Trend, disparities, and projection analysis of public data on foot fractures in Sweden: a retrospective analysis of 179 129 fractures.

INTRODUCTION: Orthopedic injuries to the foot constitute a significant portion of lower extremity injuries, necessitating an understanding of trends for effective preventive strategies and resource allocation. Demographic shifts, improved traffic safety, and lifelong physical activity may alter incidence rates, trauma mechanisms, and fracture distribution. This study explores the prevalence of foot fractures in Sweden using publicly available data. METHODS: Utilizing data from the Swedish National Board of Health and Welfare (SNBHW) spanning 2008-2022, retrospective study focuses on foot fractures in Sweden. Analysis includes calculating annual incidence rates per 100,000 person-years, assessing temporal trends, and exploring seasonal variations. Poisson regression analysis was used for projections into 2035. RESULTS: Between 2008-2022, the average annual foot fracture incidence was 11,942, with notable fluctuations influenced by the COVID-19 pandemic. Age and sex disparities impact rates, and seasonal variance highlights increased incidence in summer. By 2035, foot fractures will decreasae amongst several demographic groups. CONCLUSION: This study provides insights into temporal trends, sex differences, and seasonal variations foot fracture patterns in Sweden. The identified trends suggest the utilization of targeted preventive strategies, efficient resource allocation, and informed healthcare planning. Despite limitations, this research offers valuable insights into foot fractures within the Swedish population, utilizing publicly aggregated data.

Trends and projection of forearm fractures including elbow fractures of the Olecranon in Sweden: an analysis of 363 968 fractures using public aggregated data.

BACKGROUND: Forearm and olecranon fractures are a common orthopaedic injury. This study aimed to analyse whether the incidence of forearm injury is changing and identifying trends in the number of forearm and olecranon fractures using public aggregated data in Sweden. METHODS: The number of forearm and olecranon fractures as defined by the number of registered diagnoses with the ICD-10 code of S52 were collected and normalized per 100,000 inhabitants and stratified per sex, age, and month. Age-adjusted incidence for forearm and olecranon fractures were calculated using the direct method. Poisson regression was used to analyse monthly, seasonal and yearly change in forearm and olecranon fracture incidence. Logistical regression was used to predict future trends of forearm and olecranon fractures. RESULTS: The findings revealed a slight decreasing trend in forearm and olecranon fractures. The average incidence rate during the study period was 333 with women having a higher incidence rate than men. More fractures occurred in the winter months. Fluctuations in the number of forearm and olecranon fractures were observed during 2020 which may be influenced by the COVID-19 pandemic. Based on current data, forearm and olecranon fractures are expected to decrease in Sweden by 2035. CONCLUSION: This study describes the trend of forearm and olecranon fractures among individuals according to sex and age in Sweden using easily obtainable data. Trends in forearm and olecranon fractures are dependent on sex and age but generally show a decreasing trend. More precise studies are needed in order to properly quantify the specific incidence of various subtypes of forearm and olecranon fractures and associated risk factors.

Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs.

BACKGROUND: Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity. METHODS: We collected pelvic radiographs of 571 patients from two single-center cohorts and one multicenter cohort. The radiographs were split in half to create hip radiographs (n = 1022). One orthopaedic surgeon and one resident assessed the radiographs for hip dysplasia on either side. We used the center edge (CE) angle as the primary diagnostic criteria. Hips with a CE angle < 20°, 20° to 25°, and > 25° were labeled as dysplastic, borderline, and normal, respectively. The dysplastic hips were also classified with both Crowe and Hartofilakidis classification of dysplasia. The dataset was divided into train, validation, and test subsets using 80:10:10 split-ratio that were used to train two deep learning models to classify images into normal, borderline and (1) Crowe grade 1-4 or (2) Hartofilakidis grade 1-3. A pre-trained on Imagenet VGG16 convolutional neural network (CNN) was utilized by performing layer-wise fine-turning. RESULTS: Both models struggled with distinguishing between normal and borderline hips. However, achieved high accuracy (Model 1: 92.2% and Model 2: 83.3%) in distinguishing between normal/borderline vs. dysplastic hips. The overall accuracy of Model 1 was 68% and for Model 2 73.5%. Most misclassifications for the Crowe and Hartofilakidis classifications were +/- 1 class from the correct class. CONCLUSIONS: This pilot study shows promising results that a deep learning model distinguish between normal and dysplastic hips with high accuracy. Future research and external validation are warranted regarding the ability of deep learning models to perform complex tasks such as identifying and classifying disorders using plain radiographs. LEVEL OF EVIDENCE: Diagnostic level IV.

