Decreased Instability in High-Risk (Hip-Spine 2B) Patients After Modifications of Surgical Planning and Technique.

BACKGROUND: Patients undergoing primary total hip arthroplasty (THA) who have spinal deformity and a stiff spine are the highest-risk group for instability. Despite the increasing use of dual-mobility cups and large femoral heads, dislocation remains a major complication after THA. Preoperative planning becomes a critical aspect of ensuring precise component positioning within a safe zone. The purpose of this study was to investigate dislocation rates over a 9-year period. METHODS: A retrospective review of 4,731 THAs performed by 3 orthopaedic surgeons between January 2014 and March 2023 was performed. Spinopelvic measurements were conducted to determine the hip-spine classification group for each patient. Only patients classified as 2B (pelvic incidence-lumbar lordosis > 10° and Δsacral slope < 10°) were eligible. Both absolute and relative dislocation frequencies were then analyzed using time-series analysis techniques and Fisher's exact tests. RESULTS: A total of 281 hip-spine 2B patients undergoing primary THA were eligible for analysis (57% women; mean age, range: 66 years, 23 to 87; mean body mass index, range: 28, 16 to 45). The overall dislocation rate was 4.3%. Use of femoral head sizes ≥ 40 mm increased from 4% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001), while the use of dual-mobility cups decreased from 100% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001). Acetabular component planning was changed from the supine plane to the standing plane in February 2020. Those changes in surgical practice were notably correlated with a significant decrease in dislocation rates from 6.8% in 2014 to 2019 to 1.5% in 2020 to 2023 (P = .03). CONCLUSIONS: Our study demonstrates that the introduction of advanced preoperative THA planning to the standing plane, coupled with precise intraoperative technology for implant placement, can significantly reduce the risk of instability in high-risk THA patients. Notably, we observed a significant decrease in dislocation rates, which aligned with the shift in surgical practice. LEVEL OF EVIDENCE: IV.

No Difference in Limb Alignment Between Kinematic and Mechanical Alignment Robotic-Assisted Total Knee Arthroplasty.

BACKGROUND: Individualized alignment techniques have gained major interest in an effort to increase satisfaction among total knee arthroplasty patients. This study aimed to compare postoperative alignment between kinematic alignment (KA) and mechanical alignment (MA) and assess whether KA significantly deviates from the principle of aligning the limb as close to neutral alignment as possible. METHODS: There were 234 patients who underwent robotic-assisted total knee arthroplasty using an unrestricted KA and a strict MA technique (KA: 145, MA: 89). The lateral distal femoral angle, medial proximal tibia angle, and the resultant arithmetic hip-knee-ankle angle (aHKA) were measured. The aHKA < 0 indicated varus alignment, while the aHKA > 0 indicated valgus knee alignment. The primary outcome was the frequency of cases that resulted in an aHKA of ± 4° of neutral (0°), as assessed on full-leg standing radiographs obtained at 6 weeks postoperatively. The secondary outcome was the change in coronal plane alignment of the knee classification type from preoperative to postoperative between the MA and KA groups. RESULTS: The mean preoperative aHKA was similar between the 2 groups (P = .19). The KA group had a mean postoperative aHKA of -1.4 ± 2.4°, while the MA group had a mean postoperative aHKA of -0.5 ± 2.1°. No significant difference in limb alignment was identified between KA and MA cases that resulted in hip-knee-ankle angle of ± 4° being neutral (91.7 versus 96.6%, P = .14). There were 97.2% of cases in the KA group that fell within the ± 5° range. The MA group was associated with a significantly higher rate of coronal plane alignment of the knee classification type change from preoperatively to postoperatively (P < .001). CONCLUSIONS: Kinematic alignment achieved similar postoperative aHKA compared to MA, and thus did not significantly deviate from the principle of aligning the limb as close to neutral alignment as possible. Surgeons should feel comfortable starting to introduce individualized alignment techniques. Without being restricted by boundaries, postoperative alignment will be within 5 degrees of neutral 97% of the time.

Changing practice to a new-generation triple-taper collared femoral component reduces periprosthetic fracture rates after primary total hip arthroplasty.

