Defining the Digit-Specific Confluence of the A1 Pulley.

PURPOSE: Variations in the description of the flexor pulley system exist, particularly in whether the A1 and A2 pulleys represent discrete or confluent entities. This has potentially important clinical relevance at the time of A1 pulley release for symptomatic trigger finger, given the goal of adequate release without overrelease. The purpose of this study was to determine the relative prevalence of confluent A1 pulleys on a digit-by-digit basis employing 2.5× loupes alone, thereby simulating a clinical surgical environment. METHODS: Cadaveric anatomic specimens underwent flexor pulley system dissection under 2.5× loupe magnification by 2 hand surgeons. The presence of pulley confluence and length (measured from the proximal aspect to the distal aspect) was recorded and compared on a digit-to-digit basis. RESULTS: Forty-five digits, comprising 9 adult forearm or hand specimens (5 right and 4 left) obtained from 6 donors (4 men and 2 women, age: 67 ± 8 years), were dissected. A total of 19 confluent A1 pulleys were encountered, with notable digit-by-digit variation in the prevalence of confluent pulleys. There were 0 confluent pulleys observed in the thumb, compared with 6 confluent pulleys observed in the middle finger. The average overall A1 pulley length was 5.0 ± 1.5 mm, with a similar pulley length observed between the digits. CONCLUSIONS: A1 pulley confluence varies on a digit-to-digit basis, with no observed confluence in the thumb and the most common confluence observed in the middle finger. CLINICAL RELEVANCE: In the setting of intraoperatively observed pulley confluence, we suggest pulley release under traction in order to develop the plane between the A1 and A2 pulleys and, thus, confirm the complete and isolated release of the A1 pulley.

Effect of Radiocapitellar Joint Over/Under Stuffing on Elbow Joint Contact Pressure.

PURPOSE: Comminuted radial head fractures are commonly treated by surgical resection or replacement with a prosthesis. A potential problem with radial head replacement is overlengthening of the radial neck ("overstuffing" of the radial head), which has been shown to affect both ulnohumeral kinematics and radiocapitellar pressures. We hypothesized that an overstuffed radial head prosthesis increases capitellar pressure and reduces coronoid pressure. METHODS: Seven human cadaveric elbows were prepared on a custom-designed apparatus simulating stabilizing muscle loads, and passively flexed from 0° to 90° under gravity valgus torque while joint contact pressures were measured. Each elbow was tested sequentially with different neck lengths, starting with the intact specimen followed by insertion of understuffed (-2 mm), standard-height (0 mm), and overstuffed (+2 mm) radial head prostheses in neutral forearm rotation, 40° pronation, and 40° supination positions, respectively. RESULTS: Capitellar mean contact pressures significantly increased after insertion of an overstuffed radial head prosthesis. In valgus position with neutral forearm rotation, capitellar mean contact pressure on the joint with an intact radial head averaged 227 ± 70 kPa. Insertion of understuffed, standard-height, and overstuffed radial head prostheses changed the mean contact pressures to 152 ± 76 kPa, 212 ± 68 kPa, and 491 ± 168 kPa, respectively. The overstuffed radial head group had significantly lower whole coronoid mean contact pressures (153 ± 56 kPa) compared with the intact (390 ± 138 kPa) and standard-height (376 ± 191 kPa) radial head groups. CONCLUSIONS: An increase in radial prosthesis height significantly increases capitellar contact pressures and reduces coronoid contact pressures. CLINICAL RELEVANCE: Restoration of the anatomic radial head height is critical when performing radial head arthroplasty to maintain normal joint biomechanics. Elevated capitellar contact pressures can potentially lead to pain and early degenerative changes.

Distribution and Correlates of Hip-Knee-Ankle Angle in Early Osteoarthritis and Preoperative Total Knee Arthroplasty Patients.

