Treatment Algorithm for the Resorption of Calcific Tendinitis Using Extracorporeal Shockwave Therapy: A Data Mining Study

Author:

Chou Wen-Yi123,Cheng Jai-Hong4,Lien Yu-Jui5,Huang Tian-Hsiang6,Ho Wen-Hsien57,Chou Paul Pei-Hsi891011

Affiliation:

1. Doctoral Degree Program in Biomedical Engineering, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

2. Department of Orthopedic Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan

3. Department of Leisure and Sport Management, Cheng Shiu University, Kaohsiung, Taiwan

4. Center for Shockwave Medicine and Tissue Engineering, Department of Medical Research, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan

5. Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan

6. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu, Taiwan

7. College of Professional Studies, National Pingtung University of Science and Technology, Pingtung, Taiwan

8. Department of Sports Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

9. Division of Sports Medicine, Department of Orthopaedic Surgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

10. Department of Orthopaedics, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

11. Department of Orthopaedic Surgery, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung, Taiwan

Abstract

Background: Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment. Purpose: To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting. Study Design: Case-control study. Methods: Patients with painful calcified shoulder tendinitis treated by ESWT were enrolled. Seven clinical factors related to shoulder calcification were adopted as the input attributes: sex, age, side affected, symptom duration, pretreatment Constant-Murley score, and calcification size and type. The 5 data mining techniques assessed were multilayer perceptron (neural network), naïve Bayes, sequential minimal optimization, logistic regression, and the J48 decision tree classifier. Results: A total of 248 patients with calcified shoulder tendinitis were enrolled in this study. Shorter symptom duration yielded the highest gain ratio (0.374), followed by smaller calcification size (0.336) and calcification type (0.253). With the J48 decision tree method, the accuracy of 3 input attributes was 89.5% by 10-fold cross-validation, indicating satisfactory accuracy. A treatment algorithm using the J48 decision tree indicated that a symptom duration of ≤10 months was the most positive indicator of calcification resorption, followed by a calcification size of ≤10.82 mm. Conclusion: The J48 decision tree method demonstrated the highest precision and accuracy in the prediction of shoulder calcification resorption by ESWT. A symptom duration of ≤10 months or calcification size of ≤10.82 mm represented the clinical scenarios most likely to show resorption after ESWT.

Funder

the Ministry of Science and Technology in Taiwan

Publisher

SAGE Publications

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