Aims: Periprosthetic femoral fracture (PPF) is a major complication following total hip arthroplasty (THA). Uncemented femoral components are widely preferred in primary THA, but are associated with higher PPF risk than cemented components. Collared components have reduced PPF rates following uncemented primary THA compared to collarless components, while maintaining similar prosthetic designs. The purpose of this study was to analyze PPF rate between collarless and collared component designs in a consecutive cohort of posterior approach THAs performed by two high-volume surgeons. Methods: This retrospective series included 1,888 uncemented primary THAs using the posterior approach performed by two surgeons (PKS, JMV) from January 2016 to December 2022. Both surgeons switched from collarless to collared components in mid-2020, which was the only change in surgical practice. Data related to component design, PPF rate, and requirement for revision surgery were collected. A total of 1,123 patients (59.5%) received a collarless femoral component and 765 (40.5%) received a collared component. PPFs were identified using medical records and radiological imaging. Fracture rates between collared and collarless components were analyzed. Power analysis confirmed 80% power of the sample to detect a significant difference in PPF rates, and a Fisher's exact test was performed to determine an association between collared and collarless component use on PPF rates. Results: Overall, 17 PPFs occurred (0.9%). There were 16 fractures out of 1,123 collarless femoral components (1.42%) and one fracture out of 765 collared components (0.13%; p = 0.002). The majority of fractures (n = 14; 82.4%) occurred within 90 days of primary THA. There were ten reoperations for PPF with collarless components (0.89%) and one reoperation with a collared component (0.13%; p = 0.034). Conclusion: Collared femoral components were associated with significant decreases in PPF rate and reoperation rate for PPF compared to collarless components in uncemented primary THA. Future studies should investigate whether new-generation collared components reduce PPF rates with longer-term follow-up.

Variability of the femoral mechanical-anatomical axis angle and its implications in primary and revision total knee arthroplasty.

Aims: Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods: Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results: The algorithm measured 1,078 radiographs at a rate of 12.6 s/image (2,156 unique measurements in 3.8 hours). There was no significant difference or bias between reader and algorithm measurements for the FMAA (p = 0.130 to 0.563). The FMAA was 6.3° (SD 1.0°; 25% outside range of 5.0° (SD 2.0°)) using definition one and 4.6° (SD 1.3°; 13% outside range of 5.0° (SD 2.0°)) using definition two. Differences between males and females were observed using definition two (males more valgus; p < 0.001). Conclusion: We developed a rapid and accurate DL tool to quantify the FMAA. Considerable variation with different measurement approaches for the FMAA supports that patient-specific anatomy and surgeon-dependent technique must be accounted for when correcting for the FMAA using an intramedullary guide. The angle between the mechanical and anatomical axes of the femur fell outside the range of 5.0° (SD 2.0°) for nearly a quarter of patients.

Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty.

BACKGROUND: The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is important to minimize the risk of periprosthetic femur fracture (PFF). We utilized a deep-learning algorithm to automate femoral radiographic parameters and determined which automated parameters were associated with early PFF. METHODS: Radiographs from a publicly available database and from patients undergoing primary cementless THA at a high-volume institution (2016 to 2020) were obtained. A U-Net algorithm was trained to segment femoral landmarks for bone morphology parameter automation. Automated parameters were compared against that of a fellowship-trained surgeon and compared in an independent cohort of 100 patients who underwent THA (50 with early PFF and 50 controls matched by femoral component, age, sex, body mass index, and surgical approach). RESULTS: On the independent cohort, the algorithm generated 1,710 unique measurements for 95 images (5% lesser trochanter identification failure) in 22 minutes. Medullary canal width, femoral cortex width, canal flare index, morphological cortical index, canal bone ratio, and canal calcar ratio had good-to-excellent correlation with surgeon measurements (Pearson's correlation coefficient: 0.76 to 0.96). Canal calcar ratios (0.43 ± 0.08 versus 0.40 ± 0.07) and canal bone ratios (0.39 ± 0.06 versus 0.36 ± 0.06) were higher (P < .05) in the PFF cohort when comparing the automated parameters. CONCLUSIONS: Deep-learning automated parameters demonstrated differences in patients who had and did not have early PFF after cementless primary THA. This algorithm has the potential to complement and improve patient-specific PFF risk-prediction tools.

