Evaluation of the EQ-5D-5L, EQ-VAS stand-alone component and Oxford knee score in the Australian knee arthroplasty population utilising minimally important difference, concurrent validity, predictive validity and responsiveness.

PURPOSE: To evaluate the Oxford Knee Score (OKS), EQ-5D-5L utility index and EQ-5D visual analogue scale (EQ-VAS) for health-related quality of life outcome measurement in patients undergoing elective total knee arthroplasty (TKA) surgery. METHODS: In this prospective multi-centre study, the OKS and EQ-5D-5L index scores were collected preoperatively, six weeks (6w) and six months (6 m) following TKA. The OKS, EQ-VAS and EQ-5D-5L index were evaluated for minimally important difference (MID), concurrent validity, predictive validity (Spearman's Rho of predicted and observed values from a generalised linear regression model (GLM)), responsiveness (effect size (ES) and standard response mean (SRM)). The MID for the individual patient was determined utilising two approaches; distribution-based and anchor-based. RESULTS: 533 patients were analysed. The EQ-5D-5L utility index showed good concurrent validity with the OKS (r = 0.72 preoperatively, 0.65 at 6w and 0.69 at 6 m). Predictive validity for the EQ-5D-5L index was lower than OKS when regressed. Responsiveness was large for all fields at 6w for the EQ-5D-5L and OKS (EQ-5D-5L ES 0.87, SRM 0.84; OKS ES 1.35, SRM 1.05) and 6 m (EQ-5D-5L index ES 1.31, SRM 0.95; OKS ES 1.69, SRM 1.59). The EQ-VAS returned poorer results, at 6w an ES of 0.37 (small) and SRM of 0.36 (small). At 6 m, the EQ-VAS had an ES of 0.59 (moderate) and SRM of 0.47 (small). It, however, had similar predictive validity to the OKS, and better than the EQ-5D-5L index. MID determined using anchor approach, was shown that for OKS at 6 weeks it was 8.84 ± 9.28 and at 6 months 13.37 ± 9.89. For the EQ-5D-5L index at 6 weeks MID was 0.23 ± 0.39, and at 6 months 0.26 ± 0.36. CONCLUSIONS: The EQ-5D-5L index score and the OKS demonstrate good concurrent validity. The EQ-5D-5L index demonstrated lower predictive validity at 6w, and 6 m than the OKS, and both PROMs had adequate responsiveness. The EQ-VAS had poorer responsiveness but better predictive validity than the EQ-5D-5L index. This article includes MID estimates for the Australian knee arthroplasty population.

A multi-disciplinary program for opioid sparse arthroplasty results in reduced long-term opioid consumption: a four year prospective study.

INTRODUCTION: The current opioid epidemic poses patient safety and economic burdens to healthcare systems worldwide. Postoperative prescriptions of opioids contribute, with reported opioid prescription rates following arthroplasty as high as 89%. In this multi-centre prospective study, an opioid sparing protocol was implemented for patients undergoing knee or hip arthroplasty. The primary outcome is to report our patient outcomes in the context of this protocol, and to examine the rate of opioid prescription on discharge from our hospitals following joint arthroplasty surgery. This is possibly associated with the efficacy of the newly implemented Arthroplasty Patient Care Protocol. METHODS: Over three years, patients underwent perioperative education with the expectation to be opioid-free after surgery. Intraoperative regional analgesia, early postoperative mobilisation and multimodal analgesia were mandatory. Long-term opioid medication use was monitored and PROMs (Oxford Knee/Hip Score (OKS/OHS), EQ-5D-5 L) were evaluated pre-operatively, and at 6 weeks, 6 months and 1 year postoperatively. Primary and secondary outcomes were opiate use and PROMs at different time points. RESULTS: A total of 1,444 patients participated. Two (0.2%) knee patients used opioids to one year. Zero hip patients used opioids postoperatively at any time point after six weeks (p < 0.0001). The OKS and EQ-5D-5 L both improved for knee patients from 16 (12-22) pre-operatively to 35 (27-43) at 1 year postoperatively, and 70 (60-80) preoperatively to 80 (70-90) at 1 year postoperatively (p < 0.0001). The OHS and EQ-5D-5 L both improved for hip patients from 12 (8-19) preoperatively to 44 (36-47) at 1 year postoperatively, and 65 (50-75) preoperatively to 85 (75-90) at 1 year postoperatively (p < 0.0001). Satisfaction improved between all pre- and postoperative time points for both knee and hip patients (p < 0.0001). CONCLUSIONS: Knee and hip arthroplasty patients receiving a peri-operative education program can effectively and satisfactorily be managed without long-term opioids when coupled with multimodal perioperative management, making this a valuable approach to reduce chronic opioid use.

Routine MRI Among Patients With a Suspected Scaphoid Fracture Risks Overdiagnosis.

BACKGROUND: In the setting of a suspected scaphoid fracture, MRI may result in overdiagnosis and potential overtreatment. This is in part because of the low prevalence of true fractures among suspected fractures, but also because of potentially misleading variations in signal that may be more common than fracture-related signal changes. To better understand the risk of overdiagnosis, we first need insight into the relative prevalence of useful and potentially distracting signal changes among patients with a suspected scaphoid fracture. QUESTION/PURPOSE: What is the proportion of signal changes representing definite and possible scaphoid fractures relative to other types of signal changes on MRI among patients with a suspected scaphoid fracture? METHODS: In a retrospective study in an orthopaedic trauma clinic associated with a Level I trauma center, we evaluated MR images of patients 16 years and older with a clinically suspected scaphoid fracture. At our institution, patients with symptoms and signs of a possible scaphoid fracture and negative radiographs undergo MRI scanning. Between January 1, 2012, and September 1, 2019, a total of 310 patients 16 years or older had an MRI to evaluate a suspected scaphoid fracture. Exclusion criteria included a scaphoid fracture that was visible on radiographs before MRI as reported by the radiologist (four patients), no available radiographs before MRI (two), MRI more than 3 weeks after injury (28), unknown date of injury (nine), and repeat or bilateral MRI scans (11), leaving 256 MR images for analysis. Sixty percent (153) of patients were women, and the median age was 34 years (IQR 21 to 50 years). The images were taken a median of 8 days (IQR 2 to 12 days) after injury. MR images were screened for the presence of scaphoid signal changes. We identified the following patterns of signal change with a reliability of kappa 0.62: definite scaphoid fracture, possible scaphoid fracture, signal in the waist area other than possible or definite fractures, and other signal changes. A definite scaphoid fracture was defined as a linear, focal, and bicortical signal abnormality, with adjacent edema and a relatively transverse orientation relative to the scaphoid long axis. The transverse linear signal was visible on more than one cut in multiple planes. A possible scaphoid fracture had a transverse linear signal on more than one cut on sagittal or coronal planes, with or without adjacent edema. RESULTS: Six percent (16 of 256) of MR images were categorized as revealing definite (2% [four of 256]) or possible (5% [12 of 256]) scaphoid fractures, whereas 29% (74 of 256) were categorized as revealing nonspecific signal changes at the waist (14% [35 of 256]) and other areas (15% [39 of 256]). Of the 51 patients with scaphoid waist signal changes, 69% (35) were categorized as having distracting and potentially misleading MRI findings. CONCLUSION: The high prevalence of signal changes that are distracting and potentially misleading, the low prevalence of signal changes that clearly represent a scaphoid fracture, and the low pretest odds of a true fracture among patients with a suspected scaphoid fracture illustrate that routine MRI of suspected scaphoid fractures carries a notable risk of overdiagnosis and potential overtreatment. Two alternative strategies are supported by preliminary evidence and merit additional attention: more-selective use of MRI in people deemed at higher risk according to a clinical prediction rule and strategies for involving the patient in decisions regarding how to manage the notably small risk of future symptomatic nonunion. LEVEL OF EVIDENCE: Level IV, diagnostic study.

