Flexion contracture is a common deformity encountered in patients requiring total knee arthroplasty (TKA). Both the soft tissue envelope and articular bones are involved in the knee extension lag. A few studies in the past have assessed the relationship between bone cuts and extension deficit by using goniometers and rulers. Using navigation for TKA enables the accurate measurement of knee flexion contracture and bone cuts. The aim of this study was to try to establish a relationship between extension lag correction and the size of bone cuts made.

One hundred and four continuous TKA were completed by a single consultant using the OrthoPilot® (BBraun, Aesculap) navigation system and Columbus implants. Seventy-four knees had preoperative flexion contracture (including neutral knees) while 30 were in hyperextension. Data was recorded prospectively using the navigation system. These included preoperative flexion and extension angles, actual bone cuts of tibia and femur (both medial and lateral), postoperative correction of flexion and extension angle, size of the prosthesis with thickness of polyethylene and soft tissue release. Of the 74 knees with fixed flexion, 57 had no release and 13 had a posterior release (four had an intermediate release and were excluded from the study).

For knees with fixed flexion (n = 70) there was a significant statistical difference between the pre and post implant extension angle (p < < 0.0001). There was no correlation between the thickness of bone cuts and postoperative extension lag either for the group with no release (p = 0.495) or posterior release (p = 0.516). There was also no correlation between bone cuts and preoperative angles for either type of release (p = 0.348 and p = 0.262). There was a significant difference between the preoperative extension deformity for the two soft tissue releases performed (p = 0.00019), the mean fixed flexion angles being −4.4° and −10.4° for no release and posterior release respectively.

Flexion contracture deformity in TKA can theoretically be solved in two ways: either by extensively releasing the soft tissue or by increasing the extension gap by cutting more bone (logically the distal femur). Appropriate soft tissue management and release in TKA is crucial in balancing the prosthesis in the coronal as well as the lateral plane. This study seems to confirm the supremacy of soft tissue management and release over bone cut resection. Cutting more or less bone could in fact lead to a poorer outcome as this will change the joint line level without having any additional beneficial effect in correcting the flexion contracture. Conversely adequate soft tissue release has corrected the flexion contracture when needed. In conclusion, there was no correlation between bone cut resection and extension lag correction and with large extension deficits, a posterior soft tissue release and osteophytes resection was more important than bone cuts.

Computer assisted total knee arthroplasty (TKA) is still a relatively novel technique. Surgeons wishing to adopt any new practice undergo a learning curve. The learning curve experienced with navigated TKA, its duration and cost in terms of complications, has not been well defined in the literature. Therefore we set out to analyse the learning curve of a newly appointed consultant with no previous experience of navigated TKA by using a surgeon who has completed over 1000 TKAs in over 10 years of experience with this technique as a baseline.

The study used the inexperienced surgeon’s first ever fifty navigated TKAs and the experienced surgeon’s most recent fifty TKAs over the same period in the same theatre using the same CT free navigation system (Orthopilot^®) and prosthesis. Operative time, bone cuts and limb alignment before and after prosthesis implantation were recorded, along with the navigation specific difficulties and complications encountered by the inexperienced surgeon.

There was no statistical difference in the accuracy of postoperative limb alignment in either the coronal (p = 0.33) or sagital (p = 0.35) planes between the novice and experienced surgeon. There was also no difference in the executed bone cut angles (tibial p = 0.79, femoral p = 0.92). The operating time showed a difference between the two surgeons with the novice having a median of 80 mins (inter-quartile range of 20 mins) and the experienced surgeon had a median of 70 mins (inter-quartile range of 20 mins), p = 0.001. However there was a statistically significant reduction in operating time between the inexperienced surgeon’s first twenty and last twenty TKAs (p = 0.001). Comparison of the last 20 TKAs for each surgeon showed no difference in the operative time (medians of 70 mins and 75 mins respectively, p = 0.945). The navigation specific difficulties and complications recorded for the novice navigator were all related to the trackers: one loosening, one tibial tracker placed too proximally, one superficial infection in a tibial tracker wound and one incompletely engaged pin-tracker coupling which brought about the only conversion to manual TKA in this series.

