使用炎症预后指数对 NSTEMI 患者造影剂诱发肾病进行机器学习算法。

IF 1.9 4区医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL Biomarkers in medicine Pub Date : 2024-11-13 DOI:10.1080/17520363.2024.2422810

Faysal Şaylık, Tufan Çınar, Murat Selçuk, İbrahim Halil Tanboğa

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引用次数: 0

摘要

目的：炎症预后指数（IPI）已被证明与癌症患者的不良预后有关。我们旨在利用提名图和机器学习（ML）算法研究 IPI 对非 ST 段抬高型心肌梗死患者造影剂诱发肾病（CIN）的预测作用：共纳入178例CIN（+）患者和1511例CIN（-）患者：结果：CIN（+）患者的IPI水平较高，IPI与CIN独立相关。包括 IPI 在内的风险预测提名图具有更高的预测能力和良好的校准性。Naive Bayes 和 k-nearest neighbors 是预测 CIN 患者的最佳 ML 算法：结论：IPI 可作为一种易于获得的标记物，使用多重多重算法预测 CIN。

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Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients.

Aim: Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer patients. We aimed to investigate the predictive role of IPI for contrast-induced nephropathy (CIN) development in non-ST segment elevation myocardial infarction patients using a nomogram and performing machine learning (ML) algorithms.Materials & methods: A total of 178 patients with CIN (+) and 1511 with CIN (-) were included.Results: CIN (+) patients had higher IPI levels, and IPI was independently associated with CIN. A risk prediction nomogram including IPI had a higher predictive ability and good calibration. Naive Bayes and k-nearest neighbors were the best ML algorithms for the prediction of CIN patients.Conclusion: IPI might be used as an easily obtainable marker for CIN prediction using ML algorithms.

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来源期刊

Biomarkers in medicine 医学-医学：研究与实验

CiteScore

3.80

自引率

4.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Biomarkers are physical, functional or biochemical indicators of physiological or disease processes. These key indicators can provide vital information in determining disease prognosis, in predicting of response to therapies, adverse events and drug interactions, and in establishing baseline risk. The explosion of interest in biomarker research is driving the development of new predictive, diagnostic and prognostic products in modern medical practice, and biomarkers are also playing an increasingly important role in the discovery and development of new drugs. For the full utility of biomarkers to be realized, we require greater understanding of disease mechanisms, and the interplay between disease mechanisms, therapeutic interventions and the proposed biomarkers. However, in attempting to evaluate the pros and cons of biomarkers systematically, we are moving into new, challenging territory. Biomarkers in Medicine (ISSN 1752-0363) is a peer-reviewed, rapid publication journal delivering commentary and analysis on the advances in our understanding of biomarkers and their potential and actual applications in medicine. The journal facilitates translation of our research knowledge into the clinic to increase the effectiveness of medical practice. As the scientific rationale and regulatory acceptance for biomarkers in medicine and in drug development become more fully established, Biomarkers in Medicine provides the platform for all players in this increasingly vital area to communicate and debate all issues relating to the potential utility and applications. Each issue includes a diversity of content to provide rounded coverage for the research professional. Articles include Guest Editorials, Interviews, Reviews, Research Articles, Perspectives, Priority Paper Evaluations, Special Reports, Case Reports, Conference Reports and Company Profiles. Review coverage is divided into themed sections according to area of therapeutic utility with some issues including themed sections on an area of topical interest. Biomarkers in Medicine provides a platform for commentary and debate for all professionals with an interest in the identification of biomarkers, elucidation of their role and formalization and approval of their application in modern medicine. The audience for Biomarkers in Medicine includes academic and industrial researchers, clinicians, pathologists, clinical chemists and regulatory professionals.