Primary reverse total shoulder arthroplasty for fractures requires more revisions than for degenerative conditions 1 year after surgery: an analysis from the Dutch Arthroplasty Register.

BACKGROUND: Although reverse total shoulder arthroplasty (RTSA) is considered a viable treatment strategy for proximal humeral fractures, there is an ongoing discussion of how its revision rate compares with indications performed in the elective setting. First, this study evaluated whether RTSA for fractures conveyed a higher revision rate than RTSA for degenerative conditions (osteoarthritis, rotator cuff arthropathy, rotator cuff tear, or rheumatoid arthritis). Second, this study assessed whether there was a difference in patient-reported outcomes between these 2 groups following primary replacement. Finally, the results of conventional stem designs were compared with those of fracture-specific designs within the fracture group. MATERIALS AND METHODS: This was a retrospective comparative cohort study with registry data from the Netherlands, generated prospectively between 2014 and 2020. Patients (aged ≥ 18 years) were included if they underwent primary RTSA for a fracture (<4 weeks after trauma), osteoarthritis, rotator cuff arthropathy, rotator cuff tear, or rheumatoid arthritis, with follow-up until first revision, death, or the end of the study period. The primary outcome was the revision rate. The secondary outcomes were the Oxford Shoulder Score, EuroQol 5 Dimensions (EQ-5D) score, numerical rating scale score (pain at rest and during activity), recommendation score, and scores assessing change in daily functioning and change in pain. RESULTS: This study included 8753 patients in the degenerative condition group (mean age, 74.3 ± 7.2 years) and 2104 patients in the fracture group (mean age, 74.3 ± 7.8 years). RTSA performed for fractures showed an early steep decline in survivorship: Adjusted for time, age, sex, and arthroplasty brand, the revision risk after 1 year was significantly higher in these patients than in those with degenerative conditions (hazard ratio [HR], 2.50; 95% confidence interval, 1.66-3.77). Over time, the HR steadily decreased, with an HR of 0.98 at year 6. Apart from the recommendation score (which was slightly better within the fracture group), there were no clinically relevant differences in the patient-reported outcome measures after 12 months. Patients who received conventional stems (n = 1137) did not have a higher likelihood of undergoing a revision procedure than those who received fracture-specific stems (n = 675) (HR, 1.70; 95% confidence interval, 0.91-3.17). CONCLUSION: Patients undergoing primary RTSA for fractures have a substantially higher likelihood of undergoing revision within the first year following the procedure than patients with degenerative conditions preoperatively. Although RTSA is regarded as a reliable and safe treatment option for fractures, surgeons should inform patients accordingly and incorporate this information in decision making when opting for head replacement surgery. There were no differences in patient-reported outcomes between the 2 groups and no differences in revision rates between conventional and fracture-specific stem designs.

Three-dimensional cortical and trabecular bone microstructure of the proximal ulna.

INTRODUCTION: The three-dimensional (3D) microstructure of the cortical and trabecular bone of the proximal ulna has not yet been described by means of high-resolution 3D imaging. An improved characterization can provide a better understanding of their relative contribution to resist impact load. The aim of this study is to describe the proximal ulna bone microstructure using micro-computed tomography (micro-CT) and relate it to gross morphology and function. MATERIALS AND METHODS: Five dry cadaveric human ulnae were scanned by micro-CT (17 µm/voxel, isotropic). Both qualitative and quantitative assessments were performed on sagittal image stacks. The cortical thickness of the trochlear notch and the trabecular bone microstructure were measured in the olecranon, bare area and coronoid. RESULTS: Groups of trabecular struts starting in the bare area, spanning towards the anterior and posterior side of the proximal ulna, were observed; within the coronoid, the trabeculae were orthogonal to the joint surface. Consistently among the ulnae, the coronoid showed the highest cortical thickness (1.66 ± 0.59 mm, p = 0.04) and the olecranon the lowest (0.33 ± 0.06 mm, p = 0.04). The bare area exhibited the highest bone volume fraction (BV/TV = 43.7 ± 22.4%), trabecular thickness (Tb.Th = 0.40 ± 0.09 mm) and lowest structure model index (SMI = - 0.28 ± 2.20, indicating plate-like structure), compared to the other regions (p = 0.04). CONCLUSIONS: Our microstructural results suggest that the bare area is the region where most of the loading of the proximal ulna is concentrated, whereas the coronoid, together with its anteromedial facet, is the most important bony stabilizer of the elbow joint. Studying the proximal ulna bone microstructure helps understanding its possible everyday mechanical loading conditions and potential fractures. LEVEL OF EVIDENCE: N.A.

Management of displaced humeral surgical neck fractures in daily clinical practice: hanging does not re-align the fracture.

