Marcella Niehoff School of Nursing, Loyola University Chicago, Maywood, Illinois, United States.
Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, Illinois, United States.
Appl Clin Inform. 2022 Jan;13(1):189-202. doi: 10.1055/s-0042-1742369. Epub 2022 Feb 2.
Sepsis is associated with high mortality, especially during the novel coronavirus disease 2019 (COVID-19) pandemic. Along with high monetary health care costs for sepsis treatment, there is a lasting impact on lives of sepsis survivors and their caregivers. Early identification is necessary to reduce the negative impact of sepsis and to improve patient outcomes. Prehospital data are among the earliest information collected by health care systems. Using these untapped sources of data in machine learning (ML)-based approaches can identify patients with sepsis earlier in emergency department (ED).
This integrative literature review aims to discuss the importance of utilizing prehospital data elements in ED, summarize their current use in developing ML-based prediction models, and specifically identify those data elements that can potentially contribute to early identification of sepsis in ED when used in ML-based approaches.
Literature search strategy includes following two separate searches: (1) use of prehospital data in ML models in ED; and (2) ML models that are developed specifically to predict/detect sepsis in ED. In total, 24 articles are used in this review.
A summary of prehospital data used to identify time-sensitive conditions earlier in ED is provided. Literature related to use of ML models for early identification of sepsis in ED is limited and no studies were found related to ML models using prehospital data in prediction/early identification of sepsis in ED. Among those using ED data, ML models outperform traditional statistical models. In addition, the use of the free-text elements and natural language processing (NLP) methods could result in better prediction of sepsis in ED.
This study reviews the use of prehospital data in early decision-making in ED and suggests that researchers utilize such data elements for prediction/early identification of sepsis in ML-based approaches.
脓毒症与高死亡率相关，尤其是在新型冠状病毒病 2019（COVID-19）大流行期间。除了脓毒症治疗的高额医疗费用外，它还对脓毒症幸存者及其护理人员的生活产生持久影响。早期识别对于减轻脓毒症的负面影响和改善患者预后至关重要。院前数据是医疗系统最早收集的信息之一。在基于机器学习（ML）的方法中使用这些未开发的数据源可以在急诊科更早地识别出脓毒症患者。
本综合文献综述旨在讨论在急诊科利用院前数据元素的重要性，总结其在开发基于 ML 的预测模型中的当前用途，并特别确定当在基于 ML 的方法中使用时，哪些数据元素可有助于在急诊科中更早地识别脓毒症。
文献检索策略包括以下两个单独的搜索：（1）在急诊科的 ML 模型中使用院前数据；（2）专门开发用于在急诊科预测/检测脓毒症的 ML 模型。总共使用了 24 篇文章进行了本次综述。
提供了一份摘要，总结了用于在急诊科更早地识别时间敏感情况的院前数据。与在急诊科使用 ML 模型来早期识别脓毒症相关的文献有限，并且没有发现与使用院前数据在急诊科中预测/早期识别脓毒症的 ML 模型相关的研究。在使用 ED 数据的研究中，ML 模型优于传统的统计模型。此外，使用自由文本元素和自然语言处理（NLP）方法可以更好地预测 ED 中的脓毒症。
本研究回顾了院前数据在 ED 早期决策中的使用，并建议研究人员在基于 ML 的方法中利用这些数据元素来进行预测/早期识别脓毒症。
