PDF(Pubmed)
Abstract：
BACKGROUND: The liberal use of blood cultures in emergency departments (EDs) leads to low yields and high numbers of false-positive results. False-positive, contaminated cultures are associated with prolonged hospital stays, increased antibiotic usage and even higher hospital mortality rates. This trial aims to investigate whether a recently developed and validated machine learning model for predicting blood culture outcomes can safely and effectively guide clinicians in withholding unnecessary blood culture analysis.
METHODS: A randomised controlled, non-inferiority trial comparing current practice with a machine learning-guided approach. The primary objective is to determine whether the machine learning based approach is non-inferior to standard practice based on 30-day mortality. Secondary outcomes include hospital length-of stay and hospital admission rates. Other outcomes include model performance and antibiotic usage. Participants will be recruited in the EDs of multiple hospitals in the Netherlands. A total of 7584 participants will be included.
BACKGROUND: Possible participants will receive verbal information and a paper information brochure regarding the trial. They will be given at least 1 hour consideration time before providing informed consent. Research results will be published in peer-reviewed journals. This study has been approved by the Amsterdam University Medical Centers\' local medical ethics review committee (No 22.0567). The study will be conducted in concordance with the principles of the Declaration of Helsinki and in accordance with the Medical Research Involving Human Subjects Act, General Data Privacy Regulation and Medical Device Regulation.
BACKGROUND: NCT06163781.
METHODS: A randomised controlled, non-inferiority trial comparing current practice with a machine learning-guided approach. The primary objective is to determine whether the machine learning based approach is non-inferior to standard practice based on 30-day mortality. Secondary outcomes include hospital length-of stay and hospital admission rates. Other outcomes include model performance and antibiotic usage. Participants will be recruited in the EDs of multiple hospitals in the Netherlands. A total of 7584 participants will be included.
BACKGROUND: Possible participants will receive verbal information and a paper information brochure regarding the trial. They will be given at least 1 hour consideration time before providing informed consent. Research results will be published in peer-reviewed journals. This study has been approved by the Amsterdam University Medical Centers\' local medical ethics review committee (No 22.0567). The study will be conducted in concordance with the principles of the Declaration of Helsinki and in accordance with the Medical Research Involving Human Subjects Act, General Data Privacy Regulation and Medical Device Regulation.
BACKGROUND: NCT06163781.
摘要：
背景：在急诊科（ED）中自由使用血液培养会导致低产量和大量的假阳性结果。假阳性,受污染的文化与住院时间延长有关，增加抗生素的使用，甚至更高的医院死亡率。该试验旨在研究最近开发和验证的用于预测血培养结果的机器学习模型是否可以安全有效地指导临床医生保留不必要的血培养分析。
方法：随机对照，将当前实践与机器学习指导方法进行比较的非劣效性试验。主要目标是确定基于机器学习的方法是否不劣于基于30天死亡率的标准实践。次要结果包括住院时间和入院率。其他结果包括模型性能和抗生素使用。参与者将在荷兰多家医院的ED中招募。总共包括7584名参与者。
背景：可能的参与者将收到有关试验的口头信息和纸质信息手册。在提供知情同意之前，他们将获得至少1小时的考虑时间。研究结果将发表在同行评审的期刊上。本研究已获得阿姆斯特丹大学医学中心当地医学伦理审查委员会的批准（编号：22.0567）。这项研究将根据《赫尔辛基宣言》的原则，并根据《涉及人类受试者的医学研究法案》进行。一般数据隐私法规和医疗器械法规。
背景：NCT06163781。
方法：随机对照，将当前实践与机器学习指导方法进行比较的非劣效性试验。主要目标是确定基于机器学习的方法是否不劣于基于30天死亡率的标准实践。次要结果包括住院时间和入院率。其他结果包括模型性能和抗生素使用。参与者将在荷兰多家医院的ED中招募。总共包括7584名参与者。
背景：可能的参与者将收到有关试验的口头信息和纸质信息手册。在提供知情同意之前，他们将获得至少1小时的考虑时间。研究结果将发表在同行评审的期刊上。本研究已获得阿姆斯特丹大学医学中心当地医学伦理审查委员会的批准（编号：22.0567）。这项研究将根据《赫尔辛基宣言》的原则，并根据《涉及人类受试者的医学研究法案》进行。一般数据隐私法规和医疗器械法规。
背景：NCT06163781。