Trend, disparities, and projection analysis of public data on shoulder fractures in Sweden: a retrospective analysis of two hundred and sixty two thousand, four hundred and forty four fractures.

PURPOSE: We aimed to identify temporal trends, seasonal changes and regional differences in shoulder fractures in Sweden during 2008-2022. METHODS: Data from the Swedish National Board of Health and Welfare were used to assess incidence rates per 100,000 people, categorized by sex, age, and month. RESULTS: Results showed an average of 17,496 fractures annually, with a decline in 2020 followed by a resurgence in 2021-2022. Elderly women, especially those over 65, had higher rates. Winter months exhibited increased incidence. CONCLUSIONS: Projection analysis indicated a gradual decrease in fractures over the next 15 years. Understanding these patterns can inform preventive strategies and resource allocation for shoulder fractures in Sweden.

Long-term follow-up of bone density changes in total hip arthroplasty: comparative analysis from a randomized controlled trial of a porous titanium construct shell vs. a porous coated shell.

PURPOSE: Periacetabular bone loss poses a considerable challenge in the longevity and stability of acetabular implants used in total hip arthroplasty (THA). Innovations in implant design, specifically the introduction of three-dimensional (3D) porous titanium constructs, might reduce bone resorption. The purpose of this study was to build upon our previous randomized controlled trial, which found no change in periacetabular bone loss between a 3D porous none-hydroxyapatite coated titanium cup and a standard porous hydroxyapatite coated cup over a two year follow-up period by extending the follow-up duration to ten years post-surgery. METHODS: This was a single-centre, long-term follow-up study conducted over a ten year period in patients who had previously participated in a randomized controlled trial comparing a 3D porous titanium construct shell (PTC group) with a standard porous hydroxyapatite coated titanium shell (PC-group). The primary outcome measured was the change in bone mineral density (BMD) within four specific periacetabular zones, alongside overall bone loss, which was assessed through BMD in the lumbar spine at two, six and ten years postoperatively. Secondary outcomes included clinical outcome measures. RESULTS: In total, 18 in the PTC and 20 in the PC group were analysed for the primary endpoint up to ten years. The mean bone mineral density in zones 1-4 was 3.7% higher in the PTC group than in the PC group at six years postoperatively and 12.0% higher at ten years. Clinical outcomes, and the frequency of adverse events did not differ between the groups. CONCLUSIONS: The PTC group displayed superior long-term bone preservation compared to the PC group while maintaining similar clinical outcomes up to ten years postoperatively. Although with a small sample size, our findings suggest that porous titanium cups have the potential to minimize BMD loss around the cup which could contribute to improving THA outcomes and implant durability.

Development and validation of an artificial intelligence model for the classification of hip fractures using the AO-OTA framework.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) has the potential to aid in the accurate diagnosis of hip fractures and reduce the workload of clinicians. We primarily aimed to develop and validate a convolutional neural network (CNN) for the automated classification of hip fractures based on the 2018 AO-OTA classification system. The secondary aim was to incorporate the model's assessment of additional radiographic findings that often accompany such injuries. METHODS: 6,361 plain radiographs of the hip taken between 2002 and 2016 at Danderyd University Hospital were used to train the CNN. A separate set of 343 radiographs representing 324 unique patients was used to test the performance of the network. Performance was evaluated using area under the curve (AUC), sensitivity, specificity, and Youden's index. RESULTS: The CNN demonstrated high performance in identifying and classifying hip fracture, with AUCs ranging from 0.76 to 0.99 for different fracture categories. The AUC for hip fractures ranged from 0.86 to 0.99, for distal femur fractures from 0.76 to 0.99, and for pelvic fractures from 0.91 to 0.94. For 29 of 39 fracture categories, the AUC was ≥ 0.95. CONCLUSION: We found that AI has the potential for accurate and automated classification of hip fractures based on the AO-OTA classification system. Further training and modification of the CNN may enable its use in clinical settings.