BACKGROUND: Several studies have investigated the distribution of hip-knee-ankle (HKA) angle in healthy populations; however, few have evaluated this metric in patients undergoing total knee arthroplasty (TKA). The purpose of this study is to compare HKA angle distribution in early and advanced knee osteoarthritis (OA) patients. METHODS: Full limb radiographs were used to measure HKA angle for 983 subjects from the Osteoarthritis Initiative (OAI) cohort and 4,901 pre-TKA patients from an institutional cohort. Measurements were made using a previously validated deep learning algorithm. Linear regression models were used to determine the association of HKA alignment angle with patient characteristics. RESULTS: The mean ± standard deviation HKA angle was -1.3° ± 3.2° in the OAI cohort and -4.1° ± 6.1° in the pre-TKA cohort. In the OAI cohort, normal alignment (64%) was the most common knee alignment followed by varus (29%), and valgus (7%). In pre-TKA patients, the most common alignment was varus (62%), followed by normal (27%) and valgus (11%). In pre-TKA patients, mean HKA angle in primary knee OA, post-traumatic knee OA, and rheumatoid arthritis patients were -4.3° ± 6.1°, -3.2° ± 6.4°, and -2.9° ± 6.1°, respectively. HKA angle was strongly associated (P < .001) with gender and body mass index. CONCLUSION: TKA patients have a wider alignment distribution and more severe varus and valgus alignment than individuals "at risk" for knee OA from the OAI cohort. These epidemiologic findings improve our understanding of HKA angle distribution and its correlation with demographic characteristics in early and late-stage arthritis.

Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs.

BACKGROUND: Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. The purpose of this study is to illustrate the potential of a convolutional neural network model to assess the risk of hip dislocation based on postoperative anteroposterior pelvis radiographs. METHODS: We retrospectively evaluated radiographs for a cohort of 13,970 primary THAs with 374 dislocations over 5 years of follow-up. Overall, 1490 radiographs from dislocated and 91,094 from non-dislocated THAs were included in the analysis. A convolutional neural network object detection model (YOLO-V3) was trained to crop the images by centering on the femoral head. A ResNet18 classifier was trained to predict subsequent hip dislocation from the cropped imaging. The ResNet18 classifier was initialized with ImageNet weights and trained using FastAI (V1.0) running on PyTorch. The training was run for 15 epochs using 10-fold cross validation, data oversampling, and augmentation. RESULTS: The hip dislocation classifier achieved the following mean performance (standard deviation): accuracy = 49.5 (4.1%), sensitivity = 89.0 (2.2%), specificity = 48.8 (4.2%), positive predictive value = 3.3 (0.3%), negative predictive value = 99.5 (0.1%), and area under the receiver operating characteristic curve = 76.7 (3.6%). Saliency maps demonstrated that the model placed the greatest emphasis on the femoral head and acetabular component. CONCLUSION: Existing prediction methods fail to identify patients at high risk of dislocation following THA. Our radiographic classifier model has high sensitivity and negative predictive value, and can be combined with clinical risk factor information for rapid assessment of risk for dislocation following THA. The model further suggests radiographic locations which may be important in understanding the etiology of prosthesis dislocation. Importantly, our model is an illustration of the potential of automated imaging artificial intelligence models in orthopedics. LEVEL OF EVIDENCE: Level III.

A Deep Learning Tool for Automated Radiographic Measurement of Acetabular Component Inclination and Version After Total Hip Arthroplasty.

BACKGROUND: Inappropriate acetabular component angular position is believed to increase the risk of hip dislocation after total hip arthroplasty. However, manual measurement of these angles is time consuming and prone to interobserver variability. The purpose of this study was to develop a deep learning tool to automate the measurement of acetabular component angles on postoperative radiographs. METHODS: Two cohorts of 600 anteroposterior (AP) pelvis and 600 cross-table lateral hip postoperative radiographs were used to develop deep learning models to segment the acetabular component and the ischial tuberosities. Cohorts were manually annotated, augmented, and randomly split to train-validation-test data sets on an 8:1:1 basis. Two U-Net convolutional neural network models (one for AP and one for cross-table lateral radiographs) were trained for 50 epochs. Image processing was then deployed to measure the acetabular component angles on the predicted masks for anatomical landmarks. Performance of the tool was tested on 80 AP and 80 cross-table lateral radiographs. RESULTS: The convolutional neural network models achieved a mean Dice similarity coefficient of 0.878 and 0.903 on AP and cross-table lateral test data sets, respectively. The mean difference between human-level and machine-level measurements was 1.35° (σ = 1.07°) and 1.39° (σ = 1.27°) for the inclination and anteversion angles, respectively. Differences of 5° or more between human-level and machine-level measurements were observed in less than 2.5% of cases. CONCLUSION: We developed a highly accurate deep learning tool to automate the measurement of angular position of acetabular components for use in both clinical and research settings. LEVEL OF EVIDENCE: III.

