NDLI: Analysis of risk factors and construction of nomogram model for arthroscopic single-row rivet repair

Content Provider	Springer Nature : BioMed Central
Author	Fan, Hao Ouyang, YuLong Chen, ShuiLin Zhao, DeFei Sun, GuiCai
Abstract	Background The factors influencing the clinical outcome of arthroscopic rotator cuff repair are not fully understood. Purpose To explore the factors related to the postoperative outcome of arthroscopic single-row rivet rotator cuff repair in patients with rotator cuff injury and to construct the related nomogram risk prediction model. Methods 207 patients with rotator cuff injury who underwent arthroscopic single-row rivet rotator cuff repair were reviewed. The differences of preoperative and postoperative Visual Analogue Score (VAS) scores and University of California, Los Angeles (UCLA) scores were analyzed and compared. The postoperative UCLA score of 29 points was taken as the critical point, and the patients were divided into good recovery group and poor recovery group, and binary logstic regression analysis was performed. According to the results of multivariate logistic regression analysis, the correlation nomogram model was constructed, and the calibration chart was used, AUC, C-index. The accuracy, discrimination and clinical value of the prediction model were evaluated by decision curve analysis. Finally, internal validation is performed using self-random sampling. Results The mean follow-up time was 29.92 ± 17.20 months. There were significant differences in VAS score and UCLA score between preoperative and final follow-up (p < 0.05); multivariate regression analysis showed: Combined frozen shoulder (OR = 3.890, 95% CI: 1.544 ∼ 9.800), massive rotator cuff tear (OR = 3.809, 95%CI: 1.218 ∼ 11.908), More rivets number (OR = 2.118, 95%CI: 1.386 ∼ 3.237), lower preoperative UCLA score (OR = 0.831, 95%CI: 0.704–0.981) were adverse factors for the postoperative effect of arthroscopic rotator cuff repair. Use these factors to build a nomogram. The nomogram showed good discriminant and predictive power, with AUC of 0.849 and C-index of 0.900 (95% CI: 0.845 ∼ 0.955), and the corrected C index was as high as 0.836 in internal validation. Decision curve analysis also showed that the nomogram could be used clinically when intervention was performed at a threshold of 2%∼91%. Conclusion Combined frozen shoulders, massive rotator cuff tears, and increased number of rivets during surgery were all factors associated with poor outcome after arthroscopic rotator cuff repair, while higher preoperative UCLA scores were factors associated with good outcome after arthroscopic rotator cuff repair. This study provides clinicians with a new and relatively accurate nomogram model.
Related Links	https://bmcmusculoskeletdisord.biomedcentral.com/counter/pdf/10.1186/s12891-024-07831-1.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14712474
DOI	10.1186/s12891-024-07831-1
Journal	BMC Musculoskeletal Disorders
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-10-02
Access Restriction	Open
Subject Keyword	Orthopedics Rehabilitation Rheumatology Sports Medicine Internal Medicine Epidemiology Rotator cuff injury Arthroscopy Forecasting model Influencing factors Nomogram
Content Type	Text
Resource Type	Article
Subject	Orthopedics and Sports Medicine Rheumatology
Journal Impact Factor	2.2/2023
5-Year Journal Impact Factor	2.6/2023

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