Factors affecting the length of stay of patients with traumatic spinal cord injury in Tianjin, China
Factors affecting the length of stay of patients with traumatic spinal cord injury in Tianjin, China
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天津市创伤性脊髓损伤患者住院时间的影响因素
DOI：
10.1179/2045772313y.0000000090
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发表时间：
2013-05-01
影响因子：
1.7
通讯作者：
Feng, Shi-Qing
中科院分区：
文献类型：
--
作者：
Wu, Qiang;Ning, Guang-Zhi;Feng, Shi-Qing
摘要目的描述天津市成人创伤性脊髓损伤（TSCI）患者的急性护理住院时间（ACLOS），并确定相关的人口统计学和临床因素。方法对天津市某综合医院2004 ~ 2007年收治的TSCI患者进行回顾性分析。预测变量为人口统计学和临床因素，包括年龄、性别、病因、损伤程度、严重程度、相关损伤、手术和并发症。结果变量为ACLOS。多变量线性回归分析模型被用来检查预测变量和结果变量之间的关联。结果本研究纳入631例TSCI患者。平均ACLOS为32.4 ± 37.7天，范围为1-294天。住院天数中位数为21天。在郊区医院接受手术、尿路感染、功能状态差、压疮和相关损伤与ACLOS显著相关。结论本研究探讨了流行病学和临床因素对天津地区ACLOS的影响。影响ACLOS的因素与其他研究报道的因素不同。在中国需要更多的研究来确定这些因素对TSCI患者ACLOS的影响，并提出预测模型。
Abstract Purpose To describe the acute care length of stay (ACLOS) of adult patients with traumatic spinal cord injury (TSCI) in Tianjin, China, and identify the associated demographic and clinical factors. Methods TSCI patients admitted to a general hospital in Tianjin, China from 2004 to 2007 were identified. The predictor variables were demographic and clinical factors, including age, gender, etiology, level of injury, severity, associated injuries, surgery, and complications. The outcome variable was ACLOS. Multivariable linear regression analysis models were used to examine the association between predictor and outcome variables. Results This study included 631 TSCI patients. The mean ACLOS was 32.4 ± 37.7 days, with a range of 1–294 days. The median number of hospitalization days was 21 days. Admission to a suburban hospital surgery, urinary infection, poorer functional status, pressure ulcers, and associated injuries were significantly associated with ACLOS. Conclusion This study examined the effect of epidemiological and clinical factors on ACLOS in Tianjin, China. The factors that influenced the ACLOS were different from factors reported in other studies. More studies are needed in China to determine the effect of these factors on ACLOS in TSCI patients and to propose a predictive model.