烧伤患者医院感染的流行病学特点及风险预测模型的构建与评价

王华; 赵朋; 孙丹; 武星; 袁琴芳; 王克伟

doi:10.3760/cma.j.cn501225-20220214-00025

烧伤患者医院感染的流行病学特点及风险预测模型的构建与评价

doi: 10.3760/cma.j.cn501225-20220214-00025

王华¹,
赵朋²,
孙丹²,
武星³,
袁琴芳³,
王克伟^1, ,

1.
江南大学附属医院中西医结合研究所，无锡　　214122
2.
江南大学附属医院烧伤科，无锡　　214122
3.
江南大学附属医院医院感染科，无锡　　214122

基金项目:

无锡市“双百”中青年医疗卫生后备拔尖人才基金 HB2020040

无锡市卫生健康委2020年度重大项目 Z202007

详细信息

通讯作者:
王克伟，Email：jnwkw169@jiangnan.edu.cn

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出版历程
- 收稿日期: 2022-02-14

Epidemiological characteristics and the establishment and evaluation of a risk prediction model for nosocomial infection in burn patients

1.
Institute of Integrated Chinese and Western Medicine, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
2.
Department of Burns, Affiliated Hospital of Jiangnan University, Wuxi 214122, China
3.
Department of Hospital Infection, Affiliated Hospital of Jiangnan University, Wuxi 214122, China

Funds:

