Identification of Independent Predictors of Increased 90-Day Complication and Revision Rates Following Total Elbow Arthroplasty.

Journal of shoulder and elbow arthroplasty Pub Date : 2023-01-26 eCollection Date: 2023-01-01 DOI:10.1177/24715492231152146
Bryce F Kunkle, Nicholas A Baxter, Megan E Welsh, Richard J Friedman, Josef K Eichinger
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Abstract

Introduction: Total elbow arthroplasty (TEA) is an increasingly popular surgical option for many debilitating conditions of the elbow. There currently exists a paucity of literature regarding patient and hospital factors that lead to inferior outcomes following TEA. The purpose of this study is to identify independent predictors of increased complication and revision rates following TEA.

Methods: The National Readmissions Database (NRD) was queried from 2011 to 2018 to identify all cases of TEA (n = 8932). Relevant patient demographic factors, comorbidities, and hospital characteristics were identified and run in a univariate binomial logistic regression model. All significant variables were included in a multivariate binomial logistic regression model for data analysis.

Results: Independent predictors of increased complication rates included age, female sex, Medicare and Medicaid payer status, medium bed-sized center, and 18 of 34 medical comorbidities (all P < .05). Independent predictors of increased revision rates included medium bed-sized centers, non-teaching hospital status, chronic pulmonary disease, depression, and pulmonary circulatory disorders (all P < .05).

Conclusion: This study identified several patient and hospital characteristics that are independently associated with both increased complication and revision rates following TEA. This information can aid orthopedic surgeons during shared decision making when considering TEA in patients.

Level of evidence: Level III, retrospective cohort study.

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识别全肘关节置换术后 90 天并发症和翻修率增加的独立预测因素。
简介:全肘关节置换术(TEA)是治疗许多肘关节衰弱疾病的一种日益流行的手术方式。目前,有关导致 TEA 术后效果不佳的患者和医院因素的文献极少。本研究的目的是找出导致 TEA 术后并发症和翻修率增加的独立预测因素:方法:查询了2011年至2018年的国家再入院数据库(NRD),以确定所有TEA病例(n = 8932)。确定了相关的患者人口学因素、合并症和医院特征,并在单变量二项逻辑回归模型中运行。所有重要变量均纳入多变量二项逻辑回归模型进行数据分析:结果:并发症发生率增加的独立预测因素包括年龄、女性性别、医疗保险和医疗补助支付者身份、中等床位规模的中心以及 34 种并发症中的 18 种(均为 P P 结论:该研究确定了患者和医院的几个特征,并对这些特征进行了分析:本研究发现了一些患者和医院的特征,这些特征与TEA术后并发症和翻修率的增加有独立关联。这些信息有助于骨科医生在考虑对患者进行TEA治疗时做出共同决策:III级,回顾性队列研究。
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