Automated Detection of Periprosthetic Joint Infections and Data Elements Using Natural Language Processing.

BACKGROUND: Periprosthetic joint infection (PJI) data elements are contained in both structured and unstructured documents in electronic health records and require manual data collection. The goal of this study is to develop a natural language processing (NLP) algorithm to replicate manual chart review for PJI data elements. METHODS: PJI was identified among all total joint arthroplasty (TJA) procedures performed at a single academic institution between 2000 and 2017. Data elements that comprise the Musculoskeletal Infection Society (MSIS) criteria were manually extracted and used as the gold standard for validation. A training sample of 1208 TJA surgeries (170 PJI cases) was randomly selected to develop the prototype NLP algorithms and an additional 1179 surgeries (150 PJI cases) were randomly selected as the test sample. The algorithms were applied to all consultation notes, operative notes, pathology reports, and microbiology reports to predict the correct status of PJI based on MSIS criteria. RESULTS: The algorithm, which identified patients with PJI based on MSIS criteria, achieved an f1-score (harmonic mean of precision and recall) of 0.911. Algorithm performance in extracting the presence of sinus tract, purulence, pathologic documentation of inflammation, and growth of cultured organisms from the involved TJA achieved f1-scores that ranged from 0.771 to 0.982, sensitivity that ranged from 0.730 to 1.000, and specificity that ranged from 0.947 to 1.000. CONCLUSION: NLP-enabled algorithms have the potential to automate data collection for PJI diagnostic elements, which could directly improve patient care and augment cohort surveillance and research efforts. Further validation is needed in other hospital settings. LEVEL OF EVIDENCE: Level III, Diagnostic.

Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Knee Arthroplasty.

BACKGROUND: Natural language processing (NLP) methods have the capability to process clinical free text in electronic health records, decreasing the need for costly manual chart review, and improving data quality. We developed rule-based NLP algorithms to automatically extract surgery specific data elements from knee arthroplasty operative notes. METHODS: Within a cohort of 20,000 knee arthroplasty operative notes from 2000 to 2017 at a large tertiary institution, we randomly selected independent pairs of training and test sets to develop and evaluate NLP algorithms to detect five major data elements. The size of the training and test datasets were similar and ranged between 420 to 1592 surgeries. Expert rules using keywords in operative notes were used to implement NLP algorithms capturing: (1) category of surgery (total knee arthroplasty, unicompartmental knee arthroplasty, patellofemoral arthroplasty), (2) laterality of surgery, (3) constraint type, (4) presence of patellar resurfacing, and (5) implant model (catalog numbers). We used institutional registry data as our gold standard to evaluate the NLP algorithms. RESULTS: NLP algorithms to detect the category of surgery, laterality, constraint, and patellar resurfacing achieved 98.3%, 99.5%, 99.2%, and 99.4% accuracy on test datasets, respectively. The implant model algorithm achieved an F1-score (harmonic mean of precision and recall) of 99.9%. CONCLUSIONS: NLP algorithms are a promising alternative to costly manual chart review to automate the extraction of embedded information within knee arthroplasty operative notes. Further validation in other hospital settings will enhance widespread implementation and efficiency in data capture for research and clinical purposes. LEVEL OF EVIDENCE: Level III.

Loss of pronation-supination in patients with heterotopic ossification around the elbow.

BACKGROUND: Heterotopic ossification (HO) is a well-recognized cause of limited flexion-extension, but it can also limit pronation-supination. There is a paucity of literature concerning restriction of pronation-supination due to HO. METHODS: We conducted a retrospective review of patients who had undergone elbow surgery for HO removal between January 1, 2003, and September 27, 2013. Computed tomography scans were reviewed to determine the presence of HO restricting forearm rotation and were rated independently by 4 observers. Each elbow was given 1 of 4 scores according to the likelihood that HO was restricting forearm rotation. Agreement was achieved when 3 or 4 observers thought that HO definitely or probably caused a loss of pronation-supination. RESULTS: Of 132 post-traumatic patients undergoing HO excision for restricted elbow motion, 61 (46%) also lacked a functional arc of pronation and supination (50° and 50°, respectively). Of these 61 patients, 32 (53%) were considered to have lost forearm rotation because of HO. The remaining 29 patients (47%) were thought to have restricted forearm rotation for reasons unrelated to HO. DISCUSSION: In this study, loss of pronation-supination affected almost half of the patients (61 of 132 [46%]) undergoing HO excision around the elbow. Of these 61 patients, 32 (52%) had HO extending into the proximal forearm and affecting rotation. From our data, one can expect that about one-quarter (24% of patients in this study, or 32 of 132) with post-traumatic HO of the elbow will have a significant functional loss of pronation-supination due to HO extending into the forearm.

