Using a Decision Tree Approach to Analyze Key Factors Influencing Intraoperative-Acquired Pressure Injury

被引:5
|
作者
Shi, Guirong [1 ]
Jiang, Liping [1 ]
Liu, Ping [2 ]
Xu, Xin [3 ]
Wu, Qunfang [4 ]
Zhang, Peipei [5 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Med, Dept Nursing, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Med, Dept Nursing, Shanghai, Peoples R China
[3] Wenzhou Med Univ, Sch Nursing, Xinhua Hosp, Shanghai, Peoples R China
[4] Wenzhou Med Univ, Sch Nursing, Spine Ctr, Wenzhou, Zhejiang, Peoples R China
[5] Wenzhou Med Univ, Sch Nursing, Operating Room, Wenzhou, Zhejiang, Peoples R China
关键词
classification and regression tree; decision tree; intraoperative complications; nursing; pressure injury; risk assessment; risk factors; surgery; RISK-FACTORS; ULCER;
D O I
10.1097/ASW.0000000000000003
中图分类号
R75 [皮肤病学与性病学];
学科分类号
100206 ;
摘要
OBJECTIVE: To determine the key factors influencing intraoperative-acquired pressure injury (IAPI). METHODS: Researchers assessed 413 surgical patients in a Shanghai tertiary hospital using an information collection form and an IAPI occurrence record form. Analysis took place using the classification and regression tree algorithm and multiple logistic regression. RESULTS: A total of 43 surgical patients (10.4%) had IAPI, including 32 stage 1 cases (74.4%), and 11 stage 2 cases (25.6%). The multiple logistic regression analysis indicated that operation duration, surgical position, preoperative hypertension, and preoperative Braden Scale risk score were independently associated with IAPI development. The decision tree showed that preoperative Braden Scale score, surgical position, operation grade, operation duration, age, prealbumin level, and body mass index were important factors and that preoperative Braden Scale score was the most critical decision variable. The cross-validation method was used to indicate a model accuracy of 91.8%. CONCLUSIONS: The decision tree effectively identified key factors for IAPI, complementing the logistic regression analysis and providing a scientific basis for the further development of structural risk assessment, prevention, and treatment strategies for IAPI.
引用
收藏
页码:591 / 597
页数:7
相关论文
共 34 条
  • [1] Operative Positioning and Intraoperative-Acquired Pressure Injury: A Retrospective Cohort Study
    Xu, Xin
    Miao, Miao
    Shi, Guirong
    Zhang, Peipei
    Liu, Ping
    Zhao, Bing
    Jiang, Liping
    [J]. ADVANCES IN SKIN & WOUND CARE, 2024, 37 (03) : 148 - 154
  • [2] Based on Decision Tree Model to Analyze the Influencing Factors of Customer's Insurance Transactions
    Kuo, Che-Nan
    Lin, Yu-Da
    Nguyen, Duc-Man
    Cheng, Yu-Huei
    [J]. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2023, 39 (04) : 797 - 807
  • [3] Analysis of Factors Influencing Online Learning Using the Decision Tree Method
    Li, Xiaojie
    Tang, Huili
    [J]. HighTech and Innovation Journal, 2024, 5 (02): : 479 - 488
  • [4] Factors Influencing Aggressive Adolescent Behavior: An Analysis Using the Decision Tree Method
    Zhang, Yu
    Shi, Peipei
    Gao, Mengjuan
    Chang, Hongjuan
    [J]. JOURNAL OF GENETIC PSYCHOLOGY, 2022, 183 (06): : 537 - 548
  • [5] Exploring the Factors Influencing Traffic Accidents: An Analysis of Black Spots and Decision Tree for Injury Severity
    Abdullah P.
    Sipos T.
    [J]. Periodica Polytechnica Transportation Engineering, 2024, 52 (01): : 33 - 39
  • [7] KEY FACTORS AFFECTING RAIL SERVICE QUALITY IN THE NORTHERN ITALY: A DECISION TREE APPROACH
    De Ona, Rocio
    Eboli, Laura
    Mazzulla, Gabriella
    [J]. TRANSPORT, 2014, 29 (01) : 75 - 83
  • [8] Extracting the factors influencing chlorophyll-a concentrations in the Nakdong River using a decision tree algorithm
    Cho, Yeongdae
    Kim, Yejin
    [J]. DESALINATION AND WATER TREATMENT, 2019, 157 : 195 - 208
  • [9] Analyzing factors influencing the severity of occupational accidents in textile industry using decision tree algorithms
    Nazli Gulum Mutlu
    Serkan Altuntas
    [J]. Cluster Computing, 2024, 27 : 787 - 825
  • [10] Analyzing factors influencing the severity of occupational accidents in textile industry using decision tree algorithms
    Mutlu, Nazli Gulum
    Altuntas, Serkan
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (01): : 787 - 825