Online interpretable dynamic prediction models for clinically significant posthepatectomy liver failure based on machine learning algorithms: A retrospective cohort study

医学 回顾性队列研究 算法 肝切除术 肌酐 终末期肝病模型 人工神经网络 肝衰竭 人工智能 机器学习 外科 统计 内科学 切除术 数学 肝移植 计算机科学 移植
作者
Yuzhan Jin,Wanxia Li,Yachen Wu,Qian Wang,Zhiqiang Xiang,Zhangtao Long,Hao Liang,Jianjun Zou,Zhu Zhu,Jianjun Zou
出处
期刊:International Journal of Surgery [Wolters Kluwer]
标识
DOI:10.1097/js9.0000000000001764
摘要

Background: Posthepatectomy liver failure (PHLF) is the leading cause of mortality in patients undergoing hepatectomy. However, practical models for accurately predicting the risk of PHLF are lacking. This study aimed to develop precise prediction models for clinically significant PHLF. Methods: A total of 226 patients undergoing hepatectomy at a single center were recruited. The study outcome was clinically significant PHLF. Five pre- and postoperative machine learning (ML) models were developed and compared with four clinical scores, namely, the MELD, FIB-4, ALBI, and APRI scores. The robustness of the developed ML models was internally validated using 5-fold cross-validation by calculating the average of the evaluation metrics and was externally validated on an independent temporal dataset, including the area under the curve (AUC) and the area under the precision‒recall curve (AUPRC). SHapley Additive exPlanations analysis was performed to interpret the best performance model. Results: Clinically significant PHLF was observed in 23 of 226 patients (10.2%). The variables in the preoperative model included creatinine, total bilirubin, and Child‒Pugh grade. In addition to the above factors, the extent of resection was also a key variable for the postoperative model. The pre- and postoperative artificial neural network (ANN) models exhibited excellent performance, with mean AUCs of 0.766 and 0.851, respectively, and mean AUPRC values of 0.441 and 0.645, whereas the MELD, FIB-4, ALBI, and APRI scores reached AUCs of 0.714, 0.498, 0.536 and 0.551, respectively, and AUPRC values of 0.204, 0.111, 0.128 and 0.163, respectively. In addition, the AUCs of the pre- and postoperative ANN models were 0.720 and 0.731, respectively, and the AUPRC values were 0.380 and 0.408, respectively, on the temporal dataset. Conclusion: Our online interpretable dynamic ML models outperformed common clinical scores and could function as a clinical decision support tool to identify patients at high risk of PHLF pre- and postoperatively.
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
刚刚
zzz发布了新的文献求助10
1秒前
研友_Zzrx6Z完成签到,获得积分10
2秒前
2秒前
4秒前
4秒前
汉堡包应助NOTHING采纳,获得10
4秒前
SYLH应助科研通管家采纳,获得50
4秒前
quhayley应助科研通管家采纳,获得10
4秒前
爆米花应助科研通管家采纳,获得10
4秒前
坦率的匪应助科研通管家采纳,获得10
5秒前
田様应助科研通管家采纳,获得10
5秒前
SYLH应助科研通管家采纳,获得50
5秒前
orixero应助科研通管家采纳,获得10
5秒前
SYLH应助科研通管家采纳,获得50
5秒前
思源应助科研通管家采纳,获得10
5秒前
5秒前
隐形曼青应助科研通管家采纳,获得10
5秒前
czh应助科研通管家采纳,获得10
5秒前
赘婿应助科研通管家采纳,获得10
5秒前
华仔应助科研通管家采纳,获得10
5秒前
科研通AI2S应助科研通管家采纳,获得10
5秒前
6秒前
SYLH应助科研通管家采纳,获得50
6秒前
大模型应助科研通管家采纳,获得10
6秒前
6秒前
斯文败类应助Keyl采纳,获得10
6秒前
褪黑素应助科研通管家采纳,获得10
6秒前
完美世界应助科研通管家采纳,获得10
6秒前
6秒前
6秒前
6秒前
6秒前
6秒前
6秒前
6秒前
6秒前
6秒前
6秒前
努力科研霸王龙完成签到 ,获得积分10
7秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3988732
求助须知:如何正确求助?哪些是违规求助? 3531027
关于积分的说明 11252281
捐赠科研通 3269732
什么是DOI,文献DOI怎么找? 1804764
邀请新用户注册赠送积分活动 881869
科研通“疑难数据库(出版商)”最低求助积分说明 809021