The ceiling effects of patient reported outcome measures for total knee arthroplasty

Eckhard, Lukas; Munir, Selin; Wood, Donna J.; Talbot, Simon; Brighton, Roger; Walter, William K.; Baré, Jonathan

doi:10.1016/j.otsr.2020.102758

Cited by 45 publications

(35 citation statements)

References 23 publications

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“…Furthermore, our recent study indicated that the HURWA roboticassisted TKA system could significantly improve the accuracies of bone resection angles and levels both in the sawbone model and the sheep model. 4,12 However, the application of these robotic-assisted TKA systems was often limited due to their insufficient versatility, increased operative time and technical complexity.…”

Section: Discussionmentioning

confidence: 99%

“…Surgical planning was performed according to our previous studies and both the operator-based and AI-based 3D models were used for surgical planning. 4,12…”

Section: Surgical Planningmentioning

confidence: 99%

“…2 Despite the development in implant materials and design, thromboembolic prophylaxis, rehabilitation, computer navigation and patient-specific implants, and the use of prophylactic antibiotics, over 20% of patients reported unsatisfaction following TKA. [3][4][5][6] Robotically assisted TKA was thus developed to promote mechanical alignment, accuracy of bone resection, soft tissue protection, and implant survivorship and implant stability. [7][8][9][10] In this connection, our previous studies showed that a novel HURWA robotic-assisted TKA system (BEIJING HURWA-ROBOT Technology Co. Ltd) could enhance the accuracy of bone resection in terms of resection angles and levels in the sawbone and the sheep models.…”

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confidence: 99%

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Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Zhang

Ding

et al. 2021

Robotics Computer Surgery

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Background: Robotic-assisted total knee arthroplasty (TKA) was performed to promote the accuracy of bone resection and mechanical alignment. Among these TKA system procedures, 3D reconstruction of CT data of lower limbs consumes significant manpower. Artificial intelligence (AI) algorithms applying deep learning has been proved efficient in automated identification and visual processing.Methods: CT data of a total of 200 lower limbs scanning were used for AI-based 3D model construction and CT data of 20 lower limbs scanning were utilised for verification. Results:We showed that the performance of an AI-guided 3D reconstruction of CT data of lower limbs for robotic-assisted TKA was similar to that of the operatorbased approach. The time of 3D lower limb model construction using AI was 4.7 min. AI-based 3D models can be used for surgical planning. Conclusion:AI was used for the first time to guide the 3D reconstruction of CT data of lower limbs for facilitating robotic-assisted TKA. Incorporation of AI in 3D model reconstruction before TKA might reduce the workload of radiologists. K E Y W O R D Sartificial intelligence, HURWA robotic-assisted TKA system, robotically TKA assisted system, 3D CT model | INTRODUCTIONTotal knee arthroplasty (TKA) is an effective procedure for cases with end-stage rheumatoid arthritis or osteoarthritis of the knee. 1 The surgery was carried out in over 370,000 and 700,000 patients annually in China and USA, respectively. 2 Despite the development in implant materials and design, thromboembolic prophylaxis, rehabilitation, computer navigation and patient-specific implants, and the use of prophylactic antibiotics, over 20% of patients reported unsatisfaction following TKA. [3][4][5][6] Robotically assisted TKA was thus developed to promote mechanical alignment, accuracy of bone resection, soft tissue protection, and implant survivorship and implant stability. [7][8][9][10] In this connection, our previous studies showed that a novel HURWA robotic-assisted TKA system (BEIJING HURWA-ROBOT Technology Co. Ltd) could enhance the accuracy of bone resection in terms of resection angles and levels in the sawbone and the sheep models. 11,12 The procedures of robotic-assisted TKA system consists of the following steps: preoperative computed tomography (CT) scanning of lower limbs, three-dimensional (3D) reconstruction of CT data of lower limbs, CT-based planning, surgical planning depending on the 3D CT model, and the act of performing robotic-assisted TKA. Among

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Section: Discussionmentioning

confidence: 99%

“…Surgical planning was performed according to our previous studies and both the operator-based and AI-based 3D models were used for surgical planning. 4,12…”

Section: Surgical Planningmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Zhang

Ding

et al. 2021

Robotics Computer Surgery

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“…A variation in outcome results among high scores is poorly measured in the case of the ceiling effect. Furthermore, the scale's reactivity is reduced by the di culty to properly evaluate the patient's progress in a ceiling group [13]. WOMAC is a traditional PROM scale used to assess THA outcomes.…”

Section: Introductionmentioning

confidence: 99%

Total Hip Arthroplasty: Psychometric Validation of the Italian Version of Forgotten Joint Score (FJS-12)

Longo

Salvatore

et al. 2022

Preprint

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Background: Patient’s satisfaction after surgery was traditionally assessed by pre, and post-surgical scores and Patient-Reported Outcome Measures (PROMs) scales. Patients treated by Total hip arthroplasty (THA) usually perform well; therefore, it is useful to have a PROMs’ scale with a low ceiling effect as the Forgotten Joint Score-12 (FJS-12). PROMs have to be validated in the local language to be used. This study aims to perform a psychometric validation of the Italian version of FJS-12 in a group of consecutive patients treated by THA.Methods: Each patient completed both the Italian version of FJS-12 and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) in preoperative evaluation, after two weeks and 1 month, 3 months and 6 months postoperative follow-up. The reliability, internal consistency, test-retest reliability, and measurement error were evaluated.Results: 53 patients were included. Cronbach’s α between 0.6 and 0.9 indicated good internal consistency for the FJS-12. The test-retest reliability was acceptable. The Pearson correlation coefficient between the FJS-12 and WOMAC was 0.238 (P=0.087) at baseline, r = 0.637 (P < 0.001) at 1 month, r = 0.490 (P < 0.001) at 3 months and r = 0.572 (P < 0.001) at 6 months. The ceiling effect was above the acceptable range (15%) for FJS-12 in 1 month (26.4%) and WOMAC in 6 months follow-up (24.5%).Conclusions: An excellent test-retest reliability, a good internal consistency, and a good validity by medium-high correlation with the WOMAC were assessed for FJS-12. However, the responsiveness for the FJS-12 score was not assessed.

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“…Yet, to date, diagnostics for early OA are lacking and its treatment focuses primarily on alleviating symptoms. The development and investigation of interventions such as pharmaceutics is challenging as clinical trials currently rely on patients reported outcome measures (PROMs) that are subjective, fail to discriminate good from very good condition (low ceiling effect) [ 2 ] or are mostly influenced by pain and do not reflect function [ 3 ]. Other potential outcomes are objective but have insufficient sensitivity for detecting short term changes (e.g., medical imaging) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing Site Specificity of Osteoarthritic Gait Kinematics with Wearable Sensors and Their Association with Patient Reported Outcome Measures (PROMs): Knee versus Hip Osteoarthritis

Nüesch

Ismailidis

Koch

et al. 2021

Sensors

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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.

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The ceiling effects of patient reported outcome measures for total knee arthroplasty

Cited by 45 publications

References 23 publications

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Total Hip Arthroplasty: Psychometric Validation of the Italian Version of Forgotten Joint Score (FJS-12)

Assessing Site Specificity of Osteoarthritic Gait Kinematics with Wearable Sensors and Their Association with Patient Reported Outcome Measures (PROMs): Knee versus Hip Osteoarthritis

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