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Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis

Authors :
Yijue Zhang
Sibo Zhu
Zhiqing Yuan
Qiwei Li
Ruifeng Ding
Xunxia Bao
Timing Zhen
Zhiliang Fu
Hailong Fu
Kaichen Xing
Hongbin Yuan
Tao Chen
Source :
BMC Cancer, Vol 20, Iss 1, Pp 1-12 (2020)
Publication Year :
2020
Publisher :
BMC, 2020.

Abstract

Abstract Background Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). However, this operation is complex, and the peri-operative risk is high, making patients more likely to be admitted to the intensive care unit (ICU). Therefore, establishing a risk model that predicts admission to ICU is meaningful in preventing patients from post-operation deterioration and potentially reducing socio-economic burden. Methods We retrospectively collected 120 clinical features from 1242 PDAC patients, including demographic data, pre-operative and intra-operative blood tests, in-hospital duration, and ICU status. Machine learning pipelines, including Supporting Vector Machine (SVM), Logistic Regression, and Lasso Regression, were employed to choose an optimal model in predicting ICU admission. Ordinary least-squares regression (OLS) and Lasso Regression were adopted in the correlation analysis of post-operative bleeding, total in-hospital duration, and discharge costs. Results SVM model achieved higher performance than the other two models, resulted in an AU-ROC of 0.80. The features, such as age, duration of operation, monocyte count, and intra-operative partial arterial pressure of oxygen (PaO2), are risk factors in the ICU admission. The protective factors include RBC count, analgesic pump dexmedetomidine (DEX), and intra-operative maintenance of DEX. Basophil percentage, duration of the operation, and total infusion volume were risk variables for staying in ICU. The bilirubin, CA125, and pre-operative albumin were associated with the post-operative bleeding volume. The operation duration was the most important factor for discharge costs, while pre-lymphocyte percentage and the absolute count are responsible for less cost. Conclusions We observed that several new indicators such as DEX, monocyte count, basophil percentage, and intra-operative PaO2 showed a good predictive effect on the possibility of admission to ICU and duration of stay in ICU. This work provided an essential reference for indication in advance to PDAC operation.

Details

Language :
English
ISSN :
14712407
Volume :
20
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Cancer
Publication Type :
Academic Journal
Accession number :
edsdoj.fcbe05cd15da4ccd8c165925f476d302
Document Type :
article
Full Text :
https://doi.org/10.1186/s12885-020-07626-2