Benefits of CT Scanning for the Management of Knee Arthritis and Arthroplasty

Coronal plane alignment of the knee phenotypes distribution varies significantly as a function of geographic, osteoarthritic and sex-related factors: A systematic review and meta-analysis

PURPOSE

The coronal plane alignment of the knee (CPAK) classification is a nine-phenotype matrix based on limb alignment and joint line obliquity. This study aimed to provide a global overview of CPAK distribution, hypothesising significant geographic, osteoarthritic and sex-related variations.

METHODS

A systematic literature search (Embase, Medline/PubMed and Cochrane Library) following PRISMA guidelines was conducted, utilising the search terms "Coronal Plane Alignment of the Knee" OR "CPAK". Studies considering image modalities other than long-leg radiographs were excluded. A random-effects meta-analysis of proportions was performed, and statistical significance was defined as p < 0.05.

RESULTS

A total of 38 studies comprising 46,966 knees were analysed. The most common phenotypes worldwide were CPAK I (33.1%), II (25.9%) and III (14.4%) in the osteoarthritic population and CPAK II (34.9%), I (21.5%) and III (19.3%) in the healthy population. Among osteoarthritic populations, CPAK type I was predominant in Europe (29.2%), Asia (41.9%) and America (33.6%), type II in Australia (32.6%) and type III in Africa (28.6%). In healthy populations, type II was predominant in Europe (42.8%) and Asia (35.3%), whereas type I was most common in South America (44.8%). Significant regional differences were observed among both osteoarthritic and healthy knees, and between osteoarthritic and healthy knees in individual countries. In Europe, significant sex differences were observed in the distribution of types I (39.1% M; 23.5% F) and III (11.4% M; 24.6% F) in the osteoarthritic population, and in the distribution of types I (26.7% M; 9.4% F), II (43.9% M; 34.4% F) and III (11.3% M; 20.6% F) in the healthy population. In Asia, significant sex differences were found for type III in osteoarthritic knees (6.3% M; 11.4% F).

CONCLUSION

CPAK distribution varies significantly as a function of geographic, osteoarthritic, and sex-related factors. A personalised approach to TKA may be desirable to better accommodate these differences.

LEVEL OF EVIDENCE

Level IV.

Comparing supine CT scanogram and standing long-leg radiograph for postoperative alignment in total knee arthroplasty: a prospective study

PURPOSE

Achieving precise postoperative alignment is critical for the long-term success of total knee arthroplasty (TKA). Long-leg standing radiograph (LLR) at 6 weeks post-op is the gold standard for assessing alignment, but its reliance on weight-bearing and positioning makes it less practical in the early postoperative period. Supine computed tomography scanogram (CTS) offers a potential alternative. This study compares CTS and LLR in patients undergoing TKA with patient-specific valgus correction angles (VCA).

METHODS

A prospective study of 108 knees from 57 patients undergoing primary TKA was conducted. CTS was performed on postoperative day three in a supine non-weight-bearing position, and LLR at six weeks in an upright standing position. Coronal alignment was assessed using hip-knee-ankle angle (HKA) and compared using Bland-Altman plots, paired-samples t-tests, and Cohen's d.

RESULTS

CTS showed 57% of knees were within 3° of neutral alignment, while LLR showed 61%. The mean difference between modalities was 0.52° (SD, 3.56°; p = 0.07). Inter-observer reliability was excellent for both CTS and LLR (all ICCs > 0.9).

CONCLUSION

While CTS provides a useful alternative for early postoperative alignment assessment-particularly in situations where a standing LLR is not feasible-the observed variability suggests that LLR remains the more reliable modality when precise alignment is critical. Surgeons should therefore consider CTS as an adjunct tool for early decision-making or non-ambulatory patients in the immediate postoperative setting.

LEVEL OF EVIDENCE

Level II, Prospective Comparative Study.

Imaging in Periprosthetic Joint Infection Diagnosis: A Comprehensive Review

Various imaging methods assist in diagnosing periprosthetic joint infection (PJI). These include radiological techniques such as radiography, computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US); as well as advanced nuclear medicine techniques including bone scintigraphy (BS), anti-granulocyte antibody imaging (AGS), leukocyte scintigraphy (LS), and fluorodeoxyglucose positron emission tomography (FDG-PET and FDG-PET/CT). Each imaging technique and radiopharmaceutical has been extensively studied, with unique diagnostic accuracy, limitations, and benefits for PJI diagnosis. This review aims to detail and describe the most commonly used imaging techniques and radiopharmaceuticals for evaluating PJI, focusing particularly on knee and hip arthroplasties.

Mechanically aligned total knee arthroplasty does not yield uniform outcomes across all coronal plane alignment of the knee (CPAK) phenotypes

PURPOSE

Patient dissatisfaction rates following total knee arthroplasty (TKA) reported in the literature reach 20%. The optimal coronal alignment is still under debate. The aim of this retrospective study was to compare clinical outcomes in different coronal plane alignment of the knee (CPAK) phenotypes undergoing mechanically aligned (MA) TKA. The hypothesis was that knees with preoperative varus arithmetic hip-knee-ankle angle (aHKA) would achieve inferior clinical outcomes after surgery compared to other aHKA categories. Additionally, another objective was to assess CPAK phenotypes distribution in the study population.

