In almost 1-3% of all pregnancies, severe maternal morbidity is present requiring admission to a critical care service. To identify seriously ill patients and for the early detection of clinical deterioration, an early warning system is used in which weighted values are assigned to a number of physiological parameters according to their deviation from normality. When a defined threshold is reached, a medical assessment by a physician is performed immediately. In this way early intervention may lead to a better outcome. Because of the different physiology and hemodynamic changes in pregnancy, a modified early obstetric warning system (MEOWS) is used which applies to the pregnant population.
The first part of the ARGUS project is to develop an automated version of the MEOWS and check how accurate it would have been in predicting interventions in a cohort of last year’s patients. Logical continuations are the improvement/optimization of the warning system and development of an online version.
Because of the programming involved, some prior experience with programming is mandatory (Matlab, LabVIEW, Python, C++ and/or R).
Dr. Alex J. Eggink Tom G. Goos MSc.
Gynecologist-perinatologist Scientific researcher