Top Talent Support Program for Young and Middle-aged People of Wuxi HB2020040

Mega-project of Wuxi Commission of Health in 2020 Z202007

More Information

Corresponding author: Wang Kewei, Email: jnwkw169@jiangnan.edu.cn

摘要

摘要: 目的了解烧伤患者医院感染的流行病学特点，探寻烧伤患者发生医院感染的独立危险因素，以此构建风险预测模型并分析其预测价值。方法采用回顾性病例系列研究方法。2016年5月—2019年12月，江南大学附属医院烧伤科收治3 475例符合入选标准的烧伤患者，其中男2 290例、女1 185例，年龄1~94岁，统计其医院感染发生率、病原菌检出部位与具体构成。将患者按照大约7∶3的比例在R 4.1.3统计软件中随机分为训练组（2 434例）和验证组（1 041例），比较2组患者入院时性别、年龄、烧伤总面积、合并Ⅲ度烧伤/吸入性损伤/休克/糖尿病情况以及入住重症监护病房（ICU）情况、行中心静脉置管/气管插管/留置导尿管/手术情况、医院感染情况、抗生素使用天数、住院天数。根据是否发生医院感染，将患者分为医院感染组（102例）和未医院感染组（3 373例），统计2组患者前述除医院感染情况外的资料以及入院季节、抗生素使用种类。对前述数据行独立样本t检验、χ²检验、Mann-Whitney U检验等单因素分析，将医院感染组与未医院感染组比较差异有统计学意义的指标作为自变量纳入多因素logistic回归分析，筛选3 475例烧伤患者发生医院感染的独立危险因素。在独立危险因素与重要临床特征基础上，构建训练组烧伤患者发生医院感染风险的列线图预测模型。在训练组和验证组中，绘制预测模型预测医院感染的受试者操作特征（ROC）曲线，计算曲线下面积；绘制校准曲线，评估预测模型预测结果与实际发生情况的符合度；绘制临床决策曲线，评估预测模型的临床效用。结果该研究纳入患者医院感染发生率为2.94%（102/3 475）；共从212个标本中检出病原菌，以创面（78例，占36.79%）和血液（64例，占30.19%）标本为主；共检出病原菌250株，以革兰阴性菌（153株，占61.20%）为主。训练组和验证组患者所有临床特征情况均相近（P>0.05）。医院感染组与未医院感染组患者年龄、烧伤总面积、抗生素使用天数、抗生素使用种类、住院天数与合并Ⅲ度烧伤情况、合并吸入性损伤情况、合并休克情况、入住ICU情况、中心静脉置管情况、气管插管情况、留置导尿管情况、手术情况比较，差异均有统计学意义（Z值分别为4.41、14.95、15.70、650.32、13.73，χ²值分别为151.09、508.30、771.20、955.79、522.67、967.40、732.11、225.35，P<0.01）。入住ICU、气管插管、留置导尿管与住院天数均为3 475例烧伤患者发生医院感染的独立危险因素（比值比分别为5.99、3.39、9.32、6.21，95%置信区间分别为2.25~15.99、1.56~7.39、2.77~31.31、2.48~15.92，P<0.01）。在训练组和验证组患者中，以独立危险因素与烧伤总面积和中心静脉置管为基础构建的医院感染预测模型的ROC曲线下面积均为0.97（95%置信区间均为0.95~0.99）；校准曲线分析显示，预测模型预测结果与实际发生情况的符合度很好；临床决策曲线分析显示，预测模型有较好的临床效用。结论烧伤患者医院感染以革兰阴性菌为主，感染部位主要是创面，其独立危险因素包括入住ICU、气管插管、留置导尿管、住院天数。基于独立危险因素与重要临床特征构建的医院感染风险预测模型对烧伤患者发生医院感染具有较好的预测能力。
- 烧伤 /
- 交叉感染 /
- 危险因素 /
- 回归分析 /
- 列线图
Abstract: Objective To find the epidemiological characteristics of nosocomial infection in burn patients, to establish a risk prediction model for nosocomial infection in burn patients based on the screened independent risk factors of the infection, and to analyze its predictive value. Methods A retrospective case series study was conducted. From May 2016 to December 2019, 3 475 burn patients who were admitted to the Department of Burns of Affiliated Hospital of Jiangnan University met the inclusion criteria, including 2 290 males and 1 185 females, aged from 1 to 94 years. The incidence of nosocomial infection, the detection site and specific composition of pathogenic bacteria were counted. The patients were randomly divided into training group (2 434 cases) and verification group (1 041 cases) in R 4.1.3 statistic software with a ratio of about 7∶3. Factors including gender, age, total burn area, combination of full-thickness burn/inhalation injury/shock/diabetes on admission, admission to intensive care unit (ICU), status of central venous catheterization/endotracheal intubation/urethral catheter indwelling/surgery, nosocomial infection status, days of antibiotic use, and days of hospital stay of patients were compared between the two groups. According to the occurrence of nosocomial infection, the patients were divided into nosocomial infection group (102 cases) and non-nosocomial infection group (3 373 cases), and in addition to the aforementioned data, non-nosocomial infection related data, the season of admission and types of antibiotics used were compared between the two groups. The above-mentioned data were statistically analyzed with one-way analysis of independent sample t test, chi-square test, and Mann-Whitney U test, and the indicators with statistically