroplasty surgeons were asked to classify the 30 cases according to the proposed revision partial knee classification (RPKC) system. RESULTS: Four classes were proposed: PR1, where no bone-implant interfaces are affected; PR2, where surgery does not include conversion to total knee arthroplasty, for example, a second partial arthroplasty to a native compartment; PR3, when a standard primary total knee prosthesis is used; and PR4 when revision components are necessary. Round one resulted in 92% inter-rater agreement (Kendall's W 0.97; p < 0.005), rising to 93% in round two (Kendall's W 0.98; p < 0.001). Round three demonstrated 97% agreement (Kendall's W 0.98; p < 0.001), with high intra-rater reliability (interclass correlation coefficient (ICC) 0.99; 95% confidence interval 0.98 to 0.99). Round four resulted in 80% agreement (Kendall's W 0.92; p < 0.001). CONCLUSION: The RPKC system accounts for all procedures which may be appropriate following partial knee arthroplasty. It has been shown to be reliable, repeatable and pragmatic. The implications for patient care and health economics are discussed. Cite this article: Bone Jt Open 2021;2(8):638-645.

Perioperative Systemic Dexamethasone Reduces Length of Stay in Total Joint Arthroplasty: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: The objective of this review is to examine the effect of perioperative systemic corticosteroids at varying doses and timings on early postoperative recovery outcomes following unilateral total knee and total hip arthroplasty. The primary outcome was length of stay (LOS). METHODS: A systematic review and meta-analysis of randomized controlled trials was performed. MEDLINE, EMBASE, and Cochrane Library databases were searched from inception to June 1, 2020. Studies comparing the outcome of adult patients receiving a systemic steroid to patients who did not receive steroids were included. RESULTS: Seventeen studies were included, incorporating 1957 patients. Perioperative corticosteroids reduced hospital LOS (mean difference [MD] = -0.39 days, 95% confidence interval [CI] -0.61 to -0.18). A subsequent dose of corticosteroid at 24 hours further reduced LOS (MD = -0.33, 95% CI -0.55 to -0.11). Corticosteroids resulted in reduced levels of pain on postoperative day (POD) 0 (MD = -1.99, 95% CI -3.30 to -0.69), POD1 (MD = -1.47, 95% CI -2.15 to -0.79), and POD2. Higher doses were more effective in reducing pain with activity on POD0 (P = .006) and 1 (P = .023). Steroids reduced the incidence of PONV on POD1 (log odds ratio [OR] = -1.05, 95% CI -1.26 to -0.84) and POD2, with greater effect at higher doses (P = .046). Corticosteroids did not increase the incidence of infection (P = 1.000), venous thromboembolism (P = 1.000), or gastrointestinal hemorrhage (P = 1.000) but were associated with an increase in blood glucose (MD = 5.30 mg/dL, 95% CI 2.69-7.90). CONCLUSION: Perioperative corticosteroids are safe, facilitate earlier discharge, and improve patient recovery following unilateral total knee arthroplasty and total hip arthroplasty. Higher doses (15-20 mg of dexamethasone) are associated with further reductions in dynamic pain and PONV, and repeat dosing may further reduce LOS.

A novel patient-specific instrument design can deliver robotic level accuracy in unicompartmental knee arthroplasty.

BACKGROUND: A previous randomised controlled trial (RCT) by our group found that robotic assisted unicompartmental knee arthroplasty (UKA) surgery was significantly more accurate than conventional instrumentation. The aim of this study was to determine whether a low-cost novel PSI design could deliver the same level of accuracy as the robot in the same time efficient manner as conventional instruments. METHODS: Thirty patients undergoing medial UKA took part. Tibial component position was planned using a low dose CT-scan, and compared to a day 1 postoperative CT-scan to determine the difference between the planned and achieved positions. Operations were performed by one expert surgeon using PSI (Embody, London, UK). RESULTS: The mean absolute difference between planned and achieved tibial implant positions using PSI was 2.0° (SD 1.0°) in the coronal plane, 1.8° (SD 1.5) in the sagittal plane, and 4.5° (SD 3.3) in the axial plane. These results were not significantly different to the 13 historical robotic cases (mean difference 0.5°, 0.5°, and 1.7°, p = 0.1907, 0.2867 and 0.1049 respectively). PSI mean operating time was on average 62 min shorter than the robotic group (p < 0.0001) and 40 min shorter than the conventional instrument group (p < 0.0001). No complications were reported. CONCLUSIONS: In conclusion, this clinical trial demonstrates that for tibial component positioning in UKA, a novel design PSI guide in the hands of an expert surgeon, can safely deliver comparable accuracy to a robotic system, whilst being significantly faster than conventional instruments. NIHR Clinical Research Network Reference: 16100.

Failed high tibial osteotomy: A joint preserving alternative to total knee arthroplasty.

