Scholarlink

Introduction <p>Hip fracture (HF) is one of the leading causes of mortality and disability in older populations. Hip refracture (HRF) is more serious than HF. We examined the survival rates of patients with HRFs at the Affiliated Dongyang Hospital of Wenzhou Medical University and developed a prediction model for their survival outcomes.</p> Materials and methods <p>This study involved identifying and analyzing patients with HRFs from January 2013 to August 2023. Utilizing our hospital’s database, we systematically extracted these patients’ information. We monitored the survival time and constructed Kaplan–Meier (K–M) survival curve. Through Cox proportional hazards regression analyses, independent risk factors were identified. Based on these factors, a survival prediction model was developed and its reliability was evaluated by the receiver operating characteristic (ROC) curve. In addition, patients who refused surgery were excluded, the eligible patients were divided into a long waiting group (&gt; 48&#xa0;h) and a short waiting group (≤ 48&#xa0;h). According to a similar process, independent risk factors were identified. Another nomogram chart and ROC curve were conducted.</p> Results <p>Data from 174 patients were used, presenting a median survival time of 40 months. Both univariate and multivariate analyses identified age (HR = 1.71, 95% CI: 1.26–2.32), surgical intervention (HR = 0.34, 95% CI: 0.21–0.55), number of comorbidities (HR = 1.44, 95% CI: 1.09–1.92) and lymphocyte levels (HR = 0.75, 95% CI: 0.61–0.92) as independent influence factors. Based on these factors, a nomogram chart was constructed. The areas under the ROC curve for predicting 1-year, 2-year, and 3-year survival rates were 0.748, 0.794 and 0.806 respectively. Additionally, through regression analysis of surgical patients, some factors (age, number of comorbidities and lymphocyte levels) were supported as independent influence factors.</p> Conclusion <p>Advanced age, non-surgical management, multiple comorbidities and lower lymphocyte levels showed a higher risk of all-cause mortality among the patients with HRFs, and these factors were associated with a poor prognosis in this patient population. This study may be useful for improving these patients’ prognosis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Independent risk factors and survival prediction in hip refractures patients: insights from a 10-year cohort

  • 期刊论文

  • 发表日期：

    2025年7月18日

查看全文
  • Ruilong Qi,
  • Guohong Xu,
  • Zhengtong Lin,
  • Li-Hong Wang

摘要

Introduction

Hip fracture (HF) is one of the leading causes of mortality and disability in older populations. Hip refracture (HRF) is more serious than HF. We examined the survival rates of patients with HRFs at the Affiliated Dongyang Hospital of Wenzhou Medical University and developed a prediction model for their survival outcomes.

Materials and methods

This study involved identifying and analyzing patients with HRFs from January 2013 to August 2023. Utilizing our hospital’s database, we systematically extracted these patients’ information. We monitored the survival time and constructed Kaplan–Meier (K–M) survival curve. Through Cox proportional hazards regression analyses, independent risk factors were identified. Based on these factors, a survival prediction model was developed and its reliability was evaluated by the receiver operating characteristic (ROC) curve. In addition, patients who refused surgery were excluded, the eligible patients were divided into a long waiting group (> 48 h) and a short waiting group (≤ 48 h). According to a similar process, independent risk factors were identified. Another nomogram chart and ROC curve were conducted.

Results

Data from 174 patients were used, presenting a median survival time of 40 months. Both univariate and multivariate analyses identified age (HR = 1.71, 95% CI: 1.26–2.32), surgical intervention (HR = 0.34, 95% CI: 0.21–0.55), number of comorbidities (HR = 1.44, 95% CI: 1.09–1.92) and lymphocyte levels (HR = 0.75, 95% CI: 0.61–0.92) as independent influence factors. Based on these factors, a nomogram chart was constructed. The areas under the ROC curve for predicting 1-year, 2-year, and 3-year survival rates were 0.748, 0.794 and 0.806 respectively. Additionally, through regression analysis of surgical patients, some factors (age, number of comorbidities and lymphocyte levels) were supported as independent influence factors.

Conclusion

Advanced age, non-surgical management, multiple comorbidities and lower lymphocyte levels showed a higher risk of all-cause mortality among the patients with HRFs, and these factors were associated with a poor prognosis in this patient population. This study may be useful for improving these patients’ prognosis.