Effect of radiocapitellar Achilles disc arthroplasty on coronoid and capitellar contact pressures after radial head excision.

BACKGROUND: Long-term radiographic arthritis has been commonly reported after radial head excision. Concern over radial head arthroplasty may arise in certain situations including capitellar arthritis, radiocapitellar malalignment, and in young and active patients. We hypothesized that radial head excision increases coronoid contact pressures, which may at least be partially reduced by radiocapitellar Achilles tendon disc arthroplasty. METHODS: Coronoid and capitellar contact pressure was measured on 6 human cadaveric elbows on a custom-designed gravity-valgus simulator under passive flexion from 0° to 90°. Sequential testing, starting with the intact specimen, resection of the radial head, and finally, radiocapitellar Achilles tendon disc arthroplasty were performed on each specimen. RESULTS: Mean contact pressure of the coronoid significantly increased after radial head excision (P < .0001) and significantly improved after Achilles disc arthroplasty (P < .0001). The pressure difference was most pronounced on the lateral coronoid. From 15° to 85° of elbow flexion, mean contact pressures on the lateral coronoid were 291 kPa and 476 kPa before and after radial head excision, respectively (P < .0001). Achilles disc arthroplasty significantly lowered coronoid contact pressures to 385 kPa (P = .002); however, they remained significantly higher than those in the intact radial head group (P = .0009). CONCLUSIONS: Radial head resection increases contact pressure in the coronoid, especially the lateral coronoid. This study showed that radiocapitellar Achilles disc arthroplasty significantly improves contact pressures on the coronoid after radial head resection. Achilles disc arthroplasty could be considered in patients who are not candidates for radial head arthroplasty.

Prediction Models for Knee Osteoarthritis: Review of Current Models and Future Directions.

Background: Knee osteoarthritis (OA) is a prevalent joint disease. Clinical prediction models consider a wide range of risk factors for knee OA. This review aimed to evaluate published prediction models for knee OA and identify opportunities for future model development. Methods: We searched Scopus, PubMed, and Google Scholar using the terms knee osteoarthritis, prediction model, deep learning, and machine learning. All the identified articles were reviewed by one of the researchers and we recorded information on methodological characteristics and findings. We only included articles that were published after 2000 and reported a knee OA incidence or progression prediction model. Results: We identified 26 models of which 16 employed traditional regression-based models and 10 machine learning (ML) models. Four traditional and five ML models relied on data from the Osteoarthritis Initiative. There was significant variation in the number and type of risk factors. The median sample size for traditional and ML models was 780 and 295, respectively. The reported Area Under the Curve (AUC) ranged between 0.6 and 1.0. Regarding external validation, 6 of the 16 traditional models and only 1 of the 10 ML models validated their results in an external data set. Conclusion: Diverse use of knee OA risk factors, small, non-representative cohorts, and use of magnetic resonance imaging which is not a routine evaluation tool of knee OA in daily clinical practice are some of the main limitations of current knee OA prediction models.

Wrist Arthroscopy Using the 2R Portal: Is It Safer Than the 1,2 Portal?

BACKGROUD: The purpose of this study was to compare the 1,2 with a novel 2R portal in terms of proximity to critical structures. METHODS: Wrist arthroscopy was performed on 8 fresh frozen cadavers via the 1,2 and 2R portals. External anatomy was then dissected under loupe magnification. The closest distance between the portals and surrounding anatomical structures was measured in millimeters using digital calipers. RESULTS: The 1,2 portal was significantly closer to radial artery and first extensor compartment tendons than the 2R portal. The radial artery was on average 1.32 mm from the 1-2 portal and 14.25 mm from the 2R portal. The 2R portal was significantly closer to the second and third extensor compartment tendons. The closest branch of the superficial branch of the radial nerve (SBRN) was on average 2.04 mm from the 1-2 portal and 7.59 mm from the 2R portal, but this was not statistically significant. CONCLUSIONS: We advocate using the 2R portal preferentially to the 1,2 portal when treating radial sided wrist pathology to decrease the risk of iatrogenic radial artery and SBRN injury.

