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Volume 37 Issue 9
Sep.  2021
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Article Navigation >  CHINESE JOURNAL OF BURNS AND WOUNDS  > 2021  >  37(9): 846-852
Dong C,Yu Z,Liu W,et al.Establishment and validation of a clinical prediction model for infection risk at the placement sites of skin and soft tissue expanders[J].Chin J Burns,2021,37(9):846-852.DOI: 10.3760/cma.j.cn501120-20200619-00314.
Citation: Dong C,Yu Z,Liu W,et al.Establishment and validation of a clinical prediction model for infection risk at the placement sites of skin and soft tissue expanders[J].Chin J Burns,2021,37(9):846-852.DOI: 10.3760/cma.j.cn501120-20200619-00314.
shu

Establishment and validation of a clinical prediction model for infection risk at the placement sites of skin and soft tissue expanders

doi: 10.3760/cma.j.cn501120-20200619-00314

  • Department of Plastic Surgery, the First Affiliated Hospital of Air Force Medical University, Xi'an 710032, China

Funds:

General Program of National Natural Science Foundation of China 81671925, 81971851

Shaanxi Science and Technology Plan of China 2018ZDXM-SF-081

Discipline Promotion Project of Xijing Hospital XJZT19D03

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    Abstract
  •   Objective  To establish a clinical prediction model for infection risk at the placement sites of skin and soft tissue expanders (hereinafter termed as expanders) and to validate the predictive value of the model.  Methods  A retrospective observational study was conducted. Totally 2 934 patients who underwent skin and soft tissue dilatation surgery in the Department of Plastic Surgery of the First Affiliated Hospital of Air Force Medical University from January 2009 to December 2018 and met the selection criteria were included. There were 1 867 males and 1 067 females, with a median age of 18 years. Totally 3 053 skin and soft tissue expansion procedures were performed with 4 266 expanders implanted. The following indexes were selected as predictor variables, including patients' age, gender, marital status, ethnicity, hospital admission, surgical indication, disease duration, with/without history of smoking, history of drinking, history of blood transfusion, history of underlying diseases, and inability to use cephalosporin antibiotics due to allergy, number of expander in a single placement, rated volume of expander, water injection rate of expander in the first time, placement site of expander, anesthesia method, duration of operation, and with/without postoperative hematoma evacuation, and infection at the placement site of expander as the outcome variable. Univariate analysis of the data was performed using least absolute shrinkage and selection operator (LASSO) regression to screen the potential risk factors affecting infection at the placement sites of expanders, the factors selected by the univariate analysis were subjected to binary multivariate logistic regression analysis to screen the independent risk factors affecting infection at the placement sites of expanders, and a nomogram prediction model for the occurrence of infection at the placement sites of expanders was established. The C index and Hosmer-Lemeshow goodness of fit test were used to evaluate the discrimination and accuracy of the model, respectively, and the bootstrap resampling was used for internal verification.  Results  The results of LASSO regression showed that age, gender, hospital admission, surgical indication, disease duration, history of drinking, history of heart disease, history of viral hepatitis, history of hypertension, inability to use cephalosporin antibiotics due to allergy, number of expander in a single placement, rated volume of expander, placement site of expander, postoperative hematoma evacuation were the potential risk factors for infection at the placement sites of expanders (regression coefficient= - 0.005, 0.170, 0.999, 0.054, 0.510, - 0.003, 0.395, - 0.218, 0.029, 0.848, - 0.116, 0.175, 0.085, 0.202). Binary multivariate logistic regression analysis showed that male, emergency admission, disease duration ≤1 year, inability to use cephalosporin antibiotics due to allergy, rated volumes of expanders ≥200 mL and <400 mL or ≥400 mL, and expanders placed in the trunk or the limbs were the independent risks factors for infection at the placement sites of expanders (odds ratio=1.37, 3.21, 2.00, 2.47, 1.70, 1.73, 1.67, 2.16, 95% confidence interval=1.04 - 1.82, 1.09 - 8.34, 1.38 - 2.86, 1.29 - 4.41, 1.07 - 2.73, 1.02 - 2.94, 1.09 - 2.58, 1.07 - 4.10, P<0.05 or P<0.01). The C index for evaluating the discriminative degree of the model was 0.63, the Hosmer-Lemeshow goodness of fit test for evaluating the accuracy of the model showed P=0.685, and the C index for internal validation by the bootstrap resampling was 0.60.  Conclusions  Male, emergency admission, disease duration ≤1 year, inability to use cephalosporin antibiotics due to allergy, rated volume of expander ≥200 mL, and expanders placed in the trunk or the limbs are the independent risk factors for infection at the placement sites of expanders. The clinical prediction model for infection risk at the placement sites of expanders was successfully established based on these factors and showed a certain predictive effect.