significant differences between nosocomial infection group and non-nosocomial infection group were included as variables in multivariate logistic regression analysis to screen independent risk factors for the development of nosocomial infection in 3 475 burn patients. On the basis of independent risk factors and important clinical characteristics, a nomogram prediction model was constructed for the risk of developing nosocomial infection of burn patients in training group. In both training group and verification group, receiver operating characteristic (ROC) curves for prediction of nosocomial infection by the prediction model were plotted, and the area under the ROC curve was calculated; calibration curves were plotted to evaluate the conformity between the predicted results of the prediction model and the actual situation; clinical decision curves were plotted to evaluate the clinical utility of the prediction model. Results The incidence of nosocomial infection of patients included in this study was 2.94% (102/3 475); pathogens were detected from 212 specimens, mainly wound (78 cases, accounting for 36.79%) and blood (64 cases, accounting for 30.19%) specimens; 250 strains of pathogenic bacteria were detected, mainly gram-negative bacteria (153 strains, accounting for 61.20%). All clinical characteristics of patients between training group and verification group were similar (P>0.05). There were statistically significant differences between patients in nosocomial infection group and non-nosocomial infection group in the aspects of age, total burn area, days of antibiotic use, antibiotic use type, days of hospital stay, combination of full-thickness burn, combination of inhalation injury, combination of shock, ICU admission status, central venous catheterization status, endotracheal intubation status, urethral catheter indwelling status, surgery status (with Z values of 4.41, 14.95, 15.70, 650.32, and 13.73, χ² values of 151.09, 508.30, 771.20, 955.79, 522.67, 967.40, 732.11, and 225.35, respectively, P<0.01). ICU admission, endotracheal intubation, urethral catheter indwelling, and days of hospital stay were independent risk factors for developing nosocomial infection by 3 475 burn patients (with odds ratios of 5.99, 3.39, 9.32, and 6.21, 95% confidence intervals of 2.25-15.99, 1.56-7.39, 2.77-31.31, and 2.48-15.92, respectively, P<0.01). In training group and verification group, the area under ROC curves of the nosocomial infection prediction model based on independent risk factors, total burn area, and central vein catheterization were both 0.97 (with both 95% confidence intervals being 0.95-0.99); the calibration curve analysis showed that the prediction results of the prediction model were in good agreement with the actual situation; the clinical decision curve analysis showed that the prediction model had good clinical utility. Conclusions The nosocomial infection in burn patients is mainly caused by gram-negative bacteria, with wound as the main infection site, and the independent risk factors including ICU admission, endotracheal intubation, urethral catheter indwelling, and days of hospital stay. Based on independent risk factors and important clinical features, the risk prediction model for nosocomial infection has a good ability to predict nosocomial infection in burn patients.
- Burns /
- Cross infection /
- Risk factors /
- Regression analysis /
- Nomograms
王华、王克伟：论文撰写、统计分析；赵朋、袁琴芳：论文修改、经费支持；孙丹、武星：数据整理、研究指导