High tibial osteotomy is an attractive treatment option for young active patients wishing to return to high-level activities. However, it is not considered a long-term solution, with 30% revised at ten years. Currently, the only revision option is a total knee arthroplasty, a procedure that might not deliver the functional level expected by these highly active patients. This paper describes a novel joint preserving approach to HTO revision, using assistive technology, in the form of 3D printed guides, to reverse the osteotomy and simultaneously perform a unicompartmental knee replacement. The indications and planning aims for this procedure are discussed, and the preliminary results in four patients presented. LEVEL OF EVIDENCE: IV.

The Knee Osteoarthritis Grading System for Arthroplasty.

BACKGROUND: The aim of this study is to validate the Knee Osteoarthritis Grading System (KOGS) of progressive osteoarthritic degeneration for the tri-compartmental knee. This system defines the site and severity of osteoarthritis to determine a specific knee arthroplasty. METHODS: The radiographic sequence for KOGS includes standing coronal (anteroposterior), lateral, 30° skyline patella, 15° and 45° Rosenberg and stress views in 20° of flexion. Cohen's kappa and related agreement statistical methods were used to assess the level of concordance of the 7 evaluators between A and B cohorts for each evaluator and also against the actual arthroplasty used. Sensitivity and specificity was also assessed for the KOGS in identifying true partial knee arthroplasties (PKAs) and total knee arthroplasties (TKAs) as decided from the cohort A evaluations. RESULTS: From a cohort of 330 patients who were included in the study, 71 (22.5%) underwent a TKA procedure, 258 (78.2%) a PKA, and 1 (0.3%) was neither a TKA nor PKA. KOGS was able to identify true PKAs (sensitivity) in the range of 92.2%-98.5% across all the different evaluators. The KOGS method was able to identify a PKA or a TKA with an accuracy ranging from 92% to 98.8% across all different evaluators. The surgical results after 20 months are at least comparable with the expected average in the academic literature. CONCLUSION: The KOGS classification provides a reliable and accurate tool to assess suitability of an individual patient for undergoing PKA or TKA.

Minimally invasive percutaneous plate osteosynthesis versus intramedullary nail fixation for closed distal tibial fractures: a meta-analysis of the clinical outcomes.

INTRODUCTION: Minimally invasive percutaneous plate osteosynthesis (MIPPO) has emerged as a viable alternative for the treatment of distal tibial fractures. However, the clinical outcomes of this procedure compared to intramedullary (IM) nail fixation have yet to be established. The present meta-analysis aims to compare the clinical outcomes following MIPPO and IM nail fixation for closed distal tibial fractures. METHODS: MEDLINE and EMBASE databases were searched from date of inception to 10th April 2017. Randomized controlled trials (RCTs) comparing MIPPO with IM nail fixation for closed and Gustilo Grade I distal tibial fractures were included. Outcomes assessed included time to union, complications and functional outcomes. Quality and risk of bias of the RCTs were assessed using the Cochrane Collaboration Tool. RESULTS: Five RCTs comprising 497 patients were included. MIPPO was associated with a longer time to union (MD: 1.08, 95% CI: 0.26, 1.90, p = 0.010, I 2 = 84%) and increased risk of wound complications (RR: 1.58, 95% CI: 1.01, 2.46, p = 0.04, I 2 = 0%). Both MIPPO and IM nail fixation had comparable risks of malunion, delayed union, non-union and deep infections, with similar functional outcomes. DISCUSSION: Compared to IM nail fixation, a MIPPO fixation technique for distal tibial fractures is associated with a longer time to fracture union and an increased risk of wound complications. Neither technique demonstrates a clear advantage with regard to risk of malunion/non-union, or functional outcome. Assuming equivalent surgical expertise with both techniques, the results suggest that IM nail fixation is the treatment modality of choice for these challenging fractures.

Do patient-specific instruments (PSI) for UKA allow non-expert surgeons to achieve the same saw cut accuracy as expert surgeons?

INTRODUCTION: High-volume unicompartmental knee arthroplasty (UKA) surgeons have lower revision rates, in part due to improved intra-operative component alignment. This study set out to determine whether PSI might allow non-expert surgeons to achieve the same level of accuracy as expert surgeons. MATERIALS AND METHODS: Thirty-four surgical trainees with no prior experience of UKA, and four high-volume UKA surgeons were asked to perform the tibial saw cuts for a medial UKA in a sawbone model using both conventional and patient-specific instrumentation (PSI) with the aim of achieving a specified pre-operative plan. Half the participants in each group started with conventional instrumentation, and half with PSI. CT scans of the 76 cut sawbones were then segmented and reliably orientated in space, before saw cut position in the sagittal, coronal and axial planes was measured, and compared to the pre-operative plan. RESULTS: The compound error (absolute error in the coronal, sagittal and axial planes combined) for experts using conventional instruments was significantly less than that of the trainees (11.6°±4.0° v 7.7° ±2.3º, p = 0.029). PSI improved trainee accuracy to the same level as experts using conventional instruments (compound error 5.5° ±3.4º v 7.7° ±2.3º, p = 0.396) and patient-specific instruments (compound error 5.5° ±3.4º v 7.3° ±4.1º, p = 0.3). PSI did not improve the accuracy of high-volume surgeons (p = 0.3). CONCLUSIONS: In a sawbone model, PSI allowed inexperienced surgeons to achieve more accurate saw cuts, equivalent to expert surgeons, and thus has the potential to reduce revision rates. The next test will be to determine whether these results can be replicated in a clinical trial.