We conclude that in terms of execution and outcome, a beginner using computer assisted TKA can match the results of an experienced navigator from the outset. The only parameter assessed that underwent a clear learning curve was the operative time, which took approximately 20 procedures to approach the same as the experienced surgeon.

Computer assisted total knee arthroplasty (TKA) enables the measurement of the dynamics of the knee both before and after the implant of the prosthesis. Much time has been spent looking at the outcomes of navigated TKA however less time has been invested on understanding how the data collected pre-operatively can inform the surgeon and help the surgical decision making process. The aim of this work was to use navigation as a tool to quantify and classify preoperatively valgus knees.

Between August 2006 and September 2007 a group of 51 patients who demonstrated intra-operative initial neutral or valgus aligned knees underwent navigated TKA using the Columbus knee prosthesis and the Orthopilot^® navigation system (BBraun, Tuttlingen, Germany). Demographic data were recorded, along with the preoperative radiograph appearance and clinical assessment of alignment. During the surgery the approach used and the knee mechanical femorotibial (MFT) angle though the range of flexion were recorded. The knees were then categorised as either “True” valgus or “False” valgus based on whether the MFT angle at 30°, 60° and 90° flexion was still valgus (True) or had gone into varus (False).

Five patients were excluded from the study group as they had incomplete data in knee flexion. Of the remaining 46 patients, 28 were True valgus and 18 were False valgus. For the two groups demographic data were compared. Male to female ratio was 9:19 for the True valgus and 4:14 for the False valgus. The mean age of the True group was 70 years (range 52–85 years) and the False was 69 years (range 53–84 years). For BMI the True group had mean of 31 (range 20–40) and False of 33 (range 26–42). Twenty-five of the 28 True valgus knees showed preoperative evidence of clinical genu valgum deformity and radiologic evidence of predominantly lateral compartment osteoarthritis. Five patients had ipsilateral hip replacements in the past and five had rheumatoid arthritis. Seventeen were operated by lateral parapatellar approach. Eighteen required ilio-tibial band release with additional lateral collateral ligament release in five knees. Six true valgus knees did not require any soft tissue release. Five patients required lateral retinacular release to achieve thumb free patellar tracking. The median operating time for the True valgus group was 80 mins. Ten of the 18 false valgus knees showed evidence of clinical varus deformity and radiological evidence of predominantly medial compartment osteoarthritis. Only one patient had an ipsilateral hip replacement in the past and one had rheumatoid arthritis. All 18 knees underwent TKA by medial parapatellar approach, requiring no additional soft tissue release in 17 knees and a moderate release in one knee. The median operating time for the False valgus group was 60 mins.

True valgus knees had more significant deformities clinically and radiologically, longer surgical time and more incidence of soft tissue release when compared to the False valgus knees. False valgus knees behaved like varus knees clinically, radiologically and intra-operatively and should therefore be treated as such when making surgical choices.

Aim: To evaluate the accuracy of intra-operative point acquisition during navigated hip replacement using an ultrasound transducer probe relative to a percutaneous digitiser stylus (pointer)

To study intra- and inter-observer variability with the use of the ultra-sound transducer and percutaneous digitiser point probes

To assess the learning curve with the use of the ultrasound transducer probe

As part of a larger cadaver study evaluating navigated total hip replacement via the posterior approach, we assessed data relating to acquisition of bony landmarks of the Anterior Pelvic Plane (APP) by four surgeons with an ultrasound transducer and a percutaneous point probe. The surgeons had differing levels of experience with hip surgery in general, and also with surgical navigation per se, but none of them had previously used the ultrasound probe for the specific purpose of landmark acquisition.

Without fixing an absolute positional value for any of the bony landmarks, the points registered for individual landmarks by each surgeon were then studied, looking at the three-dimensional spread of these points relative to each other about the mean value. The data from all four surgeons were analysed, looking at the global dispersion of points acquired by the ultrasound and percutaneous point digitiser probes.

Our results show that with the exception of a few isolated outliers, the ultrasound probe generated values fell within a +/− 10 mm range. For all four surgeons, the global spread of ultrasound-registered points was noted to be less than that acquired by percutaneous point probe acquisition. Of interest was the finding that points registered by individual surgeons using the ultrasound probe tended to be grouped distinctly together but spatially separate from those of the other surgeons; it would appear that each operator was “homing” in on what he perceived to be the bony landmark in question on the projected ultrasound image.