High degree of alignment precision associated with total knee arthroplasty performed using a surgical robot or handheld navigation.

PURPOSE: The purpose of this study was to compare the precision of bony resections during total knee arthroplasty (TKA) performed using different computer-assisted technologies. METHODS: Patients who underwent a primary TKA using an imageless accelerometer-based handheld navigation system (KneeAlign2®, OrthAlign Inc.) or computed tomography-based large-console surgical robot (Mako®, Stryker Corp.) from 2017 to 2020 were retrospectively reviewed. Templated alignment targets and demographic data were collected. Coronal plane alignment of the femoral and tibial components and tibial slope were measured on postoperative radiographs. Patients with excessive flexion or rotation preventing accurate measurement were excluded. RESULTS: A total of 240 patients who underwent TKA using either a handheld (n = 120) or robotic (n = 120) system were included. There were no statistically significant differences in age, sex, and BMI between groups. A small but statistically significant difference in the precision of the distal femoral resection was observed between the handheld and robotic cohorts (1.5° vs. 1.1° difference between templated and measured alignments, p = 0.024), though this is likely clinically insignificant. There were no significant differences in the precision of the tibial resection between the handheld and robotic groups (coronal plane 0.9° vs. 1.0°, n.s.; sagittal plane 1.2° vs. 1.1°, n.s.). There were no significant differences in the rate of overall precision between cohorts (n.s.). CONCLUSIONS: A high degree of component alignment precision was observed for both imageless handheld navigation and CT-based robotic cohorts. Surgeons considering options for computer-assisted TKA should take other important factors, including surgical principles, templating software, ligament balancing, intraoperative adjustability, equipment logistics, and cost, into account. LEVEL OF EVIDENCE: III.

Patients Undergoing Primary, Cementless TKA had Similar Pain, Opioid Utilization, and Functional Outcomes Compared to Matched Patients With Cemented Fixation.

BACKGROUND: Despite renewed interest in cementless fixation of total knee implants, many surgeons have anecdotal concerns about slower recovery and higher early pain scores. We sought to analyze 90-day opioid utilizations, inhospital pain scores, and patient-reported outcome measures (PROMs) in patients undergoing primary cemented versus cementless total knee arthroplasty (TKA). METHODS: We retrospectively identified a cohort of opioid naïve patients undergoing primary TKA for osteoarthritis. There were 186 patients who had cementless TKAs matched 1:6 with 1,116 who received a cemented TKAs based on age (±6 years), body mass index (BMI) (±5), and sex. We compared inhospital pain scores, 90-day opioid utilizations in morphine milligram equivalents (MMEs), and early postoperative PROMs. RESULTS: The cemented and cementless cohorts had similar lowest (0.09 versus 0.08), highest (7.36 versus 7.34), and average (3.26 versus 3.27) pain scores using numeric rating scale (P > .05). They received similar inhospital (90 versus 102, P = .176), discharge (315 versus 315, P = .483), and total (687 versus 720, P = .547) MMEs. They had similar average inpatient hourly opioid consumption (2.5 versus 2.5 MMEs/hour, P = .965). Average refills 90 days postoperatively were similar in both cohorts (1.5 versus 1.4 refills, P = .893). Also, preoperative, 6-week, 3-month, delta 6-week, and delta 3-month PROMs scores were similar between cemented and cementless cohorts (P > .05) CONCLUSION: This matched study demonstrated similar in-hospital pain scores and opioid utilization, total MMEs prescribed within 90 days, and PROMs at 6 weeks and 3 months postoperatively between cemented and cementless TKAs. LEVEL OF EVIDENCE: III, retrospective cohort study.

An Interpretable Machine Learning Model for Predicting 10-Year Total Hip Arthroplasty Risk.