ChatGPT-4 generates orthopedic discharge documents faster than humans maintaining comparable quality: a pilot study of 6 cases.

BACKGROUND AND PURPOSE: Large language models like ChatGPT-4 have emerged. They hold the potential to reduce the administrative burden by generating everyday clinical documents, thus allowing the physician to spend more time with the patient. We aimed to assess both the quality and efficiency of discharge documents generated by ChatGPT-4 in comparison with those produced by physicians. PATIENTS AND METHODS: To emulate real-world situations, the health records of 6 fictional orthopedic cases were created. Discharge documents for each case were generated by a junior attending orthopedic surgeon and an advanced orthopedic resident. ChatGPT-4 was then prompted to generate the discharge documents using the same health record information. The quality assessment was performed by an expert panel (n = 15) blinded to the source of the documents. As secondary outcome, the time required to generate the documents was compared, logging the duration of the creation of the discharge documents by the physician and by ChatGPT-4. RESULTS: Overall, both ChatGPT-4 and physician-generated notes were comparable in quality. Notably, ChatGPT-4 generated discharge documents 10 times faster than the traditional method. 4 events of hallucinations were found in the ChatGPT-4-generated content, compared with 6 events in the human/physician produced notes. CONCLUSION: ChatGPT-4 creates orthopedic discharge notes faster than physicians, with comparable quality. This shows it has great potential for making these documents more efficient in orthopedic care. ChatGPT-4 has the potential to significantly reduce the administrative burden on healthcare professionals.

Artificial intelligence can be used in the identification and classification of shoulder osteoarthritis and avascular necrosis on plain radiographs: a training study of 7,139 radiograph sets.

BACKGROUND AND PURPOSE: Knowledge concerning the use AI models for the classification of glenohumeral osteoarthritis (GHOA) and avascular necrosis (AVN) of the humeral head is lacking. We aimed to analyze how a deep learning (DL) model trained to identify and grade GHOA on plain radiographs performs. Our secondary aim was to train a DL model to identify and grade AVN on plain radiographs. PATIENTS AND METHODS: A modified ResNet-type network was trained on a dataset of radiographic shoulder examinations from a large tertiary hospital. A total of 7,139 radiographs were included. The dataset included various projections of the shoulder, and the network was trained using stochastic gradient descent. Performance evaluation metrics, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to assess the network's performance for each outcome. RESULTS: The network demonstrated AUC values ranging from 0.73 to 0.93 for GHOA classification and > 0.90 for all AVN classification classes. The network exhibited lower AUC for mild cases compared with definitive cases of GHOA. When none and mild grades were combined, the AUC increased, suggesting difficulties in distinguishing between these 2 grades. CONCLUSION: We found that a DL model can be trained to identify and grade GHOA on plain radiographs. Furthermore, we show that a DL model can identify and grade AVN on plain radiographs. The network performed well, particularly for definitive cases of GHOA and any level of AVN. However, challenges remain in distinguishing between none and mild GHOA grades.

Comparative outcomes of uncemented and cemented stem revision in managing periprosthetic femoral fractures: a retrospective cohort study.

INTRODUCTION: Periprosthetic femoral fractures (PFFs) following hip arthroplasty, especially Vancouver B2 and B3 fractures, present a challenge due to the association with a loose femoral stem, necessitating either open reduction and internal fixation or stem revision. This study aims to compare outcomes between uncemented and cemented stem revisions in managing Vancouver B2 and B3 fractures, considering factors such as hip-related complications, reoperations, and clinical outcome. METHODS: A retrospective cohort study was conducted at Danderyd Hospital, Sweden, from 2008 to 2022, encompassing operatively treated Vancouver B2 and B3 fractures. Patients were categorized into uncemented and cemented stem revision groups, with data collected on complications, revision surgeries, fracture healing times, and clinical outcomes. RESULTS: A total of 241 patients were identified. Significant differences were observed between the two groups in patient demographics, with the cemented group comprising older patients and more females. Follow up ranged from 1 to 15 years. Average follow up time was 3.9 years for the cemented group and 5.5 years for the uncemented group. The cemented stems demonstrated lower rates of dislocation (8.9% versus 22.5%, P = 0.004) and stem loosening (0.6% versus 9.3%, P = 0.004) than the uncemented method. Moreover, the cemented group exhibited shorter fracture healing times (11.4 weeks versus 16.7 weeks, P = 0.034). There was no difference in clinical outcome between groups. Mortality was higher in the cemented group. CONCLUSIONS: This retrospective study indicates that cemented stem revision for Vancouver B2-3 fractures is correlated with lower dislocation and stem loosening rates, necessitating fewer reoperations and shorter fracture healing times compared with the uncemented approach. The cemented group had a notably higher mortality rate, urging caution in its clinical interpretation.

