Using Machine Learning to Predict Nonachievement of Clinically Significant Outcomes After Rotator Cuff Repair.

Autor:	Alaiti RK; Research, Technology, and Data Science Office, Grupo Superador, São Paulo, Brazil.; Universidade de São Paulo, São Paulo, Brazil., Vallio CS; Health Innovation, Data Science, and MLOps, Semantix, São Paulo, Brazil., Assunção JH; Faculdade de Medicina, Hospital das Clinicas FMUSP, Universidade de São Paulo, São Paulo, Brazil.; DASA, Hospital 9 de Julho, São Paulo, São Paulo, Brazil., de Andrade E Silva FB; Faculdade de Medicina, Hospital das Clinicas FMUSP, Universidade de São Paulo, São Paulo, Brazil., Gracitelli MEC; Faculdade de Medicina, Hospital das Clinicas FMUSP, Universidade de São Paulo, São Paulo, Brazil., Neto AAF; Faculdade de Medicina, Hospital das Clinicas FMUSP, Universidade de São Paulo, São Paulo, Brazil., Malavolta EA; Faculdade de Medicina, Hospital das Clinicas FMUSP, Universidade de São Paulo, São Paulo, Brazil.; Hospital do Coração, São Paulo, Brazil.
Jazyk:	angličtina
Zdroj:	Orthopaedic journal of sports medicine [Orthop J Sports Med] 2023 Oct 19; Vol. 11 (10), pp. 23259671231206180. Date of Electronic Publication: 2023 Oct 19 (Print Publication: 2023).
DOI:	10.1177/23259671231206180
Abstrakt:	Background: Although some evidence suggests that machine learning algorithms may outperform classical statistical methods in prognosis prediction for several orthopaedic surgeries, to our knowledge, no study has yet used machine learning to predict patient-reported outcome measures after rotator cuff repair. Purpose: To determine whether machine learning algorithms using preoperative data can predict the nonachievement of the minimal clinically important difference (MCID) of disability at 2 years after rotator cuff surgical repair with a similar performance to that of other machine learning studies in the orthopaedic surgery literature. Study Design: Case-control study; Level of evidence, 3. Methods: We evaluated 474 patients (n = 500 shoulders) with rotator cuff tears who underwent arthroscopic rotator cuff repair between January 2013 and April 2019. The study outcome was the difference between the preoperative and 24-month postoperative American Shoulder and Elbow Surgeons (ASES) score. A cutoff score was calculated based on the established MCID of 15.2 points to separate success (higher than the cutoff) from failure (lower than the cutoff). Routinely collected imaging, clinical, and demographic data were used to train 8 machine learning algorithms (random forest classifier; light gradient boosting machine [LightGBM]; decision tree classifier; extra trees classifier; logistic regression; extreme gradient boosting [XGBoost]; k -nearest neighbors [KNN] classifier; and CatBoost classifier). We used a random sample of 70% of patients to train the algorithms, and 30% were left for performance assessment, simulating new data. The performance of the models was evaluated with the area under the receiver operating characteristic curve (AUC). Results: The AUCs for all algorithms ranged from 0.58 to 0.68. The random forest classifier and LightGBM presented the highest AUC values (0.68 [95% CI, 0.48-0.79] and 0.67 [95% CI, 0.43-0.75], respectively) of the 8 machine learning algorithms. Most of the machine learning algorithms outperformed logistic regression (AUC, 0.59 [95% CI, 0.48-0.81]); nonetheless, their performance was lower than that of other machine learning studies in the orthopaedic surgery literature. Conclusion: Machine learning algorithms demonstrated some ability to predict the nonachievement of the MCID on the ASES 2 years after rotator cuff repair surgery. Competing Interests: The authors have declared that there are no conflicts of interest in the authorship and publication of this contribution. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto. Ethical approval for this study was obtained from the Clinical Hospital of the Medical School of the University of Sao Paulo, Sao Paulo, Brazil (protocol No. 2.778.930). (© The Author(s) 2023.)
Databáze:	MEDLINE
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