With the percutaneous pointer probe, and with the anterior superior iliac spines as the target, there was closer grouping of points around the mean positional value for the two surgeons who were experienced with its use. However, at the symphysis pubis, the spread of points for these surgeons were not much different from the other two less experienced one, with these points showing a global spread as great as 25 mm.

Regardless of the experience of the surgeon, the use of the ultrasound transducer probe appears to be more accurate than percutaneous pointer probe for acquisition of the bony landmarks that constitute the anterior pelvic plane. The learning curve associated with its use is seemingly short and steep. Its accuracy is limited by the fact that the identification of the bony land marks on the on-screen display is open to interpretation by the individual. Methods to standardise the identification of these landmarks on ultrasound images may help improve its accuracy in the future.

Introduction: A successful total knee replacement (TKR) relies upon effective soft tissue management. Historically, soft tissue balancing has been difficult to assess and quantify intraoperatively. Computer navigation permits us to accurately assess kinematics during surgery. In a previous study we performed a series of varus and valgus stress measurements in extension to devise an algorithm for soft tissue management. In this study we evaluate the effectiveness of this algorithm.

Methods: This prospective study used the Orthopilot® CT-free navigation system during TKR for 57 patients with end-stage arthritis. We collected intraoperative kinematic data for 42 varus knees. Pre- and post-operatively, a varus and valgus stress was applied at maximum extension, recording the mechanical femorotibial (MFT) angle. There were no patellar resurfacings. The following medial releases were performed based upon the kinematics and the algorithm below:

No release–MFT angle not less than −12° with varus stress, greater than 2° with valgus stress, and/or if extension deficit was not greater than 5°.

Results: Pre-operatively, the mean MFT angle was −9.6°varus (−3° to −22°) with varus stress and −0.8°varus (4° to −11°) with valgus stress. Post-operatively, the mean MFT angle was −3.5° varus (0° to −5°) with varus stress, and 2.1° valgus (4° to −1°) with valgus stress.

Using regressional analysis, there was a strong linear correlation between both varus (r=0.871, p< 0.0001) and valgus (r=0.894, p< 0.0001) stresses and the MFT angle.

Post-operatively, the mean MFT angle was maintained within a narrow range (0° to −5° with varus stress, 4° to −1° with valgus stress), with no outliers. There were no extension deficits.

Conclusions: Using computer navigation a quantifiable soft tissue management system was introduced. We evaluated this algorithm, and obtained reproducible results within a narrow range and no outliers. This algorithm may provide an effective soft tissue management plan in TKR.

Introduction: Computer navigation systems allow real time evaluation of knee behaviour intraoperatively. Measurements made by navigation reflect soft tissue balance throughout surgery. We studied three different populations of patients undergoing total knee replacement (TKR) with a CT-free navigation system where the goal was to achieve normal alignment. We compared the initial pathological kinematics in each group with the resultant kinematics after correction.

Method: The Orthopilot® was used during TKR for three groups of patients A (n=71), B(n=60) and C(n=43) all with endstage osteoarthritis. Patients in groups A and B had TKR performed by surgeon 1, and group C by surgeon 2.

Results: Pre-operatively, the mean mechanical femoral axis and the mean mechanical femoro-tibial (MFT) angle were calculated. The mean mechanical femoral axis for group A was −0.5° varus (−6° to 9°), group B was −0.68° varus(−6° to 6°), and for group C was 2.67° valgus (−12° to 10°). P< 0.0001, using Kruskal-Wallis test. Pre-operatively, the mean MFT angle for group A was −3.75° varus(−15° to 17°), group B was −2.98° varus(−17° to 13°), and for group C was 0.16° valgus(−17° to 25°). P=0.003 using Kruskal-Wallis test. These results show that the initial preoperative kinematics are different for the three different populations.

Post-operatively we measured the mean MFT angle in groups A, B and C. In group A, the mean MFT angle was −0.38° varus (−4° to 2°), group B was −0.41° varus(−5° to 2°), and group C was −0.02° varus(−3° to 5°). P=0.7 using the Kruskal-Wallis test. These results show that the post-operative kinematics are similar between the three different populations.

