Developing and optimizing a machine learning predictive model for post-thrombotic syndrome in a longitudinal cohort of patients with proximal deep venous thrombosis

血栓后综合征 接收机工作特性 医学 逻辑回归 队列 深静脉 体质指数 静脉血栓形成 决策树 随机森林 预测建模 内科学 血栓形成 外科 人工智能 机器学习 计算机科学
作者
Zhaoyu Wu,Yixuan Li,Jiahao Lei,Peng Qiu,Haichun Liu,Xinrui Yang,Tao Chen,Xinwu Lu
出处
期刊:Journal of vascular surgery. Venous and lymphatic disorders [Elsevier BV]
卷期号:11 (3): 555-564.e5 被引量:1
标识
DOI:10.1016/j.jvsv.2022.12.006
摘要

Post-thrombotic syndrome (PTS) is the most common chronic complication of deep venous thrombosis (DVT). Risk measurement and stratification of PTS are crucial for patients with DVT. This study aimed to develop predictive models of PTS using machine learning for patients with proximal DVT.Herein, hospital inpatients from a DVT registry electronic health record database were randomly divided into a derivation and a validation set, and four predictive models were constructed using logistic regression, simple decision tree, eXtreme Gradient Boosting (XGBoost), and random forest (RF) algorithms. The presence of PTS was defined according to the Villalta scale. The areas under the receiver operating characteristic curves, decision-curve analysis, and calibration curves were applied to evaluate the performance of these models. The Shapley Additive exPlanations analysis was performed to explain the predictive models.Among the 300 patients, 126 developed a PTS at 6 months after DVT. The RF model exhibited the best performance among the four models, with an area under the receiver operating characteristic curves of 0.891. The RF model demonstrated that Villalta score at admission, age, body mass index, and pain on calf compression were significant predictors for PTS, with accurate prediction at the individual level. The Shapley Additive exPlanations analysis suggested a nonlinear correlation between age and PTS, with two peak ages of onset at 50 and 70 years.The current predictive model identified significant predictors and accurately predicted PTS for patients with proximal DVT. Moreover, the model demonstrated a nonlinear correlation between age and PTS, which might be valuable in risk measurement and stratification of PTS in patients with proximal DVT.
最长约 10秒,即可获得该文献文件

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
不远完成签到,获得积分10
刚刚
冯珂完成签到 ,获得积分10
2秒前
Graham完成签到,获得积分10
2秒前
稳重乌冬面完成签到 ,获得积分10
4秒前
一苇以航完成签到 ,获得积分10
5秒前
戚雅柔完成签到 ,获得积分10
5秒前
vsvsgo完成签到,获得积分10
6秒前
米奇完成签到 ,获得积分10
6秒前
加一点荒谬完成签到,获得积分10
6秒前
6秒前
一一一给轻松白桃的求助进行了留言
8秒前
zz2905完成签到,获得积分10
8秒前
小超人完成签到 ,获得积分10
9秒前
香蕉初瑶完成签到,获得积分10
9秒前
meimei完成签到 ,获得积分10
9秒前
儒雅的菠萝吹雪完成签到,获得积分10
10秒前
10秒前
11秒前
水寒完成签到,获得积分10
11秒前
拉长的念珍完成签到,获得积分10
12秒前
大气夜山完成签到 ,获得积分10
12秒前
Tristan完成签到 ,获得积分10
14秒前
我思故我在完成签到,获得积分10
14秒前
15秒前
何浏亮完成签到,获得积分10
16秒前
阿成完成签到,获得积分10
16秒前
Pauline完成签到 ,获得积分10
16秒前
17秒前
微笑的语芙完成签到,获得积分10
17秒前
17秒前
小背包完成签到 ,获得积分10
17秒前
水寒发布了新的文献求助10
19秒前
希望天下0贩的0应助17采纳,获得10
19秒前
yu完成签到 ,获得积分10
19秒前
钟瑞乾完成签到,获得积分10
19秒前
花痴的电灯泡完成签到,获得积分10
20秒前
虚心念桃完成签到,获得积分10
21秒前
jiaolulu发布了新的文献求助10
22秒前
zyw完成签到 ,获得积分10
22秒前
ironsilica完成签到,获得积分10
25秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038235
求助须知:如何正确求助?哪些是违规求助? 3575992
关于积分的说明 11374009
捐赠科研通 3305760
什么是DOI,文献DOI怎么找? 1819276
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022