BACKGROUND: As the demand for total hip arthroplasty (THA) rises, a predictive model for THA risk may aid patients and clinicians in augmenting shared decision-making. We aimed to develop and validate a model predicting THA within 10 years in patients using demographic, clinical, and deep learning (DL)-automated radiographic measurements. METHODS: Patients enrolled in the osteoarthritis initiative were included. DL algorithms measuring osteoarthritis- and dysplasia-relevant parameters on baseline pelvis radiographs were developed. Demographic, clinical, and radiographic measurement variables were then used to train generalized additive models to predict THA within 10 years from baseline. A total of 4,796 patients were included [9,592 hips; 58% female; 230 THAs (2.4%)]. Model performance using 1) baseline demographic and clinical variables 2) radiographic variables, and 3) all variables was compared. RESULTS: Using 110 demographic and clinical variables, the model had a baseline area under the receiver operating curve (AUROC) of 0.68 and area under the precision recall curve (AUPRC) of 0.08. Using 26 DL-automated hip measurements, the AUROC was 0.77 and AUPRC was 0.22. Combining all variables, the model improved to an AUROC of 0.81 and AUPRC of 0.28. Three of the top five predictive features in the combined model were radiographic variables, including minimum joint space, along with hip pain and analgesic use. Partial dependency plots revealed predictive discontinuities for radiographic measurements consistent with literature thresholds of osteoarthritis progression and hip dysplasia. CONCLUSION: A machine learning model predicting 10-year THA performed more accurately with DL radiographic measurements. The model weighted predictive variables in concordance with clinical THA pathology assessments.

Complication Rate After Primary Total Hip Arthroplasty Using the Posterior Approach and Enabling Technology: A Consecutive Series of 2,888 Hips.

BACKGROUND: Total hip arthroplasty (THA) is a safe and effective procedure; however, complications such as dislocation, fracture, and infection still occur. It is still unclear whether the dislocation rate via the posterior approach (PA) is better, equal, or worse than the direct anterior approach. Our aim was to report the primary THA dislocation rate via the PA using enabling technology in a large consecutive series of patients. METHODS: A retrospective cohort of 2,888 primary THAs were reviewed at a single, high-volume, academic institution from January 2018 to September 2021. All patients underwent a THA by 4 fellowship-trained orthopaedic surgeons through the PA with enabling technology. Overall dislocation and readmission rates within 90 days and up to 3 years were analyzed. RESULTS: Of the 2,888 procedures, a total of 39 patients had complications related to the surgery during the 3-year follow-up period. There were 10 patients (0.35%) who experienced a dislocation, with half undergoing surgical revision. Of the 39 patients who experienced complications, 37 (1.3%) were readmitted and 2 underwent revision during their hospital stay. Postoperative periprosthetic fractures were the most common cause for readmission and reoperation at a rate of 0.52% and 0.52%, respectively. CONCLUSION: The dislocation rate of 0.35% is one of the lowest reported rates via the PA at a mean follow up of 2.1 years and is comparable to previously published rates using alternate approaches. Using contemporary THA with enabling technology, the PA is a reliable approach with respect to dislocation and complication rates after primary THA.

Deep Learning Phenotype Automation and Cohort Analyses of 1,946 Knees Using the Coronal Plane Alignment of the Knee Classification.

BACKGROUND: The Coronal Plane Alignment of the Knee (CPAK) classification allows for knee phenotyping which can be used in preoperative planning prior to total knee arthroplasty. We used deep learning (DL) to automate knee phenotyping and analyzed CPAK distributions in a large patient cohort. METHODS: Patients who had full-limb radiographs from a large arthritis database were retrospectively included. A DL algorithm was developed to automate CPAK knee alignment parameters including the lateral distal femoral, medial proximal tibia, hip-knee-ankle, and joint line obliquity angles. The algorithm was validated against a fellowship-trained arthroplasty surgeon. After applying the algorithm in a large patient cohort (n = 1,946 knees), the distribution of CPAK was compared across patient sex and baseline Kellgren-Lawrence (KL) scores. RESULTS: There was no significant difference in the CPAK angles (n = 140, P = .66-.98, inter-class correlation coefficient = 0.89-0.91) or phenotype classifications made by the algorithm and surgeon (P = .96). The deep learning algorithm measured the entire cohort (n = 1,946 knees, mean age 61 years [range, 46 to 80 years], 51% women) in < 5 hours. Women had more valgus CPAK phenotypes than men (P < .05). Patients who had higher KL grades at baseline (2 to 4) were more varus using the CPAK classification compared to lower KL grades (0 to 1) (P < .05). CONCLUSION: We applied an accurate, automated DL algorithm on a large patient cohort to determine knee phenotypes, helping to validate and strengthen the CPAK classification system. Analyses revealed that sex-specific and major bone loss adjustments may need to be accounted for when using this system.