Discussion: Populations A and B preoperatively exhibited a mean varus MFT angle (−0.5° and −0.68° respectively), compared with a mean valgus MFT angle for group C(2.67°), which were statistically significantly different. Although different surgeons operated on the 3 groups (surgeon 1 operated on groups A and B, and surgeon 2 operated on group C), post-operative kinematics were within a narrow range (−0.02° to −0.41°) and not statistically different (p=0.7).

Conclusion: The Orthopilot® results showed that these populations had different initial pathological kinematics. Despite this, and using different operators we obtained similar post-op results within a narrow range. Computer navigation produces reliable, reproducible results independent of population or operator variables.

Introduction: Accurate soft tissue balancing is an essential part of total knee replacement (TKR), but has been difficult to quantify using traditional instrumentation methods. Computer navigation systems allow us to accurately assess intra-operative kinematics, which are affected by soft tissue management. The aims of this study were to evaluate the role of varus and valgus stress measurements and subsequently devise an algorithm for soft tissue management during TKR.

Methods: We used the Orthopilot® CT-free navigation system during TKR for patients with primary end-stage arthritis. This was a prospective study with 71 patients collecting intra-operative kinematic data. 57 knees were varus, 13 valgus, and 1 well aligned.

Pre- and post-operatively, the surgeon applied a varus and valgus stress at maximum extension, recording the mechanical femorotibial (MFT) angle. There were no patellar resurfacings. We compared the kinematics of each varus knee. Based upon the kinematics and the surgeon’s experience the following medial releases were performed as usual and divided into three categories:

No release (limited medial approach).

Results: Pre-operatively, the mean MFT angle was −9.6° (−3° to −22°) with varus stress and −0.8° (4° to −11°) with valgus stress. Post-operatively, the mean MFT angle was −3.7° (−1° to −7°) with varus stress, and 1.1° (4° to −3°) with valgus stress. Using regressional analysis, there was a strong linear correlation between both varus (r=0.742, p< 0.0001) and valgus (r=0.771, p< 0.0001) stresses and the MFT angle.

With the following medial releases, these kinematics were found:

No release – MFT angle not less than −12° with varus stress, greater than 2° with valgus stress, and/or if extension deficit was not greater than 5°.

The results show that post-operatively, the mean MFT angle is maintained within a narrow range (−1° to −7° with varus stress, 4° to −3° with valgus stress). 5/57(9%) patients had a mean MFT angle of 6.4°(0° to 7°) with valgus stress, and were considered to have been over-corrected. There were no extension deficits.

Conclusions: Navigation allows us to quantify soft tissue balancing based upon the initial kinematics with varus and valgus stress testing. From these measurements, an algorithm was developed, which showed that an appropriate release was made in 52/57 (91%) patients, but this may require some adjustment to reduce the number of outlying results.

Introduction: Computer navigated total knee replacement does not require the use of intramedullary alignment rods, and is thus less invasive than traditional methods.

One previous study has suggested that the computer-assisted technique may reduce blood loss in comparison to traditional methods. This study (Kalairajah et al, 2005) used blood volume loss from drainage bottles as a primary outcome measure (n=60). Hidden (internal) blood losses were not accounted for.

Our study uses a more accurate method of assessing blood loss, and the sample size is larger (n=136; 68 standard TKR versus 68 computer assisted TKR).

Methods: 136 TKR patients were included, of which 68 had standard TKR and 68 computer assisted. Patients were matched such that in each group half had BMI in the range 20–30, and half had BMI between 30–40. Patients were also matched for gender. All patients had Tranexamic acid at the start of the procedure.

Total body blood volume was calculated using the formula of Nadler, Hidalgo & Bloch (1962). This was then used, together with haematocrit and volume re-infused or transfused, to calculate true blood loss, as described by Sehat, Evans, and Newman (2004). This method is considered to be more reliable than measuring drain output, as it takes account of “hidden” losses. The navigated and non-navigated groups were compared using Student’s t-test.

Results: The average blood loss was 583ml in the standard TKR group, and 442ml in the computer assisted TKR group. This difference was statistically significant (p=0.003).

