Dataset Information

Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study.

ABSTRACT:

Background

Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations among existing classification or prediction models, including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice.

Objective

The aim of this study was to propose a Bayesian network (BN)-based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool, which can provide decision support for health professionals.

Methods

The proposed model's structure was based on a 3-level BN structure and then retrained by the Bayesian Search (BS) learning algorithm. The model's parameters were determined by the expectation-maximization algorithm. The used dataset included backgrounds, the target disease, and predictors. The performance of the model was evaluated based on classification accuracy, area under the curve (AUC), specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV); it was also compared with other well-known classification models. A test was also performed to explore whether physical fitness tests could improve the performance of the proposed model.

Results

A total of 249 elderly people between the ages of 60 and 80 years, living in the Kongjiang community (Shanghai), were recruited from April to September 2007. A total of 157 instances were adopted as the dataset after data preprocessing. The experimental results showed that the results of the proposed model were higher than, or equal to, the mean scores of other classification models: .754 for accuracy, .78 for AUC, .78 for specificity, and .73 for sensitivity. The proposed model provided .45 for PPV and .92 for NPV at the prevalence of 20%. The proposed model also showed a significant improvement when compared with the traditional BN model: 6.3% increase in accuracy (from .709 to .754), 4.0% increase in AUC (from .75 to .78), 6.8% increase in specificity (from .73 to .78), 5.8% increase in sensitivity (from .69 to .73), 15.4% increase in PPV (from .39 to .45), and 2.2% increase in NPV (from .90 to .92). Furthermore, the test results showed that the performance of the proposed model could be largely enhanced through physical fitness tests in 3 evaluation indices: 10.6% increase in accuracy (from .682 to .754), 16.4% increase in AUC (from .67 to .78), and 30.0% increase in specificity (from .60 to .78).

Conclusions

The proposed model presents a promising method to classify people with knee OA when compared with other classification models and the traditional BN model. It could be implemented in clinical practice as a prescreening tool for knee OA, which would not only improve the quality of health care for elderly people but also reduce overall medical costs.

SUBMITTER: Sheng B

PROVIDER: S-EPMC6670282 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study.

Sheng Bo B Huang Liang L Wang Xiangbin X Zhuang Jie J Tang Lihua L Deng Chao C Zhang Yanxin Y

JMIR medical informatics 20190718 3

<h4>Background</h4>Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations among existing classification or prediction models, including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice.<h4>Objective</h4>The aim of this study was to propose a Bayesian network (BN)-based classification model to classify people with knee OA. The proposed model can ...[more]

PMID: 31322132

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Project description:BackgroundExercise-based approaches have been a cornerstone of physiotherapy management of knee osteoarthritis for many years. However, clinical effects are considered small to modest and the need for continued adherence identified as a barrier to clinical efficacy. While exercise-based approaches focus on muscle strengthening, biomechanical research has identified that people with knee osteoarthritis over activate their muscles during functional tasks. Therefore, we aimed to create a new behavioural intervention, which integrated psychologically informed practice with biofeedback training to reduce muscle overactivity, and which was suitable for delivery by a physiotherapist.MethodsThrough literature review, we created a framework linking theory from pain science with emerging biomechanical concepts related to overactivity of the knee muscles. Using recognised behaviour change theory, we then mapped a set of intervention components which were iteratively developed through ongoing testing and consultation with patients and physiotherapists.ResultsThe underlying framework incorporated ideas related to central sensitisation, motor responses to pain and also focused on the idea that increased knee muscle overactivity could result from postural compensation. Building on these ideas, we created an intervention with five components: making sense of pain, general relaxation, postural deconstruction, responding differently to pain and functional muscle retraining. The intervention incorporated a range of animated instructional videos to communicate concepts related to pain and biomechanical theory and also used EMG biofeedback to facilitate visualization of muscle patterns. User feedback was positive with patients describing the intervention as enabling them to "create a new normal" and to be "in control of their own treatment." Furthermore, large reductions in pain were observed from 11 patients who received a prototype version of the intervention.ConclusionWe have created a new intervention for knee osteoarthritis, designed to empower individuals with capability and motivation to change muscle activation patterns and beliefs associated with pain. We refer to this intervention as Cognitive Muscular Therapy. Preliminary feedback and clinical indications are positive, motivating future large-scale trials to understand potential efficacy. It is possible that this new approach could bring about improvements in the pain associated with knee osteoarthritis without the need for continued adherence to muscle strengthening programmes.Trial registrationISRCTN51913166 (Registered 24-02-2020, Retrospectively registered).

Project description:OBJECTIVES:The present study investigated (1) trends in the prevalence and incidence of knee osteoarthritis over a 20-year period (1996-2015); (2) trends in multimorbidity and (3) trends in drug prescriptions. DESIGN:Registry-based study. SETTING:Primary healthcare, Flanders, Belgium. PARTICIPANTS:Data were collected from Intego, a general practice-based morbidity registration network. In the study period between 1996 and 2015, data from 440?140 unique patients were available. OUTCOME MEASURES:Trends in prevalence and incidence rate of knee osteoarthritis were computed using joinpoint regression analysis. The mean disease count was calculated to assess trends in multimorbidity. In addition, the number of drug prescriptions was identified by the Anatomical Therapeutic Chemical Classification code and trends were equally recorded with joinpoint regression. RESULTS:The total age-standardised prevalence of knee osteoarthritis increased from 2.0% in 1996 to 3.6% in 2015. An upward trend was observed with an average annual percentage change (AAPC) of 2.5 (95% CI 2.2 to 2.9). In 2015, the prevalence rates in the 10?year age groups from the 45-54 years age group onwards were 3.1%, 5.6%, 9.0% and 13.9%, to reach 15.0% in people aged 85 years and older. The incidence remained stable with 3.75‰ in 2015 (AAPC=-0.5, 95%?CI -1.4 to 0.5). The mean disease count significantly increased from 1.63 to 2.34 (p<0.001) for incident cases with knee osteoarthritis. Finally, we observed a significantly positive trend in the overall prescription of acetaminophen (AAPC=6.7, 95%?CI 5.6 to 7.7), weak opioids (AAPC=4.0, 95%?CI 0.9 to 7.3) and glucosamine (AAPC=8.6, 95%?CI 2.4 to 15.1). Oral non-steroidal anti-inflammatory drugs were most prescribed, with a prevalence rate of 29.8% in 2015, but remained stable during the study period (AAPC=0.0, 95%?CI -1.1 to 1.1). CONCLUSIONS:Increased prevalence, multimorbidity, and number of drug prescriptions confirm an increased burden of knee osteoarthritis. In future, these trends can be used to prioritise initiatives for improvement in care.

Dataset Information

Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study.

Background

Objective

Methods

Results

Conclusions

Publications

Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study.

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