Leg-Length Discrepancy Variability on Standard Anteroposterior Pelvis Radiographs: An Analysis Using Deep Learning Measurements.

BACKGROUND: Leg-length discrepancy (LLD) is a critical factor in component selection and placement for total hip arthroplasty. However, LLD radiographic measurements are subject to variation based on the femoral/pelvic landmarks chosen. This study leveraged deep learning (DL) to automate LLD measurements on pelvis radiographs and compared LLD based on several anatomically distinct landmarks. METHODS: Patients who had baseline anteroposterior pelvis radiographs from the Osteoarthritis Initiative were included. A DL algorithm was created to identify LLD-relevant landmarks (ie, teardrop (TD), obturator foramen, ischial tuberosity, greater and lesser trochanters) and measure LLD accurately using six landmark combinations. The algorithm was then applied to automate LLD measurements in the entire cohort of patients. Interclass correlation coefficients (ICC) were calculated to assess agreement between different LLD methods. RESULTS: The DL algorithm measurements were first validated in an independent cohort for all six LLD methods (ICC = 0.73-0.98). Images from 3,689 patients (22,134 LLD measurements) were measured in 133 minutes. When using the TD and lesser trochanter landmarks as the standard LLD method, only measuring LLD using the TD and greater trochanter conferred acceptable agreement (ICC = 0.72). When comparing all six LLD methods for agreement, no combination had an ICC>0.90. Only two (13%) combinations had an ICC>0.75 and eight (53%) combinations had a poor ICC (<0.50). CONCLUSION: We leveraged DL to automate LLD measurements in a large patient cohort and found considerable variation in LLD based on the pelvic/femoral landmark selection. This emphasizes the need for the standardization of landmarks for both research and surgical planning.

Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population-level analysis using deep learning.

PURPOSE: To (1) develop a deep-learning (DL) algorithm capable of producing limb-length and knee-alignment measurements, and (2) determine the association between limb-length discrepancy (LLD), coronal-plane alignment, osteoarthritis (OA) severity, and patient-reported knee pain. METHODS: A multicenter, prospective patient cohort from the Osteoarthritis Initiative between 2004 and 2015 with full-limb standing radiographs at 12 month follow-up was included. A convolutional neural network was developed to automate measurements of the hip-knee-ankle (HKA) angle, femur, and tibia lengths, and LLD. At 12 month follow-up, patients reported their frequency of knee pain since enrollment and current level of knee pain. RESULTS: A total of 1011 patients (2022 knees, 52.3% female) with an average age of 61.2 ± 9.0 years were included. The algorithm performed 12,312 measurements in 5.4 h. ICC values of HKA and LLD ranged between 0.87 and 1.00 when compared against trained radiologist measurements. Knees producing pain most days of the month were significantly more varus (mean HKA:- 3.9° ± 2.8°) or valgus (mean HKA:2.8° ± 2.3°) compared to knees that did not produce any pain (p < 0.05). In varus knees, those producing pain on most days were part of the shorter limb compared to nonpainful knees (p < 0.05). Baseline Kellgren-Lawrence grade was significantly associated with HKA magnitude, LLD, and pain frequency at 12 month follow-up (p < 0.05 all). CONCLUSION: A higher frequency of knee pain was associated with more severe coronal plane deformity, with valgus deviation being one degree less than varus on average, suggesting that the knee tolerates less valgus deformation before symptoms become more consistent. Knee pain frequency was also associated with greater LLD and baseline KL grade, suggesting an association between radiographically apparent joint degeneration and pain frequency. LEVEL OF EVIDENCE: IV case series.

