Foundation and Deutsche Arthrose-Hilfe e.V. The funding sources had no involvement in any aspect of the study.

No abstract

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Purpose The aim of this study was to describe the native trochlear orientation of non-arthritic knees in three planes and to quantify the relationship between trochlear and distal condylar anatomy across race and sex. Methods Computed tomography scans of 1578 femora were included in this study. The mediolateral position of the trochlear sulcus, the distal trochlear sulcus angle (DTSA) the medial sulcus angle (MSA) and the lateral sulcus angle (LSA) as well as the mechanical lateral distal femoral angle (mLDFA) were measured relative to a standard reference coordinate system. Multiple linear regression analyses were performed to account for potential confounding variables. Results The mediolateral position of the trochlear sulcus had minimal mean deviation of the sagittal femoral plane. The mean DTSA was 86.1° (SD 2.2°). Multilinear regression analysis found mLDFA, sex, and age all influence DTSA (p < 0.05), with mLDFA having by far the greatest influence (r2 = 0.55). The medial facet of the trochlear sulcus was found to be flat proximally and more prominent distally. The lateral facet was relatively uniform throughout the arc. Conclusion In non-arthritic knees, due to a strong positive correlation between the DTSA and the mLDFA, the trochlear sulcus is consistently orientated in the sagittal femoral plane regardless of distal condylar anatomy. Minor deviations from the sagittal plane occur in a lateral direction in the middle part and in a medial direction at the proximal and distal part of the trochlea. These findings have relevance regarding the biomimetic design of total knee implants.

There is a great need for quantitative outcomes reflecting the functional status in patients with knee or hip osteoarthritis (OA) to advance the development and investigation of interventions for OA. The purpose of this study was to determine if gait kinematics specific to the disease—i.e., knee versus hip OA—can be identified using wearable sensors and statistical parametric mapping (SPM) and whether disease-related gait deviations are associated with patient reported outcome measures. 113 participants (N = 29 unilateral knee OA; N = 30 unilateral hip OA; N = 54 age-matched asymptomatic persons) completed gait analysis with wearable sensors and the Knee/Hip Osteoarthritis Outcome Score (KOOS/HOOS). Data were analyzed using SPM. Knee and hip kinematics differed between patients with knee OA and patients with hip OA (up to 14°, p < 0.001 for knee and 8°, p = 0.003 for hip kinematics), and differences from controls were more pronounced in the affected than unaffected leg of patients. The observed deviations in ankle, knee and hip kinematic trajectories from controls were associated with KOOS/HOOS in both groups. Capturing gait kinematics using wearables has a large potential for application as outcome in clinical trials and for monitoring treatment success in patients with knee or hip OA and in large cohorts representing a major advancement in research on musculoskeletal diseases.

Kinematic differences between patients with osteoarthritis (OA) and control participants have been reported to be influenced by gait speed. The purpose of this study was to experimentally detect the effect of walking speed on differences in spatiotemporal parameters and kinematic trajectories between patients with hip OA and age‐matched asymptomatic participants using wearable sensors and statistical parametric mapping (SPM). Twenty‐four patients with severe unilateral hip OA and 48 control participants were included in this study. Patients walked at a self‐selected normal speed and control participants at self‐selected normal and slow speeds. Spatiotemporal parameters and kinematic trajectories were measured with the inertial sensor system Rehagait®. Gait parameters were compared between patients with hip OA and control participants for normal and matched speed using SPM with independent sample t‐tests. At self‐selected normal speed, the patient group walked slower (−0.20 m/s, p < .001) and at lower cadence (−5.0 steps/minute, p < .001) as well as with smaller hip flexion (−7.4°, p < .001) and extension (−4.1°, p = .001), higher knee flexion during terminal stance (+8.0°, p < .001) and higher ankle dorsiflexion and plantarflexion (+7.1°, p < .001). While differences in spatiotemporal parameters and the ankle trajectory disappeared at matched speed, some clinically relevant and statistically significant differences in hip and knee trajectories remained. Most differences in sagittal plane gait kinematics between patients with hip OA and control participants were present for matched speed, and therefore appear to be associated with a disease rather than gait speed. Nevertheless, studies investigating hip kinematics in patients with hip OA should involve trials at matched speeds.

III.

Inertial measurement units (IMUs) are commonly used for gait assessment, yet their potential for quantifying improvements in gait function and patterns after total hip arthroplasty (THA) has not been fully explored. The primary aim of this study was to compare spatiotemporal parameters and sagittal plane kinematic patterns of patients with hip osteoarthritis (OA) before and after THA, and to asymptomatic controls.The secondary aim was to assess the association between dynamic hip range of motion (ROM) during walking and the Hip Osteoarthritis Outcome Scores (HOOS).Twenty-four patients with hip OA and 24 matched asymptomatic controls completed gait analyses using the RehaGait ® sensor system. Patients were evaluated pre-and 1 year postoperatively, controls in a single visit. Differences in kinematic data were analyzed using statistical parametric mapping, and correlations between dynamic hip ROM and HOOS were calculated. Walking speed and stride length significantly increased (+0.08 m/s, p = 0.019; +0.06 m, p = 0.048) after THA but did not reach the level of asymptomatic controls (−0.11 m/s, p = 0.028; −0.14 m, p = 0.001). Preoperative hip and knee kinematics differed significantly from controls.After THA, they improved significantly and did not differ from controls. Dynamic hip flexion-extension ROM correlated positively with all HOOS subscores (r > 0.417; p ≤ 0.001). The change in HOOS symptoms in patients was explained by the combination of baseline HOOS symptoms and change in dynamic hip ROM (r 2 = 0.748) suggesting that the additional information gained with IMU gait analysis helps to complement and objectify patient-reported outcome measures pre-and postoperatively and monitor treatment-related improvements.