Causes of Failure after Total Knee Arthroplasty in Osteoarthritis Patients 55 Years of Age or Younger

Kim, Young Ho; Lee, Song; Ko, Dong Oh; Seo, Bong Soo; Jung, Woo Shik; Chang, Byung Kwon

doi:10.5792/ksrr.2014.26.1.13

Cited by 59 publications

(51 citation statements)

References 18 publications

(42 reference statements)

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“…The DL-based pipeline is based on a DenseNet-121 with the following parameters: 16 filters in initial layer, growth rate of 32, pooling block configuration of [6,12,24,16], 4 bottleneck layers, 2 classes. The same architecture was used for the radiograph and MRI pipelines, but for the MRI pipeline, we modified the convolutional layers, batch normalization layers, pooling layers, and leaky rectified linear unit (ReLU) layers to allow for 3D image input 39 .…”

Section: Pipeline Architecturementioning

confidence: 99%

“…The procedure is also imperfect: only 66% of patients report their knees feeling "normal," and 33% of patients report some degree of pain post-implant 14 . Furthermore, the implant can fail under some circumstances: periprosthetic joint infection and wound complications can be observed, and implant instability can occur due to aseptic loosening, malpositioning of the implant, and wear of joint components 15,16 . It is thus much preferable to prolong the good health of the knee, particularly in patients where OA has not advanced to the most severe stages, thereby delaying TKR as long as possible.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

Tolpadi

Lee

Pedoia

et al. 2020

Sci Rep

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Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is available: total knee replacement (TKR). Though a generally successful procedure, only 2/3 of patients who undergo the procedure report their knees feeling "normal" post-operation, and complications can arise that require revision. This necessitates a model to identify a population at higher risk of TKR, particularly at less advanced stages of OA, such that appropriate treatments can be implemented that slow OA progression and delay TKR. Here, we present a deep learning pipeline that leverages MRI images and clinical and demographic information to predict TKR with AUC 0.834 ± 0.036 (p < 0.05). Most notably, the pipeline predicts TKR with AUC 0.943 ± 0.057 (p < 0.05) for patients without OA. Furthermore, we develop occlusion maps for case-control pairs in test data and compare regions used by the model in both, thereby identifying TKR imaging biomarkers. As such, this work takes strides towards a pipeline with clinical utility, and the biomarkers identified further our understanding of OA progression and eventual TKR onset. otherwise would, thereby reducing time spent pursuing nonsurgical alternatives with minimal probability of success while dealing with serious pain. Beyond this, if the model were to draw from medical images of the knee, it could identify anatomic regions most correlated with a TKR prediction. To this point, few studies have been conducted in this space, and those that have primarily investigate the importance of cartilage volume loss, subchondral bone defects, and bone marrow lesions [17][18][19] . An identification of more such biomarkers for TKR, however, could greatly improve understanding of both OA and TKR, and ultimately guide treatment strategies.Predictive modeling of TKR, however, has a limited history, particularly with models that use medical images. A few studies have leveraged random forest regression, Cochran-Armitage tests for trend, and t-tests to identify demographic, general health, and physical examination measurements that most strongly correlate with TKR or total joint arthroplasty (TJA) 20, 21 . Others have taken these efforts further, using techniques such as multiple regression and multivariate risk prediction models to predict TKR outright 22, 23 . To our knowledge, only one group has developed a predictive model of TKR that accepts image inputs, attaining performance that surpasses that of models using only clinical and demographic information 24 . Notably, past TKR predictive models largely measure performance by evaluating the area under the receiver operating characteristic (ROC) curve, which plots true positive rate against false positive rate 25 . However, in most datasets used in this space, the number of patients who eventually undergo TKR is dramatically higher among those who have advanced OA as opposed to those with n...

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Section: Pipeline Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

Tolpadi

Lee

Pedoia

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Projections for 2030 estimate a 7-fold increase in TKA, while 67 million Americans will be afflicted by OA [4]. Chronic conventional treatments with anti-inflammatory agents, especially in seniors, are marred by gastrointestinal and renal side effects while surgical interventions may be complicated by deep-vein thromboses, joint stiffness, and muscle atrophy and importantly by prosthesis failure due to infections, wear, and loosening especially in younger age groups [3,5,6]. Moreover, many senior patients may not be ideal candidates for surgery due to cardiovascular conditions [4] while in younger age groups, a higher risk of revision, especially for aseptic failure, is evident [6].…”

Section: Introductionmentioning

confidence: 99%

Avoidance of total knee arthroplasty in early osteoarthritis of the knee with intra-articular implantation of autologous activated peripheral blood stem cells versus hyaluronic acid: A randomized controlled trial with differential effects of growth factor addition

et al. 2017

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“…Instability is the cause of a poor clinical outcome that means second operations are required in the short term, as discussed by Song et al 19 , with the mean revision of the first implant instabilities after 3.5 years of survival. With respect to painful prostheses of no apparent cause appearing in all the published series as one of the causes of arthroplasty failure, explaining the true expectations of this type of surgery properly to patients could lead to an improvement and reduce its frequency 18 .…”

Section: Discussionmentioning

confidence: 99%

Observational Study of Total Knee Arthroplasty in Aseptic Revision Surgery: Clinical Results

Oliver¹,

Jaldin

Camprubí

et al. 2020

Orthopaedic Surgery

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Objective To review the long‐term clinical results after revision surgery and the relationship between the different clinical variables involved with a failed total knee arthroplasty (TKA) and its evolution to provide a better understanding of the current treatment methods. Methods The present study involved 89 subjects with a failed knee arthroplasty that ended up requiring revision surgery and component replacement between 2011 and 2015. The study included patients with pain remaining after TKA and indication from the knee unit surgeon to review the implant, without presenting with thromboembolic or neurological changes that could bias the results. The demographic data, surgical information, type of implant, and causes of failure were analyzed. The patients subjected to replacement surgery were specifically asked to fill out clinical and satisfaction questionnaires (Lysholm and KOOS). The mean follow‐up was 5.6 years (range, 3–11 years) and the analysis was divided into early revision (<5 years) and late revision (>5 years). The R statistical package version 3.2.5 for Windows was used, with significance less than 0.05 Cohort observational study. Results The results indicated that implant revisions accounted for 5.57% of total primary implants, with a mean survival of 6 years for primary prosthesis failure. The mean revision surgery result on the Lysholm knee scoring scale was 68.73 out of 100 points. A better score was obtained for revisions undertaken on TKA with over 5 years' survival and there were no significant differences in terms of the type of implant used. The causes of TKA failure were aseptic loosening (77.38%), instability (9.52%), and painful prosthesis (13.10%). The results were statistically significant when isolated revisions were performed on one component. Rating worse on most of the questionnaire subscales. Conclusion The clinical results were better in primary implant replacements with at least 5 years' survival. The replacement of only one of the components (tibial or femoral) provided worse clinical results than total replacement.

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Causes of Failure after Total Knee Arthroplasty in Osteoarthritis Patients 55 Years of Age or Younger

Cited by 59 publications

References 18 publications

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

Avoidance of total knee arthroplasty in early osteoarthritis of the knee with intra-articular implantation of autologous activated peripheral blood stem cells versus hyaluronic acid: A randomized controlled trial with differential effects of growth factor addition

Observational Study of Total Knee Arthroplasty in Aseptic Revision Surgery: Clinical Results

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