Proximal humerus fractures - epidemiology, comparison of mortality rates after surgical versus non-surgical treatment, and analysis of risk factors based on Medicare registry data

肱骨近端骨折——流行病学、手术治疗与非手术治疗后死亡率比较以及基于医疗保险登记数据的风险因素分析

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Abstract

AIMS: The optimal choice of management for proximal humerus fractures (PHFs) has been increasingly discussed in the literature, and this work aimed to answer the following questions: 1) what are the incidence rates of PHF in the geriatric population in the USA; 2) what is the mortality rate after PHF in the elderly population, specifically for distinct treatment procedures; and 3) what factors influence the mortality rate? METHODS: PHFs occurring between 1 January 2009 and 31 December 2019 were identified from the Medicare physician service records. Incidence rates were determined, mortality rates were calculated, and semiparametric Cox regression was applied, incorporating 23 demographic, clinical, and socioeconomic covariates, to compare the mortality risk between treatments. RESULTS: From 2009 to 2019, the incidence decreased by 11.85% from 300.4 cases/100,000 enrollees to 266.3 cases/100,000 enrollees, although this was not statistically significant (z = -1.47, p = 0.142). In comparison to matched Medicare patients without a PHF, but of the same five-year age group and sex, a mean survival difference of -17.3% was observed. The one-year mortality rate was higher after nonoperative treatment with 16.4% compared to surgical treatment with 9.3% (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.23 to 1.36; p < 0.001) and to shoulder arthroplasty with 7.4% (HR = 1.45, 95% CI 1.33 to 1.58; p < 0.001). Statistically significant mortality risk factors after operative treatment included age older than 75 years, male sex, chronic obstructive pulmonary disease (COPD), cerebrovascular disease, chronic kidney disease, a concomitant fracture, congestive heart failure, and osteoporotic fracture. CONCLUSION: Mortality risk factors for distinct treatment modes after PHF in elderly patients could be identified, which may guide clinical decision-making.Cite this article: Bone Joint Res 2023;12(2):103-112.

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