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预测髋部骨折患者院内及1年死亡率风险评分的开发与初步验证

Development and initial validation of a risk score for predicting in-hospital and 1-year mortality in patients with hip fractures.

作者信息

Jiang Hong X, Majumdar Sumit R, Dick Donald A, Moreau Marc, Raso James, Otto David D, Johnston D William C

机构信息

Royal Alexandra Hospital, Edmonton, Alberta, Canada.

出版信息

J Bone Miner Res. 2005 Mar;20(3):494-500. doi: 10.1359/JBMR.041133. Epub 2004 Nov 29.

Abstract

UNLABELLED

Our objectives were to better define the rates and determinants of in-hospital and 1-year mortality after hip fracture. We studied a population-based cohort of 3981 hip fracture patients. Using multivariable regression methods, we identified risk factors for mortality (older age, male sex, long-term care residence, 10 prefracture co-morbidities) and calculated a hip fracture-specific score that could accurately predict or risk-adjust in-hospital and 1-year mortality. Our methods, after further validation, may be useful for comparing outcomes across hospitals or regions.

INTRODUCTION

Hip fractures in the elderly are common and associated with significant mortality and variations in outcome. The rates and determinants of mortality after hip fracture are not well defined. Our objectives were (1) to define the rate of in-hospital and 1-year mortality in hip fracture patients, (2) to describe co-morbidities at the time of fracture, and (3) to develop and validate a multivariable risk-adjustment model for mortality.

MATERIALS AND METHODS

We studied a population-based cohort of 3981 hip fracture patients > or =60 years of age admitted to hospitals in a large Canadian health region from 1994 to 2000. We collected sociodemographic and prefracture co-morbidity data. Main outcomes were in-hospital and 1-year mortality. We used multivariable regression methods to first derive a risk-adjustment model for mortality in 2187 patients treated at one hospital and then validated it in 1794 patients treated at another hospital. These models were used to calculate a score that could predict or risk-adjust in-hospital and 1-year mortality after hip fracture.

RESULTS AND CONCLUSIONS

The median age of the cohort was 82 years, 71% were female, and 26% had more than four prefracture co-morbidities. In-hospital mortality was 6.3%; 10.2% for men and 4.7% for women (adjusted odds ratio, 1.8; 95% CI, 1.3-2.4). Mortality at 1 year was 30.8%; 37.5% for men and 28.2% for women (adjusted p < 0.001). Older age, male sex, long-term care residence, and 10 different co-morbidities were independently associated with mortality. Risk-adjustment models based on these variables had excellent accuracy for predicting mortality in-hospital (c-statistic = 0.82) and at 1 year (c-statistic = 0.74). We conclude that 1 in 15 elderly patients with hip fracture will die during hospitalization, and almost one-third of those who survive to discharge will die within the year. The determinants of mortality were primarily older age, male sex, and prefracture co-morbidities. Our hip fracture-specific risk-adjustment tool is pragmatic and reliable, and after further validation, may be useful for comparing outcomes across different hospitals or regions.

摘要

未标注

我们的目标是更准确地确定髋部骨折后住院期间及1年死亡率的发生率和决定因素。我们研究了一个基于人群的队列,其中包括3981例髋部骨折患者。使用多变量回归方法,我们确定了死亡率的风险因素(年龄较大、男性、长期护理机构居住、骨折前10种合并症),并计算了一个髋部骨折特异性评分,该评分可以准确预测或对住院期间及1年死亡率进行风险调整。经过进一步验证后,我们的方法可能有助于比较不同医院或地区的治疗结果。

引言

老年人髋部骨折很常见,且与显著的死亡率及治疗结果差异相关。髋部骨折后死亡率的发生率和决定因素尚未明确界定。我们的目标是:(1)确定髋部骨折患者的住院期间及1年死亡率;(2)描述骨折时的合并症情况;(3)开发并验证一个用于死亡率的多变量风险调整模型。

材料与方法

我们研究了1994年至2000年期间在加拿大一个大型健康区域内入院的年龄≥60岁的3981例基于人群的髋部骨折患者队列。我们收集了社会人口统计学和骨折前合并症数据。主要结局是住院期间及1年死亡率。我们使用多变量回归方法,首先在一家医院治疗的2187例患者中得出一个死亡率风险调整模型,然后在另一家医院治疗的1794例患者中对其进行验证。这些模型用于计算一个可以预测或对髋部骨折后住院期间及1年死亡率进行风险调整的评分。

结果与结论

该队列的中位年龄为82岁,71%为女性,26%有超过4种骨折前合并症。住院期间死亡率为6.3%;男性为10.2%,女性为4.7%(调整后的优势比为1.8;95%置信区间为1.3 - 2.4)。1年时死亡率为30.8%;男性为37.5%,女性为28.2%(调整后p < 0.001)。年龄较大、男性、长期护理机构居住以及10种不同的合并症与死亡率独立相关。基于这些变量的风险调整模型在预测住院期间死亡率(c统计量 = 0.82)和1年死亡率(c统计量 = 0.74)方面具有出色的准确性。我们得出结论,每15例老年髋部骨折患者中有1例将在住院期间死亡,而存活至出院的患者中近三分之一将在年内死亡。死亡率的决定因素主要是年龄较大、男性以及骨折前合并症。我们的髋部骨折特异性风险调整工具实用且可靠,经过进一步验证后,可能有助于比较不同医院或地区的治疗结果。

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