The machine learning algorithm screened the characteristic variables of prolonged hospital stay after hip fracture and constructed the prediction model
10.3760/cma.j.cn211501-20240116-00155
- VernacularTitle:机器学习算法筛选髋部骨折延长术后住院时间特征变量及预测模型构建
- Author:
Changying SU
1
;
Lisha HUANG
;
Jiesi ZHONG
;
Lanmei PENG
;
Jingru WANG
;
Hui GAO
;
Jun YANG
Author Information
1. 鹰潭一八四医院关节运动医学科，鹰潭 335000
- Keywords:
Machine learning;
Hip fracture;
Postoperative;
Length of stay
- From:
Chinese Journal of Practical Nursing
2024;40(19):1454-1461
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To identify risk variables for prolonged postoperative length of stay (PPOLOS) in hip fracture patients by machine learning algorithms and construct Nomogram models.Methods:A retrospective case-control study was conducted to select 248 patients with hip fracture diagnosed and treated in Yingtan 184th Hospital from June 2019 to June 2023 by convenient sampling method. Two machine learning algorithms were used (least absolute shrinkage and selection operator, LASSO and support vector machine-Recursive Feature Elimination, SVM-RFE) to screen PPOLOS risk variables. Construct a Nomogram model to predict the risk of PPOLOS in patients with hip fracture based on intersection risk variables. The model was validated using internal data sets.Results:Among the 248 patients with hip fracture, there were 79 males and 169 females, aged (64.49 ± 8.02). The mean postoperative length of hospital stay of 248 patients was (7.98 ± 5.68) d, and the median was 7 d. LASSO algorithm identifies 7 risk variables, the SVM-RFE algorithm identified 8 risk variables. Intersectional risk variables were age, body mass index (BMI), Charlson comorbidity index (CCI), type of surgery, and central granulocytocyte ratio (NLR). Multivariate Logistic regression analysis(intersection risk variables) showed that age [ OR=1.649(1.235-2.202)], BMI [1.603(1.204-2.134)], CCI [ OR=1.670(1.236-2.258)], type of surgery [ OR=1.620(1.209-2.170), 1.699(1.243-2.321)], and NLR [ OR=3.258(2.299-4.617)] were independently associated with the risk of PPOLOS (all P<0.05). The results showed that the conformity index of the Nomogram model was 0.865 (95% CI=0.768-0.945). The area under the curve was 0.852 (95% CI=0.748-0.962). When the risk threshold was >0.08, it could provide significant clinical net benefit. The clinical impact curve showed effective identification of PPOLOS patients in high-risk groups. Conclusions:This Nomogram model can guide medical staff to make clinical diagnosis and treatment decisions as soon as possible to avoid risks, allocate medical resources rationally, and improve nursing quality.
