Department of Anesthesiology, South Campus, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
School of Life Sciences, Fudan University, Shanghai, China.
BMC Cancer. 2020 Nov 27;20(1):1161. doi: 10.1186/s12885-020-07626-2.
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 (PaO), 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 PaO 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.
手术切除是治疗胰腺导管腺癌（PDAC）的主要方法。然而，这种手术复杂，围手术期风险高，使患者更有可能被收入重症监护病房（ICU）。因此，建立预测 ICU 入住的风险模型对于防止患者术后恶化并可能降低社会经济负担具有重要意义。
我们回顾性收集了 1242 例 PDAC 患者的 120 项临床特征，包括人口统计学数据、术前和术中血液检查、住院时间和 ICU 状态。采用支持向量机（SVM）、逻辑回归和套索回归等机器学习管道，选择预测 ICU 入住的最佳模型。采用普通最小二乘法（OLS）和套索回归对术后出血、总住院时间和出院费用进行相关分析。
SVM 模型的性能优于其他两种模型，AU-ROC 为 0.80。年龄、手术持续时间、单核细胞计数和术中部分动脉压（PaO）等特征是 ICU 入住的危险因素。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 ICU 停留的风险变量。胆红素、CA125 和术前白蛋白与术后出血量有关。手术持续时间是出院费用的最重要因素，而前淋巴细胞百分比和绝对值与费用较低有关。
我们观察到，DEX、单核细胞计数、嗜碱性粒细胞百分比和术中 PaO 等一些新指标对 ICU 入住和 ICU 停留时间的可能性具有良好的预测效果。这项工作为 PDAC 手术的术前指示提供了重要参考。
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