可解释的机器学习使用围手术期系列实验室结果预测腹膜炎诱导败血症患者的术后死亡率。

IF 1.2 4区 医学 Q3 SURGERY
Seung Hee Lim, Min Jeong Kim, Won Hyuk Choi, Jin Cheol Cheong, Jong Wan Kim, Kyung Joo Lee, Jun Ho Park
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引用次数: 0

摘要

目的:脓毒症是手术后最常见的死亡原因之一。已经开发了几种传统的评分系统来预测败血症的结果;然而,它们的预测能力是不够的。本研究应用可解释的机器学习算法来提高腹膜炎引起败血症患者术后死亡率的预测准确性。方法:我们对人口统计学、临床和实验室分析的数据进行了回顾性分析,包括德尔塔中性粒细胞指数(DNI)、白细胞和中性粒细胞计数以及CRP水平。在手术前、手术后12-36小时和手术后60-84小时测量实验室数据。主要研究结果是死亡率。比较了使用顺序器官衰竭评估(SOFA)和简化急性生理学评分(SAPS)3模型的几种机器学习算法的受试者工作特征曲线下面积SHapley加性exPlanations的值用于指示变量与死亡率之间关系的方向。结果:与SAPS3和SOFA(分别为0.860和0.867)相比,CatBoost模型的死亡率AUC最高(0.933)。第3天DNI的增加、感染性休克、去甲肾上腺素治疗的使用以及第3天国际标准化比率的增加对模型的死亡率预测影响最大。结论:机器学习算法提高了预测腹膜炎引起败血症患者术后死亡率的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Explainable machine learning using perioperative serial laboratory results to predict postoperative mortality in patients with peritonitis-induced sepsis.

Explainable machine learning using perioperative serial laboratory results to predict postoperative mortality in patients with peritonitis-induced sepsis.

Explainable machine learning using perioperative serial laboratory results to predict postoperative mortality in patients with peritonitis-induced sepsis.

Explainable machine learning using perioperative serial laboratory results to predict postoperative mortality in patients with peritonitis-induced sepsis.

Purpose: Sepsis is one of the most common causes of death after surgery. Several conventional scoring systems have been developed to predict the outcome of sepsis; however, their predictive power is insufficient. The present study applies explainable machine-learning algorithms to improve the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis.

Methods: We performed a retrospective analysis of data from demographic, clinical, and laboratory analyses, including the delta neutrophil index (DNI), WBC and neutrophil counts, and CRP level. Laboratory data were measured before surgery, 12-36 hours after surgery, and 60-84 hours after surgery. The primary study output was the probability of mortality. The areas under the receiver operating characteristic curves (AUCs) of several machine-learning algorithms using the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score (SAPS) 3 models were compared. 'SHapley Additive exPlanations' values were used to indicate the direction of the relationship between a variable and mortality.

Results: The CatBoost model yielded the highest AUC (0.933) for mortality compared to SAPS3 and SOFA (0.860 and 0.867, respectively). Increased DNI on day 3, septic shock, use of norepinephrine therapy, and increased international normalized ratio on day 3 had the greatest impact on the model's prediction of mortality.

Conclusion: Machine-learning algorithms increase the accuracy of predicting postoperative mortality in patients with sepsis caused by peritonitis.

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来源期刊
CiteScore
2.30
自引率
7.10%
发文量
75
期刊介绍: Manuscripts to the Annals of Surgical Treatment and Research (Ann Surg Treat Res) should be written in English according to the instructions for authors. If the details are not described below, the style should follow the Uniform Requirements for Manuscripts Submitted to Biomedical Journals: Writing and Editing for Biomedical Publications available at International Committee of Medical Journal Editors (ICMJE) website (http://www.icmje.org).
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