INTRODUCTION: It is unclear if the collar and cuff treatment improve alignment in displaced surgical neck fractures of the proximal humerus. Therefore, this study evaluated if the neckshaft angle and extent of displacement would improve between trauma and onset of radiographically visible callus in non-operatively treated surgical neck fractures (Boileau type A, B, C). MATERIALS AND METHODS: A consecutive series of patients (≥ 18 years old) were retrospectively evaluated from a level 1 trauma center in Australia (inclusion period: 2016-2020) and a level 2 trauma center in the Netherlands (inclusion period: 2004 to 2018). Patients were included if they sustained a Boileau-type fracture and underwent initial non-operative treatment. The first radiograph had to be obtained within 24 h after the initial injury and the follow-up radiograph(s) 1 week after trauma and before the start of radiographically visible callus. On each radiograph, the maximal medial gap (MMG), maximal lateral gap (MLG), and neck-shaft angle (NSA) were measured. Linear mixed modelling was performed to evaluate if these measurements would improve over time. RESULTS: Sixty-seven patients were included: 25 type A, 11 type B, and 31 type C fractures. The mean age (range) was 68 years (24-93), and the mean number (range) of follow-up radiographs per patient was 1 (1-4). Linear mixed modelling on both MMG and MLG revealed no improvement during follow-up among the three groups. Mean NSA of type A fractures improved significantly from 161° at trauma to 152° at last follow-up (p-value = 0.004). CONCLUSIONS: Apart from humeral head angulation improvement in type A, there is no increase nor reduction in displacement among the three fracture patterns. Therefore, it is advised that surgical decision-making should be performed immediately after trauma. LEVEL OF CLINICAL EVIDENCE: Level IV, retrospective case series.

The Pericapsular Nerve Group (PENG) block combined with Local Infiltration Analgesia (LIA) compared to placebo and LIA in hip arthroplasty surgery: a multi-center double-blinded randomized-controlled trial.

BACKGROUND: The PEricapsular Nerve Group (PENG) block is a novel regional analgesia technique that provides improved analgesia in patients undergoing hip surgery while preserving motor function. In this study the PENG block was investigated for analgesia in elective total hip arthroplasty (THA). METHODS: In this multi-centre double-blinded randomized-controlled trial, in addition to spinal anesthesia and local infiltration analgesia (LIA), THA patients received either a PENG block or a sham block. The primary outcome was pain score (numeric rating scale 0-10) 3 h postoperatively (Day 0). Secondary outcomes were postoperative quadriceps muscle strength, postoperative Day 1 pain scores, opiate use, complications, length of hospital stay, and patient-reported outcome measures. RESULTS: Sixty patients were randomized and equally allocated between groups. Baseline demographics were similar. Postoperative Day 0, the PENG group experienced less pain compared to the sham group (PENG: 14 (47%) patients no pain, 14 (47%) mild pain, 2 (6%) moderate/severe pain versus sham: 6 (20%) no pain, 14 (47%) mild pain, 10 (33%) moderate/severe pain; p = 0.03). There was no difference in quadriceps muscle strength between groups on Day 0 (PENG: 23 (77%) intact versus sham: 24 (80%) intact; p = 0.24) and there were no differences in other secondary outcomes. CONCLUSIONS: Patients receiving a PENG block for analgesia in elective THA experience less postoperative pain on Day 0 with preservation of quadriceps muscle strength. Despite these short-term benefits, no quality of recovery or longer lasting postoperative effects were detected.

Are 3D-printed Models of Tibial Plateau Fractures a Useful Addition to Understanding Fractures for Junior Surgeons?

BACKGROUND: Tibial plateau fractures are often complex, and they can be challenging to treat. Classifying fractures is often part of the treatment process, but intra- and interobserver reliability of fracture classification systems often is inadequate to the task, and classifications that lack reliability can mislead providers and result in harm to patients. Three-dimensionally (3D)-printed models might help in this regard, but whether that is the case for the classification of tibial plateau fractures, and whether the utility of such models might vary by the experience of the individual classifying the fractures, is unknown. QUESTIONS/PURPOSES: (1) Does the overall interobserver agreement improve when fractures are classified with 3D-printed models compared with conventional radiology? (2) Does interobserver agreement vary among attending and consultant trauma surgeons, senior surgical residents, and junior surgical residents? (3) Do surgeons' and surgical residents' confidence and accuracy improve when tibial plateau fractures are classified with an additional 3D model compared with conventional radiology? METHODS: Between 2012 and 2020, 113 patients with tibial plateau fractures were treated at a Level 1 trauma center. Forty-four patients were excluded based on the presence of bone diseases (such as osteoporosis) and the absence of a CT scan. To increase the chance to detect an improvement or deterioration and to prevent observers from losing focus during the classification, we decided to include 40 patients with tibial plateau fractures. Nine trauma surgeons, eight senior surgical residents, and eight junior surgical residents-none of whom underwent any study-specific pretraining-classified these fractures according to three often-used classification systems (Schatzker, OA/OTA, and the Luo three-column concept), with and without 3D-printed models, and they indicated their overall confidence on a 10-point Likert scale, with 0 meaning not confident at all and 10 absolutely certainty. To set the gold standard, a panel of three experienced trauma surgeons who had special expertise in knee surgery and 10 years to 25 years of experience in practice also classified the fractures until consensus was reached. The Fleiss kappa was used to determine interobserver agreement for fracture classification. Differences in confidence in assessing fractures with and without the 3D-printed model were compared using a paired t-test. Accuracy was calculated by comparing the participants' observations with the gold standard. RESULTS: The overall interobserver agreement improved minimally for fracture classification according to two of three classification systems (Schatzker: κconv = 0.514 versus κ3Dprint = 0.539; p = 0.005; AO/OTA:κconv = 0.359 versus κ3Dprint = 0.372; p = 0.03). However, none of the classification systems, even when used by our most experienced group of trauma surgeons, achieved more than moderate interobserver agreement, meaning that a large proportion of fractures were misclassified by at least one observer. Overall, there was no improvement in self-assessed confidence in classifying fractures or accuracy with 3D-printed models; confidence was high (about 7 points on a 10-point scale) as rated by all observers, despite moderate or worse accuracy and interobserver agreement. CONCLUSION: Although 3D-printed models minimally improved the overall interobserver agreement for two of three classification systems, none of the classification systems achieved more than moderate interobserver agreement. This suggests that even with 3D-printed models, many fractures would be misclassified, which could result in misleading communication, inaccurate prognostic assessments, unclear research, and incorrect treatment choices. Therefore, we cannot recommend the use of 3D-printed models in practice and research for classification of tibial plateau fractures. LEVEL OF EVIDENCE: Level III, diagnostic study.

Does the SORG Orthopaedic Research Group Hip Fracture Delirium Algorithm Perform Well on an Independent Intercontinental Cohort of Patients With Hip Fractures Who Are 60 Years or Older?