     

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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 3.3 %其他: 3.3 %其他: 0.1 %其他: 0.1 %China: 24.9 %China: 24.9 %Netherlands: 0.1 %Netherlands: 0.1 %三明: 0.3 %三明: 0.3 %三门峡: 0.1 %三门峡: 0.1 %上海: 0.8 %上海: 0.8 %东莞: 0.3 %东莞: 0.3 %临沂: 0.3 %临沂: 0.3 %佛山: 0.5 %佛山: 0.5 %保定: 0.1 %保定: 0.1 %六安: 0.3 %六安: 0.3 %北京: 0.5 %北京: 0.5 %北方邦: 0.1 %北方邦: 0.1 %南京: 0.7 %南京: 0.7 %南平: 1.2 %南平: 1.2 %南通: 0.3 %南通: 0.3 %南阳: 0.3 %南阳: 0.3 %吉安: 0.1 %吉安: 0.1 %周口: 0.1 %周口: 0.1 %咸阳: 0.1 %咸阳: 0.1 %哥伦布: 0.1 %哥伦布: 0.1 %唐山: 0.3 %唐山: 0.3 %嘉兴: 0.4 %嘉兴: 0.4 %天津: 0.3 %天津: 0.3 %宁德: 0.7 %宁德: 0.7 %宁波: 0.3 %宁波: 0.3 %宜春: 0.3 %宜春: 0.3 %宣城: 0.1 %宣城: 0.1 %宿迁: 1.1 %宿迁: 1.1 %巴音郭楞: 0.3 %巴音郭楞: 0.3 %常州: 0.1 %常州: 0.1 %常德: 0.3 %常德: 0.3 %广元: 0.4 %广元: 0.4 %广州: 0.7 %广州: 0.7 %张家口: 2.1 %张家口: 2.1 %徐州: 0.1 %徐州: 0.1 %德阳: 0.1 %德阳: 0.1 %怀化: 0.1 %怀化: 0.1 %惠州: 0.3 %惠州: 0.3 %扬州: 0.7 %扬州: 0.7 %拉贾斯坦邦: 0.4 %拉贾斯坦邦: 0.4 %朝阳: 0.4 %朝阳: 0.4 %杭州: 0.3 %杭州: 0.3 %柳州: 0.4 %柳州: 0.4 %榆林: 0.7 %榆林: 0.7 %武汉: 0.5 %武汉: 0.5 %池州: 0.1 %池州: 0.1 %沈阳: 1.1 %沈阳: 1.1 %泉州: 1.2 %泉州: 1.2 %济南: 0.3 %济南: 0.3 %海得拉巴: 0.4 %海得拉巴: 0.4 %淮北: 0.3 %淮北: 0.3 %淮南: 0.4 %淮南: 0.4 %深圳: 0.3 %深圳: 0.3 %渭南: 0.4 %渭南: 0.4 %湖州: 1.5 %湖州: 1.5 %湘西: 0.4 %湘西: 0.4 %湘西土家族苗族自治州: 0.1 %湘西土家族苗族自治州: 0.1 %湛江: 0.1 %湛江: 0.1 %滁州: 0.4 %滁州: 0.4 %滨州: 0.1 %滨州: 0.1 %漯河: 0.1 %漯河: 0.1 %漳州: 0.5 %漳州: 0.5 %盐城: 0.1 %盐城: 0.1 %盘锦: 0.1 %盘锦: 0.1 %石家庄: 0.1 %石家庄: 0.1 %福州: 0.1 %福州: 0.1 %绵阳: 0.1 %绵阳: 0.1 %舟山: 0.4 %舟山: 0.4 %芒廷维尤: 24.0 %芒廷维尤: 24.0 %芝加哥: 0.8 %芝加哥: 0.8 %茂名: 0.1 %茂名: 0.1 %荆门: 0.3 %荆门: 0.3 %莆田: 2.4 %莆田: 2.4 %葫芦岛: 0.3 %葫芦岛: 0.3 %衡水: 0.3 %衡水: 0.3 %衢州: 0.5 %衢州: 0.5 %襄阳: 0.4 %襄阳: 0.4 %西宁: 1.1 %西宁: 1.1 %西安: 0.3 %西安: 0.3 %赣州: 0.3 %赣州: 0.3 %运城: 0.3 %运城: 0.3 %连云港: 0.1 %连云港: 0.1 %遵义: 1.5 %遵义: 1.5 %邯郸: 0.1 %邯郸: 0.1 %邵阳: 0.1 %邵阳: 0.1 %郑州: 0.4 %郑州: 0.4 %郴州: 0.9 %郴州: 0.9 %鄂州: 0.1 %鄂州: 0.1 %重庆: 8.1 %重庆: 8.1 %金华: 0.4 %金华: 0.4 %铜陵: 0.9 %铜陵: 0.9 %阿什本: 0.3 %阿什本: 0.3 %随州: 0.3 %随州: 0.3 %鞍山: 0.3 %鞍山: 0.3 %韶关: 0.1 %韶关: 0.1 %黄冈: 0.5 %黄冈: 0.5 %其他其他ChinaNetherlands三明三门峡上海东莞临沂佛山保定六安北京北方邦南京南平南通南阳吉安周口咸阳哥伦布唐山嘉兴天津宁德宁波宜春宣城宿迁巴音郭楞常州常德广元广州张家口徐州德阳怀化惠州扬州拉贾斯坦邦朝阳杭州柳州榆林武汉池州沈阳泉州济南海得拉巴淮北淮南深圳渭南湖州湘西湘西土家族苗族自治州湛江滁州滨州漯河漳州盐城盘锦石家庄福州绵阳舟山芒廷维尤芝加哥茂名荆门莆田葫芦岛衡水衢州襄阳西宁西安赣州运城连云港遵义邯郸邵阳郑州郴州鄂州重庆金华铜陵阿什本随州鞍山韶关黄冈

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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