A Comprehensive Analysis of the Causes and Predictors of 30-Day Mortality Following Hip Fracture Surgery.
A Comprehensive Analysis of the Causes and Predictors of 30-Day Mortality Following Hip Fracture Surgery.
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DOI：
10.4055/cios.2017.9.1.10
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发表时间：
2017-03
影响因子：
2.5
通讯作者：
Kapoor H
中科院分区：
文献类型：
--
作者：
Sheikh HQ;Hossain FS;Aqil A;Akinbamijo B;Mushtaq V;Kapoor H
股骨颈骨折是老年人群中损伤相关死亡的主要原因。30天死亡率数字是股骨颈骨折后临床结局的一个良好利用的标志。目前的研究未能分析与30天死亡率增加相关的所有患者人口统计学、生化和共病参数。我们的目的是评估死亡率的医学风险因素，这些因素在股骨颈骨折患者入院时很容易识别。对前瞻性填充数据库进行回顾性审查，以识别2008年10月至2011年3月期间发生股骨颈骨折的所有连续患者。确定了与患者、损伤和手术相关的所有因素。关注的主要结局是30天死亡率。使用后向逐步似然比考克斯回归模型进行单变量和随后的多变量分析，以确定显著增加死亡风险的所有参数。共有1，356名患者被纳入研究。30天死亡率为8.7%。最常见的死亡原因包括肺炎、败血症和急性心肌梗死。多元回归分析显示，男性、年龄增加、入院来源不是患者自己的家、入院血红蛋白低于10 g/dL、心肌梗死病史、入院时合并胸部感染、Charlson合并症评分增加和肝脏疾病是死亡率的重要预测因素。本研究利用住院时容易收集的临床和生化信息阐明了死亡率的危险因素。这些结果可以识别脆弱的患者谁可能受益于资源的优先顺序。
A fracture neck of femur is the leading cause of injury-related mortality in the elderly population. The 30-day mortality figure is a well utilised marker of clinical outcome following a fracture neck of femur. Current studies fail to analyse all patient demographic, biochemical and comorbid parameters associated with increased 30-day mortality. We aimed to assess medical risk factors for mortality, which are easily identifiable on admission for patients presenting with a fractured neck of femur. A retrospective review of a prospectively populated database was undertaken to identify all consecutive patients with a fracture neck of femur between October 2008 and March 2011. All factors related to the patient, injury and surgery were identified. The primary outcome of interest was 30-day mortality. Univariate and subsequent multivariate analyses using a backward stepwise likelihood ratio Cox regression model were performed in order to establish all parameters that significantly increased the risk of death. A total of 1,356 patients were included in the study. The 30-day mortality was 8.7%. The most common causes of death included pneumonia, sepsis and acute myocardial infarction. Multiple regression analysis revealed male gender, increasing age, admission source other than the patient's own home, admission haemoglobin of less than 10 g/dL, a history of myocardial infarction, concomitant chest infection during admission, increasing Charlson comorbidity score and liver disease to be significant predictors of mortality. This study has elucidated risk factors for mortality using clinical and biochemical information which are easily gathered at the point of hospitalization. These results allow for identification of vulnerable patients who may benefit from a prioritisation of resources.