Multivariate Logistic Regression Analysis of the Outcomes After Humeral Head Replacement: A Case Series

Background: The aim of our study was to explore the risk factors affecting the treatment of humeral head replacement. Methods: : 52 patients with humeral head replacement surgery were enrolled in the study. The information of patients were recorded. The treatment outcomes of humeral head replacement were evaluated by ASES scores. The indicators that may affect humeral head arthroplasty surgery effect were analyzed by univariate analysis and multivariate logistic regression analysis. Results: : Univariate analysis showed: the differences of age, time from injury to surgery, time to start exercising after surgery, whether the patient regular follow-up, visual Analogue Scale(VSA), whether with shoulder dislocation, and whether with osteoporosis, were statistically significant ( P <0.05). Multivariate Logistic regression analysis showed that age, time from injury to surgery, whether with shoulder dislocation, time to start exercising after surgery, and whether the patient regular follow-up were the influencing factors, but whether with osteoporosis and VSA were not illustrated as a risk factor. Conclusion: Age, time from injury to surgery, time to start exercising after surgery, whether with shoulder dislocation, and whether the patient regular follow-up may be the chief factors affecting humeral head replacement surgery. Trial Registration: This trial was registered at the Research Registry on June 22, 2018(researchregistry3243, https://www.researchregistry.com/browse-the-registry).