Conclusions: A previous study found reduced blood loss when performing total knee replacement using computer navigation, compared with traditional methods. Our study confirmed this finding, using a larger sample size, and a more reliable method of assessing blood loss.

Our study found that overall blood loss was less for both groups, when compared to the findings of Kalairajah Y et al. We suspect that this difference was due to our departmental policy that all patients receive tranexamic acid at the start of joint replacement procedure.

The accuracy of measurement in computer-assisted total knee arthroplasty is dependent on the quality of data acquisition at the start of the procedure; errors in landmark identification could lead to misalignment and therefore poorer longterm outcomes.

Some navigation systems require the surgeon to explicitly identify the femoral epicondyles in order to calculate the trans-epicondylar axis, whereas other systems are able to interpolate the epicondylar location based on a number of points acquired from the distal femoral surface. Significant inter-observer variability in landmark identification has been previously reported in dry bone studies. The purpose of this study was to test the accuracy of identification of the epicondyles during a simulated total knee replacement on a fresh cadaveric specimen.

An unfixed fresh cadaveric left lower limb was used to perform a navigated total knee replacement using the Orthopilot® (B|Braun-Aesculap, Tuttlingen, Germany) image-free navigation system.

Sixteen surgeons attending an advanced navigation training course were invited to take part. A single consultant surgeon performed initial dissection and pin placement, up to the point of landmark acquisition. Each subject was then asked to use a pointer tool to identify the medial and lateral epicondyles, as they would in an operative situation. Data were recorded by the Orthopilot® system, and exported as a 3D array for further analysis.

Initial visualisation with a 3D scatter plot showed that points were evenly distributed within a circular pattern around each epicondyle. The length of a vector between each point on each epicondyle was calculated in turn. The maximum distances between points were 15.6mm for the medial epicondyle, and 19.9mm for the lateral epicondyle.

We then calculated the length and angulation of the trans-epicondylar axis (TEA) for each observer, equivalent to the vector between each pair of points (medial and lateral epicondyle). An average TEA was calculated, and the range and standard deviation of angulation were determined. In the x axis the range was 16.3° (–8.3° to 7.9°, SD 5.1°), in the y axis the range was 18.7° (–8.7° to 10°, SD 5.2°) and in the z axis the range was 20.5° (–10.1° to 10.4°, SD 6.5°). Range of recorded TEA length was 64.5 to 74.9mm (mean 70.6mm, SD 3.3mm).

We conclude that in this simulated operative scenario, surgeons exhibited considerable variability when locating the epicondyles. Range of angulation of the TEA exceeded 16° (SD > 5.1°) in all 3 planes. We cannot recommend the use of a trans-epicondylar axis determined from 2 single points, as a reliable landmark in navigated total knee replacement.

Computer navigated total knee replacement is less invasive than traditional methods, as it avoids the use of intramedullary alignment rods. A previous study (Kalairajah et al, 2005) has shown that computer-assisted techniques may reduce blood loss in comparison to traditional methods. Our study uses a more accurate method of assessing blood loss, and the sample size is larger.

136 TKR patients were selected from a prospectively collected database of all those undergoing arthroplasty at our institution; 68 had standard TKR and 68 had a computer assisted TKR. In each group, half had BMI in the range 20–30, and half had BMI between 30–40. There were an equal number of males and females in each group. All patients received a standardised anaesthetic, and had tranexamic acid at the start of the procedure.

Total body blood volume was calculated from patient height, weight and sex, using the model described by Nadler, Hidalgo & Bloch (1962). This was then used, together with pre- and post-op haematocrit and volume re-infused or transfused, to calculate true blood loss, as described by Sehat, Evans, and Newman (2004). This method is considered to be more reliable than measuring drain output, as it takes account of “hidden” (internal) losses.

The average blood loss was 603ml in the standard TKR group, and 448ml in the computer assisted TKR group. Student’s t-test showed that this difference was statistically significant (p = 0.007). Regression analysis showed no significant difference between obese and non-obese patients, nor a difference between sexes. Blood loss in both groups was lower than in a previous study, which we attribute to our department’s routine use of tranexamic acid.

