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

Author:

Zhang Yijue,Zhu SiboORCID,Yuan Zhiqing,Li Qiwei,Ding Ruifeng,Bao XunxiaORCID,Zhen Timing,Fu Zhiliang,Fu Hailong,Xing Kaichen,Yuan Hongbin,Chen TaoORCID

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.

Funder

Cultivating Funds of Renji Hospital, School of Medicine,Shanghai Jiao Tong University

Science and Technology Commission of Shanghai Municipality

China Postdoctoral Science Foundation

National High-tech Research and Development Program

National Natural Science Foundation of China

111 Project

Publisher

Springer Science and Business Media LLC

Subject

Cancer Research,Genetics,Oncology

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