BACKGROUND: Postoperative delirium in patients aged 60 years or older with hip fractures adversely affects clinical and functional outcomes. The economic cost of delirium is estimated to be as high as USD 25,000 per patient, with a total budgetary impact between USD 6.6 to USD 82.4 billion annually in the United States alone. Forty percent of delirium episodes are preventable, and accurate risk stratification can decrease the incidence and improve clinical outcomes in patients. A previously developed clinical prediction model (the SORG Orthopaedic Research Group hip fracture delirium machine-learning algorithm) is highly accurate on internal validation (in 28,207 patients with hip fractures aged 60 years or older in a US cohort) in identifying at-risk patients, and it can facilitate the best use of preventive interventions; however, it has not been tested in an independent population. For an algorithm to be useful in real life, it must be valid externally, meaning that it must perform well in a patient cohort different from the cohort used to "train" it. With many promising machine-learning prediction models and many promising delirium models, only few have also been externally validated, and even fewer are international validation studies. QUESTION/PURPOSE: Does the SORG hip fracture delirium algorithm, initially trained on a database from the United States, perform well on external validation in patients aged 60 years or older in Australia and New Zealand? METHODS: We previously developed a model in 2021 for assessing risk of delirium in hip fracture patients using records of 28,207 patients obtained from the American College of Surgeons National Surgical Quality Improvement Program. Variables included in the original model included age, American Society of Anesthesiologists (ASA) class, functional status (independent or partially or totally dependent for any activities of daily living), preoperative dementia, preoperative delirium, and preoperative need for a mobility aid. To assess whether this model could be applied elsewhere, we used records from an international hip fracture registry. Between June 2017 and December 2018, 6672 patients older than 60 years of age in Australia and New Zealand were treated surgically for a femoral neck, intertrochanteric hip, or subtrochanteric hip fracture and entered into the Australian & New Zealand Hip Fracture Registry. Patients were excluded if they had a pathological hip fracture or septic shock. Of all patients, 6% (402 of 6672) did not meet the inclusion criteria, leaving 94% (6270 of 6672) of patients available for inclusion in this retrospective analysis. Seventy-one percent (4249 of 5986) of patients were aged 80 years or older, after accounting for 5% (284 of 6270) of missing values; 68% (4292 of 6266) were female, after accounting for 0.06% (4 of 6270) of missing values, and 83% (4690 of 5661) of patients were classified as ASA III/IV, after accounting for 10% (609 of 6270) of missing values. Missing data were imputed using the missForest methodology. In total, 39% (2467 of 6270) of patients developed postoperative delirium. The performance of the SORG hip fracture delirium algorithm on the validation cohort was assessed by discrimination, calibration, Brier score, and a decision curve analysis. Discrimination, known as the area under the receiver operating characteristic curves (c-statistic), measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities, a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. RESULTS: The SORG hip fracture algorithm, when applied to an external patient cohort, distinguished between patients at low risk and patients at moderate to high risk of developing postoperative delirium. The SORG hip fracture algorithm performed with a c-statistic of 0.74 (95% confidence interval 0.73 to 0.76). The calibration plot showed high accuracy in the lower predicted probabilities (intercept -0.28, slope 0.52) and a Brier score of 0.22 (the null model Brier score was 0.24). The decision curve analysis showed that the model can be beneficial compared with no model or compared with characterizing all patients as at risk for developing delirium. CONCLUSION: Algorithms developed with machine learning are a potential tool for refining treatment of at-risk patients. If high-risk patients can be reliably identified, resources can be appropriately directed toward their care. Although the current iteration of SORG should not be relied on for patient care, it suggests potential utility in assessing risk. Further assessment in different populations, made easier by international collaborations and standardization of registries, would be useful in the development of universally valid prediction models. The model can be freely accessed at: https://sorg-apps.shinyapps.io/hipfxdelirium/ . LEVEL OF EVIDENCE: Level III, therapeutic study.

3D-printed Handheld Models Do Not Improve Recognition of Specific Characteristics and Patterns of Three-part and Four-part Proximal Humerus Fractures.

BACKGROUND: Reliably recognizing the overall pattern and specific characteristics of proximal humerus fractures may aid in surgical decision-making. With conventional onscreen imaging modalities, there is considerable and undesired interobserver variability, even when observers receive training in the application of the classification systems used. It is unclear whether three-dimensional (3D) models, which now can be fabricated with desktop printers at relatively little cost, can decrease interobserver variability in fracture classification. QUESTIONS/PURPOSES: Do 3D-printed handheld models of proximal humerus fractures improve agreement among residents and attending surgeons regarding (1) specific fracture characteristics and (2) patterns according to the Neer and Hertel classification systems? METHODS: Plain radiographs, as well as two-dimensional (2D) and 3D CT images, were collected from 20 patients (aged 18 years or older) who sustained a three-part or four-part proximal humerus fracture treated at a Level I trauma center between 2015 and 2019. The included images were chosen to comprise images from patients whose fractures were considered as difficult-to-classify, displaced fractures. Consequently, the images were assessed for eight fracture characteristics and categorized according to the Neer and Hertel classifications by four orthopaedic residents and four attending orthopaedic surgeons during two separate sessions. In the first session, the assessment was performed with conventional onscreen imaging (radiographs and 2D and 3D CT images). In the second session, 3D-printed handheld models were used for assessment, while onscreen imaging was also available. Although proximal humerus classifications such as the Neer classification have, in the past, been shown to have low interobserver reliability, we theorized that by receiving direct tactile and visual feedback from 3D-printed handheld fracture models, clinicians would be able to recognize the complex 3D aspects of classification systems reliably. Interobserver agreement was determined with the multirater Fleiss kappa and scored according to the categorical rating by Landis and Koch. To determine whether there was a difference between the two sessions, we calculated the delta (difference in the) kappa value with 95% confidence intervals and a two-tailed p value. Post hoc power analysis revealed that with the current sample size, a delta kappa value of 0.40 could be detected with 80% power at alpha = 0.05. RESULTS: Using 3D-printed models in addition to conventional imaging did not improve interobserver agreement of the following fracture characteristics: more than 2 mm medial hinge displacement, more than 8 mm metaphyseal extension, surgical neck fracture, anatomic neck fracture, displacement of the humeral head, more than 10 mm lesser tuberosity displacement, and more than 10 mm greater tuberosity displacement. Agreement regarding the presence of a humeral head-splitting fracture was improved but only to a level that was insufficient for clinical or scientific use (fair to substantial, delta kappa = 0.33 [95% CI 0.02 to 0.64]). Assessing 3D-printed handheld models adjunct to onscreen conventional imaging did not improve the interobserver agreement for pattern recognition according to Neer (delta kappa = 0.02 [95% CI -0.11 to 0.07]) and Hertel (delta kappa = 0.01 [95% CI -0.11 to 0.08]). There were no differences between residents and attending surgeons in terms of whether 3D models helped them classify the fractures, but there were few differences to identify fracture characteristics. However, none of the identified differences improved to almost perfect agreement (kappa value above 0.80), so even those few differences are unlikely to be clinically useful. CONCLUSION: Using 3D-printed handheld fracture models in addition to conventional onscreen imaging of three-part and four-part proximal humerus fractures does not improve agreement among residents and attending surgeons on specific fracture characteristics and patterns. Therefore, we do not recommend that clinicians expend the time and costs needed to create these models if the goal is to classify or describe patients' fracture characteristics or pattern, since doing so is unlikely to improve clinicians' abilities to select treatment or estimate prognosis. LEVEL OF EVIDENCE: Level III, diagnostic study.

