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Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit.
- Source :
-
Journal of clinical monitoring and computing [J Clin Monit Comput] 2019 Aug; Vol. 33 (4), pp. 703-711. Date of Electronic Publication: 2018 Aug 18. - Publication Year :
- 2019
-
Abstract
- Predictive analytics monitoring, the use of patient data to provide continuous risk estimation of deterioration, is a promising new application of big data analytical techniques to the care of individual patients. We tested the hypothesis that continuous display of novel electronic risk visualization of respiratory and cardiovascular events would impact intensive care unit (ICU) patient outcomes. In an adult tertiary care surgical trauma ICU, we displayed risk estimation visualizations on a large monitor, but in the medical ICU in the same institution we did not. The risk estimates were based solely on analysis of continuous cardiorespiratory monitoring. We examined 4275 individual patient records within a 7 month time period preceding and following data display. We determined cases of septic shock, emergency intubation, hemorrhage, and death to compare rates per patient care pre-and post-implementation. Following implementation, the incidence of septic shock fell by half (p < 0.01 in a multivariate model that included age and APACHE) in the surgical trauma ICU, where the data were continuously on display, but by only 10% (p = NS) in the control Medical ICU. There were no significant changes in the other outcomes. Display of a predictive analytics monitor based on continuous cardiorespiratory monitoring was followed by a reduction in the rate of septic shock, even when controlling for age and APACHE score.
- Subjects :
- APACHE
Aged
Female
Hemorrhage
Humans
Longitudinal Studies
Male
Medical Informatics
Middle Aged
Monitoring, Physiologic methods
Multivariate Analysis
Outcome Assessment, Health Care
Retrospective Studies
Risk
Shock, Septic pathology
Critical Care methods
Intensive Care Units
Monitoring, Physiologic instrumentation
Signal Processing, Computer-Assisted
Subjects
Details
- Language :
- English
- ISSN :
- 1573-2614
- Volume :
- 33
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Journal of clinical monitoring and computing
- Publication Type :
- Academic Journal
- Accession number :
- 30121744
- Full Text :
- https://doi.org/10.1007/s10877-018-0194-4