Deep learning model for measurement of shoulder critical angle and acromion index on shoulder radiographs.

Background: Several bone morphological parameters, including the anterior acromion morphology, the lateral acromial angle, the coracohumeral interval, the glenoid inclination, the acromion index (AI), and the shoulder critical angle (CSA), have been proposed to impact the development of rotator cuff tears and glenohumeral osteoarthritis. This study aimed to develop a deep learning tool to automate the measurement of CSA and AI on anteroposterior shoulder radiographs. Methods: We used MURA Dataset v1.1, which is a large publicly available musculoskeletal radiograph dataset from the Stanford University School of Medicine. All normal shoulder anteroposterior radiographs were extracted and annotated by an experienced orthopedic surgeon. The annotated images were divided into train (1004), validation (174), and test (93) sets. We use pytorch_segmentation_models for U-Net implementation and PyTorch framework for training the model. The test set was used for final evaluation of the model. Results: The mean absolute error for CSA and AI between human-performed and machine-performed measurements on the test set with 93 images was 1.68° (95% CI 1.406°-1.979°) and 0.03 (95% CI 0.02 - 0.03), respectively. Conclusions: A deep learning model can precisely and accurately measure CSA and AI in shoulder anteroposterior radiographs. A tool of this nature makes large-scale research projects feasible and holds promise as a clinical application if integrated with a radiology software program.

The Turtle Neck Sign: Identification of Severe Retracted Distal Biceps Tendon Rupture.

BACKGROUND: Chronic tendon retraction subsequent to distal biceps tendon rupture significantly increases repair difficulty and potential for tendon grafting. Biceps tendons that appear short or absent with magnetic resonance imaging (MRI) or that cannot be readily identified at surgery may erroneously be classified as irreparable. These apparent "absent" biceps tendons may actually be retracted and curled up inside the muscle, visually resembling the head-neck of a turtle retracted inside its shell (the "turtle neck sign"). When located, these tendons could be unfolded and repaired primarily. This type of tendon retraction seems to be associated with high-degree ruptures and larcertus fibrosus tears. PURPOSE: To test the hypothesis that tendon retractions with a turtle neck sign on MRI are more associated with high-degree ruptures and larcertus fibrosus tears versus tendon tears with simple linear retraction. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Retracted distal biceps tendon ruptures on sagittal MRI were categorized as linear retraction or curled-up (turtle neck) retraction. Retraction length, injury severity, and lacertus fibrosus tears were analyzed. RESULTS: The authors retrospectively analyzed the patient records of 85 consecutive traumatic distal biceps tendon ruptures from 2003 to 2019; the final study cohort was 37 patients. Injury-to-surgery timing was as follows: <3 weeks, 43% (16 cases); 3 weeks to 3 months, 32% (12 cases); and >3 months, 24% (9 cases). Overall, 19 patients had linear retraction <7 cm (mean, 3.3 ± 1.9 cm) and 18 patients had a turtle neck retraction ≥7 cm (mean, 9.1 ± 1.6 cm). The injury-to-surgery time (median [± interquartile range]) was 27 days (±90 days) in the linear retraction group and 23 days (±65 days) in the turtle neck retraction group. The turtle neck retraction group had a significantly higher occurrence of abnormal hook test findings, complete distal biceps tendon rupture, and lacertus fibrosus tears compared with the linear retraction group (100% vs 58%, 100% vs 68%, and 100% vs 37%, respectively; P ≤ .02). However, significant repairability differences were not found. CONCLUSION: Highly retracted distal biceps turtle neck sign tendon ruptures occur frequently in association with high-degree ruptures and lacertus fibrosus tears. The presence of a turtle neck retraction did not affect reparability. Surgeons should be aware of this curled-up retraction to avoid mistaking it for an absent tendon or a muscle-tendon disruption.

Deep Learning for Radiographic Measurement of Femoral Component Subsidence Following Total Hip Arthroplasty.

