[1]陈丽,杨玉妹,方朕.自回归移动平均模型在骨科Ⅰ类切口感染预测中的应用[J].军事护理,2023,40(11):36-39,44.[doi:10.3969/j.issn.2097-1826.2023.11.009]
 CHEN Li,YANG Yumei,FANG Zhen.Using ARIMA Model in Prediction of Orthopedics Type I Incision Infection[J].Nursing Journal Of Chinese People's Laberation Army,2023,40(11):36-39,44.[doi:10.3969/j.issn.2097-1826.2023.11.009]
点击复制

自回归移动平均模型在骨科Ⅰ类切口感染预测中的应用
分享到:

《军事护理》[ISSN:2097-1826/CN:31-3186/R]

卷:
40
期数:
2023年11期
页码:
36-39,44
栏目:
论著
出版日期:
2023-11-15

文章信息/Info

Title:
Using ARIMA Model in Prediction of Orthopedics Type I Incision Infection
文章编号:
2097-1826(2023)11-0036-05
作者:
陈丽杨玉妹方朕
(上海交通大学医学院附属第六人民医院 感染控制办公室,上海 200233)
Author(s):
CHEN LiYANG YumeiFANG Zhen
(Infection Control Department,Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine,Shanghai 200233,China)
关键词:
骨科 Ⅰ类切口 自回归移动平均模型 预测 感染
Keywords:
orthopedics type Ⅰ incision autoregressive integrated moving average model prediction infection
分类号:
R473.6
DOI:
10.3969/j.issn.2097-1826.2023.11.009
文献标志码:
A
摘要:
目的 应用自回归移动平均(autoregressive integrated moving average,ARIMA)模型建立骨科Ⅰ类切口感染预测模型,预测未来6个月的感染发病率。方法 回顾性分析2013年1月至2021年12月上海交通大学医学院附属第六人民医院骨科Ⅰ类切口感染发病率数据。选取2013年1月至2021年6月的数据作为训练集,建立ARIMA模型; 以2021年7-12月的发病率数据作为验证集,评价模型的预测效果,并预测未来6个月的发病率。结果 2013年1月至2021年12月骨科Ⅰ类切口手术患者共有228 647例,发生Ⅰ类切口感染628例,手术切口感染发病率为0.275%。ARIMA(1,0,0)(1,0,0)12为确定的最佳模型,2021年7-12月的实际值均落在预测值的95%可信区间范围内。采用该模型预测未来6个月的感染发病率依次分别为0.276%、0.283%、0.288%、0.285%、0.297%和0.291%。结论 ARIMA模型能有效拟合、预测骨科Ⅰ类切口感染发病率,模型预测结果提示未来6个月内的发病率呈现低水平流行的态势,可为临床干预措施的实施提供科学依据。
Abstract:
Objective To establish a model for the prediction of orthopedics type I incision infection using autoregressive integrated moving average(ARIMA)model,and to predict the infection incidence in the following 6 months.Methods A retrospective analysis of the incidence of type I incision infection from Jan 2013 to Dec 2021 was conducted in the Department of Orthopedics of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine.The data from Jan 2013 to Jun 2021 were used as the training set to conduct an ARIMA model,and the data from Jul to Dec 2021 were used as the prediction set to evaluate the predictive result of the model.The ARIMA model was used to predict the incidence in the following six months.Results From Jan 2013 to Dec 2021,228647 patients underwent orthopedics type I incision surgery,and 628 of them had type I incision infection,so the infection incidence was 0.275%.ARIMA(1,0,0)(1,0,0)12 was confirmed to be the best model.The actual values from Jul to Dec 2021 were within the 95% confidence interval of the predictive values.The infection incidence in the following six months predicted using the ARIMA model was 0.276%,0.283%,0.288%,0.285%,0.297%,and 0.291%,respectively.Conclusions The ARIMA model can fit and predict the incidence of orthopedics type I incision infection.The predictive result of the ARIMA model indicates a low-level prevalence in the following six months.It can provide a scientific basis for the implementation of clinical intervention measures.