METHODS

A retrospective selection was made of 180 patients who underwent MA TKA from April 2021 to December 2022, with a 1-year follow-up. Coronal knee alignment was classified according to the CPAK classification. Clinical outcome evaluations were measured using the Knee Society Score (KSS), Oxford Knee Score (OKS), Short Form Survey 12 and Forgotten Joint Score (FJS). Differences in clinical outcomes were considered statistically significant with a p value <0 .05.

RESULTS

Patients with varus aHKA achieved significantly inferior outcomes at final follow-up compared to other aHKA categories in KSS pt. 1 (79.7 ± 17.2 vs. 85.6 ± 14.7; p = 0.028), OKS (39.2 ± 9.2 vs. 42.2 ± 7.2; p = 0.019) and FJS (75.4 ± 31.0 vs. 87.4 ± 22.9; p =0 .003). The most common aHKA category was the varus category (39%). The most common CPAK phenotypes were apex distal Types I (23.9%), II (22.8%) and III (13.3%).

CONCLUSION

MA TKA does not yield uniform outcomes across all CPAK phenotypes. Varus aHKA category shows significantly inferior results at final follow-up. The most prevalent CPAK categories are varus aHKA and apex distal JLO, with phenotypes I, II and III being the most common. However, their gender distribution varies significantly.

LEVEL OF EVIDENCE

Level IV.

Machine Learning for Opportunistic Screening for Osteoporosis and Osteopenia Using Knee CT Scans

PURPOSE

To predict whether a patient has osteoporosis/osteopenia using the attenuation of trabecular bone obtained from knee computed tomography (CT) scans.

METHODS

Retrospective analysis of 273 patients who underwent contemporaneous knee CT scans and dual-energy X-ray absorptiometry (DXA) within 1 year. Volumetric segmentation of the trabecular bone of the distal femur, proximal tibia, patella, and proximal fibula was performed to obtain the bone CT attenuation. The data was randomly split into training/validation (78%) and test (22%) datasets and the performance in the test dataset were evaluated. The predictive properties of the CT attenuation of each bone to predict osteoporosis/osteopenia were assessed. Multivariable support vector machines (SVM) and random forest classifiers (RF) were used to predict osteoporosis/osteopenia.

RESULTS

Patients with a mean age (range) of 67.9 (50-87) years, 85% female were evaluated. Seventy-seven (28.2%) of patients had normal bone mineral density (BMD), 140 (51.3%) had osteopenia, and 56 (20.5%) had osteoporosis. The proximal tibia had the best predictive ability of all bones and a CT attenuation threshold of 96.0 Hounsfield Units (HU) had a sensitivity of .791, specificity of .706, and area under the curve (AUC) of .748. The AUC for the SVM with cubic kernel classifier (AUC = .912) was better than the RF classifier (AUC = .683, P < .001) and better than using the CT attenuation threshold of 96.0 HU at the proximal tibia (AUC = .748, P = .025).

CONCLUSIONS

Opportunistic screening for osteoporosis/osteopenia can be performed using knee CT scans. Multivariable machine learning models are more predictive than the CT attenuation of a single bone.

How Does Robotic-Arm Assisted Technology Influence Total Knee Arthroplasty Implant Placement for Surgeons in Fellowship Training?

Implant malalignment during total knee arthroplasty (TKA) may lead to suboptimal postoperative outcomes. Accuracy studies are typically performed with experienced surgeons; however, it is important to study less experienced surgeons when considering teaching hospitals where younger surgeons operate. Therefore, this study assessed whether robotic-arm assisted TKA (RATKA) allowed for more accurate and precise implant position to plan when compared with manual techniques when the surgery is performed by in-training orthopaedic surgical fellows. Two surgeons, currently in their fellowship training and having minimal RATKA experience, performed a total of six manual TKA (MTKA) and six RATKAs on paired cadaver knees. Computed tomography scans were obtained for each knee pre- and postoperatively. These scans were analyzed using a custom autosegmentation and autoregistration process to compare postoperative implant position with the preoperative planned position. Mean system errors and standard deviations were compared between RATKA and MTKA for the femoral component for sagittal, coronal, and axial planes and for the tibial component in the sagittal and coronal planes. A 2-Variance testing was performed using an α = 0.05. Although not statistically significant, RATKA was found to have greater accuracy and precision to plan than MTKA for: femoral axial plane (1.1° ± 1.1° vs. 1.6° ± 1.3°), coronal plane (0.9° ± 0.7° vs. 2.2° ± 1.0°), femoral sagittal plane (1.5° ± 1.3° vs. 3.1° ± 2.1°), tibial coronal plane (0.9° ± 0.5° vs. 1.9° ± 1.3°), and tibial sagittal plane (1.7° ± 2.6° vs. 4.7° ± 4.1°). There were no statistical differences between surgical groups or between the two surgeons performing the cases. With limited RATKA experience, fellows showed increased accuracy and precision to plan for femoral and tibial implant positions. Furthermore, these results were comparable to what has been reported for an experienced surgeon performing RATKA.