A Predictive Model for Knee Joint Replacement in Older Women

Lewis, Joshua R.; Dhaliwal, Satvinder S.; Zhu, Kun; Prince, Richard

doi:10.1371/journal.pone.0083665

Cited by 6 publications

(19 citation statements)

References 24 publications

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“…Non-imaging variables were screened for among studies and reviews detailing risk factors for knee OA progression and TKR onset [21][22][23][33][34][35][36] . Variables such as KL grade known to be deducible directly from MRI images and radiographs were not considered.…”

Section: Datamentioning

confidence: 99%

“…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 .…”

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: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

Tolpadi

Lee

Pedoia

et al. 2020

Sci Rep

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“…Previous research has found relationships between TKR and sex, BMI, and other risk factors in OAI 12 and targeted analyses of high-risk sub-populations. 43 F I G U R E 7 Mean ROC graphs for 5 years TKR predictive model. The base model includes only the demographic and clinical variables while the added T 2 model has T 2 averages as well.…”

Section: T 2 Values and Tkrmentioning

confidence: 99%

T₂ analysis of the entire osteoarthritis initiative dataset

Razmjoo

Calivá

Lee

et al. 2020

Journal Orthopaedic Research

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While substantial work has been done to understand the relationships between cartilage T 2 relaxation times and osteoarthritis (OA), diagnostic and prognostic abilities of T 2 on a large population yet need to be established. Using 3921 manually annotated 2D multi-slice multi-echo spin-echo magnetic resonance imaging volume, a segmentation model for automatic knee cartilage segmentation was built and evaluated. The optimized model was then used to calculate T 2 values on the entire osteoarthritis initiative (OAI) dataset composed of longitudinal acquisitions of 4796 unique patients, 25 729 magnetic resonance imaging studies in total. Cross-sectional relationships between T 2 values, OA risk factors, radiographic OA, and pain were analyzed in the entire OAI dataset. The performance of T 2 values in predicting the future incidence of radiographic OA as well as total knee replacement (TKR) were also explored. Automatic T 2 values were comparable with manual ones. Significant associations between T 2 relaxation times and demographic and clinical variables were found. Subjects in the highest 25% quartile of tibio-femoral T 2 values had a five times higher risk of radiographic OA incidence 2 years later. Elevation of medial femur T 2 values was significantly associated with TKR after 5 years (coeff = 0.10; P = .036; CI = [0.01,0.20]). Our investigation reinforces the predictive value of T 2 for future incidence OA and TKR. The inclusion of T 2 averages from the automatic segmentation model improved several evaluation metrics when compared to only using demographic and clinical variables. K E Y W O R D S deep learning, early osteoarthritis, imaging biomarkers, T 2 relaxometry 1 | INTRODUCTION Osteoarthritis (OA), a multifactorial disease that causes joint degeneration, affects 27 million U.S. adults 1,2 and often leads to severe disability. 3 The typical treatment for late-stage, symptomatic knee OA is joint replacement, such as total knee replacement (TKR). This surgically invasive remedy offers temporary relief, but replacements often fail after 10 to 15 years-with even shorter life spans in obese individuals. Current interventions for OA mostly target the late stages of the disease, often after the irreversible joint damage has already occurred. Proposed interventions and therapies are generally reactive rather than proactive. The development of proactive therapies is contingent upon detecting the early stages of OA. Preventative strategies such as weight reduction 4,5 and various levels of exercise 6,7 decrease OA progression, and should be rigorously enforced if individual assessment demonstrates a high long-term risk for OA.

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“…Of the 30 studies identified in the literature review, four related to predicting TKR [ [11] , [12] , [13] , [14] ], six related to predicting response/non-response to TKR [ 8 , [15] , [16] , [17] , [18] , [19] ] and the remainder reported associations with TKR (12 studies) [ [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] ] and response/non-response to TKR (eight studies) [ [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] ].…”

Section: Introductionmentioning

confidence: 99%

“…Of the studies predicting TKR, only one reported on the performance of the model (c-statistic = 0.79) [ 11 ], only one addressed missing data [ 14 ], and the statistical methods used to develop the models were not well reported in two of the studies [ 11 , 12 ]. Factors found to be predictive of TKR included measures of knee pain and physical function, use of medication for knee pain, Knee Outcome Survey-Activities of Daily Living Subscale (KOS-ADLS), 36-Item Short Form Health Survey (SF-36) general health subscale score, willingness to undergo TKR, seeing a healthcare provider for arthritis, knee osteoarthritis grade and 12-Item Short Form Survey (SF-12) mental component score [ [11] , [12] , [13] , [14] ].…”

Section: Introductionmentioning

confidence: 99%