Validating the use of 3D biplanar radiography versus CT when measuring femoral anteversion after total hip arthroplasty : a comparative study.

AIMS: Although CT is considered the benchmark to measure femoral version, 3D biplanar radiography (hipEOS) has recently emerged as a possible alternative with reduced exposure to ionizing radiation and shorter examination time. The aim of our study was to evaluate femoral stem version in postoperative total hip arthroplasty (THA) patients and compare the accuracy of hipEOS to CT. We hypothesize that there will be no significant difference in calculated femoral stem version measurements between the two imaging methods. METHODS: In this study, 45 patients who underwent THA between February 2016 and February 2020 and had both a postoperative CT and EOS scan were included for evaluation. A fellowship-trained musculoskeletal radiologist and radiological technician measured femoral version for CT and 3D EOS, respectively. Comparison of values for each imaging modality were assessed for statistical significance. RESULTS: Comparison of the mean postoperative femoral stem version measurements between CT and 3D hipEOS showed no significant difference (p = 0.862). In addition, the two version measurements were strongly correlated (r = 0.95; p < 0.001), and the mean paired difference in postoperative femoral version for CT scan and 3D biplanar radiography was -0.09° (95% confidence interval -1.09 to 0.91). Only three stem measurements (6.7%) were considered outliers with a > 5° difference. CONCLUSION: Our study supports the use of low-dose biplanar radiography for the postoperative assessment of femoral stem version after THA, demonstrating high correlation with CT. We found no significant difference for postoperative femoral version when comparing CT to 3D EOS. We believe 3D EOS is a reliable option to measure postoperative femoral version given its advantages of lower radiation dosage and shorter examination time.Cite this article: Bone Joint J 2022;104-B(11):1196-1201.

Comparison of tibial alignment parameters based on clinically relevant anatomical landmarks : a deep learning radiological analysis.

AIMS: Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. METHODS: Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli. RESULTS: A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34o (SD 2.4o) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65o (SD 0.55o) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre. CONCLUSION: The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning.Cite this article: Bone Jt Open 2022;3(10):767-776.

Spinopelvic Hypermobility Corrects After Staged Bilateral Total Hip Arthroplasty.

Background: Spinopelvic hypermobility may be secondary to a stiff osteoarthritic hip with a compliant spine. Purpose: We sought to determine if spinopelvic hypermobility resolves after total hip arthroplasty (THA) and when it resolves in patients with bilateral hip osteoarthritis (OA) undergoing staged bilateral THA. We also sought to analyze the change in spinopelvic parameters before and after the second THA. Methods: We conducted a retrospective review of 2047 THAs that were performed by 2 fellowship-trained arthroplasty surgeons from 2014 to 2018. Patients with preoperative spinopelvic hypermobility undergoing staged bilateral THA were identified. Radiographic spinopelvic parameters, including sacral slope (SS), pelvic incidence (PI), lumbar lordosis (LL), PI-LL mismatch, anterior pelvic plane tilt (APPt), and spinopelvic tilt (SPT), were measured on preoperative, 6-week postoperative, and 1-year postoperative lateral standing and sitting radiographs. Bilateral hip OA was graded using Kellgren-Lawrence criteria. Results: We identified 42 patients with preoperative spinopelvic hypermobility who underwent staged bilateral THA. Mean time (standard deviation) between surgeries was 9.4 months (±10.0). After the first THA, spinopelvic hypermobility resolved in 29% of the patients. After the second THA, it resolved in 67% at 6 weeks, increasing to 98% at 1 year postoperatively. Conclusion: Spinopelvic hypermobility resolves after staged bilateral THA in 98% of the patients, occurring most often only after the second THA. Less than one-third of the patients had resolution after the first THA, suggesting that contralateral hip OA continues to drive hip-driven spinopelvic motion. Acetabular component position targets based on functional pelvic position should incorporate these changes in spinopelvic motion with the understanding that resolution of hypermobility usually occurs after the second THA.

