Abstract
BackgroundSurgical 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.MethodsWe 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.ResultsSVM 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.ConclusionsWe 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.
Highlights
Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC)
Patients and variables Our development cohort included a total of 1242 PDAC patients, 665 (52.74%) of whom admission to intensive care unit (ICU) with a mean time of 16.84 h
Validation of training set for post-operative evaluation of ICU The average ROC curves and PR curves was shown in the predictive model establishment of ICU admission in three models, i.e., Supporting Vector Machine (SVM), Lasso, and LR (Fig. 2a and 1b)
Summary
Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). This operation is complex, and the peri-operative risk is high, making patients more likely to be admitted to the intensive care unit (ICU). Establishing a risk model that predicts admission to ICU is meaningful in preventing patients from post-operation deterioration and potentially reducing socio-economic burden. The intra-operative risk is mainly bleeding (5.9%), and postoperative complications are mainly pancreatic leakage (13%). These complications may be life-threatening, making the patient’s risk of entering the ICU increased [4, 5]. The high cost of pancreatic cancer surgery directly increases the burden on the patient and family.
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