“评估用于预测新生儿败血症的筛选参数和机器学习模型:一项系统综述。”

Peace Ezeobi Dennis , Angella Musiimenta , Wasswa William , Stella Kyoyagala
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引用次数: 0

摘要

全世界每年约有290万新生儿死亡,其中大多数发生在资源匮乏的环境中,每年造成的新生儿死亡总数约为30 - 50%。新生儿败血症发生在血液中有细菌侵入时;免疫系统开始出现系统性炎症反应综合征(SIRS),对身体造成损害,并可能迅速发展为严重的败血症、多器官衰竭,最终导致死亡。新生儿败血症的进展比成人更快;因此,及时诊断至关重要。诊断新生儿败血症的金标准测试是血培养,这至少需要72小时。因此,确定最有效的关键预测变量和模型可以帮助降低新生儿发病率和死亡率。通过检索PubMed、IEEE和Cochrane书目数据库来确定匹配的文章。纳入符合以下标准的全文文章进行分析:1)研究对象为新生儿。2)明确了新生儿脓毒症的定义。3)本研究提供新生儿脓毒症的发病定义(即发病时间)。4)研究明确描述了使用的预测变量。5)该研究清楚地描述了使用或评估任何综合筛选参数的机器学习模型。6)该研究必须提供诊断性能结果。31项研究完全符合纳入标准。ROM的持续时间被发现比其他母亲的危险因素更重要。发现心率和心率变异性比其他新生儿临床体征更重要。发现C反应蛋白和I/T比值比其他实验室检查更重要。预测变量的组合已显示加强新生儿败血症的预测,如一些审查的研究显示。迫切需要结合多个变量的预测算法来改进早期发现、预后和治疗新生儿败血症的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
“Evaluation of screening parameters and machine learning models for the prediction of neonatal sepsis: A systematic review.”
About 2.9 million neonates die every year worldwide, and most of these deaths occur in low resource settings where it causes about 30–50 % of the total neonatal deaths annually. Neonatal sepsis occurs when there is a bacterial invasion in the bloodstream; the immune system begins a systemic inflammatory response syndrome (SIRS) damaging to the body and can quickly advance to severe sepsis, multi-organ failure, and finally, death. Sepsis in neonates can progress more rapidly than in adults; therefore, timely diagnosis is critical. The gold standard test for diagnosing neonatal sepsis is blood culture, which takes at least 72 h. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality.
Matching articles were identified by searching PubMed, IEEE, and Cochrane bibliography databases. Full-text articles with the following criteria were included for analysis based on 1) the subject population are neonates. 2) the study provided a clear definition of neonatal sepsis. 3) the study provides neonatal sepsis onset definition (i.e., time of onset). 4) the study clearly described the predictor variables used. 5) the study clearly described machine learning models used or evaluated any of the consolidated screening parameters. 6) the study must have provided diagnostic performance results. Thirty-one studies met full inclusion criteria. The duration of ROM was found to be more significant than other maternal risk factors. Heart rate and heart rate variability were found to be more significant than other neonatal clinical signs. C reactive protein and I/T ratio were found to be more significant than other laboratory tests.
A combination of predictor variables has shown to strengthen neonatal sepsis prediction, as shown by some of the reviewed studies. Predictive algorithms that combine multiple variables are urgently needed to improve models for early detection, prognosis, and treatment of neonatal sepsis.
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来源期刊
Intelligence-based medicine
Intelligence-based medicine Health Informatics
CiteScore
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187 days
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