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参考文献(23)

[1]	World Health Organization The burden of health care-associated infection worldwide 2010-04-29 2022-02-14 https://www.who.int/news-room/feature-stories/detail/the-burden-of-health-care-associated-infection-worldwide World Health Organization. The burden of health care-associated infection worldwide[EB/OL]. (2010-04-29)[2022-02-14]. https://www.who.int/news-room/feature-stories/detail/the-burden-of-health-care-associated-infection-worldwide.
[2]	ÖncülO,ÖksüzS,AcarA,et al.Nosocomial infection characteristics in a burn intensive care unit: analysis of an eleven-year active surveillance[J].Burns,2014,40(5):835-841.DOI: 10.1016/j.burns.2013.11.003.
[3]	KlevensRM, EdwardsJR, RichardsCLJr, et al. Estimating health care-associated infections and deaths in U.S. hospitals, 2002[J]. Public Health Rep,2007,122(2):160-166.DOI: 10.1177/003335490712200205.
[4]	ZhengY,LinG,ZhanR,et al.Epidemiological analysis of 9,779 burn patients in China: an eight-year retrospective study at a major burn center in southwest China[J].Exp Ther Med,2019,17(4):2847-2854.DOI: 10.3892/etm.2019.7240.
[5]	AndrewsCJ,CuttleL.Comparing the reported burn conditions for different severity burns in porcine models: a systematic review[J].Int Wound J,2017,14(6):1199-1212.DOI: 10.1111/iwj.12786.
[6]	BaeL,BohannonJK,CuiW,et al.Fms-like tyrosine kinase-3 ligand increases resistance to burn wound infection through effects on plasmacytoid dendritic cells[J].BMC Immunol,2017,18(1):9.DOI: 10.1186/s12865-016-0188-2.
[7]	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. DOI: 10.3760/j:issn:0376-2491.2001.05.027.
[8]	任海涛,陈华清,韩春茂.危重烧伤患者发生急性呼吸窘迫综合征预测模型的建立及其预测价值分析[J].中华烧伤杂志,2021,37(4):333-339.DOI: 10.3760/cma.j.cn501120-20200301-00109.
[9]	VickersAJ,ElkinEB.Decision curve analysis: a novel method for evaluating prediction models[J].Med Decis Making,2006,26(6):565-574.DOI: 10.1177/0272989X06295361.
[10]	高立平,易博,廖殿晓,等.烧伤科连续5年医院感染回顾性调查[J].中国感染控制杂志,2018,17(1):77-79.DOI: 10.3969/j.issn.1671-9638.2018.01.018.
[11]	StrasslePD,WilliamsFN,WeberDJ,et al.Risk factors for healthcare-associated infections in adult burn patients[J].Infect Control Hosp Epidemiol,2017,38(12):1441-1448.DOI: 10.1017/ice.2017.220.
[12]	ChenYY,ChenIH,ChenCS,et al.Incidence and mortality of healthcare-associated infections in hospitalized patients with moderate to severe burns[J].J Crit Care,2019,54:185-190.DOI: 10.1016/j.jcrc.2019.08.024.
[13]	van LangeveldI,GagnonRC,ConradPF,et al.Multiple-drug resistance in burn patients: a retrospective study on the impact of antibiotic resistance on survival and length of stay[J].J Burn Care Res,2017,38(2):99-105.DOI: 10.1097/BCR.0000000000000479.
[14]	Escandón-VargasK,TanguaAR,MedinaP,et al.Healthcare-associated infections in burn patients: timeline and risk factors[J].Burns,2020,46(8):1775-1786.DOI: 10.1016/j.burns.2020.04.031.
[15]	陈文健,林邦长,徐陆亚运,等.烧伤患者创面感染相关因素分析及预防措施[J].中华医院感染学杂志,2017,27(23):5411-5413.DOI: 10.11816/cn.ni.2017-171928.
[16]	杨彩丽,徐文举,张伟峰,等.烧伤患者医院获得性肺炎的病原学特点及危险因素分析[J].中华医院感染学杂志,2017,27(11):2562-2564,2586.DOI: 10.11816/cn.ni.2017-163553.
[17]	LuytCE,HékimianG,KoulentiD,et al. Microbial cause of ICU-acquired pneumonia: hospital-acquired pneumonia versus ventilator-associated pneumonia[J]. Curr Opin Crit Care,2018,24(5):332-338.DOI: 10.1097/MCC.0000000000000526.
[18]	WangYY,XiangBD,MaL,et al.Development and validation of a nomogram to preoperatively estimate post-hepatectomy liver dysfunction risk and long-term survival in patients with hepatocellular carcinoma[J].Ann Surg,2021,274(6):e1209-e1217.DOI: 10.1097/SLA.0000000000003803.
[19]	LiG,TianML,BingYT,et al.Nomograms predict survival outcomes for distant metastatic pancreatic neuroendo-crine tumor: a population based STROBE compliant study[J].Medicine (Baltimore),2020,99(13):e19593.DOI: 10.1097/MD.0000000000019593.
[20]	BenoitL,BalayaV,GuaniB,et al.Nomogram predicting the likelihood of parametrial involvement in early-stage cervical cancer: avoiding unjustified radical hysterectomies[J].J Clin Med,2020,9(7):2121.DOI: 10.3390/jcm9072121.
[21]	曾庆玲,王庆梅,陶利菊,等.特重度烧伤患者死亡风险列线图预测模型的建立及预测价值[J].中华烧伤杂志,2020,36(9):845-852.DOI: 10.3760/cma.j.cn501120-20190620-00280.
[22]	DongYM, SunJ, LiYX, et al. Development and validation of a nomogram for assessing survival in patients with COVID-19 pneumonia [J]. Clin Infect Dis,2021,72(4):652-660. DOI: 10.1093/cid/ciaa963.
[23]	LuoG,TanJ,PengY,et al.Guideline for diagnosis, prophylaxis and treatment of invasive fungal infection post burn injury in China 2013[J/OL].Burns Trauma,2014,2(2):45-52[2022-02-14].https://pubmed.ncbi.nlm.nih.gov/27602362/.DOI: 10.4103/2321-3868.130182.