Can We Geographically Validate a Natural Language Processing Algorithm for Automated Detection of Incidental Durotomy Across Three Independent Cohorts From Two Continents?

BACKGROUND: Incidental durotomy is an intraoperative complication in spine surgery that can lead to postoperative complications, increased length of stay, and higher healthcare costs. Natural language processing (NLP) is an artificial intelligence method that assists in understanding free-text notes that may be useful in the automated surveillance of adverse events in orthopaedic surgery. A previously developed NLP algorithm is highly accurate in the detection of incidental durotomy on internal validation and external validation in an independent cohort from the same country. External validation in a cohort with linguistic differences is required to assess the transportability of the developed algorithm, referred to geographical validation. Ideally, the performance of a prediction model, the NLP algorithm, is constant across geographic regions to ensure reproducibility and model validity. QUESTION/PURPOSE: Can we geographically validate an NLP algorithm for the automated detection of incidental durotomy across three independent cohorts from two continents? METHODS: Patients 18 years or older undergoing a primary procedure of (thoraco)lumbar spine surgery were included. In Massachusetts, between January 2000 and June 2018, 1000 patients were included from two academic and three community medical centers. In Maryland, between July 2016 and November 2018, 1279 patients were included from one academic center, and in Australia, between January 2010 and December 2019, 944 patients were included from one academic center. The authors retrospectively studied the free-text operative notes of included patients for the primary outcome that was defined as intraoperative durotomy. Incidental durotomy occurred in 9% (93 of 1000), 8% (108 of 1279), and 6% (58 of 944) of the patients, respectively, in the Massachusetts, Maryland, and Australia cohorts. No missing reports were observed. Three datasets (Massachusetts, Australian, and combined Massachusetts and Australian) were divided into training and holdout test sets in an 80:20 ratio. An extreme gradient boosting (an efficient and flexible tree-based algorithm) NLP algorithm was individually trained on each training set, and the performance of the three NLP algorithms (respectively American, Australian, and combined) was assessed by discrimination via area under the receiver operating characteristic curves (AUC-ROC; this measures the model's ability to distinguish patients who obtained the outcomes from those who did not), calibration metrics (which plot the predicted and the observed probabilities) and Brier score (a composite of discrimination and calibration). In addition, the sensitivity (true positives, recall), specificity (true negatives), positive predictive value (also known as precision), negative predictive value, F1-score (composite of precision and recall), positive likelihood ratio, and negative likelihood ratio were calculated. RESULTS: The combined NLP algorithm (the combined Massachusetts and Australian data) achieved excellent performance on independent testing data from Australia (AUC-ROC 0.97 [95% confidence interval 0.87 to 0.99]), Massachusetts (AUC-ROC 0.99 [95% CI 0.80 to 0.99]) and Maryland (AUC-ROC 0.95 [95% CI 0.93 to 0.97]). The NLP developed based on the Massachusetts cohort had excellent performance in the Maryland cohort (AUC-ROC 0.97 [95% CI 0.95 to 0.99]) but worse performance in the Australian cohort (AUC-ROC 0.74 [95% CI 0.70 to 0.77]). CONCLUSION: We demonstrated the clinical utility and reproducibility of an NLP algorithm with combined datasets retaining excellent performance in individual countries relative to algorithms developed in the same country alone for detection of incidental durotomy. Further multi-institutional, international collaborations can facilitate the creation of universal NLP algorithms that improve the quality and safety of orthopaedic surgery globally. The combined NLP algorithm has been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/nlp_incidental_durotomy/ . Clinicians and researchers can use the tool to help incorporate the model in evaluating spine registries or quality and safety departments to automate detection of incidental durotomy and optimize prevention efforts. LEVEL OF EVIDENCE: Level III, diagnostic study.

Regional differences in the three-dimensional bone microstructure of the radial head: implications for observed fracture patterns.

INTRODUCTION: A characterization of the internal bone microstructure of the radial head could provide a better understanding of commonly occurring fracture patterns frequently involving the (antero)lateral quadrant, for which a clear explanation is still lacking. The aim of this study is to describe the radial head bone microstructure using micro-computed tomography (micro-CT) and to relate it to gross morphology, function and possible fracture patterns. MATERIALS AND METHODS: Dry cadaveric human radii were scanned by micro-CT (17 µm/pixel, isotropic). The trabecular bone microstructure was quantified on axial image stacks in four quadrants: the anterolateral (AL), posterolateral (PL), posteromedial (PM) and anteromedial (AM) quadrant. RESULTS: The AL and PL quadrants displayed the significantly lowest bone volume fraction and trabecular number (BV/TV range 12.3-25.1%, Tb.N range 0.73-1.16 mm-1) and highest trabecular separation (Tb.Sp range 0.59-0.82 mm), compared to the PM and AM quadrants (BV/TV range 19.9-36.9%, Tb.N range 0.96-1.61 mm-1, Tb.Sp range 0.45-0.74 mm) (p = 0.03). CONCLUSIONS: Our microstructural results suggest that the lateral side is the "weaker side", exhibiting lower bone volume faction, less trabeculae and higher trabecular separation, compared to the medial side. As the forearm is pronated during most falls, the underlying bone microstructure could explain commonly observed fracture patterns of the radial head, particularly more often involving the AL quadrant. If screw fixation in radial head fractures is considered, surgeons should take advantage of the "stronger" bone microstructure of the medial side of the radial head, should the fracture line allow this.

Prospective Cohort Study to Investigate Factors Associated With Continued Immobilization of a Nondisplaced Scaphoid Waist Fracture.

