Evidence map›Paper›PMID 39358790›Full record

ArticleBMC musculoskeletal disorders2024

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

Hao Fan, YuLong Ouyang, ShuiLin Chen, DeFei Zhao, GuiCai Sun

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Article in BMC musculoskeletal disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hao FanGuangdong Provincial People's Hospital Ganzhou Hospital, Ganzhou, China.
YuLong OuyangNanchang University, Nanchang, Jiangxi province, China.
ShuiLin ChenNanchang University, Nanchang, Jiangxi province, China.
DeFei ZhaoGanzhou People's Hospital, Ganzhou, China.
GuiCai SunDepartment of Sports Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi province, China. ndsfy0740@ncu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methods207 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.

resultsThe 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%.

conclusionCombined 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.

Indexed as

ArthroscopyNomogramsRotator Cuff InjuriesAdultAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedRecovery of FunctionRetrospective StudiesRisk FactorsRotator CuffTreatment OutcomeArthroscopyForecasting modelInfluencing factorsNomogramRotator cuff injury

Identifiers

PMID39358790
PMCPMC11447933

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.