Predicting osteoarthritic knee rehabilitation outcome by using a prediction model developed by data mining techniques

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

BACKGROUND

Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA.

METHODS

The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models.

RESULTS

The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001).

CONCLUSIONS

The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.

Transcutaneous electrostimulation for osteoarthritis of the knee

BACKGROUND

Osteoarthritis is the most common form of joint disease and the leading cause of pain and physical disability in the elderly. Transcutaneous electrical nerve stimulation (TENS), interferential current stimulation and pulsed electrostimulation are used widely to control both acute and chronic pain arising from several conditions, but some policy makers regard efficacy evidence as insufficient.

OBJECTIVES

To compare transcutaneous electrostimulation with sham or no specific intervention in terms of effects on pain and withdrawals due to adverse events in patients with knee osteoarthritis.

SEARCH STRATEGY

We updated the search in CENTRAL, MEDLINE, EMBASE, CINAHL and PEDro up to 5 August 2008, checked conference proceedings and reference lists, and contacted authors.

SELECTION CRITERIA

Randomised or quasi-randomised controlled trials that compared transcutaneously applied electrostimulation with a sham intervention or no intervention in patients with osteoarthritis of the knee.

DATA COLLECTION AND ANALYSIS

We extracted data using standardised forms and contacted investigators to obtain missing outcome information. Main outcomes were pain and withdrawals or dropouts due to adverse events. We calculated standardised mean differences (SMDs) for pain and relative risks for safety outcomes and used inverse-variance random-effects meta-analysis. The analysis of pain was based on predicted estimates from meta-regression using the standard error as explanatory variable.

MAIN RESULTS

In this update we identified 14 additional trials resulting in the inclusion of 18 small trials in 813 patients. Eleven trials used TENS, four interferential current stimulation, one both TENS and interferential current stimulation, and two pulsed electrostimulation. The methodological quality and the quality of reporting was poor and a high degree of heterogeneity among the trials (I(2) = 80%) was revealed. The funnel plot for pain was asymmetrical (P < 0.001). The predicted SMD of pain intensity in trials as large as the largest trial was -0.07 (95% CI -0.46 to 0.32), corresponding to a difference in pain scores between electrostimulation and control of 0.2 cm on a 10 cm visual analogue scale. There was little evidence that SMDs differed on the type of electrostimulation (P = 0.94). The relative risk of being withdrawn or dropping out due to adverse events was 0.97 (95% CI 0.2 to 6.0).

AUTHORS' CONCLUSIONS

In this update, we could not confirm that transcutaneous electrostimulation is effective for pain relief. The current systematic review is inconclusive, hampered by the inclusion of only small trials of questionable quality. Appropriately designed trials of adequate power are warranted.

Using machine learning algorithms to guide rehabilitation planning for home care clients

Background

Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms – Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) – to guide rehabilitation planning for home care clients.

Methods

This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP.

Results

The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP.

Conclusion

Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

The K-nearest neighbor algorithm predicted rehabilitation potential better than current Clinical Assessment Protocol

OBJECTIVE

There may be great potential for using computer-modeling techniques and machine-learning algorithms in clinical decision making, if these can be shown to produce results superior to clinical protocols currently in use. We aim to explore the potential to use an automatic, data-driven, machine-learning algorithm in clinical decision making.

STUDY DESIGN AND SETTING

Using a database containing comprehensive health assessment information (the interRAI-HC) on home care clients (N=24,724) from eight community-care regions in Ontario, Canada, we compare the performance of the K-nearest neighbor (KNN) algorithm and a Clinical Assessment Protocol (the "ADLCAP") currently used to predict rehabilitation potential. For our purposes, we define a patient as having rehabilitation potential if the patient had functional improvement or remained at home over a follow-up period of approximately 1 year.

RESULTS

The KNN algorithm has a lower false positive rate in all but one of the eight regions in the sample, and lower false negative rates in all regions. Compared using likelihood ratio statistics, KNN is uniformly more informative than the ADLCAP.

CONCLUSION

This article illustrates the potential for a machine-learning algorithm to enhance clinical decision making.