Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study.

IF 1.9 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL
Shao-Yu Tang, Tz-Heng Chen, Ko-Lin Kuo, Jue-Ni Huang, Chen-Tsung Kuo, Yuan-Chia Chu
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引用次数: 0

Abstract

Background: Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients.

Methods: A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts.

Results: Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies.

Conclusion: This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.

使用人工智能算法预测COVID-19大流行期间血液透析患者的总体生存:一项前瞻性队列研究
背景:血液透析(HD)患者是新冠肺炎严重并发症的高危人群。新冠肺炎部分疫苗接种对HD患者生存的影响仍不确定。这项前瞻性队列研究旨在使用人工智能算法预测部分新冠肺炎疫苗接种对HD患者的生存影响。方法:基于2021年7月1日至2022年4月29日期间评估的临床特征子集,使用433名HD患者队列开发机器学习模型。患者队列被随机分为训练组(80%)和测试组(20%),用于模型开发和评估。机器学习模型,包括分类提升(CatBoost)、光梯度提升机(LightGBM)、RandomForest和极端梯度提升模型(XGBoost),用于使用患者队列评估其判别性能。结果:在这些模型中,LightGBM的F1得分最高,为0.95,其次是CatBoost、RandomForest和XGBoost,在测试数据集上,受试者工作特性曲线下面积值为0.94。来自XGBoost模型的SHapley加性解释汇总图表明,年龄、白蛋白和疫苗接种细节等关键特征对生存率有显著影响。此外,完全接种疫苗的组表现出更高水平的抗刺突(S)受体结合结构域抗体。结论:这项前瞻性队列研究涉及使用人工智能算法预测新冠肺炎大流行期间HD患者的总体生存率。这些预测模型有助于识别高危人群,并指导HD患者的疫苗接种策略,最终改善整体预后。需要进一步的研究来在更大、更多样化的HD患者群体中验证和完善这些预测模型。
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来源期刊
Journal of the Chinese Medical Association
Journal of the Chinese Medical Association MEDICINE, GENERAL & INTERNAL-
CiteScore
6.20
自引率
13.30%
发文量
320
审稿时长
15.5 weeks
期刊介绍: Journal of the Chinese Medical Association, previously known as the Chinese Medical Journal (Taipei), has a long history of publishing scientific papers and has continuously made substantial contribution in the understanding and progress of a broad range of biomedical sciences. It is published monthly by Wolters Kluwer Health and indexed in Science Citation Index Expanded (SCIE), MEDLINE®, Index Medicus, EMBASE, CAB Abstracts, Sociedad Iberoamericana de Informacion Cientifica (SIIC) Data Bases, ScienceDirect, Scopus and Global Health. JCMA is the official and open access journal of the Chinese Medical Association, Taipei, Taiwan, Republic of China and is an international forum for scholarly reports in medicine, surgery, dentistry and basic research in biomedical science. As a vehicle of communication and education among physicians and scientists, the journal is open to the use of diverse methodological approaches. Reports of professional practice will need to demonstrate academic robustness and scientific rigor. Outstanding scholars are invited to give their update reviews on the perspectives of the evidence-based science in the related research field. Article types accepted include review articles, original articles, case reports, brief communications and letters to the editor
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