Accuracy and Precision of Acetabular Component Position Does Not Differ Between the Anterior and Posterior Approaches to Total Hip Arthroplasty With Robotic Assistance: A Matched-Pair Analysis.

Background: Deviation from planned component placement with robot-assisted total hip arthroplasty (RA-THA) may differ based on surgical approach. The purpose of this study was to compare radiographic accuracy and precision of acetabular component position using RA-THA with the direct anterior approach (DAA) or posterior approach (PA). Methods: Between 2016 and 2019, 134 PA RA-THA patients were matched to 134 DAA RA-THA patients based on age (±10 years), body mass index (±5 kg/m2), and sex (exact). Acetabular component position was assessed using (1) planned position on preoperative computed tomography, (2) intraoperative position, and (3) position on 6-week postoperative radiographs using the digital Ein Bild Röntgen Analyse system. Results: Accuracy of acetabular component inclination in the PA cohort was lower than that in the DAA cohort (PA: 4.3° ± 2.8° vs DAA: 3.1° ± 2.4°, P = .001). Inclination precision was not statistically different (PA: 3° ± 2.4° vs DAA: 2.5° ± 1.8°, P = .071). Anteversion accuracy was not statistically different (PA: 4.1° ± 3.7° vs DAA: 3.5° ± 2.5°, P = .091). Acetabular component anteversion was more precise with DAA (PA: 4.1° ± 3.7° vs DAA: 2.9° ± 2.0°, P = .001). Radiographic outliers (anteversion or inclination was >10° or <-10° from the planned target) were significantly more prevalent in the PA cohort than in the DAA cohort (12 vs 3, P = .016). Conclusions: The acetabular component can be positioned with excellent precision and accuracy when using RA-THA regardless of approach. Although the DAA resulted in a slight increase in precise placement of cup anteversion and more accurate placement of cup abduction with fewer outliers, these small differences may not be clinically meaningful.

No Effect of Surgical Approach on Discharge Outcomes in Outpatient Total Hip Arthroplasty.

Background: The interest in ambulatory total hip arthroplasty (THA) has increased recently due to a national focus on value-based care and improved rapid recovery protocols. Purpose: We sought to determine if surgical approach had an effect on discharge outcomes in outpatient THA. Methods: We performed a retrospective cohort study examining patients who underwent unilateral THA at a single institution using a standardized perioperative care pathway who were discharged home within 24 hours. In total, we compared 106 patients who underwent THA using the direct anterior approach (ATHA) and 90 patients who underwent THA using the posterior approach (PTHA). Univariate and multivariable analyses were used to compare time to ambulation, length of surgery, readmissions, and 90-day complications. Results:Time to ambulation in the ATHA and PTHA groups was 3.9 hours and 4.1 hours, respectively, and time to discharge was 5.9 hours and 6.0 hours, respectively. Length of surgery was shorter in the ATHA group than in the PTHA group (78 minutes vs 86 minutes, respectively). Complications occurred in 3 patients (3%) in the ATHA group vs 4 patients (4%) in PTHA group. In both groups, early ambulation (within 5 hours) predicted earlier time to discharge. Surgical approach was not associated with time to ambulation or time to discharge on multivariable analysis. Conclusion: In this retrospective study, outpatient THA was feasible in a well-selected population of patients undergoing anterior or posterior approaches. Further study is warranted.

Computer Navigation for Revision Total Hip Arthroplasty Reduces Dislocation Rates.