参考文献/References:

[1] WANG Z T,CAO J L,YUAN P,et al.A novel facilitated negativepressure wound therapy for thoracic incision infection after esophagetomy[J].J Thorac Dis,2017,9(4):1113-1118.
[2] 周梅.骨科术后切口感染危险因素分析及手术室干预策略研究[J].蚌埠医学院学报,2020,45(7):991-993.
[3] 许建建,计幼苗,毛美蓉,等.切口感染的手术室影响因素与病原菌分布特点[J].中华医院感染学杂志,2018,28(2):305-308.
[4] 姚虹,金鹏,刘飞.中美两国医院感染管理体系的比较分析[J].中国医院管理,2011,31(12):33-34.
[5] 王燕.应用时间序列分析[M].6版.北京:中国人民大学出版社,2022:1-7.
[6] 中华人民共和国卫生部.医院感染诊断标准(试行)[J].中华医学杂志,2001,81(5):314-320.
[7] BOX G E P,JENKINS G M.Time series analysis:forecasting and control[M].San Francisco:Holden Day,1970:181-218.
[8] BOX G E P,JENKINS G M,REINSEL G C.时间序列分析:预测与控制[M].4版.王成璋,尤梅芳,郝杨,译.北京:机械工业出版社,2011:116-244.
[9] 贾俊平,何晓群,金勇进.统计学[M].5版.北京:中国人民大学出版社,2012:334.
[10]李文强,李乐翔,王波,等.骨关节及骨肿瘤术后切口感染风险预测模型列线图的建立[J].山东医药,2020,60(19):71-74.
[11]罗丽,但敏,杨英,等.某医院骨科Ⅰ类切口清洁手术部位感染危险因素分析[J].中国消毒学杂志,2023,40(3):191-193.
[12]梁景棋,张言,温晓东,等.三平面截骨保关节手术治疗跟骨关节内骨折畸形愈合的疗效分析[J].中华创伤骨科杂志,2022,24(4):293-298.
[13]中华人民共和国国家卫生健康委员会. 关于下发《医院感染管理规范(试行)》的通知:卫医发[2000]431号[EB/OL].(2001-11-07).[2023-05-24].http://www.nhc.gov.cn/wjw/gfxwj/201304/3660a9b180ce4c49910f516b30d3768f.shtml.
[14]韩宇浩,吴茜,彭金碧,等.广东省职业性尘肺病ARIMA模型预测[J].中国职业医学,2023,50(2):150-154.
[15]梁建国,冯永亮,黄丽,等.ARIMA乘积季节模型在山西省结核病预测中的应用[J].疾病监测,2023,38(3):332-338.
[16]XU D,ZHANG Q,DING Y,et al.Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting[J].Environ Sci Pollut Res Int,2022,29(3):4128-4144.
[17]邱春芳,吴燕华,周玥娟,等.中青年脑卒中后抑郁的影响因素及其预测模型构建与验证[J].军事护理,2023,40(6):30-33.
[18]荆晨晨,孙淑青,秦德春.急性呼吸窘迫综合征患者早期风险预测模型德建立[J].中华护理杂志,2020,55(9):1285-1291.
[19]杨霜,刘芸男,杨小丽,等.基于ARIMA乘积季节模型的红细胞临床用量预测[J].郑州大学学报:医学版,2021,56(5):708-712.
[20]郝启迪,米莎莎,李凡卡.利用ARIMA和BP神经网络模型分析新疆生产建设兵团乙肝流行特征[J].现代预防医学,2021,48(18):3265-3269,3284.

备注/Memo

备注/Memo:
【收稿日期】2023-05-24 【修回日期】2023-08-19
【基金项目】上海交通大学医学院院感科研项目(Jyyg2215); 上海市第六人民医院院级管理类科研基金(lygl202205)
【作者简介】陈丽,硕士,主治医师,电话:021-24058270
【通信作者】方朕,电话:021-24058270
更新日期/Last Update: 2023-11-15