PURPOSE: The decision to continue immobilization of a nondisplaced scaphoid waist fracture is often based on radiographic appearance (despite evidence that radiographs are unreliable and inaccurate for diagnosing scaphoid union 6-12 weeks after fracture) and fracture tenderness (even though it is influenced by cognitive biases on pain). This may result in unhelpful additional immobilization. We studied nondisplaced scaphoid waist fractures to determine the factors associated with (1) the surgeon's decision to continue cast or splint immobilization at the first visit when cast removal was being considered; (2) greater pain on examination; and (3) the surgeon's concern about radiographic consolidation. METHODS: We prospectively included 46 patients with a nondisplaced scaphoid waist fracture treated nonoperatively. At the first visit when cast removal was considered - after an average of 6 weeks of immobilization - patients rated pain during 4 examination maneuvers. The treating surgeon assessed union on radiographs and decided whether to continue or discontinue immobilization. Patients completed measures of the following: (1) the degree to which pain limits activities (Patient-Reported Outcome Measure Interactive System [PROMIS] Pain Interference Computer Adaptive Test [CAT], Pain Self-Efficacy Questionnaire-2); (2) symptoms of depression (PROMIS Depression CAT); and (3) upper extremity function (PROMIS Upper Extremity Function CAT). We used multivariable regression analysis to investigate the factors associated with each outcome. RESULTS: Perceived inadequate radiographic healing and greater symptoms of depression were independently associated with continued immobilization. Pain during the examination was not associated with continued immobilization. Patient age was associated with pain on examination. Shorter immobilization duration was the only factor associated with the surgeon's perception of inadequate radiographic consolidation. CONCLUSIONS: Inadequate radiographic healing and greater symptoms of depression are associated with a surgeon's decision to continue cast or splint immobilization of a nondisplaced scaphoid waist fracture. CLINICAL RELEVANCE: Overreliance on radiographs and inadequate accounting for psychological distress may hinder the adoption of shorter immobilization times for nondisplaced waist fractures.

Factors associated with surgeon recommendation for additional cast immobilization of a CT-verified nondisplaced scaphoid waist fracture.

INTRODUCTION: Data from clinical trials suggest that CT-confirmed nondisplaced scaphoid waist fractures heal with less than the conventional 8-12 weeks of immobilization. Barriers to adopting shorter immobilization times in clinical practice may include a strong influence of fracture tenderness and radiographic appearance on decision-making. This study aimed to investigate (1) the degree to which surgeons use fracture tenderness and radiographic appearance of union, among other factors, to decide whether or not to recommend additional cast immobilization after 8 or 12 weeks of immobilization; (2) identify surgeon factors associated with the decision to continue cast immobilization after 8 or 12 weeks. MATERIALS AND METHODS: In a survey-based study, 218 surgeons reviewed 16 patient scenarios of CT-confirmed nondisplaced waist fractures treated with cast immobilization for 8 or 12 weeks and recommended for or against additional cast immobilization. Clinical variables included patient sex, age, a description of radiographic fracture consolidation, fracture tenderness and duration of cast immobilization completed (8 versus 12 weeks). To assess the impact of clinical factors on recommendation to continue immobilization we calculated posterior probabilities and determined variable importance using a random forest algorithm. Multilevel logistic mixed regression analysis was used to identify surgeon characteristics associated with recommendation for additional cast immobilization. RESULTS: Unclear fracture healing on radiographs, fracture tenderness and 8 (versus 12) weeks of completed cast immobilization were the most important factors influencing surgeons' decision to recommend continued cast immobilization. Women surgeons (OR 2.96; 95% CI 1.28-6.81, p = 0.011), surgeons not specialized in orthopedic trauma, hand and wrist or shoulder and elbow surgery (categorized as 'other') (OR 2.64; 95% CI 1.31-5.33, p = 0.007) and surgeons practicing in the United States (OR 6.53, 95% CI 2.18-19.52, p = 0.01 versus Europe) were more likely to recommend continued immobilization. CONCLUSION: Adoption of shorter immobilization times for CT-confirmed nondisplaced scaphoid waist fractures may be hindered by surgeon attention to fracture tenderness and radiographic appearance.

Factors associated with subsequent surgical procedures after intramedullary nailing for tibial shaft fractures.

INTRODUCTION: The reported rate of subsequent surgery after intramedullary nailing (IMN) of tibial shaft fractures (TSFs) is as high as 21%. However, most studies have not included the removal of symptomatic implant in these rates. The purpose of this study was to evaluate the subsequent surgery rate after IMN of TSFs, including the removal of symptomatic implants. Secondly, this study aimed to assess what factors are associated with subsequent surgery (1) to promote fracture and wound healing and (2) for the removal of symptomatic implants. METHODS: One-hundred and ninety-one patients treated with IMN for TSFs were retrospectively included. The rate of subsequent surgery was determined. Bi- and multivariable analysis was used to identify variables associated with subsequent surgery. RESULTS: Approximately half of patients (46%) underwent at least one subsequent surgical procedure. Forty-eight (25%) underwent a subsequent surgical procedure to promote fracture or wound healing. Age (P < 0.01), multi-trauma (P < 0.01), open fracture (P < 0.001) and index surgery during weekdays (P < 0.05) were associated with these procedures. Thirty-nine patients (20%) underwent a subsequent surgical procedure for removal of symptomatic implants. There was a significantly lower rate of implant removal in ASA II (11%) and ASA III-IV (14%) patients compared to ASA I patients (29%) (P < 0.05). CONCLUSIONS: Patients treated with IMN for TSFs should be consented that about one-in-two patients will undergo an additional surgical procedure. Half of these procedures are required to promote wound or fracture healing; the other half are for symptomatic implant removal. LEVEL OF EVIDENCE: Therapeutic level-IV.

Do 3-D Printed Handheld Models Improve Surgeon Reliability for Recognition of Intraarticular Distal Radius Fracture Characteristics?

