Improved Prediction of Surgical-Site Infection After Colorectal Surgery Using Machine Learning

医学 结直肠外科 接收机工作特性 逻辑回归 随机森林 梯度升压 人工神经网络 手术部位感染 人工智能 机器学习 结肠切除术 外科 结直肠癌 内科学 计算机科学 腹部外科 癌症
作者
Kevin A. Chen,Chinmaya U. Joisa,Jonathan Stem,José G. Guillem,Shawn M. Gomez,Muneera R. Kapadia
出处
期刊:Diseases of The Colon & Rectum [Lippincott Williams & Wilkins]
被引量:15
标识
DOI:10.1097/dcr.0000000000002559
摘要

BACKGROUND: Surgical site infection is a source of significant morbidity after colorectal surgery. Previous efforts to develop models that predict surgical site infection have had limited accuracy. Machine learning has shown promise in predicting post-operative outcomes by identifying non-linear patterns within large datasets. OBJECTIVE: We sought to use machine learning to develop a more accurate predictive model for colorectal surgical site infections. DESIGN: Patients who underwent colorectal surgery were identified in the American College of Surgeons National Quality Improvement Program database from years 2012-2019 and were split into training, validation, and test sets. Machine learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using area under the receiver operating characteristic curve. SETTINGS: A national, multicenter dataset. PATIENTS: Patients who underwent colorectal surgery. MAIN OUTCOME MEASURES: The primary outcome (surgical site infection) included patients who experienced superficial, deep, or organ-space surgical site infections. RESULTS: The dataset included 275,152 patients after application of exclusion criteria. 10.7% of patients experienced a surgical site infection. Artificial neural network showed the best performance with area under the receiver operating characteristic curve of 0.769 (95% CI 0.762 - 0.777), compared with 0.766 (95% CI 0.759 - 0.774) for gradient boosting, 0.764 (95% CI 0.756 - 0.772) for random forest, and 0.677 (95% CI 0.669 - 0.685) for logistic regression. For the artificial neural network model, the strongest predictors of surgical site infection were organ-space surgical site infection present at time of surgery, operative time, oral antibiotic bowel prep, and surgical approach. LIMITATIONS: Local institutional validation was not performed. CONCLUSIONS: Machine learning techniques predict colorectal surgical site infections with higher accuracy than logistic regression. These techniques may be used to identify patients at increased risk and to target preventative interventions for surgical site infection. See Video Abstract at http://links.lww.com/DCR/C88.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
哎哟我去完成签到,获得积分10
刚刚
刚刚
1秒前
牙牙发布了新的文献求助10
2秒前
量子星尘发布了新的文献求助10
2秒前
随安完成签到,获得积分20
2秒前
3秒前
NexusExplorer应助yangyangyang采纳,获得10
3秒前
雪白的面包完成签到 ,获得积分10
3秒前
张西西完成签到 ,获得积分10
4秒前
ouyggg发布了新的文献求助10
4秒前
4秒前
4秒前
布的奈何发布了新的文献求助10
4秒前
4秒前
我看你这篇有点像我要找的文献完成签到,获得积分10
4秒前
tutu发布了新的文献求助10
4秒前
自由珊完成签到 ,获得积分10
5秒前
5秒前
谷中青完成签到,获得积分10
5秒前
随安发布了新的文献求助10
5秒前
哈哈发布了新的文献求助10
6秒前
李爱国应助锅锅采纳,获得10
7秒前
HoydeA完成签到,获得积分10
7秒前
飞云发布了新的文献求助10
8秒前
hanhankeyan发布了新的文献求助50
8秒前
古月发布了新的文献求助10
8秒前
白斯特发布了新的文献求助10
9秒前
yangjiang发布了新的文献求助10
9秒前
淡淡诗柳完成签到,获得积分10
9秒前
天易车网官网完成签到,获得积分20
9秒前
万能图书馆应助啦啦啦采纳,获得10
10秒前
10秒前
10秒前
10秒前
彩色元彤发布了新的文献求助10
10秒前
可爱的函函应助tooty采纳,获得10
10秒前
情怀应助受伤的山蝶采纳,获得10
10秒前
山与月齐完成签到,获得积分10
11秒前
11秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969060
求助须知:如何正确求助?哪些是违规求助? 3513962
关于积分的说明 11171223
捐赠科研通 3249302
什么是DOI,文献DOI怎么找? 1794772
邀请新用户注册赠送积分活动 875377
科研通“疑难数据库(出版商)”最低求助积分说明 804769