3D printing and unicompartmental knee arthroplasty.

In suitable patients, unicompartmental knee arthroplasty (UKA) offers a number of advantages compared with total knee arthroplasty. However, the procedure is technically demanding, with a small tolerance for error. Assistive technology has the potential to improve the accuracy of implant positioning.This review paper describes the concept of detailed UKA planning in 3D, and the 3D printing technology that enables a plan to be delivered intraoperatively using patient-specific instrumentation (PSI).The varying guide designs that enable accurate registration are discussed and described. The system accuracy is reported.Future studies need to ascertain whether accuracy for low-volume surgeons can be delivered in the operating theatre using PSI, and reflected in improved patient reported outcome measures, and lower revision rates. Cite this article: EFORT Open Rev 2018;3 DOI: 10.1302/2058-5241.3.180001.

3D printing and high tibial osteotomy.

High tibial osteotomy (HTO) is a relatively conservative surgical option in the management of medial knee pain. Thus far, the outcomes have been variable, and apparently worse than the arthroplasty alternatives when judged using conventional metrics, owing in large part to uncertainty around the extent of the correction planned and achieved.This review paper introduces the concept of detailed 3D planning of the procedure, and describes the 3D printing technology that enables the plan to be performed.The different ways that the osteotomy can be undertaken, and the varying guide designs that enable accurate registration are discussed and described. The system accuracy is reported.In keeping with other assistive technologies, 3D printing enables the surgeon to achieve a preoperative plan with a degree of accuracy that is not possible using conventional instruments. With the advent of low dose CT, it has been possible to confirm that the procedure has been undertaken accurately too.HTO is the 'ultimate' personal intervention: the amount of correction needed for optimal offloading is not yet completely understood.For the athletic person with early medial joint line overload who still runs and enjoys life, HTO using 3D printing is an attractive option. The clinical effectiveness remains unproven. Cite this article: EFORT Open Rev 2018;3 DOI: 10.1302/2058-5241.3.170075.

A wearable mobility assessment device for total knee replacement: A longitudinal feasibility study.

BACKGROUND: Total knee replacement currently lacks robust indications and objective follow-up metrics. Patients and healthcare staff are under-equipped to optimise outcomes. This study aims to investigate the feasibility of using an ear-worn motion sensor (e-AR, Imperial College London) to conduct objective, home-based mobility assessments in the peri-operative setting. METHODS: Fourteen patients on the waiting list for knee replacement, and 15 healthy subjects, were recruited. Pre-operatively, and at 1, 3, 6, 12 and 24 weeks post-operatively, patients underwent functional mobility testing (Timed Up and Go), knee examination (including range of motion), and an activity protocol whilst wearing the e-AR sensor. Features extracted from sensor motion data were used to assess patient performance and predict patients' recovery phase. RESULTS: Sensor-derived peri-operative mobility trends correlated with clinical measures in several activities, allowing functional recovery of individual subjects to be profiled and compared, including the detection of a complication. Sensor data features enabled classification of subjects into normal, pre-operative and 24-week post-operative groups with 89% (median) accuracy. Classification accuracy was reduced to 69% when including all time intervals. DISCUSSION: This study demonstrates a novel, objective method of assessing peri-operative mobility, which could be used to supplement surgical decision-making and facilitate community-based follow-up.

Young, male, road traffic victims: a systematic review of the published trauma registry literature from low and middle income countries.

BACKGROUND: Trauma contributes significantly to the global burden of disease. We analysed published trauma registries to assess the demographics of those most affected in low and middle-income countries (LMICs). METHODS: We performed a systematic review of published trauma registry studies according to PRISMA guidelines. We included published full-text articles from trauma registries in low and middle-income countries describing the demographics of trauma registry patients. Articles from military trauma registries, articles using data not principally derived from trauma registry data, articles describing patients of only one demographic (e.g. only paediatric patients), or only one mechanism of injury, trauma registry implementation papers without demographic data, review papers and conference proceedings were excluded. RESULTS: The initial search retrieved 1868 abstracts of which 1324 remained after duplicate removal. After screening the abstracts, 78 full-text articles were scrutinised for their suitability for inclusion. Twenty three papers from 14 countries, including 103,327 patients, were deemed eligible and included for analysis. The median age of trauma victims in these articles was 27 years (IQR 25-29). The median percentage of trauma victims who were male was 75 (IQR 66-84). The median percentage of road traffic injuries (RTIs) as a percentage of total injuries caused by trauma was 46 (IQR 21-71). CONCLUSIONS: Young, male, road traffic victims represent a large proportion of the LMIC trauma burden. This information can inform and be used by local and national governments to implement road safety measures and other strategies aimed at reducing the injury rate in young males.