BACKGROUND: For fracture care, radiographs and two-dimensional (2-D) and three-dimensional (3-D) CT are primarily used for preoperative planning and postoperative evaluation. Intraarticular distal radius fractures are technically challenging to treat, and meticulous preoperative planning is paramount to improve the patient's outcome. Three-dimensionally printed handheld models might improve the surgeon's interpretation of specific fracture characteristics and patterns preoperatively and could therefore be clinically valuable; however, the additional value of 3-D printed handheld models for fractures of the distal radius, a high-volume and commonly complex fracture due to its intraarticular configuration, has yet to be determined. QUESTIONS/PURPOSES: (1) Does the reliability of assessing specific fracture characteristics that guide surgical decision-making for distal radius fractures improve with 3-D printed handheld models? (2) Does surgeon agreement on the overall fracture classification improve with 3-D printed handheld models? (3) Does the surgeon's confidence improve when assessing the overall fracture configuration with an additional 3-D model? METHODS: We consecutively included 20 intraarticular distal radius fractures treated at a Level 1 trauma center between May 2018 and November 2018. Ten surgeons evaluated the presence or absence of specific fracture characteristics (volar rim fracture, die punch, volar lunate facet, dorsal comminution, step-off > 2 mm, and gap > 2 mm), fracture classification according to the AO/Orthopaedic Trauma Association (OTA) classification scheme, and their confidence in assessing the overall fracture according to the classification scheme, rated on a scale from 0 to 10 (0 = not at all confident to 10 = very confident). Of 10 participants regularly treating distal radius fractures, seven were orthopaedic trauma surgeons and three upper limb surgeons with experience levels ranging from 1 to 25 years after completion of residency training. Fractures were assessed twice, with 1 month between each assessment. Initially, fractures were assessed using radiographs and 2-D and 3-D CT images (conventional assessment); the second time, the evaluation was based on radiographs and 2-D and 3-D CT images with an additional 3-D handheld model (3-D printed handheld model assessment). On both occasions, fracture characteristics were evaluated upon a surgeon's own interpretation, without specific instruction before assessment. We provided a sheet demonstrating the AO/OTA classification scheme before evaluation on each session. Multi-rater Fleiss's kappa was used to determine intersurgeon reliability for assessing fracture characteristics and classification. Confidence regarding assessment of the overall fracture classification was assessed using a paired t-test. RESULTS: We found that 3-D printed models of intraarticular distal radius fractures led to no change in kappa values for the reliability of all characteristics: volar rim (conventional kappa 0.19 [95% CI 0.06 to 0.32], kappa for 3-D handheld model 0.23 [95% CI 0.11 to 0.36], difference of kappas 0.04 [95% CI -0.14 to 0.22]; p = 0.66), die punch (conventional kappa 0.38 [95% CI 0.15 to 0.61], kappa for 3-D handheld model 0.50 [95% CI 0.23 to 0.78], difference of kappas 0.12 [95% CI -0.23 to 0.47]; p = 0.52), volar lunate facet (conventional kappa 0.31 [95% CI 0.14 to 0.49], kappa for 3-D handheld model 0.48 [95% CI 0.23 to 0.72], difference of kappas 0.17 [95% CI -0.12 to 0.46]; p = 0.26), dorsal comminution (conventional kappa 0.36 [95% CI 0.13 to 0.58], kappa for 3-D handheld model 0.31 [95% CI 0.11 to 0.51], difference of kappas -0.05 [95% CI -0.34 to 0.24]; p = 0.74), step-off > 2 mm (conventional kappa 0.55 [95% CI 0.29 to 0.82], kappa for 3-D handheld model 0.58 [95% CI 0.31 to 0.85], difference of kappas 0.03 [95% CI -0.34 to 0.40]; p = 0.87), gap > 2 mm (conventional kappa 0.59 [95% CI 0.39 to 0.79], kappa for 3-D handheld model 0.69 [95% CI 0.50 to 0.89], difference of kappas 0.10 [95% CI -0.17 to 0.37]; p = 0.48). Although there appeared to be categorical improvement in kappa values for some fracture characteristics, overlapping CIs indicated no change. Fracture classification did not improve (conventional diagnostics: kappa 0.27 [95% CI 0.14 to 0.39], conventional diagnostics with an additional 3-D handheld model: kappa 0.25 [95% CI 0.15 to 0.35], difference of kappas: -0.02 [95% CI -0.18 to 0.14]; p = 0.81). There was no improvement in self-assessed confidence in terms of assessment of overall fracture configuration when a 3-D model was added to the evaluation process (conventional diagnostics 7.8 [SD 0.79 {95% CI 7.2 to 8.3}], 3-D handheld model 8.5 [SD 0.71 {95% CI 8.0 to 9.0}], difference of score: 0.7 [95% CI -1.69 to 0.16], p = 0.09). CONCLUSIONS: Intersurgeon reliability for evaluating the characteristics of and classifying intraarticular distal radius fractures did not improve with an additional 3-D model. Further studies should evaluate the added value of 3-D printed handheld models for teaching surgical residents and medical trainees to define the future role of 3-D printing in caring for fractures of the distal radius. LEVEL OF EVIDENCE: Level II, diagnostic study.

Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?

BACKGROUND: Preliminary experience suggests that deep learning algorithms are nearly as good as humans in detecting common, displaced, and relatively obvious fractures (such as, distal radius or hip fractures). However, it is not known whether this also is true for subtle or relatively nondisplaced fractures that are often difficult to see on radiographs, such as scaphoid fractures. QUESTIONS/PURPOSES: (1) What is the diagnostic accuracy, sensitivity, and specificity of a deep learning algorithm in detecting radiographically visible and occult scaphoid fractures using four radiographic imaging views? (2) Does adding patient demographic (age and sex) information improve the diagnostic performance of the deep learning algorithm? (3) Are orthopaedic surgeons better at diagnostic accuracy, sensitivity, and specificity compared with deep learning? (4) What is the interobserver reliability among five human observers and between human consensus and deep learning algorithm? METHODS: We retrospectively searched the picture archiving and communication system (PACS) to identify 300 patients with a radiographic scaphoid series, until we had 150 fractures (127 visible on radiographs and 23 only visible on MRI) and 150 non-fractures with a corresponding CT or MRI as the reference standard for fracture diagnosis. At our institution, MRIs are usually ordered for patients with scaphoid tenderness and normal radiographs, and a CT with radiographically visible scaphoid fracture. We used a deep learning algorithm (a convolutional neural network [CNN]) for automated fracture detection on radiographs. Deep learning, an advanced subset of artificial intelligence, combines artificial neuronal layers to resemble a neuron cell. CNNs-essentially deep learning algorithms resembling interconnected neurons in the human brain-are most commonly used for image analysis. Area under the receiver operating characteristic curve (AUC) was used to evaluate the algorithm's diagnostic performance. An AUC of 1.0 would indicate perfect prediction, whereas 0.5 would indicate that a prediction is no better than a flip of a coin. The probability of a scaphoid fracture generated by the CNN, sex, and age were included in a multivariable logistic regression to determine whether this would improve the algorithm's diagnostic performance. Diagnostic performance characteristics (accuracy, sensitivity, and specificity) and reliability (kappa statistic) were calculated for the CNN and for the five orthopaedic surgeon observers in our study. RESULTS: The algorithm had an AUC of 0.77 (95% CI 0.66 to 0.85), 72% accuracy (95% CI 60% to 84%), 84% sensitivity (95% CI 0.74 to 0.94), and 60% specificity (95% CI 0.46 to 0.74). Adding age and sex did not improve diagnostic performance (AUC 0.81 [95% CI 0.73 to 0.89]). Orthopaedic surgeons had better specificity (0.93 [95% CI 0.93 to 0.99]; p < 0.01), while accuracy (84% [95% CI 81% to 88%]) and sensitivity (0.76 [95% CI 0.70 to 0.82]; p = 0.29) did not differ between the algorithm and human observers. Although the CNN was less specific in diagnosing relatively obvious fractures, it detected five of six occult scaphoid fractures that were missed by all human observers. The interobserver reliability among the five surgeons was substantial (Fleiss' kappa = 0.74 [95% CI 0.66 to 0.83]), but the reliability between the algorithm and human observers was only fair (Cohen's kappa = 0.34 [95% CI 0.17 to 0.50]). CONCLUSIONS: Initial experience with our deep learning algorithm suggests that it has trouble identifying scaphoid fractures that are obvious to human observers. Thirteen false positive suggestions were made by the CNN, which were correctly detected by the five surgeons. Research with larger datasets-preferably also including information from physical examination-or further algorithm refinement is merited. LEVEL OF EVIDENCE: Level III, diagnostic study.