Evaluation of Three-Dimensional Bioprinted Human Cartilage Powder Combined with Micronized Subcutaneous Adipose Tissues for the Repair of Osteochondral Defects in Beagle Dogs.

Cartilage lesions are difficult to repair due to low vascular distribution and may progress into osteoarthritis. Despite numerous attempts in the past, there is no proven method to regenerate hyaline cartilage. The purpose of this study was to investigate the ability to use a 3D printed biomatrix to repair a critical size femoral chondral defect using a canine weight-bearing model. The biomatrix was comprised of human costal-derived cartilage powder, micronized adipose tissue, and fibrin glue. Bilateral femoral condyle defects were treated on 12 mature beagles staged 12 weeks apart. Four groups, one control and three experimental, were used. Animals were euthanized at 32 weeks to collect samples. Significant differences between control and experimental groups were found in both regeneration pattern and tissue composition. In results, we observed that the experimental group with the treatment with cartilage powder and adipose tissue alleviated the inflammatory response. Moreover, it was found that the MOCART score was higher, and cartilage repair was more organized than in the other groups, suggesting that a combination of cartilage powder and adipose tissue has the potential to repair cartilage with a similarity to normal cartilage. Microscopically, there was a well-defined cartilage-like structure in which the mid junction below the surface layer was surrounded by a matrix composed of collagen type I, II, and proteoglycans. MRI examination revealed significant reduction of the inflammation level and progression of a cartilage-like growth in the experimental group. This canine study suggests a promising new surgical treatment for cartilage lesions.

Considerations on the Demography of BIA-ALCL in European Countries Based on an E(A)SAPS Survey.

BACKGROUND: A growing body of evidence indicates that breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is associated with the use of certain breast implants. Regional variations have been reported, and a genetic susceptibility has also been suggested. However, large variations in the ability to correctly diagnose BIA-ALCL and to further report and register cases exist between countries and may in part explain variations in the demography. MATERIAL AND METHODS: A survey was conducted by The European Association of Societies of Aesthetic Plastic Surgery E(A)SAPS and sent to 48 European countries. The primary aim was to identify the total number of confirmed cases of and deaths from BIA-ALCL in each country during four consecutive measurements over a two-year period. RESULTS: An increase in BIA-ALCL cases during four repeated measurements from a total of 305 in April 2019 to 434 in November 2020 was reported by 23 of the 33 responding countries. A nearly 100-fold variation in the number of cases per million inhabitants was noted, where Netherlands had the highest rate (4.12) followed by Finland (1.99). Countries with the lowest reported rates were Austria (0.078), Romania (0.052) and Turkey (0.048). CONCLUSION: The current study displays a notable variation ßin the number of confirmed BIA-ALCL cases across Europe, even for countries with established breast implant registers. Variations in diagnosis and reporting systems may explain the differences, but the influence of genetic variations and the prevalence of high-risk implants cannot be excluded. Incomplete sales data along with medical tourism preclude an absolute risk assessment. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

The influence of depression on patient-reported outcomes for hip-fracture patients 1 year after surgery: a prospective cohort study.

