University of Virginia School of Nursing, Charlottesville, VA, USA; University of Virginia Center for Advanced Medical Analytics, Charlottesville, VA, USA; School of Data Science, University of Virginia, Charlottesville, VA, USA.
University of Virginia School of Medicine, Department of Internal Medicine, Division of Cardiovascular Diseases, Charlottesville, VA, USA; University of Virginia Center for Advanced Medical Analytics, Charlottesville, VA, USA.
Intensive Crit Care Nurs. 2021 Aug;65:103035. doi: 10.1016/j.iccn.2021.103035. Epub 2021 Apr 17.
Diagnosing sepsis remains challenging. Data compiled from continuous monitoring and electronic health records allow for new opportunities to compute predictions based on machine learning techniques. There has been a lack of consensus identifying best practices for model development and validation towards early identification of sepsis.
To evaluate the modeling approach and statistical methodology of machine learning prediction models for sepsis in the adult hospital population.
PubMed, CINAHL, and Cochrane databases were searched with the Preferred Reporting Items for Systematic Reviews guided protocol development. We evaluated studies that developed or validated physiologic sepsis prediction models or implemented a model in the hospital environment.
Fourteen studies met the inclusion criteria, and the AUROC of the prediction models ranged from 0.61 to 0.96. We found a variety of sepsis definitions, methods used for event adjudication, model parameters used, and modeling methods. Two studies tested models in clinical settings; the results suggested that patient outcomes were improved with implementation of machine learning models.
Nurses have a unique perspective to offer in the development and implementation of machine learning models detecting patients at risk for sepsis. More work is needed in developing model harmonization standards and testing in clinical settings.
脓毒症的诊断仍然具有挑战性。从连续监测和电子健康记录中收集的数据为基于机器学习技术进行预测提供了新的机会。在确定用于早期识别脓毒症的模型开发和验证的最佳实践方面，尚未达成共识。
评估机器学习预测模型在成人医院人群中脓毒症的建模方法和统计方法。
根据系统评价的首选报告项目指南制定方案，检索了 PubMed、CINAHL 和 Cochrane 数据库。我们评估了开发或验证生理脓毒症预测模型或在医院环境中实施模型的研究。
符合纳入标准的有 14 项研究，预测模型的 AUC 值范围为 0.61 至 0.96。我们发现了各种脓毒症定义、用于事件裁决的方法、使用的模型参数和建模方法。有两项研究在临床环境中测试了模型；结果表明，实施机器学习模型可改善患者的预后。
护士在开发和实施检测脓毒症风险患者的机器学习模型方面具有独特的视角。需要进一步努力制定模型协调标准并在临床环境中进行测试。
