Comparison of PICC-associated thrombosis risk prediction models based on machine learning algorithm
10.3760/cma.j.cn115682-20211028-04867
- VernacularTitle:基于机器学习算法的PICC相关性血栓风险预测模型比较研究
- Author:
Shuhua WANG
1
;
Bo CHENG
;
Liqun ZHU
;
Songmei CAO
;
Yiqing LIANG
Author Information
1. 江苏大学医学院，镇江 212000
- Keywords:
Catheterization, central venous;
XGBoost;
Support Vector Machine;
Logistic regression;
Machine learning;
PICC-associated thrombosis;
Predictive model
- From:
Chinese Journal of Modern Nursing
2022;28(16):2144-2151
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To build the three different risk prediction models for peripherally inserted central catheter (PICC) -associated thrombosis based on machine learning algorithm, and compare the performance of the models, so as to provide a basis for evaluating and preventing PICC-associated thrombosis.Methods:The PICC-associated Thrombasis Risk Factor Questionnaire was developed based on the best evidence and expert consultation. From January 2016 to October 2020, convenience sampling was used to select 626 patients with PICC in the Affiliated Hospital of Jiangsu University as the research object to collect clinical data. Based on machine learning algorithms, Support Vector Machine (SVM) , XGBoost and Logistic regression methods were used to construct three different PICC-associated thrombosis risk prediction models, which were evaluated and compared.. Model evaluation indicators included Matthews correlation coefficient ( MCC) , F1 value, area under the receiver operating characteristic curve ( AUC) and Brier score. Results:A total of 30 variables were included, and the predictors included four aspects, namely, demographic data of patients, patient condition, treatment factors, and catheter-related factors. For the model verified on the test set, the Logistic regression prediction model had lower scores than the XGBoost and SVM prediction models in terms of MCC and F1 values. On AUC, the Logistic regression prediction model score was equal to SVM and smaller than XGBoost. On Brier, the Logistic regression prediction model scored higher than the XGBoost and SVM prediction models. Conclusions:The performance of the prediction model based on the machine learning algorithm XGBoost and SVM is superior to the traditional Logistic regression model in terms of sensitivity and accuracy. Thrombotic predictors can help guide medical and nursing staff to identify high-risk patients and reduce the incidence of PICC-associated thrombosis.