BACKGROUND: Depression is common in elderly hip-fracture patients and together with cognitive impairment is associated with increased risk of mortality. AIM: We aimed to examine the influence depression has on patient-reported outcome up to 1 year after acute hip fracture. METHODS: 162 hip-fracture patients participated in the prospective observational cohort study and were followed up at baseline, and 3 and 12 months using patient-reported outcome scores. Patients with cognitive impairment were excluded. Depression was defined as a score ≥ 8 on the depression subscale of the Hospital Anxiety Depression Scale (HADS D), having a diagnosis of depression or being treated with anti-depressant medication. Hip function was assessed using Harris Hip Score (HHS), EQ-5D was used to assess health status and Quality of life, and the Pain Numerical Rating Scale (PRNS) was used to assess pain levels. A linear regression model adjusted for group, age, sex, and ASA class was used to identify risk factors for functional outcome 12 months after fracture. RESULTS: 35 patients were included in the depression group versus 127 in the control group. No statistical differences were found in the demographic data (age, sex, ASA class, fracture type, operation method, living situation, activities of daily living ADL and clinical pathway) between the groups. In the regression model, we found no correlation between depression and the patient-reported outcome. CONCLUSION: In young elderly hip fracture patients without cognitive dysfunction, depression may not be of major importance for the rehabilitation of hip function in the short term.

Only 8% of major preventable adverse events after hip arthroplasty are filed as claims: a Swedish multi-center cohort study on 1,998 patients.

Background and purpose - Hip arthroplasty is one of the most performed surgeries in Sweden, and the rate of adverse events (AEs) is fairly high. All patients in publicly financed healthcare in Sweden are insured by the Mutual Insurance Company of Swedish County Councils (Löf). We assessed the proportion of patients that sustained a major preventable AE and filed an AE claim to Löf.Patients and methods - We performed retrospective record review using the Global Trigger Tool to identify AEs in a Swedish multi-center cohort consisting of 1,998 patients with a total or hemi hip arthroplasty. We compared the major preventable AEs with all patient-reported claims to Löf from the same cohort and calculated the proportion of filed claims.Results - We found 1,066 major preventable AEs in 744 patients. Löf received 62 claims for these AEs, resulting in a claim proportion of 8%. 58 of the 62 claims were accepted by Löf and received compensation. The claim proportion was 13% for the elective patients and 0.3% for the acute patients. The most common AE for filing a claim was periprosthetic joint infection; of the 150 infections found 37 were claimed.Interpretation - The proportion of filed claims for major preventable AEs is very low, even for obvious and serious AEs such as periprosthetic joint infection.

Epidemiology and regional variance of traumatic peripheral nerve injuries in Sweden: A 15-year observational study.

INTRODUCTION: Traumatic peripheral nerve injuries pose significant challenges to healthcare systems and individuals, affecting sensory function, causing neuropathic pain, and impairing quality of life. Despite their impact, comprehensive studies on the epidemiology and regional variance of these injuries are scarce. Understanding the incidence, trends, and anatomical distribution of such injuries is essential for targeted interventions and resource allocation. METHODS: This observational study utilized register-based data from the Swedish National Patient Register covering the period from 2008 to 2022. Incidence rates, trends, and anatomical distribution of traumatic peripheral nerve injuries were analyzed using descriptive statistics, Poisson regression modeling, and regional comparisons. RESULTS: Higher incidences of peripheral nerve injuries were observed among men compared to women across all age groups. The hand and wrist were the most commonly affected sites. Regional variations in incidence rates were evident, with some regions consistently exhibiting higher rates compared to others. Notably, a decreasing trend in injuries was observed over the study period. CONCLUSION: This study underscores the importance of targeted interventions and preventive strategies, considering sex, age, and regional disparities. Further research incorporating individual patient-level data is warranted to enhance our understanding and inform tailored interventions to reduce the burden of these injuries.

The COVID-19 pandemic caused gender-specific declines in knee surgery rates in Sweden from 2020 to 2021.

Purpose: Changes in knee surgery incidence are important factors for stakeholders and healthcare providers. The aim of this study was to examine trends and patterns in knee surgeries in Sweden from 2010 to 2022. The study focuses on gender-specific and overall rates of knee surgeries. Methods: The analysis is based on a data set sourced from national healthcare records. The data was stratified based on surgical rates and categorized by gender, year and the specific knee arthroplasty technique used. We tracked year-to-year changes in surgical rates to identify overarching patterns. We used Poisson regression to predict future trends. Comparisons were made between various surgical subcategories, such as those with and without cement in knee arthroplasty surgeries. Results: In 2010, the rate of knee surgeries per 100,000 person-years was 518.7 for males and 448.0 for females. These rates exhibited fluctuations over time, reaching their lowest point in 2020, attributed to the pandemic's disruption of elective procedures, with 386.4 surgeries per 100,000 males and 386.3 surgeries per 100,000 females. A resurgence was observed in 2022. The rates of primary knee arthroplasty increased, with a male rate of 106.2 and a female rate of 150.7 surgeries per 100,000 inhabitants in 2010, rising to 126.8 for males and 166.2 for females in 2022. Conclusion: This comprehensive nationwide open-source data analysis of knee surgeries in Sweden shows that the COVID-19 pandemic significantly impacted knee surgery rates in Sweden, causing a notable decline in 2020, followed by a resurgence in 2022. Furthermore, while men had higher surgery rates than women, they experienced a larger decline in the incidence of knee surgeries compared to women. Understanding these trends is crucial for stakeholders and healthcare providers to improve resource allocation, address gender disparities, and maintain the resilience of surgical services in the face of disruptions. Level of Evidence: Level III.

