Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis
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RESEARCH ARTICLE
Open Access
Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis Yijue Zhang1†, Sibo Zhu2† , Zhiqing Yuan3, Qiwei Li3, Ruifeng Ding4, Xunxia Bao5 , Timing Zhen5, Zhiliang Fu5, Hailong Fu6, Kaichen Xing5, Hongbin Yuan6* and Tao Chen1,7*
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. Keywords: Intensive care unit, Machine learning, Risk prediction, Peri-operative, Socio-economic burden, Pancreatic adenocarcinoma * Correspondence: [email protected]; [email protected] † Yijue Zhang and Sibo Zhu contributed equally to this work. 6 Department of Anesthesiology, Changzheng Hospital, Second Military Medical University, No.415 Fengyang Road, Shanghai 200003, China 1 Department of Anesthesiology, South Campus, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Crea
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