下载: 全尺寸图片幻灯片

1 列线图预测模型预测的训练组中1例烧伤总面积为30%TBSA、行中心静脉置管与气管插管及留置导尿管并住院90 d患者医院感染概率

注：第1行分数线上红色圆点为下方各分项得分对应点，红色菱形为总得分，箭头指示对应感染概率；TBSA为体表总面积，ICU为重症监护病房

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2 2组烧伤患者发生医院感染预测模型的受试者操作特征曲线。2A.训练组（2 434例患者）；2B.验证组（1 041例患者）

注：对角直线为参考线（医院感染），红色曲线为预测模型

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3 2组烧伤患者发生医院感染预测模型的校准曲线

注：训练组2 434例患者，验证组1 041例患者，对角虚线为理想线（预测概率与实际发生率完全符合）

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4 2组烧伤患者发生医院感染预测模型与模型中5个单变量的临床决策曲线。4A.训练组（2 434例患者）；4B.验证组（1 041例患者）

注：ICU为重症监护病房

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表1 训练组和验证组烧伤患者临床特征比较

组别	例数	性别（例）		年龄［岁，M（Q ₁ ，Q ₃）］	烧伤总面积［%TBSA，M（Q ₁ ，Q ₃）］	合并Ⅲ度烧伤（例）		合并吸入性损伤（例）
组别	例数	男	女	年龄［岁，M（Q ₁ ，Q ₃）］	烧伤总面积［%TBSA，M（Q ₁ ，Q ₃）］	是	否	是	否
训练组	2 434	1 599	835	36（3，52）	6（4，10）	753	1 681	129	2 305
验证组	1 041	691	350	36（3，52）	6（4，10）	320	721	51	990
统计量值		χ ²=0.15		Z=-0.06	Z=-0.80	χ ²=0.01		χ ²=0.24
P值		0.697		0.952	0.423	0.908		0.625
注：TBSA为体表总面积，ICU为重症监护病房

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表2 医院感染组和未医院感染组烧伤患者临床特征比较

组别	例数	性别（例）		年龄［岁，M（Q ₁ ，Q ₃）］	烧伤总面积［%TBSA，M（Q ₁ ，Q ₃）］	合并Ⅲ度烧伤（例）		合并吸入性损伤（例）		合并休克（例）
组别	例数	男	女	年龄［岁，M（Q ₁ ，Q ₃）］	烧伤总面积［%TBSA，M（Q ₁ ，Q ₃）］	是	否	是	否	是	否
医院感染组	102	73	29	47（34，55）	60（30，84）	88	14	55	47	62	40
未医院感染组	3 373	2 217	1 156	35（3，52）	6（4，10）	985	2 388	125	3 248	95	3 278
统计量值		χ ²=1.50		Z=4.41	Z=14.95	χ ²=151.09		χ ²=508.30		χ ²=771.20
P值		0.220		<0.001	<0.001	<0.001		<0.001		<0.001
注：TBSA为体表总面积，ICU为重症监护病房

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表3 影响3 475例烧伤患者发生医院感染的多因素logistic 回归分析阳性结果及重要临床特征结果

因素	比值比	95%置信区间	P值
住院天数（d，取对数）	6.21	2.48~15.92	<0.001
入住ICU	5.99	2.25~15.99	<0.001
气管插管	3.39	1.56~7.39	0.002
留置导尿管	9.32	2.77~31.31	<0.001
烧伤总面积（%TBSA，取对数）	2.63	1.05~7.35	0.050
中心静脉置管	0.46	0.20~1.02	0.057
注：ICU为重症监护病房，TBSA为体表总面积