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. 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. 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. 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.

中文翻译：

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胰腺导管腺癌手术的危险因素和社会经济负担：基于机器学习的分析


手术切除是治愈胰腺导管腺癌（PDAC）的主要方法。但该手术复杂，围手术期风险较高，使得患者住进重症监护病房（ICU）的可能性较大。因此，建立预测入住ICU的风险模型对于防止患者术后病情恶化并可能减轻社会经济负担具有重要意义。我们回顾性收集了 1242 名 PDAC 患者的 120 项临床特征，包括人口统计数据、术前和术中血液检查、住院时间和 ICU 状态。采用机器学习流程，包括支持向量机 (SVM)、逻辑回归和套索回归，来选择预测 ICU 入院的最佳模型。采用普通最小二乘回归（OLS）和Lasso回归对术后出血量、总住院时间、出院费用进行相关分析。 SVM 模型比其他两个模型取得了更高的性能，AU-ROC 为 0.80。年龄、手术时间、单核细胞计数和术中动脉血氧分压（PaO2）等特征是入ICU的危险因素。保护因素包​​括红细胞计数、镇痛泵右美托咪定（DEX）和术中DEX维持。嗜碱性粒细胞百分比、手术持续时间和总输液量是入住 ICU 的风险变量。胆红素、CA125和术前白蛋白与术后出血量相关。手术时间是影响出院费用的最重要因素，而前淋巴细胞百分比和绝对计数则费用较低。 我们观察到DEX、单核细胞计数、嗜碱性粒细胞百分比、术中PaO2等几个新指标对入住ICU的可能性和入住ICU的时间显示出良好的预测作用。本工作为PDAC运行的提前指示提供了重要的参考。