We conclude that computer-assisted total knee replacement leads to significant reduction in blood loss when compared with traditional techniques. This confirms previous reports.

Total knee replacement (TKR) has become the standard procedure in management of degenerative joint disease with its success depending mainly on two factors: three dimensional alignment and soft tissue balancing. The aim of this work was to develop and validate an algorithm to indicate appropriate medial soft tissue release during TKR for varus knees using initial kinematics quantified via navigation techniques.

Kinematic data was collected intra-operatively for 46 patients with primary end-stage osteoarthritis undergoing TKR surgery using a CT-free navigation system. All patients had preoperative varus knees and medial release was made using the surgeon’s experience. From this data an algorithm was developed to define the medial release based on the pre-operative mechanical femoral-tibial angle with valgus stress;

No release (tibial cut only) when valgus stress > −2/3°. Moderate release (medial aspect of tibia +/− semimembranosous tendon) when valgus stress > −5° and < −2°. Extensive release (proximal) when valgus stress < −5°. If there was a fixed flexion deformity > 5° then a posterior release was performed.

This algorithm was validated on a further set of 35 patients where it was used to determine the medial release based only on the kinematic data. The post-operative varus and valgus stress angles for the two groups were compared and showed good outcomes in terms of distribution and outliers.

The results showed that the algorithm was a suitable tool to indicate the type of release required based on intra-operatively measured pre-implant valgus stress and extension deficit angles. It reduced the percentage of releases made and the results were more appropriate than the decisions made by an experienced surgeon.

Computer technology allows real time evaluation of knee behaviour throughout flexion. These measurements reflect tibial rotation about the femoral condyles, patellar tracking and soft tissue balance throughout surgery. An understanding of intraoperative kinematics allows accurate adjustment of TKR positioning. We studied computer navigation with the femoral component aligned to Whiteside’s line.

We used CT free navigation during TKR for 71 end-stage osteoarthritic patients. Patients demographics: 29 right–42 left; 44 female −27 male; age 70.4 years (+/− 8.4); mean BMI 30.8 (+/− 4.7; 23.2–48.6); Oxford score: 43 +/− 7.7 (28–58). Preoperatively, 57/71 knees were varus knees, 1 well-aligned and 13 valgus; 75% were cruciate retaining and 25% were posterior stabilised knees.

During surgery the frontal femorotibial or Hip-Knee-Ankle (HKA) angle was measured from maximum extension through 30°,60° and 90° of flexion. Measurements of the femoro tibial angles (HKA) in 0°, 30°, 60° and 90° of knee flexion before and after TKR were collected. No patella was replaced. We compared the kinematics of each knee. Femoral component rotation was 2.06° external rotation +/−1.32° (−1°; 5°) referenced from the dorsal condylar axis. Analysis divided the 71 patients into three groups:

When the femoral component was placed between 1° internal rotation and 0° of external rotation (7 patients) HKA tended to flex into valgus.

Performing Total Knee Replacement (TKR) surgery using computer assisted navigation systems results in more reproducibly accurate component alignment. Navigation allows real time evaluation of passive knee behaviour throughout flexion. These kinematic measurements reflect tibial rotation about the femoral condyles, patellar tracking and soft tissue balance throughout surgery. In this study, we aim to study dynamic knee function in navigated and standard instrumentation TKR patients performing a range of everyday activities using gait analysis.

A prospective randomised controlled trial evaluated the functional outcome using gait analysis with 20 patients in each of three groups – Standard, Navigated and Control. The same implant (Scorpio) and navigation system (Strykervision) was used for each patient. The control group were subjects with no history of knee pathology or gait abnormality. Using an 8-camera Vicon motion analysis system set at 120Hz (real-time motion), we assessed the following functional activies: walking, rising from/sitting in chair, ascending/descending stairs. One functional outcome measure we have analysed so far is the maximum flexion angle.

The maximum flexion angle was recorded for each activity in standard, navigated and control groups respectively. ANOVA was performed, with significance set at p< 0.05. Maximum flexion angle during gait was 65.6°, 72.6° (p=0.009) and 73.5° (p=0.74), chair rising/sitting was 82.5°, 92.8° (p=0.01), and 93.5° (p=0.64), stairs ascent/descent was 81.8°, 99° (p< 0.0001), and 113.4° (p< 0.0001).

