Can a simple iPad app improve C-arm based component position in anterior THA?

AbstractThis systematic review and meta-analysis were conducted to compare the accuracy of component positioning, alignment and balancing techniques employed, patient-reported outcomes, and complications of robotic-arm assisted total knee arthroplasty (RATKA) with manual TKA (mTKA) and the associated learning curve. Searches of PubMed, Medline and Google Scholar were performed in October 2020 using PRISMA guidelines. Search terms included “robotic”, “knee” and “arthroplasty”. The criteria for inclusion were published clinical research articles reporting the learning curve for RATKA and those comparing the component position accuracy, alignment and balancing techniques, functional outcomes, or complications with mTKA. There were 198 articles identified, following full text screening, 16 studies satisfied the inclusion criteria and reported the learning curve of rTKA (n=5), component positioning accuracy (n=6), alignment and balancing techniques (n=7), functional outcomes (n=7), or complications (n=5). Two studies reported the learning curve using CUSUM analysis to establish an inflexion point for proficiency which ranged from 7 to 11 cases and there was no learning curve for component positioning accuracy. The meta-analysis showed a significantly lower difference between planned component position and implanted component position, and the spread was narrower for RATKA compared with the mTKA group (Femur coronal: mean 1.31, 95% confidence interval (CI) 1.08–1.55, p<0.00001; Tibia coronal: mean 1.56, 95% CI 1.32–1.81, p<0.00001). Three studies reported using different alignment and balancing techniques between mTKA and RATKA, two studies used the same for both group and two studies did not state the methods used in their RATKA groups. RATKA resulted in better Knee Society Score compared to mTKA in the short-to-mid-term follow up (95%CI [− 1.23, − 0.51], p=0.004). There was no difference in arthrofibrosis, superficial and deep infection, wound dehiscence, or overall complication rates. RATKA demonstrated improved accuracy of component positioning and patient-reported outcomes. The learning curve of RATKA for operating time was between 7 and 11 cases. Future well-powered studies on RATKAs should report on the knee alignment and balancing techniques utilised to enable better comparisons on which techniques maximise patient outcomes.Level of evidence III.

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What do patients with glaucoma see: a novel iPad app to improve glaucoma patient awareness of visual field loss

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2020-317034 ◽

2020 ◽

pp. bjophthalmol-2020-317034

Author(s):

Meghal Gagrani ◽

Jideofor Ndulue ◽

David Anderson ◽

Sachin Kedar ◽

Vikas Gulati ◽

...

Keyword(s):

Visual Field ◽

Vision Loss ◽

Peripheral Vision ◽

Glaucoma Patient ◽

Visual Field Loss ◽

Sensitivity Threshold ◽

Roc Curve Analysis ◽

The Mean ◽

Ipad App ◽

Field Defects

PurposeGlaucoma patients with peripheral vision loss have in the past subjectively described their field loss as ‘blurred’ or ‘no vision compromise’. We developed an iPad app for patients to self-characterise perception within areas of glaucomatous visual field loss.MethodsTwelve glaucoma patients with visual acuity ≥20/40 in each eye, stable and reliable Humphrey Visual Field (HVF) over 2 years were enrolled. An iPad app (held at 33 cm) allowed subjects to modify ‘blur’ or ‘dimness’ to match their perception of a 2×2 m wall-mounted poster at 1 m distance. Subjects fixated at the centre of the poster (spanning 45° of field from centre). The output was degree of blur/dim: normal, mild and severe noted on the iPad image at the 54 retinal loci tested by the HVF 24-2 and was compared to threshold sensitivity values at these loci. Monocular (Right eye (OD), left eye (OS)) HVF responses were used to calculate an integrated binocular (OU) visual field index (VFI). All three data sets were analysed separately.Results36 HVF and iPad responses from 12 subjects (mean age 71±8.2y) were analysed. The mean VFI was 77% OD, 76% OS, 83% OU. The most common iPad response reported was normal followed by blur. No subject reported dim response. The mean HVF sensitivity threshold was significantly associated with the iPad response at the corresponding retinal loci (For OD, OS and OU, respectively (dB): normal: 23, 25, 27; mild blur: 18, 16, 22; severe blur: 9, 9, 11). On receiver operative characteristic (ROC) curve analysis, the HVF retinal sensitivity cut-off at which subjects reported blur was 23.4 OD, 23 OS and 23.3 OU (dB).ConclusionsGlaucoma subjects self-pictorialised their field defects as blur; never dim or black. Our innovation allows translation of HVF data to quantitatively characterise visual perception in patients with glaucomatous field defects.

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True Kinematic Alignment Is Applicable in 44% of Patients Applying Restrictive Indication Criteria—A Retrospective Analysis of 111 TKA Using Robotic Assistance

Journal of Personalized Medicine ◽

10.3390/jpm11070662 ◽

2021 ◽

Vol 11 (7) ◽

pp. 662

Author(s):

Kim Huber ◽

Bernhard Christen ◽

Sarah Calliess ◽

Tilman Calliess

Keyword(s):

Soft Tissue ◽

Retrospective Analysis ◽

Patient Specific ◽

Kinematic Alignment ◽

Postoperative Phase ◽

Robotic Assistance ◽

Component Position ◽

Joint Stability ◽

Robotic Assisted ◽

Primary Tkas

Introduction: Image-based robotic assistance appears to be a promising tool for individualizing alignment in total knee arthroplasty (TKA). The patient-specific model of the knee enables a preoperative 3D planning of component position. Adjustments to the individual soft-tissue situation can be done intraoperatively. Based on this, we have established a standardized workflow to implement the idea of kinematic alignment (KA) for robotic-assisted TKA. In addition, we have defined limits for its use. If these limits are reached, we switch to a restricted KA (rKA). The aim of the study was to evaluate (1) in what percentage of patients a true KA or an rKA is applicable, (2) whether there were differences regarding knee phenotypes, and (3) what the differences of philosophies in terms of component position, joint stability, and early patient outcome were. Methods: The study included a retrospective analysis of 111 robotic-assisted primary TKAs. Based on preoperative long leg standing radiographs, the patients were categorized into a varus, valgus, or neutral subgroup. Initially, all patients were planned for KA TKA. When the defined safe zone had been exceeded, adjustments to an rKA were made. Intraoperatively, the alignment of the components and joint gaps were recorded by robotic software. Results and conclusion: With our indication for TKA and the defined boundaries, “only” 44% of the patients were suitable for a true KA with no adjustments or soft tissue releases. In the varus group, it was about 70%, whereas it was 0% in the valgus group and 25% in the neutral alignment group. Thus, significant differences with regard to knee morphotypes were evident. In the KA group, a more physiological knee balance reconstructing the trapezoidal flexion gap (+2 mm on average laterally) was seen as well as a closer reconstruction of the surface anatomy and joint line in all dimensions compared to rKA. This resulted in a higher improvement in the collected outcome scores in favor of KA in the very early postoperative phase.

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