6-year follow-up on migration outcomes: a randomised clinical trial of cemented vitamin E-stabilised highly crosslinked versus standard polyethylene cup in total hip arthroplasty.

BACKGROUND: In a previous study we have shown that a cemented vitamin E-doped highly cross-linked polyethylene (VEPE) compared to a conventional polyethylene cup in total hip arthroplasty (THA) has a slightly higher proximal migration but significantly lower wear rates up to 2 years after surgery. In this follow-up study we investigated the same cohort at 6 years. METHODS: This was a double-blinded, non-inferiority, randomised controlled trial on patients with osteoarthritis, with a mean age of 66 years. Patients were randomly assigned to receive either the conventional polyethylene cup or the VEPE cup in a 1:1 ratio. The primary endpoint was proximal implant migration of the cup measured with radiostereometric analysis (RSA). Secondary endpoints included wear rate of the cup and patient-reported outcome measurements (PROM). RESULTS: At the 6-year follow-up, 25 patients (11 controls, 14 VEPE) were available for RSA measurements, and we found no statistically significant difference in proximal migration between the VEPE and control groups. The wear rate was significantly lower in the VEPE group compared to controls, 0.03 mm/year and 0.07 mm/year, respectively with a mean difference 0.04 mm, (95% CI, 0.02-0.06 mm). There were no cup revisions and no difference in PROM between the groups. CONCLUSIONS: Based on our 6-year results, the VEPE group exhibited no statistical or clinically relevant difference compared to the control group, and the wear rate was significantly lower in the VEPE group. The use of a cemented vitamin E-doped highly cross-linked cup is a good option in total hip arthroplasty.

Timing of adverse events in patients undergoing acute and elective hip arthroplasty surgery: a multicentre cohort study using the Global Trigger Tool.

OBJECTIVE: To explore timing in relation to all types of adverse events (AEs), severity and preventability for patients undergoing acute and elective hip arthroplasty. DESIGN: A multicentre cohort study using retrospective record review with Global Trigger Tool methodology in combination with data from several registers. SETTING: 24 hospitals in 4 major regions of Sweden. PARTICIPANTS: Patients ≥18 years, undergoing acute or elective total or hemiarthroplasty of the hip, were eligible for inclusion. Reviews of weighted samples of 1998 randomly selected patient records were carried out using Global Trigger Tool methodology. The patients were followed for readmissions up to 90 days postoperatively throughout the whole country. RESULTS: The cohort consisted of 667 acute and 1331 elective patients. Most AEs occurred perioperatively and postoperatively (n=2093, 99.1%) and after discharge (n=1142, 54.1%). The median time from the day of surgery to the occurrence of AE was 8 days. The median days for different AE types ranged from 0 to 24.5 for acute and 0 to 71 for elective patients and peaked during different time periods. 40.2% of the AEs, both major and minor, occurred within postoperative days 0-5 and 86.9% of the AEs occurred within 30 days. Most of the AEs were deemed to be of major severity (n=1370, 65.5%) or preventable (n=1591, 76%). CONCLUSIONS: A wide variability was found regarding the timing of different AEs with the majority occurring within 30 days. The timing and preventability varied regarding the severity. Most of the AEs were deemed to be preventable and/or of major severity. To increase patient safety for patients undergoing hip arthroplasty surgery, a better understanding of the multifaceted nature of the timing of AEs in relation to the occurrence of differing AEs is needed.

Deep learning classification of shoulder fractures on plain radiographs of the humerus, scapula and clavicle.

