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临床试验/NCT05471193
NCT05471193已完成不适用

Prediction of Cardiac Instability in Intensive Care

Kepler University Hospital1 个研究点 分布在 1 个国家目标入组 3,069 人开始时间: 2022年6月1日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
发起方
入组人数
3,069
试验地点
1
主要终点
AUROC for Classification of Instability

研究概览

简要总结

A large number of different organ functions are recorded in real time for patients who are monitored in an intensive care unit. On the one hand, the measured values collected in this way are used for continuous monitoring of vital parameters, but they are also evaluated several times a day in order to be able to make decisions regarding further diagnostics and therapy. In the first case, threshold values can be defined, and if these are exceeded or fallen short of, the treatment team is automatically alerted. If these limits are set too liberally, then the alert will only indicate an acute risk to the patient, where extensive pathophysiological changes have already occurred. If the limits are chosen too restrictively, then there are frequent false alarms, since the limits are exceeded in most cases due to natural fluctuation, without this having any pathological value. The consequence is a so-called "alarm fatigue", which in the worst case leads to ignoring correct alarms and thus endangers the patients. By design, all of these readings only show the status quo of a patient. It is the task of the treatment team to predict from the course of these readings whether a threatening situation is developing for the patient.

For daily clinical practice, it would be better if dangerous changes in vital signs could be predicted. In this case, it would be possible to intervene therapeutically not only when a dangerous situation has arisen, but to try to avert this situation through adequate measures by changing the therapy strategy. In such a case, the treatment team would no longer be confronted with emergency alarms, but could counteract an impending deterioration with a long lead time.

The first approaches for detecting a drop in blood pressure, for example, which are based on simple models, are already in clinical use.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Retrospective

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • All adult patients that have been treated at the intensive care units of the Kepler University Hospital, Linz, Austria between 2018-03-01 and 2020-10-31.

排除标准

  • 未提供

结局指标

主要结局

AUROC for Classification of Instability

时间窗: 2018-03-01 to 2020-10-31

AUROC for Classification of Instability

次要结局

  • Confusion Matrix(2018-03-01 to 2020-10-31)
  • Descriptive Statistics This outcome measure will compare the individual feature (e. g. height in cm) in one group vs. the other. Significant difference will be described by p-value.(2018-03-01 to 2020-10-31)

研究者

发起方
Kepler University Hospital
申办方类型
Other
责任方
Sponsor

研究点 (1)

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Prediction of Cardiac Instability in Intensive Care | 临床试验