医学
相对风险
内科学
乌司他丁
置信区间
药物治疗
人口
全身炎症反应综合征
主动脉夹层
重症监护医学
外科
主动脉
环境卫生
败血症
作者
Hong Liu,Haiyang Li,Lu Han,Yingyuan Zhang,Yalan Wu,Hong Liu,Jianjun Yang,Jisheng Zhong,Yuqi Wang,Dongkai Wu,Guo-liang Fan,Junquan Chen,Shengqiang Zhang,Xing-Xing Peng,Zhipeng Zeng,Zhongjia Tang,Zhan-jie Lu,Lizhong Sun,Si-chong Qian,Yongfeng Shao,Hongjia Zhang
出处
期刊:The Innovation
[Elsevier]
日期:2023-07-01
卷期号:4 (4): 100448-100448
被引量:3
标识
DOI:10.1016/j.xinn.2023.100448
摘要
The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016–2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%–41.6%; RR 1.196 [0.698–2.049] for the predicted risk of <23.5%; RR 0.922 [95% CI 0.816–1.042] for the predicted risk of >41.6%). By using artificial intelligence to define an individual's benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI