Amrit Goyal, W. Macaulay, J. Geller, Wenbao Wang, Jonathon Nyce
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Multivariate regression model was tested by using an independent cohort for validation. Results: Correlation analyses found four variables that significantly influence short term MOMHR outcome. These include comorbidities (C, P = 0.0001), preoperative SF-12 mental (SM, P = 0.0004), BMI (P = 0.0006), and gender (G, P = 0.0454). By multivariate analysis, the subsequent regression model was obtained with an R2 value of 0.3816: Outcome = G*4.72 ‒ BMI*0.70 ‒ C*0.11 + SM*0.31 + 87.44. The average predicted outcome using this equation did not differ significantly from the observed WOMAC physical function outcome at a minimum of 1 year postoperatively. Conclusion: To the best of our knowledge, this study is the first reported multivariate analysis of factors affecting MOMHR and confirms the correlation of some of the previously proposed factors such as gender, BMI, comorbidities, and preoperative function. The multivariate regression equation can be used to predict the short-term outcome of MOMHR.","PeriodicalId":34195,"journal":{"name":"Journal of Orthopedics Traumatology and Rehabilitation","volume":"33 1","pages":"75 - 80"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting short-term outcome of Metal-on-Metal Hip Resurfacing (MOMHR): A multivariate analysis using 14 independent variables\",\"authors\":\"Amrit Goyal, W. Macaulay, J. Geller, Wenbao Wang, Jonathon Nyce\",\"doi\":\"10.4103/jotr.jotr_18_21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Introduction: The aim of this study was to research factors affecting the short-term outcome of metal-on-metal hip resurfacing (MOMHR) and develop a multivariate regression model that may predict outcome. 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By multivariate analysis, the subsequent regression model was obtained with an R2 value of 0.3816: Outcome = G*4.72 ‒ BMI*0.70 ‒ C*0.11 + SM*0.31 + 87.44. The average predicted outcome using this equation did not differ significantly from the observed WOMAC physical function outcome at a minimum of 1 year postoperatively. Conclusion: To the best of our knowledge, this study is the first reported multivariate analysis of factors affecting MOMHR and confirms the correlation of some of the previously proposed factors such as gender, BMI, comorbidities, and preoperative function. 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引用次数: 0
摘要
本研究的目的是研究影响金属对金属髋关节表面置换(MOMHR)短期预后的因素,并建立一个可能预测预后的多元回归模型。材料和方法:这是一项前瞻性研究,154例患者接受了MOMHR,随访至少1年。14个独立变量(年龄、性别、诊断、合并症、体重指数(BMI)、手术前西安大略和麦克马斯特大学骨关节炎(WOMAC)物理成分/僵硬度(S)/疼痛(P)、短形式12 (SF-12)物理(SP)、SF-12精神(SM)、髋臼和股骨成分大小、手术时间和估计失血量)采用相关和多变量回归分析进行分析。采用独立队列对多元回归模型进行验证。结果:相关分析发现四个变量显著影响短期MOMHR结果。这些包括合并症(C, P = 0.0001),术前SF-12精神(SM, P = 0.0004), BMI (P = 0.0006)和性别(G, P = 0.0454)。经多因素分析,得到后续回归模型,R2值为0.3816:Outcome = G*4.72 - BMI*0.70 - C*0.11 + SM*0.31 + 87.44。使用该方程的平均预测结果与术后至少1年观察到的WOMAC身体功能结果没有显著差异。结论:据我们所知,本研究首次报道了影响MOMHR因素的多变量分析,并证实了先前提出的一些因素(如性别、BMI、合并症和术前功能)之间的相关性。多元回归方程可用于预测MOMHR的短期预后。
Predicting short-term outcome of Metal-on-Metal Hip Resurfacing (MOMHR): A multivariate analysis using 14 independent variables
Introduction: The aim of this study was to research factors affecting the short-term outcome of metal-on-metal hip resurfacing (MOMHR) and develop a multivariate regression model that may predict outcome. Materials and Methods: This was a prospective study of 154 patients who underwent MOMHR and were followed for a minimum of 1 year. Fourteen independent variables (age, gender, diagnosis, co-morbidities, body mass index (BMI), pr-operative Western Ontario and McMaster Universities Osteoarthritis (WOMAC) physical component/stiffness (S)/pain (P), short form 12 (SF-12) physical (SP), SF-12 mental (SM), acetabular and femoral component sizes, operative time, and estimated blood loss) were analyzed using correlation and multivariate regression analyses. Multivariate regression model was tested by using an independent cohort for validation. Results: Correlation analyses found four variables that significantly influence short term MOMHR outcome. These include comorbidities (C, P = 0.0001), preoperative SF-12 mental (SM, P = 0.0004), BMI (P = 0.0006), and gender (G, P = 0.0454). By multivariate analysis, the subsequent regression model was obtained with an R2 value of 0.3816: Outcome = G*4.72 ‒ BMI*0.70 ‒ C*0.11 + SM*0.31 + 87.44. The average predicted outcome using this equation did not differ significantly from the observed WOMAC physical function outcome at a minimum of 1 year postoperatively. Conclusion: To the best of our knowledge, this study is the first reported multivariate analysis of factors affecting MOMHR and confirms the correlation of some of the previously proposed factors such as gender, BMI, comorbidities, and preoperative function. The multivariate regression equation can be used to predict the short-term outcome of MOMHR.