一般情况较差的住院患者血液透析期间危及生命的并发症的机器学习预测。

IF 2.3 3区 医学 Q3 ENGINEERING, BIOMEDICAL
Naotaka Kato, Takeshi Goto, Tomoyuki Ohira, Hirotaka Kinoshita, Kugo Kurokawa, Kouhei Naganuma, Chikako Ohminato, Junko Ogasawara, Shingo Hatakeyama, Yoshihiro Sasaki, Kazuyoshi Hirota, Chikara Ohyama
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引用次数: 0

摘要

背景:接受血液透析(HD)的患者面临心血管死亡风险显著升高,治疗期间的突发事件对生存构成严重威胁。这些风险在高危人群中尤其明显,例如心血管手术恢复期患者或正在接受败血症治疗的患者。因此,制定有效的预防策略对于改善患者预后至关重要。本研究旨在开发一种机器学习模型,该模型使用预处理患者特征来预测急性重症医院高危住院患者在HD期间和治疗后24小时内的突发不良事件。方法:他的回顾性研究分析了2018年至2021年在弘崎大学医院接受HD治疗的739例患者的数据。突发事件定义为致命性心律失常、难治性分析性低血压或呼吸骤停。从51例患者的特征(人口统计数据、临床参数、实验室数据和hd相关信息)中进行后向逐步选择,构建logistic回归模型。结果:739例患者中,17例(2.3%)发生突发事件。该模型识别了23个预hd协变量,获得了接收者工作特征曲线下面积(AUC)为0.889。关键协变量包括急诊住院(71%的突发事件患者存在)、近期手术(76%)、HD病史较短、HD前心率升高、血清白蛋白水平较低和c反应蛋白浓度较高。结论:我们的模型能够利用透析前数据早期识别接受血液透析的高危住院患者,从而支持及时的临床干预,优化资源分配,提高患者安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning-Based Prediction of Life-Threatening Complications During Hemodialysis in Hospitalized Patients With Poor General Conditions.

Background: Patients undergoing hemodialysis (HD) face a significantly elevated risk of cardiovascular mortality, with sudden events during treatment posing a critical threat to survival. These risks are particularly pronounced in high-risk populations, such as patients recovering from cardiovascular surgery or those being treated for sepsis. Therefore, the development of effective preventive strategies is essential for improving patient outcomes. This study aimed to develop a machine learning model that uses pretreatment patient characteristics to predict sudden adverse events during HD and within 24 h after treatment in high-risk inpatients at acute care hospitals.

Methods: His retrospective study analyzed data from 739 patients who underwent HD at Hirosaki University Hospital between 2018 and 2021. Sudden events were defined as fatal arrhythmia, refractory intradialytic hypotension, or respiratory arrest. A logistic regression model was constructed using backward stepwise selection from 51 patient characteristics (demographic data, clinical parameters, laboratory data, and HD-related information).

Results: Among the 739 patients, 17 (2.3%) experienced sudden events. The model identified 23 pre-HD covariates and achieved an area under the receiver operating characteristic curve (AUC) of 0.889. Key covariates included emergency hospitalization (present in 71% of patients with sudden events), recent surgery (76%), shorter HD history, elevated pre-HD heart rate, lower serum albumin levels, and higher C-reactive protein concentrations.

Conclusions: Our model enables the early identification of high-risk inpatients receiving hemodialysis using pre-dialysis data, thereby supporting timely clinical interventions, optimized resource allocation, and improved patient safety.

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来源期刊
Artificial organs
Artificial organs 工程技术-工程:生物医学
CiteScore
4.30
自引率
12.50%
发文量
303
审稿时长
4-8 weeks
期刊介绍: Artificial Organs is the official peer reviewed journal of The International Federation for Artificial Organs (Members of the Federation are: The American Society for Artificial Internal Organs, The European Society for Artificial Organs, and The Japanese Society for Artificial Organs), The International Faculty for Artificial Organs, the International Society for Rotary Blood Pumps, The International Society for Pediatric Mechanical Cardiopulmonary Support, and the Vienna International Workshop on Functional Electrical Stimulation. Artificial Organs publishes original research articles dealing with developments in artificial organs applications and treatment modalities and their clinical applications worldwide. Membership in the Societies listed above is not a prerequisite for publication. Articles are published without charge to the author except for color figures and excess page charges as noted.
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