In this study, we present a deep learning model for fracture classification on shoulder radiographs using a convolutional neural network (CNN). The primary aim was to evaluate the classification performance of the CNN for proximal humeral fractures (PHF) based on the AO/OTA classification system. Secondary objectives included evaluating the model's performance for diaphyseal humerus, clavicle, and scapula fractures. The training dataset consisted of 6,172 examinations, including 2-7 radiographs per examination. The overall area under the curve (AUC) for fracture classification was 0.89, indicating good performance. For PHF classification, 12 out of 16 classes achieved an AUC of 0.90 or greater. Additionally, the CNN model had excellent overall AUC for diaphyseal humerus fractures (0.97), clavicle fractures (0.96), and good AUC for scapula fractures (0.87). Despite the limitations of the study, such as the reliance on ground truth labels provided by students with limited radiographic assessment experience, our findings are in concordance with previous studies, further consolidating CNN as potent fracture classifiers in plain radiographs. The inclusion of multiple radiographs with different views from each examination, as well as the generally unselected nature of the sample, contributed to the overall generalizability of the study. This is the fifth study published by our group on AI in orthopaedic radiographs, which has consistently shown promising results. The next challenge for the orthopaedic research community will be to transfer these results from the research setting into clinical practice. External validation of the CNN model should be conducted in the future before it is considered for use in a clinical setting.

Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation.

BACKGROUND: Accurate and timely detection of medical adverse events (AEs) from free-text medical narratives can be challenging. Natural language processing (NLP) with deep learning has already shown great potential for analyzing free-text data, but its application for medical AE detection has been limited. METHOD: In this study, we developed deep learning based NLP (DL-NLP) models for efficient and accurate hip dislocation AE detection following primary total hip replacement from standard (radiology notes) and non-standard (follow-up telephone notes) free-text medical narratives. We benchmarked these proposed models with traditional machine learning based NLP (ML-NLP) models, and also assessed the accuracy of International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes in capturing these hip dislocation AEs in a multi-center orthopaedic registry. RESULTS: All DL-NLP models outperformed all of the ML-NLP models, with a convolutional neural network (CNN) model achieving the best overall performance (Kappa = 0.97 for radiology notes, and Kappa = 1.00 for follow-up telephone notes). On the other hand, the ICD/CPT codes of the patients who sustained a hip dislocation AE were only 75.24% accurate. CONCLUSIONS: We demonstrated that a DL-NLP model can be used in largescale orthopaedic registries for accurate and efficient detection of hip dislocation AEs. The NLP model in this study was developed with data from the most frequently used electronic medical record (EMR) system in the U.S., Epic. This NLP model could potentially be implemented in other Epic-based EMR systems to improve AE detection, and consequently, quality of care and patient outcomes.

Measuring adverse events following hip arthroplasty surgery using administrative data without relying on ICD-codes.

INTRODUCTION: Measure and monitor adverse events (AEs) following hip arthroplasty is challenging. The aim of this study was to create a model for measuring AEs after hip arthroplasty using administrative data, such as length of stay and readmissions, with equal or better precision than an ICD-code based model. MATERIALS AND METHODS: This study included 1 998 patients operated with an acute or elective hip arthroplasty in a national multi-centre study. We collected AEs within 90 days following surgery with retrospective record review. Additional data came from the Swedish Hip Arthroplasty Register, the Swedish National Patient Register and the Swedish National Board of Health and Welfare. We made a 2:1 split of the data into a training and a holdout set. We used the training set to train different machine learning models to predict if a patient had sustained an AE or not. After training and cross-validation we tested the best performing model on the holdout-set. We compared the results with an established ICD-code based measure for AEs. RESULTS: The best performing model was a logistic regression model with four natural age splines. The variables included in the model were as follows: length of stay at the orthopaedic department, discharge to acute care, age, number of readmissions and ED visits. The sensitivity and specificity for the new model was 23 and 90% for AE within 30 days, compared with 5 and 94% for the ICD-code based model. For AEs within 90 days the sensitivity and specificity were 31% and 89% compared with 16% and 92% for the ICD-code based model. CONCLUSION: We conclude that a prediction model for AEs following hip arthroplasty surgery, relying on administrative data without ICD-codes is more accurate than a model based on ICD-codes.