In terms of dynamic functional outcome, we found that the average maximum flexion angle for the navigated group was greater than for the standard group; moreover, this was similar to the maximum flexion angle for the control group when performing a variety of normal daily activities.

Purpose of the study: Knee prosthesis surgery has reached a high level of reproducibility, providing very satisfactory results in the large majority of patients. There remains however a certain lack of precision concerning this surgical procedure concerning the determination of the hip center. This point is used to establish the mechanical axis of the femur for positioning the prosthesis. Navigation systems can be used to localize this center. We conducted a cadaver study to determine the accuracy and repeatability of this method for determining the center of the hip joint.

Material and methods: A computerized navigation system was applied to seven fresh cadavers with normal hips. We compared the anatomic center of the hip joint with the point determined with the navigation system. We also compared the navigation technique using different navigation techniques: marker fixed on the iliac crest and without marker fixed on the iliac crest. We also determined the accuracy of the result as a function of hip circumduction during acquisition (5°, 8°, 10°).

Results: There was no statistical difference between investigator A (0.66±0.15, max error: 0.99) and B (0.68±0.10, max error: 0.87), p=0.98 (inter or intra-observer) for comparisons between the anatomic center of the hip joint and the point determined by the navigation system. The results were not statistically different between the navigation techniques (with and without a marker fixed on the iliac crest):(mean < 0.71 ± 032, max. error: 1.91) for each hip with the iliac marker (0.66 ± 0.20, max. error max: 0.99) or without the iliac marker (0.61 ± 0.41, max. error: 1.29) for hip 1. Accuracy was better for hip movement at 10° (0.60 ± 0.21, max. error: 0.92) than at 8° (0.81 ± 0.52, max. error: 1.91) or at 5° (0.67 ± 0.46, max. error: 1.91). In addition, without an iliac crest marker, 75% of the errors were less than 1, and 95% less than 1.5.

Discussion: Acquisition of the hip center of rotation using a computerized navigation system with or without use of markers fixed on the iliac crest is remarkably accurate.

Conclusion: New algorithms and control systems should help improve reproducibility above that obtained with the conventional technique.

Purpose of the study: Achieving correct ligament balance for total knee arthroplasty remains a serious challenge, even for the experienced surgeon. Computer-assisted surgery allows real time assessment of the knee joint behavior and gives continuous measures of HKA under stress.

Material and methods: Between January 2003 and November 2004, 25 patients with osteoarthritis of the knee joint underwent computer-assisted surgery for implantation of posterior stabilized total knee prosthesis. The series included 13 right knees and 12 left knees in 8 men and 17 women, mean age 73.6±8.1 years, age range 44–84 years. Body mass index was 29±5.5 (range 21.6–42.7). The IKS function score was 35.8±17 (range 5–70) and the IKS knee score was 51.2±8.5 (range 30–73). Measurements were made for varus and valgus stress of 0–30°. Extensive lateral or medial release was also performed for six knees. The medial parapatellar approach with removal of osteophytes was used for all procedures.

Results: Preoperatively, four patients presented valgus (185.6±4.7, range 182–191°), one correct alignment and 20 presented varus (174±3.45, range 166–178°). Pre-operatively the mean varus stress angle was 5.13±3.44 (range 0–11°), the mean valgus stress angle was 1.5±1.53, range −4 to 4°). At the end of the procedure, the varus stress angle 1.78±1.59 (0–5°) and the valgus stress angle 1.79±1.6 (0–4°). At 45 days, mean flexion was 115±10° (range 60–126°). There was mobilization in two patients, one with a 5° extension deficit and the other with an extension deficit less than 10°.

Discussion: This study demonstrates the usefulness of navigation systems to assess the effect of peripheral release and to limit the extent of release procedures (six of 25 patients). Materializing step by step release of the peripheral structures is helpful in achieving correct release.

Conclusion: This work confirms that extensive release is not always necessary. This type of technique should allow better control and fine tuning of ligament balance and tension.

This work was supported by work on cadaver specimens measuring the step by step effect of ligament release.