Femoral component subsidence following total hip arthroplasty (THA) is a worrisome radiographic finding. This study developed and evaluated a deep learning tool to automatically quantify femoral component subsidence between two serial anteroposterior (AP) hip radiographs. The authors' institutional arthroplasty registry was used to retrospectively identify patients who underwent primary THA from 2000 to 2020. A deep learning dynamic U-Net model was trained to automatically segment femur, implant, and magnification markers on a dataset of 500 randomly selected AP hip radiographs from 386 patients with polished tapered cemented femoral stems. An image processing algorithm was then developed to measure subsidence by automatically annotating reference points on the femur and implant, calibrating that with respect to magnification markers. Algorithm and manual subsidence measurements by two independent orthopedic surgeon reviewers in 135 randomly selected patients were compared. The mean, median, and SD of measurement discrepancy between the automatic and manual measurements were 0.6, 0.3, and 0.7 mm, respectively, and did not demonstrate a systematic tendency between human and machine. Automatic and manual measurements were strongly correlated and showed no evidence of significant differences. In contrast to the manual approach, the deep learning tool needs no user input to perform subsidence measurements. Keywords: Total Hip Arthroplasty, Femoral Component Subsidence, Artificial Intelligence, Deep Learning, Semantic Segmentation, Hip, Joints Supplemental material is available for this article. © RSNA, 2022.

Scapholunate ligament 360° procedure.

AIMS: The purpose was to evaluate early clinical, patient-reported, and radiological outcomes of the scapholunate ligament 360° tenodesis (SL 360) technique for treatment of scapholunate (SL) instability. METHODS: We studied the results of nine patients (eight males and one female with a mean age of 44.7 years (26 to 55)) who underwent the SL 360 procedure for reducible SL instability between January 2016 and June 2019, and who were identified from retrospective review of electronic medical records. Final follow-up of any kind was a mean of 33.7 months (12.0 to 51.3). Clinical, radiological, and patient-reported outcome data included visual analogue scale (VAS) for pain, Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH), Mayo Wrist Score (MWS), and Patient-Rated Wrist Examination (PRWE). Means were analyzed using paired t-test. RESULTS: Before surgery, patients with SL instability were significantly impaired with respect to wrist flexion, extension, and grip strength (mean flexion, 51° (20° to 85°) vs 73° (45° to 90°); mean extension, 46° (15° to 70°) vs 66° (45° to 80°); mean grip strength, 25 kg (20 to 31) vs 50 kg (35 to 68) compared to the unaffected side). The mean SL gap (4.9 mm (2.3 to 7.3) vs 2.1 mm (1.6 to 2.9)) and mean SL angle (71° (59° to 105°) vs 50° (38° to 64°) were also significantly greater in the affected wrist. At final follow-up, there was mean improvement regarding clinical, radiological, and functional outcomes comparing preoperative to final postoperative values for the VAS for pain scale, QuickDASH, MWS, PRWE, and SL gap and SL angle. CONCLUSION: In our small series, the SL 360 procedure for reducible SL instability has favourable early clinical, patient-reported, and radiological outcomes at a mean of 33.7 months (12.0 to 51.3). The suture tape and tendon construct confers robust stability, permitting earlier mobilization without the inherent disadvantages of Kirschner wire stabilization. Cite this article: Bone Joint J 2021;103-B(5):939-945.

Effect of incremental increase in radial neck height on coronoid and capitellar contact pressures.

BACKGROUND: Over-lengthening of the radial neck has been shown to affect ulnohumeral kinematics and has been proposed to affect radiocapitellar pressures. We hypothesized that an incremental increase in radial neck height increases the capitellar contact pressure and reduces the coronoid contact pressure. Knowledge of the effects of over-lengthening is clinically important in preventing pain and degenerative changes due to overstuffing. METHODS: Six human cadaveric elbows were prepared on a custom-designed apparatus simulating muscle loads and passive flexion from 0° to 90° under gravity valgus torque while measuring joint contact pressures in this biomechanical study. Each elbow was tested sequentially starting with the intact specimen followed by insertion of a radial head prosthesis with 0, +2, and +4 mm of radial neck height, respectively. RESULTS: Capitellar mean contact pressures significantly increased after insertion of +2 and +4 mm radial head prostheses (p < 0.03). The capitellar mean contact pressure with a 0 mm radial head prosthesis was 97 KPa. Insertion of +2 mm and +4 mm radial heads increased mean contact pressures to 391 KPa (p = 0.001) and 619 KPa (p = 0.001), respectively, with 90° of elbow flexion. DISCUSSION: Increasing radial prosthesis height by 2 mm significantly increases capitellar contact pressures and reduces coronoid contact pressures.