Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review.

IF 1.9 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Jessica Rahman, Aida Brankovic, Mark Tracy, Sankalp Khanna
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引用次数: 0

Abstract

Background: Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration.

Objective: This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes.

Methods: Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis.

Results: Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis.

Conclusions: The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

探索用于检测不良后果的新生儿生理信号预处理计算技术:范围审查。
背景:计算信号预处理是开发用于临床决策支持的数据驱动预测模型的先决条件。因此,确定符合临床原则的最佳实践对于确保透明度和可重复性以推动临床应用至关重要。它进一步促进了研究的可重复性、道德性和可靠性。这一程序对于建立软件质量管理系统也至关重要,以确保在开发作为医疗设备的软件时符合法规要求,从而实现临床前早期检测临床恶化:本综述以新生儿重症监护病房为重点,总结了用于预处理新生儿临床生理信号的最先进计算方法;这些信号用于开发机器学习模型,以预测不良后果的风险:采用关键词和 MeSH(医学主题词表)相结合的方法检索了五个数据库(PubMed、Web of Science、Scopus、IEEE 和 ACM Digital Library)。根据定义的检索词和纳入标准,共识别出 2013 年至 2023 年 1 月期间的 3585 篇论文。去除重复论文后,通过标题和摘要筛选出 2994 篇(83.51%)论文,并选择了 81 篇(0.03%)进行全文审阅。其中 52 篇(64%)符合纳入详细分析的条件:在所审查的 52 篇文章中,24 篇(46%)的研究侧重于诊断模型,其余(28 篇,54%)侧重于预后模型。这些研究进行的分析涉及各种生理信号,其中以心电图最为普遍。使用了不同的编程语言,其中以 MATLAB 和 Python 最为突出。生理数据的监测和捕获使用了不同的系统,影响了数据质量,并引入了研究的异质性。关注的结果包括败血症、呼吸暂停、心动过缓、死亡率、坏死性小肠结肠炎和缺氧缺血性脑病,有些研究分析了不良结果的组合。我们发现,在报告信号预处理的环境和方法时,部分或完全缺乏透明度。这包括报告处理缺失数据的方法、考虑分析的片段大小,以及有关修改最先进的生理信号处理方法以符合新生儿临床原则的详细信息。在 52 项综述研究中,只有 7 项(13%)报告了所有建议的预处理步骤,这可能会对下游分析产生影响:综述发现,新生儿生理信号预处理所使用的技术不尽相同,所报告的参数和步骤也不一致,这对于确认临床和软件质量管理系统实践的遵守情况、实用性和最佳实践的选择非常必要。提高报告的透明度和程序的标准化将促进研究的解释和可重复性,并加快临床应用,增强对研究结果的信心,简化研究成果转化为临床实践的过程,最终促进新生儿护理和患者预后的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Interactive Journal of Medical Research
Interactive Journal of Medical Research MEDICINE, RESEARCH & EXPERIMENTAL-
自引率
0.00%
发文量
45
审稿时长
12 weeks
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