The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery
The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery
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DOI：
10.1007/s10151-015-1319-0
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发表时间：
2015-07-01
影响因子：
3.3
通讯作者：
Ockrim, J. B.
中科院分区：
文献类型：
--
作者：
Francis, N. K.;Luther, A.;Ockrim, J. B.
人工神经网络（ann）可用于开发预测工具，以实现临床决策过程。本研究旨在探讨人工神经网络在预测结直肠癌手术后增强恢复结果中的应用。数据来自2002年至2009年在单一中心进行腹腔镜手术后增强恢复（ERAS）计划的连续结直肠癌患者。评估的主要结局是延迟出院和30天内再入院。使用多层感知器神经网络（MLPNN）对数据进行分析，并为每个结果创建预测工具。采用logistic回归分析，将结果与常规统计方法进行比较。共有275名癌症患者参与了这项研究。中位住院时间为6天（范围2-49天），67例（24.4%）患者住院时间超过7天。34例（12.5%）患者在30天内再次入院。预测延迟出院的重要因素与ERAS依从性的失败有关，特别是与前48小时的术后因素有关。延迟出院的MLPNN的接受者算子特征曲线（AUROC）下面积为0.817，而逻辑回归分析开发的预测工具的AUROC为0.807。预测30天再入院的因素包括对ERAS途径的总体依从性和接受直肠癌新辅助治疗。再入院时MLPNN的AUROC为0.68。这些结果可能合理地表明，人工神经网络可用于开发可靠的多因素干预结果预测工具，如ERAS。ERAS的依从性可以可靠地预测腹腔镜结直肠癌手术后延迟出院和30天再入院。
Artificial neural networks (ANNs) can be used to develop predictive tools to enable the clinical decision-making process. This study aimed to investigate the use of an ANN in predicting the outcomes from enhanced recovery after colorectal cancer surgery.Data were obtained from consecutive colorectal cancer patients undergoing laparoscopic surgery within the enhanced recovery after surgery (ERAS) program between 2002 and 2009 in a single center. The primary outcomes assessed were delayed discharge and readmission within a 30-day period. The data were analyzed using a multilayered perceptron neural network (MLPNN), and a prediction tools were created for each outcome. The results were compared with a conventional statistical method using logistic regression analysis.A total of 275 cancer patients were included in the study. The median length of stay was 6 days (range 2-49 days) with 67 patients (24.4 %) staying longer than 7 days. Thirty-four patients (12.5 %) were readmitted within 30 days. Important factors predicting delayed discharge were related to failure in compliance with ERAS, particularly with the postoperative elements in the first 48 h. The MLPNN for delayed discharge had an area under a receiver operator characteristic curve (AUROC) of 0.817, compared with an AUROC of 0.807 for the predictive tool developed from logistic regression analysis. Factors predicting 30-day readmission included overall compliance with the ERAS pathway and receiving neoadjuvant treatment for rectal cancer. The MLPNN for readmission had an AUROC of 0.68.These results may plausibly suggest that ANN can be used to develop reliable outcome predictive tools in multifactorial intervention such as ERAS. Compliance with ERAS can reliably predict both delayed discharge and 30-day readmission following laparoscopic colorectal cancer surgery.