Purpose: Computer navigation in total hip arthroplasty (THA) offers potential for more accurate placement of acetabular components, avoiding impingement, edge loading, and dislocation, all of which can necessitate revision THA (rTHA). Therefore, the use of computer navigation may be particularly beneficial in patients undergoing rTHA. The purpose of this study was to determine if the use of computer-assisted hip navigation reduces the rate of dislocation in patients undergoing rTHA. Methods: A retrospective review of 72 patients undergoing computer-navigated rTHA between February 2016 and May 2017 was performed. Demographics, indications for revision, type of procedure performed, and incidence of postoperative dislocation were collected for all patients. Clinical follow-up was recorded at 3 months, 1 year and 2 years. Results: All 72 patients (48% female; 52% male) were included for analysis. The mean age was 70.4 ± 11.2 years and mean BMI was 26.4 ± 5.2 kg/m2. 22 of 72 patients (31%) required a rTHA procedure due to instability resulting in dislocation. At 3 months, 1 year, and 2 years, there were no dislocations (0%). There was a significant reduction in dislocation rate after computer-navigated rTHA (0%) relative to that following primary THA in the same patient cohort (31%; p < 0.05). Conclusion: Our study demonstrates a significant reduction in dislocation rate following rTHA with computer navigation. Although the cause of postoperative dislocation is often multifactorial, the use of computer navigation may help to curtail femoral and acetabular malalignment in rTHA. Level of Evidence: Level III: retrospective.

John Charnley Award: Deep Learning Prediction of Hip Joint Center on Standard Pelvis Radiographs.

BACKGROUND: Accurate hip joint center (HJC) determination is critical for preoperative planning, intraoperative execution, clinical outcomes after total hip arthroplasty, and commonly used classification systems in primary and revision hip replacement. However, current methods of preoperative HJC estimation are prone to subjectivity and human error. The purpose of the study was to leverage deep learning (DL) to develop a rapid and objective HJC estimation tool on anteroposterior (AP) pelvis radiographs. METHODS: Radiographs from 3,965 patients (7,930 hips) were included. A DL model workflow was created to detect bony landmarks and estimate HJC based on a pelvic height ratio method. The workflow was utilized to conduct a grid-search for optimal nonspecific, sex-specific, and patient-specific (using contralateral hip) pelvic height ratios on the training/validation cohort (6,344 hips). Algorithm performance was assessed on an independent testing cohort for HJC estimation comparison. RESULTS: The algorithm estimated HJC for the testing cohort at a rate of 0.65 seconds/hip based on features in AP radiographs alone. The model predicted HJC within 5 mm of error for 80% of hips using nonspecific ratios, which increased to 83% with sex-specific and 91% with patient-specific pelvic height ratio models. Mean error decreased utilizing the patient-specific model (3.09 ± 1.69 mm, P < .001). CONCLUSION: Using DL, we developed nonspecific, sex-specific, and patient-specific models capable of estimating native HJC on AP pelvis radiographs. This tool may provide clinical value when considering preoperative component position in patients planned to undergo THA and in reducing the subjective variability in HJC estimation. LEVEL OF EVIDENCE: Diagnostic, level IV.

Does Low Back Pain Improve Following Total Hip Arthroplasty?

BACKGROUND: Frequently, patients indicated for total hip arthroplasty (THA) present with low back pain (LBP) and hip pain. The purpose of this study was to compare patients whose back pain resolved after THA with those where back pain did not resolve and identify how to predict this using spinopelvic parameters. METHODS: We reviewed a series of 500 patients who underwent THA for unilateral hip osteoarthritis by 2 surgeons. Patients underwent biplanar standing and sitting EOS radiographs pre-operatively. Patients with previous spine surgery or femoral neck fracture were excluded. Demographic data was analyzed at baseline. The Oswestry Disability Index (ODI) scores were calculated pre-operatively and at 1 year postoperatively. Spinopelvic parameters included, pelvic incidence and sacral slope (SS) change from standing to sitting. RESULTS: Two hundred and four patients (41%) had documented LBP before THA. The Oswestry Disability Index (ODI) for patients improved from 38.9 ± 17.8 pre-operatively to 17.0 ± 10.6 at 1 year post-operatively (P < .001). At 1- and 2-year follow-up, resolution of back pain occurred in 168 (82.4%) and 187 (91.2%) patients, respectively. Pelvic incidence was not predictive of back pain resolution. All patients whose back pain resolved had a sacral slope change from standing to sitting of >10°, while those patients whose back pain did not resolve had a change of <10°. CONCLUSION: This study demonstrates that symptomatic low back pain (LBP) resolves in 82% of patients after THA. The results of this study may be used to counsel patients on back pain and its resolution following total hip replacement.