3D mapping of scaphoid fractures and comminution.

OBJECTIVE: Acute and subacute scaphoid fractures were assessed using 3D computer tomography (CT). The aims were to describe fracture morphology, to map fractures onto a 3D scaphoid model and to correlate this to scaphoid anatomy. MATERIALS AND METHODS: A retrospective, multicentre database search was performed to identify CT studies of acute and subacute scaphoid fractures. CT scans of scaphoid fractures less than 6 weeks from time of injury were included in this retrospective, multicentre study. CTs were segmented and converted into three-dimensional models. Following virtual fracture reduction, fractures were mapped onto a three-dimensional scaphoid model. RESULTS: Seventy-five CT scans were included. The median delay from injury to CT was 29 days. Most studies were in male patients (89%). Most fractures were comminuted (52%) or displaced (64%). A total of 73% of displaced fractures had concomitant comminution. Waist fractures had higher rates of comminution and displacement when compared with all other fractures. Comminution was located along the dorsal ridge and the volar scaphoid waist. CONCLUSION: Our study is the first to describe acute fracture morphology using 3D CT and to correlate comminution and displacement to fracture types. The dorsal ridge and volar waist need prudent assessment, especially in waist fractures.

What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review.

BACKGROUND: Artificial-intelligence algorithms derive rules and patterns from large amounts of data to calculate the probabilities of various outcomes using new sets of similar data. In medicine, artificial intelligence (AI) has been applied primarily to image-recognition diagnostic tasks and evaluating the probabilities of particular outcomes after treatment. However, the performance and limitations of AI in the automated detection and classification of fractures has not been examined comprehensively. QUESTION/PURPOSES: In this systematic review, we asked (1) What is the proportion of correctly detected or classified fractures and the area under the receiving operating characteristic (AUC) curve of AI fracture detection and classification models? (2) What is the performance of AI in this setting compared with the performance of human examiners? METHODS: The PubMed, Embase, and Cochrane databases were systematically searched from the start of each respective database until September 6, 2018, using terms related to "fracture", "artificial intelligence", and "detection, prediction, or evaluation." Of 1221 identified studies, we retained 10 studies: eight studies involved fracture detection (ankle, hand, hip, spine, wrist, and ulna), one addressed fracture classification (diaphyseal femur), and one addressed both fracture detection and classification (proximal humerus). We registered the review before data collection (PROSPERO: CRD42018110167) and used the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). We reported the range of the accuracy and AUC for the performance of the predicted fracture detection and/or classification task. An AUC of 1.0 would indicate perfect prediction, whereas 0.5 would indicate a prediction is no better than a flip-of-a-coin. We conducted quality assessment using a seven-item checklist based on a modified methodologic index for nonrandomized studies instrument (MINORS). RESULTS: For fracture detection, the AUC in five studies reflected near perfect prediction (range, 0.95-1.0), and the accuracy in seven studies ranged from 83% to 98%. For fracture classification, the AUC was 0.94 in one study, and the accuracy in two studies ranged from 77% to 90%. In two studies AI outperformed human examiners for detecting and classifying hip and proximal humerus fractures, and one study showed equivalent performance for detecting wrist, hand and ankle fractures. CONCLUSIONS: Preliminary experience with fracture detection and classification using AI shows promising performance. AI may enhance processing and communicating probabilistic tasks in medicine, including orthopaedic surgery. At present, inadequate reference standard assignments to train and test AI is the biggest hurdle before integration into clinical workflow. The next step will be to apply AI to more challenging diagnostic and therapeutic scenarios when there is absence of certitude. Future studies should also seek to address legal regulation and better determine feasibility of implementation in clinical practice. LEVEL OF EVIDENCE: Level II, diagnostic study.

Use and outcome of 1,220 primary total elbow arthroplasties from the Australian Orthopaedic Association National Joint Arthroplasty Replacement Registry 2008-2018.

Background and purpose - The Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) was analyzed to determine trends in use of primary total elbow arthroplasty (TEA), the types of prostheses used, primary diagnoses, reasons for and types of revision, and whether the primary diagnosis or prosthesis design influenced the revision rate.Patients and methods - During 2008-2018, 1,220 primary TEA procedures were reported of which 140 TEAs were revised. Kaplan-Meier estimates of survivorship were used to describe the time to first revision and hazard ratios (HR) from Cox proportional hazard models, adjusted for age and sex, were used to compare revision rates.Results - The annual number of TEAs performed remained constant. The 3 most common diagnoses for primary TEA were fracture/dislocation (trauma) (36%), osteoarthritis (OA) (34%), and rheumatoid arthritis (RA) (26%). The cumulative percentage revision for all TEAs undertaken for any reason was 10%, 15%, and 19% at 3, 6, and 9 years. TEAs undertaken for OA had a higher revision rate compared with TEAs for trauma (HR = 1.8, 95% CI 1.1-3.0) and RA (HR = 2.0, CI 1.3-3.1). The Coonrad-Morrey (50%), Latitude (30%), Nexel (10%), and Discovery (9%) were the most used prosthesis designs. There was no difference in revision rates when these 4 designs were compared. The most common reasons for revision were infection (35%) and aseptic loosening (34%).Interpretation - The indications for primary and revision TEA in Australia are similar to those reported for other registries. Revision for trauma is lower than previously reported.