摘要
目的:分析上海市某三甲医院创伤中心骨盆骨折患者住院日及其预测因子。方法:抽取上海市第一人民医院南院创伤中心2013年全部62名骨盆骨折住院患者病历,对住院日进行单因素分析、相关分析和有序logistic回归。结果:研究对象中位住院日为19.00天。单因素分析显示受伤原因、骨盆骨折数、患侧和是否输血是住院日的预测因子(P<0.05)。相关分析显示手术次数、手术时长、输血量、手术失血、检验次数、CT检查次数和超声检查次数分别与住院日存在相关关系(P<0.05)。有序logistic回归表明手术次数、CT检查次数和手术失血量是住院日的独立预测因子(P<0.05)。结论:骨盆骨折住院患者平均住院时间长,住院日主要受手术次数、CT检查次数和手术失血量影响,减少不必要的影像学检查和术中出血可缩短住院日。
Objective:To investigate the Predictors of length of stay of pelvic fracture patients in a Tertiary Hospital in Shanghai.Methods:Sampled the whole pelvic fracture inpatients in Trauma Centre,Shanghai 1st People's Hospital south campus,2013.Methods on analyzing data were univariate analysis,correlation analysis and ordinal logistic regression.Results:The subject's Median length of stay was 19.00 days.Univariate analyzed showed reason of injury,number of pelvic fractures,side of injury and blood infusion are the factors of length of stay(P<0.05).Frequency of operation,length of operation,blood infusion volume,volume of operational hemorrhage,frequency of laboratory test,frequency of CT examine and frequency of ultrasound examine were respectively correlated with length of stay (P<0.05).Frequency of operations,frequency of CT examine and volume of operational hemorrhage were the independent factors of length of stay by ordinal logistic regression (P<0.05).Conclusions:The length of stay of pelvic fracture patients is long.The main Predictors of length of stay are frequency of operations,frequency of CT examine and volume of operational hemorrhage.Efforts to decline the volume of operational hemorrhage could shorten the length of stay of pelvic fracture inpatients.
作者
陈宇
马磊
马良
李文进
祝延红
CHEN Yu MA Lei MA Liang LI Wen-jin ZHU Yan-hong(School of Public Health, Shanghai Jiao Tong University Shanghai, 200025, China Department of Research Management, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, 200080, China)
出处
《现代生物医学进展》
CAS
2017年第20期3850-3854,3935,共6页
Progress in Modern Biomedicine
基金
国家自然科学基金项目(71432007)
关键词
骨盆骨折
住院日
预测因子
Pelvic fracture
Length of stay
Predictors