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

Tae Keun Yoo,Deok Won Kim,Soo Beom Choi,Ein Oh,Jee Soo Park,Jose Crispin

doi:10.1371/journal.pone.0148724

Abstract

BackgroundKnee 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.MethodsThe 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.ResultsThe 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).ConclusionsThe 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.

Highlights

Knee osteoarthritis (OA) is the most common joint disease of adults worldwide [1]
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
We provided an artificial neural network (ANN) calculator to predict the knee OA risk

Summary

Introduction

Knee osteoarthritis (OA) is the most common joint disease of adults worldwide [1]. Since there has been no effective disease-modifying therapy, the treatments of advanced radiographic knee OA are limited [2]. A screening questionnaire for symptomatic knee OA was developed based on patients' self-reported symptoms [6] This screening tool showed low specificity, and could not predict radiographic knee OA without pain. A logistic regression (LR) model was developed using well-recognized risk factors such as age, sex, body mass index, occupational factor, and joint injury [7]. Other algorithms have been based on the combination of clinical information including physical examination, blood examination for specific molecules, and genetic data [4], [5], [8] These prediction models were inefficient due to the low performance. 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. We developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA

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